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

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

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

Longitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer’s disease progression

International audienc

A Comprehensive Investigation of the Potential Role of Lipoproteins and Metabolite Profile as Biomarkers of Alzheimer’s Disease Compared to the Known CSF Biomarkers

Introduction. While cerebrospinal fluid (CSF) core biomarkers have been considered diagnostic biomarkers for a long time, special attention has been recently dedicated to lipoproteins and metabolites that could be potentially associated with Alzheimer’s disease (AD) neurodegeneration. Herein, we aimed to investigate the relationship between the levels of CSF core biomarkers including Aβ-42, TAU, and P-TAU and plasma lipoproteins and metabolites of patients with AD from the baseline cohort of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Method. Using the ADNI database, fourteen subclasses of lipoproteins as well as a number of lipids and fatty acids and low-molecular metabolites including amino acids, ketone bodies, and glycolysis-related metabolites in blood samples were measured as potential noninvasive markers, and their association with the CSF core biomarkers was statistically investigated controlling for age and gender. Results. A total number of 251 AD subjects were included, among whom 71 subjects were negative for the Apo-E ε4 allele and 150 were positive. There was no significant difference between the two groups regarding cognitive assessments, CSF core biomarkers, and lipoproteins and metabolites except the level of Aβ-42 (p<0.001) and phenylalanine (p=0.049), which were higher in the negative group. CSF TAU and P-TAU were significantly correlated with medium and small HDL in the negative group, and with extremely large VLDL in the positive group. Our results also indicated significant correlations of metabolites including unsaturated fatty acids, glycerol, and leucine with CSF core biomarkers. Conclusion. Based on our findings, a number of lipoproteins and metabolites were associated with CSF core biomarkers of AD. These correlations showed some differences in Apo-E ε4 positive and negative groups, which reminds the role of Apo-E gene status in the pathophysiology of AD development. However, further research is warranted to explore the exact association of lipoproteins and other metabolites with AD core biomarkers and pathology

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

Late-Life Depression Is Associated With Reduced Cortical Amyloid Burden : Findings From the Alzheimer's Disease Neuroimaging Initiative Depression Project

Background: We evaluated the role of cortical amyloid deposition as a factor contributing to memory dysfunction and increased risk of dementia associated with late-life depression (LLD). Methods: A total of 119 older adult participants with a current diagnosis of major depression (LLD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) Depression Project study and 119 nondepressed (ND) cognitively unimpaired participants matched on age, sex, and APOE genotype were obtained from the ADNI database. Results: Thirty-three percent of LLD participants met ADNI criteria for mild cognitive impairment. Compared with ND individuals, the LLD group exhibited less global amyloid beta (Aβ) accumulation (p = .05). The proportion of amyloid positivity in the LLD group was 19.3% compared with 31.1% for the ND participants (p = .02). Among LLD participants, global Aβ was not associated with lifetime number of depressive episodes, lifetime length of depression, length of lifetime selective serotonin reuptake inhibitor use, or lifetime length of untreated depression (p >. 21 for all). Global Aβ was associated with worse memory performance (p = .05). Similar results were found in secondary analyses restricting comparisons to the cognitively unimpaired LLD participants as well as when comparing the LLD group with an ND group that included participants with mild cognitive impairment. Conclusions: Contrary to expectation, the LLD group showed less Aβ deposition than the ND group and Aβ deposition was not associated with depression history characteristics. Aβ was associated with memory, but this relationship did not differ between LLD and ND. Our results suggest that memory deficits and accelerated cognitive decline reported in previous studies of LLD are not due to greater cortical Aβ accumulation

Alzheimer's polygenic risk scores are associated with cognitive phenotypes in Down syndrome

Funder: NeuroGenomics and Informatics CenterFunder: Washington University School of MedicineFunder: Chan Zuckerberg Initiative; doi: http://dx.doi.org/10.13039/100014989Funder: Washington UniversityFunder: National Institute on Aging; doi: http://dx.doi.org/10.13039/100000049Funder: German Center for Neurodegenerative Diseases; doi: http://dx.doi.org/10.13039/501100005224Funder: FLENIFunder: Japan Agency for Medical Research and Development; doi: http://dx.doi.org/10.13039/100009619Funder: KHIDI; doi: http://dx.doi.org/10.13039/501100003710Funder: National Institute of Biomedical Imaging and Bioengineering; doi: http://dx.doi.org/10.13039/100000070Funder: NICHD; doi: http://dx.doi.org/10.13039/100009633INTRODUCTION: This study aimed to investigate the influence of the overall Alzheimer's disease (AD) genetic architecture on Down syndrome (DS) status, cognitive measures, and cerebrospinal fluid (CSF) biomarkers. METHODS: AD polygenic risk scores (PRS) were tested for association with DS‐related traits. RESULTS: The AD risk PRS was associated with disease status in several cohorts of sporadic late‐ and early‐onset and familial late‐onset AD, but not in familial early‐onset AD or DS. On the other hand, lower DS Mental Status Examination memory scores were associated with higher PRS, independent of intellectual disability and APOE (PRS including APOE, PRS APOE , p = 2.84 × 10−4; PRS excluding APOE, PRS nonAPOE , p = 1.60 × 10−2). PRS APOE exhibited significant associations with Aβ42, tTau, pTau, and Aβ42/40 ratio in DS. DISCUSSION: These data indicate that the AD genetic architecture influences cognitive and CSF phenotypes in DS adults, supporting common pathways that influence memory decline in both traits. Highlights: Examination of the polygenic risk of AD in DS presented here is the first of its kind. AD PRS influences memory aspects in DS individuals, independently of APOE genotype. These results point to an overlap between the genes and pathways that leads to AD and those that influence dementia and memory decline in the DS population. APOE ε4 is linked to DS cognitive decline, expanding cognitive insights in adults