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Proceedings Paper

Reduced sine hyperbolic polynomial model for brain neuro-developmental analysis
Author(s): Peyman H. Kassani; Vince D. Calhoun; Yu-Ping Wang
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Paper Abstract

Several studies on brain development have only considered functional connectivity (FC) of different brain regions. In the following study, we propose to add effective connectivity (EC) through Granger causality (GC) for the task of brain maturation. We do this for two different groups of subjects, i.e., children and young adults. We aim to show that the inclusion of causal interaction may further discriminate brain connections between two age groups . We extract EC feature by a new kernel-based GC (KGC) method based on a reduced Sine hyperbolic polynomial (RSP) neural network which helps to learn nonlinearity of complex brain network. Our new EC-based feature outperformed FC-based feature evaluated on Philadelphia neurocohort (PNC) study with better separation between the two different age groups. We also showed that the fusion of two sets of features (FC + EC) improved brain age prediction accuracy by more than 4%.

Paper Details

Date Published: 28 February 2020
PDF: 5 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131706 (28 February 2020); doi: 10.1117/12.2549020
Show Author Affiliations
Peyman H. Kassani, Tulane Univ. (United States)
Vince D. Calhoun, Ctr. for Translational Research in Neuroimaging and Data Science (United States)
Yu-Ping Wang, Tulane Univ. (United States)

Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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