# Differentiable programming for functional connectomics

arXiv: https://arxiv.org/abs/2206.00649 · HTML: https://mlsanity.com/paper/2206.00649/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 60 references that are in the map.
- First posted on arXiv 31 May 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [Fine-grain atlases of functional modes for fMRI analysis](https://mlsanity.com/paper/2003.05405/) (2020)
- [High Performance I/O For Large Scale Deep Learning](https://mlsanity.com/paper/2001.01858/) (2020)
- [Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks](https://mlsanity.com/paper/1511.06352/) (2015)
- [Towards end-to-end optimisation of functional image analysis pipelines](https://mlsanity.com/paper/1610.04079/) (2016)

## Abstract (arXiv)

Mapping the functional connectome has the potential to uncover key insights into brain organisation. However, existing workflows for functional connectomics are limited in their adaptability to new data, and principled workflow design is a challenging combinatorial problem. We introduce a new analytic paradigm and software toolbox that implements common operations used in functional connectomics as fully differentiable processing blocks. Under this paradigm, workflow configurations exist as reparameterisations of a differentiable functional that interpolates them. The differentiable program that we envision occupies a niche midway between traditional pipelines and end-to-end neural networks, combining the glass-box tractability and domain knowledge of the former with the amenability to optimisation of the latter. In this preliminary work, we provide a proof of concept for differentiable connectomics, demonstrating the capacity of our processing blocks both to recapitulate canonical knowledge in neuroscience and to make new discoveries in an unsupervised setting. Our differentiable modules are competitive with state-of-the-art methods in problem domains including functional parcellation, denoising, and covariance modelling. Taken together, our results and software demonstrate the promise of differentiable programming for functional connectomics.