Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly accounting for heterogeneous measures poses important challenges related to the modeling of the variability and the interpretability of the results. These issues are here addressed by proposing a novel multi-channel stochastic generative model. We assume that a latent variable generates the data observed through different channels (e.g., clinical scores, imaging, ...) and describe an efficient way to estimate jointly the distribution of both latent variable and data generative process. Experiments on synthetic data show that the multi-channel formulation allows superior data reconstruction as opposed to the single channel one. Moreover, the derived lower bound of the model evidence represents a promising model selection criterion. Experiments on AD data show that the model parameters can be used for unsupervised patient stratification and for the joint interpretation of the heterogeneous observations. Because of its general and flexible formulation, we believe that the proposed method can find important applications as a general data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with MICCAI 2018, September 20, Granada, Spai

CSF and Brain Structural Imaging Markers of the Alzheimer's Pathological Cascade

10.1371/journal.pone.0047406PLoS ONE712

Multiple landmark detection using multi-agent reinforcement learning

The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a tedious process that requires domain-specific expertise and introduces inter-observer variability. This paper proposes a new detection approach for multiple landmarks based on multi-agent reinforcement learning. Our hypothesis is that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of others. Using a Deep Q-Network (DQN) architecture we construct an environment and agent with implicit inter-communication such that we can accommodate K agents acting and learning simultaneously, while they attempt to detect K different landmarks. During training the agents collaborate by sharing their accumulated knowledge for a collective gain. We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naïve approach of training K agents separately. Code and visualizations available: https://github.com/thanosvlo/MARL-for-Anatomical-Landmark-Detectio

Atrophy in the parahippocampal gyrus as an early biomarker of Alzheimer’s disease

The main aim of the present study was to compare volume differences in the hippocampus and parahippocampal gyrus as biomarkers of Alzheimer’s disease (AD). Based on the previous findings, we hypothesized that there would be significant volume differences between cases of healthy aging, amnestic mild cognitive impairment (aMCI), and mild AD. Furthermore, we hypothesized that there would be larger volume differences in the parahippocampal gyrus than in the hippocampus. In addition, we investigated differences between the anterior, middle, and posterior parts of both structures. We studied three groups of participants: 18 healthy participants without memory decline, 18 patients with aMCI, and 18 patients with mild AD. 3 T T1-weighted MRI scans were acquired and gray matter volumes of the anterior, middle, and posterior parts of both the hippocampus and parahippocampal gyrus were measured using a manual tracing approach. Volumes of both the hippocampus and parahippocampal gyrus were significantly different between the groups in the following order: healthy > aMCI > AD. Volume differences between the groups were relatively larger in the parahippocampal gyrus than in the hippocampus, in particular, when we compared healthy with aMCI. No substantial differences were found between the anterior, middle, and posterior parts of both structures. Our results suggest that parahippocampal volume discriminates better than hippocampal volume between cases of healthy aging, aMCI, and mild AD, in particular, in the early phase of the disease. The present results stress the importance of parahippocampal atrophy as an early biomarker of AD

Contrasting prefrontal cortex contributions to episodic memory dysfunction in behavioural variant frontotemporal dementia and alzheimer's disease

Recent evidence has questioned the integrity of episodic memory in behavioural variant frontotemporal dementia (bvFTD), where recall performance is impaired to the same extent as in Alzheimer's disease (AD). While these deficits appear to be mediated by divergent patterns of brain atrophy, there is evidence to suggest that certain prefrontal regions are implicated across both patient groups. In this study we sought to further elucidate the dorsolateral (DLPFC) and ventromedial (VMPFC) prefrontal contributions to episodic memory impairment in bvFTD and AD. Performance on episodic memory tasks and neuropsychological measures typically tapping into either DLPFC or VMPFC functions was assessed in 22 bvFTD, 32 AD patients and 35 age- and education-matched controls. Behaviourally, patient groups did not differ on measures of episodic memory recall or DLPFC-mediated executive functions. BvFTD patients were significantly more impaired on measures of VMPFC-mediated executive functions. Composite measures of the recall, DLPFC and VMPFC task scores were covaried against the T1 MRI scans of all participants to identify regions of atrophy correlating with performance on these tasks. Imaging analysis showed that impaired recall performance is associated with divergent patterns of PFC atrophy in bvFTD and AD. Whereas in bvFTD, PFC atrophy covariates for recall encompassed both DLPFC and VMPFC regions, only the DLPFC was implicated in AD. Our results suggest that episodic memory deficits in bvFTD and AD are underpinned by divergent prefrontal mechanisms. Moreover, we argue that these differences are not adequately captured by existing neuropsychological measures

Generation and quality control of lipidomics data for the alzheimers disease neuroimaging initiative cohort.

