A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data
Year: 2018
Type: preprint
Abstract: We propose a matrix factorization technique that decomposes the resting state fMRI (rs-fMRI) correlation matrices for a patient population into a sparse set of representative subnetworks, as modeled by rank one outer products. The subnetworks are combined using patient specific non-negative coefficients; these coefficients are also used to model, and subsequently predict the clinical severity of a given patient via a linear regression. Our generative-discriminative framework is able to exploit the structure of rs-fMRI correlation matrices to capture group level effects, while simultaneously accounting for patient variability. We employ ten fold cross validation to demonstrate the predictive power of our model on a cohort of fifty eight patients diagnosed with Autism Spectrum Disorder. Our method outperforms classical semi-supervised frameworks, which perform dimensionality reduction on the correlation features followed by non-linear regression to predict the clinical scores.
Source: arXiv (Cornell University)
Institutions: Johns Hopkins University, Kennedy Krieger Institute
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: G2826372249, Somatosensory Inhibitory Dysfunction in Autism Spectrum Disorder., G4655856282, Adolescent Changes in Brain and Behavior in Boys and Girls with ADHD, G6193134970, 2/2 - Anomalous Motor System Physiology in ADHD: Biomarker Validation and Modeling Domains of Function, Visual-Motor Development in Infants at High Risk for Autism, Motor Skill Learning in Autism, G8287830055
OpenAlex ID: W2950194358