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

High-order resting-state functional connectivity network for MCI classification: High-Order Correlation and FC Network

Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis

DEEP-AD: The deep learning model for diagnostic classification and prognostic prediction of alzheimer's disease

In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical judgment regarding the early detection of AD by using DL. To that aim, the system design research methodology is suggested in this dissertation for achieving three goals. The first goal is to investigate the DL models that have performed well at identifying patterns associated with AD, as well as the accuracy so far attained, limitations, and gaps. A systematic review of the literature (SLR) revealed a shortage of empirical studies on the early identification of AD through DL. In this regard, thirteen empirical studies were identified and examined. We concluded that three-dimensional (3D) DL models have been generated far less often and that their performance is also inadequate to qualify them for clinical trials. The second goal is to provide the neuroradiologist with the computer-interpretable information they need to analyze neuroimaging biomarkers. Given this context, the next step in this dissertation is to find the optimum DL model to analyze neuroimaging biomarkers. It has been achieved in two steps. In the first step, eight state-of-the-art DL models have been implemented by training from scratch using end-to-end learning (E2EL) for two binary classification tasks (AD vs. CN and AD vs. stable MCI) and compared by utilizing MRI scans from the publicly accessible datasets of neuroimaging biomarkers. Comparative analysis is carried out by utilizing efficiency-effects graphs, comprehensive indicators, and ranking mechanisms. For the training of the AD vs. sMCI task, the EfficientNet-B0 model gets the highest value for the comprehensive indicator and has the fewest parameters. DenseNet264 performed better than the others in terms of evaluation matrices, but since it has the most parameters, it costs more to train. For the AD vs. CN task by DenseNet264, we achieved 100% accuracy for training and 99.56% accuracy for testing. However, the classification accuracy was still only 82.5% for the AD vs. sMCI task. In the second step, fusion of transfer learning (TL) with E2EL is applied to train the EfficientNet-B0 for the AD vs. sMCI task, which achieved 95.29% accuracy for training and 93.10% accuracy for testing. Additionally, we have also implemented EfficientNet-B0 for the multiclass AD vs. CN vs. sMCI classification task with E2EL to be used in ensemble of models and achieved 85.66% training accuracy and 87.38% testing accuracy. To evaluate the model’s robustness, neuroradiologists must validate the implemented model. As a result, the third goal of this dissertation is to create a tool that neuroradiologists may use at their convenience. To achieve this objective, this dissertation proposes a web-based application (DEEP-AD) that has been created by making an ensemble of Efficient-Net B0 and DenseNet 264 (based on the contribution of goal 2). The accuracy of a DEEP-AD prototype has undergone repeated evaluation and improvement. First, we validated 41 subjects of Spanish MRI datasets (acquired from HT Medica, Madrid, Spain), achieving an accuracy of 82.90%, which was later verified by neuroradiologists. The results of these evaluation studies showed the accomplishment of such goals and relevant directions for future research in applied DL for the early detection of AD in clinical settings.En términos de contexto, el objetivo de esta tesis es ayudar a los neurorradiólogos en su juicio clínico sobre la detección precoz de la AD mediante el uso de DL. Para ello, en esta tesis se propone la metodología de investigación de diseño de sistemas para lograr tres objetivos. El segundo objetivo es proporcionar al neurorradiólogo la información interpretable por ordenador que necesita para analizar los biomarcadores de neuroimagen. Dado este contexto, el siguiente paso en esta tesis es encontrar el modelo DL óptimo para analizar biomarcadores de neuroimagen. Esto se ha logrado en dos pasos. En el primer paso, se han implementado ocho modelos DL de última generación mediante entrenamiento desde cero utilizando aprendizaje de extremo a extremo (E2EL) para dos tareas de clasificación binarias (AD vs. CN y AD vs. MCI estable) y se han comparado utilizando escaneos MRI de los conjuntos de datos de biomarcadores de neuroimagen de acceso público. El análisis comparativo se lleva a cabo utilizando gráficos de efecto-eficacia, indicadores exhaustivos y mecanismos de clasificación. Para el entrenamiento de la tarea AD vs. sMCI, el modelo EfficientNet-B0 obtiene el valor más alto para el indicador exhaustivo y tiene el menor número de parámetros. DenseNet264 obtuvo mejores resultados que los demás en términos de matrices de evaluación, pero al ser el que tiene más parámetros, su entrenamiento es más costoso. Para la tarea AD vs. CN de DenseNet264, conseguimos una accuracy del 100% en el entrenamiento y del 99,56% en las pruebas. Sin embargo, la accuracy de la clasificación fue sólo del 82,5% para la tarea AD vs. sMCI. En el segundo paso, se aplica la fusión del aprendizaje por transferencia (TL) con E2EL para entrenar la EfficientNet-B0 para la tarea AD vs. sMCI, que alcanzó una accuracy del 95,29% en el entrenamiento y del 93,10% en las pruebas. Además, también hemos implementado EfficientNet-B0 para la tarea de clasificación multiclase AD vs. CN vs. sMCI con E2EL para su uso en conjuntos de modelos y hemos obtenido una accuracy de entrenamiento del 85,66% y una precisión de prueba del 87,38%. Para evaluar la solidez del modelo, los neurorradiólogos deben validar el modelo implementado. Como resultado, el tercer objetivo de esta disertación es crear una herramienta que los neurorradiólogos puedan utilizar a su conveniencia. Para lograr este objetivo, esta disertación propone una aplicación basada en web (DEEP-AD) que ha sido creada haciendo un ensemble de Efficient-Net B0 y DenseNet 264 (basado en la contribución del objetivo 2). La accuracy del prototipo DEEP-AD ha sido sometida a repetidas evaluaciones y mejoras. En primer lugar, validamos 41 sujetos de conjuntos de datos de MRI españoles (adquiridos de HT Medica, Madrid, España), logrando una accuracy del 82,90%, que posteriormente fue verificada por neurorradiólogos. Los resultados de estos estudios de evaluación mostraron el cumplimiento de dichos objetivos y las direcciones relevantes para futuras investigaciones en DL, aplicada en la detección precoz de la AD en entornos clínicos.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione

Identification of Novel Fluid Biomarkers for Alzheimer\u27s Disease

Clinicopathological studies suggest that Alzheimer\u27s disease: AD) pathology begins to appear ~10-20 years before the resulting cognitive impairment draws medical attention. Biomarkers that can detect AD pathology in its early stages and predict dementia onset and progression would, therefore, be invaluable for patient care and efficient clinical trial design. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid: CSF) using an unbiased proteomics approach: two-dimensional difference gel electrophoresis with liquid chromatography tandem mass spectrometry). From this, we identified 47 proteins that differed in abundance between cognitively normal: Clinical Dementia Rating [CDR] 0) and mildly demented: CDR 1) subjects. To validate these findings, we measured a subset of the identified candidate biomarkers by enzyme linked immunosorbent assay: ELISA); promising candidates in this discovery cohort: N=47) were further evaluated by ELISA in a larger validation CSF cohort: N=292) that contained an additional very mildly demented: CDR 0.5) group. Levels of four novel biomarkers were significantly altered in AD, and Receiver-operating characteristic: ROC) analyses using a stepwise logistic regression model identified optimal panels containing these markers that distinguished CDR 0 from CDR\u3e0: tau, YKL-40, NCAM) and CDR 1 from CDR\u3c1: tau, chromogranin-A, carnosinase-I). Plasma levels of the most promising marker, YKL-40, were also found to be increased in CDR 0.5 and 1 groups and to correlate with CSF levels. Importantly, the CSF YKL-40/Aâ42 ratio predicted risk of developing cognitive impairment: CDR 0 to CDR\u3e0 conversion) as well as the best CSF biomarkers identified to date, tau/Aâ42 and p-tau181/Aâ42. Additionally, YKL-40 immunoreactivity was observed within astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Aâ deposition. Utilizing an alternative, targeted proteomics approach to identify novel biomarkers, 333 CSF samples were evaluated for levels of 190 analytes using a multiplexed Luminex platform. The mean concentrations of 37 analytes were found to differ between CDR 0 and CDR\u3e0 participants. ROC and statistical machine learning algorithms identified novel biomarker panels that improved upon the ability of the current best biomarkers to discriminate very mildly demented from cognitively normal participants, and identified a novel biomarker, Calbindin, with significant prognostic potential

Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts

Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly with a progressive decline in cognitive function significantly affecting quality of life. Both the prevalence and emotional and financial burdens of AD on patients, their families, and society are predicted to grow significantly in the near future, due to a prolongation of the lifespan. Several lines of evidence suggest that modifications of risk-enhancing life styles and initiation of pharmacological and non-pharmacological treatments in the early stage of disease, although not able to modify its course, helps to maintain personal autonomy in daily activities and significantly reduces the total costs of disease management. Moreover, many clinical trials with potentially disease-modifying drugs are devoted to prodromal stages of AD. Thus, the identification of markers of conversion from prodromal form to clinically AD may be crucial for developing strategies of early interventions. The current available markers, including volumetric magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebral spinal fluid (CSF) analysis are expensive, poorly available in community health facilities, and relatively invasive. Taking into account its low cost, widespread availability and non-invasiveness, electroencephalography (EEG) would represent a candidate for tracking the prodromal phases of cognitive decline in routine clinical settings eventually in combination with other markers. In this scenario, the present paper provides an overview of epidemiology, genetic risk factors, neuropsychological, fluid and neuroimaging biomarkers in AD and describes the potential role of EEG in AD investigation, trying in particular to point out whether advanced analysis of EEG rhythms exploring brain function has sufficient specificity/sensitivity/accuracy for the early diagnosis of AD

Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

abstract: Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to model building and their inclusion will increase training time and potentially hurt the model performance. The objective of this research is to develop novel SSL models to balance data inclusivity and usability. My dissertation research focuses on applications of SSL in healthcare, driven by problems in brain cancer radiomics, migraine imaging, and Parkinson’s Disease telemonitoring. The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain. The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models. The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Isoprenoidquantifizierung in Hirngewebe - cerebrale Regulation von FPP und GGPP bei Morbus Alzheimer und im Alter

Over the last years there has been an increasing interest in the involvement of the MVA-pathway and of members of the small GTPases, in the development and progression of AD. Earlier investigations mainly focused on the role of cholesterol in disease pathology. This research was supported by retrospective cohort studies, initially showing beneficial effects of the long-term intake of cholesterol lowering statins, on the incidence of the development of sporadic AD. However, in more recent literature increasing attention has been paid to the isoprenoids, FPP and GGPP, due to their crucial role in the post-translational modifications of members of the superfamily of small GTPases. In AD, these proteins were amongst others shown to be involved in mechanisms affecting APP processing, ROS generation and synaptic plasticity. A major factor impeding the clarification of the role of the MVA-pathway intermediates in these mechanisms was the lack of a sensitive and accurate method to determine FPP and GGPP levels in brain tissue. Hence, a state of the art HPLC-FLD method for the quantification of the isoprenoids FPP and GGPP in brain tissue was successfully developed. After the introduction of a double clean-up step from complex brain matrix samples and the synthesis of an appropriate IS (DNP), the method was fully validated according to the latest FDA guideline for bioanalytical method validation. Furthermore, this method was transferred to a faster and more sensitive, state of the art UHPLC-MS/MS application. Additionally, the method was shown to be applicable for mouse brain tissue and data was generated from an in vivo mouse simvastatin study and for different mouse models. According to the aims of the thesis, the current work describes for the first time absolute isoprenoid concentrations in human frontal cortex white and grey matter. Furthermore, this is the first report of isoprenoid levels in the frontal cortex of human AD brains. Further results were shown from mouse brains originating from different mouse models, including the Thy-1 APP mouse model mimicking AD pathology in terms of Aβ formation or C57Bl/6 mice at different ages. AD prevalence can be clearly correlated with increasing age. Therefore, three different generations of mice were investigated. The study demonstrated constant isoprenoid and cholesterol levels in the first half of their life followed by a significant increase of FPP and GGPP in the second half (between 12 and 24 month of age). Cholesterol levels were also elevated in the aged group, but again the effect was less pronounced than shown for the isoprenoids. These results lead to the tentative conclusion that cerebral isoprenoid levels are elevated during aging and that this accumulation is amplified during AD leading to accelerated neuronal dysfunction. In a different mouse study, using the C57Bl/6 mice, in vivo drug intervention with the HMG-CoA reductase inhibitor simvastatin revealed strong inhibition of the rate limiting step of the mevalonate/isoprenoid/cholesterol pathway and resulted in the first report of significantly reduced FPP and GGPP levels in brain tissue of statin treated mice. These results open for the first time the possibility to monitor drug effects on cerebral isoprenoid levels and correlate these data with a modulation of APP processing, which was shown by our group in previous studies. Interestingly, apart from the isoprenoid reduction following statin treatment the reduction of brain cholesterol was also significant but to a lesser extent. These findings support the notion that isoprenoid levels are more susceptible to statin treatment than cholesterol levels. Furthermore, this suggests a strong cellular dependence on FPP and GGPP, as the pool seems to be easily depleted, which finally could lead to cell death. The first investigations of farnesylated Ras and geranylgeranylated Rac protein levels by means of immuno-blotting, substantiated the notion of a decreased abundance of prenylated small GTPases under statin influence as a consequence of reduced isoprenoid levels. These findings demonstrate for the first time a correlation of FPP and GGPP levels with the abundance of small GTPases. These findings together with the results from the AD study prove that isoprenoid levels are not strictly subject to the same regulation as cholesterol levels. To further understand the physiological regulation in the cell, in vitro experiments with different inhibitors of the mevalonate/isoprenoid/cholesterol pathway were conducted. These