Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers

Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia

BACKGROUND

The accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer's disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data.

METHODS

Predictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis. The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab.

RESULTS

The most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable.

CONCLUSION

A predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status.

Alzheimer's disease biomarkers and their current use in clinical research and practice

While blood-based tests are readily available for various conditions, including cardiovascular diseases, type 2 diabetes, and common cancers, Alzheimer's disease (AD) and other neurodegenerative diseases lack an early blood-based screening test that can be used in primary care. Major efforts have been made towards the investigation of approaches that may lead to minimally invasive, cost-effective, and reliable tests capable of measuring brain pathological status. Here, we review past and current technologies developed to investigate biomarkers of AD, including novel blood-based approaches and the more established cerebrospinal fluid and neuroimaging biomarkers of disease. The utility of blood as a source of AD-related biomarkers in both clinical practice and interventional trials is discussed, supported by a comprehensive list of clinical trials for AD drugs and interventions that list biomarkers as primary or secondary endpoints. We highlight that identifying individuals in early preclinical AD using blood-based biomarkers will improve clinical trials and the optimization of therapeutic treatments as they become available. Lastly, we discuss challenges that remain in the field and address new approaches being developed, such as the examination of cargo packaged within extracellular vesicles of neuronal origin isolated from peripheral blood.

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.

Predicting amyloid-PET and clinical conversion in apolipoprotein E ε3/ε3 non-demented individuals with multidimensional factors

The apolipoprotein E (APOE) ε4 is a well-established risk factor of amyloid-β (Aβ) in Alzheimer's disease (AD). However, because of the high prevalence of APOE ε3, there may be a large number of people with APOE ε3/ε3 who are non-demented and have Aβ pathology. There are limited studies on assessing Aβ status and clinical conversion in the APOE ε3/ε3 non-demented population. Two hundred and ninety-three non-demented individuals with APOE ε3/ε3 from ADNI database were divided into Aβ-positron emission tomography (Aβ-PET) positivity (+) and Aβ-PET negativity (-) groups using cut-off value of >1.11. Stepwise regression searched for a single or multidimensional clinical variables for predicting Aβ-PET (+), and the receiver operating characteristic curve (ROC) assessed the accuracy of the predictive models. The Cox regression model explored the risk factors associated with clinical conversion to mild cognitive impairment (MCI) or AD. The results showed that the combination of sex, education, ventricle and white matter hyperintensity (WMH) volume can accurately predict Aβ-PET status in cognitively normal (CN), and the combination of everyday cognition study partner total (EcogSPTotal) score, age, plasma p-tau 181 and WMH can accurately predict Aβ-PET status in MCI individuals. EcogSPTotal score were independent predictors of clinical conversion to MCI or AD. The findings may provide a non-invasive and effective tool to improve the efficiency of screening Aβ-PET (+), accelerate and reduce costs of AD trial recruitment in future secondary prevention trials or help to select patients at high risk of disease progression in clinical trials.

Explaining the Variability of Alzheimer Disease Fluid Biomarker Concentrations in Memory Clinic Patients Without Dementia

BACKGROUND AND OBJECTIVES

Patients' comorbidities can affect Alzheimer disease (AD) blood biomarker concentrations. Because a limited number of factors have been explored to date, our aim was to assess the proportion of the variance in fluid biomarker levels explained by the clinical features of AD and by a large number of non-AD-related factors.

METHODS

MEMENTO enrolled 2,323 individuals with cognitive complaints or mild cognitive impairment in 26 French memory clinics. Baseline evaluation included clinical and neuropsychological assessments, brain MRI, amyloid-PET, CSF (optional), and blood sampling. Blood biomarker levels were determined using the Simoa-HDX analyzer. We performed linear regression analysis of the clinical features of AD (cognition, AD genetic risk score, and brain atrophy) to model biomarker concentrations. Next, we added covariates among routine biological tests, inflammatory markers, demographic and behavioral determinants, treatments, comorbidities, and preanalytical sample handling in final models using both stepwise selection processes and least absolute shrinkage and selection operator (LASSO).

RESULTS

In total, 2,257 participants were included in the analysis (median age 71.7, 61.8% women, 55.2% with high educational levels). For blood biomarkers, the proportion of variance explained by clinical features of AD was 13.7% for neurofilaments (NfL), 11.4% for p181-tau, 3.0% for Aβ-42/40, and 1.4% for total-tau. In final models accounting for non-AD-related factors, the variance was mainly explained by age, routine biological tests, inflammatory markers, and preanalytical sample handling. In CSF, the proportion of variance explained by clinical features of AD was 24.8% for NfL, 22.3% for Aβ-42/40, 19.8% for total-tau, and 17.2% for p181-tau. In contrast to blood biomarkers, the largest proportion of variance was explained by cognition after adjustment for covariates. The covariates that explained the largest proportion of variance were also the most frequently selected with LASSO. The performance of blood biomarkers for predicting A+ and T+ status (PET or CSF) remained unchanged after controlling for drivers of variance.

DISCUSSION

This comprehensive analysis demonstrated that the variance in AD blood biomarker concentrations was mainly explained by age, with minor contributions from cognition, brain atrophy, and genetics, conversely to CSF measures. These results challenge the use of blood biomarkers as isolated stand-alone biomarkers for AD.

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

The role of plasma neurofilament light chain and glial fibrillary acidic protein in subjective cognitive decline and mild cognitive impairment

INTRODUCTION AND AIM

NfL and GFAP are promising blood-based biomarkers for Alzheimer's disease. However, few studies have explored plasma GFAP in the prodromal and preclinical stages of AD. In our cross-sectional study, our aim is to investigate the role of these biomarkers in the earliest stages of AD.

MATERIALS AND METHODS

We enrolled 40 patients (11 SCD, 21 MCI, 8 AD dementia). All patients underwent neurological and neuropsychological examinations, analysis of CSF biomarkers (Aβ42, Aβ42/Aβ40, p-tau, t-tau), Apolipoprotein E (APOE) genotype analysis and measurement of plasma GFAP and NfL concentrations. Patients were categorized according to the ATN system as follows: normal AD biomarkers (NB), carriers of non-Alzheimer's pathology (non-AD), prodromal AD, or AD with dementia (AD-D).

RESULTS

GFAP was lower in NB compared to prodromal AD (p = 0.003, d = 1.463) and AD-D (p = 0.002, d = 1.695). NfL was lower in NB patients than in AD-D (p = 0.011, d = 1.474). NfL demonstrated fair accuracy (AUC = 0.718) in differentiating between NB and prodromal AD, with a cut-off value of 11.65 pg/mL. GFAP showed excellent accuracy in differentiating NB from prodromal AD (AUC = 0.901) with a cut-off level of 198.13 pg/mL.

CONCLUSIONS

GFAP exhibited excellent accuracy in distinguishing patients with normal CSF biomarkers from those with prodromal AD. Our results support the use of this peripheral biomarker for detecting AD in patients with subjective and objective cognitive decline.

Cost-effectiveness comparison between blood biomarkers and conventional tests in Alzheimer's disease diagnosis

Dementia management has evolved with drugs such as lecanemab, shifting management from palliative care to early diagnosis and intervention. However, the administration of these drugs presents challenges owing to the invasiveness, high cost and limited availability of amyloid-PET and cerebrospinal fluid tests for guiding drug administration. Our manuscript explores the potential of less invasive blood biomarkers as a diagnostic method, with a cost-effectiveness analysis and a comparison with traditional tests. Our findings suggest that blood biomarkers are a cost-effective alternative, but with lower accuracy, indicating the need for multiple specific biomarkers for precision. This underscores the importance of future research on new blood biomarkers and their clinical efficacy.

Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals

BACKGROUND

The pathophysiology of Alzheimer's disease (AD) involves β -amyloid (A β ) accumulation. Early identification of individuals with abnormal β -amyloid levels is crucial, but A β quantification with positron emission tomography (PET) and cerebrospinal fluid (CSF) is invasive and expensive.

METHODS

We propose a machine learning framework using standard non-invasive (MRI, demographics, APOE, neuropsychology) measures to predict future A β -positivity in A β -negative individuals. We separately study A β -positivity defined by PET and CSF.

