ArticleNeuroImage2020
Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Higher-Order Triadic Interactions: Insights Into the Multiscale Network Organization in Schizophrenia.Human brain mapping · 2025Article
- Deciphering Multiway Multiscale Brain Network Connectivity: Insights from Birth to 6 Months.bioRxiv : the preprint server for biology · 2025Article
- Providing context: Extracting non-linear and dynamic temporal motifs from brain activity.PloS one · 2025Article
- Exploring Nonlinear Dynamics In Brain Functionality Through Phase Portraits And Fuzzy Recurrence Plots.bioRxiv : the preprint server for biology · 2024Article
- Multiscale Functional Connectivity analysis of episodic memory reconstruction.Frontiers in cognition · 2024Article
- Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls.Nature. Mental health · 2024Article
- Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls.bioRxiv : the preprint server for biology · 2023Article
- Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series.Schizophrenia research · 2022Article
- Statelets: Capturing recurrent transient variations in dynamic functional network connectivity.Human brain mapping · 2022Article
- An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.Human brain mapping · 2022Article
- Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Accumulating evidence from whole brain functional magnetic resonance imaging (fMRI) suggests that the human brain at rest is functionally organized in a spatially and temporally constrained manner. However, because of their complexity, the fundamental mechanisms underlying time-varying functional networks are still not well understood. Here, we develop a novel nonlinear feature extraction framework called local space-contrastive learning (LSCL), which extracts distinctive nonlinear temporal structure hidden in time series, by training a deep temporal convolutional neural network in an unsupervised, data-driven manner. We demonstrate that LSCL identifies certain distinctive local temporal structures, referred to as temporal primitives, which repeatedly appear at different time points and spatial locations, reflecting dynamic resting-state networks. We also show that these temporal primitives are also present in task-evoked spatiotemporal responses. We further show that the temporal primitives capture unique aspects of behavioral traits such as fluid intelligence and working memory. These results highlight the importance of capturing transient spatiotemporal dynamics within fMRI data and suggest that such temporal primitives may capture fundamental information underlying both spontaneous and task-induced fMRI dynamics.
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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.