Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson\u27s disease

Parkinson\u27s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts\u27 visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis

Statistical Neuroimage Modeling, Processing and Synthesis based on Texture and Component Analysis: Tackling the Small Sample Size Problem

The rise of neuroimaging in the last years has provided physicians and radiologist with the ability to study the brain with unprecedented ease. This led to a new biological perspective in the study of neurodegenerative diseases, allowing the characterization of different anatomical and functional patterns associated with them. CAD systems use statistical techniques for preparing, processing and extracting information from neuroimaging data pursuing a major goal: optimize the process of analysis and diagnosis of neurodegenerative diseases and mental conditions. With this thesis we focus on three different stages of the CAD pipeline: preprocessing, feature extraction and validation. For preprocessing, we have developed a method that target a relatively recent concern: the confounding effect of false positives due to differences in the acquisition at multiple sites. Our method can effectively merge datasets while reducing the acquisition site effects. Regarding feature extraction, we have studied decomposition algorithms (independent component analysis, factor analysis), texture features and a complete framework called Spherical Brain Mapping, that reduces the 3-dimensional brain images to two-dimensional statistical maps. This allowed us to improve the performance of automatic systems for detecting Alzheimer's and Parkinson's diseases. Finally, we developed a brain simulation technique that can be used to validate new functional datasets as well as for educational purposes

Imaging biomarkers extraction and classification for Prion disease

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by inheritance of prion protein gene mutations or exposure to prions. To date, there are no accurate imaging biomarkers that can be used to predict the future diagnosis of a subject or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the large heterogeneity of phenotypes of prion disease and the lack of a consistent spatial pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of the human form of prion disease. Using a tailored framework, I extracted quantitative imaging biomarkers for characterisation of patients with Prion diseases. Following the extraction of patient-specific imaging biomarkers from multiple images, I implemented a Gaussian Process approach to correlated symptoms with disease types and stages. The model was used on three different tasks: diagnosis, differential diagnosis and stratification, addressing an unmet need to automatically identify patients with or at risk of developing Prion disease. The work presented in this thesis has been extensively validated in a unique Prion disease cohort, comprising both the inherited and sporadic forms of the disease. The model has shown to be effective in the prediction of this illness. Furthermore, this approach may have used in other disorders with heterogeneous imaging features, being an added value for the understanding of neurodegenerative diseases. Lastly, given the rarity of this disease, I also addressed the issue of missing data and the limitations raised by it. Overall, this work presents progress towards modelling of Prion diseases and which computational methodologies are potentially suitable for its characterisation

Mapping abnormality: Towards evaluating MRI scans for individualized diagnosis and validation of clinical course/progression in motor neuron disease

The present dissertation proposes a standardized assessment tool for meaningful interpretation of individual patients’ MRI data which may be used to infer on atrophy (or hypertrophy). Various MRI-based measures have been subject to a wide range of neuroscience studies and numerous associations between such measures and the diagnosis / prognosis with various pathological conditions have been identified. However, surprisingly, accurate individualized MRI-based assessment is still difficult, and most studies utilize group comparisons in order to describe group-based brain structural or functional differences. One of the reasons for this might be the lack of a standardized system which allows to evaluate individual MRI data. With this dissertation, it was aimed at passing this limit by describing a method to rate single subjects’ T1-weighted MRI data. The idea is straightforward, in that it rates single patient data with respect to a matched control population and uses nonparametric statistics to identify regions of unexpected thin (thick) cortical thickness. In this way, signs of atrophy (hypertrophy) can be localized for the individual. This thesis encompasses four original research papers which taken together describe and validate an individualized atrophy-assessment tool: first, the general procedure of rating an individual’s MRI data, specifically cortical thickness data, to a control population is investigated for sensitivity and specificity using simulations. The selected strategy was based on rating topographically distinct cortical regions (“mosaics”/”patches”) and is therefore referred to as “mosaic approach”. Given the reference groups were age- and gender matched, we investigated in a second study whether variance associated with these demographic variables was successfully eliminated, while maintaining information on clinical disability, studying a longitudinal data set of amyotrophic lateral sclerosis (ALS) patients (study 2). Finally, we explored the method for “external validity” in a dual approach: First, we tested whether the degree of cortical involvement is mirrored by our tool (which we hypothesized given it exclusively targets supratentorial gray matter regions) by contrasting different motor neuron diseases (MND) against each other (study 3). Second, we also explored if topographically distinct cortical pathology can be correctly localized with our tool, by including different patient subgroups from the frontotemporal dementia (FTD) spectrum. For FTD, as well as for MND, the “ground truth” localization of pathology is well-characterized by histological and previous (group-based) imaging findings, such that we could directly compare the individual results from the mosaic-approach to that knowledge. Another focus of this work was to provide an accessible visualization to easily identify regions of supposed atrophy (or hypertrophy) for the clinical user. Our results suggest that the here-proposed mosaic-approach which compares single patient’s cortical thickness data to matched control data is a viable approach to meaningfully detect signs of atrophy at the individual level; it is furthermore objective, reliable and valid. Despite clinical and methodological limitations, including small sample sizes of reference groups and varying acquisition parameters, which we are currently improving, we have high hopes that this tool can help clinical practice and can ultimately also be used as a novel endpoint for clinical trials