RESULTS

Cross-validated AUC for 4-year A β conversion prediction was 0.78 for the CSF-based and 0.68 for the PET-based A β definitions. Although not trained for the clinical status-change prediction, the CSF-based model excelled in predicting future mild cognitive impairment (MCI)/dementia conversion in cognitively normal/MCI individuals (AUCs, respectively, 0.76 and 0.89 with a separate dataset).

CONCLUSION

Standard measures have potential in detecting future A β -positivity and assessing conversion risk, even in cognitively normal individuals. The CSF-based definition led to better predictions than the PET-based definition.

Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer's disease cohorts

Introduction

Efforts to develop cost-effective approaches for detecting amyloid pathology in Alzheimer's disease (AD) have gained significant momentum with a focus on biomarker classification. Recent research has explored non-invasive and readily accessible biomarkers, including magnetic resonance imaging (MRI) biomarkers and some AD risk factors.

Methods

In this comprehensive study, we leveraged a diverse dataset, encompassing participants with varying cognitive statuses from multiple sources, including cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and our in-house Dementia Disease Initiation (DDI) cohort. As brain amyloid plaques have been proposed as sufficient for AD diagnosis, our primary aim was to assess the effectiveness of multimodal biomarkers in identifying amyloid plaques, using deep machine learning methodologies.

Results

Our findings underscore the robustness of the utilized methods in detecting amyloid beta positivity across multiple cohorts. Additionally, we investigated the potential of demographic data to enhance MRI-based amyloid detection. Notably, the inclusion of demographic risk factors significantly improved our models' ability to detect amyloid-beta positivity, particularly in early-stage cases, exemplified by an average area under the ROC curve of 0.836 in the unimpaired DDI cohort.

Discussion

These promising, non-invasive, and cost-effective predictors of MRI biomarkers and demographic variables hold the potential for further refinement through considerations like APOE genotype and plasma markers.

Automated Quality Evaluation Index for Arterial Spin Labeling Derived Cerebral Blood Flow Maps

BACKGROUND

Arterial spin labeling (ASL) derived cerebral blood flow (CBF) maps are prone to artifacts and noise that can degrade image quality.

PURPOSE

To develop an automated and objective quality evaluation index (QEI) for ASL CBF maps.

STUDY TYPE

Retrospective.

POPULATION

Data from N = 221 adults, including patients with Alzheimer's disease (AD), Parkinson's disease, and traumatic brain injury.

FIELD STRENGTH/SEQUENCE

Pulsed or pseudocontinuous ASL acquired at 3 T using non-background suppressed 2D gradient-echo echoplanar imaging or background suppressed 3D spiral spin-echo readouts.

ASSESSMENT

The QEI was developed using N = 101 2D CBF maps rated as unacceptable, poor, average, or excellent by two neuroradiologists and validated by 1) leave-one-out cross validation, 2) assessing if CBF reproducibility in N = 53 cognitively normal adults correlates inversely with QEI, 3) if iterative discarding of low QEI data improves the Cohen's d effect size for CBF differences between preclinical AD (N = 27) and controls (N = 53), 4) comparing the QEI with manual ratings for N = 50 3D CBF maps, and 5) comparing the QEI with another automated quality metric.

STATISTICAL TESTS

Inter-rater reliability and manual vs. automated QEI were quantified using Pearson's correlation. P < 0.05 was considered significant.

RESULTS

The correlation between QEI and manual ratings (R = 0.83, CI: 0.76-0.88) was similar (P = 0.56) to inter-rater correlation (R = 0.81, CI: 0.73-0.87) for the 2D data. CBF reproducibility correlated negatively (R = -0.74, CI: -0.84 to -0.59) with QEI. The effect size comparing patients and controls improved (R = 0.72, CI: 0.59-0.82) as low QEI data was discarded iteratively. The correlation between QEI and manual ratings (R = 0.86, CI: 0.77-0.92) of 3D ASL was similar (P = 0.09) to inter-rater correlation (R = 0.78, CI: 0.64-0.87). The QEI correlated (R = 0.87, CI: 0.77-0.92) significantly better with manual ratings than did an existing approach (R = 0.54, CI: 0.30-0.72).

DATA CONCLUSION

Automated QEI performed similarly to manual ratings and can provide scalable ASL quality control.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Plasma neurofilament light chain predicts Alzheimer's disease in patients with subjective cognitive decline and mild cognitive impairment: A cross-sectional and longitudinal study

BACKGROUND AND PURPOSE

We aimed to evaluate the accuracy of plasma neurofilament light chain (NfL) in predicting Alzheimer's disease (AD) and the progression of cognitive decline in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI).

METHODS

This longitudinal cohort study involved 140 patients (45 with SCD, 73 with MCI, and 22 with AD dementia [AD-D]) who underwent plasma NfL and AD biomarker assessments (cerebrospinal fluid, amyloid positron emission tomography [PET], and 18 F-fluorodeoxyglucose-PET) at baseline. The patients were rated according to the amyloid/tau/neurodegeneration (A/T/N) system and followed up for a mean time of 2.72 ± 0.95 years to detect progression from SCD to MCI and from MCI to AD. Forty-eight patients (19 SCD, 29 MCI) also underwent plasma NfL measurements 2 years after baseline.

RESULTS

At baseline, plasma NfL detected patients with biomarker profiles consistent with AD (A+/T+/N+ or A+/T+/N-) with high accuracy (area under the curve [AUC] 0.82). We identified cut-off values of 19.45 pg/mL for SCD and 20.45 pg/mL for MCI. During follow-up, nine SCD patients progressed to MCI (progressive SCD [p-SCD]), and 14 MCI patients developed AD dementia (progressive MCI [p-MCI]). The previously identified cut-off values provided good accuracy in identifying p-SCD (80% [95% confidence interval 65.69: 94.31]). The rate of NfL change was higher in p-MCI (3.52 ± 4.06 pg/mL) compared to non-progressive SCD (0.81 ± 1.25 pg/mL) and non-progressive MCI (-0.13 ± 3.24 pg/mL) patients. A rate of change lower than 1.64 pg/mL per year accurately excluded progression from MCI to AD (AUC 0.954).

CONCLUSION

Plasma NfL concentration and change over time may be a reliable, non-invasive tool to detect AD and the progression of cognitive decline at the earliest stages of the disease.

Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

Detecting Amyloid Positivity Using Morphometric Magnetic Resonance Imaging

Background

Early detection of amyloid-β (Aβ) positivity is essential for an accurate diagnosis and treatment of Alzheimer's disease (AD), but it is currently costly and/or invasive.

Objective

We aimed to classify Aβ positivity (Aβ+) using morphometric features from magnetic resonance imaging (MRI), a more accessible and non-invasive technique, in two clinical population scenarios: one containing AD, mild cognitive impairment (MCI) and cognitively normal (CN) subjects, and another only cognitively impaired subjects (AD and MCI).

Methods

Demographic, cognitive (Mini-Mental State Examination [MMSE] scores), regional morphometry MRI (volumes, areas, and thicknesses), and derived morphometric graph theory (GT) features from all subjects (302 Aβ+, age: 73.3±7.2, 150 male; 246 Aβ-, age: 71.1±7.1, 131 male) were combined in different feature sets. We implemented a machine learning workflow to find the best Aβ+ classification model.

Results

In an AD+MCI+CN population scenario, the best-performing model selected 120 features (107 GT features, 12 regional morphometric features and the MMSE total score) and achieved a negative predictive value (NPVadj) of 68.4%, and a balanced accuracy (BAC) of 66.9%. In a AD+MCI scenario, the best model obtained NPVadj of 71.6%, and BAC of 70.7%, using 180 regional morphometric features (98 volumes, 52 areas and 29 thicknesses from temporal, parietal, and frontal brain regions).

Conclusions

Although with currently limited clinical applicability, regional MRI morphometric features have clinical usefulness potential for detecting Aβ status, which may be augmented by a combination with cognitive data when cognitively normal subjects make up a substantial part of the population presenting for diagnosis.

