**Models of Information Processing in the Visual Cortex**

Vincent de Ladurantaye, Jean Rouat and Jacques Vanden-Abeele

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50616

## **1. Introduction**

226 Visual Cortex – Current Status and Perspectives

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In the most general meaning of the word, a model is a way of representing something of interest. If we ask a biologist to model the visual system, he will probably talk about neurons, dendrites and synapses. On the other hand, if we ask a mathematician to model the visual system, he will probably talk about variables, probabilities and differential equations.

There are an almost infinite number of ways to model any given system, and this is particularly true if we are interested in something as complex as the human brain, including the visual cortex. It is impossible to review every type of model present in the literature. This chapter does not, by any means, claim to be an accurate review of all the models of vision published so far. Instead, we try to regroup the different types of models into small categories, to give the reader a good overview of what is possible to achieve in terms of modeling biological vision. Each section of the chapter concentrates on one of these categories, begins with an outline of the general properties and goals of the global model category, and then explores different implementation methods (i.e. specific models).

## **1.1. Why models?**

There are many reasons why one might want to elaborate and use a model. Different kinds of models achieve different goals, however they all serve a common purpose: a tool to learn and better understand our world. Models are used as a simplification of reality. Many models only represent a specific aspect of a complex system and make arbitrary assumptions about the rest of the system which may be unrelated, judged negligible or simply unknown. A good example is the fact that current neurobiological models of the brain mainly focus on modeling neurons, neglecting interneurons and glial cells. Current models ignore glial cells, probably because they are not fully understood. Some models might purposefully ignore some parts of a system to isolate a specific structure and/or function that we want to understand.

Because of the extreme complexity of the cortex, simplification is unavoidable and even productive. Modeling is an efficient approach for simplifying reality as a way to gain a better

©2012 Rouat et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 2 Visual Cortex 228 Visual Cortex – Current Status and Perspectives Models of Information Processing in the Visual Cortex <sup>3</sup>

understanding of the system in question. In fact, one should strive to design the simplest possible model that could be complexified if necessary.

**2. Visual cortex: integrated into a cooperative network**

Even if the organization of the brain is considered to be specific to the task, the various modalities seem to use the same independent processing mechanism [57]. Specialization of the cortices appears with the stimulation through specific sensory pathways. The visual cortex is interconnected with other sensory modalities and is therefore multisensorial [6]. For example, the visual cortex is also involved in the perception of tactile orientations as observed by Zangaladze et al. [68] and other authors. Many models of interactions between the visual cortex and other modalities are presented in the literature. For example, the recent work by Ohshiro et al. [43] in which a model of multisensory neural layer is proposed. From an engineering perspective, new applications that integrate multisensory perception can be designed. Wysoski et al. [64] give an example of a multimedia application in which models of audition and vision are combined through an implementation of a network of spiking neurons

Models of Information Processing in the Visual Cortex 229

Figure 1 is our sketchy representation summarizing our integrated and general view of how functional multimodal information processing is done in the cortex. By no means, are we attempting to reproduce the biological characteristics of the cortex in relation to this figure. Emphasis is put on the main streams of information processing in relation with the visual cortex. We symbolize the cortical polysensoriality with a dedicated neural cortical layer, while there is no physiological agreement on the existence of such dedicated layer. In this section, interpretation of the functioning of the visual cortex is based on information and signal processing techniques to facilitate model designs without taking into account all physiological

**Figure 1.** Functional view of the information processing going on in the cortex and between various sensorial areas of the cortex. Layers at bottom illustrate the hierarchical structure of feature extraction, corresponding to visual, auditory, somato-sensorial, sensori-motor, etc. The upper layer represents the multisensorial areas of the cortex. Large arrows represent the feedforward flow of information going from the sensorial areas to the polysensorial "layer" and feedbacks from the upper layer to the sensorial (and hierarchical feature) layers. Dotted arrows represent the iterative process of feature extraction

**2.1. Integration into a collaborative network**

for the identification of 35 persons.

which is occuring inside the sensory layers.

complexities.

For a more philosophical discussion on the role of models in science, the work by Frigg and Hartmann [15] can be recommended. They describe two fundamental functions of models. First, models can represent a selected part of the world, and second, models can represent a theory. The two notions are not mutually exclusive, but they provide a good distinction between more practical models trying to reproduce a certain system, and more theoretical models constrained to a certain set of laws. As we don't have yet an established theory as how the brain works, this chapter focuses on the former.

Moreover, models can also be categorized into models of data and models of phenomena. This is an important distinction because they consist of two drastically different approaches to the same problem that, in our case, is understanding the visual cortex. Models of data try to reproduce data we observe in the brain, and models of phenomena try to reproduce the "miracle" of the visual cortex, which is vision. This chapter emphasizes practical models and the functioning of the visual cortex from an information-processing point of view.

## **1.2. Modeling the visual cortex**

As established in previous section, we concentrate more on models of phenomena to understand how the visual system works, instead of models of data that are trying to be biologically accurate. We do so, because science is yet to provide a full understanding of the brain, thus it is not possible to propose accurate overall models of data.

The chapter rather gives an overview of different kinds of models of vision present in the literature, whether they are biologically accurate or not. Modeling with artificial neural networks is not the only way. There are many other models based on mathematical representations, statistics, physics principles that can represent behaviors observed in the visual system. Any model able to represent some aspects of the vision process can provide valuable insights as to how the visual cortex really works.

Because of the complexity, there exists no model in the literature that can represent visual cortex as a whole. For the sake of simplicity, when designing a model, only specific functions of the visual system are usually considered . Of course, there are models more encompassing than others with more functionalities. However, most of them deal with only one of the two main visual functions, that is, localization or recognition. Ironically, this fits the often used ventral vs. dorsal stream model of the visual cortex, where the ventral stream explains the "what?" and the dorsal steam the "where?". This chapter is thus divided into three main sections:


Before doing so, we first discuss the point that the visual cortex is embedded in a huge neural network, interacting and cooperating with other cortical and sensorial areas.

## **2. Visual cortex: integrated into a cooperative network**

## **2.1. Integration into a collaborative network**

2 Visual Cortex

understanding of the system in question. In fact, one should strive to design the simplest

For a more philosophical discussion on the role of models in science, the work by Frigg and Hartmann [15] can be recommended. They describe two fundamental functions of models. First, models can represent a selected part of the world, and second, models can represent a theory. The two notions are not mutually exclusive, but they provide a good distinction between more practical models trying to reproduce a certain system, and more theoretical models constrained to a certain set of laws. As we don't have yet an established theory as

Moreover, models can also be categorized into models of data and models of phenomena. This is an important distinction because they consist of two drastically different approaches to the same problem that, in our case, is understanding the visual cortex. Models of data try to reproduce data we observe in the brain, and models of phenomena try to reproduce the "miracle" of the visual cortex, which is vision. This chapter emphasizes practical models and

As established in previous section, we concentrate more on models of phenomena to understand how the visual system works, instead of models of data that are trying to be biologically accurate. We do so, because science is yet to provide a full understanding of the

The chapter rather gives an overview of different kinds of models of vision present in the literature, whether they are biologically accurate or not. Modeling with artificial neural networks is not the only way. There are many other models based on mathematical representations, statistics, physics principles that can represent behaviors observed in the visual system. Any model able to represent some aspects of the vision process can provide

Because of the complexity, there exists no model in the literature that can represent visual cortex as a whole. For the sake of simplicity, when designing a model, only specific functions of the visual system are usually considered . Of course, there are models more encompassing than others with more functionalities. However, most of them deal with only one of the two main visual functions, that is, localization or recognition. Ironically, this fits the often used ventral vs. dorsal stream model of the visual cortex, where the ventral stream explains the "what?" and the dorsal steam the "where?". This chapter is thus divided into three main

2. A section concerning models associated with the dorsal pathway, which deals with

3. A section on models related to the ventral pathway, which mainly deals with object

Before doing so, we first discuss the point that the visual cortex is embedded in a huge neural

the functioning of the visual cortex from an information-processing point of view.

brain, thus it is not possible to propose accurate overall models of data.

1. A short section concerning models trying to be biologically accurate.

network, interacting and cooperating with other cortical and sensorial areas.

valuable insights as to how the visual cortex really works.

possible model that could be complexified if necessary.

how the brain works, this chapter focuses on the former.

**1.2. Modeling the visual cortex**

movement and localization.

sections:

recognition.

Even if the organization of the brain is considered to be specific to the task, the various modalities seem to use the same independent processing mechanism [57]. Specialization of the cortices appears with the stimulation through specific sensory pathways. The visual cortex is interconnected with other sensory modalities and is therefore multisensorial [6]. For example, the visual cortex is also involved in the perception of tactile orientations as observed by Zangaladze et al. [68] and other authors. Many models of interactions between the visual cortex and other modalities are presented in the literature. For example, the recent work by Ohshiro et al. [43] in which a model of multisensory neural layer is proposed. From an engineering perspective, new applications that integrate multisensory perception can be designed. Wysoski et al. [64] give an example of a multimedia application in which models of audition and vision are combined through an implementation of a network of spiking neurons for the identification of 35 persons.

Figure 1 is our sketchy representation summarizing our integrated and general view of how functional multimodal information processing is done in the cortex. By no means, are we attempting to reproduce the biological characteristics of the cortex in relation to this figure. Emphasis is put on the main streams of information processing in relation with the visual cortex. We symbolize the cortical polysensoriality with a dedicated neural cortical layer, while there is no physiological agreement on the existence of such dedicated layer. In this section, interpretation of the functioning of the visual cortex is based on information and signal processing techniques to facilitate model designs without taking into account all physiological complexities.

**Figure 1.** Functional view of the information processing going on in the cortex and between various sensorial areas of the cortex. Layers at bottom illustrate the hierarchical structure of feature extraction, corresponding to visual, auditory, somato-sensorial, sensori-motor, etc. The upper layer represents the multisensorial areas of the cortex. Large arrows represent the feedforward flow of information going from the sensorial areas to the polysensorial "layer" and feedbacks from the upper layer to the sensorial (and hierarchical feature) layers. Dotted arrows represent the iterative process of feature extraction which is occuring inside the sensory layers.

#### 4 Visual Cortex 230 Visual Cortex – Current Status and Perspectives Models of Information Processing in the Visual Cortex <sup>5</sup>

According to the common view, we represent sensing areas of the cortex in figure 1 as a hierarchy of layers and the "polysensorial" layer represents the connectivity between cortical areas. We known that connectivities between audition, vision and the motor system at lower levels of the senses exist (e.g. in the colliculus [6]), but these interactions are not discussed in this chapter.

(also denoted as vector). Each elementary function **w**(*t*) (or "basis function", in the signal processing jargon) represents the receptive field of a particular neuron (or group of neurons). The result of the projection of **s**(*t*) on the specific basis function **w**(*t*) is a scalar number *y*(*t*) that is added to the transmembrane potential of the neuron. It corresponds to the contribution of the stimulus input **s**(*t*) to the neuron-activity. It is summarized below in mathematical

> *y*(*t*) = **w**(*t*).**s**(*t*) or

> > *I* ∑ *i*=1

*wi*(*t*)*si*(*t*) (1)

Models of Information Processing in the Visual Cortex 231

*y*(*t*) =

*w*1(*t*) *w*2(*t*) . . *wI*(*t*)

⎤ ⎥ ⎥ ⎥ ⎦

*I* is the dimension of the stimulus and receptive field (assuming that the neuron has *I* synapses). *wi* is the efficiency at time *t* of synapse *i* from the neuron. In other words, at time *t*, *y*(*t*) (equation 1) is the degree of similarity between the input stimulus **s**(*t*) and the receptive field **w**(*t*) of the neuron. In signal processing jargon, *y*(*t*) is the basis coefficient. To some extent, a set of *I* neurons can be assimilated into a bank of *I* finite impulse response filters (FIR) which impulse response is equal to a set of *I* receptive fields **w**. Depending on the number of neurons, the characteristics of the receptive field functions **w**, and the constraints on the coefficients *y*, the analysis operation performed by a set of neurons can be equivalent to an independent component analysis (ICA) decomposition [27], a wavelet decomposition [33],

• ICA [24, 27] constraints the output coefficients *yi*(*t*) to be independent for a set of dependent input receptive fields **w**. Therefore, a layer of postsynaptic neurons can uncouple the features coming from a presynaptic layer in which receptive fields are dependent. This is particularly efficient for hierarchical analysis systems. Applications in image processing commonly use ICA to find basis functions to decompose images into

• Wavelet decomposition [34] of signals is a well established field in signal processing. A layer of neurons can approximate a wavelet decomposition as long as the receptive fields **w** respect some mathematical constraints such as limited support, bounded energy, and

• Sparsity in the activity of neurons in the visual cortex can also be taken into account with equation 1. Sparsity in the activity means that very few neurons are active, that is only a few *yi* are sufficiently high to generate a response in the neuron. **w** is called the dictionary on which the presynaptic signals are projected on. To be sparse, the size of the dictionary should be sufficient, that is, the number of neurons is sufficiently high so that the basis is

constraints on the Fourier transform of the receptive fields [17].

and **s**(*t*) =

⎡ ⎢ ⎢ ⎢ ⎣ *s*1(*t*) *s*2(*t*) . . *sI*(*t*)

⎤ ⎥ ⎥ ⎥ ⎦

⎡ ⎢ ⎢ ⎢ ⎣

**w**(*t*) =

or an overcomplete and sparse decomposition [39, 56].

terms.

with

features [26].

overcomplete.

Even if the perspective presented here is very schematic, it is still of interest for research and a better understanding of the cortex. Next subsections introduce models of feature-extraction through the visual sensory layers.

## **2.2. Hierarchical and sparse low level feature extractions**

Low level features and characteristics are hypothesized to be hierarchically obtained through a succession of feedforward and feedback loops. This process is commonly modeled as a cascade of layers made of filters that mimic the receptive fields of neurons. Each layer output is subject to nonlinear transformations. Layers are interconnected into hierarchical structures that can be feedforward or include feedbacks as illustrated at the bottom of figure 1. We introduce here two types of models based on signal processing techniques or on artificial neural networks.

*2.2.1. Signal processing models*

**Figure 2.** Illustration of the receptive field of a neuron representing an elementary analysis function and the nonlinear output being associated to a decision function. In this illustration, a hypothetic presynaptic axon is sketched and it synapses with *I* contacts on a postsynaptic neuron.

In these approaches, neurons are assumed to be made of two simple modules: analysis and then detection. The analysis is modeled as a filter which characterizes to some extent the receptive field of a neuron. Detection is modeled as a threshold or a nonlinear function. When the input to the neuron matches its receptive field, the detection module has a strong response.

There is an intense research work going on the automatic finding and generation of receptive fields to be used in computational models or artificial vision models and systems. Signal processing methods estimating the receptive fields of models are briefly summarized here.

These models assume that any stimulus **s**(*t*) (denoted as a vector) to a neuron is transformed by this neuron into a new representation by projection on an elementary function **w**(*t*) (also denoted as vector). Each elementary function **w**(*t*) (or "basis function", in the signal processing jargon) represents the receptive field of a particular neuron (or group of neurons). The result of the projection of **s**(*t*) on the specific basis function **w**(*t*) is a scalar number *y*(*t*) that is added to the transmembrane potential of the neuron. It corresponds to the contribution of the stimulus input **s**(*t*) to the neuron-activity. It is summarized below in mathematical terms.

$$y(t) = \mathbf{w}(t). \mathbf{s}(t)$$

$$\text{or}$$

$$y(t) = \sum\_{i=1}^{I} w\_i(t)s\_i(t) \tag{1}$$

with

4 Visual Cortex

According to the common view, we represent sensing areas of the cortex in figure 1 as a hierarchy of layers and the "polysensorial" layer represents the connectivity between cortical areas. We known that connectivities between audition, vision and the motor system at lower levels of the senses exist (e.g. in the colliculus [6]), but these interactions are not discussed in

Even if the perspective presented here is very schematic, it is still of interest for research and a better understanding of the cortex. Next subsections introduce models of feature-extraction

Low level features and characteristics are hypothesized to be hierarchically obtained through a succession of feedforward and feedback loops. This process is commonly modeled as a cascade of layers made of filters that mimic the receptive fields of neurons. Each layer output is subject to nonlinear transformations. Layers are interconnected into hierarchical structures that can be feedforward or include feedbacks as illustrated at the bottom of figure 1. We introduce here two types of models based on signal processing techniques or on artificial

> Dendritic tree section or dendrite

**Figure 2.** Illustration of the receptive field of a neuron representing an elementary analysis function and the nonlinear output being associated to a decision function. In this illustration, a hypothetic presynaptic

In these approaches, neurons are assumed to be made of two simple modules: analysis and then detection. The analysis is modeled as a filter which characterizes to some extent the receptive field of a neuron. Detection is modeled as a threshold or a nonlinear function. When the input to the neuron matches its receptive field, the detection module has a strong response. There is an intense research work going on the automatic finding and generation of receptive fields to be used in computational models or artificial vision models and systems. Signal processing methods estimating the receptive fields of models are briefly summarized here. These models assume that any stimulus **s**(*t*) (denoted as a vector) to a neuron is transformed by this neuron into a new representation by projection on an elementary function **w**(*t*)

Integration of Contributions

Analysis Detection

Non-linear transformation

Synaptic efficiencies

*2*

axon is sketched and it synapses with *I* contacts on a postsynaptic neuron.

*1 I*

*i*

this chapter.

neural networks.

*2.2.1. Signal processing models*

*1 2 i I*

axone

δ(**t**)

*1 2 i*

*I*

through the visual sensory layers.

**2.2. Hierarchical and sparse low level feature extractions**

$$\mathbf{w}(t) = \begin{bmatrix} w\_1(t) \\ w\_2(t) \\ \cdot \\ \cdot \\ w\_I(t) \end{bmatrix} \text{ and } \mathbf{s}(t) = \begin{bmatrix} s\_1(t) \\ s\_2(t) \\ \cdot \\ \cdot \\ s\_I(t) \end{bmatrix}$$

*I* is the dimension of the stimulus and receptive field (assuming that the neuron has *I* synapses). *wi* is the efficiency at time *t* of synapse *i* from the neuron. In other words, at time *t*, *y*(*t*) (equation 1) is the degree of similarity between the input stimulus **s**(*t*) and the receptive field **w**(*t*) of the neuron. In signal processing jargon, *y*(*t*) is the basis coefficient.

To some extent, a set of *I* neurons can be assimilated into a bank of *I* finite impulse response filters (FIR) which impulse response is equal to a set of *I* receptive fields **w**. Depending on the number of neurons, the characteristics of the receptive field functions **w**, and the constraints on the coefficients *y*, the analysis operation performed by a set of neurons can be equivalent to an independent component analysis (ICA) decomposition [27], a wavelet decomposition [33], or an overcomplete and sparse decomposition [39, 56].


