Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature

The impact of PICALM genetic variations on reserve capacity of posterior cingulate in AD continuum

Phosphatidylinositolbinding clathrin assembly protein (PICALM) gene is one novel genetic player associated with late-onset Alzheimer’s disease (LOAD), based on recent genome wide association studies (GWAS). However, how it affects AD occurrence is still unknown. Brain reserve hypothesis highlights the tolerant capacities of brain as a passive means to fight against neurodegenerations. Here, we took the baseline volume and/or thickness of LOAD-associated brain regions as proxies of brain reserve capacities and investigated whether PICALM genetic variations can influence the baseline reserve capacities and the longitudinal atrophy rate of these specific regions using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In mixed population, we found that brain region significantly affected by PICALM genetic variations was majorly restricted to posterior cingulate. In sub-population analysis, we found that one PICALM variation (C allele of rs642949) was associated with larger baseline thickness of posterior cingulate in health. We found seven variations in health and two variations (rs543293 and rs592297) in individuals with mild cognitive impairment were associated with slower atrophy rate of posterior cingulate. Our study provided preliminary evidences supporting that PICALM variations render protections by facilitating reserve capacities of posterior cingulate in non-demented elderly

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

Benefit of Phonemic Cueing in Alzheimer's Disease Patients' Naming Performance: Baseline Correlates and Predictive Utility

Word-finding difficulty, especially when confronted with naming items, is a well-known problem that many individuals with Alzheimer’s disease (AD) encounter. The use of neuropsychological measures, imaging technology, and genetic research has all contributed to the understanding of naming deficits in AD and the underlying cognitive processes involved. The effects of providing cues during confrontation naming tasks have also been studied, and research has suggested overall benefits of phonemic cueing. This research project further investigated the benefits of phonemic cueing cross-sectionally and longitudinally among a large sample (N = 1104) of individuals with mild to moderate AD. Cross-sectionally, the study examined neuropsychological and socio-demographic correlates of phonemic cueing benefit, as well as potential modifying effects of genetic vulnerability and dementia severity. Longitudinally, the study determined whether phonemic cueing benefit predicts rate of decline on several dementia severity measures. Results indicated that, consistent with previous literature, mild AD subjects benefited from phonemic cues significantly more than moderate AD subjects. Individuals with higher premorbid IQ were found to benefit more from phonemic cueing, which was expected given research findings on the effects of education on cognitive reserve. Women and men were comparable in overall confrontation naming ability, which contradicted current literature, and women were found to benefit more than men from phonemic cues. Confrontation naming ability accounted for the observed inverse relationship between age and phonemic cueing benefit. Observed differences in PCI between carriers and noncarriers of the ApoE ε4 allele were also accounted for by confrontation naming ability, with carriers performing better on naming tasks compared to noncarriers. Phonemic cueing benefit uniquely contributed to baseline cognitive performance on some semantic measures, phonemic fluency, and one non-semantic visuospatial task. Only lower levels of baseline dementia severity and older age predicted less cognitive impairment at 2-year follow-up.Psychology, Department o

Predictors of Rate of Cognitive and Functional Decline in Patients with Amnestic Mild Cognitive Impairment

Amnestic Mild Cognitive Impairment (MCI) is a known risk factor for conversion to Alzheimer’s disease (AD). Although substantial research has been conducted on the general profile of amnestic MCI subjects and predictors of conversion to AD, the research on predictors of rate of decline has been less comprehensive and studied. The present study sought to fill the gaps in this portion of research by systematically and comprehensively examining predictors of rate of decline in a longitudinal sample of individuals with MCI. Specifically, this study identified predictors of rate of cognitive and functional decline, including age, genetic vulnerability, baseline cognitive performance, baseline functional ability, and baseline neuropsychiatric severity. Participants with single or multi-domain aMCI (N = 151) were assessed at baseline and for a mean of 1.32 follow-up visits (mean interval from baseline to last follow-up = 1.61 years). Results showed that carriers of the ApoE ε4 allele declined more quickly on all three dementia severity measures, but not on instrumental activities of daily living (iADL) functioning, compared to non-carriers. Older individuals declined more rapidly on iADL functioning (but not in dementia severity). Participants with average baseline iADL ratio scores declined more quickly compared to participants with above or below average baseline iADL ratio scores. Participants with lower Executive Functions composite scores at baseline declined more quickly on dementia severity measures but more slowly on iADL functioning. In addition, lower Memory composite scores at baseline predicted faster decline on iADL functioning only. Greater memory impairment severity (operationalized as the number of memory scores in the impaired range) at baseline predicted faster decline on the MMSE in particular. Contrary to hypotheses, those with lower levels of depression at baseline declined more rapidly on dementia severity measures compared to those with higher levels of depression. Identifying potential predictors of rate of decline from amnestic MCI to AD could be clinically meaningful for prognostic purposes, understanding risk and protective factors, as well as guiding future treatments and clinical trials that could aim to target and delay progression among those patients who are particularly vulnerable to more quickly convert to AD.Psychology, Department o

Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features

The introduction of mild cognitive impairment (MCI) as a diagnostic category adds to the challenges of diagnosing Alzheimer’s Disease (AD). No single marker has been proven to accurately categorize patients into their respective diagnostic groups. Thus, previous studies have attempted to develop fused predictors of AD and MCI. These studies have two main limitations. Most do not simultaneously consider all diagnostic categories and provide suboptimal fused representations using the same set of modalities for prediction of all classes. In this work, we present a combined framework, cascaded multiview canonical correlation (CaMCCo), for fusion and cascaded classification that incorporates all diagnostic categories and optimizes classification by selectively combining a subset of modalities at each level of the cascade. CaMCCo is evaluated on a data cohort comprising 149 patients for whom neurophysiological, neuroimaging, proteomic and genomic data were available. Results suggest that fusion of select modalities for each classification task outperforms (mean AUC = 0.92) fusion of all modalities (mean AUC = 0.54) and individual modalities (mean AUC = 0.90, 0.53, 0.71, 0.73, 0.62, 0.68). In addition, CaMCCo outperforms all other multi-class classification methods for MCI prediction (PPV: 0.80 vs. 0.67, 0.63)

Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease

Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction

Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease

Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding