Biomarkers of reduced inhibition in human cortical microcircuit signals in depression
Bibliographic record
Abstract
**AMajor depressive disorder (depression) involves different mechanisms and brain scales. Altered cortical inhibition is associated with treatment-resistant depression, and reduced dendritic inhibition by somatostatin-expressing (SST) interneurons is a key component of the pathology. Electroencephalography (EEG) is an important source of biomarkers for depression to improve diagnosis and inform personalized treatments. However, whether the effects of reduced SST inhibition on microcircuit activity have signatures detectible in EEG remains unknown. We used detailed models of human cortical layer 2/3 microcircuits with normal or reduced SST inhibition to simulate resting-state activity together with the EEG signals in health and depression. We show that the healthy microcircuit models had emergent properties that reproduced key features of resting-state EEG theta-alpha frequency bands. We found that simulated EEG from depression microcircuits showed a significant increase in theta rhythmic activity and increased broadband power. Neuronal spiking showed a spike preference of EEG peak phase, and did not differ between conditions. We also showed using a realistic head model that the EEG signal biomarkers were mostly localized. Our study thus used detailed computational models to identify EEG biomarkers of reduced SST inhibition in cortical microcircuits in depression, which may serve to improve the diagnosis and stratification of depression subtypes, and in monitoring the effects of pharmacological modulation for treating depression.
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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 | 0.000 |
| 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".