# A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data

arXiv: https://arxiv.org/abs/1807.09319 · HTML: https://mlsanity.com/paper/1807.09319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Bayesian Models of Functional Connectomics and Behavior](https://mlsanity.com/paper/2301.06182/) (2023)

## Abstract (arXiv)

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.