Bayesian Models of Functional Connectomics and Behavior
- URL: http://arxiv.org/abs/2301.06182v1
- Date: Sun, 15 Jan 2023 20:42:31 GMT
- Title: Bayesian Models of Functional Connectomics and Behavior
- Authors: Niharika Shimona D'Souza
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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