ArticleNeuroImage2021
Representation learning of resting state fMRI with variational autoencoder.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- The Intrinsic Manifold of Spontaneous Activity Constrains Cortical Responses to Naturalistic Stimuli.bioRxiv : the preprint server for biology · 2026Article
- Conditioned Graph Reconstruction of Brain Functional Network Connectivity Reveals Interpretable Latent Axes of Sex and Fluid Intelligence.bioRxiv : the preprint server for biology · 2026Article
- Distinct contributions of anterior and posterior orbitofrontal cortex to outcome-guided behavior.bioRxiv : the preprint server for biology · 2025Article
- Intervention on Modifiable Lifestyle and Physiological Factors via Variational Autoencoder Reveals Changes in Functional Connectivity-Mediated Risk for Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- Deep learning-based embedding of functional connectivity profiles for precision functional mapping.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.Scientific reports · 2024Article
- Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach.NeuroImage · 2024Article
- A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.ArXiv · 2024Article
- Using a deep generation network reveals neuroanatomical specificity in hemispheres.Patterns (New York, N.Y.) · 2024Article
- Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data.Frontiers in computational neuroscience · 2024Article
- Article
- Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder.Frontiers in psychiatry · 2024Article
- Conditional Variational Autoencoder for Functional Connectivity Analysis of Autism Spectrum Disorder Functional Magnetic Resonance Imaging Data: A Comparative Study.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- Computing personalized brain functional networks from fMRI using self-supervised deep learning.Medical image analysis · 2023Article
- Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE.Proceedings of machine learning research · 2023Article
- Manifold Learning for fMRI time-varying FC.bioRxiv : the preprint server for biology · 2023Article
- Manifold learning for fMRI time-varying functional connectivity.Frontiers in human neuroscience · 2023Article
- A transformer model for learning spatiotemporal contextual representation in fMRI data.Network neuroscience (Cambridge, Mass.) · 2023Article
- Brain Decoding Using fMRI Images for Multiple Subjects through Deep Learning.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model trainable with unsupervised learning, to disentangle the unknown sources of rsfMRI activity. After being trained with large data from the Human Connectome Project, the model has learned to represent and generate patterns of cortical activity and connectivity using latent variables. The latent representation and its trajectory represent the spatiotemporal characteristics of rsfMRI activity. The latent variables reflect the principal gradients of the latent trajectory and drive activity changes in cortical networks. Representational geometry captured as covariance or correlation between latent variables, rather than cortical connectivity, can be used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data is available in each subject. Our results demonstrate that VAE is a valuable addition to existing tools, particularly suited for unsupervised representation learning of resting state fMRI activity.
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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.