Neuroimaging and Reward Processing in Major Depressive Disorder and Mild Traumatic Brain Injury
Bibliographic record
Abstract
Major Depressive Disorder (MDD) is a common and disabling condition experienced by 20-45% of individuals who have suffered a mild traumatic brain injury (mTBI). The comorbidity of MDD and mTBI is associated with cumulatively worse outcomes, yet its neurobiological and behavioural presentation remains poorly understood. Importantly, while most literature has evaluated TBI groups with varying depressive symptom levels, scant research has investigated the impact of mTBI history among those with MDD. The current research sought to clarify this interaction with a focus on anhedonia and its underlying reward system, which are central to MDD and have preliminary evidence of disruption in mTBI. The substantial overlap in frontotemporal and limbic brain regions implicated in MDD, mTBI, and reward, combined with previous neuroimaging and reward findings in each condition separately, suggest a cumulative impact. Thus, the goals of the present studies were to understand the impact of mTBI history on resting-state functional connectivity (RSFC) and cortical thickness among those with MDD, and assess the combined impact of MDD and mTBI on anhedonia and reward function. Four participant groups were recruited across two studies: (1) MDD-alone, (2) mTBI-alone, (3) MDD+mTBI, and (4) healthy control participants. Together, clinical and neuroimaging findings converged to suggest that MDD is the main contributor to the interaction between MDD and mTBI, with significant differences observed between those with and without MDD, but limited separation by mTBI history. Subtle differences between MDD-alone and MDD+mTBI groups emerged, with altered sgACC-precuneus connectivity and more intact reward learning among MDD+mTBI participants, as well as trends toward altered cortical thickness in temporal regions. However, mTBI history within MDD was not associated with large-scale network differences in this cohort. In the present sample, we also found no evidence of differences in reward valuation, motivation, or response bias based on MDD or mTBI history. This work is the first to report the impact of mTBI history on RSFC and cortical thickness among those with MDD, as well as reward outcomes in MDD+mTBI versus either alone. Findings strongly support inclusion of MDD-alone groups in future studies of TBI and depression given its substantial impact on this interaction.
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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".