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This study investigates the association between cognitive dysfunction and hippocampal volumes in Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) using Mendelian randomization. A meta-analysis of 503 healthy controls, 562 MCI patients, and 389 CE patients revealed significant reductions in hippocampal and subregion volumes in MCI and AD compared to controls. While various subregions showed volume reductions, no causal relationship between hippocampal volume and AD was established through Mendelian randomization analysis. In conclusion, significant volume reductions were observed in MCI and AD patients, highlighting the complexity of the relationship between hippocampal volume and cognitive impairment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Brain Research Organization (IBRO). Published by Elsevier Inc. All rights reserved.)

Background: Optical coherence tomography (OCT) enables high-resolution imaging of ocular structures in health and disease. Choroid thickness (CT) is a key vascular retinal parameter that can be assessed by OCT and might be relevant in the evaluation of the vascular component of cognitive decline. We aimed to investigate CT changes in a large cohort of individuals cognitive unimpaired (CU), with mild cognitive impairment due to Alzheimer's (MCI-AD), mild cognitive impairment due to cerebrovascular disease (MCI-Va), Alzheimer's disease dementia (ADD), and vascular dementia (VaD)., Methods: Clinical, demographical, ophthalmological and OCT data from the Neuro-ophthalmological Research at Fundació ACE (NORFACE) project were analyzed. CT was assessed in the macula across nine Early Treatment Diabetic Retinopathy Study (ETDRS) quadrants, average thickness, total volume, and subfoveal choroidal thickness. Differences of CT among the five diagnostic groups were assessed in a multivariate regression model, adjusting for demographic and cardiovascular risk factors and OCT image quality. A comparison between manual and automatic CT measurements in a subset of participants was also performed., Results: The study cohort comprised 1,280 participants: 301 CU, 196 MCI-AD, 112 MCI-Va, 578 ADD, and 93 VaD. CT was significantly increased in individuals with cognitive impairment compared to those CU, particularly in the VaD and MCI-Va groups and in the peripheral ETDRS regions. No significant differences were found in inner superior, center and subfoveal choroidal thickness. The interaction of sex and diagnosis had no effect in differentiating CT. Mini-Mental State Examination (MMSE) scores were not correlated to CT. Manual and automated CT measurements showed good reliability., Discussion: Our findings indicated that peripheral choroidal thickening, especially in patients with cerebrovascular disease, may serve as a potential choroidal biomarker for cognitive decline and suggest different pathogenic pathways in AD and VaD. Further research is required to explore CT as a reliable ocular biomarker for cognitive impairment., Competing Interests: Declarations Ethics approval and consent to participate This study and its informed consent were approved by the Ethics Committee of the Hospital Clínic i Provincial de Barcelona in accordance with Spanish biomedical laws (Law 14/2007, of July 3, on biomedical research; Royal Decree 1716/2011, of November 18) and followed the recommendations of the declaration of Helsinki. All participants signed an informed consent form (in the case of individuals with moderate stages of dementia, informed consent was signed by their legal representative or family member). Consent for publication Not applicable. Competing interests MB has consulted for Araclon, Avid, Grifols, Lilly, Nutricia, Roche, Eisai and Servier. She received fees from lectures and funds for research from Araclon, Biogen, Grifols, Nutricia, Roche and Servier. She reports grants/research funding from Abbvie, Araclon, Biogen Research Limited, Bioiberica, Grifols, Lilly, S.A, Merck Sharp & Dohme, Kyowa Hakko Kirin, Laboratorios Servier, Nutricia SRL, Oryzon Genomics, Piramal Imaging Limited, Roche Pharma SA, and Schwabe Farma Iberica SLU, all outside the submitted work. She has not received personal compensations from these organizations. AR is member of scientific advisory board of Landsteiner Genmed and Grifols SA. AR has stocks of Landsteiner Genmed. MM has consulted for F. Hoffmann-La Roche Ltd. The rest of authors declare that they have no competing interests., (© 2024. The Author(s).)

Beta-amyloid (Aβ), the most pivotal pathological hallmark for Alzheimer's disease (AD) diagnosis and drug evaluation, was recognized by TZ095, a high-affinity fluorescent probe developed by rational molecular design. With a TICT mechanism, TZ095 exhibited remarkable affinity with Aβ aggregates (K d  = 81.54 nM for oligomers; K d  = 66.70 nM for fibril) and substantial fluorescence enhancement (F/F 0  = 44), enabling real-time monitoring of Aβ in live cells and nematodes. Significantly, this work used TZ095 to construct a new protocol that can quickly and conveniently monitor Aβ changes at the cellular and nematode levels to evaluate the anti-AD efficacy of candidate compounds, and four reported Aβ-lowering drug candidates were administrated for validation. Imaging data demonstrated that TZ095 can visually and quantitatively track the effect of Aβ elimination after drug treatment. Furthermore, TZ095 excelled in ex vivo histological staining of 12-month-old APP/PS1 mouse brains, accurately visualizing Aβ plaques. Integrating CUBIC technology, TZ095 facilitated whole-brain, 3D imaging of Aβ distribution in APP/PS1 mice, enabling high-resolution in situ analysis of Aβ plaques. Collectively, these innovative applications of TZ095 offer a promising strategy for rapid, convenient, and real-time monitoring of Aβ levels in preclinical therapeutic assessments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Purpose: The discovery of the glymphatic system, crucial for cerebrospinal and interstitial fluid exchange, has enhanced our grasp of brain protein balance and its potential role in neurodegenerative disease prevention and therapy. Detecting early neurodegenerative shifts via noninvasive biomarkers could be key in identifying at-risk individuals for Alzheimer's disease (AD). Our research explores a diffusion tensor imaging (DTI) method that measures cortical mean diffusivity (cMD), potentially a more sensitive indicator of neurodegeneration than traditional macrostructural methods., Materials and Methods: We analyzed 67 post-traumatic stress disorder (PTSD)-diagnosed veterans from the Alzheimer's Disease Neuroimaging Initiative database. Participants underwent structural MRI, DTI, Aβ PET imaging, and cognitive testing. We focused on the DTI-ALPS technique to assess glymphatic function and its relation to cMD, cortical Aβ accumulation, and thickness, accounting for age and APOE ε4 allele variations., Results: The cohort, all male with an average age of 68.1 (SD 3.4), showed a strong inverse correlation between DTI-ALPS and cMD in AD-affected regions, especially in the entorhinal, parahippocampal, and fusiform areas. Higher DTI-ALPS readings were consistently linked with greater cortical thickness, independent of Aβ deposits and genetic risk factors. Age and cMD emerged as inversely proportional predictors of DTI-ALPS, indicating a complex interaction with age., Conclusion: The study confirms a meaningful association between glymphatic efficiency and cMD in AD-sensitive zones, accentuating cortical microstructural alterations in PTSD. It positions DTI-ALPS as a viable biomarker for assessing glymphatic function in PTSD, implicating changes in DTI-ALPS as indicative of glymphatic impairment., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The cognitive reserve (CR) hypothesis posits that individuals can differ in how their brain function is disrupted by pathology associated with aging and neurodegeneration. Here, we test this hypothesis in the continuum from cognitively normal to at-risk stages for Alzheimer's Disease (AD) to AD dementia using longitudinal data from 490 participants of the DELCODE multicentric observational study. Brain function is measured using task fMRI of visual memory encoding. Using a multivariate moderation analysis, we identify a CR-related activity pattern underlying successful memory encoding that moderates the detrimental effect of AD pathological load on cognitive performance. CR is mainly represented by a more pronounced expression of the task-active network encompassing deactivation of the default mode network (DMN) and activation of inferior temporal regions including the fusiform gyrus. We devise personalized fMRI-based CR scores that moderate the impact of AD pathology on cognitive performance and are positively associated with years of education. Furthermore, higher CR scores attenuate the effect of AD pathology on cognitive decline over time. Our findings primarily provide evidence for the maintenance of core cognitive circuits including the DMN as the neural basis of CR. Individual brain activity levels of these areas during memory encoding have prognostic value for future cognitive decline., Competing Interests: Competing interests The authors declare the following competing interests: Y.S. consults for Eisai, Lilly, and Arcadia. Columbia University licenses the Dependence Scale, and in accordance with university policy, Y.S. is entitled to royalties through this license. B.H.S. is involved in clinical studies by Roche, Biogen, and Hummingbird Diagnostics, but does not receive personal funds from any of them. S.T. is member of the DSMB of the study ENVISION (Biogen). J.W. acted as a consultant for Immungenetics, Noselab, and Roboscreen. J.W. further served on a scientific advisory board for Abbott, Biogen, Boehringer Ingelheim, Lilly, Immungenetics, MSD Sharp-Dohme, Noselab, Roboscreen, and Roche. J.W. received honoraria for presentations from Beijing Yibai Science and Technology Ltd, Eisai, Gloryren, Janssen, Pfizer, Med Update GmbH, Roche, and Lilly. The remaining authors declare no competing interests., (© 2024. The Author(s).)

