# Large Connectome Model: An fMRI Foundation Model of Brain Connectomes Empowered by Brain-Environment Interaction in Multitask Learning Landscape

arXiv: https://arxiv.org/abs/2510.18910 · HTML: https://mlsanity.com/paper/2510.18910/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Not too little, not too much: a theoretical analysis of graph (over)smoothing](https://mlsanity.com/paper/2205.12156/) (2022)
- [Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking](https://mlsanity.com/paper/2409.19407/) (2024)
- [NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics](https://mlsanity.com/paper/2306.06202/) (2023)
- [NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes](https://mlsanity.com/paper/2409.17510/) (2024)
- [Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations](https://mlsanity.com/paper/2409.11377/) (2024)

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

- [Marrying Generative Model of Healthcare Events with Digital Twin of Social Determinants of Health for Disease Reasoning](https://mlsanity.com/paper/2605.09771/) (2026)
- [Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning](https://mlsanity.com/paper/2605.28990/) (2026)
- [BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces](https://mlsanity.com/paper/2608.15266/) (2026)
- [FlatClip: Reusing Image Foundation Models for fMRI Representation Learning via Cortical Flatmaps](https://mlsanity.com/paper/2609.31204/) (2026)

## Abstract (arXiv)

A reliable foundation model of functional neuroimages is critical to promote clinical applications where the performance of current AI models is significantly impeded by a limited sample size. To that end, tremendous efforts have been made to pretraining large models on extensive unlabeled fMRI data using scalable self-supervised learning. Since self-supervision is not necessarily aligned with the brain-to-outcome relationship, most foundation models are suboptimal to the downstream task, such as predicting disease outcomes. By capitalizing on rich environmental variables and demographic data along with an unprecedented amount of functional neuroimages, we form the brain modeling as a multitask learning and present a scalable model architecture for (i) multitask pretraining by tokenizing multiple brain-environment interactions (BEI) and (ii) semi-supervised finetuning by assigning pseudo-labels of pretrained BEI. We have evaluated our foundation model on a variety of applications, including sex prediction, human behavior recognition, and disease early diagnosis of Autism, Parkinson's disease, Alzheimer's disease, and {Schizophrenia}, where promising results indicate the great potential to facilitate current neuroimaging applications in clinical routines.