Abnormally increased intrinsic neural timescales in sensory and default mode networks in cocaine use disorder

preprint OA: closed
📄 Open PDF Full text JSON View at publisher

Abstract

Cocaine use disorder (CUD) is associated with abnormal structural and functional brain changes. However, the neurodynamics and molecular underpinnings remain unclear. In this study, we mapped whole-brain intrinsic neural timescales (INTs), reflecting temporal neural processing, using resting-state functional magnetic resonance imaging data from 44 CUD patients and 44 healthy controls (HC). CUD showed increased INTs in visual, somatomotor, and default mode networks compared with HC. Mediation analysis linked local INTs abnormalities to altered dorsal attention network neurodynamics, associated with inhibitory control deficits. Notably, these changes were primarily correlated with alterations in gamma-aminobutyric acid type A receptors and the noradrenaline transporter. Machine learning classifiers based on INTs achieved a maximum accuracy of 75.5% in distinguishing CUD from HC, with a generalization accuracy of 65.0% on an independent dataset. This study elucidates aberrant neural mechanisms underlying CUD and highlights INTs as promising diagnostic biomarkers for clinical detection and intervention. Teaser Spatiotemporal neuroscience reveals intrinsic neural timescale disruptions underlying cocaine use disorder, offering novel diagnostic biomarkers.
Full text 1,356 characters · extracted from oa-doi-fallback · click to expand
Abstract Cocaine use disorder (CUD) is associated with abnormal structural and functional brain changes. However, the neurodynamics and molecular underpinnings remain unclear. In this study, we mapped whole-brain intrinsic neural timescales (INTs), reflecting temporal neural processing, using resting-state functional magnetic resonance imaging data from 44 CUD patients and 44 healthy controls (HC). CUD showed increased INTs in visual, somatomotor, and default mode networks compared with HC. Mediation analysis linked local INTs abnormalities to altered dorsal attention network neurodynamics, associated with inhibitory control deficits. Notably, these changes were primarily correlated with alterations in gamma-aminobutyric acid type A receptors and the noradrenaline transporter. Machine learning classifiers based on INTs achieved a maximum accuracy of 75.5% in distinguishing CUD from HC, with a generalization accuracy of 65.0% on an independent dataset. This study elucidates aberrant neural mechanisms underlying CUD and highlights INTs as promising diagnostic biomarkers for clinical detection and intervention. Teaser Spatiotemporal neuroscience reveals intrinsic neural timescale disruptions underlying cocaine use disorder, offering novel diagnostic biomarkers. Competing Interest Statement The authors have declared no competing interest.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00