The Alzheimer's Disease Neuroimaging Initiative and the role and contributions of the Private Partners Scientific Board (PPSB)

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Private Partners Scientific Board (PPSB) encompasses members from industry, biotechnology, diagnostic, and non-profit organizations that have until recently been managed by the Foundation for the National Institutes of Health (FNIH) and provided financial and scientific support to ADNI programs. In this article, we review some of the major activities undertaken by the PPSB, focusing on those supporting the most recently completed National Institute on Aging grant, ADNI3, and the impact it has had on streamlining biomarker discovery and validation in Alzheimer's disease. We also provide a perspective on the gaps that may be filled with future PPSB activities as part of ADNI4 and beyond. HIGHLIGHTS: The Private Partners Scientific board (PPSB) continues to play a key role in enabling several Alzheimer's Disease Neuroimaging Initiative (ADNI) activities. PPSB working groups have led landscape assessments to provide valuable feedback on new technologies, platforms, and methods that may be taken up by ADNI in current or future iterations.

Predicting amyloid-beta pathology in the general population

INTRODUCTION

Reliable models to predict amyloid beta (Aβ) positivity in the general aging population are lacking but could become cost-efficient tools to identify individuals at risk of developing Alzheimer's disease.

METHODS

We developed Aβ prediction models in the clinical Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study (n = 4,119) including a broad range of easily ascertainable predictors (demographics, cognition and daily functioning, health and lifestyle factors). Importantly, we determined the generalizability of our models in the population-based Rotterdam Study (n = 500).

RESULTS

The best performing model in the A4 Study (area under the curve [AUC] = 0.73 [0.69-0.76]), including age, apolipoprotein E (APOE) ε4 genotype, family history of dementia, and subjective and objective measures of cognition, walking duration and sleep behavior, was validated in the independent Rotterdam Study with higher accuracy (AUC = 0.85 [0.81-0.89]). Yet, the improvement relative to a model including only age and APOE ε4 was marginal.

DISCUSSION

Aβ prediction models including inexpensive and non-invasive measures were successfully applied to a general population-derived sample more representative of typical older non-demented adults.

Real-world prediction of preclinical Alzheimer's disease with a deep generative model

Amyloid positivity is an early indicator of Alzheimer's disease and is necessary to determine the disease. In this study, a deep generative model is utilized to predict the amyloid positivity of cognitively normal individuals using proxy measures, such as structural MRI scans, demographic variables, and cognitive scores, instead of invasive direct measurements. Through its remarkable efficacy in handling imperfect datasets caused by missing data or labels, and imbalanced classes, the model outperforms previous studies and widely used machine learning approaches with an AUROC of 0.8609. Furthermore, this study illuminates the model's adaptability to diverse clinical scenarios, even when feature sets or diagnostic criteria differ from the training data. We identify the brain regions and variables that contribute most to classification, including the lateral occipital lobes, posterior temporal lobe, and APOE ϵ4 allele. Taking advantage of deep generative models, our approach can not only provide inexpensive, non-invasive, and accurate diagnostics for preclinical Alzheimer's disease, but also meet real-world requirements for clinical translation of a deep learning model, including transferability and interpretability.

Longitudinal changes in Alzheimer's-related plasma biomarkers and brain amyloid

INTRODUCTION

Understanding longitudinal plasma biomarker trajectories relative to brain amyloid changes can help devise Alzheimer's progression assessment strategies.

METHODS

We examined the temporal order of changes in plasma amyloid-β ratio (A β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ ), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios (p-tau181 / A β 42 $\text{p-tau181}/\mathrm{A}{\beta}_{42}$ ,p-tau231 / A β 42 $\text{p-tau231}/\mathrm{A}{\beta}_{42}$ ) relative to 11 C-Pittsburgh compound B (PiB) positron emission tomography (PET) cortical amyloid burden (PiB-/+). Participants (n = 199) were cognitively normal at index visit with a median 6.1-year follow-up.

RESULTS

PiB groups exhibited different rates of longitudinal change inA β 42 / A β 40 ( β = 5.41 × 10 - 4 , SE = 1.95 × 10 - 4 , p = 0.0073 ) ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}\ ( {\beta \ = \ 5.41 \times {{10}}^{ - 4},{\rm{\ SE\ }} = \ 1.95 \times {{10}}^{ - 4},\ p\ = \ 0.0073} )$ . Change in brain amyloid correlated with change in GFAP (r = 0.5, 95% CI = [0.26, 0.68]). The greatest relative decline inA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ (-1%/year) preceded brain amyloid positivity by 41 years (95% CI = [32, 53]).

DISCUSSION

PlasmaA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may begin declining decades prior to brain amyloid accumulation, whereas p-tau ratios, GFAP, and NfL increase closer in time. HIGHLIGHTS PlasmaA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ declines over time among PiB- but does not change among PiB+. Phosphorylated-tau to Aβ42 ratios increase over time among PiB+ but do not change among PiB-. Rate of change in brain amyloid is correlated with change in GFAP and neurofilament light chain. The greatest decline inA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may precede brain amyloid positivity by decades.

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum

Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.

Blood-based biomarkers for Alzheimer's disease: Current state and future use in a transformed global healthcare landscape

Timely detection of the pathophysiological changes and cognitive impairment caused by Alzheimer's disease (AD) is increasingly pressing because of the advent of biomarker-guided targeted therapies that may be most effective when provided early in the disease. Currently, diagnosis and management of early AD are largely guided by clinical symptoms. FDA-approved neuroimaging and cerebrospinal fluid biomarkers can aid detection and diagnosis, but the clinical implementation of these testing modalities is limited because of availability, cost, and perceived invasiveness. Blood-based biomarkers (BBBMs) may enable earlier and faster diagnoses as well as aid in risk assessment, early detection, prognosis, and management. Herein, we review data on BBBMs that are closest to clinical implementation, particularly those based on measures of amyloid-β peptides and phosphorylated tau species. We discuss key parameters and considerations for the development and potential deployment of these BBBMs under different contexts of use and highlight challenges at the methodological, clinical, and regulatory levels.

Plasma biomarkers for prediction of Alzheimer's disease neuropathologic change

While plasma biomarkers for Alzheimer's disease (AD) are increasingly being evaluated for clinical diagnosis and prognosis, few population-based autopsy studies have evaluated their utility in the context of predicting neuropathological changes. Our goal was to investigate the utility of clinically available plasma markers in predicting Braak staging, neuritic plaque score, Thal phase, and overall AD neuropathological change (ADNC).We utilized a population-based prospective study of 350 participants with autopsy and antemortem plasma biomarker testing using clinically available antibody assay (Quanterix) consisting of Aβ42/40 ratio, p-tau181, GFAP, and NfL. We utilized a variable selection procedure in cross-validated (CV) logistic regression models to identify the best set of plasma predictors along with demographic variables, and a subset of neuropsychological tests comprising the Mayo Clinic Preclinical Alzheimer Cognitive Composite (Mayo-PACC). ADNC was best predicted with plasma GFAP, NfL, p-tau181 biomarkers along with APOE ε4 carrier status and Mayo-PACC cognitive score (CV AUC = 0.798). Braak staging was best predicted using plasma GFAP, p-tau181, and cognitive scores (CV AUC = 0.774). Neuritic plaque score was best predicted using plasma Aβ42/40 ratio, p-tau181, GFAP, and NfL biomarkers (CV AUC = 0.770). Thal phase was best predicted using GFAP, NfL, p-tau181, APOE ε4 carrier status and Mayo-PACC cognitive score (CV AUC = 0.754). We found that GFAP and p-tau provided non-overlapping information on both neuritic plaque and Braak stage scores whereas Aβ42/40 and NfL were mainly useful for prediction of neuritic plaque scores. Separating participants by cognitive status improved predictive performance, particularly when plasma biomarkers were included. Plasma biomarkers can differentially inform about overall ADNC pathology, Braak staging, and neuritic plaque score when combined with demographics and cognitive variables and have significant utility for earlier detection of AD.

Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?

