Regional flux analysis of longitudinal atrophy in Alzheimer's disease

Oral podium presentationInternational audienceBackground. The longitudinal analysis of the brain morphology in Alzheimer's disease (AD) is fundamental for discovering and quantifying the dynamics of the pathology. We can broadly identify two main paradigms for the analysis of time series of structural magnetic resonance images (MRIs): hypothesis-free and regional analysis. In the former case, the longitudinal atrophy is modeled at fine scales on the whole brain such as in the voxel/tensor based morphometry and cortical thickness analysis.These methods are useful for exploratory purposes, but usually lack robustness for a reliable quantification of the changes at the subject level. On the other hand, the regional analysis identifies volume changes in preliminary segmented regions. It is however limited to previously defined regions of interest, and therefore it might fail to detect the complex and spread pattern of changes which is likely to underlie the evolution of the pathology. In this study we propose the regional flux analysis, a new approach for the study of the brain longitudinal changes. The aim of regional flux analysis is twofold: consistently unify hypothesis-free and regional approaches to 1) reliably discovery the dynamics of brain morphological changes, and 2) at the same time provide statistically powered measures of longitudinal atrophy. Methods. We encode the morphological differences of follow-up images by longitudinal deformations estimated by non-linear image registration. We compute the scalar pressure potential associated to the non-linear deformations, and we identify the regions of maximal apparent volume change by the loci of extremal pressure. Maximum pressure points identify significant areas of volume loss (deformation sinks), while minimum pressure points identify significant areas of volume gain (deformation sources). We build an atlas of probabilistic regions of group-wise significant sources and sinks of longitudinal atrophy, which is used as reference for quantifying the volume changes of given patients as the flux of the longitudinal deformation across these regions. We tested our method on the discovery and measurement of the yearly longitudinal atrophy of 200 healthy controls, 150 subjects with mild congnitive impairment (MCI) and 142 AD patients. For each subject, baseline and 1-year images were non-linearly registered with the LCC-logDemons algorithm. The probabilistic atlas was estimated from a subset of longitudinal deformations estimated for 20 AD patients, and the resulting regions were used for the quantification of the longitudinal atrophy in the remaining subjects. Statistical power of the resulting measures was assessed by sample size analysis. Results. The estimated probabilistic atlas was composed by 44 and 18 regions of respectively deformation sink and sources. The sink regions of apparent volume loss mapped to grey/withe matter regions, and included hippocampi (bilateral), temporal areas (Sup,Mid and Inf temporal gyrus), Insula and Parahippocampal gyrus. The source regions of apparent volume gain were localized exclusively in CSF areas, among the which Posterior, Anterior and Temporal horns of the ventricles. Longitudinal atrophy measured in hippocampi, temporal regions, and temporal horn of the ventricles was the most discriminative between controls and respectively MCI and AD. Based on the whole set of longitudinal atrophy measurements, sample size analysis required 243 (95% CI: 151,441) and 556 (95% CI: 244,1273) subjects per arm when considering respectively AD and MCI for a randomized two-arm placebo controlled clinical trial for detecting 25% atrophy reduction by controlling for normal aging (80% power, p=0.05). On the head-to-head comparison, the proposed flux analysis outperformed in terms of reduced sample size previously validated quantification methods based on longitudinal hippocampal volumetry. Conclusions. Regional flux analysis of deformations is a novel approach to deformation based morphometry which combines the flexibility of voxel based methods (like tensor based morphometry) with the robustness of segmentation based methods for the quantification of longitudinal atrophy. We showed that regional flux analysis enables a fully automated and powered analysis of longitudinal atrophy in AD, and favorably compares with validated methods for the regional quantification of longitudinal atrophy. Flux analysis thus represents a promising candidate for detecting and robustly quantifying potential drugs effects in clinical trials

Instantiated mixed effects modeling of Alzheimer's disease markers

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease are of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified “marker signature” that defines a subpopulation of similar cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models

Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION: Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression. METHODS: We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD. RESULTS: We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions. DISCUSSION: Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD

Accurate risk estimation of β-amyloid positivity to identify prodromal Alzheimer's disease: Cross-validation study of practical algorithms

INTRODUCTION: The aim was to create readily available algorithms that estimate the individual risk of β-amyloid (Aβ) positivity. METHODS: The algorithms were tested in BioFINDER (n = 391, subjective cognitive decline or mild cognitive impairment) and validated in Alzheimer's Disease Neuroimaging Initiative (n = 661, subjective cognitive decline or mild cognitive impairment). The examined predictors of Aβ status were demographics; cognitive tests; white matter lesions; apolipoprotein E (APOE); and plasma Aβ₄₂/Aβ₄₀, tau, and neurofilament light. RESULTS: Aβ status was accurately estimated in BioFINDER using age, 10-word delayed recall or Mini–Mental State Examination, and APOE (area under the receiver operating characteristics curve = 0.81 [0.77–0.85] to 0.83 [0.79–0.87]). When validated, the models performed almost identical in Alzheimer's Disease Neuroimaging Initiative (area under the receiver operating characteristics curve = 0.80–0.82) and within different age, subjective cognitive decline, and mild cognitive impairment populations. Plasma Aβ₄₂/Aβ₄₀ improved the models slightly. DISCUSSION: The algorithms are implemented on http://amyloidrisk.com where the individual probability of being Aβ positive can be calculated. This is useful in the workup of prodromal Alzheimer's disease and can reduce the number needed to screen in Alzheimer's disease trials

