Reduced sine hyperbolic polynomial model for brain neuro-developmental analysis

Peyman H. Kassani; Vince D. Calhoun; Yu-Ping Wang

doi:10.1117/12.2549020

28 February 2020 Reduced sine hyperbolic polynomial model for brain neuro-developmental analysis

Peyman H. Kassani, Vince D. Calhoun, Yu-Ping Wang

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1131706 (2020) https://doi.org/10.1117/12.2549020
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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%.

Conference Presentation

Citation Download Citation

Peyman H. Kassani, Vince D. Calhoun, and Yu-Ping Wang "Reduced sine hyperbolic polynomial model for brain neuro-developmental analysis", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131706 (28 February 2020); https://doi.org/10.1117/12.2549020

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