Identifying Cognitive States Using Regularity Partitions

Ioannis Pappas and Panos Pardalos

PLOS ONE, 2015, vol. 10, issue 8, 1-15

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.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137012 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 37012&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0137012

DOI: 10.1371/journal.pone.0137012

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().