White matter differences between healthy young ApoE4 carriers and non-carriers identified with tractography and support vector machines.

The apolipoprotein E4 (ApoE4) is an established risk factor for Alzheimer's disease (AD). Previous work has shown that this allele is associated with functional (fMRI) changes as well structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess the diffusion characteristics and the white matter (WM) tracts of healthy young (20-38 years) ApoE4 carriers and non-carriers. No significant differences in diffusion indices were found between young carriers (ApoE4+) and non-carriers (ApoE4-). There were also no significant differences between the groups in terms of normalised GM or WM volume. A feature selection algorithm (ReliefF) was used to select the most salient voxels from the diffusion data for subsequent classification with support vector machines (SVMs). SVMs were capable of classifying ApoE4 carrier and non-carrier groups with an extremely high level of accuracy. The top 500 voxels selected by ReliefF were then used as seeds for tractography which identified a WM network that included regions of the parietal lobe, the cingulum bundle and the dorsolateral frontal lobe. There was a non-significant decrease in volume of this WM network in the ApoE4 carrier group. Our results indicate that there are subtle WM differences between healthy young ApoE4 carriers and non-carriers and that the WM network identified may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification

Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer’s disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation (“Which change in voxels would change the outcome most?”), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals (“Why does this person have AD?”) with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual “fingerprints” of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data

Neural Dedifferentiation in Relation to Risk for Alzheimer\u27s Disease

Functional magnetic resonance imaging (fMRI) research indicates that as an individual\u27s age increases, the task-related spatial extent of neural activation increases. This decrease in neural specificity, or dedifferentiation, is often demonstrated by older adults during challenging cognitive tasks. Cognitively intact individuals at-risk for Alzheimer\u27s disease (AD), as deemed by having an apolipoprotein-E ε4 allele or a family history of AD, demonstrate increased fMRI activation as compared to individuals at lower risk. Using a low effort, high accuracy event-related semantic memory task involving the presentation of famous and non-famous names, we examined spatial neural specificity through a measure of dedifferentiation using fMRI. In particular, the goal was to look at degree of dedifferentiation between older healthy subjects with or without risk factors for AD. Our results indicated that while there was not a significant difference between the two groups on the total amount of neural dedifferentiation, there was a significant interaction between stimulus type and risk group. Individuals at-risk for AD displayed greater dedifferentiation for non-famous names yet greater differentiation (i.e., less dedifferentiation) for famous names as compared to the low-risk group. These findings may reflect disturbances in memory formation for individuals at-risk for AD

Apolipoprotein E related Co-Morbidities and Alzheimer’s disease

The primary goal of advancement in clinical services is to provide a health care system that enhances an individual’s quality of life. Incidence of diabetes mellitus, cardiovascular disease and associated dementia coupled with the advancing age of the population, have led to an increase in the worldwide challenge to the healthcare system. In order to overcome these challenges prior knowledge of common, reliable risk factors and their effectors is essential. The oral health constitutes one such relatively unexplored but indispensable risk factor for aforementioned co-morbidities, in the form of poor oral hygiene and tooth loss during aging. Behavioural traits such as low education, smoking, poor diet, neglect of oral health, lack of exercise, and hypertension are few of the risk factors that are shared commonly amongst these conditions. In addition, common genetic susceptibility traits such as the apolipoprotein ɛ gene, together with an individual’s life style can also influence the development of co-morbidities such as periodontitis, atherosclerosis/stroke, diabetes, and Alzheimer’s disease. This review specifically addresses the susceptibility of apolipoprotein ε gene allele 4 as the plausible commonality for the etiology of co-morbidities that eventually result from periodontal diseases and ultimately progress to dementia

A Novel Cognitive Stress Test for the Detection of Early Alzheimer’s Disease in African Americans

