Depressive symptoms combined with auditory hallucinations are accompanied with severe gray matter brain impairments in patients with first-episode untreated schizophrenia – A pilot study in China
Bibliographic record
Abstract
BACKGROUND: Depressive symptoms and auditory hallucinations (AHs) are often accompanied by gray matter volume (GMV) alterations in schizophrenia. However, little is known about the effects of concurrent depressive symptoms and AHs on the GMV of patients with schizophrenia. AIM: To investigate the pathological features of gray brain matter in patients with first-episode untreated schizophrenia (FUSCH) who have concurrent moderate-to-severe depressive symptoms and AHs (FUSCH-DAH). METHODS: The Calgary Depression Scale for Schizophrenia (CDSS) and Auditory Hallucinations Rating Scale (AHRS) were adopted. Voxel-based morphometry (VBM)-based GMV analyses were used to measure cortical alterations. FUSCH-DAH patients were compared to FUSCH patients with depressive symptoms but without AHs, denoted as FUSCH-D, along with healthy controls. RESULTS: GMV reductions were more substantial in the FUSCH-DAH patients than FUSCH-D patients or healthy controls. Both FUSCH-DAH and FUSCH-D groups showed GMV reductions of the parietal, frontal, and temporal lobes, which were not apparent in the healthy controls. Compared to FUSCH-D patients, FUSCH-DAH patients demonstrated more substantial GMV reductions in the Broca area, Wernicke region, insular lobe, and prefrontal lobe. The GMV reductions were 1.06% and 0.58% in FUSCH-DAH and FUSCH-D patients, respectively, as compared with the healthy controls. CONCLUSIONS: This is the first report showing that concurrent depressive symptoms and AHs leads to severe GMV deterioration in FUSCH-DAH patients. Hence, there is a reciprocal relationship between AHs and depressive symptoms in FUSCH-DAH patients. However, the potential additive effects of concurrent AHs and depressive symptoms require further investigation in order to identify future targeted therapies for schizophrenia.
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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 | 0.001 | 0.001 |
| 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".