Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence

Reliable classification of cognitive states in longitudinal Alzheimer's Disease (AD) studies is critical for early diagnosis and intervention. However, inconsistencies in diagnostic labeling, arising from subjective assessments, evolving clinical criteria, and measurement variability, introduce noise that can impact machine learning (ML) model performance. This study explores the potential of explainable artificial intelligence to detect and characterize noisy labels in longitudinal datasets. A predictive model is trained using a Leave-One-Subject-Out validation strategy, ensuring robustness across subjects while enabling individual-level interpretability. By leveraging SHapley Additive exPlanations values, we analyze the temporal variations in feature importance across multiple patient visits, aiming to identify transitions that may reflect either genuine cognitive changes or inconsistencies in labeling. Using statistical thresholds derived from cognitively stable individuals, we propose an approach to flag potential misclassifications while preserving clinical labels. Rather than modifying diagnoses, this framework provides a structured way to highlight cases where diagnostic reassessment may be warranted. By integrating explainability into the assessment of cognitive state transitions, this approach enhances the reliability of longitudinal analyses and supports a more robust use of ML in AD research.

CLU polymorphisms and plasma clusterin levels in patients with multiple sclerosis: association with disability scores, progression rate and fingolimod therapy

OBJECTIVES

Multiple sclerosis (MS) is a chronic, demyelinating disorder of the central nervous system that is widely accepted to result from a complex interplay of genetic and environmental factors. The involvement of clusterin in neurodegenerative and autoimmune diseases has been highlighted, but its role in MS remains unclear. This study aimed to investigate the association of four single nucleotide polymorphisms (SNPs) in the clusterin gene (CLU) with MS susceptibility.

METHODS

The study group consisted of 310 patients with RRMS (pwRRMS) and 310 controls. 25 treatment-naïve pwRRMS, 25 pwRRMS on fingolimod treatment and 25 controls composed a subgroup for further analysis. The genotypes of 4 CLU SNPs were determined using either restriction endonuclease digestion following PCR (rs11136000 & rs3087554) or the real-time PCR method using TaqMan genotyping assays (rs2279590 & rs1532278). Plasma clusterin concentration was determined by ELISA in the subgroup (n = 75).

RESULTS

Our results revealed that CLU rs3087554 C allele (p = .008) and TC  +  CC genotype were significantly associated with RRMS (p = .002). Furthermore, haplotype analysis has also shown that T-C-T-T haplotype was associated with RRMS (p < .001). Moreover, plasma clusterin concentrations were significantly higher in pwRRMS on fingolimod therapy compared to treatment-naïve pwRRMS and the control group. In addition, plasma clusterin concentration was increased in patients with rs11136000 & rs1532278 CC genotypes in the subgroup.

DISCUSSION

These findings suggest that CLU SNPs and plasma clusterin concentrations could serve as significant biomarkers at different stages of MS.

The Impact of Behavioral and Psychological Symptoms on Financial Decision-Making Capacity in Mild to Moderate Dementia

OBJECTIVES

The study aims to assess the impact of behavioral and psychological symptoms on financial decision-making in individuals with mild to moderate dementia.

METHODS

A cross-sectional quantitative study assessed cognitive status, behavioral and psychological symptoms as well as financial capacities. A multiple regression hierarchical model determined the relative contributions of demographic, cognitive, and behavioral and psychological symptoms to financial capacity.

RESULTS

A total of 87 participants, with a median age of 84 years, were included in the study. Nearly all participants (94.5%) exhibited one or more behavioral and psychological symptoms. Greater dementia severity, increased behavioral and psychological symptoms, and lower educational levels were associated with poorer financial capacity.

CONCLUSIONS

This study underlines the impact of behavioral and psychological symptoms on financial decision-making in individuals with mild to moderate dementia, even when accounting education and dementia severity. Further research is necessary to elucidate the connection between these symptoms and financial capacity.

CLINICAL IMPLICATIONS

The critical need for early diagnosis of dementia and its associated behavioral and psychological symptoms is highlighted. Additionally, implementing timely behavioral and psychological management strategies and encouraging patients to engage in lifetime intellectual enrichment may be helpful for preserving financial capacity and promoting independence in individuals with dementia.

Clinical utility of CSF Aβ38 in Japanese research and clinical cohorts

INTRODUCTION

Previous studies have reported that cerebrospinal fluid (CSF) amyloid beta (Aβ42/Aβ38) performs comparably to Aβ42/Aβ40 in predicting amyloid positron emission tomography (PET) positivity in White cohorts. However, this finding has not been validated in diverse populations. Moreover, the utility of CSF Aβ38 in diagnosing various neurological diseases has not been fully understood.

METHODS

We analyzed CSF Aβ38, Aβ40, Aβ42, phosphorylated tau181, and neurofilament light chain in Japanese research and clinical cohorts with Alzheimer's clinical syndrome (ACS) or non-ACS.

RESULTS

CSF Aβ42/Aβ38 predicted amyloid PET positivity comparably to Aβ42/Aβ40. The levels of CSF Aβ38 were significantly lower in patients with progressive supranuclear palsy (PSP) and idiopathic normal pressure hydrocephalus (iNPH) than in those with other diseases.

DISCUSSION

We validated the high diagnostic performance of CSF Aβ42/Aβ38 in Japanese patients with AD. CSF Aβ38 reduction may be a characteristic feature of PSP and iNPH.

