Prediction of time between CIS onset and clinical conversion to MS using Random Forests

CIS is diagnosed after a first neurological attack and can be considered an early stage of MS as ~80% of all CIS patients will have a second relapse within 20 years. The prediction of this second clinical relapse which marks the clinical conversion to MS (i.e., clinically-definite MS, CDMS) is very challenging, and many clinical and radiological predictors of CDMS have been identified. Machine learning techniques such as support vector machines (SVMs) have been widely applied to neuroimaging data in order to associate MRI features with binary clinical outcomes. A single-centre study has shown that it is possible to predict short-time conversion after 1 and 3 years with an accuracy of ~75 % using a priori defined features from baseline MRI measures and clinical characteristics, which were applied to support vector machines (SVMs). Random forests are another type of machine learning techniques that can easily be applied to regression problems, and consist of an ensemble of decision trees for regression where each tree is created from independent bootstraps from the input data. The present study shows the feasibility of using random forests with European multi-centre MRI data (obtained at CIS onset) to predict the actual date of conversion to CDMS rather than just a binary outcome at a fixed time point

Supervised machine learning in multiple sclerosis: applications to clinically isolated syndromes

Multiple sclerosis (MS) is an inflammatory, demyelinating disease that can cause various neurological symptoms. The first episode of this disease is called a clinically isolated syndrome (CIS) and leads to the diagnosis of MS in the majority of patients in the long-term. Fast conversion from CIS to MS is associated with higher disability and more severe disease progression so that it is of high clinical interest to identify risk patients that will convert to MS within a short time. Several risk factors for conversion have been identified but they can only be applied on cohort levels. In this thesis we provide an overview of supervised machine learning approaches that can be used to distinguish individual CIS-stable patients from those who will experience a second attack within one to five years and consequently will be diagnosed with clinically definite MS. This classification is based on information available at baseline derived from routine MRI scans and complemented by clinical information such as lesion masks, age, gender, disability and CIS type of onset. We introduce the classification landscape, an overview of supervised classification studies with respect to their method and task complexity, and show that our experiments cover a large range of feature complexities in this landscape for the rather complex task of outcome prediction in CIS patients. We show that low-level voxel-based information such as tissue density of grey and white matter are not informative and lead to inconclusive results, whereas the introduction of high-level features such as lesion load, age, gender or disability improves accuracies to 71.4 % and 68 % at one- and three-year follow-up respectively in a single-centre data set. Finally, we propose a recursive feature elimination method that is able to identify specific regions that are relevant with respect to disease progression in MS and achieves accuracies of 73.9 % and 74.3 % at one- and three-year follow-up respectively even in a multi-centre setting

Localized energy for wave equations with degenerate trapping

Localized energy estimates have become a fundamental tool when studying wave equations in the presence of asymptotically at background geometry. Trapped rays necessitate a loss when compared to the estimate on Minkowski space. A loss of regularity is a common way to incorporate such. When trapping is sufficiently weak, a logarithmic loss of regularity suffices. Here, by studying a warped product manifold introduced by Christianson and Wunsch, we encounter the first explicit example of a situation where an estimate with an algebraic loss of regularity exists and this loss is sharp. Due to the global-in-time nature of the estimate for the wave equation, the situation is more complicated than for the Schr\"{o}dinger equation. An initial estimate with sub-optimal loss is first obtained, where extra care is required due to the low frequency contributions. An improved estimate is then established using energy functionals that are inspired by WKB analysis. Finally, it is shown that the loss cannot be improved by any power by saturating the estimate with a quasimode.Comment: 18 page

SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis

Esclerosi múltiple; Classificació d'aprenentatge automàtic; Selecció de funcionsEsclerosis múltiple; Clasificación de aprendizaje automático; Selección de característicasMultiple sclerosis; Machine learning classification; Feature selectionMachine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification. We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together. The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together. Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients.This project received funding from the European Union's Horizon2020 Research and Innovation Program EuroPOND under grant agreement number 666992, and it was supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. We thank all participating partners of the MAGNIMS study group for sharing their data with us

Prediction of second neurological attack in patients with clinically isolated syndrome using support vector machines

The aim of this study is to predict the conversion from clinically isolated syndrome to clinically definite multiple sclerosis using support vector machines. The two groups of converters and non-converters are classified using features that were calculated from baseline data of 73 patients. The data consists of standard magnetic resonance images, binary lesion masks, and clinical and demographic information. 15 features were calculated and all combinations of them were iteratively tested for their predictive capacity using polynomial kernels and radial basis functions with leave-one-out cross-validation. The accuracy of this prediction is up to 86.4% with a sensitivity and specificity in the same range indicating that this is a feasible approach for the prediction of a second clinical attack in patients with clinically isolated syndromes, and that the chosen features are appropriate. The two features gender and location of onset lesions have been used in all feature combinations leading to a high accuracy suggesting that they are highly predictive. However, it is necessary to add supporting features to maximise the accuracy. © 2013 IEEE

Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS

The relation between APOE genotype and cerebral microbleeds in cognitively unimpaired middle- and old-aged individuals

Positive associations between cerebral microbleeds (CMBs) and APOE-ε4 (apolipoprotein E) genotype have been reported in Alzheimer's disease, but show conflicting results. We investigated the effect of APOE genotype on CMBs in a cohort of cognitively unimpaired middle- and old-aged individuals enriched for APOE-ε4 genotype. Participants from ALFA (Alzheimer and Families) cohort were included and their magnetic resonance scans assessed (n = 564, 50% APOE-ε4 carriers). Quantitative magnetic resonance analyses included visual ratings, atrophy measures, and white matter hyperintensity (WMH) segmentations. The prevalence of CMBs was 17%, increased with age (p < 0.05), and followed an increasing trend paralleling APOE-ε4 dose. The number of CMBs was significantly higher in APOE-ε4 homozygotes compared to heterozygotes and non-carriers (p < 0.05). This association was driven by lobar CMBs (p < 0.05). CMBs co-localized with WMH (p < 0.05). No associations between CMBs and APOE-ε2, gray matter volumes, and cognitive performance were found. Our results suggest that cerebral vessels of APOE-ε4 homozygous are more fragile, especially in lobar locations. Co-occurrence of CMBs and WMH suggests that such changes localize in areas with increased vascular vulnerability

White matter microstructure disruption in early stage amyloid pathology.

Introduction: Amyloid beta (Aβ) accumulation is the first pathological hallmark of Alzheimer's disease (AD), and it is associated with altered white matter (WM) microstructure. We aimed to investigate this relationship at a regional level in a cognitively unimpaired cohort. Methods: We included 179 individuals from the European Medical Information Framework for AD (EMIF‐AD) preclinAD study, who underwent diffusion magnetic resonance (MR) to determine tract‐level fractional anisotropy (FA); mean, radial, and axial diffusivity (MD/RD/AxD); and dynamic [18F]flutemetamol) positron emission tomography (PET) imaging to assess amyloid burden. Results: Regression analyses showed a non‐linear relationship between regional amyloid burden and WM microstructure. Low amyloid burden was associated with increased FA and decreased MD/RD/AxD, followed by decreased FA and increased MD/RD/AxD upon higher amyloid burden. The strongest association was observed between amyloid burden in the precuneus and body of the corpus callosum (CC) FA and diffusivity (MD/RD) measures. In addition, amyloid burden in the anterior cingulate cortex strongly related to AxD and RD measures in the genu CC. Discussion: Early amyloid deposition is associated with changes in WM microstructure. The non‐linear relationship might reflect multiple stages of axonal damage

Eigenvector centrality dynamics are related to Alzheimer’s disease pathological changes in non-demented individuals

Amyloid-β accumulation starts in highly connected brain regions and is associated with functional connectivity alterations in the early stages of Alzheimer's disease. This regional vulnerability is related to the high neuronal activity and strong fluctuations typical of these regions. Recently, dynamic functional connectivity was introduced to investigate changes in functional network organization over time. High dynamic functional connectivity variations indicate increased regional flexibility to participate in multiple subnetworks, promoting functional integration. Currently, only a limited number of studies have explored the temporal dynamics of functional connectivity in the pre-dementia stages of Alzheimer's disease. We study the associations between abnormal cerebrospinal fluid amyloid and both static and dynamic properties of functional hubs, using eigenvector centrality, and their relationship with cognitive performance, in 701 non-demented participants from the European Prevention of Alzheimer's Dementia cohort. Voxel-wise eigenvector centrality was computed for the whole functional magnetic resonance imaging time series (static), and within a sliding window (dynamic). Differences in static eigenvector centrality between amyloid positive (A+) and negative (A-) participants and amyloid-tau groups were found in a general linear model. Dynamic eigenvector centrality standard deviation and range were compared between groups within clusters of significant static eigenvector centrality differences, and within 10 canonical resting-state networks. The effect of the interaction between amyloid status and cognitive performance on dynamic eigenvector centrality variability was also evaluated with linear models. Models were corrected for age, sex, and education level. Lower static centrality was found in A+ participants in posterior brain areas including a parietal and an occipital cluster; higher static centrality was found in a medio-frontal cluster. Lower eigenvector centrality variability (standard deviation) occurred in A+ participants in the frontal cluster. The default mode network and the dorsal visual networks of A+ participants had lower dynamic eigenvector centrality variability. Centrality variability in the default mode network and dorsal visual networks were associated with cognitive performance in the A- and A+ groups, with lower variability being observed in A+ participants with good cognitive scores. Our results support the role and timing of eigenvector centrality alterations in very early stages of Alzheimer's disease and show that centrality variability over time adds relevant information on the dynamic patterns that cause static eigenvector centrality alterations. We propose that dynamic eigenvector centrality is an early biomarker of the interplay between early Alzheimer's disease pathology and cognitive decline