The Alzheimer's Disease Neuroimaging Initiative phase 2: Increasing the length, breadth, and depth of our understanding

Contributions of the ADNI Biostatistics Core

The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.

Prediction of future dementia among patients with mild cognitive impairment (MCI) by integrating multimodal clinical data

Efficiently and objectively analyzing the complex, diverse multimodal data collected from patients at risk for dementia can be difficult in the clinical setting, contributing to high rates of underdiagnosis or misdiagnosis of this serious disorder. Patients with mild cognitive impairment (MCI) are especially at risk of developing dementia in the future. This study evaluated the ability of multi-modal machine learning (ML) methods, especially the Ensemble Integration (EI) framework, to predict future dementia development among patients with MCI. EI is a machine learning framework designed to leverage complementarity and consensus in multimodal data, which may not be adequately captured by methods used by prior dementia-related prediction studies. We tested EI's ability to predict future dementia development among MCI patients using multimodal clinical and imaging data, such as neuroanatomical measurements from structural magnetic resonance imaging (MRI) and positron emission tomography (PET) scans, from The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) challenge. For predicting future dementia development among MCI patients, on a held out test set, the EI-based model performed better (AUC = 0.81, F-measure = 0.68) than the more commonly used XGBoost (AUC = 0.68, F-measure = 0.57) and deep learning (AUC = 0.79, F-measure = 0.61) approaches. This EI-based model also suggested MRI-derived volumes of regions in the middle temporal gyrus, posterior cingulate gyrus and inferior lateral ventricle brain regions to be predictive of progression to dementia.

Harmonization of Structural Brain Connectivity through Distribution Matching

The increasing prevalence of multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power for investigating brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods exist for dMRI data harmonization, few specifically address structural brain connectivity. We introduce a new distribution-matching approach to harmonizing structural brain connectivity across different sites and scanners. We evaluate our method using structural brain connectivity data from two distinct datasets of OASIS-3 and ADNI-2, comparing its performance to the widely used ComBat method. Our approach is meant to align the statistical properties of connectivity data from these two datasets. We examine the impact of harmonization on the correlation of brain connectivity with the Mini-Mental State Examination score and age. Our results demonstrate that our distribution-matching technique more effectively harmonizes structural brain connectivity, often producing stronger and more significant correlations compared to ComBat. Qualitative assessments illustrate the desired distributional alignment of ADNI-2 with OASIS-3, while quantitative evaluations confirm robust performance. This work contributes to the growing field of dMRI harmonization, potentially improving the reliability and comparability of structural connectivity studies that combine data from different sources in neuroscientific and clinical research.

A review of deep learning-based information fusion techniques for multimodal medical image classification

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various imaging modalities to provide a more comprehensive understanding of the underlying pathology. Recently, deep learning-based multimodal fusion techniques have emerged as powerful tools for improving medical image classification. This review offers a thorough analysis of the developments in deep learning-based multimodal fusion for medical classification tasks. We explore the complementary relationships among prevalent clinical modalities and outline three main fusion schemes for multimodal classification networks: input fusion, intermediate fusion (encompassing single-level fusion, hierarchical fusion, and attention-based fusion), and output fusion. By evaluating the performance of these fusion techniques, we provide insight into the suitability of different network architectures for various multimodal fusion scenarios and application domains. Furthermore, we delve into challenges related to network architecture selection, handling incomplete multimodal data management, and the potential limitations of multimodal fusion. Finally, we spotlight the promising future of Transformer-based multimodal fusion techniques and give recommendations for future research in this rapidly evolving field.

Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example

BACKGROUND

Deep-learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain-specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real-world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders.

OBJECTIVES AND METHODS

In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network.

RESULTS

In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline.

CONCLUSIONS

We are confident that this study can help more researchers enter the field and create accessible fMRI and deep-learning diagnostic models for neurological disorders.

Mitigating diabetes associated with reactive oxygen species (ROS) and protein aggregation through pharmacological interventions

Diabetes mellitus, a complex metabolic disorder, presents a growing global health challenge. In 2021, there were 529 million diabetics worldwide. At the super-regional level, Oceania, the Middle East, and North Africa had the highest age-standardized rates. The majority of cases of diabetes in 2021 (>90.0%) were type 2 diabetes, which is largely indicative of the prevalence of diabetes in general, particularly in older adults (K. L. Ong, et al., Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021, Lancet, 2023, 402(10397), 203-234). Nowadays, slowing the progression of diabetic complications is the only effective way to manage diabetes with the available therapeutic options. However, novel biomarkers and treatments are urgently needed to control cytokine secretion, advanced glycation end products (AGEs) production, vascular inflammatory effects, and cellular death. Emerging research has highlighted the intricate interplay between reactive oxygen species (ROS) and protein aggregation in the pathogenesis of diabetes. In this scenario, the main aim of this paper is to provide a comprehensive review of the current understanding of the molecular mechanisms underlying ROS-induced cellular damage and protein aggregation, specifically focusing on their contribution to diabetes development. The role of ROS as key mediators of oxidative stress in diabetes is discussed, emphasizing their impact on cellular components and signaling. Additionally, the involvement of protein aggregation in impairing cellular function and insulin signaling is explored. The synergistic effects of ROS and protein aggregation in promoting β-cell dysfunction and insulin resistance are examined, shedding light on potential targets for therapeutic intervention.