Equation 1 is a simple model of a layer of neurons (at least for the receptive fields). Bank of neurons can be combined into hierarchical layers of filters that follow equation 1. Different combinations are possible. Depending on the architecture and on the connectivity, different hierarchical structures can be obtained.

accurate fashion have to cut corners by modeling only isolated sections of the cortex and

Models of Information Processing in the Visual Cortex 233

The section presents biologically accurate models, that is, models trying to fit the data observed in the brain. And then, some models are reviewed that are not exactly biologically

Since the primary visual cortex is the most studied and best understood, most models try to reproduce parts of the early visual system. In the work of McLaughlin et al. [36], a small local patch (1 *mm*2) of V1 is reproduced. They use a relatively complex, and thus accurate, neuron model. The work focuses upon orientation preference and selectivity, and upon the spatial distribution of neuronal responses across the cortical layer. Furthermore, they discuss how large-scale scientific computation can provide significant understanding about the possible

Similarly, Basalyga and Wennekers [4] are able to model a simplified version of the visual pathway of a cat's eye using three connected subsystems: the retina, the Thalamus and V1. They model a patch of about 1.9 *mm*<sup>2</sup> of cortical surface using a Hodgkin-Huxley neuron model. They are able to reproduce the orientation preference and direction selectivity of

While biologically accurate neuron models are closer to reality, they are also computationally very expensive. By using a simpler, "integrate-and-fire" neuron model, Rangan et al. [47] are able to model a patch of 25 *mm*2. They focus on reproducing V1 orientation preference maps with hypercolumns in a pinwheel organization. Models such as these give insights into the

Not all models are trying to directly reproduce biology. Most vision models use a more functional approach, trying to mimic the architecture and the behavior instead of the exact dynamics. Many distinct visual areas have been identified in the cerebral cortex, and functional organization of these areas has been proposed [62]. Such models do not generally use large-scale simulations, but are still trying to reproduce the biological architecture. As

A good example of such a model is based on the Adaptive Resonance Theory (ART) proposed by Grossberg. More specifically, the LAMINART model [19]. This model is a relatively complex system that is based on bottom-up, top-down and lateral interactions. The model integrates a "what" and a "where" stream, and as such, offers a very complete model of the

Another interesting work is a computational model of the human visual cortex based on the simulation of arrays of cortical columns [1]. The model suggests an architecture by which the brain can transform the external world into internal interpretable events. Furthermore, they argue that the model could be a good start for reverse engineering the brain. Modern super-computers have enough computational capacity to simulate enough cortical columns to achieve such a goal. However, the problem resides in the communication between the cortical columns. Communication between processing units in super-computer is a major

such, they are able to model a much larger part of the visual cortex.

sometimes also by simplifying the neuron model.

accurate, but that are striving to reproduce biology.

**3.1. Biologically accurate models**

cortical mechanisms.

neuronal dynamics in V1.

**3.2. Biologically realist models**

cortical cells.

visual cortex.

We introduce in the following subsection 3 types of such structures.

## *2.2.2. Hierarchical neural network structures*

Previous signal processing modules can be combined into hierarchical structures of neural networks. We list below some of the known models.


## **2.3. Summary**

Simple models of the visual cortex have been presented. The present section has opened the way to a more integrated view in which peripheral and other senses interact with the visual cortex.

## **3. Biological models**

This section reviews some models trying to reproduce as accurately as possible the biological visual system. The ultimate goal of those researches is having models able to represent the data we observe in the brain.

One of the earliest models is the Hodgkin & Huxley neuron model [23]. It is popular in the field of computational neuroscience and uses 4 differential equations to represent a biologically realistic neuron. Such models are important as they are one of the corner stones to the simulators that are used for a better understanding of the visual system. They can help us understand more precisely the different cortical mechanisms. Nowadays, computer simulations are very important in neuroscience and to the understanding of the visual cortex. Hundreds of computational models (along with their reference papers) are available to the computational neuroscientist. Many different models can be downloaded from the modelDB database [21]. Compartment, channel, spiking, continuous or discrete models cover a wide range of simulators and are available in different languages and simulators.

One of the biggest challenge when implementing biologically accurate models of the visual cortex is the amount of calculations required for the simulations. Even with massively parallel computer architectures, we cannot effectively rival the level of parallelism and computing power of the brain. As such, works claiming to represent the visual cortex in a biologically accurate fashion have to cut corners by modeling only isolated sections of the cortex and sometimes also by simplifying the neuron model.

The section presents biologically accurate models, that is, models trying to fit the data observed in the brain. And then, some models are reviewed that are not exactly biologically accurate, but that are striving to reproduce biology.

## **3.1. Biologically accurate models**

6 Visual Cortex

Equation 1 is a simple model of a layer of neurons (at least for the receptive fields). Bank of neurons can be combined into hierarchical layers of filters that follow equation 1. Different combinations are possible. Depending on the architecture and on the connectivity, different

Previous signal processing modules can be combined into hierarchical structures of neural

• Convolutional neural networks like the one proposed by Lecun et al. [30] have a mostly

• Deep learning machines [22] are layered neural networks that can be trained layer by layer. Theses layers are placed in a hierarchy to solve complex problems like image or speech recognition. First layers extract features and last layers perform a kind of classification by filtering the activity of the incoming layers. Feedbacks are established between layers. • An example of a Bayesian neural network is given by Hawkins et al. [16, 20]. The connectivity between neurons inside a layer reflects partially the architecture of the cortex. Neurons are nodes of a statistical graphical model and feedbacks between layers are also

Simple models of the visual cortex have been presented. The present section has opened the way to a more integrated view in which peripheral and other senses interact with the visual

This section reviews some models trying to reproduce as accurately as possible the biological visual system. The ultimate goal of those researches is having models able to represent the

One of the earliest models is the Hodgkin & Huxley neuron model [23]. It is popular in the field of computational neuroscience and uses 4 differential equations to represent a biologically realistic neuron. Such models are important as they are one of the corner stones to the simulators that are used for a better understanding of the visual system. They can help us understand more precisely the different cortical mechanisms. Nowadays, computer simulations are very important in neuroscience and to the understanding of the visual cortex. Hundreds of computational models (along with their reference papers) are available to the computational neuroscientist. Many different models can be downloaded from the modelDB database [21]. Compartment, channel, spiking, continuous or discrete models cover a wide

One of the biggest challenge when implementing biologically accurate models of the visual cortex is the amount of calculations required for the simulations. Even with massively parallel computer architectures, we cannot effectively rival the level of parallelism and computing power of the brain. As such, works claiming to represent the visual cortex in a biologically

range of simulators and are available in different languages and simulators.

hierarchical structures can be obtained.

*2.2.2. Hierarchical neural network structures*

feedforward architecture;

established.

**3. Biological models**

data we observe in the brain.

**2.3. Summary**

cortex.

networks. We list below some of the known models.

We introduce in the following subsection 3 types of such structures.

Since the primary visual cortex is the most studied and best understood, most models try to reproduce parts of the early visual system. In the work of McLaughlin et al. [36], a small local patch (1 *mm*2) of V1 is reproduced. They use a relatively complex, and thus accurate, neuron model. The work focuses upon orientation preference and selectivity, and upon the spatial distribution of neuronal responses across the cortical layer. Furthermore, they discuss how large-scale scientific computation can provide significant understanding about the possible cortical mechanisms.

Similarly, Basalyga and Wennekers [4] are able to model a simplified version of the visual pathway of a cat's eye using three connected subsystems: the retina, the Thalamus and V1. They model a patch of about 1.9 *mm*<sup>2</sup> of cortical surface using a Hodgkin-Huxley neuron model. They are able to reproduce the orientation preference and direction selectivity of cortical cells.

While biologically accurate neuron models are closer to reality, they are also computationally very expensive. By using a simpler, "integrate-and-fire" neuron model, Rangan et al. [47] are able to model a patch of 25 *mm*2. They focus on reproducing V1 orientation preference maps with hypercolumns in a pinwheel organization. Models such as these give insights into the neuronal dynamics in V1.

## **3.2. Biologically realist models**

Not all models are trying to directly reproduce biology. Most vision models use a more functional approach, trying to mimic the architecture and the behavior instead of the exact dynamics. Many distinct visual areas have been identified in the cerebral cortex, and functional organization of these areas has been proposed [62]. Such models do not generally use large-scale simulations, but are still trying to reproduce the biological architecture. As such, they are able to model a much larger part of the visual cortex.

A good example of such a model is based on the Adaptive Resonance Theory (ART) proposed by Grossberg. More specifically, the LAMINART model [19]. This model is a relatively complex system that is based on bottom-up, top-down and lateral interactions. The model integrates a "what" and a "where" stream, and as such, offers a very complete model of the visual cortex.

Another interesting work is a computational model of the human visual cortex based on the simulation of arrays of cortical columns [1]. The model suggests an architecture by which the brain can transform the external world into internal interpretable events. Furthermore, they argue that the model could be a good start for reverse engineering the brain. Modern super-computers have enough computational capacity to simulate enough cortical columns to achieve such a goal. However, the problem resides in the communication between the cortical columns. Communication between processing units in super-computer is a major bottleneck. There is also the question of how the model should connect those cortical columns between them. How many must communicate with each other? How much information do they communicate? Nevertheless, reverse engineering of the brain is an objective that is becoming more and more realistic.

us to perform actions such as catching a ball, by placing our hands in the predicted trajectory

Models of Information Processing in the Visual Cortex 235

Models that use such anticipation principle have applications in target tracking. Most of these models are based on a method known as Recursive Bayesian Estimation. To put things simply, the algorithm consists of a loop that constantly tracks the movement of the target based on its current position and speed. The loop estimates and corrects the previous estimation using new data acquired from the moving object. Similar principles can also apply to visual control tasks. A quick overview of the mathematical framework can be found in the work of Diard et

One of the most popular implementation of recursive Bayesian estimation is the Kalman filter. Many applications have used this algorithm to achieve target tracking. The model proposed by Bai [3] is a good example that achieves tracking of not only the target position in space, but also its size and rotation. Many different methods can be used for tracking. For instance, an approach based on a low pass filter has been shown to give better precision than traditional Kalman filters [7]. For tracking targets with more unreliable movement patterns, particle filters can be used [8]. These approaches are not making predefined assumptions about the target, and are thus more polyvalent, but generally slower. However, optimization techniques

The predictive behavior mentioned previously is used by the visual system for planning and controlling many of our actions. This section is divided in two parts. The first section present studies discussing the importance of vision in visual control models. The second section presents different models using visual stimuli for controlling actions such as pointing,

A study from Kawato [29] analyses the internal models for trajectory planning and motor control. It is suggested that humans or animals use the combination of a kinematic and dynamic model for trajectory planning and control. To catch a ball, one must anticipate the trajectory of the ball and position the hand and orient adequately the palm. Fajen and Cramer [13] study the positioning of the hand in function of distance, the angle and the speed. They discuss the implications for predictive models of catching based on visual perception. However, the targeted object is not the only thing tracked by our visual system. The movement of the hand itself needs to be anticipated. Saunders and Knill [53] study the visual feedback to control movements of the hand. Alternatively, Tucker and Ellis [59] are interested in the cortical area of visuomotor integration. They discuss on the fact that motor involvement impacts visual representations. They set up 5 experiments in which they study

The use of vision for motion control is thus a very complex process using both feedforward

Now that we know vision plays an important role in movement, we present some models using vision to achieve motion control. Fagg and Arbib [12] developed the FARS model for

such as the one proposed by Zhou et al. [69] can be used for faster processing.

the impact of grasping and touching objects on the speed of visual perception.

and feedback visual information to achieve movement.

estimated by our brain.

**4.2. Visual control**

grasping and locomotion.

*4.2.2. Visual control models*

*4.2.1. Importance of vision in motion control*

al. [10].

## **4. Visual localization and planning**

In this section we present models that are related to the "where?" pathway of the visual cortex, that is, models having to do with movement and localization. However, another important function of our visual system is to anticipate trajectories of moving objects. In computer vision, this is commonly called target tracking. Many of these algorithms are based on probabilistic approaches which we will present in the first part of this section. In the second part, we will discuss the importance of vision in motion control, which uses the concepts of localization and planning to control actions.

## **4.1. Movement detection and prediction**

Movement detection is a crucial element of our visual system. Moving objects attract more attention than stationary objects. Optical flow models implement the capture of movement in a sequence of images. Such models compute the local displacement gradient for each pixel in a sequence of images. The result is a series of gradient fields such as illustrated in figure 3. These methods can be used for a multitude of applications such as detecting motion, segmenting moving objects and analyzing stereo disparity to extract 3D information out of 2 cameras. Further information about optical flow models can be found in the work of Fleet and Weiss [14].

**Figure 3.** Example of optical flow gradient. In the left figure, a scene of forward movement is illustrated, thus, gradients are growing from the center toward the edges. In the right figure, the scene is rotating, thus forming a rotational pattern. In both scenes, the center of the image is moving slower than the rest, thus the arrows are shorter.

Optical flow models can detect movement, however, they are unable to achieve tracking in a reliable fashion because they lack anticipating behavior. The human brain is very good at predicting short term events. In fact, it is believed that the brain anticipates almost everything in our environment [20]. Indeed, brain activity is greater when we encounter an unanticipated event [2]. When we see a moving object, we automatically anticipate its trajectory. To do so, our brain unconsciously estimates the object speed and direction, and "runs" these number in a "mental physic model", which is acquired through experience". This anticipation allows us to perform actions such as catching a ball, by placing our hands in the predicted trajectory estimated by our brain.

Models that use such anticipation principle have applications in target tracking. Most of these models are based on a method known as Recursive Bayesian Estimation. To put things simply, the algorithm consists of a loop that constantly tracks the movement of the target based on its current position and speed. The loop estimates and corrects the previous estimation using new data acquired from the moving object. Similar principles can also apply to visual control tasks. A quick overview of the mathematical framework can be found in the work of Diard et al. [10].

One of the most popular implementation of recursive Bayesian estimation is the Kalman filter. Many applications have used this algorithm to achieve target tracking. The model proposed by Bai [3] is a good example that achieves tracking of not only the target position in space, but also its size and rotation. Many different methods can be used for tracking. For instance, an approach based on a low pass filter has been shown to give better precision than traditional Kalman filters [7]. For tracking targets with more unreliable movement patterns, particle filters can be used [8]. These approaches are not making predefined assumptions about the target, and are thus more polyvalent, but generally slower. However, optimization techniques such as the one proposed by Zhou et al. [69] can be used for faster processing.

## **4.2. Visual control**

8 Visual Cortex

bottleneck. There is also the question of how the model should connect those cortical columns between them. How many must communicate with each other? How much information do they communicate? Nevertheless, reverse engineering of the brain is an objective that is

In this section we present models that are related to the "where?" pathway of the visual cortex, that is, models having to do with movement and localization. However, another important function of our visual system is to anticipate trajectories of moving objects. In computer vision, this is commonly called target tracking. Many of these algorithms are based on probabilistic approaches which we will present in the first part of this section. In the second part, we will discuss the importance of vision in motion control, which uses the concepts of

Movement detection is a crucial element of our visual system. Moving objects attract more attention than stationary objects. Optical flow models implement the capture of movement in a sequence of images. Such models compute the local displacement gradient for each pixel in a sequence of images. The result is a series of gradient fields such as illustrated in figure 3. These methods can be used for a multitude of applications such as detecting motion, segmenting moving objects and analyzing stereo disparity to extract 3D information out of 2 cameras. Further information about optical flow models can be found in the work of Fleet and

**Figure 3.** Example of optical flow gradient. In the left figure, a scene of forward movement is illustrated, thus, gradients are growing from the center toward the edges. In the right figure, the scene is rotating, thus forming a rotational pattern. In both scenes, the center of the image is moving slower than the rest,

Optical flow models can detect movement, however, they are unable to achieve tracking in a reliable fashion because they lack anticipating behavior. The human brain is very good at predicting short term events. In fact, it is believed that the brain anticipates almost everything in our environment [20]. Indeed, brain activity is greater when we encounter an unanticipated event [2]. When we see a moving object, we automatically anticipate its trajectory. To do so, our brain unconsciously estimates the object speed and direction, and "runs" these number in a "mental physic model", which is acquired through experience". This anticipation allows

becoming more and more realistic.

**4. Visual localization and planning**

localization and planning to control actions.

**4.1. Movement detection and prediction**

Weiss [14].

thus the arrows are shorter.

The predictive behavior mentioned previously is used by the visual system for planning and controlling many of our actions. This section is divided in two parts. The first section present studies discussing the importance of vision in visual control models. The second section presents different models using visual stimuli for controlling actions such as pointing, grasping and locomotion.

#### *4.2.1. Importance of vision in motion control*

A study from Kawato [29] analyses the internal models for trajectory planning and motor control. It is suggested that humans or animals use the combination of a kinematic and dynamic model for trajectory planning and control. To catch a ball, one must anticipate the trajectory of the ball and position the hand and orient adequately the palm. Fajen and Cramer [13] study the positioning of the hand in function of distance, the angle and the speed. They discuss the implications for predictive models of catching based on visual perception. However, the targeted object is not the only thing tracked by our visual system. The movement of the hand itself needs to be anticipated. Saunders and Knill [53] study the visual feedback to control movements of the hand. Alternatively, Tucker and Ellis [59] are interested in the cortical area of visuomotor integration. They discuss on the fact that motor involvement impacts visual representations. They set up 5 experiments in which they study the impact of grasping and touching objects on the speed of visual perception.

The use of vision for motion control is thus a very complex process using both feedforward and feedback visual information to achieve movement.

#### *4.2.2. Visual control models*

Now that we know vision plays an important role in movement, we present some models using vision to achieve motion control. Fagg and Arbib [12] developed the FARS model for

#### 10 Visual Cortex 236 Visual Cortex – Current Status and Perspectives Models of Information Processing in the Visual Cortex <sup>11</sup>

grasping. They study the interaction between anterior intra-parietal and premotor areas. They also make predictions on neural activity patterns at population and single unit levels.

Moving on to models with more concrete applications, Yoshimi and Allen [65] propose an integrated view with a robotic application. They describe a real-time computer vision system with a gripper. A closed feedback loop control is used between the vision system and the gripper. Visual primitives are used to assist in the grasping and manipulation. Böhme and Heinke [5] implement the Selective Attention for Action model (SAAM) by taking into account the physical properties of the hand including anatomy. Their model is based on the fact that visual attention is guided by physical characteristics of objects (like the handle of a cup). Mehta and Schaal [37] study the visuomotor control of an unstable dynamic system (the balancing of a pole on a finger) with Smith predictors, Kalman filters, tapped-delay lines, and delay-uncompensated control. After validation with human participants, they exclude these models and propose the existence of a forward model in the sensory preprocessing loop.

## **5. Object recognition**

## **5.1. Overview**

In this section, we present models that are related to the ventral pathway of the visual cortex, that is, models that have to do with form and object recognition. For many years, object recognition has been one of the most challenging problems in the field of artificial intelligence. However, for humans, this task is something so simple that we do it unconsciously within a fraction of a second. For this reason, the visual cortex has been used as an inspiration in many artificial vision systems.

Image

features for passage to the next level.

outputs of many simple cells. 5.2.1.1. Hierarchical models

complex patterns.

MAX MAX

**Figure 4.** Example of a hierarchical model [51], consisting of multiple layers organized hierarchically. Each level in the hierarchy is composed of a layer of simple cells followed by a layer of complex cells. The simple cells are doing an extraction operation, combining features of the lower level to form more complex features. Complex cells are doing a pooling operation, selecting only the strongest (MAX)

in the hierarchy, they found what they called "complex cells", which are insensitive to phase and location in the visual field, but are still sensitive to oriented bars. This is explained in their model by the complex cells having a larger receptive field than simple cells, thus integrating

Inspired by the hierarchical model of Hubel and Wiesel, convolutional neural networks were proposed. Good examples of such networks are LeNet-5[30] and HMAX[54]. As illustrated in figure 4, a typical hierarchical model is composed of multiple layers of simple and complex cells. As we go up in the hierarchy, lower level features are combined into more and more

Simple cells achieve feature extraction while complex cells realize a pooling operation. Feature extraction is done using the convolution between the input and the features, hence the name "convolutional neural networks". The pooling operation can be achieved in many different ways, but the most popular is the one used in the HMAX model, that is, the MAX operation. The MAX operation selects the highest input on its receptive field, and thus only allows the

strongest features to be propagated to the next level of the hierarchy.

Simple Cells Extraction

Pooling Complex Cells

Models of Information Processing in the Visual Cortex 237

MAX

There are many ways to categorize the different object recognition models present in the literature. For the purpose of this chapter, we will discern two main categories: spiking and non-spiking models. The principal differences between those two approaches are that spiking models use bio-inspired spiking mechanism like STDP, synchrony and oscillations while non-spiking approaches tend to use statistical methods to achieve brain-like behavior. Spiking models are less popular in the literature because they tend to be computationally more expensive. However, they offer models that are more biologically plausible, and in specific cases more robust.