Early detection and accurate diagnosis of brain morphological abnormalities are essential for the effective management and treatment of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural magnetic resonance imaging (MRI) is a powerful support tool to aid in disease diagnosis and prediction. In this research study, we present an innovative approach to predict Alzheimer's disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework. The proposed method involves dividing the brain into 138 predetermined sections based on anatomical information. Next, we apply three-dimensional vision transformers (3D-ViTs) to each ROI individually, harnessing the power of deep learning. To improve prediction accuracy, we employ a deep belief network (DBN) as an ensemble learning model. Evaluating our approach on the baseline structural MRI dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and comparing it against five other competing models, we demonstrate its performance across four binary classification tasks and a three-class classification test (AD vs MCI vs CN (Cognitively Normal)). The proposed system outperforms existing models and provides interpretable insights into the brain regions that significantly contribute to solving each classification problem. Our findings align with the existing body of literature and hold promise for guiding future research directions in this domain., Competing Interests: Declarations Competing interests The authors declare no competing interests, (© 2024. The Author(s).)

Background and Objectives: Alzheimer disease (AD) is associated with a 2 to 3-fold increased risk of developing late-onset focal epilepsy, yet it remains unclear how development of focal epilepsy in AD is related to AD pathology. The objective of this study was to examine spatial relationships between the epileptogenic zone and tau deposition, amyloid deposition, and brain atrophy in individuals with AD who developed late-onset, otherwise unexplained focal epilepsy. We hypothesized that if network hyperexcitability is mechanistically linked to AD pathology, then there would be increased tau and amyloid deposition within the epileptogenic hemisphere., Methods: In this cross-sectional study, we performed tau and amyloid PET imaging, brain MRI, and overnight scalp EEG in individuals with early clinical stages of AD who developed late-onset, otherwise unexplained focal epilepsy (AD-Ep). Participants were referred from epilepsy and memory disorders clinics at our institutions. We determined epilepsy localization based on EEG findings and seizure semiology. We quantified tau deposition, amyloid deposition, and atrophy across brain regions and calculated asymmetry indices for these measures. We compared findings in AD-Ep with those in a control AD group without epilepsy (AD-NoEp)., Results: The AD-Ep group included 8 individuals with a mean age of 69.5 ± 4.2 years at PET imaging. The AD-NoEp group included 14 individuals with a mean age of 71.7 ± 9.8 years at PET imaging. In AD-Ep, we found a highly asymmetric pattern of tau deposition, with significantly greater tau in the epileptogenic hemisphere. Amyloid deposition and cortical atrophy were also greater in the epileptogenic hemisphere, although the magnitudes of asymmetry were reduced compared with tau. Compared with AD-NoEp, the AD-Ep group had significantly greater tau asymmetry and trends toward greater asymmetry of amyloid and atrophy. AD-Ep also had significantly greater amyloid burden bilaterally and trends toward greater tau burden within the epileptogenic hemisphere, compared with AD-NoEp., Discussion: Our results reveal a spatial association between the epileptogenic focus and tau deposition, amyloid deposition, and neurodegeneration in early clinical stages of AD. Within the limitations of a cross-sectional study with small sample sizes, these findings contribute to our understanding of the clinicopathologic heterogeneity of AD, demonstrating an association between focal epilepsy and lateralized pathology in AD.

Alzheimer's disease (AD) is a fatal neurodegenerative disorder, with the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) serving significant roles in monitoring its progression. We hypothesize that while cognitive assessment scores can detect AD-related brain changes, the targeted brain regions may differ. Additionally, given AD's strong association with aging, we propose that specific brain regions are influenced by both AD pathology and aging, exhibiting strong correlations with both. To test these hypotheses, we developed a 3D convolutional network with a mixed-attention mechanism to recognize AD subjects from structural magnetic resonance imaging (sMRI) data and utilize 3D convolutional methods to pinpoint brain regions significantly correlated with the AD, MMSE, CDR and age. All models were trained and internally validated on 417 samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI), and the classification model was externally validated on 382 samples from the Australian Imaging and Lifestyle flagship (AIBL). This approach provided robust support for using MMSE and CDR in assessing AD progression and visually illustrated the relationship between aging and AD. The analysis revealed correlations among the four identification tasks (AD, MMSE, CDR and age) and highlighted asymmetric brain lesions in both AD and aging. Notably, we found that AD can accelerate aging to some extent, and a significant correlation exists between the rate of aging and cognitive assessment scores. This offers new insights into the relationship between AD and aging., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Proteins work together in nanostructures in many physiological contexts and disease states. We recently developed expansion revealing (ExR), which expands proteins away from each other, in order to support better labeling with antibody tags and nanoscale imaging on conventional microscopes. Here, we report multiplexed expansion revealing (multiExR), which enables high-fidelity antibody visualization of >20 proteins in the same specimen, over serial rounds of staining and imaging. Across all datasets examined, multiExR exhibits a median round-to-round registration error of 39 nm, with a median registration error of 25 nm when the most stringent form of the protocol is used. We precisely map 23 proteins in the brain of 5xFAD Alzheimer's model mice, and find reductions in synaptic protein cluster volume, and co-localization of specific AMPA receptor subunits with amyloid-beta nanoclusters. We visualize 20 synaptic proteins in specimens of mouse primary somatosensory cortex. multiExR may be of broad use in analyzing how different kinds of protein are organized amidst normal and pathological processes in biology., (© 2024. The Author(s).)

Deficits in memory are seen as a canonical sign of aging and a prodrome to dementia in older adults. However, our understanding of age-related cognition and brain morphology occurring throughout a broader spectrum of adulthood remains limited. We quantified the relationship between cognitive function and brain morphology (sulcal width, SW) using three cross-sectional observational datasets (PISA, AIBL, ADNI) from mid-life to older adulthood, assessing the influence of age, sex, amyloid (Aβ) and genetic risk for dementia. The data comprised cognitive, genetic and neuroimaging measures of a total of 1570 non-clinical mid-life and older adults (mean age 72, range 49-90 years, 1330 males) and 1365 age- and sex-matched adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). Among non-clinical adults, we found robust modes of co-variation between regional SW and multidomain cognitive function that differed between the mid-life and older age range. These cortical and cognitive profiles derived from healthy cohorts predicted out-of-sample AD and MCI. Furthermore, Aβ-deposition and educational attainment levels were associated with cognition but not SW. These findings underscoring the complex interplay between factors influencing cognition and brain structure from mid-life onwards, providing valuable insights for future research into neurodegeneration and the development of future screening algorithms., (© 2024. The Author(s).)

Pupil dynamics has emerged as a critical non-invasive indicator of brain state changes. In particular, pupillary-light-responses (PLR) in Alzheimer's disease (AD) patients show potential as biomarkers for brain degeneration. To investigate AD-specific PLR and its underlying neuromodulatory sources, we combine high-resolution awake mouse fMRI with real-time pupillometry to map brain-wide event-related correlation patterns based on illumination-driven pupil constriction ( P c ) and post-illumination pupil dilation recovery (amplitude, P d , and time, T). The P c -driven differential analysis reveals altered visual signal processing and reduced thalamocortical activation in AD mice in comparison with wild-type (WT) control mice. In contrast, the post-illumination pupil dilation recovery-based fMRI highlights multiple brain areas associated with AD brain degeneration, including the cingulate cortex, hippocampus, septal area of the basal forebrain, medial raphe nucleus, and pontine reticular nuclei (PRN). Additionally, the brain-wide functional connectivity analysis highlights the most significant changes in PRN of AD mice, which serves as the major subcortical relay nuclei underlying oculomotor function. This work integrates non-invasive pupil-fMRI measurements in preclinical models to identify pupillary biomarkers based on brain-wide functional changes, including neuromodulatory dysfunction coupled with AD brain degeneration., (© 2024. The Author(s).)