Normative modeling of thalamic nuclear volumes

Thalamic nuclei have been implicated in neurodegenerative and neuropsychiatric disorders. Normative models for thalamic nuclear volumes have not been proposed thus far. The aim of this work was to establish normative models of thalamic nuclear volumes and subsequently investigate changes in thalamic nuclei in cognitive and psychiatric disorders. Volumes of the bilateral thalami and 12 nuclear regions were generated from T1 MRI data using a novel segmentation method (HIPS-THOMAS) in healthy control subjects (n=2374) and non-control subjects (n=695) with early and late mild cognitive impairment (EMCI, LMCI), Alzheimer's disease (AD), Early psychosis and Schizophrenia, Bipolar disorder, and Attention deficit hyperactivity disorder. Three different normative modelling methods were evaluated while controlling for sex, intracranial volume, and site. Z-scores and extreme z-score deviations were calculated and compared across phenotypes. GAMLSS models performed the best. Statistically significant shifts in z-score distributions consistent with atrophy were observed for most phenotypes. Shifts of progressively increasing magnitude were observed bilaterally from EMCI to AD with larger shifts in the left thalamic regions. Heterogeneous shifts were observed in psychiatric diagnoses with a predilection for the right thalamic regions. Here we present the first normative models of thalamic nuclear volumes and highlight their utility in evaluating heterogenous disorders such as Schizophrenia.

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.

Could Accelerated Long-Term Forgetting Be a Feature of the Higher Rate of Memory Complaints Associated with Subjective Cognitive Decline? An Exploratory Study

Background

Recently, subjective cognitive decline (SCD) was proposed as an early risk factor for future Alzheimer's disease (AD).

Objective

In this study, we investigated whether accelerated long-term forgetting (ALF), assessed with extended testing intervals than those adopted in clinical practice, might be a cognitive feature of SCD. Using an explorative MRI analysis of the SCD sample, we attempted to investigate the areas most likely involved in the ALF pattern.

Methods

We recruited 31 individuals with SCD from our memory clinic and subdivided them based on their rate of memory complaints into mild SCDs (n = 18) and severe SCDs (n = 13). A long-term forgetting procedure, involving the recall of verbal and visuo-spatial material at four testing delays (i.e., immediate, 30 min, 24 h, and 7 days post-encoding) was used to compare the two sub-groups of SCDs with a healthy control group (HC; n = 16).

Results

No significant between-group difference was found on the standard neuropsychological tests, nor in the immediate and 30 min recall of the experimental procedure. By contrast, on the verbal test severe SCDs forgot significantly more than HCs in the prolonged intervals (i.e., 24 h and 7 days), with the greatest decline between 30 min and 24 h. Finally, in the whole SCD sample, we found significant associations between functional connectivity values within some cortical networks involved in memory (default mode network, salience network, and fronto-parietal network) and verbal long-term measures.

Conclusions

Our preliminary findings suggest that long-term forgetting procedures could be a sensitive neuropsychological tool for detecting memory concerns in SCDs, contributing to early AD detection.

Predicting amyloid PET positivity using plasma p-tau181 and other blood-based biomarkers

Introduction

This study aimed to determine the efficacy of combining plasma phosphorylated tau (p-tau)181, amyloid beta (Aβ)42/Aβ40, neurofilament light (NfL), and apolipoprotein E (APOE) genotypes for detecting positive amyloid positron emission tomography (PET), which is little known in the Asian population, in two independent cohorts.

Methods

Biomarkers were measured using a single-molecule array (Simoa) in a cohort study (Asan). All participants underwent amyloid PET. Significant changes in the area under the curve (AUC) and Akaike Information Criterion values were considered to determine the best model. The generalizability of this model was tested using another cohort (KBASE-V).

Results

In the Asan cohort, after adjusting for age and sex, p-tau181 (AUC = 0.854) or APOE ε4 status (AUC = 0.769) distinguished Aβ status with high accuracy. Combining them or adding NfL and Aβ42/40 improved model fitness. The best-fit model included the plasma p-tau181, APOE ε4, NfL and Aβ42/40. The models established from the Asan cohort were tested in the KBASE-V cohort. Additionally, in the KBASE-V cohort, these three biomarker models had similar AUC in cognitively unimpaired (AUC = 0.768) and mild cognitive impairment (MCI) (AUC = 0.997) participants.

Conclusions

Plasma p-tau181 showed a high performance in determining Aβ-PET positivity. Adding plasma NfL and APOE ε4 status improved the model fit without significant improvement in AUC.

Predicting cognitive stage transition using p-tau181, Centiloid, and other measures

BACKGROUND

A combination of plasma phospho-tau (p-tau), amyloid beta (Aβ)-positron emission tomography (PET), brain magnetic resonance imaging, cognitive function tests, and other biomarkers might predict future cognitive decline. This study aimed to investigate the efficacy of combining these biomarkers in predicting future cognitive stage transitions within 3 years.

METHODS

Among the participants in the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE-V) study, 49 mild cognitive impairment (MCI) and 113 cognitively unimpaired (CU) participants with Aβ-PET and brain imaging data were analyzed.

RESULTS

Older age, increased plasma p-tau181, Aβ-PET positivity, and decreased semantic fluency were independently associated with cognitive stage transitions. Combining age, p-tau181, the Centiloid scale, semantic fluency, and hippocampal volume produced high predictive value in predicting future cognitive stage transition (area under the curve = 0.879).

CONCLUSIONS

Plasma p-tau181 and Centiloid scale alone or in combination with other biomarkers, might predict future cognitive stage transition in non-dementia patients.

HIGHLIGHTS

-Plasma p-tau181 and Centiloid scale might predict future cognitive stage transition. -Combining them or adding other biomarkers increased the predictive value. -Factors that independently associated with cognitive stage transition were demonstrated.

Quantifying brain connectivity signatures by means of polyconnectomic scoring

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.

Forecasting individual progression trajectories in Alzheimer's disease

The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.

FMRI Complexity Correlates with Tau-PET and Cognitive Decline in Late-Onset and Autosomal Dominant Alzheimer's Disease

BACKGROUND

Neurofibrillary tangle pathology detected with tau-PET correlates closely with neuronal injury and cognitive symptoms in Alzheimer's disease (AD). Complexity of rs-fMRI has been demonstrated to decrease with cognitive decline in AD.

OBJECTIVE

We hypothesize that the rs-fMRI complexity provides an index for tau-related neuronal injury and cognitive decline in the AD process.

