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Abstract
Functional Magnetic Resonance (fMRI) data can be used to depict functional connectivity of the brain. Standard techniques have been developed to construct brain networks from this data; typically nodes are considered as voxels or sets of voxels with weighted edges between them representing measures of correlation. Identifying cognitive states based on fMRI data is connected with recording voxel activity over a certain time interval. Using this information, network and machine learning techniques can be applied to discriminate the cognitive states of the subjects by exploring different features of data. In this work we wish to describe and understand the organization of brain connectivity networks under cognitive tasks. In particular, we use a regularity partitioning algorithm that finds clusters of vertices such that they all behave with each other almost like random bipartite graphs. Based on the random approximation of the graph, we calculate a lower bound on the number of triangles as well as the expectation of the distribution of the edges in each subject and state. We investigate the results by comparing them to the state of the art algorithms for exploring connectivity and we argue that during epochs that the subject is exposed to stimulus, the inspected part of the brain is organized in an efficient way that enables enhanced functionality.
Highlights
Studying the human brain has gained significant interest the past years due to the advances of neuroimaging techniques
The temporal resolution of fMRI is considered over several seconds where each second may consist of a large amount of images
We turn our attention to data collected during a specific cognitive experiment and we focus on three points that stand on the intersection of the previous remarks: 1. We wish to identify differences in functional connectivity between during different cognitive states
Summary
Studying the human brain has gained significant interest the past years due to the advances of neuroimaging techniques. A typical fMRI study is delineated as follows: Fluctuations in the oxygen levels of the blood (BOLD signals) are captured usually in low frequencies (0.01–0.05 Hz) with significant spatial resolution [1]. When recording, fMRI images are being drawn usually every 0.5 or 1 second. Each image consists of a collection of voxels that are rudimentary volume elements of the cortex (usually few cubic millimeters). Each image can contain thousands of voxels whereas each voxel may reflect the activity of a large group of neurons. The temporal resolution of fMRI is considered over several seconds where each second may consist of a large amount of images. Each voxel can be described by a specific BOLD response over time
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