Genomic Copy Number Analysis in Alzheimer's Disease and Mild Cognitive Impairment: An ADNI Study

Copy number variants (CNVs) are DNA sequence alterations, resulting in gains (duplications) and losses (deletions) of genomic segments. They often overlap genes and may play important roles in disease. Only one published study has examined CNVs in late-onset Alzheimer's disease (AD), and none have examined mild cognitive impairment (MCI). CNV calls were generated in 288 AD, 183 MCI, and 184 healthy control (HC) non-Hispanic Caucasian Alzheimer's Disease Neuroimaging Initiative participants. After quality control, 222 AD, 136 MCI, and 143 HC participants were entered into case/control association analyses, including candidate gene and whole genome approaches. Although no excess CNV burden was observed in cases (AD and/or MCI) relative to controls (HC), gene-based analyses revealed CNVs overlapping the candidate gene CHRFAM7A, as well as CSMD1, SLC35F2, HNRNPCL1, NRXN1, and ERBB4 regions, only in cases. Replication in larger samples is important, after which regions detected here may be promising targets for resequencing

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Introduction Developing cross‐validated multi‐biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. Methods We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid‐PET and fluorodeoxyglucose positron‐emission tomography (FDG‐PET) to predict rates of cognitive decline. Prediction models were trained in autosomal‐dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross‐validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model‐based risk enrichment was estimated. Results A model combining all biomarker modalities and established in ADAD predicted the 4‐year rate of decline in global cognition (R2 = 24%) and memory (R2 = 25%) in sporadic AD. Model‐based risk‐enrichment reduced the sample size required for detecting simulated intervention effects by 50%–75%. Discussion Our independently validated machine‐learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD

Comparative analytical performance of multiple plasma Aβ42 and Aβ40 assays and their ability to predict positron emission tomography amyloid positivity

INTRODUCTION: This report details the approach taken to providing a dataset allowing for analyses on the performance of recently developed assays of amyloid beta (Aβ) peptides in plasma and the extent to which they improve the prediction of amyloid positivity. METHODS: Alzheimer's Disease Neuroimaging Initiative plasma samples with corresponding amyloid positron emission tomography (PET) data were run on six plasma Aβ assays. Statistical tests were performed to determine whether the plasma Aβ measures significantly improved the area under the receiver operating characteristic curve for predicting amyloid PET status compared to age and apolipoprotein E (APOE) genotype. RESULTS: The age and APOE genotype model predicted amyloid status with an area under the curve (AUC) of 0.75. Three assays improved AUCs to 0.81, 0.81, and 0.84 (P < .05, uncorrected for multiple comparisons). DISCUSSION: Measurement of Aβ in plasma contributes to addressing the amyloid component of the ATN (amyloid/tau/neurodegeneration) framework and could be a first step before or in place of a PET or cerebrospinal fluid screening study. HIGHLIGHTS: The Foundation of the National Institutes of Health Biomarkers Consortium evaluated six plasma amyloid beta (Aβ) assays using Alzheimer's Disease Neuroimaging Initiative samples. Three assays improved prediction of amyloid status over age and apolipoprotein E (APOE) genotype. Plasma Aβ42/40 predicted amyloid positron emission tomography status better than Aβ42 or Aβ40 alone

Whole-exome rare-variant analysis of Alzheimer's disease and related biomarker traits

INTRODUCTION: Despite increasing evidence of a role of rare genetic variation in the risk of Alzheimer's disease (AD), limited attention has been paid to its contribution to AD-related biomarker traits indicative of AD-relevant pathophysiological processes. METHODS: We performed whole-exome gene-based rare-variant association studies (RVASs) of 17 AD-related traits on whole-exome sequencing (WES) data generated in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study (n = 450) and whole-genome sequencing (WGS) data from ADNI (n = 808). RESULTS: Mutation screening revealed a novel probably pathogenic mutation (PSEN1 p.Leu232Phe). Gene-based RVAS revealed the exome-wide significant contribution of rare coding variation in RBKS and OR7A10 to cognitive performance and protection against left hippocampal atrophy, respectively. DISCUSSION: The identification of these novel gene-trait associations offers new perspectives into the role of rare coding variation in the distinct pathophysiological processes culminating in AD, which may lead to identification of novel therapeutic and diagnostic targets