Test–retest reliability of resting-state EEG intrinsic neural timescales

Intrinsic neural timescales, which reflect the duration of neural information storage within local brain regions and capacity for information integration, are typically measured using autocorrelation windows (ACWs). Extraction of intrinsic neural timescales from resting-state brain activity has been extensively applied in psychiatric disease research. Given the potential of intrinsic neural timescales as a neural marker for psychiatric disorders, investigating their reliability is crucial. This study, using an open-source database, aimed to evaluate the test-retest reliability of ACW-0 and ACW-50 under both eyes-open and eyes-closed conditions across three sessions. The intraclass correlation coefficients (ICCs) were employed to quantify the reliability of the intrinsic neural timescales. Our results showed that intrinsic neural timescales exhibited good reliability (ICC > 0.6) at the whole-brain level across different index types and eye states. Spatially, except for the right temporal region in the eyes-open condition, all other regions showed moderate-to-high ICCs. Over 60% of the electrodes demonstrated moderate-to-high intrinsic neural timescale ICCs under both eyes-open and eyes-closed conditions, with ACW-0 being more stable than ACW-50. Moreover, in the new dataset, the above results were consistently reproduced. The present study comprehensively assessed the reliability of intrinsic neural timescale under various conditions, providing robust evidence for their stability in neuroscience and psychiatry.

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Publication Details

Journal
Cerebral Cortex
Published
2025-02-01
DOI
https://doi.org/10.1093/cercor/bhaf034
Citations
3
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
4.29

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article

Test–retest reliability of resting-state EEG intrinsic neural timescales

Xiaoling Tang, Wenbo Luo, Xinye Xu, Mingming Zhang et al.
3 citations
Cerebral Cortex
Neural dynamics and brain function
4.29
article

Test–retest reliability of resting-state EEG intrinsic neural timescales

Xiaoling Tang, Wenbo Luo, Xinye Xu, Mingming Zhang, Shan Wang
article en
3 citations

Abstract

Intrinsic neural timescales, which reflect the duration of neural information storage within local brain regions and capacity for information integration, are typically measured using autocorrelation windows (ACWs). Extraction of intrinsic neural timescales from resting-state brain activity has been extensively applied in psychiatric disease research. Given the potential of intrinsic neural timescales as a neural marker for psychiatric disorders, investigating their reliability is crucial. This study, using an open-source database, aimed to evaluate the test-retest reliability of ACW-0 and ACW-50 under both eyes-open and eyes-closed conditions across three sessions. The intraclass correlation coefficients (ICCs) were employed to quantify the reliability of the intrinsic neural timescales. Our results showed that intrinsic neural timescales exhibited good reliability (ICC > 0.6) at the whole-brain level across different index types and eye states. Spatially, except for the right temporal region in the eyes-open condition, all other regions showed moderate-to-high ICCs. Over 60% of the electrodes demonstrated moderate-to-high intrinsic neural timescale ICCs under both eyes-open and eyes-closed conditions, with ACW-0 being more stable than ACW-50. Moreover, in the new dataset, the above results were consistently reproduced. The present study comprehensively assessed the reliability of intrinsic neural timescale under various conditions, providing robust evidence for their stability in neuroscience and psychiatry.

Cerebral CortexVol. 35(2)
Liaoning Normal University (CN), Institut des Sciences Cognitives Marc Jeannerod (FR)
National Natural Science Foundation of China
Openalex Percentile: Top 7%
Neural dynamics and brain function
4.29
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Test–retest reliability of resting-state EEG intrinsic neural timescales — Xiaoling Tang, Wenbo Luo, et al. · Cerebral Cortex (2025) | TGRS Research Map | TGRS