Background and Purpose: Brain atrophy occurs in the late stage of dementia, yet structural MRI is widely used in the work-up. Atrophy patterns can suggest a diagnosis of Alzheimer disease (AD) or frontotemporal dementia (FTD) but are difficult to assess visually. We hypothesized that the availability of a quantitative volumetric brain MRI report would increase neuroradiologists' accuracy in diagnosing AD, FTD, or healthy controls compared with visual assessment., Materials and Methods: Twenty-two patients with AD, 17 with FTD, and 21 cognitively healthy patients were identified from the electronic health systems record and a behavioral neurology clinic. Four neuroradiologists evaluated T1-weighted anatomic MRI studies with and without a volumetric report. Outcome measures were the proportion of correct diagnoses of neurodegenerative disease versus normal aging ("rough accuracy") and AD versus FTD ("exact accuracy"). Generalized linear mixed models were fit to assess whether the use of a volumetric report was associated with higher accuracy, accounting for random effects of within-rater and within-subject variability. Post hoc within-group analysis was performed with multiple comparisons correction. Residualized volumes were tested for an association with the diagnosis using ANOVA., Results: There was no statistically significant effect of the report on overall correct diagnoses. The proportion of "exact" correct diagnoses was higher with the report versus without the report for AD (0.52 versus 0.38) and FTD (0.49 versus 0.32) and lower for cognitively healthy (0.75 versus 0.89). The proportion of "rough" correct diagnoses of neurodegenerative disease was higher with the report than without the report within the AD group (0.59 versus 0.41), and it was similar within the FTD group (0.66 versus 0.63). Post hoc within-group analysis suggested that the report increased the accuracy in AD (OR = 2.77) and decreased the accuracy in cognitively healthy (OR = 0.25). Residualized hippocampal volumes were smaller in AD (mean difference -1.8; multiple comparisons correction, -2.8 to -0.8; P < .001) and FTD (mean difference -1.2; multiple comparisons correction, -2.2 to -0.1; P = .02) compared with cognitively healthy., Conclusions: The availability of a brain volumetric report did not improve neuroradiologists' accuracy over visual assessment in diagnosing AD or FTD in this limited sample. Post hoc analysis suggested that the report may have biased readers incorrectly toward a diagnosis of neurodegeneration in cognitively healthy adults., (© 2024 by American Journal of Neuroradiology.)

Background: Psychotic symptoms (hallucinations and delusions) are a type of neuropsychiatric symptom found during Alzheimer's Disease (AD)., Objective: This systematic review aims to comprehensively capture, analyse, and evaluate the body of evidence that has investigated associations between brain regions/networks and psychotic symptoms in AD., Methods: The protocol, created according to the PRISMA guidelines, was pre-registered on OSF (https://osf.io/tg8xp/). Searches were performed using PubMed, Web of Science and PsycInfo. A partial coordinate-based meta-analysis (CBMA) was performed based on data availability., Results: Eighty-two papers were selected: delusions were found to be associated mainly with right fronto-temporal brain regions and the insula; hallucinations mainly with fronto-occipital areas; both were frequently associated with the anterior cingulate cortex. The CBMA, performed on the findings of fourteen papers on delusions, identified a cluster in the frontal lobe, one in the putamen, and a smaller one in the insula., Conclusions: The available evidence highlights that key brain regions, predominantly in the right frontal lobe, the anterior cingulate cortex, and temporo-occipital areas, appear to underpin the different manifestations of psychotic symptoms in AD and MCI. The fronto-temporal areas identified in relation to delusions may underpin a failure to assimilate correct information and consider alternative possibilities (which might generate and maintain the delusional belief), and dysfunction within the salience network (anterior cingulate cortex and insula) may suggest a contribution for how internal and external stimuli are identified; the fronto-occipital areas linked to hallucinations may indicate diminished sensory processing and non-optimal predictive processing, that together contribute to misinterpretation of stimuli and misperceptions; the fronto-temporal and occipital areas, as well as the anterior cingulate cortex were linked to the psychotic cluster., Competing Interests: Declaration of competing interest The authors declare no potential conflicts of interest with respect to the research, authorship, or publication of this article., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Early detection of Alzheimer's disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses of cognitive impairment, limiting understanding of AD risk among those with normal cognition. The objective was to establish AD progression models through a deep learning approach to analyze heterogeneous, multi-modal datasets, including clustering analyses of population subsets. A multi-head deep-learning architecture was built to process and learn from biomedical and imaging data from the National Alzheimer's Coordinating Center. Shapley additive explanation algorithms for feature importance ranking and pairwise correlation analysis were used to identify predictors of disease progression. Four primary disease progression clusters (slow, moderate and rapid converters or non-converters) were subdivided into groups by race and sex, yielding 16 sub-clusters of participants with distinct progression patterns. A multi-head and early-fusion convolutional neural network achieved the most competitive performance and demonstrated superiority over a single-head deep learning architecture and conventional tree-based machine-learning methods, with 97% test accuracy, 96% F1 score and 0.19 root mean square error. From 447 features, 2 sets of 100 predictors of disease progression were extracted. Feature importance ranking, correlation analysis and descriptive statistics further enriched cluster analysis and validation of the heterogeneity of risk factors., (© 2024. The Author(s).)

The clinical manifestation of Parkinson's disease exhibits significant heterogeneity in the prevalence of non-motor symptoms and the rate of progression of motor symptoms, suggesting that Parkinson's disease can be classified into distinct subtypes. In this study, we aimed to explore this heterogeneity by identifying a set of subtypes with distinct patterns of spatiotemporal trajectories of neurodegeneration. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning algorithm that combined disease progression modelling with clustering methods, to cortical and subcortical neurodegeneration visible on 3 T structural MRI of a large cross-sectional sample of 504 patients and 279 healthy controls. Serial longitudinal data were available for a subset of 178 patients at the 2-year follow-up and for 140 patients at the 4-year follow-up. In a subset of 210 patients, concomitant Alzheimer's disease pathology was assessed by evaluating amyloid-β concentrations in the CSF or via the amyloid-specific radiotracer 18F-flutemetamol with PET. The SuStaIn analysis revealed three distinct subtypes, each characterized by unique patterns of spatiotemporal evolution of brain atrophy: neocortical, limbic and brainstem. In the neocortical subtype, a reduction in brain volume occurred in the frontal and parietal cortices in the earliest disease stage and progressed across the entire neocortex during the early stage, although with relative sparing of the striatum, pallidum, accumbens area and brainstem. The limbic subtype represented comparative regional vulnerability, which was characterized by early volume loss in the amygdala, accumbens area, striatum and temporal cortex, subsequently spreading to the parietal and frontal cortices across disease stage. The brainstem subtype showed gradual rostral progression from the brainstem extending to the amygdala and hippocampus, followed by the temporal and other cortices. Longitudinal MRI data confirmed that 77.8% of participants at the 2-year follow-up and 84.0% at the 4-year follow-up were assigned to subtypes consistent with estimates from the cross-sectional data. This three-subtype model aligned with empirically proposed subtypes based on age at onset, because the neocortical subtype demonstrated characteristics similar to those found in the old-onset phenotype, including older onset and cognitive decline symptoms (P < 0.05). Moreover, the subtypes correspond to the three categories of the neuropathological consensus criteria for symptomatic patients with Lewy pathology, proposing neocortex-, limbic- and brainstem-predominant patterns as different subgroups of α-synuclein distributions. Among the subtypes, the prevalence of biomarker evidence of amyloid-β pathology was comparable. Upon validation, the subtype model might be applied to individual cases, potentially serving as a biomarker to track disease progression and predict temporal evolution., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

In recent years, the glymphatic system has received increasing attention due to its possible implications in biological mechanisms associated with neurodegeneration. In the field of human brain mapping, this led to the development of diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. While this index has been repeatedly used to investigate possible differences between neurodegenerative disorders and healthy controls, a comprehensive evaluation of its stability across multiple measurements and different disorders is still missing. In this study, we perform a Bayesian meta-analysis aiming to assess the consistency of the DTI-ALPS results previously reported for 12 studies on Parkinson's disease and 11 studies on Alzheimer's disease. We also evaluated if the measured value of the DTI-ALPS index can quantitatively inform the diagnostic process, allowing disambiguation between these two disorders. Our results, expressed in terms of Bayes' Factor values, confirmed that the DTI-ALPS index is consistent in measuring the different functioning of the glymphatic system between healthy subjects and patients for both Parkinson's disease (Log10(BF10) = 30) and Alzheimer's disease (Log10(BF10) = 10). Moreover, we showed that the DTI-ALPS can be used to compare these two disorders directly, therefore providing a first proof of concept supporting the reliability of taking into consideration this neuroimaging measurement in the diagnostic process. Our study underscores the potential of the DTI-ALPS index in advancing our understanding of neurodegenerative pathologies and enhancing clinical diagnostics., (© 2024. The Author(s).)

