Reduced Short-Latency Afferent Inhibition in Prefrontal but not Motor Cortex and Its Association With Executive Function in Schizophrenia: A Combined TMS-EEG Study
Bibliographic record
Abstract
BACKGROUND: Cholinergic dysfunction is increasingly assumed to be involved in the pathophysiology of schizophrenia. Short-latency afferent inhibition (SAI) is a transcranial magnetic stimulation (TMS) paradigm that has been shown to assay central cholinergic activity from the motor cortex (M1). Recently, we established a method to index SAI from the dorsolateral prefrontal cortex (DLPFC), an area implicated in the pathophysiology of schizophrenia. We investigated SAI in M1 and DLPFC in schizophrenia. We hypothesized that modulation of N100 on TMS-evoked potentials (TEPs) from the DLPFC would be attenuated in patients with schizophrenia compared to healthy controls. METHODS: SAI was examined in 12 patients, whose age was matched to controls, using TMS combined with electroencephalography (EEG). SAI was recorded with TMS applied to left M1 (M1-SAI) and DLPFC (DLPFC-SAI). For group comparison, we used the SAI data of healthy participants in our previous study. RESULTS: In patients, N100 TEP was significantly attenuated with DLPFC-SAI, whereas P180 TEP was significantly increased with M1-SAI. Between patients and controls, there were significant differences in modulation of P180 TEP by M1-SAI (t22 = -2.748, P = .012; patients > controls) and N100 TEP by DLPFC-SAI (t22 = 5.456, P < .0001; patients < controls). Further, modulation of N100 TEP by DLPFC-SAI significantly correlated with executive function (r = -.740, P = .006, N = 12). CONCLUSION: Our findings suggest that DLPFC-SAI but not M1-SAI were reduced in patients with schizophrenia and this was linked to deficits in cognition. This may reflect prefrontal cholinergic deficits and represent a biomarker for cholinergic and executive dysfunction in patients with schizophrenia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".