Leveraging a machine learning based predictive framework to study brain-phenotype relationships

An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the overarching question of how to best structure and run experiments ambiguous. In this work, I cover two explicit pieces of this larger question, the relationship between data representation and predictive performance and a case study on issues related to data collected from disparate sites and cohorts. I then present the Brain Predictability toolbox, a soft- ware package to explicitly codify and make more broadly accessible to researchers the recommended steps in performing a predictive experiment, everything from framing a question to reporting results. This unique perspective ultimately offers recommen- dations, explicit analytical strategies, and example applications for using machine learning to study the brain

Strukturální podklady kognitivního deficitu v zobrazování magnetické rezonance.

Předkládaná dizertační práce se ve své hlavní části zabývá možnostmi detekce strukturálních a difuzních změn v MR zobrazení u pacientů s kognitivním deficitem. V širším kontextu je nejprve zmíněn podklad klinických změn a nálezů při neurozobrazení u pacientů s demencí, a to se zvláštním zaměřením na Alzheimerovu chorobu (ACh) a její diferenciální diagnostiku. Druhá část práce obsahuje čtyři experimentální studie v rámci našeho výzkumu. Hlavním cílem prvních dvou studií bylo získání strukturální a mikrostrukturální informace o neurodegenerativních procesech charakteristických pro ACh - na globální i regionální úrovni. Pro tento účel bylo použito několik komplementárních přístupů se zaměřením především na evaluaci šedé, a následně i bílé hmoty mozku. V následujících částech jsme se zaměřili na popis kontextu mikrostrukturálních změn bílé hmoty u normotenzního hydrocefalu (NPH) a charakteristických vzorců dezintegrace bílé hmoty u epilepsií temporálního laloku (TLE). Nejdůležitějším závěrem, který lze vyvodit z našich studií je, že strukturální a difuzní zobrazování se ukázalo jako užitečné při identifikaci regionálně specifické a disproporcionální ztráty objemu mozku a mikrostruktury u některých patologických procesů, které jsou základem kognitivního zhoršení. Použití několika různých morfometrických...Structural and diffusion imaging patterns that can be evaluated using MRI in patients with cognitive deficits are the central theme of the proposed work. First, the clinical and neuroimaging background of dementias has been reviewed in a broader context, with a special focus on Alzheimer's disease (AD) and differential diagnoses. The second part of this thesis contains four consecutive experimental studies. The primary objective of the first two studies was to obtain structural and microstructural information on the neurodegenerative processes characteristic for AD on global and regional levels. For this purpose, several complementary approaches were used and the focus was shifted from grey to white matter (GM/WM). The following two studies focused on the differential context of WM microstructural alterations in normal pressure hydrocephalus (NPH) and distinctive patterns of WM disintegrity in temporal lobe epilepsy (TLE). The most important conclusion of our studies is that structural and diffusion imaging proved to be useful in identifying regionally specific and disproportionate loss of brain volume and microstructure in several pathological processes underlying cognitive deterioration. The use of distinctive morphometric methods yielded complementary information on AD-related atrophy patterns,...Department of Neurosurgery and Neurooncology First Faculty of Medicine and Central Military HospitalNeurochirurgická a neuroonkologická klinika 1. LF UK a ÚVN1. lékařská fakultaFirst Faculty of Medicin

Sleep medicine: Practice, challenges and new frontiers

Sleep medicine is an ambitious cross-disciplinary challenge, requiring the mutual integration between complementary specialists in order to build a solid framework. Although knowledge in the sleep field is growing impressively thanks to technical and brain imaging support and through detailed clinic-epidemiologic observations, several topics are still dominated by outdated paradigms. In this review we explore the main novelties and gaps in the field of sleep medicine, assess the commonest sleep disturbances, provide advices for routine clinical practice and offer alternative insights and perspectives on the future of sleep research