EPA-1603 - Changes of resting-state eeg and functional connectivity in the sensor and source space of patients with major depression
Bibliographic record
Abstract
Dyfunctions of prefrontal neuronal circuits contribute to the pathophysiology of depression. Previous studies showed increased functional MRI and EEG connectivity in patients with depression. In this study we investigated a large sample of patients with major depression (n=228) and healthy subjects (n=215). Spectrotemporal dynamics during resting state with closed eyes were analyzed in sensor and source space to examine functional EEG connectivity (fcEEG) alterations between groups. Quantitative measures of delta, theta, alpha, beta and gamma power, hemispheric asymmetry, coherence, phase and current source density (CSD, eLORETA) analyses were calculated from artifact-free EEG recordings. EEG theta power was increased in all brain regions in the group of patients with a focus in frontal regions and increased frontal theta and alpha power. Excessive coherence differences were detected in the delta, theta and alpha-bands in frontal, frontal-temporal and frontal-parietal regions. There were changes in phase differences in the delta, theta, alpha-bands between patients and healthy subjects. Differences in CSD were found for the delta, theta, alpha-band in the (rostral and subgenual) anterior cingulate cortex (ACC) with increased CSD in the patients. The main finding of the present study was an increase in cortical slow-wave activity in sensor and source space in patients with depression revealing marked differences in prefrontal cortical networks. Functional delta, theta and alpha connectivity (coherence and phase) were altered with a predominance in the left hemisphere. Dysfunctions of the ACC, together with alterations in fcEEG may contribute to the pathophysiology of major 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 | 0.001 |
| 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) | 0.002 | 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".