Early Diagnosis Of AD Research Articles

A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer's Disease Diagnosis: Addressing Incomplete Modalities.

This paper was motivated by the challenge of early AD diagnosis, particularly in scenarios when clinicians encounter varied availability of patient imaging data, such as MRI and PET scans, often constrained by cost or accessibility issues. We propose an incomplete multimodal learning framework that produces tailored models for patients with only MRI and patients with both MRI and PET. This approach improves the accuracy and effectiveness of early AD diagnosis, especially when imaging resources are limited, via bi-directional knowledge transfer. We introduced a teacher model that prioritizes extracting common information between different modalities, significantly enhancing the student model's learning process. This paper includes theoretical analysis, simulation study, and realworld case study to illustrate the method's promising potential in early AD detection. However, practitioners should be mindful of the complexities involved in model tuning. Future work will focus on improving model interpretability and expanding its application. This includes developing methods to discover the key brain regions for predictions, enhancing clinical trust, and extending the framework to incorporate a broader range of imaging modalities, demographic information, and clinical data. These advancements aim to provide a more comprehensive view of patient health and improve diagnostic accuracy across various neurodegenerative diseases.

S2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI.

To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.

A Framework for Best Practices and Readiness in the Advent of Anti-Amyloid Therapy for Early Alzheimer's Disease in Asia.

Advances in biomarker-based diagnostic modalities, recent approval of anti-amyloid monoclonal antibodies for early Alzheimer's disease (AD; mild cognitive impairment or mild dementia due to AD) and late-stage clinical development of other disease-modifying therapies for AD necessitate a significant paradigm shift in the early detection, diagnosis and management of AD. Anti-amyloid monoclonal antibodies target the underlying pathophysiological mechanisms of AD and have demonstrated a significant reduction in the rate of clinical decline in cognitive and functional outcome measures in patients with early AD. With growing recognition of the benefit of early interventions in AD, an increasing number of people may seek diagnosis for their subjective cognitive problems in an already busy medical system. Various factors such as limited examination time, lack of expertise for cognitive assessment and limited access to specialized tests can impact diagnostic accuracy and timely detection of AD. To overcome these challenges, a new model of care will be required. In this paper, we provide practical guidance for institutional readiness for anti-amyloid therapies for early AD in Asia, in terms of best practices for identifying eligible patients and diagnosing them appropriately, safe administration of anti-amyloid monoclonal antibodies and monitoring of treatment, managing potential adverse events such as infusion reactions and amyloid-related imaging abnormalities, and cross-disciplinary collaboration. Education and training will be the cornerstone for the establishment of new pathways of care for the identification of patients with early AD and delivery of anti-amyloid therapies in a safe and efficient manner to eligible patients.

Plasma lipidomics in early APP/PS1 female mouse model and its relationship with brain: Is it affected by the estrous cycle?

BackgroundAlzheimer's disease (AD) is the most prevalent dementia, showing higher incidence in women. Besides, lipids play an essential role in brain, and they could be dysregulated in neurodegeneration. Specifically, impaired plasma lipid levels could predict early AD diagnosis. This work aims to identify the main plasma lipids altered in early AD female mouse model and evaluate their relationship with brain lipidome. Also, the possible involvement of the estrous cycle in lipid metabolism has been evaluated.MethodsPlasma samples of wild-type (n = 10) and APP/PS1 (n = 10) female mice of 5 months of age were collected, processed, and analysed using a lipidomic mass spectrometry-based method. A statistical analysis involving univariate and multivariate approaches was performed to identify significant lipid differences related to AD between groups. Also, cytology tests were conducted to confirm estrous cycle phases.ResultsThree hundred thirty lipids were detected in plasma, 18 of them showed significant differences between groups; specifically, some triacylglycerols, cholesteryl esters, lysophosphatidylcholines, phosphatidylcholines, and ether-linked phosphatidylcholines, increased in early AD; while other phosphatidylcholines, phosphatidylethanolamines, ceramides, and ether-linked phosphatidylethanolamines decreased in early AD. A multivariate approach was developed from some lipid variables, showing high diagnostic indexes (70% sensitivity, 90% specificity, 80% accuracy). From brain and plasma lipidome, some significant correlations were observed, mainly in the glycerophospholipid family. Also, some differences were found in both plasma and brain lipids, according to the estrous cycle phase.ConclusionsTherefore, lipid alterations can be identified in plasma at early AD stages in mice females, with a relationship with brain lipid metabolism for most of the lipid subfamilies, suggesting some lipids as potential AD biomarkers. In addition, the estrous cycle monitoring could be relevant in female studies.

