Cognition-associated gray matter volume alterations in long-COVID show sex-specific patterns
Bibliographic record
Abstract
Introduction: The long-term effects of the coronavirus disease 2019 (COVID-19) are a major concern in today's society, with cognitive impairment being an important manifestation. Notably, men and women exhibit differences in disease progression and the prevalence of long-COVID. This study aims to investigate sex differences in cognitively impaired long-COVID individuals and their potential association with alterations in gray matter volume (GMV). Methods: We conducted MRI at 3 Tesla to investigate brain structural correlates of cognitive impairment in long-COVID patients using voxel-based morphometry (VBM) and compared these patients to a healthy control (HC) group (n=30, female=13, male=17). Long-COVID patients underwent scanning and neuropsychiatric assessment on average 9.9 months after their acute and mostly mild COVID-19 infection. Based on Montreal Cognitive Assessment (MoCA) scores, they were classified into two groups: the PCn group, showing preserved cognitive function with MoCA scores of 26 or higher (n=36, female=23, male=13), and the PCcog group, characterized by cognitive impairment with MoCA scores below 26 (n=28, female=15, male=13). Subsequent analyses were performed separately for males and females to investigate sex-specific brain structural correlates of cognitive impairment. Results: Our analysis revealed significant GMV alterations in long-COVID patients across various brain regions, encompassing both shared and sex-specific regional changes. In females, these alterations were more restricted, affecting anterior frontal, limbic, and diencephalic regions. In males, GMV alterations were more widespread, involving neocortical regions such as the parietal, occipital, and motor cortices, and were characterized by a greater number of affected clusters. Discussion: Our findings demonstrate GMV alterations in both men and women with cognitive impairment, exhibiting sex-specific differences in affected regions. These differences suggest potentially distinct underlying mechanisms, highlighting the need for further research into their functional implications and relevance for personalized treatment strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.003 | 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".