results confirmed the isoprenoid and cholesterol reducing effects of statin treatment as observed in the aforementioned in vivo mouse study. Interestingly, cholesterol synthesis inhibition targeted after FPP as the branch point, led to significantly elevated FPP levels. FTase inhibition led to significantly reduced FPP levels, whereas inhibition of the GGTase I did not show a significant change of either isoprenoid levels.Im Zusammenhang mit der Entstehung und dem Fortschreiten der Alzheimer Demenz (AD) spielt speziell der Mevalonat-Biosyntheseweg eine bedeutsame Rolle. Frühere Arbeiten beschäftigten sich hauptsächlich, gestützt durch die Ergebnisse retrospektiver Studien mit Statinen, mit der Rolle des Cholesterins. Aktuellere Arbeiten richten den Fokus der Forschung stärker auf die ebenfalls aus dem Mevalonat-Biosyntheseweges entstammenden Isoprenoide, Farnesyl- (FPP) und Gernaylgeranylpyrophosphat (GGPP). Beide Isoprenoide sind maßgeblich an der post-translationalen Modifikation von Proteinen aus der Familie der kleinen Rho-GTPasen beteiligt. Bezüglich der AD konnte gezeigt werden, dass bestimmte Mitglieder dieser Familie in Mechanismen involviert sind die an der Entstehung von reaktiven Sauerstoffspezies und neurotoxischem Amyloid beta beteiligt sind. Weiterhin spielen sie bei der synaptischen Plastizität eine zentrale Rolle. Bisherige Forschungen zur Aufklärung der genauen biochemischen Funktion von FPP und GGPP sowie deren Regulation, speziell in Hirngewebe, wurden durch das Fehlen einer sensitiven und validen Analytik eingeschränkt. Die vorgelegte Arbeit beschreibt die Entwicklung, Validierung und erfolgreiche Anwendung einer HPLC-Fluoreszenz Methode zur Quantifizierung der beiden Isoprenoide FPP und GGPP in Hirngewebe. Der Erarbeitung eines komplexen Protokolls zur Methodenaufarbeitung und der Synthese eines geeigneten internen Standards folgte eine komplette Validierung nach den aktuellen FDA Richtlinien für bioanalytische Methodenvalidierung. Des Weiteren wurde diese Methode auf eine schnellere, sensitivere und selektivere, hoch-moderne UHPLC-MS/MS Methode übertragen und neben der Validierung der Methode für humane Hirnproben wurde auch die Übertragbarkeit auf Mäusehirne gezeigt. Entsprechend der Zielsetzung dieser Arbeit wurden erstmalig FPP und GGPP Konzentrationen in der weißen und grauen Substanz des menschlichen frontalen Kortex quantifiziert. Weiter konnten unter Verwendung der neu etablierten Methode zum ersten Mal erhöhte FPP und GGPP Spiegel im frontalen Kortex von AD Patienten verglichen mit gleichaltrigen Kontroll-Hirn Proben nachgewiesen werden. Für die Cholesterin Spiegel der identischen Proben konnten gezeigt werden, dass diese unverändert waren, was frühere Arbeiten bestätigt. Die vorgelegte Arbeit liefert weiterhin neue Ergebnisse zu der Korrelation von cerebralen Isoprenoid- und Cholesterin-Spiegeln in einem in vivo Thy-1 APP Maus-Model, welches typischer Weise herangezogen wird um den, der AD zugrunde liegenden Amyloid beta Metabolismus zu untersuchen. Weiter wurden Studien an einem Alterungsmodell mit C57Bl/6 Mäusen durchgeführt, die eine zunächst (zwischen Mäusen im Alter zwischen 3 und 12 Monaten) unveränderte FPP und GGPP Homöostase zeigten, während in der dritten und ältesten Gruppe (24 Monate) ein signifikanter Anstieg zu verzeichnen war. Diese Ergebnisse wurden auch durch die gemessenen Cholesterin Spiegel reflektiert, die ebenfalls nur im Alter erhöht waren. Da das Auftreten der AD deutlich mit dem Alter korreliert führten diese Ergebnisse, zusammen mit den Daten von den humanen AD Hirnproben zu der Hypothese, dass Isoprenoid Spiegel mit dem Alter im Gehirn ansteigen und dieser Effekt bei der AD möglicherweise potenziert ist. In weiteren in vivo Versuchen mit C57Bl/6 Mäusen konnte erstmalig der pharmakologische Effekt von Simvastatin auf die Gehalte von FPP und GGPP im Gehirn gezeigt werden. Die orale Verabreichung des HMG-CoA Reduktase Inhibitors für 21 Tage führte zu stark reduzierten FPP und GGPP Konzentrationen im Gehirn der behandelten Tiere. Cholesterin wurde durch die Behandlung ebenfalls reduziert, wobei dieser Effekt vergleichsweise schwach ausgeprägt war. Dies lässt darauf schließen, dass Isoprenoid-Spiegel sensibler auf eine Hemmung der HMG-CoA Reduktase reagieren als Cholesterin-Spiegel. Mevalonat-Biosyntheseweges als Mevalonat/Isoprenoid/Cholesterin-Biosyntheseweg. Um weitere Einblicke in die physiologische Regulation dieses speziellen Biosyntheseweges zu erlangen, wurden einerseits in mehreren Kooperationen eine ganze Reihe von weiteren Metaboliten des Mevalonat-Biosyntheseweges an dem zuvor beschriebenen in vivo Alterungsmodels untersucht (Daten noch in Bearbeitung). In der Gesamtheit wurden, wie oben beschrieben bei FPP und GGPP die stärksten Veränderungen im hohen Alter festgestellt, was die bedeutende Rolle der beiden Isoprenoide in diesem Biosyntheseweg unterstreicht. Andererseits wurden im Zellmodel (SY5Y Zellen) unterschiedliche Inhibitoren des Mevalonat/Isoprenoid/Cholesterin-Biosyntheseweges untersucht. Hierbei konnte der Statin-Effekt aus dem in vivo Versuch ebenfalls in vitro gezeigt werden. Inhibition der Squalensynthase, die den Cholesterin-Biosyntheseweg nach der FPP Synthesestufe hemmt, führte zu signifikant erhöhten FPP Spiegel, was auf eine Akkumulation des Isoprenoids hindeutet