Alzheimer's diagnosis beyond cerebrospinal fluid: Probe-Free Detection of Tau Proteins using MXene based redox systems and molecularly imprinted polymers

Phosphorylated Tau proteins are promising biomarkers for the diagnosis and prognosis of Alzheimer's disease. This study presents a novel voltametric sensor using a vanadium MXene polydopamine (VxPDA) redox active composite and a Tau-441-specific polyaniline molecularly imprinted polymer (PANI MIP) for the sensitive detection of Tau-441 in interstitial fluid (ISF) and plasma. The VxPDA/PANI MIP sensor demonstrates a broad detection range of 5 fg/mL to 5 ng/mL (122 aM/L to 122 pM/L) in ISF without the use of redox mediators, with a lower limit of detection (LOD) of 2.3 fg/mL (60 aM/L). Furthermore, a handheld device utilizing this technology successfully detects Tau-441 in artificial serum with high sensitivity (5 fg/mL to 150 fg/mL (122 aM/L to 366 aM/L)) and specificity within a clinically relevant range. The rapid detection time (∼32 min) and low cost (∼£20/device) of this sensor highlight its potential for minimally invasive, early AD diagnosis in clinical settings. This advancement aims to facilitate a transition away from invasive cerebrospinal fluid (CSF)-based diagnostic techniques for AD.

Profiling of microglial-originated microvesicles to unearthing their lurking potential as potent foreseeable biomarkers for the diagnosis of Alzheimer's disease: A systematic review.

Alzheimer's Disease is a neurodegenerative disease characterized by accumulation of phosphorylated tau and amyloid deposits within the brain tissues in the elderly population. Numerous studies established that amassment of these toxic accretions within the brain tissues initiates neuronal demise and synaptic impairment which becomes the underlying basis for memory loss and cognitive abnormalities in these patients. Hypoxia, oxidative stress, and inflammation are commonly encountered perils in the neuronal milieu that derail the neuron-synapse interactions and maneuver them to undergo apoptosis. A spinoff from neuronal desecration is microglial activation which forms a cardinal role in mounting innate immune defenses for warding off and reversing off toxic stimulus encountered. A potential ramification of microglial activation in this context is assembly, processing and exuding of micro-vesicles into the extracellular space. These micro-vesicles will be packaged with amyloid and tau deposits which accumulate intracellularly within microglial cells secondary to their professional scavenging function. These microglial MVs are prone to seed tau and amyloid beta into the surrounding neuron-synapse framework, thus are implicated in spreading the disease pathology in AD. Therefore, these MVs can be considered as an omen for disease initiation, progression, monitoring as well gauging the treatment response in the clinical AD cohorts. We speculate future research studies to unmask the dormant potential of these microglial MVs as reliable markers for diagnosis, evaluating the disease progression as well as treatment in AD. This will open the door for early diagnosis of AD so as to prioritize management and optimize clinical outcomes..

The pattern of cortical thickness associated with executive dysfunction in MCI and SCC: The MEMENTO cohort

