Functional MRI Data Analysis Using Connectivity Strengths to Identify Cognitive States

This paper considers a graphical framework for identifying connectivity strengths in cognitive dataset. Functional MRI (fMRI) data with two cognitive states used for the analysis. In general fMRI data expressed in terms of number of voxels and Region of Interests (ROI). Task specific brain activatio...

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Published in2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) pp. 578 - 582
Main Authors Ramakrishna, J. Siva, Ramasangu, Hariharan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2018
Subjects
Brain
Classification
Cognitive states
Correlation
Functional connectivity
Functional magnetic resonance imaging
Functional MRI
Graph analysis
Graph theory
Roi
Task analysis
Time series analysis
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Summary:This paper considers a graphical framework for identifying connectivity strengths in cognitive dataset. Functional MRI (fMRI) data with two cognitive states used for the analysis. In general fMRI data expressed in terms of number of voxels and Region of Interests (ROI). Task specific brain activation detection using fMRI is a challenging activity. The connectivity analysis among the ROIs gives inference about connectivity strengths among ROIs. In this paper it is identified through a graphical approach. In which both global and nodal parameters are considered for determination of connectivity strengths. The graph analysis considers connectivity matrices, Calculation of nodal and global measures and group comparisons through non-parameteric permutations. The results depicts interactions between ROIs is varying for a task specific cognitive data. Further the obtained results show significant change in participation of a ROI with other ROIs while subject performing a task. The graphical analysis is performed on a well known StarPlus fMRI data. The connectivity strength for each ROI is measured.
DOI:10.1109/ICACCI.2018.8554941