Investigating Cortical Inhibition in First-Degree Relatives and Probands in Schizophrenia
Bibliographic record
Abstract
Deficits in GABAergic inhibitory neurotransmission are a reliable finding in schizophrenia (SCZ) patients. Previous studies have reported that unaffected first-degree relatives of patients with SCZ demonstrate neurophysiological abnormalities that are intermediate between probands and healthy controls. In this study, first-degree relatives of patients with SCZ and their related probands were investigated to assess frontal cortical inhibition. Long-interval cortical inhibition (LICI) was measured from the dorsolateral prefrontal cortex (DLPFC) using combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG). The study presents an extended sample of 129 subjects (66 subjects have been previously reported): 19 patients with SCZ or schizoaffective disorder, 30 unaffected first-degree relatives of these SCZ patients, 13 obsessive-compulsive disorder (OCD) patients, 18 unaffected first-degree relatives of these OCD patients and 49 healthy subjects. In the DLPFC, cortical inhibition was significantly decreased in patients with SCZ compared to healthy subjects. First-degree relatives of patients with SCZ showed significantly more cortical inhibition than their SCZ probands. No differences were demonstrated between first-degree relatives of SCZ patients and healthy subjects. Taken together, these findings show that more studies are needed to establish an objective biological marker for potential diagnostic usage in severe psychiatric disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".