BackgroundThe association between the pattern of cortical thickness (CT) and executive dysfunction (ED) in mild cognitive impairment (MCI) and subjective cognitive complaints (SCC) is still poorly understood. We aimed to investigate the association between CT and ED in a large French cohort (MEMENTO) of 2323 participants with MCI or SCC. MethodsAll participants with available CT and executive function data (verbal fluency and Trail Making Test [TMT]) were selected (n=1924). Linear regressions were performed to determine relationships between executive performance and the brain parenchymal fraction (BPF) and CT using FreeSurfer. ResultsThe global executive function score was related to the BPF (sß: 0.091, P<0.001) and CT in the right supramarginal (sß: 0.060, P=0.041) and right isthmus cingulate (sß: 0.062, P=0.011) regions. Literal verbal fluency was related to the BPF (sß: 0.125, P<0.001) and CT in the left parsorbitalis region (sß: 0.045, P=0.045). Semantic verbal fluency was related to the BPF (sß: 0.101, P<0.001) and CT in the right supramarginal region (sß: 0.061, P=0.042). The time difference between the TMT parts B and A was related to the BPF (sß: 0.048, P=0.045) and CT in the right precuneus (sß: 0.073, P=0.019) and right isthmus cingulate region (sß: 0.054, P=0.032). ConclusionsIn a large clinically based cohort of participants presenting with either MCI or SCC (a potential early stage of Alzheimer's disease [AD]), ED was related to the BPF and CT in the left pars orbitalis, right precuneus, right supramarginal, and right isthmus cingulate regions. This pattern of lesions adds knowledge to the conventional anatomy of ED and could contribute to the early diagnosis of AD.

MSFNet‐2SE: A multi‐scale fusion convolutional network for Alzheimer's disease classification on magnetic resonance images

AbstractAlzheimer's disease (AD) is an irreversible neurodegenerative disease, and the early diagnosis and effective intervention of AD is essential for patients and doctors. Intelligence diagnosis based on magnetic resonance imaging and deep learning has become one of the useful methods for AD identification. To improve the diagnosis effect of AD in early stages, a novel multi‐scale classification model of AD, called MSFNet‐2SE, is proposed. First, a new multi‐scale fusion (MSF) feature extraction module is designed based on the idea of the feature maps split into feature subsets of Res2Net. Second, a channel attention module is embedded into the MSF module through integrating two Squeeze‐and‐Excitation (SE) blocks. Finally, a gradient centralization Adam optimizer is used to improve the model classification performance. Experimental results illustrate that, compared with other available state‐of‐the‐art classification models of AD, the proposed model has excellent classification performance. It is helpful to improve the clinical diagnosis efficiency.

Lipoproteins and metabolites in diagnosing and predicting Alzheimer’s disease using machine learning

BackgroundAlzheimer’s disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD; however, the key factors influencing AD onset remain unclear. This study aimed to analyze the key lipoprotein and metabolite factors influencing AD onset using machine-learning methods. It provides new insights for researchers and medical personnel to understand AD and provides a reference for the early diagnosis, treatment, and early prevention of AD.MethodsA total of 603 participants, including controls and patients with AD with complete lipoprotein and metabolite data from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database between 2005 and 2016, were enrolled. Random forest, Lasso regression, and CatBoost algorithms were employed to rank and filter 213 lipoprotein and metabolite variables. Variables with consistently high importance rankings from any two methods were incorporated into the models. Finally, the variables selected from the three methods, with the participants’ age, sex, and marital status, were used to construct a random forest predictive model.ResultsFourteen lipoprotein and metabolite variables were screened using the three methods, and 17 variables were included in the AD prediction model based on age, sex, and marital status of the participants. The optimal random forest modeling was constructed with “mtry” set to 3 and “ntree” set to 300. The model exhibited an accuracy of 71.01%, a sensitivity of 79.59%, a specificity of 65.28%, and an AUC (95%CI) of 0.724 (0.645–0.804). When Mean Decrease Accuracy and Gini were used to rank the proteins, age, phospholipids to total lipids ratio in intermediate-density lipoproteins (IDL_PL_PCT), and creatinine were among the top five variables.ConclusionsAge, IDL_PL_PCT, and creatinine levels play crucial roles in AD onset. Regular monitoring of lipoproteins and their metabolites in older individuals is significant for early AD diagnosis and prevention.

Interpretable medical deep framework by logits-constraint attention guiding graph-based multi-scale fusion for Alzheimer’s disease analysis

Deep learning using structural MRI has been widely applied to early diagnosis study of Alzheimer’s disease. Among existing methods, attention-based 3D subject-level methods can not only provide diagnosis results but also interpret the significant brain regions, thereby attracting considerable attention. However, the performance of previous attention-based methods might be still restricted by: (i) the gap between attention scores and semantic significant regions; (ii) using only single-scale features or simply fusing multi-scale information by addition or concatenation for classification decision-making. To overcome these two issues, we propose an innovative dual-branch model called LA-GMF, which consists of two major modules: logits-constraint attention (LA) and graph-based multi-scale fusion (GMF). The LA module is designed to guide the model to focus on key areas to enhance the diagnostic performance of local lesions, by reducing the inconsistency between attention scores and class prediction probabilities. Meanwhile, by combining the graph neural network and the self-attention mechanism, the GMF module not only introduces the interaction between patches, but also explores the correlation and complementarity between features at different scales, thereby extracting feature representations more comprehensively. Experiments on the popular ADNI and AIBL datasets validate the potential of our model in boosting early AD diagnosis accuracy. Additionally, our interpretation experiments demonstrate the superior interpretability performance of the proposed method over recent state-of-the-art attention-based methods. Our source codes are released at: https://github.com/nollexu/LA-GMF.

A review on the new age methodologies for early detection of Alzheimer's and Parkinson's disease.

Neurodegenerative diseases (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are often diagnosed late, impeding effective treatment; therefore, early detection is imperative. Modern methodologies can serve a pivotal role in fulfilling the crucial need for timely detection and intervention in this context. Evaluate early detection's significance and summarize key technologies (biomarkers, neuroimaging, AI/ML, genetics, digital health) for enhanced diagnostic strategies in AD and PD. This study employs a focused descriptive review approach, encompassing analysis of peer-reviewed articles and clinical trials from existing literature, to provide a nuanced exploration of the subject matter. This review underscores the efficacy of non-invasive biomarkers, biosensors and emerging promising technologies for advancing early diagnosis of AD and PD. The landscape of early NDD detection has been reshaped by technology, yet challenges persist, encompassing the domains of validation and ethics. A collaborative effort between medical professionals, researchers and technologists is imperative to effectively address and combat NDDs.

Constructing hierarchical attentive functional brain networks for early AD diagnosis

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing FBN in a hierarchical manner for more effective analysis of the complementary diagnostic insights at different scales. To this end, this paper proposes to build hierarchical FBNs adaptively within the Transformer framework. Specifically, a sparse attention-based node-merging module is designed to work alongside the conventional network feature extraction modules in each layer. The proposed module generates coarser nodes for further FBN construction and analysis by combining fine-grained nodes. By stacking multiple such layers, a hierarchical representation of FBN can be adaptively learned in an end-to-end manner. The hierarchical structure can not only integrate the complementary information from multiscale FBN for joint analysis, but also reduce the model complexity due to decreasing node sizes. Moreover, this paper argues that the nodes defined by the existing atlases are not necessarily the optimal starting level to build FBN hierarchy and exploring finer nodes may further enrich the FBN representation. In this regard, each predefined node in an atlas is split into multiple sub-nodes, overcoming the scale limitation of the existing atlases. Extensive experiments conducted on various data sets consistently demonstrate the superior performance of the proposed method over the competing methods.

Role of Imaging Genetics in Alzheimer's Disease: A Systematic Review and Current Update.

Alzheimer's disease is a neurodegenerative disorder characterized by severe cognitive, behavioral, and psychological symptoms, such as dementia, cognitive decline, apathy, and depression. There are no accurate methods to diagnose the disease or proper therapeutic interventions to treat AD. Therefore, there is a need for novel diagnostic methods and markers to identify AD efficiently before its onset. Recently, there has been a rise in the use of imaging techniques like Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) as diagnostic approaches in detecting the structural and functional changes in the brain, which help in the early and accurate diagnosis of AD. In addition, these changes in the brain have been reported to be affected by variations in genes involved in different pathways involved in the pathophysiology of AD. A literature review was carried out to identify studies that reported the association of genetic variants with structural and functional changes in the brain in AD patients. Databases like PubMed, Google Scholar, and Web of Science were accessed to retrieve relevant studies. Keywords like 'fMRI', 'Alzheimer's', 'SNP', and 'imaging' were used, and the studies were screened using different inclusion and exclusion criteria. 15 studies that found an association of genetic variations with structural and functional changes in the brain were retrieved from the literature. Based on this, 33 genes were identified to play a role in the development of disease. These genes were mainly involved in neurogenesis, cell proliferation, neural differentiation, inflammation and apoptosis. Few genes like FAS, TOM40, APOE, TRIB3 and SIRT1 were found to have a high association with AD. In addition, other genes that could be potential candidates were also identified. Imaging genetics is a powerful tool in diagnosing and predicting AD and has the potential to identify genetic biomarkers and endophenotypes associated with the development of the disorder.

