# Bayesian Models of Functional Connectomics and Behavior

arXiv: https://arxiv.org/abs/2301.06182 · HTML: https://mlsanity.com/paper/2301.06182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [Machine learning in resting-state fMRI analysis](https://mlsanity.com/paper/1812.11477/) (2018)
- [A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes](https://mlsanity.com/paper/2105.14409/) (2021)
- [Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations](https://mlsanity.com/paper/2008.12410/) (2020)
- [A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism](https://mlsanity.com/paper/2007.01931/) (2020)
- [A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data](https://mlsanity.com/paper/2009.03238/) (2020)
- [A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity](https://mlsanity.com/paper/2011.08813/) (2020)
- [Hierarchical organization of functional connectivity in the mouse brain: a complex network approach](https://mlsanity.com/paper/1601.07574/) (2016)
- [A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data](https://mlsanity.com/paper/1807.09319/) (2018)
- [A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces](https://mlsanity.com/paper/2007.01929/) (2020)

## Abstract (arXiv)

The problem of jointly analysing functional connectomics and behavioral data is extremely challenging owing to the complex interactions between the two domains. In addition, clinical rs-fMRI studies often have to contend with limited samples, especially in the case of rare disorders. This data-starved regimen can severely restrict the reliability of classical machine learning or deep learning designed to predict behavior from connectivity data. In this work, we approach this problem from the lens of representation learning and bayesian modeling. To model the distributional characteristics of the domains, we first examine the ability of approaches such as Bayesian Linear Regression, Stochastic Search Variable Selection after performing a classical covariance decomposition. Finally, we present a fully bayesian formulation for joint representation learning and prediction. We present preliminary results on a subset of a publicly available clinical rs-fMRI study on patients with Autism Spectrum Disorder.