Frameworks to Investigate Robustness and Disease Characterization/Prediction Utility of Time-Varying Functional Connectivity State Profiles of the Human Brain at Rest

Neuroimaging technologies aim at delineating the highly complex structural and functional organization of the human brain. In recent years, several unimodal as well as multimodal analyses of structural MRI (sMRI) and functional MRI (fMRI) neuroimaging modalities, leveraging advanced signal processing and machine learning based feature extraction algorithms, have opened new avenues in diagnosis of complex brain syndromes and neurocognitive disorders. Generically regarding these neuroimaging modalities as filtered, complimentary insights of brain’s anatomical and functional organization, multimodal data fusion efforts could enable more comprehensive mapping of brain structure and function. Large scale functional organization of the brain is often studied by viewing the brain as a complex, integrative network composed of spatially distributed, but functionally interacting, sub-networks that continually share and process information. Such whole-brain functional interactions, also referred to as patterns of functional connectivity (FC), are typically examined as levels of synchronous co-activation in the different functional networks of the brain. More recently, there has been a major paradigm shift from measuring the whole-brain FC in an oversimplified, time-averaged manner to additional exploration of time-varying mechanisms to identify the recurring, transient brain configurations or brain states, referred to as time-varying FC state profiles in this dissertation. Notably, prior studies based on time-varying FC approaches have made use of these relatively lower dimensional fMRI features to characterize pathophysiology and have also been reported to relate to demographic characterization, consciousness levels and cognition. In this dissertation, we corroborate the efficacy of time-varying FC state profiles of the human brain at rest by implementing statistical frameworks to evaluate their robustness and statistical significance through an in-depth, novel evaluation on multiple, independent partitions of a very large rest-fMRI dataset, as well as extensive validation testing on surrogate rest-fMRI datasets. In the following, we present a novel data-driven, blind source separation based multimodal (sMRI-fMRI) data fusion framework that uses the time-varying FC state profiles as features from the fMRI modality to characterize diseased brain conditions and substantiate brain structure-function relationships. Finally, we present a novel data-driven, deep learning based multimodal (sMRI-fMRI) data fusion framework that examines the degree of diagnostic and prognostic performance improvement based on time-varying FC state profiles as features from the fMRI modality. The approaches developed and tested in this dissertation evince high levels of robustness and highlight the utility of time-varying FC state profiles as potential biomarkers to characterize, diagnose and predict diseased brain conditions. As such, the findings in this work argue in favor of the view of FC investigations of the brain that are centered on time-varying FC approaches, and also highlight the benefits of combining multiple neuroimaging data modalities via data fusion