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OBJECTIVE Endovascular thrombectomy (EVT) for acute large-vessel occlusion stroke is well established, yet its role in patients presenting with minor stroke symptoms (National Institutes of Health Stroke Scale [NIHSS] score < 6) remains unclear. Prior studies have not accounted for intraprocedural factors that may influence outcomes in this subgroup. METHODS The authors analyzed 5693 patients from the STAR (Stroke and Thrombectomy and Aneurysm Registry), including 398 with low NIHSS scores, to evaluate the association between procedure time (PT) and outcomes. Among patients with low NIHSS scores, propensity score matching was used to compare outcomes between those with PT ≤ 35 versus > 35 minutes, a cutoff defined using receiver operating characteristic curve analysis. The authors performed a meta-analysis of studies reporting best medical management (BMM) outcomes in patients with low NIHSS scores. To compare these with EVT-treated patients, a matched BMM comparator cohort using stratification on pooled baseline characteristics was constructed. Outcomes included 90-day functional independence (modified Rankin Scale [mRS] scores 0–2), intracranial hemorrhage, and mortality. RESULTS PT had a stronger negative effect on outcomes in patients with low NIHSS scores compared with those with high scores, with a steeper decline in functional independence per minute of PT (slope: –0.45% vs –0.21%; p = 0.03 for interaction). Among patients with low scores, PT ≤ 35 minutes was associated with higher 90-day functional independence compared with the propensity score–matching cohort with PT > 35 minutes (82.6% vs 59.0%, p < 0.001). When benchmarked against BMM (75.2% mRS scores 0–2), patients undergoing EVT within 35 minutes had superior outcomes (85.1%, p = 0.047). In contrast, EVT with PT > 35 minutes resulted in worse outcomes than BMM (mRS scores 0–2: 59.8%, p = 0.008). The number needed to treat for PT ≤ 35 minutes was 10, whereas PT > 35 minutes yielded a number needed to harm of 6. CONCLUSIONS PT critically modulates the benefit of EVT in minor stroke. While efficient EVT yields better outcomes than BMM, prolonged procedures are associated with harm. These findings may explain prior inconsistent evidence on EVT in patients with low NIHSS scores and underscore the need to individualize procedural thresholds in this population.

Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

BACKGROUND: Aortic enlargement is a powerful predictor of dissection and rupture, yet it is rarely evaluated during routine myocardial perfusion imaging, despite the widespread availability of computed tomography (CT) attenuation correction scans. The aim of this study was to determine whether fully automated, opportunistically derived, artificial intelligence-based aortic measurements from myocardial perfusion imaging CT attenuation correction scans are associated with adverse outcomes in a large multicenter cohort. METHODS: CT attenuation correction scans from patients undergoing positron emission tomography/CT and single-photon emission CT/CT myocardial perfusion imaging across 10 centers were included. A deep learning model automatically segmented the thoracic aorta, and a postprocessing algorithm extracted maximum ascending and descending diameters. The aortic size index was calculated by indexing the diameter to body surface area. RESULTS: A total of 29 339 patients (56% men; median age, 66 years [interquartile range, 58–75 years]) were included. Over a median follow-up of 3.5 years (interquartile range, 1.9–5.0 years), 5083 (17.3%) patients died. Median ascending and descending aortic size index values were 1.8 cm/m 2 (interquartile range, 1.6–2.0) and 1.5 cm/m 2 (interquartile range, 1.4–1.6), respectively, with an increase with age and higher values in females. Elevated aortic size index thresholds (ascending >2.2 cm/m 2 ; descending >1.6 cm/m 2 ) were significantly associated with increased all-cause mortality (ascending: adjusted hazard ratio, 1.16 [95% CI, 1.07–1.26], P <0.001; descending: adjusted hazard ratio, 1.23 [95% CI, 1.14–1.31]; P <0.001). Notably, the prognostic value of an abnormal aortic size index persisted independent of age, sex, and perfusion abnormalities. CONCLUSIONS: Artificial intelligence can unlock previously unused information within routine myocardial perfusion imaging CT attenuation correction scans by rapidly and automatically quantifying aortic size at scale. Opportunistic aortic measurements derived from CT attenuation correction may serve as an adjunctive risk biomarker and could add prognostic value to standard myocardial perfusion imaging without additional imaging or radiation.

