Identifying Cognitive States Using Regularity Partitions

PloS One
Ioannis Pappas, Panos Pardalos

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 e...Continue Reading

References

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Aug 5, 2005·Human Brain Mapping·Geng LiEdward S Yang
Sep 21, 2007·Proceedings of the National Academy of Sciences of the United States of America·Marta Sales-PardoLuís A Nunes Amaral
Feb 5, 2009·Nature Reviews. Neuroscience·Ed Bullmore, Olaf Sporns
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Dec 30, 2014·Frontiers in Neuroscience·Huifang E WangChristophe Bernard
Jun 17, 2015·Proceedings of the National Academy of Sciences of the United States of America·Sara SpadoneMaurizio Corbetta

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