Abnormal Resting-State Connectivity in a Substantia Nigra-Related Striato-Thalamo-Cortical Network in a Large Sample of First-Episode Drug-Naïve Patients With Schizophrenia
Bibliographic record
Abstract
Objective: The dopamine hypothesis is one of the most influential theories of the neurobiological background of schizophrenia (SCZ). However, direct evidence for abnormal dopamine-related subcortical-cortical circuitry disconnectivity is still lacking. The aim of this study was therefore to test dopamine-related substantia nigra (SN)-based striato-thalamo-cortical resting-state functional connectivity (FC) in SCZ. Method: Based on our a priori hypothesis, we analyzed a large sample resting-state functional magnetic resonance imaging (fMRI) dataset from first-episode drug-naïve SCZ patients (n = 112) and healthy controls (n = 82) using the SN as the seed region for an investigation of striato-thalamo-cortical FC. This was done in the standard band of slow frequency oscillations and then in its subfrequency bands (Slow4 and Slow5). Results: The analysis showed in SCZ: (1) reciprocal functional hypo-connectivity between SN and striatum, with differential patterns for Slow5 and Slow4; (2) functional hypo-connectivity between striatum and thalamus, as well as functional hyper-connectivity between thalamus and sensorimotor cortical areas, specifically in Slow4; (3) correlation of thalamo-sensorimotor functional hyper-connectivity with psychopathological symptoms. Conclusions: We demonstrate abnormal dopamine-related SN-based striato-thalamo-cortical FC in slow frequency oscillations in first-episode drug-naive SCZ. This suggests that altered dopaminergic function in the SN leads to abnormal neuronal synchronization (as indexed by FC) within subcortical-cortical circuitry, complementing the dopamine hypothesis in SCZ on the regional level of resting-state activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".