Automated classification of MRI images for mild cognitive impairment and Alzheimer’s disease detection using log gabor filters based deep meta-heuristic classification

In older people, mild cognitive impairment (MCI) is a precursor to more severe forms of dementia like AD (AD). In diagnosing patients with primary AD and amnestic MCI, modern neuroimaging techniques, especially MRI, play a key role. To efficiently categorize MRI images as normal or abnormal, the research presents a machine learning-based automatic labelling system, with a focus on boosting performance via texture feature analysis. To this end, the research implements a preprocessing phase employing Log Gabor filters, which are particularly well-suited for spatial frequency analysis. In addition, the research uses Gray Wolf Optimization (GWO) to acquire useful information from the images. For classification tasks using the MRI images, the research also make use of DenseNets, a form of deep neural network. The proposed method leverages Log Gabor filters for preprocessing, Gray Wolf Optimization (GWO) for feature extraction, and DenseNets for classification, resulting in a robust approach for categorizing MRI images as normal or abnormal. When compared to earlier trials performed without optimization, the proposed systematic technique shows a significant increase in classification accuracy of 15%. For neuroimaging applications, our research emphasizes the use of Log Gabor filters for preprocessing, GWO for feature extraction, and DenseNets for classification, which can help with the early detection and diagnosis of MCI and AD.

Development of Ru-polypyridyl complexes for real-time monitoring of Aβ oligomers and inhibition of Aβ fibril formation.

The aggregation of amyloid-β (Aβ) is one of the important pathological markers of Alzheimer's disease. Ruthenium(II) complexes have good stability, low cytotoxicity, a high fluorescence quantum yield, and a good Stokes shift as fluorescent probes. Based on this, we constructed a fluorescent probe for in vivo real-time imaging and inhibition of Aβ-fibril formation using a complex of Ru polypyridine with organic fluorophores (N,N-dimethylaniline) and hydrophobic peptides (KLVFF). DLS and TEM studies have shown that Ru-YH has an inhibitory effect on the fibrotic aggregation of Aβ. Both in vivo and in vitro studies have shown that Ru-WJ and Ru-YH can quickly cross the blood-brain barrier and successfully detect Aβ in early (2.5-month old) transgenic mouse models. In summary, we have explored the potential of Ru complex based biological probes for early diagnosis and inhibition of AD.

Structural neuroimaging changes associated with subjective cognitive decline from a clinical sample

