Machine Learning for Multiclass Classification and Prediction of Alzheimer\u27s Disease

Alzheimer\u27s disease (AD) is an irreversible neurodegenerative disorder and a common form of dementia. This research aims to develop machine learning algorithms that diagnose and predict the progression of AD from multimodal heterogonous biomarkers with a focus placed on the early diagnosis. To meet this goal, several machine learning-based methods with their unique characteristics for feature extraction and automated classification, prediction, and visualization have been developed to discern subtle progression trends and predict the trajectory of disease progression. The methodology envisioned aims to enhance both the multiclass classification accuracy and prediction outcomes by effectively modeling the interplay between the multimodal biomarkers, handle the missing data challenge, and adequately extract all the relevant features that will be fed into the machine learning framework, all in order to understand the subtle changes that happen in the different stages of the disease. This research will also investigate the notion of multitasking to discover how the two processes of multiclass classification and prediction relate to one another in terms of the features they share and whether they could learn from one another for optimizing multiclass classification and prediction accuracy. This research work also delves into predicting cognitive scores of specific tests over time, using multimodal longitudinal data. The intent is to augment our prospects for analyzing the interplay between the different multimodal features used in the input space to the predicted cognitive scores. Moreover, the power of modality fusion, kernelization, and tensorization have also been investigated to efficiently extract important features hidden in the lower-dimensional feature space without being distracted by those deemed as irrelevant. With the adage that a picture is worth a thousand words, this dissertation introduces a unique color-coded visualization system with a fully integrated machine learning model for the enhanced diagnosis and prognosis of Alzheimer\u27s disease. The incentive here is to show that through visualization, the challenges imposed by both the variability and interrelatedness of the multimodal features could be overcome. Ultimately, this form of visualization via machine learning informs on the challenges faced with multiclass classification and adds insight into the decision-making process for a diagnosis and prognosis

Supervised machine learning in psychiatry:towards application in clinical practice

In recent years, the field of machine learning (often named with the more general term artificial intelligence) has literally exploded and its application has been proposed in basically all fields, including psychiatry and mental health. This has been motivated by the promise of using machine learning to develop new clinical tools that could help perform personalized predictions and recommendations, ultimately improving the results achievable in the psychiatric clinical practice that still faces only a limited success in the fight against mental diseases. However, despite this huge interest, there is still a substantial lack of tools in psychiatry that are based on machine learning algorithms. Massimiliano Grassi, in his Ph.D. thesis, investigates the challenges of translating machine learning algorithms into clinical practice and proposes innovative solutions to these challenges. The thesis presents the development and validation of new algorithms for the prediction of the onset of Alzheimer’s disease, the remission of obsessive-compulsive disorder, and the automatization of sleep staging in polysomnography, a method to diagnose sleep disorders. The results from these studies demonstrate that the use of machine learning in psychiatric clinical practice is not just a promise, and it is possible to develop machine learning algorithms that achieve clinically relevant performance even if based solely on information that can be easily accessible in the daily clinical routine

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

Classification and early detection of dementia and cognitive decline with magnetic resonance imaging

Dementie is een verwoestende ziekte waar wereldwijd miljoenen mensen aan leiden. De meest voorkomende oorzaak van dementie is de ziekte van Alzheimer. Voor het ontwikkelen van effectieve behandelingen is het belangrijk om dementie in een vroeg stadium te detecteren. Traditioneel alzheimeronderzoek is voornamelijk gericht op groepsverschillen tussen patiënten en controles. Recent onderzoek is deels verschoven naar individuele classificatie met machine learning. In dit proefschrift onderzoeken we het gebruik van magnetic resonance imaging (MRI) voor automatische detectie van de ziekte van Alzheimer, en vroege detectie van cognitieve achteruitgang. In dit proefschrift laten we zien dat het combineren van MRI modaliteiten de classificatie kan verbeteren. Ook laten we zien dat diffusie MRI een goede maat is om alzheimer te diagnosticeren. Bij toepassing van dezelfde methoden op een groep presymptomatische gendragers die amyloïdangiopathie zullen ontwikkelen vonden we geen verschillen tussen de gendragers en controles. Tevens waren we niet in staat om cognitieve achteruitgang na 4 jaar te voorspellen in een groep ouderen met verhoogd risico op achteruitgang. Met MRI kunnen betrouwbare individuele uitspraken gedaan kan worden over patiënten, maar het is met de huidige methoden niet gevoelig voor vroege detectie van cognitieve achteruitgang.Alzheimer NederlandLUMC / Geneeskund

