ArticleJournal of neuroscience methods2019
Autoconnectivity: A new perspective on human brain function.
Article in Journal of neuroscience methods, 2019. 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, 15 citations in OpenAlex.
- Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia.Cerebellum (London, England) · 2026Article
- Temporal and Spatial Scales of Human Resting-State Cortical Activity across the Lifespan.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia.Research square · 2025Article
- Characterization of a novel transgenic mouse model to investigate brain-wide activation of astrocyte Gq signaling.Lab animal · 2025Article
- How do tasks impact the reliability of fMRI functional connectivity?Human brain mapping · 2024Article
- Static and dynamic functional connectomes represent largely similar information.bioRxiv : the preprint server for biology · 2023Article
- Functional brain networks reflect spatial and temporal autocorrelation.Nature neuroscience · 2023Article
- Single voxel autocorrelation uncovers gradients of temporal dynamics in the hippocampus and entorhinal cortex during rest and navigation.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Static and dynamic fMRI-derived functional connectomes represent largely similar information.Network neuroscience (Cambridge, Mass.) · 2023Article
- Statistical perspective on functional and causal neural connectomics: The Time-Aware PC algorithm.PLoS computational biology · 2022Article
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Corrections and comments
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Authors and funding
12 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundAutocorrelation (AC) in fMRI time-series is a well-known phenomenon, typically attributed to colored noise and therefore removed from the data. We hypothesize that AC reflects systematic and meaningful signal fluctuations that may be tied to neural activity and provide evidence to support this hypothesis. NEW
methodEach fMRI time-series is modeled as an autoregressive process from which the autocorrelation is quantified. Then, autocorrelation during resting-state fMRI and auditory oddball (AOD) task in schizophrenia and healthy volunteers is examined.
resultsDuring resting-state, AC was higher in the visual cortex while during AOD task, frontal part of the brain exhibited higher AC in both groups. AC values were significantly lower in specific brain regions in schizophrenia patients (such as thalamus during resting-state) compared to healthy controls in two independent datasets. Moreover, AC values had significant negative correlation with patients' symptoms. AC differences discriminated patients from healthy controls with high accuracy (resting-state). COMPARISON WITH EXISTING
methodsContrary to most prior works, the results suggest AC shows meaningful patterns that are discriminative between patients and controls. Our results are in line with recent works attributing autocorrelation to feedback loop of brain's regulatory circuit.
conclusionsAutoconnectivity is cognitive state dependent (resting-state vs. task) and mental state dependent (healthy vs. schizophrenia). The concept of autoconnectivity resembles a recurrent neural network and provides a new perspective of functional integration in the brain. These findings may have important implications for understanding of brain function in health and disease as well as for analysis of fMRI time-series.
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Registered trials
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