The aim of the present study was to predict amyloid-beta positivity using a conventional T1-weighted image, radiomics, and a diffusion-tensor image obtained by magnetic resonance imaging (MRI). We included 186 patients with mild cognitive impairment (MCI) who underwent Florbetaben positron emission tomography (PET), MRI (three-dimensional T1-weighted and diffusion-tensor images), and neuropsychological tests at the Asan Medical Center. We developed a stepwise machine learning algorithm using demographics, T1 MRI features (volume, cortical thickness and radiomics), and diffusion-tensor image to distinguish amyloid-beta positivity on Florbetaben PET. We compared the performance of each algorithm based on the MRI features used. The study population included 72 patients with MCI in the amyloid-beta-negative group and 114 patients with MCI in the amyloid-beta-positive group. The machine learning algorithm using T1 volume performed better than that using only clinical information (mean area under the curve [AUC]: 0.73 vs. 0.69, p < 0.001). The machine learning algorithm using T1 volume showed better performance than that using cortical thickness (mean AUC: 0.73 vs. 0.68, p < 0.001) or texture (mean AUC: 0.73 vs. 0.71, p = 0.002). The performance of the machine learning algorithm using fractional anisotropy in addition to T1 volume was not better than that using T1 volume alone (mean AUC: 0.73 vs. 0.73, p = 0.60). Among MRI features, T1 volume was the best predictor of amyloid PET positivity. Radiomics or diffusion-tensor images did not provide additional benefits.

Detection of Tau-PET Positivity in Clinically Diagnosed Mild Cognitive Impairment with Multidimensional Features

BACKGROUND

The way to evaluate brain tau pathology in vivo is tau positron emission tomography (tau-PET) or cerebrospinal fluid (CSF) analysis. In the clinically diagnosed mild cognitive impairment (MCI), a significant proportion of tau-PET are negative. Interest in less expensive and convenient ways to detect tau pathology in Alzheimer's disease has increased due to the high cost of tau-PET and the invasiveness of lumbar puncture, which typically slows down the cost and enrollment of clinical trials.

OBJECTIVE

We aimed to investigate one simple and effective method in predicting tau-PET status in MCI individuals.

METHODS

The sample included 154 individuals which were dichotomized into tau-PET (+) and tau-PET (-) using a cut-off of >1.33. We used stepwise regression to select the unitary or combination of variables that best predicted tau-PET. The receiver operating characteristic curve was used to assess the accuracy of single and multiple clinical markers.

RESULTS

The combined performance of three variables [Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog13), Mini-Mental State Examination (MMSE), ADNI-Memory summary score (ADNI-MEM)] in neurocognitive measures demonstrated good predictive accuracy of tau-PET status [accuracy = 85.7%, area under the curve (AUC) = 0.879]. The combination of clinical markers model (APOEɛ4, neurocognitive measures and structural MRI imaging of middle temporal) had the best discriminative power (AUC = 0.946).

CONCLUSION

As a noninvasive test, the combination of APOEɛ4, neurocognitive measures and structural MRI imaging of middle temporal accurately predicts tau-PET status. The finding may provide a non-invasive, cost-effective tool for clinical application in predicting tau pathology among MCI individuals.

Phosphorylated tau in Alzheimer's disease

There is a need for blood biomarkers to detect individuals at different Alzheimer's disease (AD) stages because obtaining cerebrospinal fluid-based biomarkers is invasive and costly. Plasma phosphorylated tau proteins (p-tau) have shown potential as such biomarkers. This systematic review was conducted according to the PRISMA guidelines and aimed to determine whether quantification of plasma tau phosphorylated at threonine 181 (p-tau181), threonine 217 (p-tau217) and threonine 231 (p-tau231) is informative in the diagnosis of AD. All p-tau isoforms increase as a function of Aβ-accumulation and discriminate healthy individuals from those at preclinical AD stages with high accuracy. P-tau231 increases earliest, followed by p-tau181 and p-tau217. In advanced stages, all p-tau isoforms are associated with the clinical classification of AD and increase with disease severity, with the greatest increase seen for p-tau217. This is also reflected by a better correlation of p-tau217 with Aβ scans, whereas both, p-tau217 and p-tau181 correlated equally with tau scans. However, at the very advanced stages, p-tau181 begins to plateau, which may mirror the trajectory of the Aβ pathology and indicate an association with a more intermediate risk of AD. Across the AD continuum, the incremental increase in all biomarkers is associated with structural changes in widespread brain regions and underlying cognitive decline. Furthermore, all isoforms differentiate AD from non-AD neurodegenerative disorders, making them specific for AD. Incorporating p-tau181, p-tau217 and p-tau231 in clinical use requires further studies to examine ideal cut-points and harmonize assays.

Evaluation of glymphatic system activity by diffusion tensor image analysis along the perivascular space (DTI-ALPS) in dementia patients

OBJECTIVES

Dementia is a clinical syndrome caused by multiple etiologies, usually manifests with progressive and diffuse brain dysfunction. The activity of the human glymphatic system was evaluated in cases of dementia by the diffusion tensor image analysis along the perivascular space (DTI-ALPS).

METHODS

We recruited 28 healthy subjects and 77 patients, including 38 with Alzheimer's disease (AD),18 with mild cognitive impairment (MCI), 28 with normal controls (NC) and 21 with vascular cognitive impairment (VCI). All participants underwent DTI scanning. Diffusivities in the X, Y and Z axes were obtained in the lateral ventricle body plane of all subjects. We assessed the diffusivity along the perivascular spaces, as well as projection fibers and association fibers, respectively, in order to acquire an DTI-ALPS-index and correlated them with mini mental state examination (MMSE) and montreal cognitive assessment (MOCA) scores using partial correlation which the influence of age was controlled.

RESULTS

The AD, MCI, and VCI patients showed significantly lower DTI-ALPS-index (p < 0.001) compared to the NC. Besides, the VCI group had significantly higher DTI-ALPS-index than the AD group (p = 0.007). There was a significant positive correlation between DTI-ALPS-index and MMSE and MOCA scores (the effect of age was controlled), showing that lower water diffusivity along the perivascular spaces associated with dementia.The higher Dzassoc led to the reduced DTI-ALPS-index in VCI, while lower Dxassoc contributed to the decrease of DTI-ALPS-index in AD.

CONCLUSION

The evaluation of DTI-ALPS demonstrates impairment of the glymphatic system in dementia patients by decreased DTI-ALPS-index. Different from AD, the VCI patients show glymphatic drainage disorder rather than glymphatic system impairment.

ADVANCES IN KNOWLEDGE

This article comprehensively covers several types of dementia and performs the comparison of VCI, AD and MCI in glymphatic system dysfunction.

Longitudinal changes in Alzheimer's-related plasma biomarkers and brain amyloid

INTRODUCTION

Understanding longitudinal plasma biomarker trajectories relative to brain amyloid changes can help devise Alzheimer's progression assessment strategies.

METHODS

We examined the temporal order of changes in plasma amyloid-β ratio (Aβ 42 /Aβ 40 ), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios (p-tau181/Aβ 42 , p-tau231/Aβ 42 ) relative to 11 C-Pittsburgh compound B (PiB) positron emission tomography (PET) cortical amyloid burden (PiB-/+). Participants (n = 199) were cognitively normal at index visit with a median 6.1-year follow-up.

RESULTS

PiB groups exhibited different rates of longitudinal change in Aβ 42 /Aβ 40 (β = 5.41 × 10^ -4 , SE = 1.95 × 10 -4 , p = 0.0073). Change in brain amyloid was correlated with change in GFAP (r = 0.5, 95% CI = [0.26, 0.68]). Greatest relative decline in Aβ 42 /Aβ 40 (-1%/year) preceded brain amyloid positivity onset by 41 years (95% CI = [32, 53]).

DISCUSSION

Plasma Aβ 42 /Aβ 40 may begin declining decades prior to brain amyloid accumulation, whereas p-tau ratios, GFAP, and NfL increase closer in time.

Annualized changes in rate of amyloid deposition and neurodegeneration are greater in participants who become amyloid positive than those who remain amyloid negative

This study longitudinally examined participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who underwent a conversion in amyloid-beta (Aβ) status in comparison to a group of ADNI participants who did not show a change in amyloid status over the same follow-up period. Participants included 136 ADNI dementia-free participants with 2 florbetapir positron emission tomography (PET) scans. Of these participants, 68 showed amyloid conversion as measured on florbetapir PET, and the other 68 did not. Amyloid converters and non-converters were chosen to have representative demographic data (age, education, sex, diagnostic status, and race). The amyloid converter group showed increased prevalence of APOE ε4 (p < 0.001), greater annualized percent volume loss in selected magnetic resonance imaging (MRI) regions (p < 0.05), lower cerebrospinal fluid Aβ_1-42 (p < 0.001), and greater amyloid retention (as measured by standard uptake value ratios) on florbetapir PET scans (p < 0.001) in comparison to the non-converter group. These results provide compelling evidence that important neuropathological changes are occurring alongside amyloid conversion.