Background Synaptic loss is an important factor in Alzheimer disease (AD); however, blood assays that conveniently and rapidly reflect changes in synaptic density are lacking. Purpose To correlate multiple potential synaptic blood markers with synaptic density measured using 18 F-SynVesT-1, a fluorine 18 ( 18 F)-labeled radiotracer, brain PET and to explore the independent associations between these markers and synaptic density. Materials and Methods This prospective study included 50 cognitively unimpaired (mean age, 65.0 years ± 8.3 [SD]; 37 female) participants and 70 participants with cognitive impairment (mean age, 69.5 years ± 7.9; 43 female) from the Memory Clinic of Shanghai Jiao Tong University Affiliated Ruijin Hospital and communities in Shanghai. Amyloid-β (Aβ) and tau were assessed using 18 F-florbetapir and 18 F-MK6240 PET/CT. Synaptic density was evaluated with 18 F-SynVesT-1 PET/MRI. Pearson correlation analysis was used to investigate relationships of plasma (Aβ42/40 ratio, phosphorylated tau 181 [p-tau-181], glial fibrillary acid protein [GFAP], neurofilament light) and serum (C-reactive protein, tumor necrosis factor-α, α-synuclein, neurogranin, active plasminogen activator inhibitor-1, tissue plasminogen activator) biomarkers with synaptic density. Linear regression models and mediation analysis were used to explore effects of other AD-related pathologies on these relationships. Results Correlations were observed between increased p-tau-181 and GFAP and decreased synaptic density in global cortex ( r p-tau-181 = -0.352, r GFAP = -0.386; both P < .001) and hippocampus ( r p-tau-181 = -0.361, r GFAP = -0.369; both P < .001) at 18 F-SynVesT-1 PET/MRI. The relationships between p-tau-181 and GFAP with 18 F-SynVesT-1 PET/MRI persisted after controlling for plasma Aβ42/40 ratio, Aβ PET, or cortical thickness ( P value range, <.001-.01). This association disappeared after controlling for tau PET ( P value range, .08-.83). Conclusion Plasma p-tau-181 and GFAP are closely associated with synaptic density measured using 18 F-SynVesT-1 PET/MRI, with the relationship primarily influenced by tau accumulation rather than Aβ deposition or cortical thickness. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Giannakopoulos in this issue.

Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [ 18 F]-florbetapir (FBP) and [ 11 C]-Pittsburgh compound-B (PiB). One hundred thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within 90 days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. In the head-to-head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP-to-PiB proportions. ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Background:  Alzheimer's disease (AD) is the most common cause of dementia and affects a large portion of the elderly population worldwide., Objective:  To analyze the relationship between lamina cribrosa thickness (LCT) and hippocampal volume in patients with AD and mild cognitive impairment (MCI)., Methods:  The sample in the present study consisted of 20 recently diagnosed MCI patients, 20 recently diagnosed AD patients, and 20 matched healthy volunteers. Every patient underwent magnetic resonance imaging (MRI) scans. The VolBrain software (open-access platform for MRI brain analysis) was used to calculate the hippocampal volume. Optical coherence tomography was performed to measure the LCT. Analysis of variance and Pearson chi-squared tests were employed to assess the results., Results:  The lowest total hippocampal volume ( p  < 0.05) was in the AD group, which was 6.14 ± 0.66 mm 3 , while in the control group, it was 7.7 ± 9.65 mm 3 , and 6.69 ± 0.46 mm 3 in the MCI group. In comparison to the rest of the groups, in the AD group, the LCT was the thinnest (202.17 ± 16.35 µm). As per the results of the study population as a whole, low hippocampal volume causes low LCT, which shows an important relationship (r: 0.41; p  < 0.05)., Conclusion:  The current findings present evidence of the relationship between hippocampal volume and LCT in patients with AD and MCI., Competing Interests: The authors have no conflict of interest to declare., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution 4.0 International License, permitting copying and reproduction so long as the original work is given appropriate credit (https://creativecommons.org/licenses/by/4.0/).)

Background: Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and tau proteins, leading to neurofibrillary tangles. A biomarker-based diagnostic method called the ATN system categorizes AD pathology into amyloid-β (A), tau (T), and neurodegeneration (N). The relationship between regional tau deposition and reduced glucose metabolism in the preclinical AD stage is not well understood., Objective: We presented voxel-by-voxel metabolic/tau deposition ratio (MTR) images to investigate the effects of tau deposition on metabolism in AD brains on a stage-by-stage basis., Methods: We selected 174 subjects who underwent 3D-MRI, FDG-PET, amyloid PET, and tau PET scans. MTR images were created by normalizing FDG-PET toMK6240 PET images. Voxel-wise comparisons among 63 cognitively normal amyloid-negative (CNA) subjects, 49 subjects with AD dementia (ADD), 23 subjects with mild cognitive impairment due to AD (MCA), and 39 preclinical AD (PRC) subjects were conducted., Results: There was reduced glucose metabolism in ADD and MCA groups compared to CNA, predominantly in parietotemporal areas. Tau deposition was observed in wider areas in ADD and restricted to the medial temporal lobes in MCA. MTR exhibited significant reductions in broader regions in ADD and MCA, indicating simultaneous glucose metabolism decrease and tau deposition. At the MCA and PRC stages, glucose metabolism impairment and tau deposition were shown in separate regions by FDG PET and tau PET, respectively, while MTR images showed impairment in both regions., Conclusions: Our findings suggest that MTR imaging provides insights into AD pathophysiology by simultaneously assessing glucose metabolism and tau deposition. In the early stage of the AD continuum (MCA and PRC), metabolic decline and tau deposition occur independently in different brain regions., Competing Interests: Declaration of conflicting interestsKazunari Ishii received lecture fees from Nihon Medi-physics Co., Ltd.

Background: Reserves' mechanisms explain inconsistencies between accumulation of neuropathological damage and clinical manifestations. Leisure activities are believed to promote reserves., Objective: This study evaluates whether cognitive, social, and physical leisure activities performed over life-span predict current cognitive functioning in normal aging and Alzheimer's disease (AD) continuum., Methods: 35 AD, 24 amnestic-Mild Cognitive Impairment (a-MCI) patients, 21 individuals with subjective cognitive complaint (SCD), and 25 controls underwent a questionnaire developed to quantify leisure activities in different life periods, the Addenbrooke's Cognitive Examination-Revised (ACE-R), and T1-weighted 3T-MRI scans for brain volumetrics and cortical thickness quantification. Partial/total leisure activities' scores and demographic and brain variables were entered as predictors, while ACE-R scores as dependent variables in linear regression analyses., Results: Current level of cognition was predicted by (i) social and physical activities performed in middle age and current cognitive activity in AD; (ii) cognitive and social activities performed in middle age, current age and cortical thickness in a-MCI; (iii) recreational activities the set of lifetime, current age, and brain features in SCD; (iv) education and the set of lifetime leisure activities over lifespan in controls., Conclusions: This study shows a funnel effect due to gradual reduction of stimulatory activities in the transition from healthy aging to AD. Reserve indices taking into account different types of stimulatory activities allow to capture even smallest residual effects of reserves accumulated over lifespan, until their complete depletion at advanced AD stages. These results may help target tailored interventions during normal and pathological aging., Competing Interests: Declaration of conflicting interestsL.S. and M.B. are Editorial Board Members of this journal but were not involved in the peer-review process of this article nor had access to any information regarding its peer-review. The remaining authors have no conflict of interest to report.

Background: Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a variety of environmental and mechanical factors., Objective: The aim of this research work is to addresses the issue of noise reduction and to predict Alzheimer's disease detection efficiently., Methods: First, we present a genetic programming (GP) technique for reducing Rician noise in MRI images to pre-process the dataset. To effectively reduce Rician noise, this GP approach combines a Feature Extraction component, GP Optimal Expression, and an Optimum Removal Estimation component. In the second phase, we design and develop an explainable Deep Learning framework. This framework uses a local data-driven interpretation technique based on SHAP values to investigate the relationship between the neural network's estimated AD diagnosis and the input MRI images. In addition, we handle class distribution by combining an oversampling strategy with a minority approach. Several assessment metrics are used to analyze the performance of our proposed model., Results: The proposed method is tested on a variety of medical samples, and the results are compared to those obtained using other comparable approaches. We also test and compare our model to three cutting-edge models: DenseNet169, VGGNet15, and Inceptionv3., Conclusions: The empirical results show that our proposed model outperforms others, particularly in handling basic structures with limited spectral features, lower computational complexity, and less overfitting. This research worked addressed Rician noise issue in MRI images and predict AD severity prediction using explainable deep learning framework., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: The neurodegenerative diseases like Alzheimer's disease (AD) can result in progressive decline in both cognitive functions and motor skills, which have critical need for accurate early diagnosis. However, current diagnosis approaches primarily rely on timely clinical magnetic resonance imaging (MRI) scans, which impede widely application for potential patients. Leveraging handwriting as a diagnostic tool offers significant potential for identifying AD in its early stages., Objective: This study aims to develop an efficient, rapid, and accurate method for early diagnosis of AD by utilizing handwriting analysis, a promising avenue due to its association with compromised motor skills in neurodegenerative diseases., Methods: We propose a novel methodology that leverages self-attention mechanisms for the early diagnosis of AD. Our approach integrates data from 25 distinct handwriting tasks available in the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset., Results: The Self-Attention model achieved an accuracy of 94.3% and an F1-score of 94.5%, outperforming other state-of-the-art models, including traditional machine learning and deep learning approaches. Specially, the Self-Attention model surpassed the previous best model, the convolutional neural networks, by approximately 4% in both accuracy and F1-score. Additionally, the model demonstrated superior precision (94.7%), sensitivity (94.5%), and specificity (94.1%), indicating high reliability and excellent identification of true positive and true negative cases, which is crucial in medical diagnostics., Conclusions: Handwriting analysis, powered by self-attention mechanisms, offers significant potential as a diagnostic tool for identifying AD in its early stages, providing an effective alternative to traditional MRI-based diagnosis., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Data availabilityThe data and codes used for the analysis are available from the corresponding authors upon request.

Background: Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD)., Methods: Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task)., Findings: Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits., Interpretation: The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life., Funding: The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute., Competing Interests: Declaration of interests TB has received investigator-initiated research awards from the NIH, the Alzheimer’s Association, the Foundation at Barnes-Jewish Hospital, Siemens Healthineers, Hyperfine and Avid Radiopharmaceuticals (a wholly-owned subsidiary of Eli Lilly and Company). She participates as a site investigator in clinical trials sponsored by Eli Lilly and Company, Biogen, Eisai, Jaansen, and Roche. She has served as a paid and unpaid consultant to Eisai, Siemens, Biogen, Janssen, Hyperfine, Merck Lilly, and Bristol-Myers Squibb. JCM has served as a paid consultant to the Barcelona Brain Research Center and the Native Alzheimer Disease-related Resource Center in Minority Aging Research. He also received payments for presentations at the AAIM meeting, Longer Life Foundation and the International Brain Health Symposium. JCM has received travel support to attend meetings including: AAIM, DIAN, AD/PD, ATRI/ADNI, ADRC, ADC, the International conference on Health Aging & Biomarkers and the International Brain Health Symposium. He has served on the advisory board for the Cure Alzheimer’s Fund and LEADS at Indiana University. IMN has received payments from Premier, Inc for participating in an advisory board, from Peerview for an educational talk, and from Subtle Medical, Inc for consulting. DW has served as a paid consultant to Qynapse, Beckman Coulter and Eli Lilly. He also received grants from the NIH and Biogen paid to his institution and received travel support from the Alzheimer's Association. SR is an NIA IRP employee and has served on the advisory board of Dementia Platforms, UK, the Canadian Consortium on Neurodegeneration in Aging and the Adult Aging Brain Connectome. She has received travel support from the McKnight Foundation to attend an annual meeting., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Blood tests have the potential to improve the accuracy of Alzheimer's disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes., Methods: Plasma samples from the Alzheimer's Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes., Results: Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes., Discussion: This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes., Highlights: Plasma p-tau217 measures most accurately classified amyloid and tau status. Plasma Aβ42/Aβ40 had relatively low accuracy in classification of amyloid status. Plasma p-tau217 measures had higher correlations with cortical thickness than NfL. Correlations of plasma biomarkers with dementia symptoms were relatively low., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: Microglial responses are an integral part of Alzheimer's disease (AD) pathology and are associated with amyloid beta (Aβ) deposition. This study aimed to investigate the effects of Aβ and microglial responses on global cognitive impairment., Methods: In this longitudinal study, 28 patients with mild cognitive impairment and 11 healthy controls underwent 11 C-PK11195 and 11 C-Pittsburgh compound B positron emission tomography (PET), structural magnetic resonance imaging scans, and global cognitive ratings at baseline and 2-year follow-up. Correlations between PET uptake and global cognition were assessed. Additionally, the mediation effect of the microglial response on the association between Aβ load and global cognition was assessed., Results: Aβ load and the microglial response were both independently detrimental to global cognitive performance at baseline; however, at 2-year follow-up the association between Aβ load and global cognitive ratings was partially mediated by the microglial response., Discussion: As AD progresses, the associated microglial response partially mediates the detrimental effect of aggregated Aβ on cognition., Highlights: This was a longitudinal study of amyloid beta (Aβ), microglial responses, and global cognitive performance. Aβ and microglial responses both affect cognition in early Alzheimer's disease. Microglial response partially mediates the effect of Aβ on cognition in later stages., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: Understanding the relationship between amyloid beta (Aβ) positron emission tomography (PET) and Aβ cerebrospinal fluid (CSF) biomarkers will define their potential utility in Aβ treatment. Few population-based or neuropathologic comparisons have been reported., Methods: Participants 50+ years with Aβ PET and Aβ CSF biomarkers (phosphorylated tau [p-tau]181/Aβ42, n = 505, and Aβ42/40, n = 54) were included from the Mayo Clinic Study on Aging. From these participants, an autopsy subgroup was identified (n = 47). The relationships of Aβ PET and Aβ CSF biomarkers were assessed cross-sectionally in all participants and longitudinally in autopsy data., Results: Cross-sectionally, more participants were Aβ PET+ versus Aβ CSF- than Aβ PET- versus Aβ CSF+ with an incremental effect when using Aβ PET regions selected for early Aβ deposition. The sensitivity for the first detection of Thal phase ≥ 1 in longitudinal data was higher for Aβ PET (89%) than p-tau181/Aβ42 (64%)., Discussion: Aβ PET can detect earlier cortical Aβ deposition than Aβ CSF biomarkers. Aβ PET+ versus Aβ CSF- findings are several-fold greater using regional Aβ PET analyses and in peri-threshold-standardized uptake value ratio participants., Highlights: Amyloid beta (Aβ) positron emission tomography (PET) has greater sensitivity for Aβ deposition than Aβ cerebrospinal fluid (CSF) in early Aβ development. A population-based sample of participants (n = 505) with PET and CSF tests was used. Cortical regions showing early Aβ on Aβ PET were also used in these analyses. Neuropathology was used to validate detection of Aβ by Aβ PET and Aβ CSF biomarkers., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: Recent technological advances have increased the risk that de-identified brain images could be re-identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants' privacy., Methods: An independent expert committee evaluated 11 face-deidentification ("de-facing") methods and selected four for formal testing., Results: Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended mri&#95;reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4., Discussion: ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. Trained analysts inspect de-faced images to confirm complete face removal and complete non-modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI., Highlights: ADNI is implementing "de-facing" of MRI and PET beginning in ADNI4. "De-facing" alters face imagery in brain images to help protect privacy. Four algorithms were extensively compared for ADNI and mri&#95;reface was chosen. Validation confirms mri&#95;reface is robust and effective for ADNI sequences. Validation confirms mri&#95;reface negligibly affects ADNI brain measurements., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Objective: To explore changes in cerebral cortex activation and functional connectivity during resting-state and verbal fluency tasks in patients with different types of dementia., Methods: We recorded oxygenated hemoglobin concentration (HbO) signals detected by functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex, partial parietal cortex, and cortex of the temporal lobe in four groups of participants: mild cognitive impairment (MCI), Lewy body dementia (LBD), Alzheimer's disease (AD), and cognitively normal (CN)., Results: The study recruited 120 older adults with MCI (n = 30), LBD (n = 28), AD (n = 30), or CN (n = 32). The mean functional connectivity of the frontal and temporal lobe in resting state was significantly less in the AD (0.19 ± 0.11) group than in the MCI (0.23 ± 0.11), LBD (0.29 ± 0.12), and CN (0.40 ± 0.11) groups (p < 0.001). Further, the mean HbO concentrations in the brain regions and channels were significantly lower in the AD group than in the LBD and MCI groups (p < 0.001). Cognitive levels correlated significantly with the mean HbO concentrations in the resting state and verbal fluency task conditions., Conclusion: The fNIRS HbO signals significantly differed in the cerebral cortex regions in participants with different types of dementia. These findings suggest that fNIRS can effectively enhance the differential diagnosis and assessment of dementia., Competing Interests: Declaration of competing interest All authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has fostered collaboration among researchers around the world, catalyzing innovation and accelerating progress in the field. In Latin America, this initiative advanced the validation and development of Alzheimer's disease biomarkers for the first time in our region. In 2011, as part of the international ADNI, Argentina-ADNI (Arg-ADNI) was founded. The following years were characterized by strong support from entities such as the Alzheimer's Association, transforming into the emergence of several multinational studies focusing on prevention and diagnosis, and treatment of dementias. These studies are heirs to the tradition of Arg-ADNI: the Dominantly Inherited Alzheimer Network, Latin American Initiative for Lifestyle Intervention to Prevent Cognitive Decline, Longitudinal Early-Onset Alzheimer's Disease Study, and Initiative for the Study of Down Syndrome and Alzheimer's Disease. These initiatives have contributed significantly to the development of regional research and serve as essential tools for health policy planning in Latin America. HIGHLIGHTS: In Latin America, the Alzheimer's Disease Neuroimaging Initiative (ADNI) advanced the validation and development of Alzheimer's disease biomarkers for the first time in our region. ADNI has been the basis that boosted several multinational studies focusing on both prevention and diagnosis, and treatment of dementias (Dominantly Inherited Alzheimer Network, LatAm-FINGER, LEADS). These initiatives with the support from the Alzheimer's Association have contributed significantly to the development of regional research This is an example of collaboration between high-income countries with low- and middle-income countries aimed at global scientific development and inclusion., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: Biomarkers for Alzheimer's disease neuropathologic change (ADNC) have been instrumental in developing effective disease-modifying therapeutics. However, to prevent/treat dementia effectively, we require biomarkers for non-AD neuropathologies; for this, neuropathologic examinations and annotated tissue samples are essential., Methods: We conducted clinicopathologic correlation for the first 100 Alzheimer's Disease Neuroimaging Initiative (ADNI) Neuropathology Core (NPC) cases., Results: Clinical syndromes in this cohort showed 95% sensitivity and 79% specificity for predicting high/intermediate ADNC, a 21% false positive rate, and a ∼44% false negative rate. In addition, 60% with high/intermediate ADNC harbored additional potentially dementing co-pathologies., Discussion: These results suggest that clinical presentation imperfectly predicts ADNC and that accurate prediction of high/intermediate ADNC does not exclude co-pathology that may modify presentation, biomarkers, and therapeutic responses. Therefore, new biomarkers are needed for non-AD neuropathologies. The ADNI NPC supports this mission with well-characterized tissue samples (available through ADNI and the National Institute on Aging) and "gold-standard" diagnostic information (soon to include digital histology)., Highlights: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Neuropathology Core (NPC) brain donation cohort now exceeds 200 cases. ADNI NPC data in National Alzheimer's Coordinating Center format are available through the Laboratory of Neuro Imaging. Digitized slide files from the ADNI NPC will be available in 2025. Requests for ADNI brain tissue samples can be submitted online for ADNI/National Institute on Aging evaluation. Clinical diagnoses of Alzheimer's disease (AD)/AD and related dementias (ADRD) do not always predict post mortem neuropathology. Neuropathology is essential for the development of novel AD/ADRD biomarkers., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