Alzheimers disease (AD) is a major public health priority with a large socioeconomic burden and complex etiology. The Alzheimer Disease Metabolomics Consortium (ADMC) and the Alzheimer Disease Neuroimaging Initiative (ADNI) aim to gain new biological insights in the disease etiology. We report here an untargeted lipidomics of serum specimens of 806 subjects within the ADNI1 cohort (188 AD, 392 mild cognitive impairment and 226 cognitively normal subjects) along with 83 quality control samples. Lipids were detected and measured using an ultra-high-performance liquid chromatography quadruple/time-of-flight mass spectrometry (UHPLC-QTOF MS) instrument operated in both negative and positive electrospray ionization modes. The dataset includes a total 513 unique lipid species out of which 341 are known lipids. For over 95% of the detected lipids, a relative standard deviation of better than 20% was achieved in the quality control samples, indicating high technical reproducibility. Association modeling of this dataset and available clinical, metabolomics and drug-use data will provide novel insights into the AD etiology. These datasets are available at the ADNI repository at http://adni.loni.usc.edu/

Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease

Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression

Complexity in the genetic architecture of leukoaraiosis in hypertensive sibships from the GENOA Study

<p>Abstract</p> <p>Background</p> <p>Subcortical white matter hyperintensity on magnetic resonance imaging (MRI) of the brain, referred to as leukoaraiosis, is associated with increased risk of stroke and dementia. Hypertension may contribute to leukoaraiosis by accelerating the process of arteriosclerosis involving penetrating small arteries and arterioles in the brain. Leukoaraiosis volume is highly heritable but shows significant inter-individual variability that is not predicted well by any clinical covariates (except for age) or by single SNPs.</p> <p>Methods</p> <p>As part of the Genetics of Microangiopathic Brain Injury (GMBI) Study, 777 individuals (74% hypertensive) underwent brain MRI and were genotyped for 1649 SNPs from genes known or hypothesized to be involved in arteriosclerosis and related pathways. We examined SNP main effects, epistatic (gene-gene) interactions, and context-dependent (gene-environment) interactions between these SNPs and covariates (including conventional and novel risk factors for arteriosclerosis) for association with leukoaraiosis volume. Three methods were used to reduce the chance of false positive associations: 1) false discovery rate (FDR) adjustment for multiple testing, 2) an internal replication design, and 3) a ten-iteration four-fold cross-validation scheme.</p> <p>Results</p> <p>Four SNP main effects (in <it>F3</it>, <it>KITLG</it>, <it>CAPN10</it>, and <it>MMP2</it>), 12 SNP-covariate interactions (including interactions between <it>KITLG </it>and homocysteine, and between <it>TGFB3 </it>and both physical activity and C-reactive protein), and 173 SNP-SNP interactions were significant, replicated, and cross-validated. While a model containing the top single SNPs with main effects predicted only 3.72% of variation in leukoaraiosis in independent test samples, a multiple variable model that included the four most highly predictive SNP-SNP and SNP-covariate interactions predicted 11.83%.</p> <p>Conclusion</p> <p>These results indicate that the genetic architecture of leukoaraiosis is complex, yet predictive, when the contributions of SNP main effects are considered in combination with effects of SNP interactions with other genes and covariates.</p

Amyloid imaging in the differential diagnosis of dementia: review and potential clinical applications

In the past decade, positron emission tomography (PET) with carbon-11-labeled Pittsburgh Compound B (PIB) has revolutionized the neuroimaging of aging and dementia by enabling in vivo detection of amyloid plaques, a core pathologic feature of Alzheimer's disease (AD). Studies suggest that PIB-PET is sensitive for AD pathology, can distinguish AD from non-AD dementia (for example, frontotemporal lobar degeneration), and can help determine whether mild cognitive impairment is due to AD. Although the short half-life of the carbon-11 radiolabel has thus far limited the use of PIB to research, a second generation of tracers labeled with fluorine-18 has made it possible for amyloid PET to enter the clinical era. In the present review, we summarize the literature on amyloid imaging in a range of neurodegenerative conditions. We focus on potential clinical applications of amyloid PET and its role in the differential diagnosis of dementia. We suggest that amyloid imaging will be particularly useful in the evaluation of mildly affected, clinically atypical or early age-at-onset patients, and illustrate this with case vignettes from our practice. We emphasize that amyloid imaging should supplement (not replace) a detailed clinical evaluation. We caution against screening asymptomatic individuals, and discuss the limited positive predictive value in older populations. Finally, we review limitations and unresolved questions related to this exciting new technique