Constrained Decoupled Dictionary Learning of Task fMRI for Brain Health Biomarkers

Sreekrishna Ramakrishnapillai, Harris R. Lieberman, Jennifer C. Rood, Stefan M. Pasiakos, Lydia Bazzano, Preetham Shankapal, Owen Carmichael, Kori Murray

Keywords: Task-fMRI, SVM, Prediction

Abstract: Sparse dictionary learning methods have shown promise for representing task-based functional magnetic resonance imaging (tfMRI) data in terms of coherent spatiotemporal patterns of brain activity. However, methods that leverage prior knowledge, provide space-efficient representations, and correlate well with relevant clinical and scientific variables have been elusive. We propose a constrained decoupled dictionary learning method that uses prior knowledge related to task paradigms and the spatial locations of task-related brain regions to estimate decoupled spatial and temporal patterns that represent the fMRI data. The method identifies a high percentage of the spatial and temporal patterns programmed into simulated fMRI data. When applied to two real-world fMRI data sets, the method: 1. automatically identifies temporal and spatial patterns that were known by neuroscientists to be task-related a priori but were not provided as inputs to the method; 2. provides measurements that differ significantly between differing tasks; and 3. provides measurements that correlate well with relevant health information (e.g., fasting glucose level) in support vector regression models.

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Paper Type: both

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Integration of Imaging and Clinical Data

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