Validity and Performance of Blood Biomarkers for Alzheimer Disease to Predict Dementia Risk in a Large Clinic-Based Cohort

BACKGROUND AND OBJECTIVE

Blood biomarkers for Alzheimer disease (AD) have consistently proven to be associated with CSF or PET biomarkers and effectively discriminate AD from other neurodegenerative diseases. Our aim was to test their utility in clinical practice, from a multicentric unselected prospective cohort where patients presented with a large spectrum of cognitive deficits or complaints.

METHODS

The MEMENTO cohort enrolled 2,323 outpatients with subjective cognitive complaint (SCC) or mild cognitive impairment (MCI) consulting in 26 French memory clinics. Participants had neuropsychological assessments, MRI, and blood sampling at baseline. CSF sampling and amyloid PET were optional. Baseline blood Aβ42/40 ratio, total tau, p181-tau, and neurofilament light chain (NfL) were measured using a Simoa HD-X analyzer. An expert committee validated incident dementia cases during a 5-year follow-up period.

RESULTS

Overall, 2,277 individuals had at least 1 baseline blood biomarker available (n = 357 for CSF subsample, n = 649 for PET subsample), among whom 257 were diagnosed with clinical AD/mixed dementia during follow-up. All blood biomarkers but total tau were mildly correlated with their equivalence in the CSF (r = 0.33 to 0.46, p < 0.0001) and were associated with amyloid-PET status (p < 0.0001). Blood p181-tau was the best blood biomarker to identify amyloid-PET positivity (area under the curve = 0.74 [95% CI = 0.69; 0.79]). Higher blood and CSF p181-tau and NfL concentrations were associated with accelerated time to AD dementia onset with similar incidence rates, whereas blood Aβ42/40 was less efficient than CSF Aβ42/40. Blood p181-tau alone was the best blood predictor of 5-year AD/mixed dementia risk (c-index = 0.73 [95% CI = 0.69; 0.77]); its accuracy was higher in patients with clinical dementia rating (CDR) = 0 (c-index = 0.83 [95% CI = 0.69; 0.97]) than in patients with CDR = 0.5 (c-index = 0.70 [95% CI = 0.66; 0.74]). A "clinical" reference model (combining demographics and neuropsychological assessment) predicted AD/mixed dementia risk with a c-index = 0.88 [95% CI = 0.86-0.91] and performance increased to 0.90 [95% CI = 0.88; 0.92] when adding blood p181-tau + Aβ42/40. A "research" reference model (clinical model + apolipoprotein E genotype and AD signature on MRI) had a c-index = 0.91 [95% CI = 0.89-0.93] increasing to 0.92 [95% CI = 0.90; 0.93] when adding blood p181-tau + Aβ42/40. Chronic kidney disease and vascular comorbidities did not affect predictive performances.

DISCUSSION

In a clinic-based cohort of patients with SCC or MCI, blood biomarkers may be good hallmarks of underlying pathology but add little to 5-year dementia risk prediction models including traditional predictors.

Clinical performance of an antibody-free assay for plasma Aβ42/Aβ40 to detect early alterations of Alzheimer's disease in individuals with subjective cognitive decline

BACKGROUND

Accessible and cost-effective diagnostic tools are urgently needed to accurately quantify blood biomarkers to support early diagnosis of Alzheimer's disease (AD). In this study, we investigated the ability of plasma amyloid-beta (Aβ)42/Aβ40 ratio measured by an antibody-free mass-spectrometric (MS) method, ABtest-MS, to detect early pathological changes of AD.

METHODS

This cohort study included data from the baseline and 2-year follow-up visits from the Fundació ACE Healthy Brain Initiative (FACEHBI) study. Plasma Aβ42/Aβ40 was measured with ABtest-MS and compared to ¹⁸F-Florbetaben PET as the reference standard (cutoff for early amyloid deposition of 13.5 centiloids). Cross-validation was performed in an independent DPUK-Korean cohort. Additionally, associations of plasma Aβ42/Aβ40 with episodic memory performance and brain atrophy were assessed.

RESULTS

The FACEHBI cohort at baseline included 200 healthy individuals with subjective cognitive decline (SCD), of which 36 (18%) were Aβ-PET positive. Plasma Aβ42/Aβ40 levels were significantly lower in Aβ-PET positive individuals (median [interquartile range, IQR], 0.215 [0.203-0.236]) versus Aβ-PET negative subjects (median [IQR], 0.261 [0.244-0.279]) (P < .001). Plasma Aβ42/Aβ40 was significantly correlated with Aβ-PET levels (rho = -0.390; P < .001) and identified Aβ-PET status with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI], 0.80-0.93). A cutoff for the Aβ42/Aβ40 ratio of 0.241 (maximum Youden index) yielded a sensitivity of 86.1% and a specificity of 80.5%. These findings were cross-validated in an independent DPUK-Korean cohort (AUC 0.86 [95% CI 0.77-0.95]). Lower plasma Aβ42/Aβ40 ratio was associated with worse episodic memory performance and increased brain atrophy. Plasma Aβ42/Aβ40 at baseline predicted clinical conversion to mild cognitive impairment and longitudinal changes in amyloid deposition and brain atrophy at 2-year follow-up.

CONCLUSIONS

This study suggests that plasma Aβ42/Aβ40, as determined by this MS-based assay, has potential value as an accurate and cost-effective tool to identify individuals in the earliest stages of AD, supporting its implementation in clinical trials, preventative strategies and clinical practice.

Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding

BACKGROUND

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development).

OBJECTIVE

To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements.

METHODS

With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction.

RESULTS

We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics.

CONCLUSION

The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

The performance of plasma amyloid beta measurements in identifying amyloid plaques in Alzheimer's disease: a literature review

The extracellular buildup of amyloid beta (Aβ) plaques in the brain is a hallmark of Alzheimer's disease (AD). Detection of Aβ pathology is essential for AD diagnosis and for identifying and recruiting research participants for clinical trials evaluating disease-modifying therapies. Currently, AD diagnoses are usually made by clinical assessments, although detection of AD pathology with positron emission tomography (PET) scans or cerebrospinal fluid (CSF) analysis can be used by specialty clinics. These measures of Aβ aggregation, e.g. plaques, protofibrils, and oligomers, are medically invasive and often only available at specialized medical centers or not covered by medical insurance, and PET scans are costly. Therefore, a major goal in recent years has been to identify blood-based biomarkers that can accurately detect AD pathology with cost-effective, minimally invasive procedures.To assess the performance of plasma Aβ assays in predicting amyloid burden in the central nervous system (CNS), this review compares twenty-one different manuscripts that used measurements of 42 and 40 amino acid-long Aβ (Aβ42 and Aβ40) in plasma to predict CNS amyloid status. Methodologies that quantitate Aβ42 and 40 peptides in blood via immunoassay or immunoprecipitation-mass spectrometry (IP-MS) were considered, and their ability to distinguish participants with amyloidosis compared to amyloid PET and CSF Aβ measures as reference standards was evaluated. Recent studies indicate that some IP-MS assays perform well in accurately and precisely measuring Aβ and detecting brain amyloid aggregates.

A remote speech-based AI system to screen for early Alzheimer's disease via smartphones

Introduction

Artificial intelligence (AI) systems leveraging speech and language changes could support timely detection of Alzheimer's disease (AD).

Methods

The AMYPRED study (NCT04828122) recruited 133 subjects with an established amyloid beta (Aβ) biomarker (66 Aβ+, 67 Aβ-) and clinical status (71 cognitively unimpaired [CU], 62 mild cognitive impairment [MCI] or mild AD). Daily story recall tasks were administered via smartphones and analyzed with an AI system to predict MCI/mild AD and Aβ positivity.

