Exploring the Effects of Temperament on Gray Matter Volume of Frontal Cortex in Patients with Mood Disorders
Bibliographic record
Abstract
BACKGROUND: Patients with bipolar disorder (BD) and patients with major depressive disorder (MDD) have relatively specific temperament and structural abnormalities of brain regions related to emotion and cognition. However, the effects of temperament factors on the structure of frontal and temporal cortex is still unclear. The aims of this study were to explore the differences and relationships between temperament characteristics and the gray matter volume of frontal and temporal cortex in patients with BD or MDD. METHODS: T1-weighted magnetic resonance imaging (MRI) data, demographic and clinical information were obtained from 279 depressed patients (90 patients with BD, 189 patients with MDD) and 162 healthy controls (HC). Temperament was assessed with the Chinese short version of Temperament Evaluation of Memphis, Pisa and San Diego - Auto questionnaire (TEMPS-A). The Desikan-Killiany atlas was used for yielding gray matter volume by FreeSurfer 6.0 software suite. A total of 22 frontal and temporal regions were chosen as regions of interest (ROIs). RESULTS: Compared with patients with MDD, patients with BD had higher TEMPS-A total scores and scores on cyclothymic, irritable and hyperthymic subscales. The gray matter volume in bilateral rostral middle frontal gyrus (RMFG), left temporal pole and right superior frontal gyrus were reduced in patients with BD. Patients with MDD only had lower gray matter volume in bilateral temporal pole. In the pooled patients, there were negative associations between hyperthymia and gray matter volume in right RMFG. CONCLUSION: Patients with BD and MDD had different temperament characteristics. The prominent temperament subscales in patients with BD were cyclothymia, irritable and hyperthymia. Patients with greater hyperthymia had lower gray matter volume in right frontal gyrus. Temperament may reflect an endophenotype in patients with mood disorders, especially in BD.
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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 | 0.001 | 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".