Highlights

The diagnostic value of cerebrospinal fluid (CSF) amyloid beta (Aβ)38 was examined in Japanese research and clinical cohorts.CSF Aβ42/Aβ38 and Aβ42/Aβ40 showed comparable performance to detect brain Aβ deposition.CSF Aβ42/Aβ38 and Aβ42/Aβ40 discordant group showed a characteristic profile.CSF Aβ38 and Aβ40 were prominently decreased in progressive supranuclear palsy and idiopathic normal pressure hydrocephalus.

Defining benefit: Clinically and biologically meaningful outcomes in the next-generation Alzheimer's disease clinical care pathway

To understand the potential benefits of emerging Alzheimer's disease (AD) therapies within and beyond clinical trial settings, there is a need to advance current outcome measurements into meaningful information relevant to all stakeholders. The relationship between the impact on disease biology and clinically measurable outcomes in cognition, function, and behavior must be considered when defining the meaningful benefit of early AD therapies. In this review, we discuss: (1) the lack of consideration for biomarkers in the current concept of meaningfulness in AD; (2) the lack of gold standards for determining minimal biologically and clinically important differences (MBCIDs) in AD trials; (3) how the treatment benefits of disease-modifying treatments are cumulative and increase over time; and (4) the different concepts of meaningfulness among key stakeholders. This review utilizes the future clinical biological framework of AD and aims to further integrate and expand the parameters of meaningful benefits toward a precision medicine framework. HIGHLIGHTS: Definition of meaningful benefit from Alzheimer's disease (AD) treatment varies across disease stage and stakeholder perspectives. Observable and meaningful outcomes must consider the clinical-biological nature of AD. Statistically significant effects or outcomes do not always equate to clinically meaningfulness. Assessment tools must reflect stage-specific subtle changes following treatment. Real-world evidence will support consensus, definition, and interpretation of clinical meaningfulness.

Correlation Between Regulation of Intestinal Flora by Danggui-Shaoyao-San and Improvement of Cognitive Impairment in Mice With Alzheimer's Disease

PURPOSE

The abnormal central glucose metabolism in Alzheimer's disease (AD) is related to the brain-gut axis. This study aims to explore the target of Danggui-Shaoyao-San (DSS) in improving cognitive impairment.

METHOD

This study analyzed the differences in mice intestinal flora by 16S rRNA sequencing. The cognitive protective effects of DSS were observed through the Morris water maze and the new object recognition. The mitigation effects of DSS on Aβ and p-tau, regulatory effects on glucose metabolism targets, and intestinal structure effects were observed through brain and colon slices staining. The differences in neural ultrastructure were compared by transmission electron microscopy.

FINDING

The results showed that DSS affected the composition of intestinal dominant bacteria and bacteria genera and regulated the abundance of intestinal bacteria in AD mice. DSS improved the behavior of AD mice, alleviated the deposition of AD pathological products in the brain and colon, regulated the expression of glycometabolism-related proteins, and improved the colon barrier structure and neural ultrastructure in the brain of mice with AD.

CONCLUSION

Our findings suggest that DSS may affect AD central glucose metabolism and improve cognition by regulating the gut-brain axis.

Mild Cognitive Impairment: Quantifying a Qualitative Disorder

Mild cognitive impairment (MCI) has been described as a transitional state between normal aging and dementia, which can be both identified and tracked over time from qualitative and/or quantitative perspectives. Each definition of MCI involves some subjective cognitive complaint, some level of objective cognitive impairment, and generally intact daily functioning. Progression to dementia is common on follow-up in MCI, but stability and reversion to normal cognition can also occur. Quantitative methods might allow health care providers to evaluate and follow the subtle declines in MCI, as well as examine possible benefits of interventions with this at-risk condition.

The Alzheimer's Disease Neuroimaging Initiative Clinical Core

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical Core is responsible for coordination of all clinical activities at the ADNI sites, including project management, regulatory oversight, and site management and monitoring, as well as the collection of all clinical data and management of all study data. The Clinical Core is also charged with determining the clinical classifications and criteria for enrollment in evolving AD trials and enabling the ongoing characterization of the cross-sectional features and longitudinal trajectories of the ADNI cohorts with application of these findings to optimal clinical trial designs. More than 2400 individuals have been enrolled in the cohorts since the inception of ADNI, facilitating refinement of our understanding of the AD trajectory and allowing academic and industry investigators to model therapeutic trials across the disease spectrum from the presymptomatic stage through dementia. HIGHLIGHTS: Since 2004, the Alzheimer's Disease Neuroimaging Initiative (ADNI) Clinical Core has overseen the enrollment of > 2400 participants with mild cognitive impairment, mild Alzheimer's disease (AD) dementia, and normal cognition. The longitudinal dataset has elucidated the full cognitive and clinical trajectory of AD from its presymptomatic stage through the onset of dementia. The ADNI data have supported the design of most major trials in the field.

Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery

Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.

HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression

Objective

We aimed to develop and validate a novel multimodal framework Hierarchical Multi-task Auxiliary Learning (HiMAL) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD).

Materials and Methods

HiMAL utilized multimodal longitudinal visit data including imaging features, cognitive assessment scores, and clinical variables from MCI patients in the Alzheimer's Disease Neuroimaging Initiative dataset, to predict at each visit if an MCI patient will progress to AD within the next 6 months. Performance of HiMAL was compared with state-of-the-art single-task and multitask baselines using area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. An ablation study was performed to assess the impact of each input modality on model performance. Additionally, longitudinal explanations regarding risk of disease progression were provided to interpret the predicted cognitive decline.

