Hakhu, Sasha

Background/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T1-weighted, T2-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T1/T2-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas—a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P2 (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 − ODI) and P2 values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 − ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P2 was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema.

Background: The potential long-term effects of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) infection on the brain structure have not yet been fully elucidated. Even though existing studies have reported structural changes in the post-COVID-19 period, the results remain highly inconsistent and controversial. As such, identifying an imaging biomarker for post-COVID brains is still under investigation. This review aims to comprehensively summarize the structural MRI (sMRI) studies that focus on volumetric brain changes at least two weeks following COVID-19 infection. Methods: A systematic literature search was conducted on PubMed, SCOPUS, Web of Science, EMBASE, and Google Scholar up to 9 September 2025. Studies that utilized sMRI to assess volumetric brain changes post-COVID at greater than two weeks following infection were included. Exclusion criteria encompassed research involving pediatric or adolescent populations and imaging modalities other than sMRI. Preprints, reviews, case reports, case series and post-mortem studies were also excluded. Results: Forty-one studies satisfied the inclusion criteria and consisted of 2895 patients and 1729 healthy controls. Despite the wide variability in image acquisition protocols, data processing methods, and comorbidities between studies, multiple studies reported statistically significant volumetric reductions in the hippocampus, amygdala, thalamus, basal ganglia, nucleus accumbens and the cerebellum months to years after infection, especially in older hospitalized patients with severe COVID-19. Conclusions: The emerging literature reports long-term volume changes across various brain regions in individuals previously infected with COVID-19; however, the evidence is inconsistent. Specific imaging biomarkers following exposure to SARS-CoV-2 infection and the underlying mechanisms of these changes are yet to be identified. Future studies with harmonized imaging protocols, longitudinal designs, and integrated biomarker and clinical data are needed to define robust biomarkers and elucidate the pathophysiology of these findings.