Citations

Objective To characterize regional brain atrophy patterns, evaluate their association with regional leucine‐rich glioma‐inactivated‐1 ( LGI1 ) gene expression, and identify clinical predictors of hippocampal atrophy in LGI1 autoimmune encephalitis (AE). Methods This study compared regional brain volumes in LGI1‐AE patients versus age‐ and sex‐matched healthy controls (HC) and Alzheimer's disease (AD) patients. LGI1 gene expression was derived from the Allen Human Brain Atlas. Multivariable linear regression and generalized linear models were utilized. Results A total of 55 LGI1‐AE patients were included. Median age was 68 years (IQR 62.5–74.2 years) at volumetric analysis, after 19 months (IQR 11–45 months) from symptom onset. Compared with HC ( n = 55) and AD patients ( n = 55), LGI1‐AE patients had greater regional atrophy of the hippocampus, insula, caudate, and putamen ( p < 0.002). Longitudinal analyses demonstrated hippocampal and pallidal atrophy after covariate adjustment. Among those who did not fulfill limbic encephalitis criteria and/or had non‐lesional magnetic resonance imaging, limbic and striatal atrophy were identified compared with HC ( p < 0.01). Regions with the greatest differential atrophy corresponded to higher LGI1 gene expression (Spearman ρ = 0.7, p = 0.005). Severe cognitive deficits at diagnosis ( β = −0.56; p = 0.002) and longer disease duration ( β = −0.01; p = 0.001) independently predicted total hippocampal volume ( R 2 = 0.31). Interpretation LGI1‐AE demonstrates a distinct pattern of limbic and striatal atrophy that differs from AD and aligns with regional LGI1 expression, supporting a model of selective network vulnerability. Greater baseline cognitive impairment and longer disease duration independently associated with hippocampal atrophy, underscoring the potential importance of early diagnosis and treatment to limit structural brain injury. ANN NEUROL 2026

Background Premature and early menopause involve ovarian hormone deprivation, which may contribute to later-life vulnerability to cognitive decline and neurodegenerative diseases. Multimodal neuroimaging biomarkers provide a means of detecting structural, microstructural, functional, and molecular brain changes before clinical symptoms emerge. Methods Following PRISMA guidelines, we conducted a systematic search of PubMed. Study quality was assessed using the Newcastle–Ottawa Scale. Results Neuroimaging modalities included in vivo structural magnetic resonance imaging (MRI) ( n = 16), functional MRI ( n = 2), diffusion tensor imaging ( n = 4), and positron emission tomography ( n = 3). Women with premature or early menopause showed regionally specific structural and microstructural brain abnormalities, including lower gray-matter volume or cortical thickness in medial temporal, frontal, and basal forebrain regions; white-matter microstructural abnormalities in the anterior corona radiata, corpus callosum, and fronto-occipital tracts; and reduced posterior hippocampal activation during memory encoding. In one study, earlier age at menopause was associated with higher amyloid- β (Aβ) burden in women who underwent premenopausal bilateral oophorectomy (PBO), and elevated tau deposition was reported predominantly among women with higher Aβ levels. Conclusion Converging multimodal neuroimaging evidence suggests that premature and early menopause may be associated with Alzheimer’s disease-relevant brain changes. However, findings remain heterogeneous, and longitudinal studies are needed to clarify the temporal relationships between ovarian hormone deprivation and neurodegeneration, and to determine the clinical implication of these findings.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system in which environmental factors play an important role in shaping disease risk, activity, and progression. Over the past decade, human and experimental studies have consistently shown alterations in the gut microbiome across the phenotypic spectrum of MS and have linked these changes to immune dysregulation, barrier dysfunction, neuroinflammation, and demyelination. Additionally, emerging evidence indicates that microbial function, particularly metabolite production plays a more direct role in shaping immune responses and associated neuropathology. Evidence from both human studies and experimental autoimmune encephalomyelitis models supports a functional role for microbial metabolites in shaping neuroimmune responses. Bacterially derived metabolites such as short-chain fatty acids, bile acids, polyamines, phytoestrogen metabolites, and tryptophan-derived compounds can influence T-cell differentiation, glial activation, epithelial integrity, and neuroimmune communication. Recent longitudinal studies also show associations between metabolite profiles and disability worsening. Because disease-modifying therapies, diet, and microbiome-directed interventions can reshape microbial metabolism, microbial metabolites may represent promising therapeutic targets in the gut-immune-brain axis. In this Review , we integrate current evidence to propose a mechanistic framework in which microbial metabolites act as central regulators of mucosal and systemic immunity that influence different aspects of MS biology. We discuss how this perspective shifts gut microbiome research from descriptive associations to biological mechanisms that more directly link the gut to immune responses and downstream neuropathology. We then evaluate therapeutic strategies that target microbial metabolism and outline key priorities for longitudinal, multi-omics, and interventional studies that are needed to enable microbiome-informed precision therapies in MS.

