ArticleScientific reports2022
Interpreting models interpreting brain dynamics.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Disease detection and classification in temporal lobe epilepsy: step-wise versus simultaneous AI decision models in a multisite neuroimaging study.Brain communications · 2026Article
- Interpretable Disorder Signatures: Probing Neural Latent Spaces for Schizophrenia, Alzheimer's, and Autism Stratification.Brain sciences · 2025Article
- Explainable Self-Supervised Dynamic Neuroimaging Using Time Reversal.Brain sciences · 2025Article
- Discovering individual fingerprints in resting-state functional connectivity using deep neural networks.Human brain mapping · 2024Article
- Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia.Frontiers in psychiatry · 2024Article
- A pilot evaluation of the diagnostic accuracy of ChatGPT-3.5 for multiple sclerosis from case reports.Translational neuroscience · 2024Article
- Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics.Neuroimage. Reports · 2023Article
- Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.Sensors (Basel, Switzerland) · 2023Article
- Explainable Fuzzy Clustering Framework Reveals Divergent Default Mode Network Connectivity Dynamics in Schizophrenia.bioRxiv : the preprint server for biology · 2023Article
- Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches.Informatics in medicine unlocked · 2023Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Yet, the difficulty of reliable training on high-dimensional low sample size datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this work, we introduce a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. Results successfully demonstrate that the proposed framework enables learning the dynamics of resting-state fMRI directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.