METHODS

Data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI3) and the Estudio de la Enfermedad de Alzheimer en Jalisciences (EEAJ) study. Associations between tau-PET and rs-fMRI complexity were calculated. Potential pathways relating complexity to cognitive function mediated through tau-PET were assessed by path analysis.

RESULTS

We found significant negative correlations between rs-fMRI complexity and tau-PET in medial temporal lobe of both cohorts, and associations of rs-fMRI complexity with cognitive scores were mediated through tau-PET.

CONCLUSION

The association of rs-fMRI complexity with tau-PET and cognition, suggests that a reduction in complexity is indicative of tau-related neuropathology and cognitive decline in AD processes.

Grey matter changes on brain MRI in subjective cognitive decline: a systematic review

Introduction

People with subjective cognitive decline (SCD) report cognitive deterioration. However, their performance in neuropsychological evaluation falls within the normal range. The present study aims to analyse whether structural magnetic resonance imaging (MRI) reveals grey matter changes in the SCD population compared with healthy normal controls (HC).

Methods

Parallel systematic searches in PubMed and Web of Science databases were conducted, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Quality assessment was completed using the Newcastle-Ottawa Scale (NOS).

Results

Fifty-one MRI studies were included. Thirty-five studies used a region of interest (ROI) analysis, 15 used a voxel-based morphometry (VBM) analysis and 10 studies used a cortical thickness (CTh) analysis. Ten studies combined both, VBM or CTh analysis with ROI analysis.

Conclusions

Medial temporal structures, like the hippocampus or the entorhinal cortex (EC), seemed to present grey matter reduction in SCD compared with HC, but the samples and results are heterogeneous. Larger sample sizes could help to better determine if these grey matter changes are consistent in SCD subjects.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01031-6.

Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders

INTRODUCTION

Carrying out a randomized controlled trial to estimate the causal effects of regional brain atrophy due to Alzheimer's disease (AD) is impossible. Instead, we must estimate causal effects from observational data. However, this generally requires knowing and having recorded all confounders, which is often unrealistic.

METHODS

We provide an approach that leverages the dependencies among multiple neuroanatomical measures to estimate causal effects from observational neuroimaging data without the need to know and record all confounders.

RESULTS

Our analyses of N = 732 $N=732$ subjects from the Alzheimer's Disease Neuroimaging Initiative demonstrate that using our approach results in biologically meaningful conclusions, whereas ignoring unobserved confounding yields results that conflict with established knowledge on cognitive decline due to AD.

DISCUSSION

The findings provide evidence that the impact of unobserved confounding can be substantial. To ensure trustworthy scientific insights, future AD research can account for unobserved confounding via the proposed approach.

Hallmarks of neurodegenerative disease: A systems pharmacology perspective

Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

Association between sleep parameters and longitudinal shortening of telomere length

BACKGROUND

The relationship between sleep parameters and longitudinal shortening of telomere length is unclear. This study aimed to investigate the relationship between sleep parameters and the shortening of leukocyte telomere length (LTL) over a year.

METHODS

Among the participants in the validation cohort of the Korea Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease, participants who measured both baseline and follow-up (two years later) of LTL were analyzed. They were dichotomized according to the degree of LTL attrition over two years. Clinical characteristics were compared between the faster and slower LTL shortening groups (cut-off points: -0.710 kbp, n = 119 each). Multivariable logistic regression analyses were performed to determine independent relationships between faster shortening of LTL length and sleep parameters.

RESULTS

A total of 238 participants, aged 55-88 years, were included. Participants with faster LTL shortening had a shorter duration of sleep (P = 0.013) and longer sleep latency (P = 0.007). Among the components of the PSQI, subjective measures of sleep quality, sleep latency, sleep duration, and sleep efficiency were significantly worse in participants with faster LTL shortening. Multivariate logistic regression analysis showed that sleep duration (per hour, OR = 0.831, 95% CI = 0.698-0.989), sleep latency (per minute, OR = 1.013, 95% CI = 1.002-1.024), global PSQI score (OR = 1.134, 95% CI = 1.040-1.236), shortest sleep duration (OR = 5.173, 95% CI = 1.563-17.126), and lowest sleep efficiency (OR = 7.351, 95% CI = 1.943-27.946) were independently associated with faster LTL shortening.

CONCLUSIONS

Poor sleep quality, specifically short sleep duration, long sleep latency, and low sleep efficiency were associated with faster longitudinal shortening of LTL.

Serum neurofilament light chain level as a predictor of cognitive stage transition

BACKGROUND

Neurofilament light chain (NFL) level has been suggested as a blood-based biomarker for neurodegeneration in dementia. However, the association between baseline NFL levels and cognitive stage transition or cortical thickness is unclear. This study aimed to investigate whether baseline NFL levels are associated with cognitive stage transition or cortical thickness in mild cognitive impairment (MCI) and cognitively unimpaired (CU) participants.

METHODS

This study analyzed data on participants from the independent validation cohort of the Korea Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE-V) study. Among the participants of KBASE-V study, 53 MCI and 146 CU participants who were followed up for ≥ 2 years and had data on the serum NFL levels were eligible for inclusion in this study. Participants were classified into three groups according to baseline serum NFL levels of low, middle, or high.

RESULTS

The Kaplan-Meier analysis showed association between the serum NFL tertiles and risk of cognitive stage transition in MCI (P = 0.002) and CU (P = 0.028) participants, analyzed separately. The same is true upon analysis of MCI and CU participants together (P < 0.001). In MCI participants, the highest serum NFL tertile and amyloid-beta positivity were independent predictors for cognitive stage transition after adjusting for covariates. For CU participants, only amyloid-beta positivity was identified to be an independent predictor.

CONCLUSION

The study shows that higher serum NFL tertile levels correlate with increased risk of cognitive stage transition in both MCI and CU participants. Serum NFL levels were negatively correlated with the mean cortical thickness of the whole-brain and specific brain regions.

