ArticleHuman brain mapping2018
Spatio-temporal dynamics of resting-state brain networks improve single-subject prediction of schizophrenia diagnosis.
Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Catecholaminergic Modulation of Large-Scale Network Dynamics Is Tied to the Reconfiguration of Corticostriatal Connectivity.Human brain mapping · 2024Trial
- Causal disconnectomics of motion perception networks: insights from transcranial magnetic stimulation-induced BOLD responses.The Journal of physiology · 2026Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.bioRxiv : the preprint server for biology · 2024Article
- Overcoming the translational crisis of contemporary psychiatry - converging phenomenological and spatiotemporal psychopathology.Molecular psychiatry · 2023Review
- Altered functional dynamics gradient in schizophrenia with cigarette smoking.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia.Frontiers in neuroimaging · 2023Article
- A schizophrenia study based on multi-frequency dynamic functional connectivity analysis of fMRI.Frontiers in human neuroscience · 2023Article
- Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study.Human brain mapping · 2022Article
- Alterations of dynamic functional connectivity between visual and executive-control networks in schizophrenia.Brain imaging and behavior · 2022Article
- Multi-timepoint pattern analysis: Influence of personality and behavior on decoding context-dependent brain connectivity dynamics.Human brain mapping · 2022Article
- A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning.Journal of neuroscience methods · 2022Article
- Relationship between Dynamic Blood-Oxygen-Level-Dependent Activity and Functional Network Connectivity: Characterization of Schizophrenia Subgroups.Brain connectivity · 2021Article
- Functional near-infrared spectroscopy (fNIRS) as a tool to assist the diagnosis of major psychiatric disorders in a Chinese population.European archives of psychiatry and clinical neuroscience · 2021Article
- Dynamic functional connectivity and its anatomical substrate reveal treatment outcome in first-episode drug-naïve schizophrenia.Translational psychiatry · 2021Article
- Graph-theoretical analysis identifies transient spatial states of resting-state dynamic functional network connectivity and reveals dysconnectivity in schizophrenia.Journal of neuroscience methods · 2021Article
- Brain features of nearly drug-naïve female monozygotic twins with first-episode schizophrenia and the classification accuracy of brain feature patterns: A pilot study.Brain and behavior · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding-window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting-state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting-state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting-state dynamics are not trivially attributable to sampling variability and head motion.
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