ArticleImaging neuroscience (Cambridge, Mass.)2024
Identification of rare cortical folding patterns using unsupervised deep learning.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Motifs of brain cortical folding from birth to adulthood.Nature communications · 2026Article
- Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease.Medical image analysis · 2025Article
- Phylogenetic differences in the morphology and shape of the central sulcus in great apes and humans: implications for the evolution of motor functions.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Orbitofrontal sulcal patterns in catatonia.European psychiatry : the journal of the Association of European Psychiatrists · 2023Article
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9 authors.
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Abstract
Like fingerprints, cortical folding patterns are unique to each brain even though they follow a general species-specific organization. Some folding patterns have been linked with neurodevelopmental disorders. However, due to the high inter-individual variability, the identification of rare folding patterns that could become biomarkers remains a very complex task. This paper proposes a novel unsupervised deep learning approach to identify rare folding patterns and assess the degree of deviations that can be detected. To this end, we preprocess the brain MR images to focus the learning on the folding morphology and train a beta variational auto-encoder (
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