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INTRODUCTION Dementia with Lewy bodies (DLB) is typically characterized by parietal, temporal, and occipital atrophy, but less is known about the newly defined prodromal phases. The objective of this study was to evaluate structural brain alterations in prodromal DLB (p‐DLB) as compared to healthy controls (HC) and full‐blown dementia (DLB‐DEM). METHODS The study included 42 DLB patients (n = 20 p‐DLB; n = 22 DLB‐DEM) and 27 HC with a standardized neurological assessment and 3‐tesla magnetic resonance imaging. Voxel‐wise analyses on gray‐matter and cortical thickness were implemented to evaluate differences between p‐DLB, DLB‐DEM, and HC. RESULTS p‐DLB and DLB‐DEM exhibited reduced occipital and posterior parieto‐temporal volume and thickness, extending from prodromal to dementia stages. Occipital atrophy was more sensitive than insular atrophy in differentiating p‐DLB and HC. Occipital atrophy correlated to frontotemporal structural damage increasing from p‐DLB to DLB‐DEM. DISCUSSION Occipital and posterior‐temporal structural alterations are an early signature of the DLB continuum and correlate with a long‐distance pattern of atrophy.

We evaluated 128 GBA and 432 nonGBA Parkinson’s disease (PD) subjects available from Parkinson’s Progression Markers Initiative. Baseline clinical features and dopaminergic activity were assessed, together with clinical follow-up (6.87 ± 3.2 years). Survival analyses assessed the independent and interactive effects of sex and GBA1 mutations on cognitive decline. At baseline, GBA-PD males showed severe motor impairment, sleep disorders and memory deficits. Despite milder motor deficit, compared to GBA-PD males, GBA-PD females showed greater dopaminergic denervation, suggesting the effect of neural reserve. In longitudinal assessment, GBA-PD males showed greater MoCA rate of change per year and greater risk of cognitive impairment than GBA-PD females and nonGBA-PD. In GBA-PD males, both late age at onset and “severe/mild” GBA variants were associated with increased risk of cognitive impairment. Male sex and GBA1 carrier status have an additive value in increasing the risk of cognitive decline in PD. The effect of sex on GBA1-related pathology warrants further examination to address future trials design and patients’ selection.

Methods This study assessed data from two cohorts of patients with alpha‐synucleinopathies (University of Brescia and University of Rome Tor‐Vergata cohorts). Consecutive participants with video‐polysomnography‐confirmed iRBD, Parkinson's disease (PD), dementia with Lewy bodies (DLB) and controls underwent neurological, clinical and 123I‐FP‐CIT SPECT imaging assessments. Individuals with iRBD were longitudinally monitored to collect clinical phenoconversion to PD or DLB. The main outcome was to identify whole brain 123 I‐FP‐CIT SPECT measures reflecting monoaminergic deficits in each clinical group as compared to controls. Results The cohort (n = 184) included 45 patients with iRBD, 47 PD, 42 DLB and 50 age‐matched controls. Individuals with iRBD were categorized as RBD‐DAT− (n = 32) and RBD‐DAT+ (n = 13), according to nigrostriatal assessment used in clinical practice. Compared to controls, RBD‐DAT− showed an early involvement of the left insula, which increased in RBD‐DAT+, and was present in patients with Parkinson's disease and dementia with Lewy bodies. Longitudinal cox regression analyses revealed a higher risk of phenoconversion in individuals with iRBD and insular monoaminergic deficits [HR = 3.387; CI 95%: 1.18–10.27]. Interpretation In this study, altered insular monoaminergic binding in iRBD was associated with phenoconversion to DLB or PD. These findings may provide a helpful stratification approach for future pharmacological or non‐pharmacological interventions.