## **5.2. Non-spiking models**

Since there are so many non spiking visual models in the literature, we further divide this section into three parts. We first review bottom-up models, which are strictly feed forward. We then review models incorporating top-down components, that is, models using feedback from previous or learned data to influence the input. We then take a quick look at models based on modeling visual attention.

#### *5.2.1. Bottom-up models*

One of the earliest models of the visual cortex originated in the work of Hubel and Wiesel[25]. They described a hierarchical organization of the primary visual cortex where the lower level cells were responsive to visual patterns containing bars (edges-like). These "simple cells", as they called them, are selective to bars of specific orientation, location and phase. Higher up

**Figure 4.** Example of a hierarchical model [51], consisting of multiple layers organized hierarchically. Each level in the hierarchy is composed of a layer of simple cells followed by a layer of complex cells. The simple cells are doing an extraction operation, combining features of the lower level to form more complex features. Complex cells are doing a pooling operation, selecting only the strongest (MAX) features for passage to the next level.

in the hierarchy, they found what they called "complex cells", which are insensitive to phase and location in the visual field, but are still sensitive to oriented bars. This is explained in their model by the complex cells having a larger receptive field than simple cells, thus integrating outputs of many simple cells.

#### 5.2.1.1. Hierarchical models

10 Visual Cortex

grasping. They study the interaction between anterior intra-parietal and premotor areas. They also make predictions on neural activity patterns at population and single unit levels.

Moving on to models with more concrete applications, Yoshimi and Allen [65] propose an integrated view with a robotic application. They describe a real-time computer vision system with a gripper. A closed feedback loop control is used between the vision system and the gripper. Visual primitives are used to assist in the grasping and manipulation. Böhme and Heinke [5] implement the Selective Attention for Action model (SAAM) by taking into account the physical properties of the hand including anatomy. Their model is based on the fact that visual attention is guided by physical characteristics of objects (like the handle of a cup). Mehta and Schaal [37] study the visuomotor control of an unstable dynamic system (the balancing of a pole on a finger) with Smith predictors, Kalman filters, tapped-delay lines, and delay-uncompensated control. After validation with human participants, they exclude these models and propose the existence of a forward model in the sensory preprocessing loop.

In this section, we present models that are related to the ventral pathway of the visual cortex, that is, models that have to do with form and object recognition. For many years, object recognition has been one of the most challenging problems in the field of artificial intelligence. However, for humans, this task is something so simple that we do it unconsciously within a fraction of a second. For this reason, the visual cortex has been used as an inspiration in many

There are many ways to categorize the different object recognition models present in the literature. For the purpose of this chapter, we will discern two main categories: spiking and non-spiking models. The principal differences between those two approaches are that spiking models use bio-inspired spiking mechanism like STDP, synchrony and oscillations while non-spiking approaches tend to use statistical methods to achieve brain-like behavior. Spiking models are less popular in the literature because they tend to be computationally more expensive. However, they offer models that are more biologically plausible, and in

Since there are so many non spiking visual models in the literature, we further divide this section into three parts. We first review bottom-up models, which are strictly feed forward. We then review models incorporating top-down components, that is, models using feedback from previous or learned data to influence the input. We then take a quick look at models

One of the earliest models of the visual cortex originated in the work of Hubel and Wiesel[25]. They described a hierarchical organization of the primary visual cortex where the lower level cells were responsive to visual patterns containing bars (edges-like). These "simple cells", as they called them, are selective to bars of specific orientation, location and phase. Higher up

**5. Object recognition**

artificial vision systems.

specific cases more robust.

**5.2. Non-spiking models**

*5.2.1. Bottom-up models*

based on modeling visual attention.

**5.1. Overview**

Inspired by the hierarchical model of Hubel and Wiesel, convolutional neural networks were proposed. Good examples of such networks are LeNet-5[30] and HMAX[54]. As illustrated in figure 4, a typical hierarchical model is composed of multiple layers of simple and complex cells. As we go up in the hierarchy, lower level features are combined into more and more complex patterns.

Simple cells achieve feature extraction while complex cells realize a pooling operation. Feature extraction is done using the convolution between the input and the features, hence the name "convolutional neural networks". The pooling operation can be achieved in many different ways, but the most popular is the one used in the HMAX model, that is, the MAX operation. The MAX operation selects the highest input on its receptive field, and thus only allows the strongest features to be propagated to the next level of the hierarchy.

#### 5.2.1.2. Learning feature extractors

The biggest problem with convolutional networks is to find a way to learn good features to extract at each level. In our visual cortex, visual stimuli are perceived from birth and visual features are learned in function of what we see in our everyday life. In the primary visual cortex, these features happen to be, as discussed earlier, edge extractors. Interestingly enough, using natural image statistics, mathematical algorithms are able to reproduce this result. Figure 5 shows some of the features learned with different algorithms. Moreover, we can see that it is possible to learn these features in a topographical organization, just like cortical maps.

on the context and the shape of the object helps the recognition process. This section presents

Models of Information Processing in the Visual Cortex 239

One of the fist models to include a top-down process involving prediction was an hierarchical neural network incorporating a Kalman Filter [49]. An improved version of this model has since been proposed [41], where sparse coding is used as a pretreatment. Another model using top-down prediction to influence inputs is Grossberg's ART model (Adaptive Resonance Theory)[18]. If complementarity is detected between ascendant (bottom-up) and descendent (top-down) data, the network will enter a resonance state, amplifying complementary data. Hawkins has proposed the HTM model (Hierarchical Temporal Memory) [20] [16], which is

Another category of top-down models are the ones learning a compositional representation of objects. They learn object structures by combining objects parts, and can then use this representation in a top-down fashion to help recognition or fill in missing elements. One such approach is known as hierarchical recursive composition [70]. This method is able to form segments of objects and combine them in a hierarchical representation that can be used to segment and recognize objects. Another approach using a compositional method is based on a Bayesian network [45]. They demonstrate the power of their system by showing their ability

Visual attention models try to reproduce the saccadic behavior of the eyes by analyzing specific regions of the image in sequence, instead of perceiving the whole image simultaneously. One of the first models of pattern recognition using the concept of visual attention has been proposed by Olshausen [44]. The visual attention process is driven by the interaction between a top-down process (memory) and bottom-up data coming from the input. In this section, we quickly review some of the most recent models for both bottom-up

Bottom-up visual attention models mostly rely on visual saliency maps. Such maps use algorithms trying to reproduce the visual saliency properties observed in the biological visual cortex. Examples of such maps are given in figure . Many algorithms for computing such maps have been proposed. For a recent review of the most popular of these algorithms, refer

All the neural models presented until now in this section are using simpler neuron models that do not have a time component. They give a numerical value as output, that can be interpreted as a strong firing rate if the value is strong, or weak otherwise. However, they do not generate spikes as biological neurons do. By modeling spikes, we can model a phenomenon such as

This section presents general models using spiking neurons in a way that is not using synchrony or oscillation. Many models have been proposed to achieve spike-timing-

some of the models trying to reproduce this prediction process.

to construct an image of an object using only their top-down process.

based on a hierarchical Bayesian network.

and top-down guidance of the visual glaze.

**5.3. Spiking and synchronisation models**

synchronization, oscillation and Hebbian learning.

*5.2.3. Visual attention models*

to the work of Toet [58].

*5.3.1. Spiking models*

**Figure 5.** Visual features learned using image statistics. The features on the right are obtained using TICA (Topographic Independent Component Analysis) [26] and the left features are obtained using the IPSD algorithm [28]. In both cases, the features are learned in a pinwheel fashion, using unsupervised learning.

However, those features are only first level features, and learning good features for higher levels is a great challenge. Networks composed of multiple layers are typically referred to as "Deep Belief Networks". Hinton et al. [22] were the first to be able to effectively learn multi-level features using his RBM (Restricted Boltzman Machine) approach. This learning method has later been extended to deep convolutional networks [48]. An approach combining RBM and a convolutional approach has also been proposed [31].

However, deep belief networks are not the only way to learn feature extractors. The SIFT features [32] are a very popular and for a long time have been considered state of the art. More recently, it has also been shown that the concepts behind SIFT features are biologically plausible [40].

#### *5.2.2. Top-down models*

As mentioned earlier in the planning section, the brain is very good at anticipating our environment. While this is obvious for applications such as target tracking, this behavior is also applied to object recognition. When we see an object, a top-down process based partly on the context and the shape of the object helps the recognition process. This section presents some of the models trying to reproduce this prediction process.

One of the fist models to include a top-down process involving prediction was an hierarchical neural network incorporating a Kalman Filter [49]. An improved version of this model has since been proposed [41], where sparse coding is used as a pretreatment. Another model using top-down prediction to influence inputs is Grossberg's ART model (Adaptive Resonance Theory)[18]. If complementarity is detected between ascendant (bottom-up) and descendent (top-down) data, the network will enter a resonance state, amplifying complementary data. Hawkins has proposed the HTM model (Hierarchical Temporal Memory) [20] [16], which is based on a hierarchical Bayesian network.

Another category of top-down models are the ones learning a compositional representation of objects. They learn object structures by combining objects parts, and can then use this representation in a top-down fashion to help recognition or fill in missing elements. One such approach is known as hierarchical recursive composition [70]. This method is able to form segments of objects and combine them in a hierarchical representation that can be used to segment and recognize objects. Another approach using a compositional method is based on a Bayesian network [45]. They demonstrate the power of their system by showing their ability to construct an image of an object using only their top-down process.

### *5.2.3. Visual attention models*

12 Visual Cortex

The biggest problem with convolutional networks is to find a way to learn good features to extract at each level. In our visual cortex, visual stimuli are perceived from birth and visual features are learned in function of what we see in our everyday life. In the primary visual cortex, these features happen to be, as discussed earlier, edge extractors. Interestingly enough, using natural image statistics, mathematical algorithms are able to reproduce this result. Figure 5 shows some of the features learned with different algorithms. Moreover, we can see that it is possible to learn these features in a topographical organization, just like

**Figure 5.** Visual features learned using image statistics. The features on the right are obtained using TICA (Topographic Independent Component Analysis) [26] and the left features are obtained using the IPSD algorithm [28]. In both cases, the features are learned in a pinwheel fashion, using unsupervised

However, those features are only first level features, and learning good features for higher levels is a great challenge. Networks composed of multiple layers are typically referred to as "Deep Belief Networks". Hinton et al. [22] were the first to be able to effectively learn multi-level features using his RBM (Restricted Boltzman Machine) approach. This learning method has later been extended to deep convolutional networks [48]. An approach combining

However, deep belief networks are not the only way to learn feature extractors. The SIFT features [32] are a very popular and for a long time have been considered state of the art. More recently, it has also been shown that the concepts behind SIFT features are biologically

As mentioned earlier in the planning section, the brain is very good at anticipating our environment. While this is obvious for applications such as target tracking, this behavior is also applied to object recognition. When we see an object, a top-down process based partly

RBM and a convolutional approach has also been proposed [31].

5.2.1.2. Learning feature extractors

cortical maps.

learning.

plausible [40].

*5.2.2. Top-down models*

Visual attention models try to reproduce the saccadic behavior of the eyes by analyzing specific regions of the image in sequence, instead of perceiving the whole image simultaneously. One of the first models of pattern recognition using the concept of visual attention has been proposed by Olshausen [44]. The visual attention process is driven by the interaction between a top-down process (memory) and bottom-up data coming from the input. In this section, we quickly review some of the most recent models for both bottom-up and top-down guidance of the visual glaze.

Bottom-up visual attention models mostly rely on visual saliency maps. Such maps use algorithms trying to reproduce the visual saliency properties observed in the biological visual cortex. Examples of such maps are given in figure . Many algorithms for computing such maps have been proposed. For a recent review of the most popular of these algorithms, refer to the work of Toet [58].

## **5.3. Spiking and synchronisation models**

All the neural models presented until now in this section are using simpler neuron models that do not have a time component. They give a numerical value as output, that can be interpreted as a strong firing rate if the value is strong, or weak otherwise. However, they do not generate spikes as biological neurons do. By modeling spikes, we can model a phenomenon such as synchronization, oscillation and Hebbian learning.

#### *5.3.1. Spiking models*

This section presents general models using spiking neurons in a way that is not using synchrony or oscillation. Many models have been proposed to achieve spike-timing-

**Figure 6.** Example of saliency map. Using a JET color map, regions in red are considred to be more visualy salient. The saliency was computed using the FastSUN algorithm [42].

dependent plasticity (STDP). Thorpe and Masquelier have proposed an implementation of HMAX using spiking neurons [35]. They show that such hierarchical models can be implemented and trained using STDP.

**Figure 7.** The binding problem [11]. In the leftmost column, we have an image from which 4 features are responding. Each feature is represented by a neuron spiking through time in the lower part of the figure.

Models of Information Processing in the Visual Cortex 241

of groups of neurons encoding an extended object [60]. Most studies however agree that synchrony and oscillation plays a role in the functioning of the visual cortex. Mechanisms such as binding-by-synchrony and temporal binding could very well be occurring in higher

One of the most popular models based on temporal binding is the LEGION network[63]. It was first developed to provide a neurocomputational foundation to the temporal binding theory. There are many applications for legion in scene analysis, particularly for image segmentation[67]. Pichevar and Rouat then proposed the ODLM network [46], which extends the LEGION model. ODLM is able to perform binding and matching in signal processing applications that are insensitive to affine transforms. Therefore, patterns can be recognized independently of their size, position and rotations. They have shown how binding can be used to segment and/or compare images based on pixel-values, and also to achieve sound

Slotine and his group have also proposed a model based on synchronization [66] that achieves visual grouping of orientation features. However, they do not use real images, but only orientation features. The only binding model that reproduces the vision process from real images is the Maplet model [52] from Malsburg's lab. Their model is computationally quite

The binding problem consists in grouping these features together to recognize objects. The binding-by-synchrony offers a solution to this problem by using rhythmic oscillations. Features belonging to a same object will oscillate in synchrony, as shown in the middle column, where all the features combine to form a vase. In the rightmost column, with the same 4 features responding, 2

distinct groups of oscillation occur, thus forming 2 faces looking at each other.

areas of the brain which are harder to study and understand [60].

5.3.2.2. Binding-by-synchrony models

intensive, but is able to achieve face recognition.

source separation.

Another concept that can be implemented using spiking neurons is rank-order coding (ROC). In ROC, the activation order of neurons transports information. Neurons firing first represent more important information than neurons firing at a later time after presentation of stimuli. A recognition model based on this principle is the SpikeNet network [9].

Many spiking models are now proposed to achieve different vision tasks. For instance, a vision system achieving image recognition using spiking neurons [55] has been shown to be robust to rotation and occlusion.

#### *5.3.2. Synchrony models*

Most of the vision models based on synchrony and oscillations are based on the binding problem and the binding-by-synchrony hypothesis. In this section, we first give a quick overview of what is the binding problem and we then describes some of the models implementing it.

#### 5.3.2.1. The binding problem

A fundamental dilemma in the brain, or in any recognition system, is the combinational problem. When a signal is perceived, it is broken down into a multitude of features, which then need to be recombined to achieve recognition. This has been defined by Malsburg [61] as the "Binding Problem".

One solution proposed to solve the binding problem is the binding-by-synchrony hypothesis. According to Milner [38] and van der Malsburg [61], groups of neurons in the brain can be bound by synchrony when these groups belong to a same external entity. The validity of this hypothesis is however highly debated in the literature. The binding problem itself is highly controversial [50]. In fact, no real evidence has yet been found regarding the synchronization

**Figure 7.** The binding problem [11]. In the leftmost column, we have an image from which 4 features are responding. Each feature is represented by a neuron spiking through time in the lower part of the figure. The binding problem consists in grouping these features together to recognize objects. The binding-by-synchrony offers a solution to this problem by using rhythmic oscillations. Features belonging to a same object will oscillate in synchrony, as shown in the middle column, where all the features combine to form a vase. In the rightmost column, with the same 4 features responding, 2 distinct groups of oscillation occur, thus forming 2 faces looking at each other.

of groups of neurons encoding an extended object [60]. Most studies however agree that synchrony and oscillation plays a role in the functioning of the visual cortex. Mechanisms such as binding-by-synchrony and temporal binding could very well be occurring in higher areas of the brain which are harder to study and understand [60].

#### 5.3.2.2. Binding-by-synchrony models

14 Visual Cortex

**Figure 6.** Example of saliency map. Using a JET color map, regions in red are considred to be more

dependent plasticity (STDP). Thorpe and Masquelier have proposed an implementation of HMAX using spiking neurons [35]. They show that such hierarchical models can be

Another concept that can be implemented using spiking neurons is rank-order coding (ROC). In ROC, the activation order of neurons transports information. Neurons firing first represent more important information than neurons firing at a later time after presentation of stimuli.

Many spiking models are now proposed to achieve different vision tasks. For instance, a vision system achieving image recognition using spiking neurons [55] has been shown to be

Most of the vision models based on synchrony and oscillations are based on the binding problem and the binding-by-synchrony hypothesis. In this section, we first give a quick overview of what is the binding problem and we then describes some of the models

A fundamental dilemma in the brain, or in any recognition system, is the combinational problem. When a signal is perceived, it is broken down into a multitude of features, which then need to be recombined to achieve recognition. This has been defined by Malsburg [61] as

One solution proposed to solve the binding problem is the binding-by-synchrony hypothesis. According to Milner [38] and van der Malsburg [61], groups of neurons in the brain can be bound by synchrony when these groups belong to a same external entity. The validity of this hypothesis is however highly debated in the literature. The binding problem itself is highly controversial [50]. In fact, no real evidence has yet been found regarding the synchronization

visualy salient. The saliency was computed using the FastSUN algorithm [42].

A recognition model based on this principle is the SpikeNet network [9].

implemented and trained using STDP.

robust to rotation and occlusion.

*5.3.2. Synchrony models*

5.3.2.1. The binding problem

the "Binding Problem".

implementing it.

One of the most popular models based on temporal binding is the LEGION network[63]. It was first developed to provide a neurocomputational foundation to the temporal binding theory. There are many applications for legion in scene analysis, particularly for image segmentation[67]. Pichevar and Rouat then proposed the ODLM network [46], which extends the LEGION model. ODLM is able to perform binding and matching in signal processing applications that are insensitive to affine transforms. Therefore, patterns can be recognized independently of their size, position and rotations. They have shown how binding can be used to segment and/or compare images based on pixel-values, and also to achieve sound source separation.

Slotine and his group have also proposed a model based on synchronization [66] that achieves visual grouping of orientation features. However, they do not use real images, but only orientation features. The only binding model that reproduces the vision process from real images is the Maplet model [52] from Malsburg's lab. Their model is computationally quite intensive, but is able to achieve face recognition.

## **6. Conclusion**

This chapter presented different types of models of the visual cortex. We first discussed the idea that the visual cortex is in fact only a part of a more complex system, cooperating with other sensorial areas. However, because models are mainly used for simplifying reality, we then focused on vision specific models. We first presented models closely related to biology, with models directly trying to reproduce biological data, and others trying to reproduce the global architecture of the visual cortex. We then presented models related to movement detection and planning. Anticipation being an important mechanism in the brain we presented models using vision for planning and controlling movements. Finally, we glanced through vision models related to form and object recognition. We looked at feedforward and top-down models, as well as models based on neural synchrony and oscillations. Since all these models are very different, there is no consensus on how the visual cortex should be modeled. Also, all models do not have the same goals. The best possible model for a given situation is the simplest model that allows reaching the targeted goals.

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Models of Information Processing in the Visual Cortex 243

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We have only given a small overview of all the models of vision reported in the literature. This chapter was only intended to have a quick peek into the world of visual cortex models. We also have to keep in mind that our current understanding of the brain is far from complete. What we are doing right now is akin to trying to understand the mechanics of the ocean by studying one drop of water at a time. Considering the rapid evolution of knowledge about the brain, it is quite possible that some of the currently available models may become obsolete. But this is the basic characteristic of any scientific endeavor.

