Ensemble of classifiers based data fusion of EEG and MRI for diagnosis of neurodegenerative disorders

The prevalence of Alzheimer\u27s disease (AD), Parkinson\u27s disease (PD), and mild cognitive impairment (MCI) are rising at an alarming rate as the average age of the population increases, especially in developing nations. The efficacy of the new medical treatments critically depends on the ability to diagnose these diseases at the earliest stages. To facilitate the availability of early diagnosis in community hospitals, an accurate, inexpensive, and noninvasive diagnostic tool must be made available. As biomarkers, the event related potentials (ERP) of the electroencephalogram (EEG) - which has previously shown promise in automated diagnosis - in addition to volumetric magnetic resonance imaging (MRI), are relatively low cost and readily available tools that can be used as an automated diagnosis tool. 16-electrode EEG data were collected from 175 subjects afflicted with Alzheimer\u27s disease, Parkinson\u27s disease, mild cognitive impairment, as well as non-disease (normal control) subjects. T2 weighted MRI volumetric data were also collected from 161 of these subjects. Feature extraction methods were used to separate diagnostic information from the raw data. The EEG signals were decomposed using the discrete wavelet transform in order to isolate informative frequency bands. The MR images were processed through segmentation software to provide volumetric data of various brain regions in order to quantize potential brain tissue atrophy. Both of these data sources were utilized in a pattern recognition based classification algorithm to serve as a diagnostic tool for Alzheimer\u27s and Parkinson\u27s disease. Support vector machine and multilayer perceptron classifiers were used to create a classification algorithm trained with the EEG and MRI data. Extracted features were used to train individual classifiers, each learning a particular subset of the training data, whose decisions were combined using decision level fusion. Additionally, a severity analysis was performed to diagnose between various stages of AD as well as a cognitively normal state. The study found that EEG and MRI data hold complimentary information for the diagnosis of AD as well as PD. The use of both data types with a decision level fusion improves diagnostic accuracy over the diagnostic accuracy of each individual data source. In the case of AD only diagnosis, ERP data only provided a 78% diagnostic performance, MRI alone was 89% and ERP and MRI combined was 94%. For PD only diagnosis, ERP only performance was 67%, MRI only was 70%, and combined performance was 78%. MCI only diagnosis exhibited a similar effect with a 71% ERP performance, 82% MRI performance, and 85% combined performance. Diagnosis among three subject groups showed the same trend. For PD, AD, and normal diagnosis ERP only performance was 43%, MRI only was 66%, and combined performance was 71%. The severity analysis for mild AD, severe AD, and normal subjects showed the same combined effect

An Integrated Neuroimaging Approach for the Prediction and Analysis of Alzheimer’s Disease and its Prodromal Stages

This dissertation proposes to combine magnetic resonance imaging (MRI), positron emission tomography (PET) and a neuropsychological test, Mini-Mental State Examination (MMSE), as input to a multidimensional space for the classification of Alzheimer’s disease (AD) and it’s prodromal stages including amnestic MCI (aMCI) and non-amnestic MCI (naMCI). An assessment is provided on the effect of different MRI normalization techniques on the prediction of AD. Statistically significant variables selected for each combination model were used to construct the classification space using support vector machines. To combine MRI and PET, orthogonal partial least squares to latent structures is used as a multivariate analysis to discriminate between AD, early and late MCI (EMCI and LMCI) from cognitively normal (CN)s. In addition, this dissertation proposes a new effective mean indicator (EMI) method for distinguishing stages of AD from CN. EMI utilizes the mean of specific top-ranked measures, determined by incremental error analysis, to achieve optimal separation of AD and CN. For AD vs. CN, the two most discriminative volumetric variables (right hippocampus and left inferior lateral ventricle), when combined with MMSE scores, provided an average accuracy of 92.4% (sensitivity: 84.0%; specificity: 96.1%). MMSE scores were found to improve classification accuracy by 8.2% and 12% for aMCI vs. CN and naMCI vs. CN, respectively. Brain atrophy was almost evenly seen on both sides of the brain for AD subjects, which was different from right side dominance for aMCI and left side dominance for naMCI. Findings suggest that subcortical volume need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or the mean thickness. Furthermore, MRI and PET had comparable predictive power in separating AD from CN. For the EMCI prediction, cortical thickness was found to be the best predictor, even better than using all features together. Validation with an external test set demonstrated that best of feature-selected models for the LMCI group was able to classify 83% of the LMCI subjects. The EMI-based method achieved an accuracy of 92.7% using only MRI features. The performance of the EMI-based method along with its simplicity suggests great potential for its use in clinical trials

