Post‐ <scp>COVID</scp> Fatigue Is Associated With Reduced Cortical Thickness After Hospitalization
Bibliographic record
Abstract
OBJECTIVE: Neuropsychiatric symptoms are among the most prevalent sequelae of COVID-19, particularly among hospitalized patients. Recent research has identified volumetric brain changes associated with COVID-19. However, it currently remains poorly understood how brain changes relate to post-COVID fatigue and cognitive deficits. We, therefore, aimed to assess structural brain changes after hospitalization for COVID-19 and their associations with cognitive performance and fatigue. METHODS: We analyzed data from n = 57 patients previously hospitalized for COVID-19 (63% male, mean age 52 years) from the prospective, multicentric high-resolution platform of the German National Pandemic Cohort Network (NAPKON-HAP) and n = 57 matched healthy control participants (HC). We assessed cortical thickness and subcortical volumes in high-resolution T1-weighted MRI and their associations with cognitive performance (Montreal Cognitive Assessment) and fatigue (Fatigue Severity Scale). RESULTS: Patients exhibited statistically significant reductions of cortical thickness in parahippocampal gyri and the temporal lobe (all p[FDR-corrected] < 0.05) as well as reduced hippocampal volumes compared to HC (left, Cohen's d [95% CI] = 0.50 [0.12-0.8]; right d = 0.43 [0.05-0.80]). Higher acute COVID-19 severity was associated with reduced cortical thickness, particularly in the olfactory system. Furthermore, reduced cortical thickness of the temporal poles and the anterior and posterior cingulate gyrus was associated with more severe post-acute fatigue. INTERPRETATION: Our results identify long-lasting macrostructural brain changes after moderate to severe COVID-19 that correlate with acute disease severity and long-term fatigue. Early identification and targeted interventions for patients at risk of persistent brain changes are needed. TRIAL REGISTRATION: NAPKON-HAP is registered at clinicaltrials.gov (NCT04747366).
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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) | 0.002 | 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".