Prospective Metamemory Monitoring of Episodic Visual Memory in Community-Dwelling Older Adults with Subjective Cognitive Decline and Mild Cognitive Impairment

OBJECTIVE

Metamemory tasks have been utilized to investigate anosognosia in older adults with dementia, though previous research has not systematically compared memory self-awareness in prodromal dementia groups. This represents an important oversight given that remedial and interventional efforts may be most beneficial before individuals' transition to clinical dementia. We examine differences in memory self-awareness and memory self-monitoring between cognitively healthy elderly controls and prodromal dementia groups.

METHODS

Participants with subjective cognitive decline despite intact objective neuropsychological functioning (SCD; n = 82), amnestic mild cognitive impairment (aMCI; n = 18), nonamnestic mild cognitive impairment (naMCI; n = 38), and normal cognitive functioning (HC; n = 120) were recruited from the Einstein Aging Study for a cross-sectional study. Participants completed an experimental visual memory-based global metamemory prediction task and subjective assessments of memory/cognition and self-awareness.

RESULTS

While, relative to HC, memory self-awareness and memory self-monitoring were preserved for delayed memory performance in SCD and aMCI, these processes were impaired in naMCI. Furthermore, results suggest that poor metamemory accuracy captured by our experimental task can be generalized to everyday memory problems.

CONCLUSIONS

Within the framework of the Cognitive Awareness Model, our findings provide preliminary evidence that poor memory self-awareness/self-monitoring in naMCI may reflect an executive or primary anosognosia, with implications for tailored rehabilitative interventions.

Cilostazol Versus Aspirin on White Matter Changes in Cerebral Small Vessel Disease: A Randomized Controlled Trial

BACKGROUND AND PURPOSE

Cerebral small vessel disease is characterized by progressive cerebral white matter changes (WMCs). This study aimed to compare the effects of cilostazol and aspirin on changes in WMC volume in patients with cerebral small vessel disease.

METHODS

In a multicenter, double-blind, randomized controlled trial, participants with moderate or severe WMCs and at least one lacunar infarction detected on brain magnetic resonance imaging were randomly assigned to the cilostazol and aspirin groups in a 1:1 ratio. Cilostazol slow release (200 mg) or aspirin (100 mg) capsules were administered once daily for 2 years. The primary outcome was the change in WMC volume on magnetic resonance images from baseline to 2 years. Secondary imaging outcomes include changes in the number of lacunes or cerebral microbleeds, fractional anisotropy, and mean diffusivity on diffusion tensor images, and brain atrophy. Secondary clinical outcomes include all ischemic strokes, all ischemic vascular events, and changes in cognition, motor function, mood, urinary symptoms, and disability.

RESULTS

Between July 2013 and August 2016, 256 participants were randomly assigned to the cilostazol (n=127) and aspirin (n=129) groups. Over 2 years, the percentage of WMC volume to total WM volume and the percentage of WMC volume to intracranial volume increased in both groups, but neither analysis showed significant differences between the groups. The peak height of the mean diffusivity histogram in normal-appearing WMs was significantly reduced in the aspirin group compared with the cilostazol group. Cilostazol significantly reduced the risk of ischemic vascular event compared with aspirin (0.5 versus 4.5 cases per 100 person-years; hazard ratio, 0.11 [95% CI, 0.02-0.89]).

CONCLUSIONS

There was no significant difference between the effects of cilostazol and aspirin on WMC progression in patients with cerebral small vessel disease. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01932203.

Conductance-Based Structural Brain Connectivity in Aging and Dementia

Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.

Head injury is associated with tau deposition on PET in MCI and AD patients

INTRODUCTION

Head injuries (HI) are a risk factor for dementia, but the underlying etiology is not fully known. Understanding whether tau might mediate this relationship is important.

METHODS

Cognition and tau deposition were compared between 752 individuals with (impaired, n = 302) or without cognitive impairment (CN, n = 450) with amyloid and [18F]flortaucipir positron emission tomography, HI history information, and cognitive testing from the Alzheimer's Disease Neuroimaging Initiative and the Indiana Memory and Aging Study.

RESULTS

Sixty-three (38 CN, 25 impaired) reported a history of HI. Higher neuropsychiatric scores and poorer memory were observed in those with a history of HI. Tau was higher in individuals with a history of HI, especially those who experienced a loss of consciousness (LOC). Results were driven by impaired individuals, especially amyloid beta-positive individuals with history of HI with LOC.

DISCUSSION

These findings suggest biological changes, such as greater tau, are associated with HI in individuals with cognitive impairment. Small effect sizes were observed; thus, further studies should replicate and extend these results.

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling

Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.

Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study

OBJECTIVES

Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer's disease (AD). Neuroimaging studies suggest that abnormal brain connectivity plays an important role in the pathophysiology of SCD. However, most previous studies focused on single modalities only. Multimodal combinations can more effectively utilize various information and little is known about their diagnostic value in SCD.

METHODS

One hundred ten SCD individuals and well-matched healthy controls (HCs) were recruited in this study (the primary sample: 35 SCD and 36 HC; the validation sample: 21 SCD and 18 HC). Multimodal imaging data were used to construct functional, anatomical, and morphological networks, respectively. These networks were used in combination with a multiple kernel learning-support vector machine to predict SCD individuals. We validated our model on another independent sample. Multiple linear regression (MLR) analyses were conducted to investigate the relationships among network metrics, cognition, and pathological biomarkers.

RESULTS

We found that the characteristics identified from the multimodal network were primarily located in the default mode network (DMN) and salience network (SN), achieving an accuracy of 88.73% (an accuracy of 79.49% for an independent sample) based on the integration of the three modalities. MLR analyses showed that increased AV45 SUVRs were significantly associated with impaired memory function, the enhanced functional connectivity, and the decreased morphological connectivity.

CONCLUSION

This study suggests that abnormal multimodal connections within DMN and SN can be used as effective biomarkers to identify SCD and provide insight into understanding the pathophysiological mechanisms underlying SCD.

KEY POINTS

• Multimodal brain networks improve the detection accuracy of SCD. • Abnormal connections within DMN and SN can be used as effective biomarkers for the identification of SCD.

Forgetting Rates on the Recency Portion of a Word List Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease

Amnestic mild cognitive impairment has a greater risk of progressing to Alzheimer's disease (AD). Consistent with AD patients' distinctive deficit in consolidating new memory traces, in a recent study we demonstrated that the forgetting rate on the recency portion of a word list differentiates AD from other forms of dementia. In line with this finding, the aim of this study was to investigate whether increased recency forgetting could be a reliable index for predicting amnestic mild cognitive impairment (MCI) patients' conversion to AD. For this purpose, we compared accuracy in immediate and delayed recall from different portions of a word list in a group of patients with amnestic MCI who converted (C-MCI) or did not convert (S-MCI) to AD during a three-year follow-up period and in a group of normal controls. The results of the present study show that the forgetting from the recency portion of the list (operationalized as a ratio between immediate and delayed recall) was significantly larger in C-MCI than in S-MCI patients. Consistently, the hierarchical logistic regression analyses demonstrated that the recency ratio is a strong predictor of group membership. Similar to what occurs in full-blown AD patients, the results of our study suggest that the increased forgetting rate from the recency portion of the list in C-MCI patients is due to severely reduced efficiency in converting transitory short-term memory representations into stable long-term memory traces. This is consistent with prominent involvement of neuropathological changes in the cortical areas of the medial-temporal lobes and suggests that the recency ratio is a cognitive marker able to identify MCI patients who have a greater likelihood of progressing to AD.

Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis

There has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such open-source framework is the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) that works on Windows, macOS, and Linux operating systems and leverages containerized analysis pipelines to analyze neuroimaging data stored locally across multiple physical locations without the need for pooling the data at any point during the analysis. In this paper, the COINSTAC team partnered with a data collection consortium to implement the first-ever decentralized voxelwise analysis of brain imaging data performed outside the COINSTAC development group. Decentralized voxel-based morphometry analysis of over 2000 structural magnetic resonance imaging data sets collected at 14 different sites across two cohorts and co-located in different countries was performed to study the structural changes in brain gray matter which linked to age, body mass index (BMI), and smoking. Results produced by the decentralized analysis were consistent with and extended previous findings in the literature. In particular, a widespread cortical gray matter reduction (resembling a 'default mode network' pattern) and hippocampal increase with age, bilateral increases in the hypothalamus and basal ganglia with BMI, and cingulate and thalamic decreases with smoking. This work provides a critical real-world test of the COINSTAC framework in a "Large-N" study. It showcases the potential benefits of performing multivoxel and multivariate analyses of large-scale neuroimaging data located at multiple sites.

Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases.

Retrospective metamemory monitoring of semantic memory in community-dwelling older adults with subjective cognitive decline and mild cognitive impairment

In neurodegenerative conditions, better memory/cognitive awareness, indexed by greater "metamemory monitoring accuracy", is linked to stronger cognitive remediation outcomes. Differences in metamemory monitoring accuracy in predementia conditions, which could inform treatment effectiveness, have not been systematically investigated. We utilized a retrospective confidence judgment (RCJ) task for general knowledge recognition in community-dwelling older adults: 106 cognitively healthy (HC), 68 subjective cognitive decline (SCD) despite intact neuropsychological function, 14 amnestic mild cognitive impairment (aMCI), and 31 non-amnestic mild cognitive impairment (naMCI). Participants gave confidence ratings after making recognition responses to general knowledge questions. Recognition accuracy, confidence levels, and absolute and relative RCJ accuracy (i.e., metamemory monitoring accuracy) were analysed. Compared to HC and SCD, absolute RCJ accuracy was significantly poorer in both MCI groups but relative RCJ accuracy was significantly poorer in naMCI, but not aMCI. This novel result may be driven by lower confidence for correct recognition responses in naMCI and suggests that poorer RCJ accuracy in naMCI may be attributable to poorer performance monitoring. We discuss results in relation to the possibility that individuals in distinct preclinical dementia conditions, who have different levels of memory/cognitive awareness, may differentially benefit from cognitive remediation strategies tailored to their levels of memory/cognitive awareness.

MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI

The neurological disorder mild cognitive impairment (MCI) demonstrates minor impacts on the patient's daily activities and may be ignored as the status of normal aging. But some of the MCI patients may further develop into severe statuses like Alzheimer's disease (AD). The brain functional connectivity network (BFCN) was usually constructed from the resting-state functional magnetic resonance imaging (rs-fMRI) data. This technology has been widely used to detect the neurodegenerative dementia and to reveal the intrinsic mechanism of neural activities. The BFCN edge was usually determined by the pairwise correlation between the brain regions. This study proposed a weighted voting model of multi-source connectivity networks (MuscNet) by integrating multiple BFCNs of different correlation coefficients. Our model was further improved by removing redundant features. The experimental data demonstrated that different BFCNs contributed complementary information to each other and MuscNet outperformed the existing models on detecting MCI patients. The previous study suggested the existence of multiple solutions with similarly good performance for a machine learning problem. The proposed model MuscNet utilized a weighted voting strategy to slightly outperform the existing studies, suggesting an effective way to fuse multiple base models. The reason may need further theoretical investigations about why different base models contribute to each other for the MCI prediction.

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease

Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.

