# Learning and comparing functional connectomes across subjects

arXiv: https://arxiv.org/abs/1304.3880 · HTML: https://mlsanity.com/paper/1304.3880/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2013.

## Built on by (most cited first, 4 of 4)

- [Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification](https://mlsanity.com/paper/2404.19467/) (2024)
- [Generalizable Representation Learning for fMRI-based Neurological Disorder Identification](https://mlsanity.com/paper/2412.16197/) (2024)
- [A foundation-model approach to pediatric headache classification from rs-fMRI](https://mlsanity.com/paper/2608.07287/) (2026)
- [A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regression](https://mlsanity.com/paper/2609.22654/) (2026)

## Abstract (arXiv)

Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes.