Alzheimer's Research & Therapy

BackgroundAdvance care planning (ACP) and advance directives (AD) are tools for supporting person-centered decision-making. In dementia care, the progression of cognitive decline, complex family dynamics and variability in healthcare systems pose unique challenges to effective ACP/AD implementation for people with dementia (PWD). MethodsWe conducted a scoping review of the literature related to ACP/AD in dementia care between 2014-2024. Studies were screened and thematically analyzed to identify current approaches, gaps and recommendations for dementia-specific ACP/AD. We identified key stakeholders involved in decision-making and highlighted procedural components for ACP/AD according to stakeholder groups. ResultsForty studies were included. Key stakeholders included healthcare professionals (HCPs); family members and caregivers; PWD; dyads (PWD and their caregivers); the broader public; policymakers; and researchers. Prominent findings included: the role and training of HCPs; educational and decision-support needs; early and ongoing engagement of PWD; development and evaluation of dementia-specific tools; ethical and procedural challenges in end-of-life decision-making; and the importance of outreach and cultural sensitivity. Promising interventions include structured communication models, psychoeducational programs and tools, although few have been fully adapted for dementia. ConclusionDementia-specific ACP/AD require a relational, flexible and ethically grounded approach that evolves with the individuals condition. While ACP/AD should reflect the autonomous preferences of the PWD, during late-stage dementia, shared decision-making becomes central to providing care that aligns with the persons goals and preferences. Future research should focus on inclusive tools and training; timing and process facilitation; and public health strategies to improve access and equity.

BackgroundBiological subtypes in Alzheimers disease (AD), originally identified on neuropathological data, have been translated to in vivo biomarkers such as structural magnetic resonance imaging (sMRI) and positron emission tomography (PET), to disentangle the heterogeneity within AD. Although there is methodological variability across studies, comparable characteristics of subtypes are reported at the group level. In this study, we investigated whether group-level similarities translate to individual-level agreement across subtyping methods, in a head-to-head context. MethodsWe compared five previously published subtyping methods. Firstly, we validated the subtyping methods in 89 amyloid-beta positive (A{beta}+) AD dementia patients (reference group: 70 A{beta}-healthy individuals; HC) using sMRI. Secondly, we extended and applied the subtyping methods to 53 A{beta}+ prodromal AD and 30 A{beta}+ AD dementia patients (reference group: 200 A{beta}-HC) using both sMRI and tau PET. Subtyping methods were implemented as outlined in each original study. Group-level and individual-level comparisons across methods were performed. ResultsEach individual method was replicated and the proof-of-concept was established. All methods captured subtypes with similar patterns of demographic and clinical characteristics, and with similar maps of cortical thinning and tau PET uptake, at the group level. However, large disagreements were found at the individual level. ConclusionsAlthough characteristics of subtypes may be comparable at the group level, there is a large disagreement at the individual level across subtyping methods. Therefore, there is an urgent need for consensus and harmonization across subtyping methods. We call for establishment of an open benchmarking framework to overcome this problem.

Understanding tau pathology progression across the Alzheimers disease (AD) continuum is critical for diagnosis and stratification. This study examined how age of onset and disease stage influence regional tau deposition using [{superscript 1}F]RO948-PET, and its relationship with plasma biomarkers, cognition, and cortical atrophy. In total, 57 participants underwent tau-PET, MRI, blood sampling, and neuropsychological testing: 39 patients with MCI (A{beta}-/A{beta}+) or AD, and 18 cognitively normal controls. The MCI A{beta}+ and AD groups were further divided into early-onset (EOAD, <65y) and late-onset (LOAD, >65y) subgroups. MCI A{beta}+ patients showed early tau accumulation in medial-temporal regions, extending to inferior-temporal cortex. MCI-EOAD exhibited more advanced neocortical tau binding, while MCI-LOAD showed intermediate lateral temporal involvement. In AD, EOAD patients had higher parietal tau burden than LOAD. Plasma biomarkers (p-tau181, p-tau217, p-tau231, GFAP, NFL) were elevated in MCI A{beta}+ and AD. Plasma p-tau217 showed strong correlations with tau-PET in medial and inferior temporal regions, with weaker correlations in neocortical areas. EOAD showed non-linear tau-PET/p-tau217 associations, contrasting with LOADs linear pattern. Tau-PET was negatively correlated with global cognition and executive function, while p-tau217 better reflected early episodic memory decline. Both tau measures correlated with cortical thinning, especially in the entorhinal cortex. These findings highlight [{superscript 1}F]RO948-PETs sensitivity in detecting early tau pathology and superiority in capturing individual differences in tau burden, particularly in advanced stages where plasma biomarkers plateaued. Tau-PET demonstrated superior resolution of disease progression and individual variability, reinforcing its value as a prognostic biomarker and a critical tool for patient stratification in clinical trials. One Sentence Summary[{superscript 1}F]RO948 tau-PET detects early tau pathology and onset-related patterns, outperforming plasma biomarkers in tracking Alzheimers progression.

AimsAmyloid-related imaging abnormalities (ARIA) have hampered clinical trials and therapeutic use of amyloid-{beta} (A{beta}) immunotherapy for Alzheimers disease (AD), with the cause of the white matter oedema (ARIA-E) unknown. Aquaporin 4 (AQP4), present in astrocyte endfeet, controls water flow across the blood-brain barrier. Experimental studies suggest that as A{beta} plaques are cleared following immunotherapy, capillary angiopathy (capCAA) increases, displacing astrocyte endfeet allowing influx of extracellular water (oedema). We sought neuropathological evidence for this mechanism in immunised AD patients. MethodsBrains of 16 Alzheimers patients immunised against A{beta}42 (iAD, AN1792, Elan Pharmaceuticals) and 28 unimmunized Alzheimers (cAD) cases were immunolabelled and quantified for A{beta}42 and AQP4. ResultsCapCAA was 3.5 times higher in iAD (p=0.009). No difference between the groups was identified in the proportion of capillaries wrapped by AQP4 or AQP4 protein load. However, capCAA in iAD negatively correlated with AQP4 load (r = -0.498, p<0.001), suggesting disturbance of AQP4 in presence of capCAA. ConclusionsAfter A{beta} immunotherapy, capCAA was increased, likely reflecting the drainage of soluble A{beta} towards the vasculature and providing a potential mechanism to disrupt AQP4-containing astrocyte endfeet, resulting in ARIA-E. We did not identify alterations in AQP4, potentially because of limitations in the timing of the post-mortem analysis. Given the recent licencing of A{beta} immunotherapy, the field must prioritise obtaining neuropathological correlates of ARIA to explore its mechanisms further.

INTRODUCTIONWe investigated the presence of neurofibrillary tangles (NFT) and their association with amyloid plaques and fluid biomarkers of Alzheimers disease (AD) in cognitively unimpaired individuals (CU). METHODSNinety-nine CU individuals from the ALFA+ cohort underwent tau PET using 18F-RO-948, amyloid PET, MRI, cognitive assessments, and (CSF)/plasma biomarkers. Tau PET SUVRs calculated in Braak regions, and associations with AD biomarkers were assessed. Thresholds for positivity were derived, and ROC analysis evaluated biomarker accuracy in predicting tau PET positivity. RESULTSNine cases were Braak-I/II positive, five Braak-III/IV, and one Braak-V/VI. For CSF biomarkers, the strongest associations with Braak-I/II reached r=0.58 [95%CI:0.26-0.79, p<0.001, CSF ptau217] and, for plasma, r=0.25 [95%CI:0.03-0.45, p=0.01, plasma ptau181/A{beta}42], but showed low PPVs [0.09-0.33] for Braak-I/II positivity. DISCUSSION18F-RO-948 PET was capable of detecting positive cases in the earliest preclinical AD stages. The studied fluid biomarkers alone showed limited accuracy for screening CU individuals for tau PET positivity.