Telomere shortening reflecting physical aging is associated with cognitive decline and dementia conversion in mild cognitive impairment due to Alzheimer's disease

We investigated whether telomere length (TL) reflecting physical rather than chronological aging is associated with disease progression in the different cognitive stages of Alzheimer’s disease (AD). Study participants included 89 subjects with amyloid pathology (A+), determined through amyloid PET or cerebrospinal fluid analysis, including 26 cognitively unimpaired (CU A+) individuals, 28 subjects with mild cognitive impairment (MCI A+), and 35 subjects with AD dementia (ADD A+). As controls, 104 CU A- individuals were selected. The participants were evaluated annually over two years from baseline. Compared to the highest TL quartile group of MCI A+ participants, the lowest TL quartile group yielded 2-year differences of -9.438 (95% confidence interval [CI] = -14.567 ~ -4.309), -26.708 (-41.576 ~ -11.839), 3.198 (1.323 ~ 5.056), and 2.549 (0.527 ~ 4.571) on the Mini-Mental State Examination, Consortium to Establish a Registry for AD, Clinical Dementia Rating-Sum of Boxes, and Blessed Dementia Scale-Activities of Daily Living, respectively. With this group, the lowest TL quartile group had a significantly greater probability of progressing to ADD than the highest TL quartile group (hazard ratio = 13.16, 95% CI = 1.11 ~ 156.61). Telomere shortening may be associated with rapid cognitive decline and conversion to dementia in MCI A+.

A Comparison Study of Cilostazol and Aspirin on Changes in Volume of Cerebral Small Vessel Disease White Matter Changes: Protocol of a Multicenter, Randomized Controlled Trial

Background and Purpose

Cerebral small vessel disease (CSVD) is the most common cause of vascular dementia and a major contributor to mixed dementia. CSVD is characterized by progressive cerebral white matter changes (WMC) due to chronic low perfusion and loss of autoregulation. In addition to its antiplatelet effect, cilostazol exerts a vasodilating effect and improves endothelial function. This study aims to compare the effects of cilostazol and aspirin on changes in WMC volume in CSVD.

Methods

The comparison study of Cilostazol and aspirin on cHAnges in volume of cerebral smaLL vEssel disease white matter chaNGEs (CHALLENGE) is a double blind, randomized trial involving 19 hospitals across South Korea. Patients with moderate or severe WMC and ≥ 1 lacunar infarction detected on brain magnetic resonance imaging (MRI) are eligible; the projected sample size is 254. Participants are randomly assigned to a cilostazol or aspirin group at a 1:1 ratio. Cilostazol slow release 200 mg or aspirin 100 mg are taken once daily for 2 years. The primary outcome measure is the change in WMC volume on MRI from baseline to 104 weeks. Secondary imaging outcomes include changes in the number of lacunes and cerebral microbleeds, fractional anisotropy and mean diffusivity on diffusion tensor imaging, and brain atrophy. Secondary clinical outcomes include all ischemic strokes, all vascular events, and changes in cognition, motor function, mood, urinary symptoms, and disability.

Conclusions

CHALLENGE will provide evidence to support the selection of long-term antiplatelet therapy in CSVD.

Trial Registration

ClinicalTrials.gov Identifier: NCT01932203.

Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in survival analysis are often constrained by strong parametric assumptions and limited in their ability to learn from high-dimensional data. This paper develops a novel convolutional approach that addresses the drawbacks of both traditional statistical approaches as well as recent neural network models for survival. We present Match-Net: a missingness-aware temporal convolutional hitting-time network, designed to capture temporal dependencies and heterogeneous interactions in covariate trajectories and patterns of missingness. To the best of our knowledge, this is the first investigation of temporal convolutions in the context of dynamic prediction for personalized risk prognosis. Using real-world data from the Alzheimer's disease neuroimaging initiative, we demonstrate state-of-the-art performance without making any assumptions regarding underlying longitudinal or time-to-event processes-attesting to the model's potential utility in clinical decision support.

Quantification of structural brain connectivity via a conductance model

Connectomics has proved promising in quantifying and understanding the effects of development, aging and an array of diseases on the brain. In this work, we propose a new structural connectivity measure from diffusion MRI that allows us to incorporate direct brain connections, as well as indirect ones that would not be otherwise accounted for by standard techniques and that may be key for the better understanding of function from structure. From our experiments on the Human Connectome Project dataset, we find that our measure of structural connectivity better correlates with functional connectivity than streamline tractography does, meaning that it provides new structural information related to function. Through additional experiments on the ADNI-2 dataset, we demonstrate the ability of this new measure to better discriminate different stages of Alzheimer's disease. Our findings suggest that this measure is useful in the study of the normal brain structure, and for quantifying the effects of disease on the brain structure.

Clinical and Biomarker Characteristics According to Clinical Spectrum of Alzheimer's Disease (AD) in the Validation Cohort of Korean Brain Aging Study for the Early Diagnosis and Prediction of AD

We aimed to present the study design of an independent validation cohort from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (AD) (KBASE-V) and to investigate the baseline characteristics of the participants according to the AD clinical spectrum. We recruited 71 cognitively normal (CN) participants, 96 with subjective cognitive decline (SCD), 72 with mild cognitive impairment (MCI), and 56 with AD dementia (ADD). The participants are followed for three years. The Consortium to Establish a Registry for AD scores was significantly different between all of the groups. The logical memory delayed recall scores were significantly different between all groups, except between the MCI and ADD groups. The Mini-Mental State Examination score, hippocampal volume, and cerebrospinal fluid (CSF) amyloid-β42 level were significant difference among the SCD, MCI, and ADD groups. The frequencies of participants with amyloid pathology according to PET or CSF studies were 8.9%, 25.6%, 48.3%, and 90.0% in the CN, SCD, MCI, and ADD groups, respectively. According to ATN classification, A+/T+/N+ or A+/T+/N- was observed in 0%, 15.5%, 31.0%, and 78.3% in the CN, SCD, MCI, and ADD groups, respectively. The KBASE-V showed a clear difference according to the AD clinical spectrum in neuropsychological tests and AD biomarkers.

