| Title: | A self-supervised learning framework for discovering cortical folding patterns under genetic influence: Application to the Anterior Cingulate Cortex |
| Journal: | Imaging Neuroscience |
| Published: | 5 Nov 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41211100/ |
| DOI: | https://doi.org/10.1162/imag.a.987 |
| URL: | https://direct.mit.edu/imag/article-pdf/doi/10.1162/IMAG.a.987/2559428/imag.a.987.pdf |
Publication 16713
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Cortical folding patterns can serve as a macroscopic probe for hidden events that occur during the development of the human brain. However, the large inter-individual variability of these patterns makes them very difficult to associate with developmental pathologies. We propose a framework for discovering candidate patterns under genetic influence and illustrate its use in the anterior cingulate cortex region, where the paracingulate sulcus shapes have aroused interest in psychiatry. This framework is based on a comprehensive regional representation of fold variability inferred from a self-supervised deep learning algorithm applied to 36,000 white British ancestry subjects from the UK Biobank database. This representation can be used to train linear classification models, in order to learn to discern folding patterns from labeled databases and generalize to larger databases. In our case, the generalization of paracingulate sulcus labeling on UK BioBank subjects did not lead to clear genetic associations. Secondly, we show that new loci associated with cortical folding patterns can be discovered directly from the representation. We find in the discovery cohort 4 loci for the right hemisphere and 10 loci for the left hemisphere related to specific folding patterns of the anterior cingulate cortex ( p < 5 × 10 - 8 ). Even if only one locus is replicated on a smaller cohort of non white British ancestry subjects, many of the discovered loci have already been associated with brain anatomy or psychiatry.</p>
9 Authors
- Antoine Dufournet
- Julien Laval
- Denis Rivière
- Héloïse de Vareilles
- Graham K. Murray
- Arnaud Cachia
- Joël Chavas
- Vincent Frouin
- Jean-François Mangin
1 Application
| Application ID | Title |
|---|---|
| 64984 | Validation and transfer-learning for biomarkers obtained in several clinical cohorts of neuro developmental and neuro degenerative diseases |
Enabling scientific discoveries that improve human health