## **Author details**

Vincent de Ladurantaye, Jean Rouat and Jacques Vanden-Abeele *NECOTIS, Université de Sherbrooke, Québec, Canada*

## **7. References**


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16 Visual Cortex

This chapter presented different types of models of the visual cortex. We first discussed the idea that the visual cortex is in fact only a part of a more complex system, cooperating with other sensorial areas. However, because models are mainly used for simplifying reality, we then focused on vision specific models. We first presented models closely related to biology, with models directly trying to reproduce biological data, and others trying to reproduce the global architecture of the visual cortex. We then presented models related to movement detection and planning. Anticipation being an important mechanism in the brain we presented models using vision for planning and controlling movements. Finally, we glanced through vision models related to form and object recognition. We looked at feedforward and top-down models, as well as models based on neural synchrony and oscillations. Since all these models are very different, there is no consensus on how the visual cortex should be modeled. Also, all models do not have the same goals. The best possible model for a given

We have only given a small overview of all the models of vision reported in the literature. This chapter was only intended to have a quick peek into the world of visual cortex models. We also have to keep in mind that our current understanding of the brain is far from complete. What we are doing right now is akin to trying to understand the mechanics of the ocean by studying one drop of water at a time. Considering the rapid evolution of knowledge about the brain, it is quite possible that some of the currently available models may become obsolete.

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*NECOTIS, Université de Sherbrooke, Québec, Canada*

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**6. Conclusion**

**Author details**

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© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

**Neurovascular and Neurometabolic** 

The quest to understand the functional organization of the brain by assessing changes in brain circulation has occupied scientists for more than a century. Significant increases in cerebral blood flow (CBF) accompany brain activity. The direct relationship between CBF and brain activity has been known since 1881(1). For more than a century, neuroscientists have assumed that increased metabolic demands in response to brain activation drive the elevation of CBF. Researchers could not investigate the hypothesis further until the 1980s, with the development of positron emission tomography (PET), a noninvasive imaging method to measure hemodynamic and metabolic changes in response to taskinduced brain activation. Using PET and visual stimulation, Fox and colleagues (2) found that CBF increases were linearly coupled with neuronal activity as expected; however, changes in the cerebral metabolic rate of oxygen (CMRO2) were significantly lower than that of CBF. The CBF–CMRO2 "uncoupling" phenomenon therefore contradicts the early

The "uncoupling" discovery not only reshaped our understanding of the flow–metabolism interaction in response to brain activation but also led to the discovery of blood oxygenation level–dependent (BOLD) functional magnetic resonance imaging (fMRI) in the 1990s (3, 4). Further, the discovery evoked heated debate on physiological interpretations—for example, whether oxidative metabolism or glycolysis meets the energy demand during brain activation, and whether oxygen demand or some other mechanism mediates increases in CBF. In this chapter, we review the historical events that led to using PET to discover the neurovascular and neurometabolic "uncoupling"; the invention of BOLD fMRI techniques; and the development of physiological hypotheses to resolve the "uncoupling" mystery in

**Uncoupling in the Visual Cortex** 

Ai-Ling Lin, Jia-Hong Gao and Peter T. Fox

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51074

**1. Introduction** 

hypothesis.

the visual cortex.

## **Neurovascular and Neurometabolic Uncoupling in the Visual Cortex**

Ai-Ling Lin, Jia-Hong Gao and Peter T. Fox

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51074

## **1. Introduction**

20 Visual Cortex

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The quest to understand the functional organization of the brain by assessing changes in brain circulation has occupied scientists for more than a century. Significant increases in cerebral blood flow (CBF) accompany brain activity. The direct relationship between CBF and brain activity has been known since 1881(1). For more than a century, neuroscientists have assumed that increased metabolic demands in response to brain activation drive the elevation of CBF. Researchers could not investigate the hypothesis further until the 1980s, with the development of positron emission tomography (PET), a noninvasive imaging method to measure hemodynamic and metabolic changes in response to taskinduced brain activation. Using PET and visual stimulation, Fox and colleagues (2) found that CBF increases were linearly coupled with neuronal activity as expected; however, changes in the cerebral metabolic rate of oxygen (CMRO2) were significantly lower than that of CBF. The CBF–CMRO2 "uncoupling" phenomenon therefore contradicts the early hypothesis.

The "uncoupling" discovery not only reshaped our understanding of the flow–metabolism interaction in response to brain activation but also led to the discovery of blood oxygenation level–dependent (BOLD) functional magnetic resonance imaging (fMRI) in the 1990s (3, 4). Further, the discovery evoked heated debate on physiological interpretations—for example, whether oxidative metabolism or glycolysis meets the energy demand during brain activation, and whether oxygen demand or some other mechanism mediates increases in CBF. In this chapter, we review the historical events that led to using PET to discover the neurovascular and neurometabolic "uncoupling"; the invention of BOLD fMRI techniques; and the development of physiological hypotheses to resolve the "uncoupling" mystery in the visual cortex.

© 2012 Lin et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **2. Neurovascular and neurometabolic uncoupling in the visual cortex**

Neurovascular and Neurometabolic Uncoupling in the Visual Cortex 249

**Figure 2. (A)** The apparatus Mosso used to record Bertino's brain pulsations. **(B)** Mosso's recordings, taken from the forearm (f) and the brain (b), show stronger brain pulsations after events (marked by the

These studies suggested that measuring CBF might be an important way to assess brain function during mental activity. Roy and Sherrington, two distinguished British physiologists, further characterized the relationship between brain function and CBF (6). They attributed task-induced vasodilation to an increased demand for cerebral metabolism in response to neuronal activity. They stated, "the chemical products of cerebral metabolism contained in the lymph which bathes the walls of the arterioles of the brain can cause variations of the caliber of the cerebral vessels: that in this re-action the brain possesses an intrinsic mechanism by which its vascular supply can be varied locally in correspondence with local variations of functional activity". This statement implies that cerebral functional activity, energy metabolism, and blood flow are closely related. Researchers have interpreted the Roy–Sherrington principle to mean that CBF changes reflect a tight coupling

between cellular energy requirements and vascular delivery of glucose and oxygen.

Over the past century, the visual cortex has made significant contributions to the evolving investigations of the Roy–Sherrington principle. The first report of the close relationship between CBF and neuronal activation in the visual cortex was in 1928 by Dr. John Fulton, of the neurosurgery clinic at the Peter Bent Brigham Hospital in Boston (7). Fulton's patient had suffered a gradual loss of vision due to a collection of congenitally abnormal blood vessels serving his visual cortex. Unlike the smooth, silent blood flow in normal blood vessels, the blood flow through these abnormal vessels was turbulent and created a brief rushing sound with each heartbeat. The patient could hear this sound, as could his physicians when they listened with their stethoscope through a defect in the skull (Figure 3A). The sound increased whenever the patient opened his eyes and especially when he

arrows) that stimulated brain activity. Source: (1).

read a newspaper (Figure 3B).

## **2.1. Brain Activity vs. Cerebral Blood Flow**

Italian physiologist Angelo Mosso first observed the relationship between CBF and neuronal activity in the late nineteenth century (1). Mosso invented an apparatus to simultaneously compare human intracranial pressure changes measured through a traumatic skull injury with pressure changes in the forearm or foot (Figure 1). Using this approach, he investigated particular cerebral hemodynamic patterns during emotional and cognitive experiences. His most famous report was the case of Michele Bertino, a 37-year-old farmer who had a large fracture to the skull (1). The fractured bone pieces were removed and the cerebral mass was exposed through a 2-cm bone breach in the right frontal region. Mosso recorded changes in brain volume related to CBF through a button fixed to the wooden cupola with a sheet of gutta-percha resting on Bertino's exposed dura mater and connected to a screw on the recording drum (Figure 2A).

**Figure 1.** Mosso's devices for recording the blood volumes in arm and brain. 1, Forearm pulsation recording; 2, brain pulsation recording. Source: (5).

When the blood volume in the brain changed, the pulsation of the brain increased, increasing the pressure on the button and on the screw, thus compressing the air inside the drum. Changes in air compression were transmitted to a second recording drum and then written on a rotating cylinder. When Mosso asked Bertino to multiply 8 12, the pulsations of the brain increased within a few seconds after the request—but pulsations in the forearm did not. Similarly, when Mosso asked whether the chiming of the local church bell reminded Bertino that he had forgotten his midday prayers, Bertino said yes, and his brain pulsated again (Figure 2B).

recording drum (Figure 2A).

recording; 2, brain pulsation recording. Source: (5).

again (Figure 2B).

**2.1. Brain Activity vs. Cerebral Blood Flow** 

**2. Neurovascular and neurometabolic uncoupling in the visual cortex** 

**Figure 1.** Mosso's devices for recording the blood volumes in arm and brain. 1, Forearm pulsation

When the blood volume in the brain changed, the pulsation of the brain increased, increasing the pressure on the button and on the screw, thus compressing the air inside the drum. Changes in air compression were transmitted to a second recording drum and then written on a rotating cylinder. When Mosso asked Bertino to multiply 8 12, the pulsations of the brain increased within a few seconds after the request—but pulsations in the forearm did not. Similarly, when Mosso asked whether the chiming of the local church bell reminded Bertino that he had forgotten his midday prayers, Bertino said yes, and his brain pulsated

Italian physiologist Angelo Mosso first observed the relationship between CBF and neuronal activity in the late nineteenth century (1). Mosso invented an apparatus to simultaneously compare human intracranial pressure changes measured through a traumatic skull injury with pressure changes in the forearm or foot (Figure 1). Using this approach, he investigated particular cerebral hemodynamic patterns during emotional and cognitive experiences. His most famous report was the case of Michele Bertino, a 37-year-old farmer who had a large fracture to the skull (1). The fractured bone pieces were removed and the cerebral mass was exposed through a 2-cm bone breach in the right frontal region. Mosso recorded changes in brain volume related to CBF through a button fixed to the wooden cupola with a sheet of gutta-percha resting on Bertino's exposed dura mater and connected to a screw on the

**Figure 2. (A)** The apparatus Mosso used to record Bertino's brain pulsations. **(B)** Mosso's recordings, taken from the forearm (f) and the brain (b), show stronger brain pulsations after events (marked by the arrows) that stimulated brain activity. Source: (1).

These studies suggested that measuring CBF might be an important way to assess brain function during mental activity. Roy and Sherrington, two distinguished British physiologists, further characterized the relationship between brain function and CBF (6). They attributed task-induced vasodilation to an increased demand for cerebral metabolism in response to neuronal activity. They stated, "the chemical products of cerebral metabolism contained in the lymph which bathes the walls of the arterioles of the brain can cause variations of the caliber of the cerebral vessels: that in this re-action the brain possesses an intrinsic mechanism by which its vascular supply can be varied locally in correspondence with local variations of functional activity". This statement implies that cerebral functional activity, energy metabolism, and blood flow are closely related. Researchers have interpreted the Roy–Sherrington principle to mean that CBF changes reflect a tight coupling between cellular energy requirements and vascular delivery of glucose and oxygen.

Over the past century, the visual cortex has made significant contributions to the evolving investigations of the Roy–Sherrington principle. The first report of the close relationship between CBF and neuronal activation in the visual cortex was in 1928 by Dr. John Fulton, of the neurosurgery clinic at the Peter Bent Brigham Hospital in Boston (7). Fulton's patient had suffered a gradual loss of vision due to a collection of congenitally abnormal blood vessels serving his visual cortex. Unlike the smooth, silent blood flow in normal blood vessels, the blood flow through these abnormal vessels was turbulent and created a brief rushing sound with each heartbeat. The patient could hear this sound, as could his physicians when they listened with their stethoscope through a defect in the skull (Figure 3A). The sound increased whenever the patient opened his eyes and especially when he read a newspaper (Figure 3B).

Neurovascular and Neurometabolic Uncoupling in the Visual Cortex 251

**Figure 4. (A)** Checkerboard stimulation. **(B)** CBF image during the initial unstimulated state. **(C)** CBF

**Figure 5. (A)** Photic stimulation caused a selective regional CBF (rCBF) increase in the striate cortex up to a maximum response at 7.8 Hz. The rCBF declined with frequency increases beyond 7.8 Hz but remained above the unstimulated state. Each point and error bar represent the mean ± SD at each stimulus frequency. rCBF response is expressed as the percent change from the initial unstimulated scan. **(B)** Striate cortex percent change in rCBF from the initial unstimulated state, varied as a linear function of stimulus frequency between 0 and 7.8 Hz. The function of the linear regression is given and

image during 16-Hz photic stimulation. **(D)** CBF image of panel C (16 Hz) versus panel B (unstimulated). The position and configuration of the regional CBF response elicited by repetitive

patterned-flash stimuli are illustrated. Source: (8).

the regression line plotted. Source: (8).

**Figure 3. (A)** Patient 3 weeks after operation, showing the region explored. The arrow indicates the point of maximum intensity of the bruit. This overlies the region of greatest vascularity of the angioma. **(B)** Typical electrophonograms of bruit **(a)**, after 10 minutes' rest in a darkened room; **(b)**, after 2 minutes' reading of small print illuminated by a single 40-watt tungsten bulb at 5 feet; **(c)**, 3 minutes later, after 2.5 minutes' rest in a darkened room. Time above: 0–2 sec. Source: (7).

The correlation between CBF changes in a specific sensory system (visual system) during mental activity (reading) was remarkable. However, the full importance of this work was not appreciated until the development of accurate techniques to measure CBF and brain metabolism in laboratory animals and in humans.

The development of positron emission tomography (PET) in the 1960s further advanced the field of research. PET is a nuclear medicine imaging technique that produces a threedimensional, in vivo image of functional processes in the body. Using 15O-labeled water (i.e., H215O; half-life = 123 sec) as a freely diffusible tracer, PET can measure CBF in less than 1 min. With these techniques, Fox and Raichle (8) revisited the task-induced neurovascular coupling in the human visual cortex in vivo. They measured CBF changes with a visual system that delivered full-field flashes of fixed wavelength, luminance, and duration over frequencies from 1 to 60 Hz (Figure 4A). For the first time, noninvasive neuroimaging showed localized CBF changes in the human visual cortex in response to neuronal activation (Figure 4, B to D). Knowing that the time for full recovery of the visualevoked potential was approximately 125 msec and that H215O PET measurements integrated over 40 sec, Fox and colleagues reasoned that the CBF response should be linear at least up to a repetition rate of 8 Hz (i.e., 1/0.125 sec; the stimulation is not effective for stimulus frequency >8 Hz). The findings were in excellent agreement with their hypothesis: CBF rose linearly with stimulus rate (*r* = 0.886; *p* < 0.0005), peaking at 7.8 Hz (Figure 5, A and B). Response locations also were tightly grouped. The rate–response function soon was replicated and extended by using a reversing checkerboard, constant-luminance stimulus (9). The pioneer works by Fox and colleagues were significant in two aspects: they were the first studies to show CBF in response to visual stimulation in conscious adult humans, and they confirmed that CBF is linear coupled with neuronal activity till it reaches the maximal visual-evoked potential.

**Figure 3. (A)** Patient 3 weeks after operation, showing the region explored. The arrow indicates the point of maximum intensity of the bruit. This overlies the region of greatest vascularity of the angioma. **(B)** Typical electrophonograms of bruit **(a)**, after 10 minutes' rest in a darkened room; **(b)**, after 2 minutes' reading of small print illuminated by a single 40-watt tungsten bulb at 5 feet; **(c)**, 3 minutes

The correlation between CBF changes in a specific sensory system (visual system) during mental activity (reading) was remarkable. However, the full importance of this work was not appreciated until the development of accurate techniques to measure CBF and brain

The development of positron emission tomography (PET) in the 1960s further advanced the field of research. PET is a nuclear medicine imaging technique that produces a threedimensional, in vivo image of functional processes in the body. Using 15O-labeled water (i.e., H215O; half-life = 123 sec) as a freely diffusible tracer, PET can measure CBF in less than 1 min. With these techniques, Fox and Raichle (8) revisited the task-induced neurovascular coupling in the human visual cortex in vivo. They measured CBF changes with a visual system that delivered full-field flashes of fixed wavelength, luminance, and duration over frequencies from 1 to 60 Hz (Figure 4A). For the first time, noninvasive neuroimaging showed localized CBF changes in the human visual cortex in response to neuronal activation (Figure 4, B to D). Knowing that the time for full recovery of the visualevoked potential was approximately 125 msec and that H215O PET measurements integrated over 40 sec, Fox and colleagues reasoned that the CBF response should be linear at least up to a repetition rate of 8 Hz (i.e., 1/0.125 sec; the stimulation is not effective for stimulus frequency >8 Hz). The findings were in excellent agreement with their hypothesis: CBF rose linearly with stimulus rate (*r* = 0.886; *p* < 0.0005), peaking at 7.8 Hz (Figure 5, A and B). Response locations also were tightly grouped. The rate–response function soon was replicated and extended by using a reversing checkerboard, constant-luminance stimulus (9). The pioneer works by Fox and colleagues were significant in two aspects: they were the first studies to show CBF in response to visual stimulation in conscious adult humans, and they confirmed that CBF is linear coupled with neuronal activity till it reaches the maximal

later, after 2.5 minutes' rest in a darkened room. Time above: 0–2 sec. Source: (7).

metabolism in laboratory animals and in humans.

visual-evoked potential.

**Figure 4. (A)** Checkerboard stimulation. **(B)** CBF image during the initial unstimulated state. **(C)** CBF image during 16-Hz photic stimulation. **(D)** CBF image of panel C (16 Hz) versus panel B (unstimulated). The position and configuration of the regional CBF response elicited by repetitive patterned-flash stimuli are illustrated. Source: (8).

**Figure 5. (A)** Photic stimulation caused a selective regional CBF (rCBF) increase in the striate cortex up to a maximum response at 7.8 Hz. The rCBF declined with frequency increases beyond 7.8 Hz but remained above the unstimulated state. Each point and error bar represent the mean ± SD at each stimulus frequency. rCBF response is expressed as the percent change from the initial unstimulated scan. **(B)** Striate cortex percent change in rCBF from the initial unstimulated state, varied as a linear function of stimulus frequency between 0 and 7.8 Hz. The function of the linear regression is given and the regression line plotted. Source: (8).

## **2.2. Discovery of the neurovascular and neurometabolic uncoupling**

With these powerful tools in hand, Fox and colleagues took a further step to test the Roy– Sherrington principle, that is, to determine whether increased metabolic demands (i.e., glucose and oxygen consumption) in response to neuronal activity drove the task-evoked CBF changes. At first, researchers rationally assumed that cerebral metabolic rate of glucose (CMRGlc) and oxygen (CMRO2) would increase proportionally with the increased CBF and neuronal activity because neurons account for most of the energy consumption during brain activation through ATP generated by oxidative phosphorylation of glucose. In addition to CBF, they measured CMRGlc by using 18F-labeled 2-fluoro-2-deoxy-D-glucose (18FDG) and CMRO2 by using 15O-labeled oxygen (15O2). Again, using visual stimulation as a paradigm, Fox and colleagues used PET to simultaneously measure the task-induced changes in CBF, CMRGlc, and CMRO2 in the activated human cortex. As expected, task performance reliably elicited large, highly focal increases in CBF (2) and CMRGlc (2, 10) (Figure 6A). The observed increases in CBF and CMRGlc were similar in magnitude, typically 30%–50%.