Clinical and radiological characteristics of early versus late mild cognitive impairment in patients with comorbid depressive disorder

OBJECTIVE

The classification of mild cognitive impairment (MCI) continues to be debated though it has recently been subtyped into late (LMCI) versus early (EMCI) stages. Older adults presenting with both a depressive disorder (DEP) and cognitive impairment (CI) represent a unique, understudied population. Our aim was to examine baseline characteristics of DEP-CI patients in the DOTCODE trial, a randomized controlled trial of open antidepressant treatment for 16 weeks followed by add-on donepezil or placebo for 62 weeks.

METHODS/DESIGN

Key inclusion criteria were diagnosis of major depression or dysthymic disorder with Hamilton Depression Rating Scale (HAM-D) score >14, and cognitive impairment defined by MMSE score ≥21 and impaired performance on the WMS-R Logical Memory II test. Patients were classified as EMCI or LMCI based on the 1.5 SD cutoff on tests of verbal memory, and compared on baseline clinical, neuropsychological, and anatomical characteristics.

RESULTS

Seventy-nine DEP-CI patients were recruited of whom 39 met criteria for EMCI and 40 for LMCI. The mean age was 68.9, and mean HAM-D was 23.0. Late mild cognitive impairment patients had significantly worse ADAS-Cog (P < .001), MMSE (P = .004), Block Design (P = .024), Visual Rep II (P = .006), CFL Animal (P = .006), UPSIT (P = .051), as well as smaller right hippocampal volume (P = .037) compared to EMCI patients. MRI indices of cerebrovascular disease did not differ between EMCI and LMCI patients.

CONCLUSIONS

Cognitive and neuronal loss markers differed between EMCI and LMCI among patients with DEP-CI, with LMCI being more likely to have the clinical and neuronal loss markers known to be associated with Alzheimer's disease.

Aberrant functional connectivity network in subjective memory complaint individuals relates to pathological biomarkers

Background

Individuals with subjective memory complaints (SMC) feature a higher risk of cognitive decline and clinical progression of Alzheimer's disease (AD). However, the pathological mechanism underlying SMC remains unclear. We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in SMC individuals.

Methods

We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional MRI and positron emission tomography (PET). Based on graph theory approaches, we detected local and global functional connectivity across the whole brain by using degree centrality (DC) and eigenvector centrality (EC) respectively. Additionally, we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and cerebrospinal fluid (CSF) data. The voxel-wise two-sample T-test analysis was used to examine between-group differences in the intrinsic functional network and cerebral amyloid deposition. Then, we correlated these network metrics with pathological results.

Results

The SMC individuals showed higher DC in the bilateral hippocampus (HP) and left fusiform gyrus and lower DC in the inferior parietal region than controls. Across all subjects, the DC of the bilateral HP and left fusiform gyrus was positively associated with total tau and phosphorylated tau₁₈₁. However, no significant between-group difference existed in EC and cerebral amyloid deposition.

Conclusion

We found impaired local, but not global, intrinsic connectivity networks in SMC individuals. Given the relationships between DC value and tau level, we hypothesized that functional changes in SMC individuals might relate to pathological biomarkers.

Prediction of Cerebral Amyloid With Common Information Obtained From Memory Clinic Practice

Background: Given the barriers prohibiting the broader utilization of amyloid imaging and high screening failure rate in clinical trials, an easily available and valid screening method for identifying cognitively impaired patients with cerebral amyloid deposition is needed. Therefore, we developed a prediction model for cerebral amyloid positivity in cognitively impaired patients using variables that are routinely obtained in memory clinics.

Methods: Six hundred and fifty two cognitively impaired subjects from the Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer disease (KBASE) and the Alzheimer’s Disease Neuroimaging Initiative-2 (ADNI-2) cohorts were included in this study (107 amnestic mild cognitive impairment (MCI) and 69 Alzheimer’s disease (AD) dementia patients for KBASE cohort, and 332 MCI and 144 AD dementia patients for ADNI-2 cohort). Using the cross-sectional dataset from the KBASE cohort, a multivariate stepwise logistic regression analysis was conducted to develop a cerebral amyloid prediction model using variables commonly obtained in memory clinics. For each participant, the logit value derived from the final model was calculated, and the probability for being amyloid positive, which was calculated from the logit value, was named the amyloid prediction index. The final model was validated using an independent dataset from the ADNI-2 cohort.

Results: The final model included age, sex, years of education, history of hypertension, apolipoprotein ε4 positivity, and score from a word list recall test. The model predicted that younger age, female sex, higher educational level, absence of hypertension history, presence of apolipoprotein ε4 allele, and lower score of word list recall test are associated with higher probability for being amyloid positive. The amyloid prediction index derived from the model was proven to be valid across the two cohorts. The area under the curve was 0.873 (95% confidence interval 0.815 to 0.918) for the KBASE cohort, and 0.808 (95% confidence interval = 0.769 to 0.842) for ADNI-2 cohort.

Conclusion: The amyloid prediction index, which was based on commonly available clinical information, can be useful for screening cognitively impaired individuals with a high probability of amyloid deposition in therapeutic trials for early Alzheimer’s disease as well as in clinical practice.

Alzheimer's Disease and Type 2 Diabetes: A Critical Assessment of the Shared Pathological Traits

Alzheimer's disease (AD) and Type 2 Diabetes Mellitus (T2DM) are two of the most prevalent diseases in the elderly population worldwide. A growing body of epidemiological studies suggest that people with T2DM are at a higher risk of developing AD. Likewise, AD brains are less capable of glucose uptake from the surroundings resembling a condition of brain insulin resistance. Pathologically AD is characterized by extracellular plaques of Aβ and intracellular neurofibrillary tangles of hyperphosphorylated tau. T2DM, on the other hand is a metabolic disorder characterized by hyperglycemia and insulin resistance. In this review we have discussed how Insulin resistance in T2DM directly exacerbates Aβ and tau pathologies and elucidated the pathophysiological traits of synaptic dysfunction, inflammation, and autophagic impairments that are common to both diseases and indirectly impact Aβ and tau functions in the neurons. Elucidation of the underlying pathways that connect these two diseases will be immensely valuable for designing novel drug targets for Alzheimer's disease.

