**1. Introduction**

108 Neuroimaging – Cognitive and Clinical Neuroscience

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> Functional Magnetic resonance imaging (fMRI, (Ogawa et al., 1990)) in the absence of experimental tasks and behavioral responses, performed with the patient in a relaxed "resting" state (rs-fMRI), takes advantage of the neural origin of spontaneous blood-oxygenlevel-dependent (BOLD) signal fluctuations (Biswal et al., 1995) to represent the rate and timing of activity synchronization across the entire brain (Damoiseaux et al., 2006; Mantini et al., 2007; van de Ven et al., 2004).

> Independent component analysis (ICA) (Hyvarinen et al., 2001), when applied to wholebrain rs-fMRI, allows extracting from each individual patient data set a series of activation images describing the BOLD signal temporal correlations within and between functionally connected brain regions, forming highly reproducible neural networks called resting state networks (RSN) (Damoiseaux et al., 2006; Mantini et al., 2007). Particularly, ICA transforms individual patient rs-fMRI data sets into series of RSN maps, allowing for a voxel-based population analysis of whole-brain functional connectivity without the need to specify "a priori" the regions of interest constituting the layout of the neural network (McKeown et al., 1998; van de Ven et al., 2004).

> In normal volunteers there are at least six RSNs consistently found whose neurological significance has been established according to the functional specialization and anatomical connectivity of the constituent regions (Greicius et al., 2009; van den Heuvel et al., 2009) as well as to the possible association with neuro-electrical rhythms (Mantini et al., 2007). Altogether the functional connectivity of these RSNs represents a basic physiological condition of the human resting brain (Gusnard & Raichle, 2001).

> While the number, role, meaning and potential of RSNs in representing and interpreting the functional architecture of the human brain is still debated and sometimes controversial (Morcom & Fletcher, 2007), a number of voxel-based population rs-fMRI studies have uncovered significant differences between normal and clinical populations in various neurological disorders, and a particular attention has been given to cognitive decline as a

Neuronal Networks Observed with Resting State

colors for the different networks.

referential (SRN) network.

(Rocca et al., 2011)) subnetworks.

Functional Magnetic Resonance Imaging in Clinical Populations 111

(Greicius et al., 2003; Raichle et al., 2001); the visual network (VIS) involving bilaterally the retinotopic occipital cortex up to the temporal-occipital junctions and middle temporal gyri (Lowe et al., 1998; Wang et al., 2008); the fronto-parietal network (FPN) including, bilaterally, the intra-parietal cortex and the superior-lateral frontal cortex (Corbetta & Shulman, 2002); the sensori-motor network (SMN) involving, bilaterally, the pre- and postcentral gyri, the medial frontal gyrus, the primary and supplementary motor and the primary and secondary sensory areas (Biswal et al., 1995); the auditory network (AUD), involving, bilaterally, the superior and middle temporal cortex (Seifritz et al., 2002) and the self-referential network (SRN) involving the ventro-medial prefrontal cortex and the perigenual anterior cingulate cortex (D'Argembeau et al., 2007). The brain maps of these six typical RSNs in a normal population are exemplarily shown in figure 1 using different

Fig. 1. Typical RSN maps. Visual network (VIS), default-mode network (DMN), frontoparietal network (FPN), sensori-motor network (SMN), auditory network (AUD) self-

As anticipated (and exemplified in figure 1), all the RSNs consist of anatomically separated, but functionally connected regions, sharing and supporting the same sensitive, motor or cognitive functions (Cordes et al., 2000). The RSNs reported in the normative literature have generally resulted to be quite consistent across studies, despite some differences in data acquisition and analysis techniques that partially account for the variability observed in the number and lay out of the networks. For instance, the DMN has been sometimes distinguished into two separate subnetworks, the anterior and posterior DMN (see, e. g., (Damoiseaux et al., 2008)), and the FPN as two lateralized networks (right and left FPNs, RFPN and LFPN) (see, e. g., (Damoiseaux et al., 2006; Tedeschi et al., 2010)). The auditory and visual networks have been presented in terms of a one single network (see, e. g., (Mantini et al., 2007)), two (see, e. g., (Damoiseaux et al., 2006)) or even three (see, e. g.,

Understanding the functional correlate of a given RSN under normal physiological conditions is crucial to correctly address any possible link between altered rs-fMRI patterns and behavioral and clinical variables. However, it should also be recognized that cytoarchitectonically distinct brain regions are kept functionally connected by white matter

primary or secondary aspect of neurodegeneration (Bonavita et al., 2011; Cherkassky et al., 2006; Greicius et al., 2007; Greicius et al., 2004; Mohammadi et al., 2009; Nakamura et al., 2009; Rocca et al., 2010; Rombouts et al., 2005; Roosendaal et al., 2010; Sorg et al., 2007; Sorg et al., 2009; Tedeschi et al., 2010).

In this chapter we will review the physiological and technical background of resting state neural networks and the ICA methodology currently used for observing and analyzing RSNs in normal and clinical populations. The main physiological RSNs will be illustrated and discussed with special emphasis to those exhibiting functional abnormalities in neurological disorders. In addition, two clinical applications will be presented, where this methodology showed pathological changes in amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) patients in comparison to normal subjects.
