Reduced intrinsic neural timescales and their relationship to global network connectivity in depression
Bibliographic record
Abstract
INTRODUCTION: Major Depressive Disorder (MDD) is characterized by widespread alterations in brain network organization, but the relationship between temporal dynamics and network connectivity remains poorly understood. We investigated how intrinsic neural timescales (INT), measured through the autocorrelation window (ACW), relate to functional connectivity (FC) in MDD during a disorder-specific task paradigm. METHODS: 21 MDD patients, 19 schizophrenia patients (clinical comparison), and 21 healthy controls underwent fMRI scanning during a self-evaluation task. We analyzed temporal dynamics using ACW and examined FC using Network-Based Statistics. The relationship between ACW and FC was assessed using Bayesian modeling. RESULTS: MDD patients showed significantly reduced ACW compared to controls, with the strongest reductions in Control (β = -0.264), Default Mode (β = -0.203), and Somatomotor (β = -0.177) networks. Network-Based Statistics revealed increased FC in MDD (243 unique connections) particularly within Default and Control networks. MDD patients exhibited a disorder-specific inverse relationship between ACW and FC (β = -0.119) that was absent in controls and contrasted with the positive relationship observed in schizophrenia patients (β = 0.189). DISCUSSION: Our findings reveal that MDD involves both temporal dysregulation (shortened neural timescales) and spatial disruption (increased connectivity), with a specific alteration in how these properties are coupled. This suggests that MDD might be understood as involving disrupted integration of temporal and spatial brain dynamics, offering novel perspectives for biomarker development and therapeutic interventions targeting temporal processing abnormalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".