INTRODUCTION Growing evidence suggests a connection between insulin resistance and apolipoprotein E (APOE) genotype in Alzheimer's disease (AD) pathogenesis, but the mechanisms are unclear. We examined effects of insulin resistance and APOE genotype on blood–brain barrier (BBB) integrity in AD. METHODS BBB integrity was measured in 196 biologically‐confirmed non‐diabetic patients with AD evaluating CSF/serum albumin ratio, kappa and lambda free light chains (FLCs). Insulin resistance was assessed using triglyceride–glucose index (TyG). The impact of TyG on BBB integrity, and its interaction with APOE genotypes, was analyzed using multivariate models. RESULTS Sixty‐four percent of patients with AD showed altered TyG, with the 21.8% classified as high TyG. TyG subgroups were associated with BBB abnormalities, with similar AD clinical and biomarkers profile. A significant interaction between TyG and APOE ε4/ε4 genotype on BBB permeability was found in multivariate analyses. DISCUSSION Insulin resistance is a common feature in non‐diabetic AD and correlates with altered BBB permeability, interacting synergistically with APOE genotype. Highlights Insulin resistance and apolipoprotein E (APOE) genotype are well‐recognized risk factors for Alzheimer's disease (AD). Insulin resistance shows high prevalence in patients with AD. Insulin resistance is related to damage in blood–brain barrier (BBB) integrity. The association between the triglyceride–glucose (TyG) index and BBB permeability varies in relation to APOE genotype; patients with the APOE ε4/ε4 displayed higher BBB permeability.

INTRODUCTION Amnestic mild cognitive impairment (aMCI) is emerging as a heterogeneous condition. METHODS We looked at a cohort of N = 207 aMCI subjects, with baseline fluorodeoxyglucose positron emission tomography (FDG‐PET), T1 magnetic resonance imaging, cerebrospinal fluid (CSF), apolipoprotein E (APOE), and neuropsychological assessment. An algorithm based on FDG‐PET hypometabolism classified each subject into subtypes, then compared biomarker measures and clinical progression. RESULTS Three subtypes emerged: hippocampal sparing–cortical hypometabolism, associated with younger age and the highest level of Alzheimer's disease (AD)‐CSF pathology; hippocampal/cortical hypometabolism, associated with a high percentage of APOE ε3/ε4 or ε4/ε4 carriers; medial–temporal hypometabolism, characterized by older age, the lowest AD‐CSF pathology, the most severe hippocampal atrophy, and a benign course. Within the whole cohort, the severity of temporo‐parietal hypometabolism, correlated with AD‐CSF pathology and marked the rate of progression of cognitive decline. DISCUSSION FDG‐PET can distinguish clinically comparable aMCI at single‐subject level with different risk of progression to AD dementia or stability. The obtained results can be useful for the optimization of pharmacological trials and automated‐classification models. Highlights Algorithm based on FDG‐PET hypometabolism demonstrates distinct subtypes across aMCI; Three different subtypes show heterogeneous biological profiles and risk of progression; The cortical hypometabolism is associated with AD pathology and cognitive decline; MTL hypometabolism is associated with the lowest conversion rate and CSF‐AD pathology.

Background The Boston Cognitive Assessment (BOCA) is a self-administered online test developed for cognitive screening and longitudinal monitoring of brain health in an aging population. The study aimed to validate BOCA in an Italian population and to investigate the convergent validity with the Montreal Cognitive Assessment (MOCA) in healthy ageing population and patients within the Alzheimer Disease spectrum. Methods BOCA was administered to 150 participants, including cognitively healthy controls (HC, n  = 50), patients with mild cognitive impairment (MCI, n  = 50), and dementia (DEM, n  = 50). The BOCA reliability was assessed using (i) Spearman’s correlation analysis between subscales; (ii) Cronbach’s alpha calculation, and (iii) Principal Component Analysis. Repeated-measures ANOVA was employed to assess the impact of the sequence of test administrations between the groups. BOCA performance between HS, MCI and DEM and within different severity subgroups were compared using Kruskall Wallis test. Furthermore, a comparison was conducted between MCI patients who tested positive for amyloid and those who tested negative, utilizing Mann Whitney’s U-test. Results Test scores were significantly different between patients and controls ( p  < 0.001) suggesting good discriminative ability. The Cronbach’s alpha was 0.82 indicating a good internal consistency of the BOCA subscales and strong-to-moderate Spearman’s correlation coefficients between them. BOCA total and subscores differ across different MoCA severity subgroups and demonstrated strong correlation with MoCA scores (rho = 0.790, p  < 0.001). Conclusions The Italian version of the BOCA test exhibited validity, feasibility, and accurate discrimination closely performing as MoCA.