In the last decade, extensive research has emerged into understanding the impact of risk factors for Alzheimer's Disease (AD) on brain in pre-symptomatic stages. We investigated the neuroimaging correlates of the APOEe4 genetic risk factor for AD in young adulthood, its relationship with cognition, and potential effects of other variables on the findings. While conventional volumetric analyses revealed no consistent differences, more sophisticated analyses identified subtle structural differences between APOEe4 carriers and non-carriers. Findings from diffusion studies were limited, but functional studies demonstrated consistent alterations in connectivity and activity. The complex relationship between APOE genotype, neuroimaging variables, and cognition revealed no consensus on the directionality of findings. Methodological choices, including analytical approaches, sample size, and the influence of other genes, gender, and ethnicity, varied across studies, impacting comparability and generalizability. Recommendations for future research include multimodal and longitudinal imaging, standardisation of pipelines, advanced analytical techniques, and collaborative data pooling., Competing Interests: Declaration of Competing Interest LK, XX, PR, OT, EJ, JM, WH, MD have no competing interests or financial disclosures related to present article. LS was funded by Alzheimer’s Research UK Senior Research Fellowship (ARUK-SRF2017B-1) and the Lewy Body Society (LS002/2019). This research was supported by the NIHR Sheffield Biomedical Research Centre (BRC) / NIHR Sheffield Clinical Research Facility (CRF). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care (DHSC)., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Subjective cognitive decline (SCD) may be an early marker of Alzheimer's disease (AD) pathology. Until recently, it was impossible to measure biomarkers specific for α-synuclein pathology; therefore, its association with subjective reports of cognitive decline is unknown., Methods: Alzheimer's Disease Neuroimaging Initiative participants without dementia (n = 918) were classified as positive or negative for amyloid beta (Aβ+ or Aβ-) and α-synuclein (α-syn+ or α-syn-) biomarkers. Self- and study partner-reported cognitive decline was measured with the Everyday Cognition (ECog) questionnaire., Results: Per self-report, Aβ+/α-syn+ had the greatest cognitive decline. Aβ-/α-syn+ did not differ from Aβ-/α-syn- across ECog scores. Study partner-reported results had a similar pattern, but Aβ+/α-syn- and Aβ+/α-syn+ did not differ across ECog scores. Mild cognitive impairment classification moderated the study partner-reported memory score., Discussion: While α-syn+ alone did not increase subjective reports of cognitive decline, Aβ+/α-syn+ had the most self- and study partner-rated cognitive decline. Therefore, the presence of multiple pathologies was associated with greater SCD., Highlights: Cerebrospinal fluid α-synuclein (α-syn) seed amplification assay was used to determine α-syn positivity. Amyloid beta (Aβ)-/α-syn-, Aβ-/α-syn+, Aβ+/α-syn-, and Aβ+/α-syn+ biomarker groups were created. Aβ+/α-syn+ had greater subjective cognitive decline (SCD) than the other biomarker groups. Aβ-/α-syn+ did not differ from Aβ-/α-syn- across self- or study-partner reported SCD scores. Study partner-reported subjective memory results were largely driven by participants with mild cognitive impairment., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