Results

Out of 634 MCI patients (mean [IQR] age: 72.8 [67-78], 60% male), 209 (32%) progressed to AD. HiMAL showed better prediction performance compared to all state-of-the-art longitudinal single-modality singe-task baselines (AUROC = 0.923 [0.915-0.937]; AUPRC = 0.623 [0.605-0.644]; all P < .05). Ablation analysis highlighted that imaging and cognition scores with maximum contribution towards prediction of disease progression.

Discussion

Clinically informative model explanations anticipate cognitive decline 6 months in advance, aiding clinicians in future disease progression assessment. HiMAL relies on routinely collected electronic health records (EHR) variables for proximal (6 months) prediction of AD onset, indicating its translational potential for point-of-care monitoring and managing of high-risk patients.

MG-TCCA: Tensor Canonical Correlation Analysis across Multiple Groups

Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA and Sparse TCCA (STCCA) in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.

Lower Prevalence of Asymptomatic Alzheimer's Disease Among Healthy African Americans

OBJECTIVE

Alzheimer's disease (AD) is believed to be more common in African Americans (AA), but biomarker studies in AA populations are limited. This report represents the largest study to date examining cerebrospinal fluid AD biomarkers in AA individuals.

METHODS

We analyzed 3,006 cerebrospinal fluid samples from controls, AD cases, and non-AD cases, including 495 (16.5%) self-identified black/AA and 2,456 (81.7%) white/European individuals using cutoffs derived from the Alzheimer's Disease Neuroimaging Initiative, and using a data-driven multivariate Gaussian mixture of regressions.

RESULTS

Distinct effects of race were found in different groups. Total Tauand phospho181-Tau were lower among AA individuals in all groups (p < 0.0001), and Aβ42 was markedly lower in AA controls compared with white controls (p < 0.0001). Gaussian mixture of regressions modeling of cerebrospinal fluid distributions incorporating adjustments for covariates revealed coefficient estimates for AA race comparable with 2-decade change in age. Using Alzheimer's Disease Neuroimaging Initiative cutoffs, fewer AA controls were classified as biomarker-positive asymptomatic AD (8.0% vs 13.4%). After adjusting for covariates, our Gaussian mixture of regressions model reduced this difference, but continued to predict lower prevalence of asymptomatic AD among AA controls (9.3% vs 13.5%).

INTERPRETATION

Although the risk of dementia is higher, data-driven modeling indicates lower frequency of asymptomatic AD in AA controls, suggesting that dementia among AA populations may not be driven by higher rates of AD. ANN NEUROL 2024;96:463-475.

Early Alzheimer's Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment

Alzheimer's disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer's disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer's disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer's disease detection and highlights future research directions.

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis

BACKGROUND

With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research.

OBJECTIVE

The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally.

METHODS

This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels.

RESULTS

To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers.

CONCLUSIONS

The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.

PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge

Fairness is crucial in machine learning to prevent bias based on sensitive attributes in classifier predictions. However, the pursuit of strict fairness often sacrifices accuracy, particularly when significant prevalence disparities exist among groups, making classifiers less practical. For example, Alzheimer's disease (AD) is more prevalent in women than men, making equal treatment inequitable for females. Accounting for prevalence ratios among groups is essential for fair decision-making. In this paper, we introduce prior knowledge for fairness, which incorporates prevalence ratio information into the fairness constraint within the Empirical Risk Minimization (ERM) framework. We develop the Prior-knowledge-guided Fair ERM (PFERM) framework, aiming to minimize expected risk within a specified function class while adhering to a prior-knowledge-guided fairness constraint. This approach strikes a flexible balance between accuracy and fairness. Empirical results confirm its effectiveness in preserving fairness without compromising accuracy.

Topology-based Clustering of Functional Brain Networks in an Alzheimer's Disease Cohort

Alzheimer's disease is a progressive neurodegenerative disease with many identifying biomarkers for diagnosis. However, whole-brain phenomena, particularly in functional MRI modalities, are not fully understood nor characterized. Here we employ the novel application of topological data analysis (TDA)-based methods of persistent homology to functional brain networks from ADNI-3 cohort to perform a subtyping experiment using unsupervised clustering techniques. We then investigate variations in QT-PAD challenge features across the identified clusters. Using a Wasserstein distance kernel with a variety of clustering algorithms, we found that the 0th-homology Wasserstein distance kernel and spectral clustering yielded clusters with significant differences in whole brain and medial temporal lobe (MTL) volume, thus demonstrating an intrinsic link between whole brain functional topology and brain morphometric structure. These findings demonstrate the importance of MTL in functional connectivity and the efficacy of using TDA-based machine learning methods in network neuroscience and neurodegenerative disease subtyping.

