Citations

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.

Abstract BACKGROUND Patients with primary brain tumors often have very complex histories, but an accurate understanding of their care timeline is critical to provide the best care possible. Extracting pertinent facts from the medical record can be time consuming and frustrating. We sought to apply generative AI large language models (LLM) to this problem in order to supply providers with concise, accurate, and focused data including graphs and tables. METHODS After evaluating multiple LLMs, GEMINI 2.5 Pro was selected. Prompt engineering was iteratively refined across a range of patients to generate concise outputs using tables, graphs, and prose to describe timing of chemoradiation, tumor markers, procedures, and trends in MRI results. The model inferred BT-RADS scores from the MRI report prose. Using a 5-point Likert scale, neuroradiologists assessed the AI-output both prospectively and retrospectively comparing it to the medical record. LLM’s compared AI summaries to source material. RESULTS Six neuroradiologists each ranked 25 prospective patient outputs as accurate (82%/16%/1%), useful (94%/6%/0%), trustworthy (86%/13%/1%), and satisfactory (86%/10%/1%) for Likert ratings of highly agree/agree/neutral, respectively. No responses were disagree or highly disagree. Interobserver agreement was high (Gwent AC2 > 0.95). The LLM evaluation of referenced portions of the record determined > 85% of summary statements were supported by source documents. While users estimated time savings of a 1-10 minutes per case, they felt the greater benefit was having more organized data presented efficiently. CONCLUSIONS AI summaries of the medical record of complex patients with primary brain tumors give providers an accurate, well-organized display of the pertinent facts necessary for high quality care and save time in chart review. These summaries, which are generated early each morning, have been found to be helpful and are now being incorporated into our clinical practice and multidisciplinary tumor board.

Intermuscular adipose tissue (IMAT) accumulates in the lower leg musculature of healthy older adults. IMAT was negatively associated with the rate of glucose infusion during a hyperinsulinemic-euglycemic clamp and muscle oxidative capacity. Exercise training decreased IMAT to a greater extent in young compared with older adults. Together these data support the implication that IMAT accumulation is linked with metabolic derangements in older adults but the restorative potential of exercise in older adults needs further evaluation.

Abstract Insulin resistance (IR) is a modifiable risk factor for dementia, yet its effects on brain metabolism and function remain unclear. In older adults, greater IR was associated with reduced cerebral glucose uptake (indicating impaired mitochondrial metabolism), atrophy, and weakened connectivity between brain regions critical for cognition. In neuron-specific insulin receptor knockout mice, brain IR produced deficits in hippocampal- and prefrontal-dependent tasks accompanied by reduced brain mitochondrial ATP and elevated reactive oxygen species. To evaluate reversibility of IR-induced brain deficits, forty older adults with IR were randomized to 40-weeks of metformin or placebo. Metformin improved insulin sensitivity, increased brain glucose uptake, strengthened cognitive network connectivity, and preserved whole-brain and regional volumes implicated in decision-making and learning. Metformin also improved processing speed and working memory. Collectively, these findings highlight IR as a driver of brain metabolism and support the concept that insulin sensitization can prevent neurobiological deficits in older people with IR.

The study assesses the relationship between thalamic proton-MR spectroscopy (1H-MRS) metabolites and thalamic 11C-ER176 translocator-protein positron emission tomography (TSPO-PET) standardized uptake value ratios (SUVR) to advance our understanding of thalamic involvement in multiple sclerosis (MS)-associated neurodegeneration and disability. In this prospective cross-sectional study, patients with MS (pwMS) and controls underwent 3T-MRI, 1H-MRS, and 11C-ER176-PET targeting the thalamus. MRI-derived thalamic volume was normalized by intracranial volume. 1H-MRS metabolites—N-acetylaspartate (NAA), glutamate (Glu), glutamine (Gln), total choline (tCho), and myo-inositol (mIns)—were normalized to total creatine (tCr). Clinical disability was evaluated using MS-specific tests of Expanded Disability Status Scale-EDSS and MS-functional composite-MSFC (including Paced Auditory Serial Addition Test-PASAT). Compared to controls (n = 30), pwMS (n = 21) exhibited smaller thalamic volume, higher thalamic 1H-MRS mIns/tCr (putative gliosis marker), and higher thalamic 11C-ER176-PET SUVR (glial density marker). In pwMS, higher thalamic mIns/tCr (r = −0.67) and tCho/tCr (r = −0.52) correlated with smaller thalamic volume. In pwMS, higher thalamic mIns/tCr correlated with higher thalamic 11C-ER176-PET SUVR (r = 0.48) and decreased cognitive function (PASAT, rho = −0.48). In controls, decreased thalamic NAA/tCr correlated with increased thalamic 11C-ER176-PET SUVR (r = −0.41). Thalamus, a core central nervous system relay, is affected early in MS disease course. Glial-mediated innate immune activation in the thalamus, evaluated by increased 1H-MRS mIns/tCr and 11C-ER176-PET SUVR, is associated with loss of thalamic volume and increased disability in pwMS. The multimodal imaging approach with 1H-MRS mIns/tCr and 11C-ER176-PET SUVR emerges as potential glial biomarkers, to better understand disease mechanisms and evaluate therapeutic interventions targeting glial activity in MS.