There are recognized neuroimaging regions of interest in typical Alzheimer's disease which have been used to track disease progression and aid prognostication. However, there is a need for validated baseline imaging markers to predict clinical decline in atypical Alzheimer's Disease. We aimed to address this need by producing models from baseline imaging features using penalized regression and evaluating their predictive performance on various clinical measures. Baseline multimodal imaging data, in combination with clinical testing data at two time points from 46 atypical Alzheimer's Disease patients with a diagnosis of logopenic progressive aphasia (N = 24) or posterior cortical atrophy (N = 22), were used to generate our models. An additional 15 patients (logopenic progressive aphasia = 7, posterior cortical atrophy = 8), whose data were not used in our original analysis, were used to test our models. Patients underwent MRI, FDG-PET and Tau-PET imaging and a full neurologic battery at two time points. The Schaefer functional atlas was used to extract network-based and regional gray matter volume or PET SUVR values from baseline imaging. Penalized regression (Elastic Net) was used to create models to predict scores on testing at Time 2 while controlling for baseline performance, education, age, and sex. In addition, we created models using clinical or Meta Region of Interested (ROI) data to serve as comparisons. We found the degree of baseline involvement on neuroimaging was predictive of future performance on cognitive testing while controlling for the above measures on all three imaging modalities. In many cases, model predictability improved with the addition of network-based neuroimaging data to clinical data. We also found our network-based models performed superiorly to the comparison models comprised of only clinical or a Meta ROI score. Creating predictive models from imaging studies at a baseline time point that are agnostic to clinical diagnosis as we have described could prove invaluable in both the clinical and research setting, particularly in the development and implementation of future disease modifying therapies., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objectives: Based on the literature, tensor-based morphometry (TBM) parameters were related to neurocognitive functions such as memory, learning, language ability, and executive functions. The present study aims to evaluate the associations between TBM indices with executive functions, memory, language, and visuospatial abilities and the value of TBM in the clinical diagnosis of Alzheimer's disease (AD) among individuals with Alzheimer's disease continuum and mild cognitive impairment (MCI) from Alzheimer's Disease Neuroimaging Initiative (ADNI)., Methods: The authors used ADNI-memory (ADNI-MEM), ADNI-executive functions (ADNI-EF), ADNI-language (ADNI-LAN), and ADNI-visuospatial (ADNI-VS) composite scores. TBM parameters, including measure 1, which represents average within a statistically defined region-of-interest inside the temporal lobes and measure 2 which indicates average within an anatomically defined region-of-interest including bilateral temporal lobes were utilized in the current study. Statistical analysis was performed using IBM SPSS Statistics version 26, and Pearson's correlation, Bonferroni's correction, and multiple linear regression were utilized for data analysis. P <0.05 was considered statistically significant., Results: After screening 800 participants, 270 (151 men, 119 women) were selected for a study with TBM scores and cognition-related assessments at 6, 12, and 24 months. Groups included healthy controls (n=53), MCI (n=158), and Alzheimer's Disease (AD) (n=59). TBM indices correlated with cognitive scores in MCI and AD groups but not healthy controls. Changes in TBM indices and cognitive scores were significantly correlated in MCI and AD groups over 24 months. TBM indices were weak predictors of cognitive decline at all time points., Conclusions: TBM can help physicians diagnose MCI and AD early. However, TBM could not strongly predict cognitive functions decline at all time points., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: Alzheimer's disease (AD) is a common neurodegenerative disorder that primarily affects older individuals. Due to its high incidence, an accurate and efficient stratification system could greatly aid in the clinical diagnosis and prognosis of AD patients. Convolutional neural networks (CNN) approaches have demonstrated exceptional performance in the automated stratification of AD, mild cognitive impairment (MCI) and cognitively normal (CN) participants using MRI, owing to their high predictive accuracy and reliability. Therefore, we aimed to develop an algorithm based on CNN and radiomic features derived from ROIs of bilateral hippocampus and amygdala in brain MRI for stratification between AD, MCI and CN., Methods: In this study, we proposed a CNN and radiomic features-based algorithm using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. T1-weighted images were used. We utilized three datasets, including AD (199 cases, 602 images), MCI (200 cases, 948 images), and CN (200 cases, 853 images), to perform binary classification (AD vs. CN, AD vs. MCI, and MCI vs. CN). Finally, we obtained the accuracy (ACC) and the area under the curve of the receiver operating characteristic curve (AUC) to evaluate the performance of the algorithm., Results: Our proposed algorithm achieved acceptable overall discrimination accuracy. In the term of AD vs CN, radiomic-based algorithm alone obtained ACC of 82.6 % and AUC of 88.8, CNN-based algorithm obtained ACC of 80 % and AUC of 87.2 and their fusion showed ACC of 84.4 % and AUC of 90. In the term of MCI vs CN, radiomic-based algorithm alone obtained ACC of 71.6 % and AUC of 77.8, CNN-based algorithm obtained ACC of 69 % and AUC of 75 and their fusion showed ACC of 72.7 % and AUC of 80. In the term of AD vs MCI, radiomic-based algorithm alone obtained ACC of 57 % and AUC of 57.5, CNN-based algorithm obtained ACC of 56.6 % and AUC of 57.7 and their fusion showed ACC of 58 % and AUC of 59.5., Conclusion: In conclusion, it has been determined that hippocampus and amygdala-based stratification using CNN features and radiomic features-based algorithm is a promising method for the classification of AD, MCI, and CN participants., Advances in Knowledge: This study proposed an automated procedures based on MRI-derived radiomic features and CNN for classification between AD, MCI and CN., Competing Interests: Declaration of competing interest The author(s) declare that they have no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The colloidal gold nanoparticle (AuNP)-based colorimetric lateral flow assay (LFA) is one of the most promising analytical tools for point-of-care disease diagnosis. However, the low sensitivity and insufficient accuracy still limit its clinical application. In this work, a machine learning (ML)-optimized colorimetric LFA with ultrasound enrichment is developed to achieve the sensitive and accurate detection of tau proteins for early screening of Alzheimer's disease (AD). The LFA device is integrated with a portable ultrasonic actuator to rapidly enrich microparticles using ultrasound, which is essential for sample pre-enrichment to improve the sensitivity, followed by ML algorithms to classify and predict the enhanced colorimetric signals. The results of the undiluted serum sample testing show that the protocol enables efficient classification and accurate quantification of the AD biomarker tau protein concentration with an average classification accuracy of 98.11% and an average prediction accuracy of 99.99%, achieving a limit of detection (LOD) as sensitive as 10.30 pg mL -1 . Further point-of-care testing (POCT) of human plasma samples demonstrates the potential use of LFA in clinical trials. Such a reliable lateral flow immunosensor with high precision and superb sensing performance is expected to put LFA in perspective as an AD clinical diagnostic platform., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Simplified methods of acquisition and quantification would facilitate the use of synaptic density imaging in multicenter and longitudinal studies of Alzheimer disease (AD). We validated a simplified tissue-to-reference ratio method using SUV ratios (SUVRs) for estimating synaptic density with [ 11 C]UCB-J PET. Methods: Participants included 31 older adults with AD and 16 with normal cognition. The distribution volume ratio (DVR) using simplified reference tissue model 2 was compared with SUVR at short scan windows using a whole-cerebellum reference region. Results: Synaptic density was reduced in AD participants using DVR or SUVR. SUVR using later scan windows (60-90 or 70-90 min) was minimally biased, with the strongest correlation with DVR. Effect sizes using SUVR at these late time windows were minimally reduced compared with effect sizes with DVR. Conclusion: A simplified tissue-to-reference method may be useful for multicenter and longitudinal studies seeking to measure synaptic density in AD., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Introduction: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment., Methods: Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI)., Results: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI., Discussion: Our risk score holds great potential to improve the precision of early risk assessment., Highlights: Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

This paper for the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI) provides an overview of magnetic resonance imaging (MRI) of medial temporal lobe (MTL) subregions in ADNI using a dedicated high-resolution T2-weighted sequence. A review of the work that supported the inclusion of this imaging modality into ADNI Phase 3 is followed by a brief description of the ADNI MTL imaging and analysis protocols and a summary of studies that have used these data. This review is supplemented by a new study that uses novel surface-based tools to characterize MTL neurodegeneration across biomarker-defined AD stages. This analysis reveals a pattern of spreading cortical thinning associated with increasing levels of tau pathology in the presence of elevated amyloid beta, with apparent epicenters in the transentorhinal region and inferior hippocampal subfields. The paper concludes with an outlook for high-resolution imaging of the MTL in ADNI Phase 4. HIGHLIGHTS: As of Phase 3, the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) protocol includes a high-resolution T2-weighted MRI scan optimized for imaging hippocampal subfields and medial temporal lobe (MTL) subregions. These scans are processed by the ADNI core to obtain automatic segmentations of MTL subregions and to derive morphologic measurements. More detailed granular examination of MTL neurodegeneration in response to disease progression is achieved by applying surface-based modeling techniques. Surface-based analysis of gray matter loss in MTL subregions reveals increasing and spatially expanding patterns of neurodegeneration with advancing stages of Alzheimer's disease (AD), as defined based on amyloid and tau positron emission tomography biomarkers in accordance with recently proposed criteria. These patterns closely align with post mortem literature on spread of pathological tau in AD, supporting the role of tau pathology in the presence of elevated levels of amyloid beta as the driver of neurodegeneration., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Purpose: Numerous studies have highlighted a close link between metabolic imbalances and Alzheimer's Disease (AD). The advancement of metabolomics has recently enabled the exploration of characteristic metabolic changes associated with AD. Studies indicate that serum glutamate (Glu) levels may correlate with mild cognitive impairment (MCI) and AD. This study aims to further elucidate the characteristics of baseline serum Glu levels in MCI and AD., Methods: This study included 783 participants from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) cohort, categorized into cognitively normal (CN, n = 224), stable MCI (sMCI, n = 181), progressive MCI (pMCI, n = 193), and AD (n = 185). The study aimed to analyze the diagnostic value of baseline serum Glu, to explore its predictive capability for the progression from CN to MCI or AD, and from MCI to AD, and to analyze the relationship between serum Glu and cerebrospinal fluid (CSF) biomarkers and cognitive functions in different diagnostic groups., Results: Compared to the CN and sMCI groups, the pMCI group showed significantly lower levels of serum Glu, and the AD group also had lower Glu levels compared to the sMCI group. However, serum Glu did not show significant diagnostic value for MCI and AD. Lower levels of serum Glu could predict the progression from MCI to AD., Conclusion: Serum Glu levels can predict the progression from MCI to AD, suggesting that it could provide new insights into the pathophysiological mechanisms of AD. However, serum Glu may not be an ideal peripheral biomarker for AD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Blood-based biomarkers (BBM) are becoming easily detectable tools to reveal pathological changes in Alzheimer's disease (AD). A comprehensive and up-to-date overview of the association between BBM and brain MRI parameters is not available. This systematic review aimed to summarize the literature on the associations between the main BBM and MRI markers across the clinical AD continuum. A systematic literature search was carried out on PubMed and Web of Science and a total of 33 articles were included. Hippocampal volume was positively correlated with Aβ42 and Aβ42/Aβ40 and negatively with Aβ40 plasma levels. P-tau181 and p-tau217 concentrations were negatively correlated with temporal grey matter volume and cortical thickness. NfL levels were negatively correlated with white matter microstructural integrity, whereas GFAP levels were positively correlated with myo-inositol values in the posterior cingulate cortex/precuneus. These findings highlight consistent associations between various BBM and brain MRI markers even in the pre-clinical and prodromal stages of AD. This suggests a possible advantage in combining multiple AD-related markers to improve accuracy of early diagnosis, prognosis, progression monitoring and treatment response., Competing Interests: Declarations Conflicts of interest The authors have no competing interests to declare that are relevant to the content of this article., (© 2024. The Author(s).)