Results

Eighty-six percent of participants (115/133) completed remote assessments. The AI system predicted MCI/mild AD (area under the curve [AUC] = 0.85, ±0.07) but not Aβ (AUC = 0.62 ±0.11) in the full sample, and predicted Aβ in clinical subsamples (MCI/mild AD: AUC = 0.78 ±0.14; CU: AUC = 0.74 ±0.13) on short story variants (immediate recall). Long stories and delayed retellings delivered broadly similar results.

Discussion

Speech-based testing offers simple and accessible screening for early-stage AD.

Leveraging speech and artificial intelligence to screen for early Alzheimer's disease and amyloid beta positivity

Early detection of Alzheimer's disease is required to identify patients suitable for disease-modifying medications and to improve access to non-pharmacological preventative interventions. Prior research shows detectable changes in speech in Alzheimer's dementia and its clinical precursors. The current study assesses whether a fully automated speech-based artificial intelligence system can detect cognitive impairment and amyloid beta positivity, which characterize early stages of Alzheimer's disease. Two hundred participants (age 54-85, mean 70.6; 114 female, 86 male) from sister studies in the UK (NCT04828122) and the USA (NCT04928976), completed the same assessments and were combined in the current analyses. Participants were recruited from prior clinical trials where amyloid beta status (97 amyloid positive, 103 amyloid negative, as established via PET or CSF test) and clinical diagnostic status was known (94 cognitively unimpaired, 106 with mild cognitive impairment or mild Alzheimer's disease). The automatic story recall task was administered during supervised in-person or telemedicine assessments, where participants were asked to recall stories immediately and after a brief delay. An artificial intelligence text-pair evaluation model produced vector-based outputs from the original story text and recorded and transcribed participant recalls, quantifying differences between them. Vector-based representations were fed into logistic regression models, trained with tournament leave-pair-out cross-validation analysis to predict amyloid beta status (primary endpoint), mild cognitive impairment and amyloid beta status in diagnostic subgroups (secondary endpoints). Predictions were assessed by the area under the receiver operating characteristic curve for the test result in comparison with reference standards (diagnostic and amyloid status). Simulation analysis evaluated two potential benefits of speech-based screening: (i) mild cognitive impairment screening in primary care compared with the Mini-Mental State Exam, and (ii) pre-screening prior to PET scanning when identifying an amyloid positive sample. Speech-based screening predicted amyloid beta positivity (area under the curve = 0.77) and mild cognitive impairment or mild Alzheimer's disease (area under the curve = 0.83) in the full sample, and predicted amyloid beta in subsamples (mild cognitive impairment or mild Alzheimer's disease: area under the curve = 0.82; cognitively unimpaired: area under the curve = 0.71). Simulation analyses indicated that in primary care, speech-based screening could modestly improve detection of mild cognitive impairment (+8.5%), while reducing false positives (-59.1%). Furthermore, speech-based amyloid pre-screening was estimated to reduce the number of PET scans required by 35.3% and 35.5% in individuals with mild cognitive impairment and cognitively unimpaired individuals, respectively. Speech-based assessment offers accessible and scalable screening for mild cognitive impairment and amyloid beta positivity.

Establishment of combined diagnostic models of Alzheimer's disease in a Chinese cohort: the Chongqing Ageing & Dementia Study (CADS)

Cerebrospinal fluid (CSF) biomarkers are essential for the accurate diagnosis of Alzheimer's disease (AD), yet their measurement levels vary widely across centers and regions, leaving no uniform cutoff values to date. Diagnostic cutoff values of CSF biomarkers for AD are lacking for the Chinese population. As a member of the Alzheimer's Association Quality Control program for CSF biomarkers, we aimed to establish diagnostic models based on CSF biomarkers and risk factors for AD in a Chinese cohort. A total of 64 AD dementia patients and 105 age- and sex-matched cognitively normal (CN) controls from the Chongqing Ageing & Dementia Study cohort were included. CSF Aβ42, P-tau181, and T-tau levels were measured by ELISA. Combined biomarker models and integrative models with demographic characteristics were established by logistic regression. The cutoff values to distinguish AD from CN were 933 pg/mL for Aβ42, 48.7 pg/mL for P-tau181 and 313 pg/mL for T-tau. The AN model, including Aβ42 and T-tau, had a higher diagnostic accuracy of 89.9%. Integrating age and APOE ε4 status to AN model (the ANA'E model) increased the diagnostic accuracy to 90.5% and improved the model performance. This study established cutoff values of CSF biomarkers and optimal combined models for AD diagnosis in a Chinese cohort.

Predicting Amyloid Positivity in Cognitively Unimpaired Older Adults: A Machine Learning Approach Using A4 Data

BACKGROUND AND OBJECTIVES

To develop and test the performance of the Positive Aβ Risk Score (PARS) for prediction of β-amyloid (Aβ) positivity in cognitively unimpaired individuals for use in clinical research. Detecting Aβ positivity is essential for identifying at-risk individuals who are candidates for early intervention with amyloid targeted treatments.

METHODS

We used data from 4,134 cognitively normal individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study. The sample was divided into training and test sets. A modified version of AutoScore, a machine learning-based software tool, was used to develop a scoring system using the training set. Three risk scores were developed using candidate predictors in various combinations from the following categories: demographics (age, sex, education, race, family history, body mass index, marital status, and ethnicity), subjective measures (Alzheimer's Disease Cooperative Study Activities of Daily Living-Prevention Instrument, Geriatric Depression Scale, and Memory Complaint Questionnaire), objective measures (free recall, Mini-Mental State Examination, immediate recall, digit symbol substitution, and delayed logical memory scores), and APOE4 status. Performance of the risk scores was evaluated in the independent test set.

RESULTS

PARS model 1 included age, body mass index (BMI), and family history and had an area under the curve (AUC) of 0.60 (95% CI 0.57-0.64). PARS model 2 included free recall in addition to the PARS model 1 variables and had an AUC of 0.61 (0.58-0.64). PARS model 3, which consisted of age, BMI, and APOE4 information, had an AUC of 0.73 (0.70-0.76). PARS model 3 showed the highest, but still moderate, performance metrics in comparison with other models with sensitivity of 72.0% (67.6%-76.4%), specificity of 62.1% (58.8%-65.4%), accuracy of 65.3% (62.7%-68.0%), and positive predictive value of 48.1% (44.1%-52.1%).

DISCUSSION

PARS models are a set of simple and practical risk scores that may improve our ability to identify individuals more likely to be amyloid positive. The models can potentially be used to enrich trials and serve as a screening step in research settings. This approach can be followed by the use of additional variables for the development of improved risk scores.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in cognitively unimpaired individuals PARS models predict Aβ positivity with moderate accuracy.

Differential diagnostic performance of a panel of plasma biomarkers for different types of dementia

Introduction

We explored what combination of blood‐based biomarkers (amyloid beta [Aβ]_{1‐42/1‐40}, phosphorylated tau [p‐tau]181, neurofilament light [NfL], glial fibrillary acidic protein [GFAP]) differentiates Alzheimer's disease (AD) dementia, frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB).

Methods

We measured the biomarkers with Simoa in two separate cohorts (n = 160 and n = 152). In one cohort, Aβ_{1‐42/1‐40} was also measured with mass spectrometry (MS). We assessed the differential diagnostic value of the markers, by logistic regression with Wald's backward selection.

Results

MS and Simoa Aβ_{1‐42/1‐40} similarly differentiated AD from controls. The Simoa panel that optimally differentiated AD from FTD consisted of NfL and p‐tau181 (area under the curve [AUC] = 0.94; cohort 1) or NfL, GFAP, and p‐tau181 (AUC = 0.90; cohort 2). For AD from DLB, the panel consisted of NfL, p‐tau181, and GFAP (AUC = 0.88; cohort 1), and only p‐tau181 (AUC = 0.81; cohort 2).

Discussion

A combination of plasma p‐tau181, NfL, and GFAP, but not Aβ_{1‐42/1‐40}, might be useful to discriminate AD, FTD, and DLB.