Introduction Neurological post-acute sequelae of COVID-19 (neuroPASC) are associated with persistent cognitive dysfunction and quality-of-life decline. We aimed to identify clinical, behavioral and sociodemographic factors associated with neuroPASC symptom burden two years after COVID-19 among individuals without prior neurological disease. Methods In this prospective, observational study, individuals with neuroPASC ( n = 102) and controls without symptomatic COVID-19 ( n = 74), all without prior neurological, psychiatric, or post-viral conditions, were enrolled between February 2022 and June 2024 across five academic sites. An unsupervised algorithm identified clusters with differing self-reported neurological symptom burden within the neuroPASC group. Functional differences between clusters were evaluated using quality-of-life, neurological and cognitive evaluations. Demographics, behavioral history, comorbidities, and blood biomarkers were compared across clusters and controls. Multivariable logistic regression assessed predictors of neuroPASC severity, including demographics, body-mass-index, Charlson Comorbidity Index, Framingham Risk Score, pre-existing endocrine/metabolic and/or gastrointestinal/hepatobiliary conditions, COVID-19 vaccination prior to infection, hospitalization during acute infection, and cumulative alcohol use. Results Two clusters emerged based on neurological symptom burden, labeled “high-burden” and “low-burden” neuroPASC, reflecting differences in the number and frequency of symptoms. Both clusters had deficits in quality-of-life and cognitive function compared to controls, with greater impairment in high-burden than low-burden neuroPASC. The clusters did not differ by sex, education, tobacco and cannabis use, blood pressure, body-mass-index, HbA1C, days since infection, hospitalization during COVID-19, pre-COVID vaccination rate, antibody-positivity, inflammation, and neurodegeneration biomarkers. The high-burden cluster was older and exhibited higher comorbidity burden and greater cumulative alcohol use compared with the low-burden cluster and controls. Pre-existing endocrine/metabolic and gastrointestinal/hepatobiliary conditions were more common in high-burden (63%) than in low-burden neuroPASC (35%). After adjusting for clinical and demographic factors, these pre-existing conditions remained the only independent predictor of severity, conferring a 3.5-fold increase in the odds of high-burden versus low-burden neuroPASC. Discussion Older age, higher comorbidity burden, greater cumulative alcohol use, and endocrine/metabolic and gastrointestinal conditions, rather than acute COVID-19 severity, were observed in the high-burden neuroPASC cluster. After multivariable adjustment, only pre-existing endocrine/metabolic and/or gastrointestinal/hepatobiliary conditions remained independently associated with high-burden neuroPASC, conferring a 3.5-fold increase in odds and highlighting the need for targeted post-infection monitoring in at-risk patients.

Abstract Background Neuroimaging data typically arrives as Digital Imaging and Communications in Medicine (DICOM) files, batched by exam. We use exam or study to denote a single imaging session of a participant, containing up to ∼40 series (the generic term for scans and derived products). Different series have different properties and applications, making their labeling complex. Filenames often change in transit, and Series Description tags (0008, 103E) are arbitrary, operator‐entered, and unreliably coupled to the series type. Furthermore, label preferences vary from site to site, making the application of a common classification system mandatory for multisite studies. Here we describe the system that we have developed and applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) and SCAN. Method Although Series Description is unreliable, DICOM includes other tags that parameterize MR pulse sequences and suggest their purpose. The mapping from these features to types is difficult to explicitly specify, especially since different manufacturers and protocols use different conventions. Thus we developed a machine learning classifier using 4060 semiautomatically labeled head and spinal series from 1.5 to 7T MR scanners, including both site‐specific and standard protocols designed to study a wide range of neurological conditions, including dementia, hydrocephalus, and multiple sclerosis. An 80%/20% split, stratified by type, was used for the training/test sets. After initial training, a few cases in the training set with high errors due to rarity were augmented and the model retrained. Performance was evaluated on the clean test set. Result The taxonomy is presented in Table 1. The classifier correctly identified 807 out of 812 (99.4%) series, with the error cases coming from outside the ADNI or SCAN protocols (Table 2). The confusion matrix is shown in Figure 1. Conclusion We implemented an automatic series type classifier with labels chosen to be generally useful for the brain MRI community. Some errors were noted, but the test set was deliberately challenging and the errors appear to be ones that could easily be made by a human. The classifier is expected to have good tolerance for as yet unseen head MR protocols but is likely unsatisfactory for body MRI.

Abstract Background Neuroimaging data typically arrives as Digital Imaging and Communications in Medicine (DICOM) files, batched by exam. We use exam or study to denote a single imaging session of a participant, containing up to ∼40 series (the generic term for scans and derived products). Different series have different properties and applications, making their labeling complex. Filenames often change in transit, and Series Description tags (0008, 103E) are arbitrary, operator‐entered, and unreliably coupled to the series type. Furthermore, label preferences vary from site to site, making the application of a common classification system mandatory for multisite studies. Here we describe the system that we have developed and applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and SCAN. Method Although Series Description is unreliable, DICOM includes other tags that parameterize MR pulse sequences and suggest their purpose. The mapping from these features to types is difficult to explicitly specify, especially since different manufacturers and protocols use different conventions. Thus we developed a machine learning classifier using 4060 semiautomatically labeled head and spinal series from 1.5 to 7T MR scanners, including both site‐specific and standard protocols designed to study a wide range of neurological conditions, including dementia, hydrocephalus, and multiple sclerosis. An 80%/20% split, stratified by type, was used for the training/test sets. After initial training, a few cases in the training set with high errors due to rarity were augmented and the model retrained. Performance was evaluated on the clean test set. Result The taxonomy is presented in Table 1. The classifier correctly identified 807 out of 812 (99.4%) series, with the error cases coming from outside the ADNI or SCAN protocols (Table 2). The confusion matrix is shown in Figure 1. Conclusion We implemented an automatic series type classifier with labels chosen to be generally useful for the brain MRI community. Some errors were noted, but the test set was deliberately challenging and the errors appear to be ones that could easily be made by a human. The classifier is expected to have good tolerance for as yet unseen head MR protocols but is likely unsatisfactory for body MRI.