Fluid and imaging biomarkers for Alzheimer's disease: Where we stand and where to head to

There is increasing evidence that a number of potentially informative biomarkers for Alzheimer disease (AD) can improve the accuracy of diagnosing this form of dementia, especially when used as a panel of diagnostic assays and interpreted in the context of neuroimaging and clinical data. Moreover, by combining the power of CSF biomarkers with neuroimaging techniques to visualize Aβ deposits (or neurodegenerative lesions), it might be possible to better identify individuals at greatest risk for developing MCI and converting to AD. The objective of this article was to review recent progress in selected imaging and chemical biomarkers for prediction, early diagnosis and progression of AD. We present our view point of a scenario that places CSF and imaging markers on the verge of general utility based on accuracy levels that already match (or even surpass) current clinical precision.

Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline

OBJECTIVE

To test the hypothesis that cortical and hippocampal volumes, measured in vivo from volumetric MRI (vMRI) scans, could be used to identify variant subtypes of Alzheimer disease (AD) and to prospectively predict the rate of clinical decline.

METHODS

Amyloid-positive participants with AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 1 and ADNI2 with baseline MRI scans (n = 229) and 2-year clinical follow-up (n = 100) were included. AD subtypes (hippocampal sparing [HpSp_MRI], limbic predominant [LP_MRI], typical AD [tAD_MRI]) were defined according to an algorithm analogous to one recently proposed for tau neuropathology. Relationships between baseline hippocampal volume to cortical volume ratio (HV:CTV) and clinical variables were examined by both continuous regression and categorical models.

RESULTS

When participants were divided categorically, the HpSp_MRI group showed significantly more AD-like hypometabolism on ¹⁸F-fluorodeoxyglucose-PET (p < 0.05) and poorer baseline executive function (p < 0.001). Other baseline clinical measures did not differ across the 3 groups. Participants with HpSp_MRI also showed faster subsequent clinical decline than participants with LP_MRI on the Alzheimer's Disease Assessment Scale, 13-Item Subscale (ADAS-Cog₁₃), Mini-Mental State Examination (MMSE), and Functional Assessment Questionnaire (all p < 0.05) and tAD_MRI on the MMSE and Clinical Dementia Rating Sum of Boxes (CDR-SB) (both p < 0.05). Finally, a larger HV:CTV was associated with poorer baseline executive function and a faster slope of decline in CDR-SB, MMSE, and ADAS-Cog₁₃ score (p < 0.05). These associations were driven mostly by the amount of cortical rather than hippocampal atrophy.

CONCLUSIONS

AD subtypes with phenotypes consistent with those observed with tau neuropathology can be identified in vivo with vMRI. An increased HV:CTV ratio was predictive of faster clinical decline in participants with AD who were clinically indistinguishable at baseline except for a greater dysexecutive presentation.

Plasma Levels of Aβ42 and Tau Identified Probable Alzheimer's Dementia: Findings in Two Cohorts

The utility of plasma amyloid beta (Aβ) and tau levels for the clinical diagnosis of Alzheimer’s disease (AD) dementia has been controversial. The main objective of this study was to compare Aβ42 and tau levels measured by the ultra-sensitive immunomagnetic reduction (IMR) assays in plasma samples collected at the Banner Sun Health Institute (BSHRI) (United States) with those from the National Taiwan University Hospital (NTUH) (Taiwan). Significant increase in tau levels were detected in AD subjects from both cohorts, while Aβ42 levels were increased only in the NTUH cohort. A regression model incorporating age showed that tau levels identified probable ADs with 81 and 96% accuracy in the BSHRI and NTUH cohorts, respectively, while computed products of Aβ42 and tau increased the accuracy to 84% in the BSHRI cohorts. Using 382.68 (pg/ml)² as the cut-off value, the product achieved 92% accuracy in identifying AD in the combined cohorts. Overall findings support that plasma Aβ42 and tau assayed by IMR technology can be used to assist in the clinical diagnosis of AD.

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

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

METHODS

We used standard searches to find publications using ADNI data.

RESULTS

(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.

DISCUSSION

Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

Synergistic interaction between amyloid and tau predicts the progression to dementia

INTRODUCTION

Recent literature proposes that amyloid β (Aβ) and phosphorylated tau (p-tau) synergism accelerates biomarker abnormalities in controls. Yet, it remains to be answered whether this synergism is the driving force behind Alzheimer disease (AD) dementia.

METHODS

We stratified 314 mild cognitive impairment individuals using [¹⁸F]florbetapir positron emission tomography Aβ imaging and cerebrospinal fluid p-tau. Regression and voxel-based logistic regression models with interaction terms evaluated 2-year changes in cognition and clinical status as a function of baseline biomarkers.

RESULTS

We found that the synergism between [¹⁸F]florbetapir and p-tau, rather than their additive effects, was associated with the cognitive decline and progression to AD. Furthermore, voxel-based analysis revealed that temporal and inferior parietal were the regions where the synergism determined an increased likelihood of developing AD.

DISCUSSION

Together, the present results support that progression to AD dementia is driven by the synergistic rather than a mere additive effect between Aβ and p-tau proteins.

Perspective: A Critical Look at the Ancillary Age-Related Eye Disease Study 2: Nutrition and Cognitive Function Results in Older Individuals with Age-Related Macular Degeneration

A large body of literature suggests that the dietary carotenoids lutein and zeaxanthin and long-chain polyunsaturated fatty acids such as docosahexaenoic acid are related to improved cognitive function across the life span. A recent report by the Age-Related Eye Disease Study (AREDS) group appears to contradict the general findings of others in the field. In this review, we look critically at the methods, study designs, and analysis techniques used in the larger body of literature and compare them with the recent AREDS reports.