BackgroundAlzheimer’s disease (AD) is characterized by progressive deterioration of cognitive functions. Some individuals with subjective cognitive decline (SCD) are in the early phase of the disease and subsequently progress through the AD continuum. Although neuroimaging biomarkers could be used for the accurate and early diagnosis of preclinical AD, the findings in SCD samples have been heterogeneous. This study established the morphological differences in brain magnetic resonance imaging (MRI) findings between individuals with SCD and those without cognitive impairment based on a clinical sample of patients defined according to SCD-Initiative recommendations. Moreover, we investigated baseline structural changes in the brains of participants who remained stable or progressed to mild cognitive impairment or dementia. MethodsThis study included 309 participants with SCD and 43 healthy controls (HCs) with high-quality brain MRI at baseline. Among the 99 subjects in the SCD group who were followed clinically, 32 progressed (SCDp) and 67 remained stable (SCDnp). A voxel-wise statistical comparison of gray and white matter (WM) volume was performed between the HC and SCD groups and between the HC, SCDp, and SCDnp groups. XTRACT ATLAS was used to define the anatomical location of WM tract damage. Region-of-interest (ROI) analyses were performed to determine brain volumetric differences. White matter lesion (WML) burden was established in each group. ResultsVoxel-based morphometry (VBM) analysis revealed that the SCD group exhibited gray matter atrophy in the middle frontal gyri, superior orbital gyri, superior frontal gyri, right rectal gyrus, whole occipital lobule, and both thalami and precunei. Meanwhile, ROI analysis revealed decreased volume in the left rectal gyrus, bilateral medial orbital gyri, middle frontal gyri, superior frontal gyri, calcarine fissure, and left thalamus. The SCDp group exhibited greater hippocampal atrophy (p < 0.001) than the SCDnp and HC groups on ROI analyses. On VBM analysis, however, the SCDp group exhibited increased hippocampal atrophy only when compared to the SCDnp group (p < 0.001). The SCD group demonstrated lower WM volume in the uncinate fasciculus, cingulum, inferior fronto-occipital fasciculus, anterior thalamic radiation, and callosum forceps than the HC group. However, no significant differences in WML number (p = 0.345) or volume (p = 0.156) were observed between the SCD and HC groups. ConclusionsThe SCD group showed brain atrophy mainly in the frontal and occipital lobes. However, only the SCDp group demonstrated atrophy in the medial temporal lobe at baseline. Structural damage in the brain regions was anatomically connected, which may contribute to early memory decline.

EEG and speech signal based multi-class recognition manoeuvre by exploiting a Hyb-SGTS and a dual stage deep CNN architecture for an early diagnosis of HC, AD and PD neurological diseases

EEG and speech signal based multi-class recognition manoeuvre by exploiting a Hyb-SGTS and a dual stage deep CNN architecture for an early diagnosis of HC, AD and PD neurological diseases

A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer’s Disease using MRI Images

Recently, the early diagnosis of Alzheimer’s disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. The methodology of this study consisted of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classifiers were selected to combine and constitute the ensemble model. The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92 for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classification groups, respectively. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classification. Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no significant differences among various ensemble approaches. The validation results revealed the low performance of individual models in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model.

Plasma p-tau 181 outperforms FDG-PET in the early diagnosis of biological AD

Plasma p-tau 181 outperforms FDG-PET in the early diagnosis of biological AD

Association of AD risk factors with plasma GFAP levels in a memory clinic population

AbstractBackgroundPlasma glial fibrillary acidic protein (GFAP) is an emerging AD biomarker. We aimed to study how plasma GFAP correlated with age, clinical stage, sex, and the APOE4 status and whether amyloid pathology may mediate these associations.MethodsCohort of 330 individuals ranging from cognitively unimpaired to dementia due to AD from the Geneva Memory Center. Plasma GFAP was measured using Single molecule array (Simoa) technology. The effect of the variables of interest on plasma GFAP levels was assessed using linear regression models and partial eta squared (eta2 p). In a subset of 169 subject with amyloid PET, mediation analysis was performed to test the mediating effect of amyloid levels on the association between variable of interest and plasma GFAP.ResultsAge had the strongest effect on plasma GFAP levels (eta2 p = 0.39, p&lt; 0.0001), followed by clinical stage (eta2 p = 0.10, p&lt; 0.0001), sex (eta2 p = 0.06, p&lt; 0.0001), and APOE4 (eta2 p = 0.02, p = 0.007). The effect of APOE4 status on plasma GFAP was fully mediated via the cortical amyloid uptake. The bootstrapped indirect effect was 0.09 (p&lt; 0.0001) and the proportion of the effect of APOE4 status on GFAP that goes through the cortical amyloid uptake was 69%. No mediating effect by amyloid was found for age and sex.ConclusionThese results show that in a memory clinic population, i) AD risk factors and severity affected plasma GFAP levels and, ii) the effect of APOE4 on GFAP is amyloid‐dependent whereas that of age and sex is amyloid‐independent. GFAP cut‐offs for the early diagnosis of AD should take age and sex into account.