Plasma Aβ as a biomarker for predicting Aβ-PET status in Alzheimer's disease：a systematic review with meta-analysis

OBJECTIVE

Amyloid-β positron emission tomography (Aβ-PET) scan has been proposed to detect amyloid-β (Aβ) deposition in the brain. However, this approach is costly and not ideal for the early diagnosis of Alzheimer's disease. Blood-based Aβ measurement offers a scalable alternative to the costly or invasive biomarkers. The aim of this study was to statistically validate whether plasma Aβ could predict Aβ-PET status via meta-analysis.

METHODS

We systematically searched for eligible studies from PubMed, Embase and Cochrane Library, which reported plasma Aβ levels of amyloid-β positron emission tomography-positive (PET (+)) and amyloid-β positron emission tomography-negative (PET (-)) subjects. We generated pooled estimates using random effects meta-analyses. For any study that has significant heterogeneity, metaregression and subgroup analysis were further conducted. Publication bias was appraised by funnel plots and Egger's test.

RESULTS

16 studies with 3047 participants were included in the meta-analysis. Among all the enrolled studies, 10 studies reported plasma Aβ40 values, while 9 studies reported plasma Aβ42 values and 13 studies reported Aβ42/Aβ40 ratio. The pooled standardised mean difference (SMD) was 0.76 (95% CI -0.61 to 2.14, p=0.28) in the plasma Aβ40 values group. Plasma Aβ42 values group has a pooled SMD of -0.60 (95% CI -0.80 to -0.41, p<0.0001). In the plasma Aβ42/Aβ40 ratio group, the pooled SMD was -1.44 (95% CI -2.17 to -0.72, p<0.0001).

CONCLUSION

Plasma Aβ40 values might not distinguish between PET (+) and PET (-) people. However, plasma Aβ42 values and plasma Aβ42/Aβ40 ratio could be served as independent biomarkers for predicting Aβ-PET status.

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

METHODS

We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

RESULTS

Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

DISCUSSION

ADNI has had a profound impact in improving clinical trials for AD.

A study of the longitudinal changes in multiple cerebrospinal fluid and volumetric magnetic resonance imaging biomarkers on converter and non-converter Alzheimer's disease subjects with consideration for their amyloid beta status

INTRODUCTION

This study aims to determine whether newly introduced biomarkers Visinin-like protein-1 (VILIP-1), chitinase-3-like protein 1 (YKL-40), synaptosomal-associated protein 25 (SNAP-25), and neurogranin (NG) in cerebrospinal fluid are useful in evaluating the asymptomatic and early symptomatic stages of Alzheimer's disease (AD). It further aims to shed new insight into the differences between stable subjects and those who progress to AD by associating cerebrospinal fluid (CSF) biomarkers and specific magnetic resonance imaging (MRI) regions with disease progression, more deeply exploring how such biomarkers relate to AD pathology.

METHODS

We examined baseline and longitudinal changes over a 7-year span and the longitudinal interactions between CSF and MRI biomarkers for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We stratified all CSF (140) and MRI (525) cohort participants into five diagnostic groups (including converters) further dichotomized by CSF amyloid beta (Aβ) status. Linear mixed models were used to compare within-person rates of change across diagnostic groups and to evaluate the association of CSF biomarkers as predictors of magnetic resonance imaging (MRI) biomarkers. CSF biomarkers and disease-prone MRI regions are assessed for CSF proteins levels and brain structural changes.

RESULTS

VILIP-1 and SNAP-25 displayed within-person increments in early symptomatic, amyloid-positive groups. CSF amyloid-positive (Aβ+) subjects showed elevated baseline levels of total tau (tTau), phospho-tau181 (pTau), VILIP-1, and NG. YKL-40, SNAP-25, and NG are positively intercorrelated. Aβ+ subjects showed negative MRI biomarker changes. YKL-40, tTau, pTau, and VILIP-1 are longitudinally associated with MRI biomarkers atrophy.

DISCUSSION

Converters (CNc, MCIc) highlight the evolution of biomarkers during the disease progression. Results show that underlying amyloid pathology is associated with accelerated cognitive impairment. CSF levels of Aβ42, pTau, tTau, VILIP-1, and SNAP-25 show utility to discriminate between mild cognitive impairment (MCI) converter and control subjects (CN). Higher levels of YKL-40 in the Aβ+ group were longitudinally associated with declines in temporal pole and entorhinal thickness. Increased levels of tTau, pTau, and VILIP-1 in the Aβ+ groups were longitudinally associated with declines in hippocampal volume. These CSF biomarkers should be used in assessing the characterization of the AD progression.

Validation of Plasma Amyloid-β 42/40 for Detecting Alzheimer Disease Amyloid Plaques

BACKGROUND AND OBJECTIVES

To determine the diagnostic accuracy of a plasma Aβ42/Aβ40 assay in classifying amyloid PET status across global research studies using samples collected by multiple centers that utilize different blood collection and processing protocols.

METHODS

Plasma samples (n = 465) were obtained from 3 large Alzheimer disease (AD) research cohorts in the United States (n = 182), Australia (n = 183), and Sweden (n = 100). Plasma Aβ42/Aβ40 was measured by a high precision immunoprecipitation mass spectrometry (IPMS) assay and compared to the reference standards of amyloid PET and CSF Aβ42/Aβ40.

RESULTS

In the combined cohort of 465 participants, plasma Aβ42/Aβ40 had good concordance with amyloid PET status (receiver operating characteristic area under the curve [AUC] 0.84, 95% confidence interval [CI] 0.80-0.87); concordance improved with the inclusion of APOE ε4 carrier status (AUC 0.88, 95% CI 0.85-0.91). The AUC of plasma Aβ42/Aβ40 with CSF amyloid status was 0.85 (95% CI 0.78-0.91) and improved to 0.93 (95% CI 0.89-0.97) with APOE ε4 status. These findings were consistent across the 3 cohorts, despite differences in protocols. The assay performed similarly in both cognitively unimpaired and impaired individuals.

DISCUSSION

Plasma Aβ42/Aβ40 is a robust measure for detecting amyloid plaques and can be utilized to aid in the diagnosis of AD, identify those at risk for future dementia due to AD, and improve the diversity of populations enrolled in AD research and clinical trials.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that plasma Aβ42/Aβ40, as measured by a high precision IPMS assay, accurately diagnoses brain amyloidosis in both cognitively unimpaired and impaired research participants.

Selective reduction of astrocyte apoE3 and apoE4 strongly reduces Aβ accumulation and plaque-related pathology in a mouse model of amyloidosis

Background

One of the key pathological hallmarks of Alzheimer disease (AD) is the accumulation of the amyloid-β (Aβ) peptide into amyloid plaques. The apolipoprotein E (APOE) gene is the strongest genetic risk factor for late-onset AD and has been shown to influence the accumulation of Aβ in the brain in an isoform-dependent manner. ApoE can be produced by different cell types in the brain, with astrocytes being the largest producer of apoE, although reactive microglia also express high levels of apoE. While studies have shown that altering apoE levels in the brain can influence the development of Aβ plaque pathology, it is not fully known how apoE produced by specific cell types, such as astrocytes, contributes to amyloid pathology.

Methods

We utilized APOE knock-in mice capable of having APOE selectively removed from astrocytes in a tamoxifen-inducible manner and crossed them with the APP/PS1-21 mouse model of amyloidosis. We analyzed the changes to Aβ plaque levels and assessed the impact on cellular responses to Aβ plaques when astrocytic APOE is removed.

Results

Tamoxifen administration was capable of strongly reducing apoE levels in the brain by markedly reducing astrocyte apoE, while microglial apoE expression remained. Reduction of astrocytic apoE3 and apoE4 led to a large decrease in Aβ plaque deposition and less compact plaques. While overall Iba1⁺ microglia were unchanged in the cortex after reducing astrocyte apoE, the expression of the disease-associated microglial markers Clec7a and apoE were lower around amyloid plaques, indicating decreased microglial activation. Additionally, astrocyte GFAP levels are unchanged around amyloid plaques, but overall GFAP levels are reduced in the cortex of female apoE4 mice after a reduction in astrocytic apoE. Finally, while the amount of neuritic dystrophy around remaining individual plaques was increased with the removal of astrocytic apoE, the overall amount of cortical amyloid-associated neuritic dystrophy was significantly decreased.

