The pathology-based classification of Alzheimer’s disease (AD) and other neurodegenerative diseases is a work in progress that is important for both clinicians and basic scientists. Analyses of large autopsy series, biomarker studies, and genomics analyses have provided important insights about AD and shed light on previously unrecognized conditions, enabling a deeper understanding of neurodegenerative diseases in general. After demonstrating the importance of correct disease classification for AD and primary age-related tauopathy, we emphasize the public health impact of an underappreciated AD “mimic,” which has been termed “hippocampal sclerosis of aging” or “hippocampal sclerosis dementia.” This pathology affects >20% of individuals older than 85 years and is strongly associated with cognitive impairment. In this review, we provide an overview of current hypotheses about how genetic risk factors ( GRN , TMEM106B , ABCC9 , and KCNMB2 ), and other pathogenetic influences contribute to TDP-43 pathology and hippocampal sclerosis. Because hippocampal sclerosis of aging affects the “oldest-old” with arteriolosclerosis and TDP-43 pathologies that extend well beyond the hippocampus, more appropriate terminology for this disease is required. We recommend “cerebral age-related TDP-43 and sclerosis” (CARTS). A detailed case report is presented, which includes neuroimaging and longitudinal neurocognitive data. Finally, we suggest a neuropathology-based diagnostic rubric for CARTS.

Subcortical white matter hyperintensities (WMHs) in the aging population frequently represent vascular injury that may lead to cognitive impairment. WMH progression is well described, but the factors underlying WMH regression remain poorly understood. A sample of 351 participants from the Alzheimer’s Disease Neuroimaging Initiative 2 (ADNI2) was explored who had WMH volumetric quantification, structural brain measures, and cognitive measures (memory and executive function) at baseline and after approximately 2 years. Selected participants were categorized into three groups based on WMH change over time, including those that demonstrated regression (n = 96; 25.5%), stability (n = 72; 19.1%), and progression (n = 209; 55.4%). There were no significant differences in age, education, sex, or cognitive status between groups. Analysis of variance demonstrated significant differences in atrophy between the progression and both regression (p = 0.004) and stable groups (p = 0.012). Memory assessments improved over time in the regression and stable groups but declined in the progression group (p = 0.003; p = 0.018). WMH regression is associated with decreased brain atrophy and improvement in memory performance over two years compared to those with WMH progression, in whom memory and brain atrophy worsened. These data suggest that WMHs are dynamic and associated with changes in atrophy and cognition.

Introduction: In recent years, artificial intelligence (AI) has emerged as a transformative tool for enhancing stroke diagnosis, aiding treatment decision making, and improving overall patient care. Leading AI-driven platforms such as RapidAI, Brainomix^®, and Viz.ai have been developed to assist healthcare professionals in the swift and accurate assessment of stroke patients. Methods: Following the PRISMA guidelines, a comprehensive systematic review was conducted using PubMed, Embase, Web of Science, and Scopus. Characteristic descriptive measures were gathered as appropriate from all included studies, including the sensitivity, specificity, accuracy, and comparison of the available tools. Results: A total of 31 studies were included, of which 29 studies focused on detecting acute ischemic stroke (AIS) or large vessel occlusions (LVOs), and 2 studies focused on hemorrhagic strokes. The four main tools used were Viz.ai, RapidAI, Brainomix^®, and deep learning modules. Conclusions: AI tools in the treatment of stroke have demonstrated usefulness for diagnosing different stroke types, providing high levels of accuracy and helping to make quicker and more precise clinical judgments.

Objective: About 25% of patients with acute ischemic stroke (AIS) present within the intravenous thrombolytic (IVT) therapeutic window of <4.5 h. This study is to elucidate the safety and efficacy of IVT in the extended therapeutic window (ETW) in patients with AIS.Methods: Using PRISMA guidelines, a systematic review was conducted using PubMed, Embase, and Scopus. A rigorous risk of bias assessment was conducted using the RoB2 tool. Rates of excellent and good functional outcome (mRS 0–1 and mRS 0–2) at 90 days, symptomatic intracranial hemorrhage (sICH), and mortality at 90 days were pooled using generalized linear mixed model and compared with controls. Meta‐analyses were conducted employing random‐effect models with risk ratio (RR) and 95% confidence intervals (CIs). Subgroup analysis was performed to assess the effect of imaging modalities used for patient selection.Results: Eight randomized controlled trials (n = 2221, 59% male) were included. At 90 days IVT showed higher rates of functional recovery: mRS 0–1: RR 1.21 95% CI 1.1–1.34, p < 0.001, and mRS 0–2: RR 1.11 95% CI 1.03–1.18, p = 0.004. Rate of mortality at 90 day was not different between groups: RR 1.17 95% CI 0.93–1.48, p = 0.17. However, the rate of sICH was higher among IVT group: RR 2.93 95% CI 1.53–5.6, p = 0.001. Subgroup analysis showed higher mRS 0–1 among patients who were selected based on perfusion imaging (p < 0.05).Interpretation: The use of IVT in AIS in ETW is beneficial especially with the use of perfusion imaging for patients' selection.