Neural Correlates of Parkinsonian Syndromes

The thesis investigated objective neuroimaging biomarkers in parkinsonian syndromes, which could be applied to increase diagnostic accuracy. To find convergence of the literature concerning disease-specific patterns in Parkinson’s disease and progressive supranuclear palsy, we conducted meta-analyses. In Parkinson’s disease glucose hypometabolism was re- vealed in bilateral inferior parietal cortex and left caudate nucleus and focal gray matter atrophy in the middle occipital gyrus. In progressive supranu- clear palsy we identified gray matter atrophy in the midbrain and white mat- ter atrophy in the cerebral/cerebellar pedunculi and midbrain. In sum, in Parkinson’s disease hypometabolism outperforms atrophy and in progres- sive supranuclear palsy we validated pathognomonic markers as disease- specific. Our studies create a novel framework to investigate disease- specific regional alterations for use in clinical routine. Further, we inves- tigated neural correlates by voxel-based morphometry and discriminated disease and clinical syndrome by multivariate pattern recognition in sin- gle patients with corticobasal syndrome and corticobasal syndrome with a unique syndrome - alien/ anarchic limb phenomenon. We found gray matter volume differences between patients and controls in asymmetric frontotem- poral/ occipital regions, motor areas, and insulae. The frontoparietal gyrus including the supplementary motor area contralateral to the side of the af- fected limb was specific for alien/ anarchic limb phenomenon. The predic- tion of the disease among controls was 79.0% accurate. The prediction of the specific syndrome within a disease reached an accuracy of 81.3%. In conclusion, we reliably classified patients and controls by objective pattern recognition. Moreover, we were able to predict a specific clinical syndrome within a disease, paving the way to individualized disease prediction.:SELBSTSTÄNDIGKEITSERKLÄRUNG I ACKNOWLEDGMENTS II SUMMARY III ZUSAMMENFASSUNG VIII BIBLIOGRAPHISCHE DARSTELLUNG XIV CONTENTS XVI 1 GENERAL INTRODUCTION 1 1.1 ParkinsonianSyndromes .................... 2 1.2 Parkinson’sDisease ....................... 2 1.2.1 DiagnosticCriteria .................... 3 1.3 ProgressiveSupranuclearPalsy ................ 4 1.3.1 DiagnosticCriteria .................... 5 1.4 CorticobasalDegeneration ................... 5 1.4.1 DiagnosticCriteria .................... 7 1.5 ImagingBiomarkers ....................... 7 1.6 CurrentThesis .......................... 9 1.6.1 MotivationandFramework ............... 9 1.6.2 ResearchQuestions................... 9 2 GENERAL MATERIALS AND METHODS 12 2.1 MagneticResonanceImaging.................. 12 2.2 AnalyticalMethods........................ 13 2.2.1 Meta-Analysis ...................... 13 2.2.2 Voxel-BasedMorphometry ............... 14 2.2.3 Support-Vector Machine Classification . . . . . . . . . 15 2.3 Multi-CentricData ........................ 16 2.4 ClinicalAssessment ....................... 17 3 Study 1 4 Study 2 5 Study 3 6 Study 4 7 Study 5 8 DISCUSSION 73 8.1 MainFindings........................... 73 8.2 Statistical Approaches to Find Imaging Biomarker . . . . . . 76 8.3 Brain Alterations and their Utility as Imaging Biomarker . . . . 77 8.4 Limitations ............................ 78 8.5 Contributions of the Current Thesis and Future Directions . . 79 9 REFERENCES APPENDIX XVIII LIST OF AUTHORSHIP XXVII CURRICULUM VITÆ XXXVII