Neurovascular and Neurometabolic Uncoupling in the Visual Cortex 253

glycolysis alone may provide the energy needed for the transient changes in brain activity; and (iii) factors other than oxidative metabolism and total energy demand must regulate

Using 1H nuclear magnetic resonance spectroscopy (MRS), a method that exploits the magnetic properties of metabolites, Prichard and colleagues observed that tissue lactate concentration [Lac] significantly increases during visual stimulation (Figure 7, A and B). The observation confirmed that the stimulus-evoked increase in glucose consumption observed

**Figure 7. (A)** 1H spectra from 13 cc of human visual cortex before, during, and after photic stimulation (stim) by red dot grids flashing at 16 Hz in front of each eye. The frequency axis is in ppm and was set from the prominent resonance of *N*-acetylaspartate (NAA) at 2.02 ppm. The shaded area highlights the lactate methyl proton resonance at 1.33 ppm, with its characteristic 7-Hz splitting. The creatine

resonance at 3.04 ppm is the total signal from methyl protons of phosphocreatine and creatine. **(B)** Time course of the experiment illustrated in panel A, with intensities of the lactate resonance plotted as a

Later studies further identified that CBF–CMRO2 uncoupling also depended on rate and duration (13, 14). Using graded visual stimulation, Vafaee and Gjedde (13) found that the percent fractional changes (%Δ) in CBF peaked at 8 Hz, similar to the finding of Fox and colleagues, whereas %ΔCMRO2 reached a maximum at 4 Hz (Figure 8). With prolonged visual stimulation (25 min), Mintun and colleagues (14) found that after 1 min of stimulation, CMRO2 increased only 4.7% compared with baseline and CBF increased 40.7% (Figure 9, A and B). However, after 25 min of stimulation, the increase in CMRO2 compared with baseline was 15.0%, having tripled from that measured at 1 min (Figure 9B). CBF decreased to 37.1% after 1 min of visual stimulation and then returned almost to baseline values after 25 min of activation (Figure 9A). These two studies further supported the Fox group's argument that factors other than oxidative metabolism regulate CBF response.

CBF response.

with PET is at least partially nonoxidative (11, 12).

percentage of its control intensity. Source: (11).

**Figure 6. (A)** Glucose metabolic rate (lower row) and blood flow (upper row) were closely coupled throughout the brain both at rest and during visual stimulation. Phasic neural activation (visual stimulation) increased regional glucose uptake and blood flow by a similar amount (51% and 50%, respectively). **(B)** The metabolic rate of oxygen (lower row) and blood flow (upper row) were closely coupled throughout the brain at rest. Visual stimulation, however, increased regional oxygen consumption minimally (5%) while markedly increasing blood flow (50%). Source: (2).

However, to their surprise, Fox and colleagues (2) observed that task-induced increases in CMRO2 (5%) were much lower than those in CBF or CMRGlc (Figure 6B). The CMRO2 shortfall during focal neuronal activation, in fact, caused a local oxygen surplus, with the oxygen extraction fraction falling from a resting value of ~40% to a task-state value of ~20%. These findings contradicted the Roy–Sherrington hypothesis.

Because either oxidative or nonoxidative (i.e., lactate producing) pathways can metabolize glucose and because the increase of CMRO2 was minimal, Fox and colleagues suggested that (i) glucose is predominately metabolized by anaerobic glycolysis; (ii) the energy demand associated with neuronal activation is small (as opposed to resting-state demand), and glycolysis alone may provide the energy needed for the transient changes in brain activity; and (iii) factors other than oxidative metabolism and total energy demand must regulate CBF response.

252 Visual Cortex – Current Status and Perspectives

**2.2. Discovery of the neurovascular and neurometabolic uncoupling** 

increases in CBF and CMRGlc were similar in magnitude, typically 30%–50%.

**Figure 6. (A)** Glucose metabolic rate (lower row) and blood flow (upper row) were closely coupled throughout the brain both at rest and during visual stimulation. Phasic neural activation (visual stimulation) increased regional glucose uptake and blood flow by a similar amount (51% and 50%, respectively). **(B)** The metabolic rate of oxygen (lower row) and blood flow (upper row) were closely coupled throughout the brain at rest. Visual stimulation, however, increased regional oxygen consumption minimally (5%) while markedly increasing blood flow (50%). Source: (2).

However, to their surprise, Fox and colleagues (2) observed that task-induced increases in CMRO2 (5%) were much lower than those in CBF or CMRGlc (Figure 6B). The CMRO2 shortfall during focal neuronal activation, in fact, caused a local oxygen surplus, with the oxygen extraction fraction falling from a resting value of ~40% to a task-state value of ~20%.

Because either oxidative or nonoxidative (i.e., lactate producing) pathways can metabolize glucose and because the increase of CMRO2 was minimal, Fox and colleagues suggested that (i) glucose is predominately metabolized by anaerobic glycolysis; (ii) the energy demand associated with neuronal activation is small (as opposed to resting-state demand), and

These findings contradicted the Roy–Sherrington hypothesis.

With these powerful tools in hand, Fox and colleagues took a further step to test the Roy– Sherrington principle, that is, to determine whether increased metabolic demands (i.e., glucose and oxygen consumption) in response to neuronal activity drove the task-evoked CBF changes. At first, researchers rationally assumed that cerebral metabolic rate of glucose (CMRGlc) and oxygen (CMRO2) would increase proportionally with the increased CBF and neuronal activity because neurons account for most of the energy consumption during brain activation through ATP generated by oxidative phosphorylation of glucose. In addition to CBF, they measured CMRGlc by using 18F-labeled 2-fluoro-2-deoxy-D-glucose (18FDG) and CMRO2 by using 15O-labeled oxygen (15O2). Again, using visual stimulation as a paradigm, Fox and colleagues used PET to simultaneously measure the task-induced changes in CBF, CMRGlc, and CMRO2 in the activated human cortex. As expected, task performance reliably elicited large, highly focal increases in CBF (2) and CMRGlc (2, 10) (Figure 6A). The observed

Using 1H nuclear magnetic resonance spectroscopy (MRS), a method that exploits the magnetic properties of metabolites, Prichard and colleagues observed that tissue lactate concentration [Lac] significantly increases during visual stimulation (Figure 7, A and B). The observation confirmed that the stimulus-evoked increase in glucose consumption observed with PET is at least partially nonoxidative (11, 12).

**Figure 7. (A)** 1H spectra from 13 cc of human visual cortex before, during, and after photic stimulation (stim) by red dot grids flashing at 16 Hz in front of each eye. The frequency axis is in ppm and was set from the prominent resonance of *N*-acetylaspartate (NAA) at 2.02 ppm. The shaded area highlights the lactate methyl proton resonance at 1.33 ppm, with its characteristic 7-Hz splitting. The creatine resonance at 3.04 ppm is the total signal from methyl protons of phosphocreatine and creatine. **(B)** Time course of the experiment illustrated in panel A, with intensities of the lactate resonance plotted as a percentage of its control intensity. Source: (11).

Later studies further identified that CBF–CMRO2 uncoupling also depended on rate and duration (13, 14). Using graded visual stimulation, Vafaee and Gjedde (13) found that the percent fractional changes (%Δ) in CBF peaked at 8 Hz, similar to the finding of Fox and colleagues, whereas %ΔCMRO2 reached a maximum at 4 Hz (Figure 8). With prolonged visual stimulation (25 min), Mintun and colleagues (14) found that after 1 min of stimulation, CMRO2 increased only 4.7% compared with baseline and CBF increased 40.7% (Figure 9, A and B). However, after 25 min of stimulation, the increase in CMRO2 compared with baseline was 15.0%, having tripled from that measured at 1 min (Figure 9B). CBF decreased to 37.1% after 1 min of visual stimulation and then returned almost to baseline values after 25 min of activation (Figure 9A). These two studies further supported the Fox group's argument that factors other than oxidative metabolism regulate CBF response.

Neurovascular and Neurometabolic Uncoupling in the Visual Cortex 255

In 1992, Kwong et al. used the BOLD contrast to study activation in the human visual cortex (15). During stimulation, they detected local increases in signal intensity in the medialposterior regions of the occipital lobes along the calcarine fissures (Figure 10A). Their results independently confirm PET observations that little or no increase in tissue oxygen consumption accompanies activation-induced changes in blood flow and volume (2). They also observed rate-dependent increases of BOLD signal, peaking at 8 Hz, consistent with the

**Figure 10. (A)** Noninvasive, real-time MRI mapping of V1 activation during visual stimulation. A baseline image acquired during darkness (upper left) was subtracted from subsequent images. Eight of these subtraction images are displayed, chosen when the image intensities reached a steady-state signal level, during darkness (OFF) and during 8-Hz photic stimulation (ON). **(B)** BOLD signal as a function of the frequency of light stimulus (0 Hz = darkness). Signal response is expressed as percent change from baseline unstimulated level. The largest observed response occurred at 8 Hz. For comparison with data from Fox and colleagues, CBF percent changes obtained by PET with the same stimulation paradigm at

Using BOLD signal, Davis and colleagues developed mathematical modeling to determine task-induced changes in CMRO2. By combining with MRI-based CBF measurement using arterial spin labeling techniques (16), researchers can compute relative CMRO2 changes (rCMRO2) by measuring evoked-induced changes in BOLD and CBF and basal BOLD relaxation rate (*M* value) (Eq. [1]). Consistent with the PET-based measurement, CMRO2 changes (16%) were much smaller than CBF changes (45%) during visual stimulation (17).

1 /

 

() 1 rCMRO ( ) ( ) 1 *BOLD t t CBF t*

1/

(1)

*M*

0, 1, 4, 8, 16, and 32 Hz are superimposed on the MR data. Source: (15).

2

PET observations for CBF (8) (Figure 10B).

**Figure 8.** Percent changes of CBF, CMRO2, and CMRO2:CBF ratio index (*I*O2) in the primary visual cortex as a function of checkerboard contrast reversal rate. Note maximum *I*O2 change at 8 Hz. Source: (13).

**Figure 9.** Effects of continuous visual stimulation on rCBF **(A)** and CMRO2 **(B)**. Data shown represent the mean percent change from baseline (fixation) in seven subjects. CBF increased significantly early after the onset of visual stimulation and then had a nonsignificant tendency to decrease. Initial increase in CMRO2 was small (4.7%); however, it tripled during continued stimulation and reached significant levels (15%, *p* < 0.01) at 25 min. Source: (14).

#### **2.3. Discovery of functional magnetic resonance imaging**

The CBF–CMRO2 uncoupling phenomenon led to the discovery of functional magnetic resonance imaging (fMRI), known as the blood oxygenation level–dependent (BOLD) contrast, by Ogawa and others (3, 4). The physical effect is based on the magnetic property difference between deoxyhemoglobin (dHb; i.e., hemoglobin not bound with oxygen) and oxyhemoglobin (Hb; i.e., hemoglobin that binds with oxygen). Magnetic fields can weakly repel Hb, which is diamagnetic, whereas they can attract dHb, which is paramagnetic (which makes the magnetic field less uniform and thus decreases MR signal). The dramatic increases in CBF, relative to CMRO2, bring in excess Hb (relative to dHb) to the venous blood, which causes measurable changes in the MRI signal (BOLD contrast).

In 1992, Kwong et al. used the BOLD contrast to study activation in the human visual cortex (15). During stimulation, they detected local increases in signal intensity in the medialposterior regions of the occipital lobes along the calcarine fissures (Figure 10A). Their results independently confirm PET observations that little or no increase in tissue oxygen consumption accompanies activation-induced changes in blood flow and volume (2). They also observed rate-dependent increases of BOLD signal, peaking at 8 Hz, consistent with the PET observations for CBF (8) (Figure 10B).

254 Visual Cortex – Current Status and Perspectives

levels (15%, *p* < 0.01) at 25 min. Source: (14).

**2.3. Discovery of functional magnetic resonance imaging** 

blood, which causes measurable changes in the MRI signal (BOLD contrast).

(13).

**Figure 8.** Percent changes of CBF, CMRO2, and CMRO2:CBF ratio index (*I*O2) in the primary visual cortex as a function of checkerboard contrast reversal rate. Note maximum *I*O2 change at 8 Hz. Source:

**Figure 9.** Effects of continuous visual stimulation on rCBF **(A)** and CMRO2 **(B)**. Data shown represent the mean percent change from baseline (fixation) in seven subjects. CBF increased significantly early after the onset of visual stimulation and then had a nonsignificant tendency to decrease. Initial increase in CMRO2 was small (4.7%); however, it tripled during continued stimulation and reached significant

The CBF–CMRO2 uncoupling phenomenon led to the discovery of functional magnetic resonance imaging (fMRI), known as the blood oxygenation level–dependent (BOLD) contrast, by Ogawa and others (3, 4). The physical effect is based on the magnetic property difference between deoxyhemoglobin (dHb; i.e., hemoglobin not bound with oxygen) and oxyhemoglobin (Hb; i.e., hemoglobin that binds with oxygen). Magnetic fields can weakly repel Hb, which is diamagnetic, whereas they can attract dHb, which is paramagnetic (which makes the magnetic field less uniform and thus decreases MR signal). The dramatic increases in CBF, relative to CMRO2, bring in excess Hb (relative to dHb) to the venous

**Figure 10. (A)** Noninvasive, real-time MRI mapping of V1 activation during visual stimulation. A baseline image acquired during darkness (upper left) was subtracted from subsequent images. Eight of these subtraction images are displayed, chosen when the image intensities reached a steady-state signal level, during darkness (OFF) and during 8-Hz photic stimulation (ON). **(B)** BOLD signal as a function of the frequency of light stimulus (0 Hz = darkness). Signal response is expressed as percent change from baseline unstimulated level. The largest observed response occurred at 8 Hz. For comparison with data from Fox and colleagues, CBF percent changes obtained by PET with the same stimulation paradigm at 0, 1, 4, 8, 16, and 32 Hz are superimposed on the MR data. Source: (15).

Using BOLD signal, Davis and colleagues developed mathematical modeling to determine task-induced changes in CMRO2. By combining with MRI-based CBF measurement using arterial spin labeling techniques (16), researchers can compute relative CMRO2 changes (rCMRO2) by measuring evoked-induced changes in BOLD and CBF and basal BOLD relaxation rate (*M* value) (Eq. [1]). Consistent with the PET-based measurement, CMRO2 changes (16%) were much smaller than CBF changes (45%) during visual stimulation (17).

$$\text{rCMRO}\_2(t) = \text{CBF}(t)^{1-\alpha/\beta} \left( 1 - \frac{BOLD(t) - 1}{M} \right)^{1/\beta} \tag{1}$$

Neurovascular and Neurometabolic Uncoupling in the Visual Cortex 257

**Figure 12.** Visual stimulation produces marked changes in activity in visual areas of the brain. Blood flow, glucose utilization, and oxygen availability significantly increase, but the change of the oxygen

that significant increases of CBF were needed to facilitate oxygen delivery and that predominately oxidative metabolism should meet energy demands (19). To clarify whether oxidative metabolism or glycolysis regulates CBF and energy demands, Lin and colleagues (20) performed a concurrent fMRI and 1H MRS study using graded visual stimulation (4, 8, and 16 Hz). They used fMRI to determine %ΔCBF and %ΔCMRO2 and used 1H MRS to

Findings for %ΔCBF, %ΔCMRO2, and %Δ[Lac] varied with frequency, with %ΔCBF and %Δ[Lac] peaking at 8 Hz, whereas %ΔCMRO2 reached a maximum at 4 Hz (Figure 13, A and B). The magnitudes of %ΔCBF (57.1%–65.1%) and %Δ[Lac] (31.3%–50.0%) were much larger than that of %ΔCMRO2 (12.2%–17.0%). As a result, %ΔCBF was tightly coupled with lactate production rate (Figure 14A) but negatively correlated with %CMRO2 (Figure 14B). ATP production (JATP) was calculated, using a stoichiometric equation, from the determined %ΔCMRO2 and lactate production rate. JATP was not significantly different among the three stimulus frequencies, and the increment was smaller (12%–17%) than the baseline (Rest) state. Further, oxidative metabolism predominately contributed to JATP and linearly

Taken together, the major findings from the study were as follows: (i) Increases in oxygen metabolism and energy demand of task-induced neuronal activation were small (12%–17%); (ii) oxidative (indexed by CMRO2) and nonoxidative (indexed by lactate production) metabolism coexisted during visual stimulation; and (iii) CBF increase was much larger (52%–65%) than the increase in energy demand and highly correlated with lactate production, but not with CMRO2. In response to the two main debated questions mentioned

utilization is minimal. Source: (18).

correlated with %ΔCMRO2 (Figure 14C and Figure 15).

determine %Δ[Lac].

**Figure 11.** The left column shows BOLD task activation responses as color overlays: colors represent signal increases from 1% (red) to 3% (yellow). The center column shows CBF task activation increasing from 20% to 80%. The rightmost column shows rCMRO2 for each subject, from 3% to 30%. All subjects show a confluent patch of increased rCMRO2 in the visual cortex, averaging from 13% to 19%. Some peaks reach up to 30% increase in metabolism, corresponding to peaks of blood flow up to 70%. Source: (17).

Taken together, the PET and fMRI results showed that the increases in CBF and CMRO2 during visual stimulation are uncoupled, summarized as follows (18): Compared with viewing a blank screen, visual stimulation produces marked changes in activity in visual areas of the brain, as shown in the PET images (from small increase, blue, to major increase, red; Figure 12). Increases in both blood flow and glucose use in the visual cortex could be observed, without similar increases in oxygen use. As a result, local oxygen availability increases because of the increased supply of oxygen from flowing blood that exceeds the increased local demand for oxygen, which forms the fMRI BOLD contrast.

## **2.4. Bioenergetics in the Visual Cortex**

Despite the agreement across imaging modalities in the observation of CBF–CMRO2 uncoupling, the physiological interpretation for this phenomenon has been controversial. Two major debates were whether oxidative metabolism regulates CBF and whether oxidative metabolism or nonoxidative glycolysis meets the energy demands for neuronal activity.

In contrast to the suggestion of Fox and colleagues that oxygen demand did not regulate CBF increases and that glycolysis can meet the neuronal energy requirements, others argued

(17).

activity.

**Figure 11.** The left column shows BOLD task activation responses as color overlays: colors represent signal increases from 1% (red) to 3% (yellow). The center column shows CBF task activation increasing from 20% to 80%. The rightmost column shows rCMRO2 for each subject, from 3% to 30%. All subjects show a confluent patch of increased rCMRO2 in the visual cortex, averaging from 13% to 19%. Some peaks reach up to 30% increase in metabolism, corresponding to peaks of blood flow up to 70%. Source:

Taken together, the PET and fMRI results showed that the increases in CBF and CMRO2 during visual stimulation are uncoupled, summarized as follows (18): Compared with viewing a blank screen, visual stimulation produces marked changes in activity in visual areas of the brain, as shown in the PET images (from small increase, blue, to major increase, red; Figure 12). Increases in both blood flow and glucose use in the visual cortex could be observed, without similar increases in oxygen use. As a result, local oxygen availability increases because of the increased supply of oxygen from flowing blood that exceeds the

Despite the agreement across imaging modalities in the observation of CBF–CMRO2 uncoupling, the physiological interpretation for this phenomenon has been controversial. Two major debates were whether oxidative metabolism regulates CBF and whether oxidative metabolism or nonoxidative glycolysis meets the energy demands for neuronal

In contrast to the suggestion of Fox and colleagues that oxygen demand did not regulate CBF increases and that glycolysis can meet the neuronal energy requirements, others argued

increased local demand for oxygen, which forms the fMRI BOLD contrast.

**2.4. Bioenergetics in the Visual Cortex** 

**Figure 12.** Visual stimulation produces marked changes in activity in visual areas of the brain. Blood flow, glucose utilization, and oxygen availability significantly increase, but the change of the oxygen utilization is minimal. Source: (18).

that significant increases of CBF were needed to facilitate oxygen delivery and that predominately oxidative metabolism should meet energy demands (19). To clarify whether oxidative metabolism or glycolysis regulates CBF and energy demands, Lin and colleagues (20) performed a concurrent fMRI and 1H MRS study using graded visual stimulation (4, 8, and 16 Hz). They used fMRI to determine %ΔCBF and %ΔCMRO2 and used 1H MRS to determine %Δ[Lac].

Findings for %ΔCBF, %ΔCMRO2, and %Δ[Lac] varied with frequency, with %ΔCBF and %Δ[Lac] peaking at 8 Hz, whereas %ΔCMRO2 reached a maximum at 4 Hz (Figure 13, A and B). The magnitudes of %ΔCBF (57.1%–65.1%) and %Δ[Lac] (31.3%–50.0%) were much larger than that of %ΔCMRO2 (12.2%–17.0%). As a result, %ΔCBF was tightly coupled with lactate production rate (Figure 14A) but negatively correlated with %CMRO2 (Figure 14B).

ATP production (JATP) was calculated, using a stoichiometric equation, from the determined %ΔCMRO2 and lactate production rate. JATP was not significantly different among the three stimulus frequencies, and the increment was smaller (12%–17%) than the baseline (Rest) state. Further, oxidative metabolism predominately contributed to JATP and linearly correlated with %ΔCMRO2 (Figure 14C and Figure 15).

Taken together, the major findings from the study were as follows: (i) Increases in oxygen metabolism and energy demand of task-induced neuronal activation were small (12%–17%); (ii) oxidative (indexed by CMRO2) and nonoxidative (indexed by lactate production) metabolism coexisted during visual stimulation; and (iii) CBF increase was much larger (52%–65%) than the increase in energy demand and highly correlated with lactate production, but not with CMRO2. In response to the two main debated questions mentioned

above, Lin et al's observations supported that (i) anaerobic glycolysis, rather than oxygen demand, drives CBF response to neuronal activity, and (ii) energy demand is predominately met through the oxidative metabolic pathway even though the CMRO2 increases are much lower than those of [Lac] (19, 21, 22).

Neurovascular and Neurometabolic Uncoupling in the Visual Cortex 259

**Figure 15. (A)** JATP at rest and the three levels of visual stimulation. The JATP values during activation are independent of stimulus rate. The increments at activation are smaller (1.4–2.0 μmol/g/min) than the rest value (11.1 μmol/g/min). **(B)** Aerobic and anaerobic relative contributions (%) to ΔJATP. The ΔJATP at the three stimulation rates is due predominately to aerobic metabolism (~98%, including both neuronal

The collective evidence from the functional imaging literature (PET, fMRI, and MRS) has forced the development of alternatives to the Roy–Sherrington hypothesis. Of these, the astrocyte–neuron lactate shuttle (ANLS) model (Figure 16) is the most conceptually evolved and widely accepted (23, 24). The ANLS model posits a cooperation between neurons and astrocytes in meeting the activation-induced needs both for energy production and for neurotransmitter production. Upon neuronal firing, both neurons and astrocytes take up glucose. Astrocytes take up most of the glucose, and neurons take up the rest. Though the level of glucose metabolism in neurons is low, the process is entirely aerobic to support neurotransmission (25, 26). The level of astrocytic glucose consumption, however, is high but much less energetically efficient because it is predominately anaerobic. Astrocytic glycolysis (2 ATP) is used to support Na+/K+ ion pumping and glutamate (Glu)–glutamine (Gln) conversion. Lactate generated by astrocytic glycolysis is eventually transported to neurons as fuel, but with some loss into the circulation, which increases hyperemia (26, 27). The ANLS hypothesis implies that (i) oxygen demand does not drive increases in CMRGlc, which serve other purposes such as astrocyte-mediated neurotransmitter recycling; (ii) taskinduced oxygen demand is small; and (iii) factors other than oxidative metabolism regulate

The three findings that Lin and colleagues reported, described above, were in good agreement with the ANLS hypothesis. The first finding (small CMRO2 increase) was in line with the ANLS implication that the energy demands of acute, transient increases in neuronal activity are small (~15% increase in CMRO2; at most, 30%). Oxidative metabolism should increase as neuronal activation continues because neurons eventually take up astrocytic lactate into the tricarboxylic acid cycle as a fuel substrate. In support of this formulation, prolonged visual stimulation (>20 min) can induce gradually rising levels of CMRO2 and gradually decreasing CMRGlc, JLac, and CBF under high-frequency stimulation

and astrocytic contributions). Source: (20).

CBF increases.

**2.5. Astrocyte–Neuron Lactate Shuttle (ANLS) Model** 

**Figure 13. (A)** Location and magnitude of %ΔCBF and %ΔCMRO2 in the primary visual cortex during 4-, 8-, and 16-Hz visual stimulation. **(B)** Magnitude of %ΔCBF, %Δ[Lac], and %ΔCMRO2 in the primary visual cortex during 4-, 8-, and 16-Hz visual stimulation. Source: (20).

**Figure 14. (A)** CBF–lactate coupling. Significant correlation existed between %ΔCBF and %ΔJLac at the three visual stimulation rates (*r* = 0.91; *p* < 0.001). **(B)** CBF–CMRO2 coupling. Negative correlation existed between %ΔCBF and %ΔCMRO2 at the three visual stimulation rates (*r* = –0.64; *p* = 0.024). **(C)** CMRO2–ATP coupling. Significant correlation existed between %ΔCMRO2 and %ΔJATP at the three visual stimulation rates (*r* = 1.00; *p* < 0.001). Source: (20).

**Figure 15. (A)** JATP at rest and the three levels of visual stimulation. The JATP values during activation are independent of stimulus rate. The increments at activation are smaller (1.4–2.0 μmol/g/min) than the rest value (11.1 μmol/g/min). **(B)** Aerobic and anaerobic relative contributions (%) to ΔJATP. The ΔJATP at the three stimulation rates is due predominately to aerobic metabolism (~98%, including both neuronal and astrocytic contributions). Source: (20).

#### **2.5. Astrocyte–Neuron Lactate Shuttle (ANLS) Model**

258 Visual Cortex – Current Status and Perspectives

lower than those of [Lac] (19, 21, 22).

above, Lin et al's observations supported that (i) anaerobic glycolysis, rather than oxygen demand, drives CBF response to neuronal activity, and (ii) energy demand is predominately met through the oxidative metabolic pathway even though the CMRO2 increases are much

**Figure 13. (A)** Location and magnitude of %ΔCBF and %ΔCMRO2 in the primary visual cortex during 4-, 8-, and 16-Hz visual stimulation. **(B)** Magnitude of %ΔCBF, %Δ[Lac], and %ΔCMRO2 in the primary

**Figure 14. (A)** CBF–lactate coupling. Significant correlation existed between %ΔCBF and %ΔJLac at the three visual stimulation rates (*r* = 0.91; *p* < 0.001). **(B)** CBF–CMRO2 coupling. Negative correlation existed between %ΔCBF and %ΔCMRO2 at the three visual stimulation rates (*r* = –0.64; *p* = 0.024). **(C)** CMRO2–ATP coupling. Significant correlation existed between %ΔCMRO2 and %ΔJATP at the three

visual cortex during 4-, 8-, and 16-Hz visual stimulation. Source: (20).

visual stimulation rates (*r* = 1.00; *p* < 0.001). Source: (20).

The collective evidence from the functional imaging literature (PET, fMRI, and MRS) has forced the development of alternatives to the Roy–Sherrington hypothesis. Of these, the astrocyte–neuron lactate shuttle (ANLS) model (Figure 16) is the most conceptually evolved and widely accepted (23, 24). The ANLS model posits a cooperation between neurons and astrocytes in meeting the activation-induced needs both for energy production and for neurotransmitter production. Upon neuronal firing, both neurons and astrocytes take up glucose. Astrocytes take up most of the glucose, and neurons take up the rest. Though the level of glucose metabolism in neurons is low, the process is entirely aerobic to support neurotransmission (25, 26). The level of astrocytic glucose consumption, however, is high but much less energetically efficient because it is predominately anaerobic. Astrocytic glycolysis (2 ATP) is used to support Na+/K+ ion pumping and glutamate (Glu)–glutamine (Gln) conversion. Lactate generated by astrocytic glycolysis is eventually transported to neurons as fuel, but with some loss into the circulation, which increases hyperemia (26, 27). The ANLS hypothesis implies that (i) oxygen demand does not drive increases in CMRGlc, which serve other purposes such as astrocyte-mediated neurotransmitter recycling; (ii) taskinduced oxygen demand is small; and (iii) factors other than oxidative metabolism regulate CBF increases.

The three findings that Lin and colleagues reported, described above, were in good agreement with the ANLS hypothesis. The first finding (small CMRO2 increase) was in line with the ANLS implication that the energy demands of acute, transient increases in neuronal activity are small (~15% increase in CMRO2; at most, 30%). Oxidative metabolism should increase as neuronal activation continues because neurons eventually take up astrocytic lactate into the tricarboxylic acid cycle as a fuel substrate. In support of this formulation, prolonged visual stimulation (>20 min) can induce gradually rising levels of CMRO2 and gradually decreasing CMRGlc, JLac, and CBF under high-frequency stimulation

Neurovascular and Neurometabolic Uncoupling in the Visual Cortex 261

Although findings from (20) support current neurophysiological hypotheses, such as the ANLS hypothesis, theories of neurovascular and neurometabolic mechanisms continue to evolve (34). Some aspects of the ANLS hypothesis remain controversial. For example, whether the lactate transferred to neurons as a fuel substrate is from astrocytic or neuronal activity (30), and whether lactate is the preferential substrate of neurons for neurotransmission-related energy needs are still under debate. (See review in (35)). Finally, whether JATP is constant during continuous stimulation, as this study assumes, remains

In summary, the ANLS model offers a more explicit and comprehensive explanation of the interplay of neuronal activation, metabolism, and hemodynamics that is based on the

Over the past century, the visual cortex has played a significant role in revealing the fundamental relationship among brain activity, metabolism, and hemodynamics. The development of PET imaging facilitated the investigations and led to the discovery of flow– metabolism uncoupling, the development of fMRI BOLD techniques, and the evolution of the physiological interpretation. These revolutionized changes enable us to better understand the metabolic physiology of brain activity, giving us the bases to predict metabolic physiology in neurological disorders, including stroke or Alzheimer's disease.

*Research Imaging Institute, University of Texas Health Science Center, San Antonio, TX, USA* 

[1] Mosso A. *Ueber den Kreislauf des Blutes im menschlichen Gehirn*. Leipzig: Verlag von Veit.

[2] Fox PT, Raichle ME, Mintun MA, Dence C. Nonoxidative glucose consumption during focal physiologic neural activity. Science. 1988;241(4864):462-4. Epub 1988/07/22. [3] Ogawa S, Menon RS, Tank DW, Kim SG, Merkle H, Ellermann JM, et al. Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophys J.

[4] Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle H, et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc Natl Acad Sci U S A. 1992;89(13):5951-5. Epub 1992/07/01.

*Brain Research Imaging Center, University of Chicago, Chicago, IL, USA* 

open. Further investigations are needed to resolve these issues.

discovery of the CBF–CMRO2 uncoupling phenomenon.

**3. Conclusion** 

**Author details** 

Jia-Hong Gao

**4. References** 

1881.

Ai-Ling Lin and Peter T. Fox

1993;64(3):803-12. Epub 1993/03/01.

**Figure 16.** The Astrocyte-neuron lactate shuttle (ANLS) model. Glucose (green arrows) is delivered via the capillaries to both neurons (beige) and astrocytes (blue). In neurons, glucose consumption is predominately oxidative, generating 38 ATP at a glucose oxidation rate of 1.00 μmol/g/min. In astrocytes, glucose consumption is both oxidative and nonoxidative, generating 3 ATP oxidatively and 2 ATP nonoxidatively (at a glucose oxidation rate of 1.00 μmol/g/min). The energy from nonoxidative metabolism is used to convert glutamate to glutamine, the predominant excitatory neurotransmitter. Neurons take up the lactate that glycolysis generates to fuel further glucose oxidation. A small amount of lactate will efflux to the capillaries and thus increase cerebral blood flow (CBF). Source: (26).

(e.g., 8 Hz) (11, 14, 28-30). Consequently, %CBF and %CMRO2 were recoupled as stimulation continued (14, 28), as mentioned in section 2.2.

The second finding supported the ANLS hypothesis construct of two metabolic pathways (oxidative and nonoxidative) that coexist, are dissociable, and serve different purposes in maintaining neuronal functions during visual stimulation. Oxidative metabolism is predominantly neuronal and supports ATP production for the release of neurotransmitters, whereas nonoxidative metabolism occurs mainly in astrocytes and supports Glu–Gln recycling and lactogenesis-mediated hyperemia.

The third finding was consistent with the ANLS prediction that some lactate produced by anaerobic glycolysis in astrocytes is effluxed into the circulation. The increased lactate:pyruvate and NADH:NAD+ ratios in blood then activate the nitric oxide signaling pathway, to increase local CBF (27, 29, 31). However, astrocyte-mediated glycolytic metabolism may not be the sole mechanism to elicit this CBF response. Local CBF increase also has been proposed via Ca2+, K+, and adenosine signaling pathways (29, 32, 33).

Although findings from (20) support current neurophysiological hypotheses, such as the ANLS hypothesis, theories of neurovascular and neurometabolic mechanisms continue to evolve (34). Some aspects of the ANLS hypothesis remain controversial. For example, whether the lactate transferred to neurons as a fuel substrate is from astrocytic or neuronal activity (30), and whether lactate is the preferential substrate of neurons for neurotransmission-related energy needs are still under debate. (See review in (35)). Finally, whether JATP is constant during continuous stimulation, as this study assumes, remains open. Further investigations are needed to resolve these issues.

In summary, the ANLS model offers a more explicit and comprehensive explanation of the interplay of neuronal activation, metabolism, and hemodynamics that is based on the discovery of the CBF–CMRO2 uncoupling phenomenon.

## **3. Conclusion**

260 Visual Cortex – Current Status and Perspectives

**Figure 16.** The Astrocyte-neuron lactate shuttle (ANLS) model. Glucose (green arrows) is delivered via the capillaries to both neurons (beige) and astrocytes (blue). In neurons, glucose consumption is predominately oxidative, generating 38 ATP at a glucose oxidation rate of 1.00 μmol/g/min. In

astrocytes, glucose consumption is both oxidative and nonoxidative, generating 3 ATP oxidatively and 2 ATP nonoxidatively (at a glucose oxidation rate of 1.00 μmol/g/min). The energy from nonoxidative metabolism is used to convert glutamate to glutamine, the predominant excitatory neurotransmitter. Neurons take up the lactate that glycolysis generates to fuel further glucose oxidation. A small amount of lactate will efflux to the capillaries and thus increase cerebral blood flow (CBF). Source: (26).

(e.g., 8 Hz) (11, 14, 28-30). Consequently, %CBF and %CMRO2 were recoupled as

The second finding supported the ANLS hypothesis construct of two metabolic pathways (oxidative and nonoxidative) that coexist, are dissociable, and serve different purposes in maintaining neuronal functions during visual stimulation. Oxidative metabolism is predominantly neuronal and supports ATP production for the release of neurotransmitters, whereas nonoxidative metabolism occurs mainly in astrocytes and supports Glu–Gln

The third finding was consistent with the ANLS prediction that some lactate produced by anaerobic glycolysis in astrocytes is effluxed into the circulation. The increased lactate:pyruvate and NADH:NAD+ ratios in blood then activate the nitric oxide signaling pathway, to increase local CBF (27, 29, 31). However, astrocyte-mediated glycolytic metabolism may not be the sole mechanism to elicit this CBF response. Local CBF increase

also has been proposed via Ca2+, K+, and adenosine signaling pathways (29, 32, 33).

stimulation continued (14, 28), as mentioned in section 2.2.

recycling and lactogenesis-mediated hyperemia.

Over the past century, the visual cortex has played a significant role in revealing the fundamental relationship among brain activity, metabolism, and hemodynamics. The development of PET imaging facilitated the investigations and led to the discovery of flow– metabolism uncoupling, the development of fMRI BOLD techniques, and the evolution of the physiological interpretation. These revolutionized changes enable us to better understand the metabolic physiology of brain activity, giving us the bases to predict metabolic physiology in neurological disorders, including stroke or Alzheimer's disease.

## **Author details**

Ai-Ling Lin and Peter T. Fox *Research Imaging Institute, University of Texas Health Science Center, San Antonio, TX, USA* 

Jia-Hong Gao *Brain Research Imaging Center, University of Chicago, Chicago, IL, USA* 

## **4. References**


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	- [35] Mangia S, Giove F, Tkac I, Logothetis NK, Henry PG, Olman CA, et al. Metabolic and hemodynamic events after changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings. J Cereb Blood Flow Metab. 2009;29(3):441-63. Epub 2008/11/13.

**Chapter 12** 

© 2012 Argandoña et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Experience Mediated Development** 

**of the Visual Cortex Vascularization** 

Irantzu Rico-Barrio and José Vicente Lafuente

Additional information is available at the end of the chapter

Brain Barrier function and the dense capillary network.

http://dx.doi.org/10.5772/48749

**1. Introduction** 

[1].

Enrike G. Argandoña, Harkaitz Bengoetxea, Naiara Ortuzar, Susana Bulnes,

The vascular system of every organ is composed of an afferent arterial system that ensures metabolic support, and an efferent venous drainage system that evacuates the substances produced by the organ as well as the catabolites that are generated. Both systems communicate via a terminal network in which the arterial capillaries anastomose with the venous ones. Vascular organisation depends on the structure and function of each organ, thus there is not a general vascular system, but an organ-specific one. The large blood vessels supplying the brain are the carotid and vertebral arteries, which then branch to form the network of pial arteries covering the surface of the brain. In the cerebral cortex, the pial vessels branch into smaller arteries, which enter the brain tissue itself and are called the penetrating arterioles. These arterioles branch into secondary and tertiary arterioles, until they reach the smallest vessel supplying the brain tissue, the capillary, which is only wide enough for one red blood cell to pass through it at a time. The capillaries then feed into the venules and veins, which carry the blood away

Brain vascularisation is especially important due to brain metabolic peculiarities. Although the brain represents only 2% of the body weight and vascularisation is only 1% of brain size, it receives 20% of the cardiac output, 20% of total body oxygen consumption, and 25% of total body glucose utilization [2, 3]. As the brain lacks a glucose storage system, most of it has to be supported by a constant blood supply. Within the brain vascularisation, the vascularisation of the cerebral cortex has differential features compared not only to other body regions, but also to other brain areas. The two main differential features are the Blood

and reproduction in any medium, provided the original work is properly cited.

## **Experience Mediated Development of the Visual Cortex Vascularization**

Enrike G. Argandoña, Harkaitz Bengoetxea, Naiara Ortuzar, Susana Bulnes, Irantzu Rico-Barrio and José Vicente Lafuente

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48749

## **1. Introduction**

264 Visual Cortex – Current Status and Perspectives

2009;29(3):441-63. Epub 2008/11/13.

[35] Mangia S, Giove F, Tkac I, Logothetis NK, Henry PG, Olman CA, et al. Metabolic and hemodynamic events after changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings. J Cereb Blood Flow Metab.

> The vascular system of every organ is composed of an afferent arterial system that ensures metabolic support, and an efferent venous drainage system that evacuates the substances produced by the organ as well as the catabolites that are generated. Both systems communicate via a terminal network in which the arterial capillaries anastomose with the venous ones. Vascular organisation depends on the structure and function of each organ, thus there is not a general vascular system, but an organ-specific one. The large blood vessels supplying the brain are the carotid and vertebral arteries, which then branch to form the network of pial arteries covering the surface of the brain. In the cerebral cortex, the pial vessels branch into smaller arteries, which enter the brain tissue itself and are called the penetrating arterioles. These arterioles branch into secondary and tertiary arterioles, until they reach the smallest vessel supplying the brain tissue, the capillary, which is only wide enough for one red blood cell to pass through it at a time. The capillaries then feed into the venules and veins, which carry the blood away [1].

> Brain vascularisation is especially important due to brain metabolic peculiarities. Although the brain represents only 2% of the body weight and vascularisation is only 1% of brain size, it receives 20% of the cardiac output, 20% of total body oxygen consumption, and 25% of total body glucose utilization [2, 3]. As the brain lacks a glucose storage system, most of it has to be supported by a constant blood supply. Within the brain vascularisation, the vascularisation of the cerebral cortex has differential features compared not only to other body regions, but also to other brain areas. The two main differential features are the Blood Brain Barrier function and the dense capillary network.

© 2012 Argandoña et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **2. Blood brain barrier**

The endothelium of the CNS vessels is a structure specialised in the maintenance of the homeostasis of the internal environment of the brain parenchyma. This function is crucial in the CNS, as its function requires a strict regulation of the neuronal environment, which is extremely sensitive to ionic and/or metabolic changes. In fact, most of the substances freely available in blood from daily food intake that are constantly metabolised and excreted, are potentially neurotoxic [4]. This function is known as the Blood Brain Barrier, and was first described in 1885 by Paul Ehrlich, who found that soluble dyes injected into the system stained all organs but the brain and the spinal cord [5].

Experience Mediated Development of the Visual Cortex Vascularization 267

levels in endothelial intervention, they are less involved than expected in the structural

d. Extracellular matrix. Apart from the afore-mentioned cells and neurons, that also regulate blood flow to cope with energy requirements and that even regulate vascular permeability, the extracellular matrix plays a key role in the interaction with vascular permeability, even regulating the expression of proteins that constitute the TJ of the BBB. On the other hand, the ECM has to be digested in order to allow angiogenesis, thus liberating non-soluble VEGF. This function is performed by the matrix

Due to the cellular heterogeneity that constitutes the BBB, all the elements can be described as a neurogliovascular unit, where all elements are interrelated, as can be seen in pathological processes such as Alzheimer's, Parkinson's or stroke, where all elements are

Among the structural elements of the BBB, tight junctions play a crucial role in the control of the paracellular diffusion of blood compounds to brain parenchyma. Tight Junctions coexist with other junction structures, such as belt desmosomes and gap junctions; nevertheless,

The proteins that constitute the tight junctions share a common cytoplasmatic location, and are linked to the actin cytoskeleton (ZO-1 and 2, cingulin, AF-6 and 7H6). The transmembrane proteins are JAM-1, occludin and claudin [24]. Actin plays an even greater

Although the development of cortical vascularisation is closely related to the development of the cortical function, in previous work we have demonstrated that despite the effects of sensorial deprivation on the development of the vascularisation of the visual cortex, the maturation of the BBB is not related to the functional maturation of the cortex, as neither visual deprivation nor environmental enrichment induced changes in the maturation of early and late markers of barrier maturation [14]. As most of the barrier structural and functional markers are fully developed prior to the beginning of the critical period,

On the other hand, 90% of the cortical vascularisation is constituted by a fine capillary network that spreads all over the cortex and whose density is related to local neuronal activity. This network was described by Galen, who called it *rete mirabile* [25, 26]. Estimations suggest that the human brain contains up to 100 billion vessels, suggesting a ratio of one vessel per neuron [3]. In prior work, we have demonstrated that this relationship is maintained in the visual cortex despite deprivation of visual inputs. Indeed, both the number of vessels per neuron and the vascular surface per neuron maintain similar values when comparing normal and visually deprived rats [27]. In contrast, visual environmental enrichment does increase the ratio of vessels per neuron in response to the

experience-mediated modifications do not appear to influence barrier maturation.

properties of the BBB [4].

metalloproteases [22].

tight junctions are still the main ones [23].

role than TJ proteins in the maintenance of BBB integrity [22].

implicated [4, 23].

**3. Capillary network** 

increased demand [28, 29].

The Blood Brain Barrier is the set of mechanisms (physical and metabolic) that regulate the passage of compounds from blood to brain, allowing the regulation of the internal environment of the CNS with independence of fluctuations in blood composition. These mechanisms include, among others, the enzymatic systems that transform some compounds during their passage through the endothelium, as well as the specific transporters of some substances, such as glucose or aminoacids. Bigger molecules, such as insulin, use exocytosis systems mediated by receptors. Some of these enzymatic mechanisms have been used to quantify vascular distribution, e.g., butyryl cholinesterase histochemistry [6], alkaline phosphatase histochemistry [7], LEA lectin histochemistry [8-10] and immunohistochemistry against antigens such as glucose transporter-1 (GluT-1) [11], PECAM [12], RECA-1 [13] and the endothelial barrier antigen (EBA) [11, 14-18].

Among the cellular components of the BBB, we could mention the following:


levels in endothelial intervention, they are less involved than expected in the structural properties of the BBB [4].

d. Extracellular matrix. Apart from the afore-mentioned cells and neurons, that also regulate blood flow to cope with energy requirements and that even regulate vascular permeability, the extracellular matrix plays a key role in the interaction with vascular permeability, even regulating the expression of proteins that constitute the TJ of the BBB. On the other hand, the ECM has to be digested in order to allow angiogenesis, thus liberating non-soluble VEGF. This function is performed by the matrix metalloproteases [22].

Due to the cellular heterogeneity that constitutes the BBB, all the elements can be described as a neurogliovascular unit, where all elements are interrelated, as can be seen in pathological processes such as Alzheimer's, Parkinson's or stroke, where all elements are implicated [4, 23].

Among the structural elements of the BBB, tight junctions play a crucial role in the control of the paracellular diffusion of blood compounds to brain parenchyma. Tight Junctions coexist with other junction structures, such as belt desmosomes and gap junctions; nevertheless, tight junctions are still the main ones [23].

The proteins that constitute the tight junctions share a common cytoplasmatic location, and are linked to the actin cytoskeleton (ZO-1 and 2, cingulin, AF-6 and 7H6). The transmembrane proteins are JAM-1, occludin and claudin [24]. Actin plays an even greater role than TJ proteins in the maintenance of BBB integrity [22].

Although the development of cortical vascularisation is closely related to the development of the cortical function, in previous work we have demonstrated that despite the effects of sensorial deprivation on the development of the vascularisation of the visual cortex, the maturation of the BBB is not related to the functional maturation of the cortex, as neither visual deprivation nor environmental enrichment induced changes in the maturation of early and late markers of barrier maturation [14]. As most of the barrier structural and functional markers are fully developed prior to the beginning of the critical period, experience-mediated modifications do not appear to influence barrier maturation.

## **3. Capillary network**

266 Visual Cortex – Current Status and Perspectives

stained all organs but the brain and the spinal cord [5].

The endothelium of the CNS vessels is a structure specialised in the maintenance of the homeostasis of the internal environment of the brain parenchyma. This function is crucial in the CNS, as its function requires a strict regulation of the neuronal environment, which is extremely sensitive to ionic and/or metabolic changes. In fact, most of the substances freely available in blood from daily food intake that are constantly metabolised and excreted, are potentially neurotoxic [4]. This function is known as the Blood Brain Barrier, and was first described in 1885 by Paul Ehrlich, who found that soluble dyes injected into the system

The Blood Brain Barrier is the set of mechanisms (physical and metabolic) that regulate the passage of compounds from blood to brain, allowing the regulation of the internal environment of the CNS with independence of fluctuations in blood composition. These mechanisms include, among others, the enzymatic systems that transform some compounds during their passage through the endothelium, as well as the specific transporters of some substances, such as glucose or aminoacids. Bigger molecules, such as insulin, use exocytosis systems mediated by receptors. Some of these enzymatic mechanisms have been used to quantify vascular distribution, e.g., butyryl cholinesterase histochemistry [6], alkaline phosphatase histochemistry [7], LEA lectin histochemistry [8-10] and immunohistochemistry against antigens such as glucose transporter-1 (GluT-1) [11],

a. Endothelial cells. The cortical endothelium is formed by a layer of endothelial cells with a higher mitochondrial component, almost complete absence of pinocytic activity,

b. Pericytes. Joined to the abluminal membrane of the endothelium, pericytes are included in the same basal membrane as the endothelium. The pericyte is a cell of heterogeneous origin that is related to macrophages, muscle cells, etc. Despite the fact that they have been largely neglected, recent studies show that they play a relevant role in angiogenesis and in the BBB function, by, among other mechanisms, inhibiting apoptosis of endothelial cells. Apart from their vascular role, recent studies have demonstrated a crucial role of pericytes in the formation of the glial scars produced after brain injuries, thus linking the maintenance of the Blood Brain Barrier with

scarring and tissue repair, a role that has long been attributed to astrocytes [21]. c. Astrocytes. These play a relevant role in the dual nature of the BBB, physical and metabolic. On the one hand, their prolongations ensheathing the endothelial wall (astrocytic endfeet) are closely related to the basal membrane. Astrocytes induce several metabolic BBB properties, such as the enzymatic activity of the capillary wall, the uptake of glucose and the establishment of tight junctions. In general, astrocytes play a key role in the induction, expression and maintenance of the BBB. Although astrocytes are implicated in flow regulation and microvascular permeability by elevating calcium

PECAM [12], RECA-1 [13] and the endothelial barrier antigen (EBA) [11, 14-18].

absence of fenestrations and presence of interendothelial junctions [19, 20].

Among the cellular components of the BBB, we could mention the following:

**2. Blood brain barrier** 

On the other hand, 90% of the cortical vascularisation is constituted by a fine capillary network that spreads all over the cortex and whose density is related to local neuronal activity. This network was described by Galen, who called it *rete mirabile* [25, 26]. Estimations suggest that the human brain contains up to 100 billion vessels, suggesting a ratio of one vessel per neuron [3]. In prior work, we have demonstrated that this relationship is maintained in the visual cortex despite deprivation of visual inputs. Indeed, both the number of vessels per neuron and the vascular surface per neuron maintain similar values when comparing normal and visually deprived rats [27]. In contrast, visual environmental enrichment does increase the ratio of vessels per neuron in response to the increased demand [28, 29].

The vascular system of the rat cerebral cortex is organised from the penetrating vessels perpendicular to the surface, emanating from the leptomeningeal vascular system. These vascular trunks branch, forming the capillary network that is the essential nutritional sector of the cortex, although metabolic exchange also takes place in the microcirculation sectors preceding the capillary network in small calibre arterioles [6].

Experience Mediated Development of the Visual Cortex Vascularization 269

**4. Development of the vascularisation of the visual cortex** 

The development of new blood vessels can occur via two mechanisms: vasculogenesis and angiogenesis. Vasculogenesis is the development of new vessels from differentiated endothelial cells in situ. Angiogenesis is the development of capillaries from preexisting vessels and is the way that cerebral vessels develop. Angiogenesis is a process that coordinates the precise timing and location of all the cells belonging to the NeuroGlioVascular unit to form a hierarchical vascular network with CNS specifications, including BBB function, reciprocal interactions between neurons, glia and pericytes and a vascular niche for neural stem cells [31]. The angiogenic process starts when one endothelial cell, in response to the VEGF secreted following local hypoxia, differentiates into a tip cell that advances according to the VEGF signal. The adjacent endothelial cells are destined to become stalk cells that follow the tip cell, providing a lumenized endothelial cell chain. The signal to inhibit tip cell differentiation and to become stalk cell is mediated by the Notch

pathway [32, 33]. This process is functionally similar to axon growth cones [34, 35].

'en bouquet' [36] terminations. This is the vascular pattern of the mature neocortex.

a. Extracerebral vascularisation (leptomeningeal)

final pattern after the arterial [36]. b. Intracerebral vascularisation

during postnatal life [38].

The development of intracortical vascularisation starts at 12 days post coitum with radially penetrating stem vessels following the pattern of neural tube growth, and is completed when these vessels form new cortical branches terminating in different cellular layers with

 The first draft of brain vessels is formed starting from where the aortic arches approach the ventral neural tube to form a primary vascular plexus. Vessels from the primary plexus reach the vesicles developing in the telencephalic basolateral surface, and vascular buds penetrate perpendicularly to the walls of the hemisphere [3, 37]. The development of the cerebral vasculature is divided into two stages, one extracerebral and the other intracerebral.

The arterial and venous vessels that cover the entire cerebral cortex are formed from undifferentiated capillary plexus. This perineural vascular system ends when the development of intracerebral vascularisation finishes. At first, the density of leptomeningeal arteries decreases, and then the veins, which indicates that the venous system reaches its

The vascularisation of the cerebral cortex begins with the development of vascular trunks penetrating radially, perpendicular to the cortical surface. The development of these early intracerebral vessels begins in the early days of embryonic development and is completed

The first vascular sprouts penetrate the cortex before the development of the cortical plate, so that the first vessels reach the ventricular zone perpendicularly opposite the leptomeningeal plexus. The earliest vessels that arise during development penetrate the cortical plate throughout its depth. This is reflected in the vascular structure of the adult cortex, where we can distinguish the radial trunks originated at first and going through all

When determining the three types of intracerebral vessels, although the vessel size is not a firm criterion for differentiating between capillary venules and arterioles, in general it can be established that the arterioles are vessels of 10 to 100 microns gauge. Arterioles with a caliber between 50 and 100 microns are called large arterioles, and arterioles smaller than 50 microns are called terminals. The capillaries are vessels under 10 microns, while venules are vessels of about 30 microns in diameter.

The main differences between these two types of vessel are in the structure of the wall:


The study of the vascular system of the cerebral cortex requires the establishment of the topographical relationships between neurons, and blood vessels must provide a sufficient supply. Vascular density is closely related to local metabolic activity and oxygen consumption of different cortical regions. In areas with increased activity, characterised by an increase in mitochondrial volume density and increased local consumption of glucose, there was an increase in capillary density [2].

The differences in capillary density between different areas are mainly due to neuronal density and activity, and more specifically due to synaptic density. Being structurally similar, the differences will be based solely on the degree of activity. Furthermore, the development of the vascular bed is the result and runs parallel to the development of the cortex. Thus, in animal species in which the cerebral cortex is not fully mature at birth, there is little cortical vascularisation, and as the cortex matures, it develops its vascular architecture. This does not happen in lower species that are born relatively mature in which we see a vascular network similar to the adult animal brain. Similarly, phylogenetically older regions such as the entorhinal cortex have a more primitive vascular structure [30].

## **4. Development of the vascularisation of the visual cortex**

268 Visual Cortex – Current Status and Perspectives

vessels of about 30 microns in diameter.

structurally similar to the capillary structure.

structuring the blood brain barrier.

there was an increase in capillary density [2].

vascular structure [30].

exchange of nutrients.

preceding the capillary network in small calibre arterioles [6].

The vascular system of the rat cerebral cortex is organised from the penetrating vessels perpendicular to the surface, emanating from the leptomeningeal vascular system. These vascular trunks branch, forming the capillary network that is the essential nutritional sector of the cortex, although metabolic exchange also takes place in the microcirculation sectors

When determining the three types of intracerebral vessels, although the vessel size is not a firm criterion for differentiating between capillary venules and arterioles, in general it can be established that the arterioles are vessels of 10 to 100 microns gauge. Arterioles with a caliber between 50 and 100 microns are called large arterioles, and arterioles smaller than 50 microns are called terminals. The capillaries are vessels under 10 microns, while venules are

The main differences between these two types of vessel are in the structure of the wall:

 Arterioles lack a complete internal elastic membrane despite having a middle layer consisting of three layers of smooth muscle. A distinctive feature of the arteriolar wall structure is the adventitia, much thinner in cerebral arterioles than in the arterioles of the rest of the body, becoming discontinuous in some points, thus allowing the

 Venules lack a distinct muscular middle layer. In its place is a layer of periendotelial cells. Periendotelial cells are a cell type that does not correspond clearly to smooth muscle cells or to pericytes, and that play a phagocytic function. The endothelium is

 The capillary wall thickness is 4 to 10 times lower than the arteriolar wall thickness. The capillary wall has no muscle layer and is in close relationship with glia, physically

The study of the vascular system of the cerebral cortex requires the establishment of the topographical relationships between neurons, and blood vessels must provide a sufficient supply. Vascular density is closely related to local metabolic activity and oxygen consumption of different cortical regions. In areas with increased activity, characterised by an increase in mitochondrial volume density and increased local consumption of glucose,

The differences in capillary density between different areas are mainly due to neuronal density and activity, and more specifically due to synaptic density. Being structurally similar, the differences will be based solely on the degree of activity. Furthermore, the development of the vascular bed is the result and runs parallel to the development of the cortex. Thus, in animal species in which the cerebral cortex is not fully mature at birth, there is little cortical vascularisation, and as the cortex matures, it develops its vascular architecture. This does not happen in lower species that are born relatively mature in which we see a vascular network similar to the adult animal brain. Similarly, phylogenetically older regions such as the entorhinal cortex have a more primitive The development of new blood vessels can occur via two mechanisms: vasculogenesis and angiogenesis. Vasculogenesis is the development of new vessels from differentiated endothelial cells in situ. Angiogenesis is the development of capillaries from preexisting vessels and is the way that cerebral vessels develop. Angiogenesis is a process that coordinates the precise timing and location of all the cells belonging to the NeuroGlioVascular unit to form a hierarchical vascular network with CNS specifications, including BBB function, reciprocal interactions between neurons, glia and pericytes and a vascular niche for neural stem cells [31]. The angiogenic process starts when one endothelial cell, in response to the VEGF secreted following local hypoxia, differentiates into a tip cell that advances according to the VEGF signal. The adjacent endothelial cells are destined to become stalk cells that follow the tip cell, providing a lumenized endothelial cell chain. The signal to inhibit tip cell differentiation and to become stalk cell is mediated by the Notch pathway [32, 33]. This process is functionally similar to axon growth cones [34, 35].

The development of intracortical vascularisation starts at 12 days post coitum with radially penetrating stem vessels following the pattern of neural tube growth, and is completed when these vessels form new cortical branches terminating in different cellular layers with 'en bouquet' [36] terminations. This is the vascular pattern of the mature neocortex.

 The first draft of brain vessels is formed starting from where the aortic arches approach the ventral neural tube to form a primary vascular plexus. Vessels from the primary plexus reach the vesicles developing in the telencephalic basolateral surface, and vascular buds penetrate perpendicularly to the walls of the hemisphere [3, 37]. The development of the cerebral vasculature is divided into two stages, one extracerebral and the other intracerebral.

a. Extracerebral vascularisation (leptomeningeal)

The arterial and venous vessels that cover the entire cerebral cortex are formed from undifferentiated capillary plexus. This perineural vascular system ends when the development of intracerebral vascularisation finishes. At first, the density of leptomeningeal arteries decreases, and then the veins, which indicates that the venous system reaches its final pattern after the arterial [36].

b. Intracerebral vascularisation

The vascularisation of the cerebral cortex begins with the development of vascular trunks penetrating radially, perpendicular to the cortical surface. The development of these early intracerebral vessels begins in the early days of embryonic development and is completed during postnatal life [38].

The first vascular sprouts penetrate the cortex before the development of the cortical plate, so that the first vessels reach the ventricular zone perpendicularly opposite the leptomeningeal plexus. The earliest vessels that arise during development penetrate the cortical plate throughout its depth. This is reflected in the vascular structure of the adult cortex, where we can distinguish the radial trunks originated at first and going through all cortical layers without collateral branches. During development of the cortex, the vascular leptomeningeal system issues new branches that penetrate the cortical plate, covering a territory that corresponds to their age so that the first vessels supply the deeper layers and the later ones supply the outermost layers. In the rat, from the second week of postnatal life there is no new perpendicular growth that enters the cortex from the leptomeningeal vascular system. The volume and surface area of the cortex continue to increase, so the vessel density decreases in proportion, penetrating perpendicularly to the cortical surface.

Experience Mediated Development of the Visual Cortex Vascularization 271

**Figure 1.** Schematic representation and histochemical sections of the development of the

and cover the whole territory they serve; d,e,f) show visual cortex vascularisation by

P60 (third phase), when the rats reach adulthood.

vascularisation of the visual cortex: a) first phase where vascular sprouts penetrate the cortex; b) during the second phase an increase in the vascular bed occurs; c) in the third phase the vessels grow in length

butyrylcholinesterase histochemistry during these phases at P0 (first phase), P14 (second phase) and

The cerebral cortex, in addition to thickening, undergoes a three-dimensional growth process accompanied by vascular arborization involving penetrating vessels. As was the case with the increase in thickness, the most ancient vessels provide greater horizontal branches due to the fact that older vessels occupy a larger area.

After maturation of the capillary network there is no budding of new vascular branches, indicating that the process of budding of new vascular branches ends when the vascular wall has fully developed. During the first phase of vascular development, a basic pattern is provided to suit the needs of each cortical area, showing specific regional differences in relation to the further development of different areas. During the second phase of vascularisation, a large increase in the vascular bed density occurs which corresponds to a large increase of the capillary surface closely associated with increased metabolic demands of the tissue. Between 8 and 20 days of postnatal life the microvascular endothelial cells proliferate rapidly. It is during this period that virtually all vascular branches form. The mitotic activity of endothelial cells drops sharply during the third week of postnatal life, as can be verified by tritiated thymidine incorporation into endothelial cells [36].

The third phase of vascular development is the elongation of existing branches. Originally, successive vascular branches are established, and then the vessels grow in length to cover the whole territory they serve. This elongation stage of pre-existing capillaries extends from day 20 until adulthood. This is the physiological pattern of development. In animals kept under normal conditions the definitive pattern of vascularisation culminates in the third week of postnatal life. Subsequent changes in transient local metabolic demands are supplied by local flow changes, but there are special circumstances that occur in which definitive increases in metabolic demand are offset by extending or adapting the normal pattern of development, as happens in adaptation to altitude hypoxia [39].

The development of the vascularisation of the visual cortex has one main specificity in rats, as the first phases of cortical development occur prior to eye opening, and thus are not experience-mediated. So, the experience-mediated vascular development happens during the so-called critical period. Sensory modifications during the early critical period result in substantial plasticity and are a crucial factor in establishing the mature circuitry [hooks]. This time window of postnatal life is specific for each brain area, and is experiencemediated. The critical period in the rat visual system is located between the 3rd and 5th postnatal weeks with a peak at the 4th [40]. Previously, we have shown that vascular density is closely related to neuronal activity, and by increasing and decreasing visual experience, we have found that the peak of VEGF expression is also at the 4th postnatal week [41], corresponding to the activity peak.

cortical layers without collateral branches. During development of the cortex, the vascular leptomeningeal system issues new branches that penetrate the cortical plate, covering a territory that corresponds to their age so that the first vessels supply the deeper layers and the later ones supply the outermost layers. In the rat, from the second week of postnatal life there is no new perpendicular growth that enters the cortex from the leptomeningeal vascular system. The volume and surface area of the cortex continue to increase, so the vessel density decreases in proportion, penetrating perpendicularly to the cortical surface.

The cerebral cortex, in addition to thickening, undergoes a three-dimensional growth process accompanied by vascular arborization involving penetrating vessels. As was the case with the increase in thickness, the most ancient vessels provide greater horizontal

After maturation of the capillary network there is no budding of new vascular branches, indicating that the process of budding of new vascular branches ends when the vascular wall has fully developed. During the first phase of vascular development, a basic pattern is provided to suit the needs of each cortical area, showing specific regional differences in relation to the further development of different areas. During the second phase of vascularisation, a large increase in the vascular bed density occurs which corresponds to a large increase of the capillary surface closely associated with increased metabolic demands of the tissue. Between 8 and 20 days of postnatal life the microvascular endothelial cells proliferate rapidly. It is during this period that virtually all vascular branches form. The mitotic activity of endothelial cells drops sharply during the third week of postnatal life, as

The third phase of vascular development is the elongation of existing branches. Originally, successive vascular branches are established, and then the vessels grow in length to cover the whole territory they serve. This elongation stage of pre-existing capillaries extends from day 20 until adulthood. This is the physiological pattern of development. In animals kept under normal conditions the definitive pattern of vascularisation culminates in the third week of postnatal life. Subsequent changes in transient local metabolic demands are supplied by local flow changes, but there are special circumstances that occur in which definitive increases in metabolic demand are offset by extending or adapting the normal

The development of the vascularisation of the visual cortex has one main specificity in rats, as the first phases of cortical development occur prior to eye opening, and thus are not experience-mediated. So, the experience-mediated vascular development happens during the so-called critical period. Sensory modifications during the early critical period result in substantial plasticity and are a crucial factor in establishing the mature circuitry [hooks]. This time window of postnatal life is specific for each brain area, and is experiencemediated. The critical period in the rat visual system is located between the 3rd and 5th postnatal weeks with a peak at the 4th [40]. Previously, we have shown that vascular density is closely related to neuronal activity, and by increasing and decreasing visual experience, we have found that the peak of VEGF expression is also at the 4th postnatal

can be verified by tritiated thymidine incorporation into endothelial cells [36].

pattern of development, as happens in adaptation to altitude hypoxia [39].

week [41], corresponding to the activity peak.

branches due to the fact that older vessels occupy a larger area.

**Figure 1.** Schematic representation and histochemical sections of the development of the vascularisation of the visual cortex: a) first phase where vascular sprouts penetrate the cortex; b) during the second phase an increase in the vascular bed occurs; c) in the third phase the vessels grow in length and cover the whole territory they serve; d,e,f) show visual cortex vascularisation by butyrylcholinesterase histochemistry during these phases at P0 (first phase), P14 (second phase) and P60 (third phase), when the rats reach adulthood.

Thus, during the first two weeks of postnatal development, most of the vessels are the perpendicularly penetrating vessels, and this terminates at the end of the second postnatal week, just at the opening of the eyes. At this point, the adult pattern starts, characterised by a dense capillary network that is thicker in the most active areas, such as layer IV [6, 30]. The influence of experience can be demonstrated in visually-deprived dark-reared rats, that have an immature vascular pattern characterised by a higher prevalence of perpendicularly penetrant trunks and a much sparser vascular network. In contrast, environmental enrichment induces just the opposite, a faster development of the mature pattern and a higher vascular density [14]. These changes occur in parallel to the development of the rest of the elements of the neurogliovascular unit, and we have demonstrated that the development of the astroglial population in layer IV of the visual cortex mirrors the vascular behaviour [42, 43]. In a similar way, vascular density is also higher in layer IV of the auditory cortex and lower in layer I [30]. On the other hand, we have recently described upregulated neuropeptides in the visual system, and as some of them have also strong angiogenic properties, and as neuropeptides are among the main regulators of the critical period in the visual cortex along with angioglioneurins, this finding is also consistent with the common patterns for neural and vascular development [44].

Experience Mediated Development of the Visual Cortex Vascularization 273

cells induced by VEGF [61-63] and is related to the blood-brain barrier (BBB) opening in brain injury [54, 64, 65]. A neuroprotective role for VEGF via VEGFR-2 has also been described [59, 66-68], which occurs via the PI3k/Akt and the mitogen-activated protein kinase/ERK kinase/extracellular signal-regulated protein kinase (MEK/ERK) pathways

But despite VEGF being the main angiogenic molecule, there are others that also play a multicellular role. Molecules that affect both neural and vascular cell processes have recently been termed angioneurins [71]. Angioneurins include molecules first described as vascular growth factors, such as Vascular Endothelial Growth Factor (VEGF), molecules first described as neurotrophins such as Brain-Derived Neurotrophic Factor (BDNF), and other factors such as Insulin-Like Growth Factor-1 (IGF-I) or Erythropoietin (EPO). Independently of their origin, all angioneurins share a common action on vascular and neuronal function. As most of these molecules also have effects on the third component of the neurogliovascular unit, the glia, we propose the term angioglioneurins to describe

**Figure 2.** Schematic representation of referred angioglioneurins and their receptors. Some

from Zacchigna et al., Nat Rev Neurosci; 9(3):169-81. Review).

angioglioneurins were originally discovered through their angiogenic effects and then later found to also have neuroprotective activity (for example, vascular endothelial growth factor (VEGF)); some neurotrophic factors were originally discovered through their neuronal effects and then later found to also have angiogenic activity (for example, brain derived neurotrophic factor (BDNF)); others are more pleiotropic but still have relevant neurovascular activities, being involved in both angiogenesis and neuroprotection (for example, insulin-like growth factor 1 (IGF1) and erythropoietin (EPO)) (modified

[69, 70].

them [39, 72].

There is a direct relationship between vascular density and metabolic activity. This is evidenced by the correlation between vascular density and mitochondria. Local capillary density, local utilization of glucose and local cerebral blood flow [45, 46] have also been found to be correlated. The relationship between the increase in neural activity and the increase in perfusion to supply this requirement is the so-called neurovascular coupling, that is the basis of modern neuroimaging techniques and that has been recently described as neurogenic instead of metabolic [47].

## **5. Angioglioneurins**

Neovascularisation is mediated by a variety of cytokines, including Vascular Endothelial Growth Factor (VEGF). VEGF is a hypoxia-inducible secreted homodimeric glycoprotein of 45,000 daltons that plays a major role in developmental [48-51] and pathological angiogenesis [52-57]. Five major isoforms of human VEGF exist, of which VEGF165 is the predominant one in most mammals. However, VEGF164 is shorter by one amino acid in rodents. VEGF isoforms are differentially expressed in disease, suggesting differences between pathological entities in the mechanisms of VEGF up-regulation as well as in their employment of distinct isoforms for neovascularisation [58]. The main receptors for the Vascular Endothelial Growth Factor (VEGF) family are the feline sarcoma virus-like tyrosine kinase receptor (Flt-1 or VEGFR-1) and the fetal liver kinase receptor (Flk-1) or VEGFR-2, also known as KDR [49, 51, 59, 60].

VEGFR-2 plays a critical role in some permeability-enhancing effects of VEGF [54]. In pathological conditions, VEGFR-2 mediates an antiapoptotic effect via Phosphoinositide 3-kinase (PI3K)-dependent signalling pathways which promote the survival of endothelial cells induced by VEGF [61-63] and is related to the blood-brain barrier (BBB) opening in brain injury [54, 64, 65]. A neuroprotective role for VEGF via VEGFR-2 has also been described [59, 66-68], which occurs via the PI3k/Akt and the mitogen-activated protein kinase/ERK kinase/extracellular signal-regulated protein kinase (MEK/ERK) pathways [69, 70].

272 Visual Cortex – Current Status and Perspectives

neurogenic instead of metabolic [47].

also known as KDR [49, 51, 59, 60].

**5. Angioglioneurins** 

[44].

Thus, during the first two weeks of postnatal development, most of the vessels are the perpendicularly penetrating vessels, and this terminates at the end of the second postnatal week, just at the opening of the eyes. At this point, the adult pattern starts, characterised by a dense capillary network that is thicker in the most active areas, such as layer IV [6, 30]. The influence of experience can be demonstrated in visually-deprived dark-reared rats, that have an immature vascular pattern characterised by a higher prevalence of perpendicularly penetrant trunks and a much sparser vascular network. In contrast, environmental enrichment induces just the opposite, a faster development of the mature pattern and a higher vascular density [14]. These changes occur in parallel to the development of the rest of the elements of the neurogliovascular unit, and we have demonstrated that the development of the astroglial population in layer IV of the visual cortex mirrors the vascular behaviour [42, 43]. In a similar way, vascular density is also higher in layer IV of the auditory cortex and lower in layer I [30]. On the other hand, we have recently described upregulated neuropeptides in the visual system, and as some of them have also strong angiogenic properties, and as neuropeptides are among the main regulators of the critical period in the visual cortex along with angioglioneurins, this finding is also consistent with the common patterns for neural and vascular development

There is a direct relationship between vascular density and metabolic activity. This is evidenced by the correlation between vascular density and mitochondria. Local capillary density, local utilization of glucose and local cerebral blood flow [45, 46] have also been found to be correlated. The relationship between the increase in neural activity and the increase in perfusion to supply this requirement is the so-called neurovascular coupling, that is the basis of modern neuroimaging techniques and that has been recently described as

Neovascularisation is mediated by a variety of cytokines, including Vascular Endothelial Growth Factor (VEGF). VEGF is a hypoxia-inducible secreted homodimeric glycoprotein of 45,000 daltons that plays a major role in developmental [48-51] and pathological angiogenesis [52-57]. Five major isoforms of human VEGF exist, of which VEGF165 is the predominant one in most mammals. However, VEGF164 is shorter by one amino acid in rodents. VEGF isoforms are differentially expressed in disease, suggesting differences between pathological entities in the mechanisms of VEGF up-regulation as well as in their employment of distinct isoforms for neovascularisation [58]. The main receptors for the Vascular Endothelial Growth Factor (VEGF) family are the feline sarcoma virus-like tyrosine kinase receptor (Flt-1 or VEGFR-1) and the fetal liver kinase receptor (Flk-1) or VEGFR-2,

VEGFR-2 plays a critical role in some permeability-enhancing effects of VEGF [54]. In pathological conditions, VEGFR-2 mediates an antiapoptotic effect via Phosphoinositide 3-kinase (PI3K)-dependent signalling pathways which promote the survival of endothelial But despite VEGF being the main angiogenic molecule, there are others that also play a multicellular role. Molecules that affect both neural and vascular cell processes have recently been termed angioneurins [71]. Angioneurins include molecules first described as vascular growth factors, such as Vascular Endothelial Growth Factor (VEGF), molecules first described as neurotrophins such as Brain-Derived Neurotrophic Factor (BDNF), and other factors such as Insulin-Like Growth Factor-1 (IGF-I) or Erythropoietin (EPO). Independently of their origin, all angioneurins share a common action on vascular and neuronal function. As most of these molecules also have effects on the third component of the neurogliovascular unit, the glia, we propose the term angioglioneurins to describe them [39, 72].

## **6. Conclusions**

The brain is highly vascularised, containing a very intricate network of capillaries (nearly every brain cell is located within 20 μm of a capillary). The endothelial cells that form the brain capillaries are sealed together by tight junctions, and have no fenestrations and very low pinocytosis. This combination of features creates the BBB, which is both a physical and enzymatic barrier.

Experience Mediated Development of the Visual Cortex Vascularization 275

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Angiogenesis is one of the main adaptive mechanisms of brain microcirculation to changing needs of the CNS, as happens in the development of the visual cortex under the influence of visual activity, mainly during the critical period.

The nervous system needs a stable internal environment, which is created by the Blood Brain Barrier (BBB). The BBB function is related to structural and functional features of the vascular endothelium. Both the development of angioarchitecture and the functional maturation of the BBB occur postnatally and are regulated by tissue microenvironment and external environment. The development of the vascular tree, the acquisition of functional competence by the BBB and the induction and modulation of neoangiogenesis are closely dependent on the changes in metabolic demand induced by functional modifications (increases or decreases of stimuli).

The vascular network plays a crucial role in development and function of the CNS. It adapts to specific changes of metabolic demand and local flow modifications. However, if these changes become permanent, the supply is ensured by neoangiogenesis. Angiogenic, neurotrophic and neuroprotective factors participate in the development and maintenance of vascular, astroglial and neuronal structures, suggesting that the neurogliovascular unit preserves brain integrity. The improvement of the neurogliovascular unit by mechanisms such as the direct administration or the indirect stimulation of secretion of angioglioneurins could be an efficient strategy in brain diseases.

## **Author details**

Enrike G. Argandoña *Department of Medicine, Unit of Anatomy, University of Fribourg, Fribourg, Switzerland* 

Harkaitz Bengoetxea, Naiara Ortuzar, Susana Bulnes, Irantzu Rico-Barrio and José Vicente Lafuente *Laboratory of Experimental Neuroscience LaNCE, Department of Neuroscience, Faculty of Medicine and Odontology, University of the Basque Country (UPV/EHU), Sarriena Auzoa, Leioa, Spain* 

## **7. References**

[1] MacVicar BA, Salter MW (2006) Neuroscience: controlled capillaries. Nature. 443(7112):642-643.

[2] Belanger M, Allaman I, Magistretti PJ (2011) Brain energy metabolism: focus on astrocyte-neuron metabolic cooperation. Cell Metab. 14(6):724-738.

274 Visual Cortex – Current Status and Perspectives

(increases or decreases of stimuli).

could be an efficient strategy in brain diseases.

Harkaitz Bengoetxea, Naiara Ortuzar, Susana Bulnes,

Irantzu Rico-Barrio and José Vicente Lafuente

visual activity, mainly during the critical period.

The brain is highly vascularised, containing a very intricate network of capillaries (nearly every brain cell is located within 20 μm of a capillary). The endothelial cells that form the brain capillaries are sealed together by tight junctions, and have no fenestrations and very low pinocytosis. This combination of features creates the BBB, which is both a physical and

Angiogenesis is one of the main adaptive mechanisms of brain microcirculation to changing needs of the CNS, as happens in the development of the visual cortex under the influence of

The nervous system needs a stable internal environment, which is created by the Blood Brain Barrier (BBB). The BBB function is related to structural and functional features of the vascular endothelium. Both the development of angioarchitecture and the functional maturation of the BBB occur postnatally and are regulated by tissue microenvironment and external environment. The development of the vascular tree, the acquisition of functional competence by the BBB and the induction and modulation of neoangiogenesis are closely dependent on the changes in metabolic demand induced by functional modifications

The vascular network plays a crucial role in development and function of the CNS. It adapts to specific changes of metabolic demand and local flow modifications. However, if these changes become permanent, the supply is ensured by neoangiogenesis. Angiogenic, neurotrophic and neuroprotective factors participate in the development and maintenance of vascular, astroglial and neuronal structures, suggesting that the neurogliovascular unit preserves brain integrity. The improvement of the neurogliovascular unit by mechanisms such as the direct administration or the indirect stimulation of secretion of angioglioneurins

*Department of Medicine, Unit of Anatomy, University of Fribourg, Fribourg, Switzerland* 

[1] MacVicar BA, Salter MW (2006) Neuroscience: controlled capillaries. Nature.

*Laboratory of Experimental Neuroscience LaNCE, Department of Neuroscience, Faculty of Medicine and Odontology, University of the Basque Country (UPV/EHU),* 

**6. Conclusions** 

enzymatic barrier.

**Author details** 

Enrike G. Argandoña

*Sarriena Auzoa, Leioa, Spain* 

443(7112):642-643.

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**Chapter 13** 

© Maya-Vetencourt and Caleo, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Insights into Visual Cortex Plasticity: Interaction** 

The central nervous system architecture is highly dynamic and continuously modified by sensory experience through processes of neuronal plasticity. Interactions with the external world, mediated by sensory input, update and modify the structural and functional architecture of the nervous system, particularly during short-term sensitive periods in early life (critical period (CP) for brain plasticity) during which experience drives the consolidation of synaptic circuitries (Katz & Shatz 1996; Berardi et al., 2000; Levelt & Hubener 2012). However, experience-dependent reorganization of neuronal circuitries

The visual cortex (VC) is a classical model to examine the influence of sensory experience on brain structure and function. Early electrophysiological studies demonstrated that occlusion of one eye (monocular deprivation, MD) during the CP leads to an ocular dominance (OD) shift of visual cortical neurons, i.e., a decrease in the number of cells responding to the deprived eye that is accompanied by an increment of cells driven by the open eye (Wiesel & Hubel 1963; Hubel & Wiesel 1970; Frenkel & Bear 2004). In addition, the deprived eye becomes amblyopic: its spatial acuity and contrast sensitivity are markedly impaired. At structural level, unilateral eyelid suture causes a reduction in the arborisation of geniculocortical terminals that serve the deprived eye, which parallels an increased spread of terminals serving the open eye (Antonini & Stryker 1993). Because MD does not cause amblyopia in adult life this is a typical example of a CP. These findings highlight the notion

that sensory experience is critical for normal development of the nervous system.

The importance of sensory experience in development of the human brain is well exemplified by cases of strabismic or anisometric children that underwent no clinical treatment during early development. In either pathological condition proper visual experience is altered, causing a marked impairment of normal visual functions (amblyopia)

and reproduction in any medium, provided the original work is properly cited.

**Between Genes and Sensory Experience** 

José Fernando Maya-Vetencourt and Matteo Caleo

Additional information is available at the end of the chapter

continues in adult life, at least to some extent.

http://dx.doi.org/10.5772/50721

**1. Introduction** 