Background While nearly half of all dementia cases are potentially preventable, early detection and targeted interventions are critical. Artificial intelligence (AI)-enhanced clinical prediction models offer promising tools to improve diagnostic and prognostic accuracy by leveraging machine learning (ML) to integrate diverse data sources. This systematic review evaluates the development, performance, and clinical applicability of AI-based prediction models in dementia. Methods Searches of PubMed, Embase, and Web of Science identified peer-reviewed studies up to October 2024, focusing on AI-based models predicting dementia onset. Included studies were assessed for model accuracy, bias, and generalizability using the PROBAST tool. Data extraction adhered to the TRIPOD and CHARMS frameworks, capturing study design, participant demographics, predictor variables, and performance metrics. Results Among 2699 articles initially screened, 21 studies were included, encompassing over 1 million participants. AI models, extremely heterogenous for their nature, demonstrated good predictive accuracy, with a mean area under the curve of 0.845. While internal validation was conducted in all studies, external validation was limited. Models incorporating ML methods like random forests and support vector machines outperformed traditional approaches. The most used parameters were clinical and cognitive data, whilst data about biomarkers were the less used. Risk of bias was generally low, though calibration and generalizability remained challenges. Conclusions AI-based prediction models show strong potential for early dementia detection and personalized care. However, their integration into clinical practice requires addressing issues of external validation, data representativeness, and model interpretability. Further research should focus on robust validation and ethical implementation to optimize their utility in dementia care.

To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort. Clustering analysis stratified dopaminergic denervation, measured with I-FP-CIT-SPECT, and motor impairment into mild [D and M] and severe [D+ and M+]. Differences in terms of biomarkers and clinical progression were assessed across subgroups. Causal mediation analysis evaluated the effect of co-pathology on the relationship between subgroups and cognitive decline. Four subgroups were identified. Two subgroups showed concordant profiles: the severe dopaminergic and motor impairment subgroup [D+/M+] exhibited poorer memory performance, pathological Aβ , as well as higher longitudinal Levodopa equivalent daily dose (LEDD) values and faster progression of motor disability; the mild dopaminergic and motor deficits [D/M] subgroup displayed a benign clinical profile and stable disease progression. Two subgroups exhibited dopaminergic and motor severity mismatch: the mild dopaminergic but severe motor impairment [D/M+] subgroup showed severe and rapidly progressive rigidity. CSF Aβ levels mediated the association between D+/M+ and cognitive decline in patients who were cognitively preserved at onset, accounting for 13% of the total effect. The external cohort supported the malignancy of D+/M+ and the presence of rigidity in D/M+. Concordant severe impairment reflects a malignant profile linked to Aβ-related cognitive decline, while mild concordant cases show stable progression. Mismatch subgroups display distinct clinical patterns, underscoring the value of integrating imaging and motor features for early disease stratification.

A bstract We study the influence of angular momentum on quantum complexity for CFT states holographically dual to rotating black holes. Using the holographic complexity=action (CA) and complexity=volume (CV) proposals, we study the full time dependence of complexity and the complexity of formation for two dimensional states dual to rotating BTZ. The obtained results and their dependence on angular momentum turn out to be analogous to those of charged states dual to Reissner-Nordström AdS black holes. For CA, our computation carefully accounts for the counterterm in the gravity action, which was not included in previous analysis in the literature. This affects the complexity early time dependence and its effect becomes negligible close to extremality. In the grand canonical ensemble, the CA and CV complexity of formation are linear in the temperature, and diverge with the same structure in the speed of light angular velocity limit. For CA the inclusion of the counterterm is crucial for both effects. We also address the problem of studying holographic complexity for higher dimensional rotating black holes, focusing on the four dimensional Kerr-AdS case. Carefully taking into account all ingredients, we show that the late time limit of the CA growth rate saturates the expected bound, and find the CV complexity of formation of large black holes diverges in the critical angular velocity limit. Our holographic analysis is complemented by the study of circuit complexity in a two dimensional free scalar model for a thermofield double (TFD) state with angular momentum. We show how this can be given a description in terms of non-rotating TFD states introducing mode-by-mode effective temperatures and times. We comment on the similarities and differences of the holographic and QFT complexity results.