FUNCTIONAL NETWORK CONNECTIVITY IN HUMAN BRAIN AND ITS APPLICATIONS IN AUTOMATIC DIAGNOSIS OF BRAIN DISORDERS

The human brain is one of the most complex systems known to the mankind. Over the past 3500 years, mankind has constantly investigated this remarkable system in order to understand its structure and function. Emerging of neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have opened a non-invasive in-vivo window into brain function. Moreover, fMRI has made it possible to study brain disorders such as schizophrenia from a different angle unknown to researchers before. Human brain function can be divided into two categories: functional segregation and integration. It is well-understood that each region in the brain is specialized in certain cognitive or motor tasks. The information processed in these specialized regions in different temporal and spatial scales must be integrated in order to form a unified cognition or behavior. One way to assess functional integration is by measuring functional connectivity (FC) among specialized regions in the brain. Recently, there is growing interest in studying the FC among brain functional networks. This type of connectivity, which can be considered as a higher level of FC, is termed functional network connectivity (FNC) and measures the statistical dependencies among brain functional networks. Each functional network may consist of multiple remote brain regions. Four studies related to FNC are presented in this work. First FNC is compared during the resting-state and auditory oddball task (AOD). Most previous FNC studies have been focused on either resting-state or task-based data but have not directly compared these two. Secondly we propose an automatic diagnosis framework based on resting-state FNC features for mental disorders such as schizophrenia. Then, we investigate the proper preprocessing for fMRI time-series in order to conduct FNC studies. Specifically the impact of autocorrelated time-series on FNC will be comprehensively assessed in theory, simulation and real fMRI data. At the end, the notion of autoconnectivity as a new perspective on human brain functionality will be proposed. It will be shown that autoconnectivity is cognitive-state and mental-state dependent and we discuss how this source of information, previously believed to originate from physical and physiological noise, can be used to discriminate schizophrenia patients with high accuracy

Using Multimodal MRI Techniques to Derive a Biomarker for Tracking the Pathological Changes Occurring at Different Stages of Cognitive Decline in Parkinson's Disease in a Cross-Sectional Study Design

Cognitive impairment is common in Parkinson's disease (PD) and can range from mild cognitive impairment (PD-MCI) to dementia (PDD). The aim of this study was to derive a multi-modal MRI-based biomarker for the reliable discrimination of PD patients at different stages of cognitive decline and to identify pathologic patterns related with dementia risk. The resting-state functional MRI (rs-fMRI), diffusion tensor imaging (DTI), Arterial Spin Labeling and MR spectroscopic imaging data of 60 PD patients (PD-N, PD-MCI, PDD) were collected. The rs-fMRI data revealed a combination of resting-state networks with significant discriminative power based on the expression scores of the resting-state networks. In combination with the DTI data we obtained a successful model for the discrimination of PDD patients and were able to identify progressive pathological changes that can be used as biomarker for PDD and could be established as clinical diagnostic tool for PD patients with high dementia risk

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Plasticité cérébrale dans le système olfactif : étude du modèle des sommeliers