Anosognosia en la enfermedad de alzheimer en el momento del diagnóstico: prevalencia e influencia en la evolución de la enfermedad

La anosognosia es un trastorno poco estudiado en la Enfermedad de Alzheimer (EA) pero muy frecuente y con importantes consecuencias. Objetivos: Evaluar la prevalencia de la anosognosia en el momento del diagnóstico de EA, analizar sus características, evolución, factores asociados y su influencia sobre la progresión de la enfermedad. Métodos: Estudio epidemiológico observacional, longitudinal y prospectivo. Se realizó en pacientes con EA en el momento del diagnóstico, con una segunda evaluación a los 18 meses. Resultados: La prevalencia de anosognosia fue del 70.9%. La edad avanzada, menor escolaridad y mayor afectación neuropsiquiátrica fueron variables predictoras de anosognosia. La anosognosia basal fue similar en los grupos con y sin progresión clínica. Conclusiones: La prevalencia de anosognosia en el momento del diagnóstico es elevada, asociándose con mayor edad, menor escolaridad y mayor afectación conductual. No encontramos influencia de la anosognosia sobre la evolución de la EA tras 18 meses del diagnósticoDepartamento de Medicina, Dermatología y ToxicologíaDoctorado en Investigación en Ciencias de la Salu

Neuroimaging biomarkers in genetic frontotemporal dementia : towards a timely diagnosis

Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disease characterised by the progressive degeneration of the frontal and temporal lobes, which results in behavioural (behavioural variant FTD) and language (primary progressive aphasia) disorders. No effective therapies currently exist to cure FTD or slow disease progression. However, efforts are being made to develop disease modifying treatments, which aim to reverse or inhibit pathological processes leading up to neuronal cell death. Therefore, the ability to diagnose FTD before brain atrophy (i.e., irreversible brain damage) is crucial. Approximately 10–30% of all FTD patients have a familial form, often caused by mutations in the genes MAPT, GRN or a repeat expansion in the gene C9orf72. These families offer the unique opportunity to study mutation carriers in the presymptomatic stage, where early pathological changes may already occur, but subjects are cognitively healthy. In this dissertation, we used multimodal MRI and machine learning to investigate whether MRI biomarkers for FTD have diagnostic value on the single-subject level to detect FTD-related differences in the presymptomatic disease stage. Furthermore, we aimed to advance the combination of resting-state functional MRI data between scanners. Lastly, we studied potential biomarkers for the differentiation between early stages of FTD and Alzheimer’s disease. LUMC / Geneeskund

Defining the neuropsychological and neuroimaging phenotype of behavioural variant frontotemporal dementia

Frontotemporal dementia (FTD) is the second most common cause of early-onset dementia after Alzheimer’s disease (AD). There exists a paucity of quantifiable, sensitive, and specific biomarkers to detect this disease and track its manifestation and progression. The primary aim of this thesis was to develop and investigate new biomarkers for FTD, and focused on the examination of neuropsychological biomarkers in the behavioural variant of FTD (bvFTD) and their neuroanaotmical correlates. Chapters 4 and 5 explored social cognition in patients with FTD and the neural correlates of this behaviour. bvFTD patients displayed gross dysfunction in the perception of sarcasm and the ability to understand basic social signals, and this mapped onto a larger social cognition neural network that has previously been defined in the literature. These findings delineate a brain network substrate for the social impairment that characterises FTD syndromes. In Chapters 6 and 7, I explored the executive functions of task switching, reaction time, and neural timing in patients with FTD. Results indicated several dissociable executive capacities, which mapped onto discrete neural areas as part of a larger executive function network, suggesting that structures implicated in aspects of executive functioning can be targeted by FTD and may underpin aspects of the bvFTD phenotype. In the final Chapter, I devised a novel battery to examine the bases of psychosis in FTD patients with the C9ORF72 mutation, which demonstrated a specific and unique impairment in the ability to interpret somatosensory proprioceptive information in these patients, which may represent a candidate mechanism for psychosis. The studies described in this thesis contribute to the growing interest in characterising and understanding the neuropsychological phenotypes of bvFTD. Improved understanding of the anatomical associations of neuropsycholgical performance in this patient population could potentially facilitate earlier and more accurate diagnosis and symptom managemen