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**0**

**19**

*Canada*

**Graphical Models of Functional MRI Data for**

<sup>2</sup>*Department of Medicine (Neurology), Pacific Parkinson's Research Centre*

Modern neuroimaging technologies have allowed researchers to non-invasively observe indirect markers of brain activity *in vivo* (Fig. 1). This has resulted in a rapid growth of studies trying to ascertain what brain loci are associated with certain cognitive, sensory and motor tasks. In particular, the recent development of functional magnetic resonance imaging (fMRI) has allowed researchers to non-invasively investigate brain activity at excellent spatial resolution and relatively good temporal resolution. While probing aspects of brain function is typically under the domain of neuroscientists, fMRI work is inherently interdisciplinary: it involves MR physicists who determine MRI sequences sensitive to small changes in the brain, neuroscientists who design the behavioural experiments and interpret the observations, statisticians to assess significance of changes, and increasingly, people with signal processing

Analysis of fMRI data sets represents a special challenge for traditional statistical methods that were originally designed for a large number of samples of low-dimensional data points. The number of "voxels" (ie. representing a specific locus in the brain) to be analyzed are large (<sup>≈</sup> 105), yet the number of time points (<sup>≈</sup> <sup>10</sup>2) is relatively small. Most early fMRI analysis methods were designed to ascertain the regions where brain functions are localized

Even when simple tasks are performed in the MRI scanner, widespread activation can be observed in the brain with fMRI. These and other studies suggest that the brain is active at multiple spatial and time scales supporting both segregated and distributed information processing (Bassett & Bullmore, 2006). In fact, the advent of non-invasive functional neuroimaging has re-ignited a centuries-old debate about whether or not cognitive and motor tasks are encoded in discrete loci or are more diffusely and fluidly represented, the latter

While connectivity appears to be of critical importance for understanding and assessment of brain function, it can be difficult to define in a rigorous sense with current technologies that can only probe brain activity at certain spatial and temporal scales (see Fig. 1). Conventionally, brain connectivity can be studied at three levels: anatomical, functional, and effective connectivity (see Fig. 2). Anatomical connectivity refers to actual physical connections

emphasizing the importance of assessing brain connectivity (Catani & ffytche, 2005).

expertise to derive more and more information from the time series extracted.

**1. Introduction**

**1.1 Brain connectivity and fMRI**

by performing voxel-wise analysis.

**Assessing Brain Connectivity**

<sup>1</sup>*Department of Electrical and Computer Engineering*

*University of British Columbia*

Junning Li1, Z. Jane Wang1 and Martin J. McKeown<sup>2</sup>