Multivariate mediation analysis with voxel-based morphometry revealed the neurodegeneration pathways from genetic variants to Alzheimer's Disease

Neurodegenerative processes are increasingly recognized as potential causative factors in Alzheimer's disease (AD) pathogenesis. While many studies have leveraged mediation analysis models to elucidate the underlying mechanisms linking genetic variants to AD diagnostic outcomes, the majority have predominantly focused on regional brain measure as a mediator, thereby compromising the granularity of the imaging data. In our investigation, using the imaging genetics data from a landmark AD cohort, we contrasted both region-based and voxel-based brain measurements as imaging endophenotypes, and examined their roles in mediating genetic effects on AD outcomes. Our findings underscored that using voxel-based morphometry offers enhanced statistical power. Moreover, we delineated specific mediation pathways between SNP, brain volume, and AD outcomes, shedding light on the intricate relationship among these variables.

Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.

Association between cognitive function and body composition in older adults: data from NHANES (1999-2002)

Aim

To investigate the association between cognitive function and body composition in older adults.

Methods

We collected data on 2080 older adults (>60 years of age) from the National Health and Nutrition Examination Survey (NHANES) for the years 1999-2000 and 2001-2002. Candidate variables included: demographic data (sex, age, race, education level, marital status, poverty-to-income ratio), alcohol consumption, cardiovascular disease, diabetes, osteoporosis, total bone mineral density, and total fat mass. A logistic regression model was established to analyze the association between cognitive function and body composition in older adults. In addition, stratified logics regression analysis was performed by sex and age.

Results

Bone mineral density significantly affects cognitive function in older adults (p<0.01). When examining the data according to sex, this correlation is present for women (p < 0.01). For men, though, it is not significant (p = 0.081). Stratified by age, total bone mineral density was significantly correlated with cognitive function in 60-70 and 70-80 years old people, but not in older adults older than 80 years(for 60-70 years old, p = 0.019; for 70-80 years old, p = 0.022). There was no significant correlation between total bone mineral density and cognitive function (p = 0.575).

Conclusion

The decrease of total bone mineral density was significantly correlated with cognitive decline in the older adults, especially among women and older people in the 60 to 80 age group. There was no connection between total fat mass, total percent fat, total lean mass, appendicular lean mass, appendicular lean mass /BMI and cognitive function in the older adults.

Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study

Introduction

Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia.

Methods

In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined.

Results

Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions.

Discussion

The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.

Distance-weighted Sinkhorn loss for Alzheimer's disease classification

Traditional loss functions such as cross-entropy loss often quantify the penalty for each mis-classified training sample without adequately considering its distance from the ground truth class distribution in the feature space. Intuitively, the larger this distance is, the higher the penalty should be. With this observation, we propose a penalty called distance-weighted Sinkhorn (DWS) loss. For each mis-classified training sample (with predicted label A and true label B), its contribution to the DWS loss positively correlates to the distance the training sample needs to travel to reach the ground truth distribution of all the A samples. We apply the DWS framework with a neural network to classify different stages of Alzheimer's disease. Our empirical results demonstrate that the DWS framework outperforms the traditional neural network loss functions and is comparable or better to traditional machine learning methods, highlighting its potential in biomedical informatics and data science.

The relationship between atherosclerosis and dementia

OBJECTIVE

The main objective is to confirm a hypothesis that atherosclerosis, through various mechanisms, considerably influences cognitive impairment and significantly increases the risk for developing dementia. Complete sample should be 920 individuals. The present study aimed to analyse epidemiological data from a questionnaire survey.

METHODS

The work was carried out in the form of an epidemiological case control study. Subjects are enrolled in the study based on results of the following examinations carried out in neurology departments and outpatient centres during the project NU20-09-00119 from 2020 to 2023. Respondents were divided into four research groups according to the results of clinical examination for the presence of atherosclerosis and dementia. The survey was mainly concerned with risk factors for both atherosclerosis and dementia. It contained questions on lifestyle factors, cardiovascular risk factors, leisure activities, and hobbies.

RESULTS

Analysis of the as yet incomplete sample of 877 subjects has yielded the following selected results: on average, 16% of subjects without dementia had primary education while the proportion was 45.2% in the group with both dementia and atherosclerosis. Subjects with dementia did mainly physical work. Low physical activity was more frequently noted in dementia groups (Group 2 - 54.4% and Group 3 - 47.2%) than in subjects without dementia (Group 1 - 19.6% and Group 4 - 25.8%). Coronary heart disease was more frequently reported by dementia patients (33.95%) than those without dementia (16.05%).

CONCLUSION

Cognitively impaired individuals, in particular those with vascular cognitive impairment, have poorer quality of life and shorter survival. Risk factors contributing to such impairment are similar to those for ischaemic or haemorrhagic stroke. It may be concluded that most of the analysed risk factors play a role in the development of both atherosclerosis and dementia.

Polygenic effects on the risk of Alzheimer's disease in the Japanese population

BACKGROUND

Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer's disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans.

METHODS

In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD.

RESULTS

The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results.

CONCLUSIONS

We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.

Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis

We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals' cognitive status.

Thinner inner retinal layers are associated with lower cognitive performance, lower brain volume, and altered white matter network structure-The Maastricht Study

INTRODUCTION

The retina may provide non-invasive, scalable biomarkers for monitoring cerebral neurodegeneration.

METHODS

We used cross-sectional data from The Maastricht study (n = 3436; mean age 59.3 years; 48% men; and 21% with type 2 diabetes [the latter oversampled by design]). We evaluated associations of retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses with cognitive performance and magnetic resonance imaging indices (global grey and white matter volume, hippocampal volume, whole brain node degree, global efficiency, clustering coefficient, and local efficiency).