Conclusion

This study reveals an important role of astrocytic apoE3 and apoE4 on the deposition and accumulation of Aβ plaques as well as on certain Aβ-associated downstream effects.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13024-022-00516-0.

Comparing tau status determined via plasma pTau181, pTau231 and [18F]MK6240 tau-PET

BACKGROUND

Tau in Alzheimer's disease (AD) is assessed via cerebrospinal fluid (CSF) and Positron emission tomography (PET). Novel methods to detect phosphorylated tau (pTau) in blood have been recently developed. We aim to investigate agreement of tau status as determined by [18F]MK6240 tau-PET, plasma pTau181 and pTau231.

METHODS

We assessed cognitively unimpaired young, cognitively unimpaired, mild cognitive impairment and AD individuals with [18F]MK6240, plasma pTau181, pTau 231, [18F]AZD4694 amyloid-PET and MRI. A subset underwent CSF assessment. We conducted ROC curves to obtain cut-off values for plasma pTau epitopes. Individuals were categorized as positive or negative in all biomarkers. We then compared the distribution among concordant and discordant groups in relation to diagnosis, Aβ status, APOEε4 status, [18F]AZD4694 global SUVR, hippocampal volume and CSF pTau181.

FINDINGS

The threshold for positivity was 15.085 pg/mL for plasma pTau181 and 17.652 pg/mL for plasma pTau231. Most individuals had concordant statuses, however, 18% of plasma181/PET, 26% of plasma231/PET and 25% of the pTau231/pTau181 were discordant. Positivity to at least one biomarker was often accompanied by diagnosis of cognitive impairment, Aβ positivity, APOEε4 carriership, higher levels of [18F]AZD4694 global SUVR, hippocampal atrophy and CSF pTau181.

INTERPRETATION

Plasma pTau181, pTau231 and [18F]MK6240 seem to reflect different stages of tau progression. Plasma biomarkers can be useful in the context of diagnostic information and clinical trials, to evaluate the disease stage. Moreover, they seem to confidently evaluate tau-PET positivity.

FUNDING

Moreover, this study was supported by Weston Brain Institute, Canadian Institute of Health Research and Fonds de Recherche du Québec.

Blood Biomarkers from Research Use to Clinical Practice: What Must Be Done? A Report from the EU/US CTAD Task Force

Timely and accurate diagnosis of Alzheimer's disease (AD) in clinical practice remains challenging. PET and CSF biomarkers are the most widely used biomarkers to aid diagnosis in clinical research but present limitations for clinical practice (i.e., cost, accessibility). Emerging blood-based markers have the potential to be accurate, cost-effective, and easily accessible for widespread clinical use, and could facilitate timely diagnosis. The EU/US CTAD Task Force met in May 2022 in a virtual meeting to discuss pathways to implementation of blood-based markers in clinical practice. Specifically, the CTAD Task Force assessed: the state-of-art for blood-based markers, the current use of blood-based markers in clinical trials, the potential use of blood-based markers in clinical practice, the current challenges with blood-based markers, and the next steps needed for broader adoption in clinical practice.

Revisiting the grammar of Tau aggregation and pathology formation: how new insights from brain pathology are shaping how we study and target Tauopathies

Converging evidence continues to point towards Tau aggregation and pathology formation as central events in the pathogenesis of Alzheimer's disease and other Tauopathies. Despite significant advances in understanding the morphological and structural properties of Tau fibrils, many fundamental questions remain about what causes Tau to aggregate in the first place. The exact roles of cofactors, Tau post-translational modifications, and Tau interactome in regulating Tau aggregation, pathology formation, and toxicity remain unknown. Recent studies have put the spotlight on the wide gap between the complexity of Tau structures, aggregation, and pathology formation in the brain and the simplicity of experimental approaches used for modeling these processes in research laboratories. Embracing and deconstructing this complexity is an essential first step to understanding the role of Tau in health and disease. To help deconstruct this complexity and understand its implication for the development of effective Tau targeting diagnostics and therapies, we firstly review how our understanding of Tau aggregation and pathology formation has evolved over the past few decades. Secondly, we present an analysis of new findings and insights from recent studies illustrating the biochemical, structural, and functional heterogeneity of Tau aggregates. Thirdly, we discuss the importance of adopting new experimental approaches that embrace the complexity of Tau aggregation and pathology as an important first step towards developing mechanism- and structure-based therapies that account for the pathological and clinical heterogeneity of Alzheimer's disease and Tauopathies. We believe that this is essential to develop effective diagnostics and therapies to treat these devastating diseases.

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.

Performance of Plasma Amyloid β, Total Tau, and Neurofilament Light Chain in the Identification of Probable Alzheimer's Disease in South China

Background: Alzheimer's disease (AD) is the most common type of dementia and has no effective treatment to date. It is essential to develop a minimally invasive blood-based biomarker as a tool for screening the general population, but the efficacy remains controversial. This cross-sectional study aimed to evaluate the ability of plasma biomarkers, including amyloid β (Aβ), total tau (t-tau), and neurofilament light chain (NfL), to detect probable AD in the South Chinese population.

Methods: A total of 277 patients with a clinical diagnosis of probable AD and 153 healthy controls with normal cognitive function (CN) were enrolled in this study. The levels of plasma Aβ42, Aβ40, t-tau, and NfL were detected using ultra-sensitive immune-based assays (SIMOA). Lumbar puncture was conducted in 89 patients with AD to detect Aβ42, Aβ40, t-tau, and phosphorylated (p)-tau levels in the cerebrospinal fluid (CSF) and to evaluate the consistency between plasma and CSF biomarkers through correlation analysis. Finally, the diagnostic value of plasma biomarkers was further assessed by constructing a receiver operating characteristic (ROC) curve.

Results: After adjusting for age, sex, and the apolipoprotein E (APOE) alleles, compared to the CN group, the plasma t-tau, and NfL were significantly increased in the AD group (p < 0.01, Bonferroni correction). Correlation analysis showed that only the plasma t-tau level was positively correlated with the CSF t-tau levels (r = 0.319, p = 0.003). The diagnostic model combining plasma t-tau and NfL levels, and age, sex, and APOE alleles, showed the best performance for the identification of probable AD [area under the curve (AUC) = 0.89, sensitivity = 82.31%, specificity = 83.66%].

Conclusion: Blood biomarkers can effectively distinguish patients with probable AD from controls and may be a non-invasive and efficient method for AD pre-screening.

Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases

Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

Plasma Proteomic Biomarkers Relating to Alzheimer's Disease: A Meta-Analysis Based on Our Own Studies

Background and Objective: Plasma biomarkers for the diagnosis and stratification of Alzheimer's disease (AD) are intensively sought. However, no plasma markers are well established so far for AD diagnosis. Our group has identified and validated various blood-based proteomic biomarkers relating to AD pathology in multiple cohorts. The study aims to conduct a meta-analysis based on our own studies to systematically assess the diagnostic performance of our previously identified blood biomarkers. Methods: To do this, we included seven studies that our group has conducted during the last decade. These studies used either Luminex xMAP or ELISA to measure proteomic biomarkers. As proteins measured in these studies differed, we selected protein based on the criteria that it must be measured in at least four studies. We then examined biomarker performance using random-effect meta-analyses based on the mean difference between biomarker concentrations in AD and controls (CTL), AD and mild cognitive impairment (MCI), MCI, and CTL as well as MCI converted to dementia (MCIc) and non-converted (MCInc) individuals. Results: An overall of 2,879 subjects were retrieved for meta-analysis including 1,053 CTL, 895 MCI, 882 AD, and 49 frontotemporal dementia (FTD) patients. Six proteins were measured in at least four studies and were chosen for meta-analyses for AD diagnosis. Of them, three proteins had significant difference between AD and controls, among which alpha-2-macroglobulin (A2M) and ficolin-2 (FCN2) increased in AD while fibrinogen gamma chain (FGG) decreased in AD compared to CTL. Furthermore, FGG significantly increased in FTD compared to AD. None of the proteins passed the significance between AD and MCI, or MCI and CTL, or MCIc and MCInc, although complement component 4 (CC4) tended to increase in MCIc individuals compared to MCInc. Conclusions: The results suggest that A2M, FCN2, and FGG are promising biomarkers to discriminate AD patients from controls, which are worthy of further validation.