Mental Health and Brain Morphology: Insights from Long Covid cohort in underrepresented individuals
Bibliographic record
Abstract
BACKGROUND: Neurological manifestations in individuals with Long COVID range from headaches to cognitive impairment and mental health issues. However, it remains unclear whether these individuals exhibit structural changes, functional changes, or both in the brain. In this study, we investigated the impact of Long COVID on mental health symptoms, cortical grey matter volume and thickness, and hippocampal volume in Brazilian individuals. METHOD: Individuals were divided into two groups based on Long COVID status: covid (symptoms of Long COVID) and control (No symptoms of Long COVID). Simultaneously, PHQ-9 and GAD-7 tests were applied on participants to evaluate severity of depression and generalized anxiety symptoms, respectively. Brain magnetic resonance imaging (MRI) of individuals presenting with Long COVID (n = 58) and of healthy control individuals (n = 21) were used for extracting volume and cortical thickness (CT) of regions of interest using FreeSurfer (v7.4.1). We performed an ANCOVA analysis and a linear regression to assess the difference between groups in PHQ-9, GAD-7, mean cortical thickness (CT), mean hippocampal volume, and total cortical grey matter volume. The data were corrected for age, sex, and years of formal education. RESULT: The Long Covid group presented significantly lower scores on PHQ-9 and GAD-7 than the control group (Beta = 6.20017 and 3.3105; p <0,001 and p <0,006, respectively). However, when comparing Long covid and control groups, we found no significant differences in the mean hippocampal volume (p = 0,831) and in the mean cortical grey matter volume (p = 0.193). CONCLUSION: These preliminary data indicate significant changes in mental health among individuals with Long Covid; however, these changes do not correspond to observable alterations in brain volume as seen in MRI scans. This suggests that the pathophysiological changes associated with these symptoms are likely functional and metabolic in nature rather than structural and may not be detectable through imaging studies that primarily focus on brain anatomy, such as MRI.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".