Abstract Background Lysosomal dysfunction plays a crucial role in the pathogenesis of Parkinson’s disease (PD), particularly among GBA1 mutation carriers. Beyond GBA1 , genes such as TMEM175 , SCARB2 , and CTSB identified in genome-wide association studies (GWAS) are also implicated in lysosomal pathways contributing to PD risk, although their functional effects in patients remain unclear. Proteins encoded by these lysosome-related genes have been explored as potential therapeutic targets in experimental models. Biomarker profiles, including clinical measures, α-syn seeding activity, lysosomal proteins, and sphingolipids, may facilitate patient stratification and support therapeutic monitoring in future clinical trials. Aim The aim of this study is to investigate the impact of genetic variants of three lysosomal-related genes ( TMEM175 , SCARB2 , and CTSB ) on biomarker profiles in PD with and without GBA1 variants. Cross-sectional data from two German cohorts: the Tuebingen Parkinson Cohort (TUEPAC) and the DESCRIBE PD cohort of the German Center for Neurodegenerative Diseases were used as explorative cohorts, and data from Accelerating Medicines Partnership® Parkinson’s Disease (AMP-PD) were used as a validation cohort. The ultimate goal is to provide new data for patient stratification based on genetics, which might serve as a readout for target engagement and treatment efficiency assessment. Methods Three cohorts were analyzed: TUEPAC, DESCRIBE PD, and AMP-PD. TUEPAC and DESCRIBE PD were combined into a single German discovery cohort (TUEPAC-DESCRIBE-PD), while AMP-PD served as an independent validation cohort. Within each cohort, for subgroup analyses, PD patients were classified as the overall PD cohort (PD all ), and further stratified by GBA1 mutation status into PD patients without GBA1 mutations (PD GBA1 _wildtype ), and PD patients carrying GBA1 mutations (PD GBA1 ). We evaluated cognitive and motor function, as well as depression using the Montreal Cognitive Assessment (MoCA), Unified Parkinson Disease Rating Scale-part I II (UPDRS III), and Beck Depression Inventory-II(BDI-II) scales. Analyzed biomarkers included CSF α-syn seeding activity using seed amplification assay (SAA), CSF lysosomal protein levels of lysosomal integral membrane protein 2 (LIMP2), also known as SCARB2, cathepsin B (CTSB) and lysosome-associated membrane protein 2 (LAMP2), blood-based enzyme activity of the lysosomal glucocerebrosidase (GCase), and CSF sphingolipid profiles. PD patients carrying risk alleles in TMEM175 , SCARB2 , and CTSB were compared to non-carriers. Results Genotype-phenotype correlation analysis in TUEPAC-DESCRIBE-PD and AMP-PD revealed: (1) In PD all , the TMEM175 p.M393T risk variant was nominally associated with decreased cognitive function when adjusted for GBA1 mutation status in TUEPAC-DESCRIBE-PD; this association could not be replicated, although a similar trend was observed in the slightly smaller, but multicentric AMP-PD cohort; TMEM175 p.M393T was not significantly associated with BDI-I I or UPDRS-III scores in either cohort. (2) In PD GBA1 _wildtype , GCase activity was significantly lower in PD patients with SCARB2 rs6812193 risk allele in TUEPAC-DESCRIBE-PD, while a similar but non-significant trend was observed in AMP-PD; (3) In PD all , CSF levels of CTSB were nominally lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; in PD GBA1 _wildtype , LAMP2 was significantly lower in carriers of CTSB rs1293298 risk allele compared to carriers of CTSB rs1293298 protective allele in TUEPAC-DESCRIBE-PD; (4) In PD all , TMEM175 p.M393T risk allele was nominally associated with altered sphingolipid profiles across both TUEPAC-DESCRIBE-PD and AMP-PD cohorts. Conclusion These findings demonstrate that genetic variants in lysosomal-related genes ( TMEM175 , SCARB2 , and CTSB ) have a functional impact on biomarker profiles in PD patients. Integrating genetic characterization with biochemical profiling provides a framework for patient stratification and may serve as a translational strategy to monitor target engagement and evaluate treatment efficacy in future clinical trials.

Abstract Background Sleep disturbances are common in Alzheimer's disease and have been reported in its preclinical stage. A detailed characterization of sleep disruption across the Alzheimer’s disease continuum is required to clarify its role as a potential therapeutic target and to evaluate the utility of sleep-related biomarkers. Methods Sleep architecture was characterized through detailed spectral and temporal analysis of the slow-wave activity using polysomnography across the Alzheimer’s disease continuum . Participants included: cognitively unimpaired individuals without abnormal Alzheimer’s disease biomarkers (n = 30), cognitively unimpaired individuals with abnormal Alzheimer’s disease biomarkers (n = 45), and individuals with mild cognitive impairment or mild dementia due to Alzheimer’s disease (n = 41). Associations between slow-wave features and longitudinal cognitive performance were tested in cognitively unimpaired participants. Sex differences were also explored. Results A stepwise increased wake after sleep onset and latency to rapid eye movement sleep was observed, alongside reduced spectral power of slow-wave activity and a progressive flattening of slow-waves across the Alzheimer’s disease continuum in both sexes. In cognitively unimpaired individuals, higher slow-wave slope and greater peak-to-peak amplitude were associated with better cognitive trajectories over time. Conclusions Sleep macro- and microarchitecture are altered early and progressively deteriorate across the Alzheimer’s disease continuum . Slow-wave features relate to cognitive trajectories in preclinical Alzheimer’s disease, with exploratory analyses suggesting stronger associations in men. These findings support slow-wave morphology as a candidate non-invasive marker for future sleep-based intervention studies.

Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes.

ABSTRACT Background and Objectives Converging evidence hints at neurodevelopmental effects in genetic frontotemporal degeneration (FTD). In cross-sectional studies, for some genes, young adult FTD variant carriers show differences in brain volumes and cognition compared to familial non-carriers. However, longitudinal trajectories may more sensitively capture FTD-related neurodevelopmental vs. neurodegenerative changes than cross-sectional approaches. This study examined longitudinal trajectories of brain volumes, executive function, and plasma biomarkers in young adult carriers compared to familial non-carriers, as measures of neurodevelopmental and neurodegenerative outcomes of FTD-causing variants. Methods This longitudinal cohort study comprised participants, aged 18-30 years, from the FTD Prevention Initiative across Europe, Canada, and the USA. Genetic groups included C9orf72 (47%), MAPT (30%), and GRN (23%). Linear mixed-effects models were computed to assess longitudinal outcomes across age between groups, controlling for sex, scanner (for brain volumes), and education (for executive function); random effects accounted for between-subject variability nested within family membership. Results Variant carriers ( n =147) and familial non-carriers ( n =113) did not differ in age (mean±SD, 25.9±3.2 years), sex (53% female), or number of visits (2.1±1.7). Young adult C9orf72 repeat expansion carriers exhibited smaller thalamic volumes than non-carriers at the reference age of 26 years ( b =-982.8mm 3 , SE=317.0, p= 0.0046, f 2 =0.32), with relatively stable trajectories across ages 18-30 (i.e., no change over time). Trajectories of rostral anterior cingulate volumes differed in C9orf72 carriers and non-carriers across age, where carriers showed relatively stable trajectories and non-carriers showed age-appropriate declines ( b =64.4mm 3 , SE=29.9, p= 0.035, f 2 =0.07). For MAPT and GRN , there were little to no differences in total brain, cortical, or subcortical volumes between groups and over time. No longitudinal differences were observed between carriers and non-carriers in executive function, or plasma NfL or GFAP for any genetic group. Discussion C9orf72 repeat expansions were linked to smaller average thalamic volumes and stable trajectories between ages 18 to 30, supporting potential neurodevelopmental origins. The modest evidence supporting an absence of difference in neurodegenerative biomarkers and executive function suggests minimal early neurodegeneration and functional preservation in young adulthood.

BACKGROUND: Although the prognostic utility of positron emission tomography (PET) myocardial flow reserve (MFR) is well established, emerging data suggest that reduced subendocardial flows also predict adverse outcomes. However, the incremental value of subendocardial MFR (MFR SE ) beyond transmural MFR (MFR TM ) remains unclear. METHODS: We studied patients in a multicenter PET registry with normal perfusion on stress/rest Rb-82 PET, excluding those with a previous history of coronary artery bypass surgery, heart transplantation, or left ventricular ejection fraction <40%. The optimal MFR SE cutoff for predicting major adverse cardiovascular events (MACEs; death, myocardial infarction [MI], revascularization, or heart failure [HF] hospitalization) was determined using Youden’s index. Patients were stratified into 3 groups: concordant-normal (MFR TM ≥2.0; MFR SE ≥2.1), discordant (low-MFR SE , normal MFR TM ), and abnormal MFR TM . Clinical outcomes were compared by MFR groups. RESULTS: Among 6603 patients (normal N=4103; discordant N=885; abnormal N=1615) the mean age was 66.3±12.4 years, and 54% were women. Compared with the concordant-normal group, patients with discordant low-MFR SE were older and more likely to have hypertension, diabetes, peripheral artery disease, and previous percutaneous coronary intervention. The median MFR TM for normal, discordant, and abnormal groups were 2.86, 2.15, and 1.72, respectively. Over a median follow-up of 4.9 years, 1661 MACE events occurred. Discordant low-MFR SE patients had a higher risk of MACE (hazard ratio [HR], 1.41; 95% CI, 1.22–1.64) and all-cause mortality (HR, 1.36; 95% CI, 1.14–1.61) compared with concordant-normal patients. The discordant group had an intermediate absolute risk of MACE, with an adjusted annualized event rate of 5.79% (95% CI, 5.10–6.49) compared with 3.99% (95% CI, 3.67–4.30; P <0.001) in the concordant-normal group and 8.35% (95% CI, 7.71–9.00; P <0.001) in the abnormal MFR TM group. CONCLUSIONS: Subendocardial MFR reveals clinically meaningful risk heterogeneity among patients with preserved transmural flow reserve, helping refine risk stratification beyond traditional PET metrics.