Introduction: We examined the relations of misfolded alpha synuclein (α-synuclein) with Alzheimer's disease (AD) biomarkers in two large independent cohorts., Methods: We included Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably Two (BioFINDER-2) and Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (n = 2315, cognitively unimpaired, mild cognitive impairment, AD dementia) who had cross-sectional cerebrospinal fluid (CSF) α-synuclein measurement from seed-amplification assay as well as cross-sectional and longitudinal amyloid beta (Aβ) and tau levels (measured in CSF and/or by positron emission tomography). All analyses were adjusted for age, sex, and cognitive status., Results: Across cohorts, the main biomarker associated with α-synuclein positivity at baseline was higher levels of Aβ pathology (all p values ≤ 0.02), but not tau. Looking at longitudinal measures of AD biomarkers, α-synuclein -positive participants had a statistically significant faster increase of Aβ load, although of modest magnitude (1.11 Centiloid/year, p = 0.02), compared to α-synuclein -negative participants in BioFINDER-2 but not in ADNI., Discussion: We showed associations between concurrent misfolded α-synuclein and Aβ levels, providing in vivo evidence of links between these two molecular disease pathways in humans., Highlights: Amyloid beta (Aβ), but not tau, was associated with alpha-synuclein (α-synuclein) positivity. Such association was consistent across two cohorts, beyond the effect of age, sex, and cognitive status. α-synuclein-positive participants had a small, statistically significant faster increase in Aβ positron emission tomography levels in one of the two cohorts., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: We investigated the interactive associations between amyloid and hypertension on the entorhinal cortex (EC) tau and atrophy and the role of cerebral blood flow (CBF) as a shared mechanism by which amyloid and hypertension contribute to EC tau and regional white matter hyperintensities (WMHs)., Methods: We analyzed data from older adults without dementia participating in the Add-Tau study (NCT02958670, n = 138) or Alzheimer's Disease Neuroimaging Initiative (ADNI) (n = 523) who had available amyloid-positron emission tomography (PET), tau-PET, fluid-attenuated inversion recovery (FLAIR), and T1-weighted magnetic resonance imaging (MRI). A subsample in both cohorts had available arterial spin labeling (ASL) MRI (Add-Tau: n = 78; ADNI: n = 89)., Results: The detrimental effects of hypertension on AD pathology and EC thickness were more pronounced in the Add-Tau cohort. Increased amyloid burden was associated with decreased occipital gray matter CBF in the ADNI cohort. In both cohorts, lower regional gray matter CBF was associated with higher EC tau and posterior WMH burden., Discussion: Reduced cerebral perfusion may be one common mechanism through which hypertension and amyloid are related to increased EC tau and WMH volume., Highlights: Hypertension is associated with increased entorhinal cortex (EC) tau, particularly in the presence of amyloid. Decreased cortical cerebral blood flow (CBF) is associated with higher regional white matter hyperintensity volume. Increasing amyloid burden is associated with decreasing CBF in the occipital lobe. MTL CBF and amyloid are synergistically associated with EC tau., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Here we highlight the Alzheimer's Association's role since its inception, as a strategic collaborator with National Institutes of Health-National Institute on Aging in the development of the modern era of the Alzheimer's Movement and in making Alzheimer's disease (AD) a national priority in the United States by developing several initiatives to advance knowledge about the cause, diagnosis, and treatment of dementia. Among these collaborative undertakings, the Alzheimer's Disease Neuroimaging Initiative (ADNI) is an exemplary case, launched with groundwork by the Neuroimaging Working Group sponsored by the Association's Ronald and Nancy Reagan Research Institute on AD. The unique contribution of the Association to the development of ADNI includes participation as a member of ADNI's Private Partner Scientific Board and involvement in developing an AD biomarker standardization and validation subproject, which has led to a conceptual shift in the field to define AD based on its underlying biology. Furthermore, the creation of Worldwide ADNI (WW-ADNI) is highlighted, underscoring the global impact of these efforts. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a keystone undertaking in the evolving landscape of Alzheimer's disease (AD) research, and is now in its fourth iteration. The Alzheimer's Association has partnered with ADNI since its inception. ADNI 4 and the Association continue to collaborate, ensuring representation within the study population., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Alzheimer's disease (AD) severely impacts the lives of many patients and their families. Predicting the progression of the disease from the early stage of mild cognitive impairment (MCI) is of substantial value for treatment, medical research and clinical trials. In this paper, we propose a novel dual attention network to classify progressive MCI (pMCI) and stable MCI (sMCI) using both magnetic resonance imaging (MRI) and neurocognitive metadata. A 3D CNN ShuffleNet V2 model is used as the network backbone to extract MRI image features. Then, neurocognitive metadata is used to guide the spatial attention mechanism to steer the model to focus attention on the most discriminative regions of the brain. In contrast to traditional fusion methods, we propose a ViT based self attention fusion mechanism to fuse the neurocognitive metadata with the 3D CNN feature maps. The experimental results show that our proposed model achieves an accuracy, AUC, and sensitivity of 81.34%, 0.874, and 0.85 respectively using 5-fold cross validation evaluation. A comprehensive experimental study shows our proposed approach significantly outperforms all previous methods for MCI progression classification. In addition, an ablation study shows both fusion methods contribute to the high final performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Alzheimer's disease is associated both with imbalances in Al 3+ production and changes in viscosity in cells. Their simultaneous measurement could therefore provide valuable insights into Alzheimer's disease pathology. Their simultaneous measurement would therefore be of great value in investigating the pathological mechanism of Alzheimer's disease. We designed a fluorescent probe YM2T with AIE effect that is capable of selectively responding to Al 3+ by fluorescence colormetrics and to viscosity by fluorescence "turn on" modes. Additionally, Al 3+ and viscosity were simultaneously detected in PC12 cells using the low cytotoxic probe YM2T via blue and green fluorescence channels. More importantly, the YM2T probe was used to image mice with AD. Hence, the YM2T probe shows potential as a useful molecular instrument for studying the pathological impact of Al 3+ and viscosity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: Anti-amyloid antibody therapies such as lecanemab are increasingly being used to treat Alzheimer's disease (AD). These therapies are associated with a high rate of amyloid-related imaging abnormalities (ARIA)., Methods: We review the case history of a patient who developed ARIA associated with lecanemab treatment., Results: In addition to microhemorrhages and cerebral edema that are recognized features of ARIA, the patient developed several ischemic strokes. The patient also experienced frequent electrographic seizures without overt clinical seizures. The patient demonstrated clinical and radiographic improvement after steroid treatment., Discussion: Our case suggests that ischemic strokes may be a feature of ARIA and highlights the importance of having a high clinical suspicion for seizures in ARIA. As anti-amyloid therapies are likely going to be increasingly used to treat AD, it is important to appreciate the spectrum of clinical and radiographic findings that can result as side effects from this class of therapies., Highlights: We report a patient who developed severe amyloid-related imaging abnormalities (ARIA) after treatment with lecanemab. Our report suggests that ischemic strokes may be a novel imaging feature of ARIA. Our report highlights the need for high clinical suspicion for seizures in ARIA., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)