The U.S. population is currently undergoing a major demographic transition, with increasing racial and ethnic diversity of the older adult population. As the growing population of older adults advances in age, memory complaints are projected to increase in prevalence particularly among African Americans and present a challenge to clinicians who must differentiate between normal aging and progressive neurocognitive conditions (Celsis, 2000; Sherwin, 2000). As targeted therapeutic interventions and emerging therapies for AD are much more likely to be effective in the earlier stages of the disease (Loewenstein, Curiel, Duara & Buschke, 2017), early assessment and detection of AD, especially in groups more likely to develop the disorder, such as African Americans, has become increasingly important. As such, the current study examined the performance of African Americans, both cognitively normal and those with amnestic-mild cognitive impairment (aMCI), on a novel cognitive stress test, the Loewenstein-Acevedo Scale of Semantic Interference and Learning (LASSI-L) and found that those with aMCI exhibit more impairment in their initial learning and storage of information and suffer from proactive semantic interference due to their inability to inhibit responses. Additionally, this study found that the LASSI-L serves as a better predictor of diagnostic group classification compared to traditional neuropsychological measures. Taken together these findings suggest that the LASSI-L is a highly promising test for the assessment of mild cognitive impairment among African American older adults, which will hopefully guide prevention and treatment planning within this underserved population

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Motivation: Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results: We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype

Predicting Cognitive Decline in Older Adults Through Multi-Voxel Pattern Analysis

Alzheimer\u27s disease (AD) is a progressive neurodegenerative disorder that is associated with cognitive and structural decline beyond what is seen in normal, healthy aging. Functional magnetic resonance imaging (fMRI) research indicates that prior to the onset of measureable cognitive impairment, individuals at-risk for AD demonstrate different patterns of neural activation than individuals at lower risk. Thus, differences in task-activated fMRI may be beneficial in predicting cognitive decline at a pre-symptomatic stage. The present study utilizes multi-voxel pattern analysis (MVPA) of baseline fMRI task-related activation to predict cognitive decline, with the hypothesis that famous and non-famous name task activation will discriminate older adults who go on to experience cognitive decline from those who do not. Ninety-nine cognitively intact older adults underwent neuropsychological testing and a semantic memory fMRI task (famous name discrimination). After follow-up neuropsychological testing 18-months later, participants were grouped as Stable (n = 65) or Declining (n = 34) based on \u3e 1.0 SD decline in performance on cognitive measures. MVPA classification accuracy was 90% for stimulus type (famous and non-famous names), thereby supporting the general approach. Mean MVPA classification accuracy for famous and non-famous names was 83% for both the Stable and Declining groups. Finally, MVPA produced greater than chance classification accuracy of participant groups for both famous name activation (56%) and non-famous name activation (55%) as determined via binomial distribution. The results of the current study suggest that MVPA possesses potential in predicting cognitive decline in older adults

Functional Magnetic Resonance Imaging of Semantic Memory as a Presymptomatic Biomarker of Alzheimer’s Disease Risk

Extensive research efforts have been directed toward strategies for predicting risk of developing Alzheimer\u27s disease (AD) prior to the appearance of observable symptoms. Existing approaches for early detection of AD vary in terms of their efficacy, invasiveness, and ease of implementation. Several non-invasive magnetic resonance imaging strategies have been developed for predicting decline in cognitively healthy older adults. This review will survey a number of studies, beginning with the development of a famous name discrimination task used to identify neural regions that participate in semantic memory retrieval and to test predictions of several key theories of the role of the hippocampus in memory. This task has revealed medial temporal and neocortical contributions to recent and remote memory retrieval, and it has been used to demonstrate compensatory neural recruitment in older adults, apolipoprotein E ε4 carriers, and amnestic mild cognitive impairment patients. Recently, we have also found that the famous name discrimination task provides predictive value for forecasting episodic memory decline among asymptomatic older adults. Other studies investigating the predictive value of semantic memory tasks will also be presented. We suggest several advantages associated with the use of semantic processing tasks, particularly those based on person identification, in comparison to episodic memory tasks to study AD risk. Future directions for research and potential clinical uses of semantic memory paradigms are also discussed. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease

Translational approaches to understanding resilience to Alzheimer\u27s disease.

Individuals who maintain cognitive function despite high levels of Alzheimer\u27s disease (AD)-associated pathology are said to be \u27resilient\u27 to AD. Identifying mechanisms underlying resilience represents an exciting therapeutic opportunity. Human studies have identified a number of molecular and genetic factors associated with resilience, but the complexity of these cohorts prohibits a complete understanding of which factors are causal or simply correlated with resilience. Genetically and phenotypically diverse mouse models of AD provide new and translationally relevant opportunities to identify and prioritize new resilience mechanisms for further cross-species investigation. This review will discuss insights into resilience gained from both human and animal studies and highlight future approaches that may help translate these insights into therapeutics designed to prevent or delay AD-related dementia

Network-based biomarkers in Alzheimer's disease: review and future directions

By 2050 it is estimated that the number of worldwide Alzheimer?s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD