Depression vulnerability and gray matter integrity of interoceptive networks in remitted depressed outpatients
Bibliographic record
Abstract
BACKGROUND: Interoception, the representation of internal body states, plays an important role in mental health. While functional neuroimaging links Major Depressive Disorder (MDD) relapse vulnerability to stress-induced inhibition of sensorimotor regions, its association with structural changes in interoceptive networks remains unclear. METHODS: A secondary analysis explored relationships between gray matter volume and relapse vulnerability in remitted MDD patients (N = 85), with two data acquisitions surrounding eight-weeks of prophylactic psychotherapy followed by a two-year follow-up. Participants were randomly assigned to either Cognitive Behavioral Therapy or Mindfulness-Based Cognitive Therapy (MBCT). Mixed-effects models were applied to study the relationships between cortical thickness, time, and intervention type with clinical variables such as relapse status, residual symptoms, and decentering, adjusting for relevant covariates. Analyses were conducted at whole brain levels as well as in pre-defined regions of interest, focusing on sensory regions implicated by prior research. RESULTS: Relapse was consistently linked to greater cortical thickness in the left superior circular sulcus of the insula and the left anterior occipital sulcus. Residual symptoms correlated with increased cortical thickness in the left insula and right precentral regions, while decentering was linked to reduced thickness in the middle temporal and inferior parietal regions. MBCT participants showed greater cortical thickness increases in the right superior temporal gyrus over time. CONCLUSIONS: MDD vulnerability was unexpectedly linked to greater cortical thickness in sensory and prefrontal brain regions, suggesting that depression vulnerability may reflect maladaptive skill acquisition. MBCT may promote gray matter growth in the right superior temporal region. TRIAL REGISTRATION: ClinicalTrials.govNCT01178424.
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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) | 0.001 | 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".