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Abstract #2348

Class-Aware Hidden Markov Model for simultaneous functional connectivity estimation and classification

Chendi Han1, Pavithran Pattiam Giriprakash1, Rajesh Nandy2, and Dietmar Cordes1
1Cleveland Clinic, Las Vegas, NV, United States, 2Department of Biostatistics and Epidemiology, School of Public Health, University of North Texas Health Science Center, Fort Worth, TX, United States

Synopsis

Keywords: Functional Connectivity, fMRI (resting state)

Motivation: Hidden Markov Models (HMM) are widely used for modeling resting-state fMRI data. However, current classification approaches usually involve additional parameter extraction steps, which make the model complicated and reduce sensitivity.

Goal(s): Our goal is to develop a one-step classification method based on HMMs that is more efficient, accurate, and maintains interpretability.

Approach: We utilize Hidden Conditional Random Fields (HCRF), combining HMM and discriminative learning into a unified model that eliminates the need for separate parameter extraction.

Results: In both simulated and real data, the one-step model outperforms the traditional two-step model. Supervised learning could further improve classification accuracy.

Impact: For fMRI classification problems, the one-step Class-Aware HMM is simpler and more accurate compared to two-step classification while maintaining model interpretability. This could help in understanding brain connectivity and disease diagnosis.

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Keywords