Radiological markers of neurological manifestations of post-acute sequelae of SARS-CoV-2 infection: a mini-review
Bibliographic record
Abstract
The neurological impact of COVID-19 is a rising concern among medical professionals, as patients continue to experience symptoms long after their recovery. This condition, known as neurological post-acute sequelae of COVID-19 (Neuro-PASC), can last for more than 12 weeks and includes symptoms such as attention disorders, brain fog, fatigue, and memory loss. However, researchers and health professionals face significant challenges in understanding how COVID-19 affects the brain, limiting the development of effective prevention and treatment strategies. In this mini-review, we provide readers with up-to-date information on the imaging techniques currently available for measuring the neurological impact of post-SARS-CoV-2 infection. Our search of PubMed and Google Scholar databases yielded 38 articles on various brain imaging techniques, including structural MRI (magnetic resonance imaging), functional MRI, diffusion MRI, susceptibility-weighted imaging, SPECT (single-photon emission computed tomography) imaging, and PET (positron emission tomography) imaging. We also discuss the optimal usage, limitations, and potential benefits of these techniques. Our findings show that various cerebral imaging techniques have been evaluated to identify a reliable marker for Neuro-PASC. For instance, 18F-FDG-PET/CT and functional MRI have demonstrated hypometabolism in cerebral regions that are directly linked to patient symptoms. Structural MRI studies have revealed different findings, such as infarcts, white matter atrophy, and changes in gray matter volumes. One SPECT imaging study noted frontal lobe hypometabolism, while diffusion MRI showed increased diffusivity in the limbic and olfactory cortical systems. The sequence SWI showed abnormalities primarily in white matter near the gray-white matter junction. A study on 18F-amyloid PET/CT found amyloid lesions in frontal and anterior cingulate cortex areas, and a study on arterial spin labeling (ASL) found hypoperfusion primarily in the frontal lobe. While accessibility and cost limit the widespread use of 18F-FDG-PET/CT scans and functional MRI, they seem to be the most promising techniques. SPECT, SWI sequence, and 18F-amyloid PET/CT require further investigation. Nevertheless, imaging remains a reliable tool for diagnosing Neuro-PASC and monitoring recovery.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".