# Self-Supervised Mental Disorder Classifiers via Time Reversal

arXiv: https://arxiv.org/abs/2211.16398 · HTML: https://mlsanity.com/paper/2211.16398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling](https://mlsanity.com/paper/2104.04569/) (2021)
- [Whole MILC: generalizing learned dynamics across tasks, datasets, and populations](https://mlsanity.com/paper/2007.16041/) (2020)
- [Leveraging Time Irreversibility with Order-Contrastive Pre-training](https://mlsanity.com/paper/2111.02599/) (2021)
- [Learnt dynamics generalizes across tasks, datasets, and populations](https://mlsanity.com/paper/1912.03130/) (2019)

## Abstract (arXiv)

Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task, for example, classifying diseases from healthy controls in fMRI data. We train a Deep Neural Network on Independent components derived from fMRI data using the Independent component analysis (ICA) technique. It learns time direction in the ICA-based data. This pre-trained model is further trained to classify brain disorders in different datasets. Through various experiments, we have shown that learning time direction helps a model learn some causal relation in fMRI data that helps in faster convergence, and consequently, the model generalizes well in downstream classification tasks even with fewer data records.