Neurophysiological Biomarkers in Schizophrenia—P50, Mismatch Negativity, and TMS-EMG and TMS-EEG
Bibliographic record
Abstract
Impaired early auditory processing is a well characterized finding in schizophrenia that is theorized to contribute to clinical symptoms, cognitive impairment and social dysfunction in patients. Two neurophysiological measures of early auditory processing, P50 gating (“P50”) and mismatch negativity (MMN), which measure sensory gating and detection of change in auditory stimuli, respectively, are consistently shown to be impaired in patients with schizophrenia. Transcranial magnetic stimulation (TMS) may also be a potential method by which sensory processing can be assessed, since TMS paradigms can be used to measure GABAB-mediated cortical inhibition that is linked with sensory gating. In this review, we examine the potential of P50, MMN and two TMS paradigms, cortical silent period (CSP) and long-interval intracortical inhibition (LICI), as endophenotypes as well as their ability to be used as predictive markers for interventions targeted at cognitive and psychosocial functioning. Studies consistently support a link between MMN, P50 and cognitive dysfunction, with robust evidence for a link between MMN and psychosocial functioning in schizophrenia as well. Importantly, studies have demonstrated that MMN can be used to predict performance in social and cognitive training tasks. A growing body of studies also support the potential of MMN to be used as an endophenotype, and future studies are needed to determine if MMN can be used as an endophenotype specifically in schizophrenia. P50, however, has weaker evidence supporting its use as an endophenotype. While CSP and LICI are not as extensively investigated, growing evidence is supporting their potential to be used as an endophenotype in schizophrenia. Future studies that assess the ability of P50, MMN and TMS neurophysiological measures to predict performance in cognitive and social training programs may identify markers that inform clinical decisions in the treatment of neurocognitive impairments in 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".