RESULTS

After adjustment, lower thicknesses of most inner retinal layers were significantly associated with worse cognitive performance, lower grey and white matter volume, lower hippocampal volume, and worse brain white matter network structure assessed from lower whole brain node degree, lower global efficiency, higher clustering coefficient, and higher local efficiency.

DISCUSSION

The retina may provide biomarkers that are informative of cerebral neurodegenerative changes in the pathobiology of dementia.

A Pragmatic, Investigator-Driven Process for Disclosure of Amyloid PET Scan Results to ADNI-4 Research Participants

BACKGROUND

Prior studies of Alzheimer's disease (AD) biomarker disclosure have answered important questions about individuals' safety after learning and comprehending their amyloid PET results; however, these studies have typically employed highly structured disclosure protocols and focused on the psychological impact of disclosure (e.g., anxiety, depression, and suicidality) in homogeneous populations. More work is needed to develop flexible disclosure protocols and study outcomes in ethnoculturally representative samples.

METHODS

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is formally incorporating amyloid PET disclosure into the newest protocol (ADNI-4). Participants across the cognitive spectrum who wish to know their amyloid PET results may learn them. The pragmatic disclosure process spans four timepoints: (1) a pre-disclosure visit, (2) the PET scan and its read, (3) a disclosure visit, and (4) a post-disclosure check-in. This process applies to all participants, with slight modifications to account for their cognitive status. In designing this process, special emphasis was placed on utilizing investigator discretion. Participant measures include perceived risk of dementia, purpose in life, and disclosure satisfaction. Investigator assessment of the disclosure visit (e.g., challenges encountered, topics discussed, etc.) is also included.

RESULTS

Data collection is ongoing. Results will allow for more robust characterization of the impact of learning amyloid PET results on individuals and describe the perspectives of investigators.

CONCLUSION

The pragmatic design of the disclosure process in ADNI-4 coupled with the novel participant and investigator data will inform future disclosure practices. This is especially important as disclosure of biomarker results expands in research and care.

Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden

Alzheimer's disease (AD) is the most common type of dementia, characterized by early memory impairments and gradual worsening of daily functions. AD-related pathology, such as amyloid-beta (Aβ) plaques, begins to accumulate many years before the onset of clinical symptoms. Predicting risk for AD via related pathology is critical as the preclinical stage could serve as a therapeutic time window, allowing for early management of the disease and reducing health and economic costs. Current methods for detecting AD pathology, however, are often expensive and invasive, limiting wide and easy access to a clinical setting. A non-invasive, cost-efficient platform, such as computerized cognitive tests, could be potentially useful to identify at-risk individuals as early as possible. In this study, we examined the diagnostic value of an episodic memory task, the mnemonic discrimination task (MDT), for predicting risk of cognitive impairment or Aβ burden. We constructed a random forest classification algorithm, utilizing MDT performance metrics and various neuropsychological test scores as input features, and assessed model performance using area under the curve (AUC). Models based on MDT performance metrics achieved classification results with an AUC of 0.83 for cognitive status and an AUC of 0.64 for Aβ status. Our findings suggest that mnemonic discrimination function may be a useful predictor of progression to prodromal AD or increased risk of Aβ load, which could be a cost-efficient, noninvasive cognitive testing solution for potentially wide-scale assessment of AD pathological and cognitive risk.

Fair Canonical Correlation Analysis

This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables. We present a framework that alleviates unfairness by minimizing the correlation disparity error associated with protected attributes. Our approach enables CCA to learn global projection matrices from all data points while ensuring that these matrices yield comparable correlation levels to group-specific projection matrices. Experimental evaluation on both synthetic and real-world datasets demonstrates the efficacy of our method in reducing correlation disparity error without compromising CCA accuracy.

Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection

Alzheimer's disease (AD) leads to irreversible cognitive decline, with Mild Cognitive Impairment (MCI) as its prodromal stage. Early detection of AD and related dementia is crucial for timely treatment and slowing disease progression. However, classifying cognitive normal (CN), MCI, and AD subjects using machine learning models faces class imbalance, necessitating the use of balanced accuracy as a suitable metric. To enhance model performance and balanced accuracy, we introduce a novel method called VS-Opt-Net. This approach incorporates the recently developed vector-scaling (VS) loss into a machine learning pipeline named STREAMLINE. Moreover, it employs Bayesian optimization for hyperparameter learning of both the model and loss function. VS-Opt-Net not only amplifies the contribution of minority examples in proportion to the imbalance level but also addresses the challenge of generalization in training deep networks. In our empirical study, we use MRI-based brain regional measurements as features to conduct the CN vs MCI and AD vs MCI binary classifications. We compare the balanced accuracy of our model with other machine learning models and deep neural network loss functions that also employ class-balanced strategies. Our findings demonstrate that after hyperparameter optimization, the deep neural network using the VS loss function substantially improves balanced accuracy. It also surpasses other models in performance on the AD dataset. Moreover, our feature importance analysis highlights VS-Opt-Net's ability to elucidate biomarker differences across dementia stages.

Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

A new weakly supervised deep neural network for recognizing Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease that mainly affects older adults, causing memory loss and decline in thinking skills. In recent years, many traditional machine learning and deep learning methods have been used to assist in the diagnosis of AD, and most existing methods focus on early prediction of disease on a supervised basis. In reality, there is a massive amount of medical data available. However, some of those data have problems with the low-quality or lack of labels, and the cost of labeling them will be too high. To solve above problem, a new Weakly Supervised Deep Learning model (WSDL) is proposed, which adds attention mechanisms and consistency regularization to the EfficientNet framework and uses data augmentation techniques on the original data that can take full advantage of this unlabeled data. Validation of the proposed WSDL method on the brain MRI datasets of the Alzheimer's Disease Neuroimaging Program by setting five different unlabeled ratios to complete weakly supervised training showed better performance according to the compared experimental results with others baselines.

Multi-Group Tensor Canonical Correlation Analysis

Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.

Fairness-Aware Class Imbalanced Learning on Multiple Subgroups

We present a novel Bayesian-based optimization framework that addresses the challenge of generalization in overparameterized models when dealing with imbalanced subgroups and limited samples per subgroup. Our proposed tri-level optimization framework utilizes local predictors, which are trained on a small amount of data, as well as a fair and class-balanced predictor at the middle and lower levels. To effectively overcome saddle points for minority classes, our lower-level formulation incorporates sharpness-aware minimization. Meanwhile, at the upper level, the framework dynamically adjusts the loss function based on validation loss, ensuring a close alignment between the global predictor and local predictors. Theoretical analysis demonstrates the framework's ability to enhance classification and fairness generalization, potentially resulting in improvements in the generalization bound. Empirical results validate the superior performance of our tri-level framework compared to existing state-of-the-art approaches. The source code can be found at https://github.com/PennShenLab/FACIMS.

Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline

Amyloid imaging has been widely used in Alzheimer's disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.

The image and data archive at the laboratory of neuro imaging

The Image and Data Archive (IDA) is a secure online resource for archiving, exploring, and sharing neuroscience data run by the Laboratory of Neuro Imaging (LONI). The laboratory first started managing neuroimaging data for multi-centered research studies in the late 1990's and since has become a nexus for many multi-site collaborations. By providing management and informatics tools and resources for de-identifying, integrating, searching, visualizing, and sharing a diverse range of neuroscience data, study investigators maintain complete control over data stored in the IDA while benefiting from a robust and reliable infrastructure that protects and preserves research data to maximize data collection investment.

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging

Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI via intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. The accelerated protocol resulted in a 1.94 × gain in imaging throughput. This translated to a 72.51% increase in MR Value-defined in this work as the ratio of the sum of median object-masked local SNR values across all contrasts to the protocol's acquisition duration. We also computed PSNR, local SNR, MS-SSIM, and variance of the Laplacian values for image quality evaluation on 25 retrospective datasets. The minimum/maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. MS-SSIM gains were: 0.003/0.065 and 0.01/0.066; variance of the Laplacian (lower is better): 0.104/-0.135 and 0.13/-0.143. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimer's Disease project. Therefore, we also demonstrate the potential for AD-imaging via automated volumetry of relevant brain anatomies. We performed statistical analysis on these volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols, and found that 27 locations were in excellent agreement. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.

Antibodies against Periodontal Microorganisms and Cognition in Older Adults

INTRODUCTION

Markers of poor oral health are associated with impaired cognition and higher risk of Alzheimer disease (AD) and thus may help predict AD.

OBJECTIVES

The aim of this study was to evaluate the cross-sectional association between empirically derived groups of 19 IgG antibodies against periodontal microorganisms and cognition in middle-aged and older adults.

METHODS

The study population consisted of participants of the third National Health and Nutrition Examination Survey (NHANES III) (1988 to 1994), who were 60 y and older, among whom cognition and IgG antibodies against 19 periodontal microorganisms were measured (N = 5,162).

RESULTS

In multivariable quantile regression analyses, the Orange-Red (Prevotella melaninogenica, Prevotella intermedia, Prevotella nigrescens, Porphyromonas gingivalis) and Yellow-Orange (Staphylococcus intermedius, Streptococcus oralis, Streptococcus mutans, Fusobacterium nucleatum, Peptostreptococcus micros, Capnocytophaga ochracea) cluster scores were negatively associated with cognition. A 1-unit higher cluster score for the Orange-Red cluster was associated on average with a lower cognitive score (β for 30th quantile = -0.2640; 95% confidence interval [CI], -0.3431 to -0.1848). Similarly, a 1-unit higher score for the Yellow-Orange cluster was associated with a lower cognitive score (β for 30th quantile = -0.2445; 95% CI, -0.3517 to -0.1372).

CONCLUSION

Groups of IgG antibodies against periodontal microorganisms were associated with lower cognition among free living adults 60 years and older, who were previously undiagnosed with cognitive impairment. Though poor oral health precedes the development of dementia and AD, oral health information is currently not used, to our knowledge, to predict dementia or AD risk. Combining our findings with current algorithms may improve risk prediction for dementia and AD.

KNOWLEDGE TRANSLATION STATEMENT

IgG antibodies against periodontal microorganisms were associated with lower cognition among adults 60 years and older previously undiagnosed with cognitive impairment. Periodontal disease may predict cognition among older adults.

Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis

MOTIVATION

With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way.

METHOD

Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods.

RESULTS

We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models.

AVAILABILITY

Code are publicly available at https://github.com/JingxuanBao/SBFA.

CONTACT

qlong@upenn.edu.

Association of Circulating Caprylic Acid with Risk of Mild Cognitive Impairment and Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort

OBJECTIVE

Medium-chain fatty acids (MCFAs) can rapidly cross the blood-brain barrier and provide an alternative energy source for the brain. This study aims to determine 1) whether plasma caprylic acid (C8:0) is associated with risk of incident mild cognitive impairment (MCI) among baseline cognitively normal (CN) participants, and incident Alzheimer's Disease (AD) among baseline MCI participants; and 2) whether these associations differ by sex, comorbidity of cardiometabolic diseases, apolipoprotein E (APOE) ε4 alleles, and ADAS-Cog 13.

METHODS

Within the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, plasma C8:0 was measured at baseline in 618 AD-free participants aged 55 to 91. Logistic regression models were used to estimate odds ratios (ORs) and 95% CIs with incident MCI and AD as dependent variables, separately.

RESULTS

The inverse association between circulating C8:0 and risk of incident MCI was of borderline significance. The inverse association between circulating levels of C8:0 and risk of incident MCI was significant among CN participants with ≥1 cardiometabolic diseases [OR (95% CI): 0.75 (0.58-0.98) (P=0.03)], those with one copy of APOE ε4 alleles [OR (95% CI): 0.43 (0.21-0.89) (P=0.02)], female [OR (95% CI): 0.60 (0.38-0.94) (P=0.02)], and ADAS-Cog 13 above the median [OR (95%CI): 0.69 (0.50-0.97)(P=0.03)] after adjusting for all covariates.

CONCLUSION

The inverse associations were present only among subgroups of CN participants, including female individuals, those with one or more cardiometabolic diseases, or one APOE ε4 allele, or higher ADAS-Cog 13 scores. If confirmed, this finding will facilitate precision prevention of MCI, in turn, AD among CN older adults.

Increasing participant diversity in AD research: Plans for digital screening, blood testing, and a community-engaged approach in the Alzheimer's Disease Neuroimaging Initiative 4

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to validate biomarkers for Alzheimer's disease (AD) clinical trials. To improve generalizability, ADNI4 aims to enroll 50-60% of its new participants from underrepresented populations (URPs) using new biofluid and digital technologies. ADNI4 has received funding from the National Institute on Aging beginning September 2022.

METHODS

ADNI4 will recruit URPs using community-engaged approaches. An online portal will screen 20,000 participants, 4000 of whom (50-60% URPs) will be tested for plasma biomarkers and APOE. From this, 500 new participants will undergo in-clinic assessment joining 500 ADNI3 rollover participants. Remaining participants (∼3500) will undergo longitudinal plasma and digital cognitive testing. ADNI4 will add MRI sequences and new PET tracers. Project 1 will optimize biomarkers in AD clinical trials.

RESULTS AND DISCUSSION

ADNI4 will improve generalizability of results, use remote digital and blood screening, and continue providing longitudinal clinical, biomarker, and autopsy data to investigators.

Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer's Disease

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment

BACKGROUND

In Alzheimer's Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities.

RESULTS

DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify.

CONCLUSION

Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI.

Schisandrin alleviates the cognitive impairment in rats with Alzheimer’s disease by altering the gut microbiota composition to modulate the levels of endogenous metabolites in the plasma, brain, and feces

Schisandrin is one of the main active compounds isolated from the fruit of Schisandrae chinensis Fructus, which is scientifically proven to have beneficial effects on Alzheimer’s disease (AD) treatment at the cellular and whole organism level. However, the oral availability of schisandrin is very low, thus implying that the underlying mechanism of therapeutic effect on AD treatment is yet to be clarified fully. Therefore, we speculated that the therapeutic effect of schisandrin on AD is mainly by regulating the imbalance of the gut microbiota (GM). In this study, behavioral experiments and H&E staining were used to confirm the pharmacological effects of schisandrin on rats with AD. 16S rDNA gene sequencing and feces, plasma, and brain metabolomics techniques were utilized to investigate the therapeutic effects and the underlying mechanisms of schisandrin on cognitive impairment in rats with AD. The results indicated that schisandrin improved cognitive impairment and hippocampal cell loss in rats. The UPLC-QTOF/MS-based metabolomics studies of the feces, plasma, and brain revealed that 44, 96, and 40 potential biomarkers, respectively, were involved in the treatment mechanism of schisandrin. Schisandrin improved the metabolic imbalance in rats with AD, and the metabolic changes mainly affected the primary bile acid biosynthesis, sphingolipid metabolism, glycerophospholipid metabolism, and unsaturated fatty acid biosynthesis. Schisandrin can improve the GM structure disorder and increase the abundance of beneficial bacteria in the gut of rats with AD. The predictive metagenomics analysis indicated that the altered GM was mainly involved in lipid metabolism, steroid hormone biosynthesis, arachidonic acid metabolism, biosynthesis of unsaturated fatty acids, and bacterial invasion of epithelial cells. Spearman’s correlation analysis showed a significant correlation between affected bacteria and metabolites in various metabolic pathways. Overall, the data underline that schisandrin improves the cognitive impairment in rats with AD by affecting the composition of the GM community, thus suggesting the potential therapeutic effect of schisandrin on the brain–gut axis in rats with AD at the metabolic level.

Different AT(N) profiles and clinical progression classified by two different N markers using total tau and neurofilament light chain in cerebrospinal fluid

Background

The AT(N) classification was proposed for categorising individuals according to biomarkers. However, AT(N) profiles may vary depending on the markers chosen and the target population.

Methods

We stratified 177 individuals who participated in the Japanese Alzheimer's Disease Neuroimaging Initiative by AT(N) classification according to cerebrospinal fluid (CSF) biomarkers. We compared the frequency of AT(N) profiles between the classification using total tau and neurofilament light chain (NfL) as N markers (AT(N)_tau and AT(N)_NfL). Baseline characteristics, and longitudinal biological and clinical changes were examined between AT(N) profiles.

Results

We found that 9% of cognitively unimpaired subjects, 49% of subjects with mild cognitive impairment, and 61% of patients with Alzheimer's disease (AD) dementia had the biological AD profile (ie, A+T+) in the cohort. The frequency of AT(N) profiles substantially differed between the AT(N)_tau and AT(N)_NfL classifications. When we used t-tau as the N marker (AT(N)_tau), those who had T- were more frequently assigned to (N)-, whereas those who had T+were more frequently assigned to (N)+ than when we used NfL as the N marker (AT(N)_NfL). During a follow-up, the AD continuum group progressed clinically and biologically compared with the normal biomarker group in both the AT(N)_tau and AT(N)_NfL classifications. More frequent conversion to dementia was observed in the non-AD pathological change group in the AT(N)_tau classification, but not in the AT(N)_NfL classification.

Conclusions

AT(N)_tau and AT(N)_NfL in CSF may capture different aspects of neurodegeneration and provide a different prognostic value. The AT(N) classification aids in understanding the AD continuum biology in various populations.

Predicting conversion from mild cognitive impairment to Alzheimer's disease with multimodal latent factors

INTRODUCTION

We studied the ability of latent factor scores to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and investigated whether multimodal factor scores improve predictive power, relative to single-modal factor scores.

METHOD

We conducted exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) of the baseline data of MCI subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to generate factor scores for three data modalities: neuropsychological (NP), magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF). Factor scores from single or multiple modalities were entered in logistic regression models to predict MCI to AD conversion for 160 ADNI subjects over a 2-year interval.

RESULTS

NP factors attained an area under the curve (AUC) of .80, with a sensitivity of .66 and a specificity of .77. MRI factors reached a comparable level of performance (AUC = .80, sensitivity = .66, specificity = .78), whereas CSF factors produced weaker prediction (AUC = .70, sensitivity = .56, specificity = .79). Combining NP factors with MRI or CSF factors produced better prediction than either MRI or CSF factors alone. Similarly, adding MRI factors to NP or CSF factors produced improvements in prediction relative to NP or CSF factors alone. However, adding CSF factors to either NP or MRI factors produced no improvement in prediction.

CONCLUSIONS

Latent factor scores provided good accuracy for predicting MCI to AD conversion. Adding NP or MRI factors to factors from other modalities enhanced predictive power but adding CSF factors did not.

Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling

Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer's disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual.

Getting Fit to Counteract Cognitive Aging: Evidence and Future Directions

Physical activity has shown tremendous promise for counteracting cognitive aging, but also tremendous variability in cognitive benefits. We describe evidence for how exercise affects cognitive and brain aging, and whether cardiorespiratory fitness is a key factor. We highlight a brain network framework as a valuable paradigm for the mechanistic insight needed to tailor physical activity for cognitive benefits.

Spatially resolved neural slowing predicts impairment and amyloid burden in Alzheimer's disease

An extensive electrophysiological literature has proposed a pathological 'slowing' of neuronal activity in patients on the Alzheimer's disease spectrum. Supported by numerous studies reporting increases in low-frequency and decreases in high-frequency neural oscillations, this pattern has been suggested as a stable biomarker with potential clinical utility. However, no spatially resolved metric of such slowing exists, stymieing efforts to understand its relation to proteinopathy and clinical outcomes. Further, the assumption that this slowing is occurring in spatially overlapping populations of neurons has not been empirically validated. In the current study, we collected cross-sectional resting state measures of neuronal activity using magnetoencephalography from 38 biomarker-confirmed patients on the Alzheimer's disease spectrum and 20 cognitively normal biomarker-negative older adults. From these data, we compute and validate a new metric of spatially resolved oscillatory deviations from healthy ageing for each patient on the Alzheimer's disease spectrum. Using this Pathological Oscillatory Slowing Index, we show that patients on the Alzheimer's disease spectrum exhibit robust neuronal slowing across a network of temporal, parietal, cerebellar and prefrontal cortices. This slowing effect is shown to be directly relevant to clinical outcomes, as oscillatory slowing in temporal and parietal cortices significantly predicted both general (i.e. Montreal Cognitive Assessment scores) and domain-specific (i.e. attention, language and processing speed) cognitive function. Further, regional amyloid-β accumulation, as measured by quantitative 18F florbetapir PET, robustly predicted the magnitude of this pathological neural slowing effect, and the strength of this relationship between amyloid-β burden and neural slowing also predicted attentional impairments across patients. These findings provide empirical support for a spatially overlapping effect of oscillatory neural slowing in biomarker-confirmed patients on the Alzheimer's disease spectrum, and link this effect to both regional proteinopathy and cognitive outcomes in a spatially resolved manner. The Pathological Oscillatory Slowing Index also represents a novel metric that is of potentially high utility across a number of clinical neuroimaging applications, as oscillatory slowing has also been extensively documented in other patient populations, most notably Parkinson's disease, with divergent spectral and spatial features.