Bayesian inference and role of astrocytes in amyloid-beta dynamics with modelling of Alzheimer's disease using clinical data

Alzheimer's disease (AD) is a prominent, worldwide, age-related neurodegenerative disease that currently has no systemic treatment. Strong evidence suggests that permeable amyloid-beta peptide (Abeta) oligomers, astrogliosis and reactive astrocytosis cause neuronal damage in AD. A large amount of Abeta is secreted by astrocytes, which contributes to the total Abeta deposition in the brain. This suggests that astrocytes may also play a role in AD, leading to increased attention to their dynamics and associated mechanisms. Therefore, in the present study, we developed and evaluated novel stochastic models for Abeta growth using ADNI data to predict the effect of astrocytes on AD progression in a clinical trial. In the AD case, accurate prediction is required for a successful clinical treatment plan. Given that AD studies are observational in nature and involve routine patient visits, stochastic models provide a suitable framework for modelling AD. Using the approximate Bayesian computation (ABC) approach, the AD etiology may be modelled as a multi-state disease process. As a result, we use this approach to examine the weak and strong influence of astrocytes at multiple disease progression stages using ADNI data from the baseline to 2-year visits for AD patients whose ages ranged from 50 to 90 years. Based on ADNI data, we discovered that the strong astrocyte effect (i.e., a higher concentration of astrocytes as compared to Abeta) could help to lower or clear the growth of Abeta, which is a key to slowing down AD progression.Comment: 10, figures and 30 page

Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets

Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image

The effect of network template from normal subjects in the detection of network impairment

This study aimed to provide a simple way to approach group differences by independent component analysis when researching functional connectivity changes of resting‑state network in brain disorders. We used baseline resting state functional magnetic resonance imaging from the Alzheimer's disease neuroimaging initiative dataset and performed independent component analysis based on different kinds of subject selection, by including two downloaded templates and single‑subject independent component analysis method. All conditions were used to calculate the functional connectivity of the default mode network, and to test group differences and evaluate correlation with cognitive measurements and hippocampal volume. The default mode network functional connectivity results most fitting clinical evaluations were from templates based on young healthy subjects and the worst results were from heterogeneous or more severe disease groups or single‑subject independent component analysis method. Using independent component analysis network maps derived from normal young subjects to extract all individual functional connectivities provides significant correlations with clinical evaluations

MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation

Accurately detecting Alzheimer's disease (AD) and predicting mini-mental state examination (MMSE) score are important tasks in elderly health by magnetic resonance imaging (MRI). Most of the previous methods on these two tasks are based on single-task learning and rarely consider the correlation between them. Since the MMSE score, which is an important basis for AD diagnosis, can also reflect the progress of cognitive impairment, some studies have begun to apply multi-task learning methods to these two tasks. However, how to exploit feature correlation remains a challenging problem for these methods. To comprehensively address this challenge, we propose a MRI-based multi-task decoupled learning method for AD detection and MMSE score prediction. First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks. Each multi-task interaction layer contains two feature decoupling modules and one feature interaction module. Furthermore, to enhance the generalization between tasks of the features selected by the feature decoupling module, we propose the feature consistency loss constrained feature decoupling module. Finally, in order to exploit the specific distribution information of MMSE score in different groups, a distribution loss is proposed to further enhance the model performance. We evaluate our proposed method on multi-site datasets. Experimental results show that our proposed multi-task decoupled representation learning method achieves good performance, outperforming single-task learning and other existing state-of-the-art methods.Comment: 15 page

A Weighted Prognostic Covariate Adjustment Method for Efficient and Powerful Treatment Effect Inferences in Randomized Controlled Trials

A crucial task for a randomized controlled trial (RCT) is to specify a statistical method that can yield an efficient estimator and powerful test for the treatment effect. A novel and effective strategy to obtain efficient and powerful treatment effect inferences is to incorporate predictions from generative artificial intelligence (AI) algorithms into covariate adjustment for the regression analysis of a RCT. Training a generative AI algorithm on historical control data enables one to construct a digital twin generator (DTG) for RCT participants, which utilizes a participant's baseline covariates to generate a probability distribution for their potential control outcome. Summaries of the probability distribution from the DTG are highly predictive of the trial outcome, and adjusting for these features via regression can thus improve the quality of treatment effect inferences, while satisfying regulatory guidelines on statistical analyses, for a RCT. However, a critical assumption in this strategy is homoskedasticity, or constant variance of the outcome conditional on the covariates. In the case of heteroskedasticity, existing covariate adjustment methods yield inefficient estimators and underpowered tests. We propose to address heteroskedasticity via a weighted prognostic covariate adjustment methodology (Weighted PROCOVA) that adjusts for both the mean and variance of the regression model using information obtained from the DTG. We prove that our method yields unbiased treatment effect estimators, and demonstrate via comprehensive simulation studies and case studies from Alzheimer's disease that it can reduce the variance of the treatment effect estimator, maintain the Type I error rate, and increase the power of the test for the treatment effect from 80% to 85%~90% when the variances from the DTG can explain 5%~10% of the variation in the RCT participants' outcomes.Comment: 49 pages, 6 figures, 12 table

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