Cette thèse s’intéresse à la capacité du cerveau à s’adapter à un environnement changeant. Plus spécifiquement, elle s’intéresse à la plasticité cérébrale dans le système olfactif. Les sommeliers, experts dans le domaine de l’olfaction, ont constitué notre modèle. Une première étude nous a permis d’établir un protocole afin de tester la performance olfactive des sommeliers. Dans une deuxième étude, nous avons testé des étudiants en sommellerie au début de leur formation d’un an et demi qui mène à la profession de sommelier. Nous avons observé que ces futurs experts de l’olfaction présentaient déjà, au cours des deux premiers mois, des capacités olfactives supérieures. Dans une troisième étude, nous avons de nouveau testé les étudiants à la fin de leur formation, afin d’examiner les effets d’un entraînement olfactif à long terme sur la performance olfactive et sur le cerveau : en plus de mesurer les capacités olfactives avec le test des Sniffin’ Sticks, nous avons utilisé l’imagerie par résonance magnétique (IRM) pour évaluer l’évolution du cerveau au cours de la formation en sommellerie. Nos principales observations concernent des changements au niveau de la structure cérébrale. Premièrement, le volume du bulbe olfactif a augmenté au cours de la formation, ce qui est en accord avec la littérature disponible à propos de cette structure. Deuxièmement, nous avons observé un épaississement au niveau du cortex entorhinal mais aussi un amincissement au niveau d’autres régions du cortex. Mises en relation avec les résultats d’études antérieures, ces observations soutiennent le récent modèle de surproduction-élagage selon lequel les changements dus à la plasticité liée à l’entraînement ne sont pas linéaires mais font intervenir différents processus en plusieurs phases. Ce modèle constitue une avancée importante dans la compréhension des mécanismes impliqués dans la plasticité cérébrale et devrait être pris en compte dans les futures études sur la plasticité. Bien que les résultats sur le plan neuroimagerie soient intéressants, les résultats de l’étude longitudinale relatifs à la performance olfactive n’étaient pas concluants sur le plan comportemental. Nous avons donc mis en place dans une quatrième étude une tâche d’identification d’odorants au sein de mélanges plus complexe et plus adaptée aux sommeliers qui a confirmé la supériorité de leurs capacités olfactives. Nous avons aussi entraîné des novices sur cette tâche pendant cinq jours pour tester les effets d’un court entraînement olfactif. Cette thèse est organisée sous forme de thèse par articles. Le premier chapitre correspond à l’introduction générale, qui est elle-même organisée en plusieurs grandes parties. Ces différentes parties définissent les concepts-clés de cette thèse : l’olfaction, les corrélations neuroanatomiques dans le système olfactif, la plasticité cérébrale, la plasticité liée à l’entraînement dans le système olfactif, la neuroimagerie. La dernière partie conclut l’introduction en présentant les objectifs et hypothèses de recherche. Les chapitres suivants correspondent aux articles rédigés au cours du doctorat et présentant les résultats des recherches. Le dernier chapitre constitue une discussion générale. Enfin, en annexes se trouvent deux articles publiés lors du doctorat, un chapitre à paraître dans un livre ainsi que des résultats non publiés.This thesis is about the brain’s ability to adapt to an ever-changing environment. More specifically, it is about brain plasticity in the olfactory system. We used sommeliers, who are experts in olfaction, as our model. A first study allowed us to instate a protocol to assess sommeliers’ olfactory function. In a second study, we tested sommelier students at the start of their year-and-a-half-long training which is the prerequisite to become a professional sommelier. We observed that these future experts in olfaction already had, during the first two months of training, superior olfactory abilities. In a third study, we tested sommelier students again at the end of their training to examine the effects of a long-term olfactory training on olfactory performance and on the brain: beside assessing olfactory performance with the Sniffin’ Sticks test, we used magnetic resonance imaging (MRI) to examine the evolution of brain structure and function during sommelier training. Changes in brain structure constituted our main results. Firstly, olfactory bulb volume increased during sommelier training, which is in line with previous reports about this structure. Secondly, we observed a cortical thickness increase in the entorhinal cortex but also cortical thinning in other brain areas. Put together with findings from previous studies, these results support the recent overproduction-pruning model of plasticity according to which changes due to training-related brain plasticity are nonlinear but involve different processes and different phases. This model constitutes a great advance in the understanding of brain plasticity and its underlying mechanisms and should be considered in future studies about plasticity. Though neuroimaging results were interesting, results from olfactory tests in our longitudinal study were not conclusive so we conducted a fourth study to test the ability to identify odorants within mixtures, a task which is more complex and suitable for sommeliers than the Sniffin’ Sticks test. Sommeliers performed better. We also tested novices that we had trained on this task for five days to evaluate the effects of a short-term olfactory training. This thesis is organized by articles. The first chapter is a general introduction, itself organized in several parts. These different parts define the major concepts of this thesis: olfaction, neuroanatomical correlations in the olfactory system, brain plasticity, plasticity in the olfactory system, neuroimaging. The last part concludes the introduction with aims and hypotheses. The following chapters are articles written during PhD that present the results of our research. The last chapter is a general discussion of all the results. Finally, two articles published during PhD, a chapter that is to be published in a book and unpublished results are presented as appendices

An insight into the brain of patients with type-2 diabetes mellitus and impaired glucose tolerance using multi-modal magnetic resonance image processing

The purpose of this thesis was to investigate brain anatomy and physiology of subjects with impaired glucose tolerance (IGT - 12 subjects), type-2 diabetes (T2DM - 17 subjects) and normoglycemia (16 subjects) using multi-modal magnetic resonance imaging (MRI) at 3T. Perfusion imaging using quantitative STAR labeling of arterial regions (QUASAR) arterial spin labeling (ASL) was the core dataset. Optimization of the post-processing methodology for this sequence was performed and the outcome was used for hemodynamic analysis of the cohort. Typical perfusion-related parameters, along with novel hemodynamic features were quantified. High-resolution structural, angiographic and carotid flow scans were also acquired and processed. Functional acquisitions were repeated following a vasodilating stimulus. Differences between the groups were examined using statistical analysis and a machine-learning framework. Hemodynamic parameters differing between the groups emerged from both baseline and post-stimulus scans for T2DM and mainly from the post-stimulus scan for IGT. It was demonstrated that quantification of not-typically determined hemodynamic features could lead to optimal group-separation. Such features captured the pattern of delayed delivery of the blood to the arterial and tissue compartments of the hyperglycemic groups. Alterations in gray and white matter, cerebral vasculature and carotid blood flow were detected for the T2DM group. The IGT cohort was structurally similar to the healthy cohort but demonstrated functional similarities to T2DM. When combining all extracted MRI metrics, features driving optimal separation between different glycemic conditions emerged mainly from the QUASAR scan. The only highly discriminant non-QUASAR feature, when comparing T2DM to healthy subjects, emerged from the cerebral angiogram. In this thesis, it was demonstrated that MRI-derived features could lead to potentially optimal differentiation between normoglycemia and hyperglycemia. More importantly, it was shown that an impaired cerebral hemodynamic pattern exists in both IGT and T2DM and that the IGT group exhibits functional alterations similar to the T2DM group