Diffusion tensor imaging after COVID-19 infection: A systematic review
Bibliographic record
Abstract
BACKGROUND: Most COVID-19 neuroimaging research focuses on clinically evident lesions occurring during the acute period after infection. Chronic effects on brain structure, especially at a microstructural level, are less well defined. Existing advanced neuroimaging studies report inconsistent differences in white matter integrity after COVID-19 infection. Our aim was to systematically evaluate the advanced neuroimaging literature with a specific focus on examining diffusion MRI (dMRI) abnormalities observable after the resolution of the acute phase of COVID-19 illness. METHODS: A search of the literature was conducted on PubMed, Embase, and Scopus on May 27th, 2023, and an updated search was performed September 20th, 2024. Inclusion criteria were a quantitative comparison of dMRI metrics between COVID-19 patients and non-COVID-19 volunteers with MRI acquired >6 weeks after COVID-19. Studies that included only subgroups of COVID-19 patients with specific symptoms, case reports, and post-mortem studies were excluded. Forwards and backwards citation chasing were performed. RESULTS: The initial search identified 1709 unique records, and 11 met inclusion criteria. Most studies included hospitalized COVID-19 patients, with brain MRI acquired between 2 and 6 months after COVID-19 infection. The majority of studies reported lower fractional anisotropy and higher mean diffusivity in the post-COVID-19 cohort, compared to non-COVID-19 controls. However, there were inconsistent findings, with one study reporting higher fractional anisotropy after COVID-19 infection. Cohorts with a more severe acute COVID-19 illness tended to have lower fractional anisotropy and higher mean diffusivity than cohorts with a milder illness course. Compared to shorter follow-up periods, a longer time between COVID-19 and MRI was associated with fewer differences between COVID-19 patients and non-COVID-19 volunteers. CONCLUSION: A review of the literature indicates that the heterogeneity of findings regarding dMRI metrics after the resolution of the acute phase of COVID-19 illness may be due in part to the severity of COVID-19 illness and the time between COVID-19 and MRI. Future studies should also consider how different SARS-CoV-2 variants differentially affect the structural brain differences after COVID-19.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".