Shared latent structures between imaging features and biomarkers in early stages of Alzheimer's disease: a predictive study

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e. the capacity of predicting biomarker values) is assessed in a cross-validation framework.Peer ReviewedPostprint (author's final draft

Multipurpose virtual reality environment for biomedical and health applications

Virtual reality is a trending, widely accessible, and contemporary technology of increasing utility to biomedical and health applications. However, most implementations of virtual reality environments are tailored to specific applications. We describe the complete development of a novel, open-source virtual reality environment that is suitable for multipurpose biomedical and healthcare applications. This environment can be interfaced with different hardware and data sources, ranging from gyroscopes to fMRI scanners. The developed environment simulates an immersive (first-person perspective) run in the countryside, in a virtual landscape with various salient features. The utility of the developed VR environment has been validated via two test applications: an application in the context of motor rehabilitation following injury of the lower limbs and an application in the context of real-time functional magnetic resonance imaging neurofeedback, to regulate brain function in specific brain regions of interest. Both applications were tested by pilot subjects that unanimously provided very positive feedback, suggesting that appropriately designed VR environments can indeed be robustly and efficiently used for multiple biomedical purposes. We attribute the versatility of our approach on three principles implicit in the design: selectivity, immersiveness, and adaptability. The software, including both applications, is publicly available free of charge, via a GitHub repository, in support of the Open Science Initiative. Although using this software requires specialized hardware and engineering know-how, we anticipate our contribution to catalyze further progress, interdisciplinary collaborations and replicability, with regards to the usage of virtual reality in biomedical and health applications.Peer ReviewedPostprint (author's final draft

Projection to latent spaces disentangles pathological effects on brain morphology in the asymptomatic phase of Alzheimer's disease

Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aß, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD.This work has been partially supported by the project MALEGRA TEC2016-75976-R financed by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). AC was supported by the Spanish Ministerio de Educación, Cultura y Deporte FPU Research Fellowship. JG holds a Ramón y Cajal fellowship (RYC-2013-13054).Peer ReviewedPostprint (published version

Projection to latent spaces disentangles specific cerebral morphometric patterns associated to aging and preclinical AD”,

Peer ReviewedPostprint (published version

Genetically predicted telomere length and its relationship with neurodegenerative diseases and life expectancy

Telomere length (TL) is a biomarker of biological aging. Shorter telomeres have been associated with mortality and increased rates of age-related diseases. However, observational studies are unable to conclude whether TL is causally associated with those outcomes. Mendelian randomization (MR) was developed for assessing causality using genetic variants in epidemiological research. The objective of this study was to test the potential causal role of TL in neurodegenerative disorders and life expectancy through MR analysis. Summary level data were extracted from the most recent genome-wide association studies for TL, Alzheimer's disease (AD), Parkinson's disease, Frontotemporal dementia, Amyotrophic Lateral Sclerosis, Progressive Supranuclear Palsy and life expectancy. MR estimates revealed that longer telomeres inferred a protective effect on risk of AD (OR = 0.964; adjusted p-value = 0.039). Moreover, longer telomeres were significantly associated with increased life expectancy (beta(IVW) = 0.011; adjusted p-value = 0.039). Sensitivity analyses suggested evidence for directional pleiotropy in AD analyses. Our results showed that genetically predicted longer TL may increase life expectancy and play a protective causal effect on AD. We did not observe significant causal relationships between longer TL and other neurodegenerative diseases. This suggests that the involvement of TL on specific biological mechanisms might differ between AD and life expectancy, with respect to that in other neurodegenerative diseases. Moreover, the presence of pleiotropy may reflect the complex interplay between TL homeostasis and AD pathophysiology. Further observational studies are needed to confirm these results. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!

Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neu- roimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimag- ing data

MRI-based screening of preclinical Alzheimer's disease for prevention clinical trials

The final publication is available at IOS Press through http://dx.doi.org/10.3233/JAD-180299”.The identification of healthy individuals harboring amyloid pathology represents one important challenge for secondary prevention clinical trials in Alzheimer’s disease (AD). Consequently, noninvasive and cost-efficient techniques to detect preclinical AD constitute an unmet need of critical importance. In this manuscript, we apply machine learning to structural MRI (T1 and DTI) of 96 cognitively normal subjects to identify amyloid-positive ones. Models were trained on public ADNI data and validated on an independent local cohort. Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60% of unnecessary CSF/PET tests and to reduce 47% of the cost of recruitment. This recruitment strategy capitalizes on available MR scans to reduce the overall amount of invasive PET/CSF tests in prevention trials, demonstrating a potential value as a tool for preclinical AD screening. This protocol could foster the development of secondary prevention strategies for AD.Peer ReviewedPostprint (author's final draft

Structural networks for brain age prediction

Biological networks have gained considerable attention within the Deep Learning community because of the promising framework of Graph Neural Networks (GNN), neural models that operate in complex networks. In the context of neuroimaging, GNNs have successfully been employed for functional MRI processing but their application to ROI-level structural MRI (sMRI) remains mostly unexplored. In this work we analyze the implementation of these geometric models with sMRI by building graphs of ROIs (ROI graphs) using tools from Graph Signal Processing literature and evaluate their performance in a downstream supervised task, age prediction. We first make a qualitative and quantitative comparison of the resulting networks obtained with common graph topology learning strategies. In a second stage, we train GNN-based models for brain age prediction. Since the order of every ROI graph is exactly the same and each vertex is an entity by itself (a ROI), we evaluate whether including ROI information during message-passing or global pooling operations is beneficial and compare the performance of GNNs against a Fully-Connected Neural Network baseline. The results show that ROI-level information is needed during the global pooling operation in order to achieve competitive results. However, no relevant improvement has been detected when it is incorporated during the message passing. These models achieve a MAE of 4.27 in hold-out test data, which is a performance very similar to the baseline, suggesting that the inductive bias included with the obtained graph connectivity is relevant and useful to reduce the dimensionality of the problem.This work has been supported by the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033 and the FI-AGAUR grant funded by Direcció General de Recerca (DGR) of Departament de Recerca i Universitats (REU) of the Generalitat de Catalunya.Peer ReviewedPostprint (published version

Effects of APOE-ε4 allele load on brain morphology in a cohort of middle-aged healthy individuals with enriched genetic risk for Alzheimer's disease

INTRODUCTION: Apolipoprotein E (APOE)-ε4 is the major genetic risk factor for Alzheimer's disease. However, the dose-dependent impact of this allele on brain morphology of healthy individuals remains unclear. METHODS: We analyzed gray matter volumes (GMvs) in a sample of 533 healthy middle-aged individuals with a substantial representation of ε4-carriers (207 heterozygotes and 65 homozygotes). RESULTS: We found APOE-ε4 additive GMv reductions in the right hippocampus, caudate, precentral gyrus, and cerebellar crus. In these regions, the APOE genotype interacted with age, with homozygotes displaying lower GMv after the fifth decade of life. APOE-ε4 was also associated to greater GMv in the right thalamus, left occipital gyrus, and right frontal cortex. DISCUSSION: Our data indicate that APOE-ε4 exerts additive effects on GMv in regions relevant for Alzheimer's disease pathophysiology already in healthy individuals. These findings elucidate the mechanisms underlying the increased Alzheimer's disease risk in ε4-carriers, suggesting a dose-dependent disease vulnerability on the brain structure level