BackgroundFocused ultrasound (FUS) in combination with microbubbles has recently shown great promise in facilitating blood-brain barrier (BBB) opening for drug delivery and immunotherapy in Alzheimers disease (AD). However, it is currently limited to systems integrated within the MRI suites or requiring post-surgical implants, thus restricting its widespread clinical adoption. In this pilot study, we investigate the clinical safety and feasibility of a portable, non-invasive neuronavigation-guided FUS (NgFUS) system with integrated real-time 2-D microbubble cavitation mapping. MethodsA phase 1 clinical study with mild to moderate AD patients (N=6) underwent a single session of microbubble-mediated NgFUS to induce transient BBB opening (BBBO). Microbubble activity under FUS was monitored with real-time 2-D cavitation maps and dosing to ensure the efficacy and safety of the NgFUS treatment. Post-operative MRI was used for BBB opening and closure confirmation as well as safety assessment. Changes in AD biomarker levels in both blood serum and extracellular vesicles (EVs) were evaluated, while changes in amyloid-beta (A{beta}) load in the brain were assessed through 18F-Florbetapir PET. ResultsBBBO was achieved in 5 out of 6 subjects with an average volume of 983{+/-}626 mm3 following FUS at the right frontal lobe both in white and gray matter regions. The outpatient treatment was completed within 34.8{+/-}10.7 min. Cavitation dose significantly correlated with the BBBO volume (R2>0.9, N=4), demonstrating the portable NgFUS systems capability of predicting opening volumes. The cavitation maps co-localized closely with the BBBO location, representing the first report of real-time transcranial 2-D cavitation mapping in the human brain. Larger opening volumes correlated with increased levels of AD biomarkers, including A{beta}42 (R2=0.74), Tau (R2=0.95), and P-Tau181 (R2=0.86), assayed in serum-derived EVs sampled 3 days after FUS (N=5). From PET scans, subjects showed a lower A{beta} load increase in the treated frontal lobe region compared to the contralateral region. Reduction in asymmetry standardized uptake value ratios (SUVR) correlated with the cavitation dose (R2>0.9, N=3). Clinical changes in the mini-mental state examination over 6 months were within the expected range of cognitive decline with no additional changes observed as a result of FUS. ConclusionWe showed the safety and feasibility of this cost-effective and time-efficient portable NgFUS treatment for BBBO in AD patients with the first demonstration of real-time 2-D cavitation mapping. The cavitation dose correlated with BBBO volume, a slowed increase in pathology, and serum detection of AD proteins. Our study highlights the potential for accessible FUS treatment in AD, with or without drug delivery.

Rationale and ObjectivesAlthough neuroimaging studies suggest that the cerebral ventricle is independently associated with APOE {varepsilon}4, cerebrospinal fluid (CSF) biomarkers, and neuropsychological scores in aging and Alzheimers disease (AD), there is no formal synthesis of these findings. We summarized the association of ventricular changes with APOE {varepsilon}4, CSF biomarkers, and neuropsychological measures. Materials and MethodsThe Preferred Reporting Items for Systematic reviews and Meta-Analyses guideline was used. PubMed, Scopus, Ovid, Cochrane, and grey literature were searched, and assessment of eligible articles was conducted using the Newcastle-Ottawa Scale. Results24 studies met the inclusion criteria. Progressive ventricular volume is increased in AD patients at an average volume of 4.4 - 4.7 cm3/ year compared to average volumes of 2.7 - 2.9 cm3/ year and 1.1 - 1.4 cm3/year for patients with MCI and healthy controls (HCs) respectively. The ventricular volume is estimated to increase by 1.7 cm3/year for progression from MCI to AD. APOE {varepsilon}4 is an independent risk factor for ventricular enlargement in aging and dementia, with AD patients most affected. The combination of CSF A{beta}42 with ventricular volume compared to tau is more robust, for tracking the progression of the AD continuum. Further, the combination of ventricular volume with mini-mental state examination (MMSE) scores is the most robust for differentiating AD and MCI from HCs and tracking the progression of the disease. ConclusionThe combination of ventricular volume with APOE {varepsilon}4, CSF A{beta}42, and MMSE scores independently may be potentially useful biomarkers for differentiating and tracking the progression of AD.

18F-flortaucipir-PET received FDA approval to visualize tauopathy in the brains of adult patients with cognitive impairment being evaluated for Alzheimer disease (AD). However, manufacturers guidelines for the visual interpretation of 18F-flortaucipir-PET differs greatly from how 18F-flortaucipir-PET has been measured in research settings using standardized uptake value ratios (SUVRs). How visual interpretation relates to 18F-flortaucipir-PET SUVR, CSF biomarkers, or longitudinal clinical assessment is not well understood. Here we compare these various diagnostic methods in participants enrolled in studies of aging and memory (n=189, of whom 23 were cognitively impaired). Visual interpretation had high agreement with SUVR (98.4%); discordant participants had hemorrhagic infarcts or atypical AD tauopathies. Visual interpretation had moderate agreement with CSF p-tau181 (86.1%). Two participants demonstrated 18F-flortaucipir uptake from meningiomas. Visual interpretation could not predict follow-up clinical assessment in 9.52% of cases. We conclude that close association between AD tauopathy and clinical onset in group-level studies does not always hold at the individual level, with discrepancies arising from atypical AD, vascular dementia, or frontotemporal dementia. A better understanding of relationships across imaging, CSF biomarkers, and clinical assessment is needed to provide appropriate diagnoses for these individuals.

Predicting the likelihood of developing Alzheimers disease (AD) dementia in at-risk individuals is important for the design of and optimal recruitment for clinical trials of disease-modifying therapies. Machine learning (ML) has been shown to excel in this task; however, there remains a lack of models developed specifically for the preclinical AD population, who display early signs of abnormal brain amyloidosis but remain cognitively unimpaired. Here, we trained and evaluated ML classifiers to predict whether individuals with preclinical AD will progress to mild cognitive impairment or dementia within multiple fixed time windows, ranging from one to five years. Models were trained on regional imaging features extracted from amyloid positron emission tomography and magnetic resonance imaging pooled across seven independent sites and from two amyloid radiotracers ([18F]-florbetapir and [11C]-Pittsburgh-compound-B). Out-of-sample generalizability was evaluated via a leave-one-site-out and leave-one-tracer-out cross-validation. Classifiers achieved an out-of-sample receiver operating characteristic area-under-the-curve of 0.66 or greater when applied to all except one hold-out sites and 0.72 or greater when applied to each hold-out radiotracer. Additionally, when applying our models in a retroactive cohort enrichment analysis on A4 clinical trial data, we observed increased statistical power of detecting differences in amyloid accumulation between placebo and treatment arms after enrichment by ML stratifications. As emerging investigations of new disease-modifying therapies for AD increasingly focus on asymptomatic, preclinical populations, our findings underscore the potential applicability of ML-based patient stratification for recruiting more homogeneous cohorts and improving statistical power for detecting treatment effects for future clinical trials. HighlightsO_LIMachine learning can predict future cognitive impairment in preclinical Alzheimers C_LIO_LIModels achieved high out-of-sample ROC-AUC on external sites and PET tracers C_LIO_LIModels were able to distinguish cognitively stable from decliners in the A4 cohort C_LIO_LIML cohort enrichment enhanced secondary treatment effect detection in the A4 cohort C_LI

INTRODUCTIONWidespread language complaints in the cognitively unimpaired (CU) may reflect Alzheimers Disease (AD) pathology and future objective impairments. METHODS138 CU, 45 mild cognitive impairment and 28 dementia participants from the TRIAD cohort underwent 18F-MK-6240 tau-PET and 18F-AZD-4694 amyloid-PET. Word-finding complaints, confrontation naming, semantic and phonemic fluency and word-knowledge were evaluated. Covariance, direct and stepwise discriminant, and voxel-wise regression analyses were conducted. RESULTSWord-finding complaints appeared in early tau stages (Braak 1-2), followed by naming difficulties (Braak 3-4), and widespread language impairments in later stages (Braak 5-6). Complaints over forgetting the names of objects, naming, and APOE significantly improved classification of early AD pathology. In CU, complaints over forgetting names of objects were linked to left fusiform and inferior temporal gyri tau accumulation. DISCUSSIONLanguage measures are useful in detecting and tracking AD-related pathophysiologies. Results encourage refinement of clinical tools for early detection and disease monitoring. HighlightsLanguage decline parallels tau buildup across PET-based Braak stages of AD. Subjective anomia marks earliest tau-related language symptom (Braak 1-2). Objective naming deficits emerge in the middle tau spread stages (Braak 3-4). Advanced tau spread reflects significant and widespread language impairments. Word-finding complaints correlate with left fusiform and inferior temporal tau. Research in contextSystematic review: The literature was reviewed using traditional sources. The core biological definition of Alzheimers disease (AD) has recently been linked to its defining cognitive clinical features of episodic memory impairments. Widespread subjective language complaints amongst cognitively unimpaired (CU) older adults and objective language impairments observed across the AD continuum suggests these measures and the further bridging of biological and clinical definitions of AD could play a critical, cost-effective role in disease detection and monitoring. Interpretation: Results extend to tau previous findings describing language changes in AD and related to amyloid status and grey-matter atrophy. They also establish the likely staging of language impairments across the biological AD continuum. Future directions: The manuscript contextualises the use of subjective word-finding complaints, alongside genetic risks to significantly enhance the prediction of underlying AD related pathology in CU. Languages measures used in clinical practice remain limited however and better test should be utilized and developed.

BACKGROUNDThe heterogeneity within Alzheimers disease (AD) seriously challenges the development of disease modifying treatments. We investigated volume of the basal forebrain, hippocampus, and precuneus in atrophy subtypes of AD, and explored the relevance of subtype stratification in a clinical trial on encapsulated cell biodelivery (ECB) of nerve growth factor (NGF) to the basal forebrain. METHODSStructural MRI data was collected for 90 amyloid-positive patients and 69 amyloid-negative healthy controls at baseline, 6-, 12-, and 24-month follow-up. The effect of the NGF treatment was investigated in 10 biopsy verified AD patients with structural MRI data at baseline and at 6- or 12-months follow-up. Patients were classified as typical, limbic-predominant, hippocampal-sparing, or minimal atrophy AD, using a validated visual assessment method. Volumetric analyses were performed using a region-of-interest approach. RESULTSAll AD subtypes showed reduced basal forebrain volume as compared with controls. Limbic-predominant subtype showed fastest basal forebrain atrophy rate, whereas minimal atrophy subtype did not show significant volume decline over time. Atrophy rates of hippocampus and precuneus also differed across subtypes. The NGF treatment seemed to slow the rate of atrophy in precuneus and hippocampus, particularly in the hippocampal-sparing AD subtype. CONCLUSIONSThe cholinergic system is differentially affected in distinct atrophy subtypes of AD, possibly contributing to their differential response to cholinergic treatment. Our findings suggest that future clinical trials should target specific subtypes of AD, or at least report treatment effects stratifying by subtype. Trial registrationClinicalTrials.gov identifier: NCT01163825. Registered 14 July 2010 - https://clinicaltrials.gov/ct2/show/NCT01163825

BACKGROUNDAmyloid-beta(A{beta})-plaques accumulate in non-demented individuals, particularly at advanced ages. The unclear association between A{beta}-pathology and cognition in elderly raises the question whether A{beta}-pathology should be considered a benign consequence of aging. METHODSPost-mortem brains of 95 centenarians and 27 Alzheimers disease(AD) patients were evaluated for A{beta}-plaque distribution according to the Thal phase and quantitative A{beta}-load in the neocortex. For centenarians, A{beta}-pathology was correlated to APOE-genotype and performance on 12 cognitive tests administered shortly before death. FINDINGSWhile 35% of centenarians exhibited A{beta}-loads similar to AD patients, cortical A{beta}-load was limited in 65% of centenarians, some of which had the highest Thal phase. Cortical A{beta}-load, as opposed to Thal phase, associated with APOE-genotype and cognitive performance in centenarians. DISCUSSIONDespite increasing A{beta}-accumulation in various brain regions with age, actual A{beta}-loads remain low in cognitively healthy centenarians. Therefore, A{beta}-pathology in the oldest-old may not be considered a benign consequence of aging.

Staging the severity of Alzheimers disease pathology using biomarkers is central to early detection and therapeutic trial design. In this cross-sectional study, we standardized amyloid and tau PET data across multiple cohorts to characterize the frequency of amyloid and tau PET-based stages across the clinical continuum. We examined amyloid and tau severity in 10,396 participants (mean [SD] age, 71.9 [7.1] years) with amyloid PET imaging and a subset (n = 3,295) with tau PET imaging. Clinical stage was defined using cohort-specific criteria and categorized as cognitively unimpaired (n = 7,764), mild cognitive impairment (n = 1,480), or dementia (n = 1,152). Amyloid positivity was defined as [&ge;]25 centiloids and amyloid severity was staged using centiloids bins (e.g., <10, 10-24, 25-49, 50-74, 75-99, [&ge;]100). Tau PET severity was staged using a hierarchical Braak staging schema (e.g., T-, T12+, T34+, T56+), and combined with amyloid status to operationalize PET-based Alzheimers disease biological stages (e.g., Stage A: A+T-; Stage B: A+T12+; Stage C: A+T34+; Stage D: A+T56+). The cumulative probabilities of PET-based stages were estimated using ordinal logistic regression models. In cognitively unimpaired individuals, the frequency of amyloid levels [&ge;]10 centiloids increased with age. Similarly, amyloid levels [&ge;]25 centiloids increased with age in mild cognitive impairment. Overall, elevated amyloid ([&ge;]25 CL) was more likely with increasing age among non-demented individuals. By contrast, this age association was attenuated in dementia where severe amyloid burden (e.g., [&ge;]100 CL) was most common. In the tau PET subsample (n = 3,295), there was a three-way interaction between amyloid, age, and clinical impairment on likelihood of tau severity. Both higher amyloid and greater clinical impairment were associated with increased tau severity; however, the strength and direction of these associations varied with age. At lower amyloid levels, the odds of tau severity increased with older age among cognitively unimpaired and mild cognitive impairment. Conversely, at higher amyloid levels, the odds of higher tau severity (e.g., T56+) decreased with older age in mild cognitive impairment and dementia. A similar age-related pattern was observed in the frequency of biological stages (n = 1,154), where Stage D (e.g., A+T56+) was most frequent in younger individuals with dementia. These findings underscore the dual importance of amyloid and tau PET severity as biomarkers for staging and characterizing Alzheimers disease progression. They also demonstrate the feasibility of applying PET-based staging frameworks for the diagnosis of Alzheimers disease across multiple tracers and cohorts.

Alzheimers disease (AD) Resilient individuals are characterized by having a degree of amyloid plaques at level with that of demented individuals, but a reduced amount of abnormal neurofibrillary protein "tangles" (NFTs). NFTs, also known to be upregulated under hypoxic conditions, become clinically relevant when involved in the stratum radiatum. In this paper, we show this region and more to have significant increases of hypoxic adaptive protein, HIF-2, within AD resilient cases. Pericyte staining was present in the stratum lacunosum and radiatum of all cases affected by AD pathology (n = 4) but in AD resilient cases were increased by 12-fold (n=3) p<.0001. No staining was detected in normal cases (n=2). HIF-2 was also only present in hippocampal neuronal nuclei of AD resilient cases, including the dentate gyrus and CA1. Cytoplasmic staining of pyramidal neurons within the subiculum was seen in all cases affected by AD pathology. The intensity of HIF-2 appears to be specific to known regions of protection in AD resilience and to increase on a gradient that corresponds to protection against dementia. These results also highlight the stratum lacunosum and radiatum as regions critically impacted by hypoxic insult among AD cases. SignificanceHIF-2 directly regulates expression of erythropoietin (EPO), a neuroprotective glycoprotein that in brain pericytes is completely dependent upon activation of HIF-2. To date, only indirect evidence exists that shows that brain pericyte-derived EPO can reach the bloodstream via HIF-2 expression (Urrutia et al, 2016). In this study, we provide novel preliminary findings that directly show HIF-2 expression in pericytes of human brains. Additionally, its localization is specific to the CA1 of the hippocampus, a region critical for hypoxic adaptation and the progression of Alzheimers disease. Finally, we present evidence of neuronal expression of HIF-2 in other critical regions of protection within AD resilient cases.

BackgroundPositron emission tomography (PET) imaging has greatly improved the diagnosis and monitoring of Alzheimers disease (AD). The recently developed neuroinformatic field is expanding analytical and computational strategies to study multimodal neuroscience data. One approach is integrating PET imaging and omics to provide new insights into AD pathophysiology. MethodsHippocampal and blood transcriptomic data of cognitively unimpaired (CU) and cognitively impaired (CI) individuals were obtained from Gene Expression Omnibus (GEO) databases and the Alzheimers Disease Neuroimaging Initiative (ADNI). We used the differentially expressed genes (DEGs) from these datasets to implement a modular dimension reduction approach based on Gene Ontology (GO) and reverse engineering of transcriptional networks centered on transcription factors (TF). GO clusters and regulatory units of TF were selected to undergo integration with [18F]Fluorodeoxyglucose ([18F]FDG)-PET images using voxel-wise linear regression models adjusted for age, gender, years of education, and APOE {varepsilon}4 status. ResultsThe GO semantic similarity resulted in 16 GO clusters enriched with overlapping DEGs in blood and the brain. Voxel-wise analysis revealed a strong association between the cluster related to the regulation of protein serine/threonine kinase activity and the [18F]FDG-PET signal in the brain. The master regulator analysis showed 61 regulatory units of TF significantly enriched with DEGs. The voxel-wise analysis of these regulons showed that zinc-finger-related regulatory units had the closest association with brain glucose metabolism. ConclusionWe identified multiple biological processes and regulatory units of TF associated with [18F]FDG-PET metabolism in the brain of individuals across the aging and AD clinical spectrum. Furthermore, the prominent enrichment of protein serine/threonine kinase activity-related GO cluster and the zinc-finger-related regulatory units highlight the potential gene signatures associated with changes in glucose metabolism due to AD pathology.

Alzheimers disease (AD) presents a significant societal challenge, with no current cure and an increasing prevalence among older adults. This study addresses the pressing need for early detection by harnessing the potential of machine learning applied to longitudinal MRI data. The dataset, sourced from the Open Access Series of Imaging Studies (OASIS) project, comprises MRI records of 150 subjects aged 60 to 96, each scanned at least once. Notably, 72 subjects were classified as Nondemented, 64 as Demented, and 14 underwent a transition from Nondemented to Demented, forming the Converted category. What we propose is to develop a machine learning sound model capable of predicting the progression of mild cognitive impairment, leveraging key biomarkers extracted from MRI data. The chosen biomarkers include years of education (EDUC), socioeconomic status (SES), Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF). Prior work in the field is referenced, highlighting studies that predominantly focused on raw MRI data analysis. In contrast, this study introduces a unique approach by utilizing a curated set of biomarkers, allowing for a more targeted and potentially interpretable model. Machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest Classifier, and AdaBoost are employed, with performance measured using established metrics. Information about severity and state are stored during the EADDLS module and used for ADmod. ADmod uses the stored MRI data during the EADDLS module to model the growth of amyloid {beta} build-up in the brain using convolution, resulting in both generalizable approaches and patient-specific approaches. There have been numerous mathematical instantiations to model amyloid {beta} build-up using partial differential equations (or PDEs), these however have remained unincorporated due to prolonged runtimes and storage limitations along with those of pre-set conditions. We propose a novel amyloid {beta} growth model using deep encoder-decoder networks in conjunction with convolution. The study contributes to the growing body of research in early Alzheimers detection, offering insights, results, and a discussion of limitations. The conclusion outlines a unique approach, emphasizes the practical implementation of the proposed model, acknowledges limitations, and suggests avenues for further research. Early detection of AD can significantly better the patients quality of care and lead to future preventative or risk assessment measures.

BackgroundAccessible datasets are of fundamental importance to the advancement of Alzheimers disease (AD) research. The AddNeuroMed consortium conducted a longitudinal observational cohort study with the aim to discover AD biomarkers. During this study, a broad selection of data modalities was measured including clinical assessments, magnetic resonance imaging, genotyping, transcriptomic profiling and blood plasma proteomics. Some of the collected data were shared with third-party researchers. However, this data was incomplete, erroneous and lacking in interoperability. MethodsWe systematically addressed several limitations of the originally shared data and provide additional unreleased data to enhance the patient-level dataset. ResultsIn this work, we publish and describe ANMerge, a new version of the AddNeuroMed dataset. ANMerge includes multimodal data from 1702 study participants and is accessible to the research community via a centralized portal. ConclusionsANMerge is an information rich patient-level data resource that can serve as a discovery and validation cohort for data-driven AD research, such as for example machine learning and artificial intelligence approaches. ANMerge can be downloaded here: https://doi.org/10.7303/syn22252881

Alzheimers disease (AD) is the most common form of age-related dementia, leading to a decline in memory, reasoning, and social skills. While numerous studies have investigated the genetic risk factors associated with AD, less attention has been given to identifying a brain imaging-based measure of AD risk. This study introduces a novel approach to assess mild cognitive impairment MCI, as a stage before AD, risk using neuroimaging data, referred to as a brain-wide risk score (BRS), which incorporates multimodal brain imaging. To begin, we first categorized participants from the Open Access Series of Imaging Studies (OASIS)-3 cohort into two groups: controls (CN) and individuals with MCI. Next, we computed structure and functional imaging features from all the OASIS data as well as all the UK Biobank data. For resting functional magnetic resonance imaging (fMRI) data, we computed functional network connectivity (FNC) matrices using fully automated spatially constrained independent component analysis. For structural MRI data we computed gray matter (GM) segmentation maps. We then evaluated the similarity between each participants neuroimaging features from the UK Biobank and the difference in the average of those features between CN individuals and those with MCI, which we refer to as the brain-wide risk score (BRS). Both GM and FNC features were utilized in determining the BRS. We first evaluated the differences in the distribution of the BRS for CN vs MCI within the OASIS-3 (using OASIS-3 as the reference group). Next, we evaluated the BRS in the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort (using OASIS-3 as the reference group), showing that the BRS can differentiate MCI from CN in an independent data set. Subsequently, using the sMRI BRS, we identified 10 distinct subgroups and similarly, we identified another set of 10 subgroups using the FNC BRS. For sMRI and FNC we observed results that mutually validate each other, with certain aspects being complementary. For the unimodal analysis, sMRI provides greater differentiation between MCI and CN individuals than the fMRI data, consistent with prior work. Additionally, by utilizing a multimodal BRS approach, which combines both GM and FNC assessments, we identified two groups of subjects using the multimodal BRS scores. One group exhibits high MCI risk with both negative GM and FNC BRS, while the other shows low MCI risk with both positive GM and FNC BRS. Moreover, in the UKBB we have 46 participants diagnosed with AD showed FNC and GM patterns similar to those in high-risk groups, defined in both unimodal and multimodal BRS. Finally, to ensure the reproducibility of our findings, we conducted a validation analysis using the ADNI as an additional reference dataset and repeated the above analysis. The results were consistently replicated across different reference groups, highlighting the potential of FNC and sMRI-based BRS in early Alzheimers detection.

BackgroundAlzheimers disease (AD) is the most prevalent form of dementia, exerting substantial personal and societal impacts. The apolipoprotein E (APOE) {varepsilon}4 allele is a known genetic factor that increases the risk of AD, contributing to more severe brain atrophy and exacerbated symptoms. PurposeWe aim to provide a comprehensive review of the impacts of the APOE {varepsilon}4 allele on brain atrophy in AD and mild cognitive impairment (MCI) as a transitional stage of AD. MethodsWe performed a coordinate-based meta-analysis of voxel-based morphometry (VBM) studies to identify the patterns of grey matter atrophy in APOE {varepsilon}4 carriers vs. non-carriers. We obtained coordinate-based structural magnetic resonance imaging (MRI) data for 1135 individuals from 12 studies on PubMed and Google Scholar that met our inclusion criteria. ResultsWe found significant atrophy in the hippocampus and parahippocampus of APOE {varepsilon}4 carriers compared to non-carriers, especially within the AD and MCI groups, while healthy controls showed no significant atrophy in these regions. ConclusionOur meta-analysis sheds light on the significant link between the APOE {varepsilon}4 allele and hippocampal atrophy in both AD and MCI, emphasizing the alleles critical influence on neurodegeneration, especially in the hippocampus. Our findings contribute to the understanding of the diseases pathology, potentially facilitating progress in early detection, targeted interventions, and personalized care strategies for individuals with the APOE {varepsilon}4 allele who are at risk for Alzheimers Disease.

High-throughput proteomic platforms have a crucial role in identifying novel Alzheimers disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan(R) 7k) and antibody-based (Olink(R) Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and interplatform reproducibility was evaluated through correlations between two independent SomaScan(R) assays analyzing the same samples and between SomaScan(R) and Olink(R) results. Our 12-category metric of reproducibility combining both correlation analyses identified 2,428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels.