A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

Understanding Cognitive Variability in Alzheimer’s Disease

Alzheimer’s Disease (AD) is highly heterogenous, both clinically and biologically. This variability is exacerbated by the ways within which, the clinical presentation is assessed with cognitive measures. This inhibits clinical trial success and earlier diagnosis of individuals. Marrying the clinical presentation to the pathology of the disease has so far proved troublesome. This thesis will look at how cognitive measures can best capture the clinical presentation of AD and how these measures can link to the underlying pathology using machine learning methods. This thesis studied this problem across four analyses and two cohorts. Each study looked at a different aspect of cognitive testing within AD. This was done with the overarching aim to interrogate the cognitive variability across the spectrum of AD. Study 1 showed a novel discrepancy score is different to memory measures at screening for AD. It also showed it tracks with AD severity, in the same way memory recall does. Studies 2 & 3 uncovered broad psychometric variance within amnestic measurement of impairment due to AD. This was done in two different populations across two different constructs of amnestic measurement, story recall and verbal list learning. These tests are frequently used interchangeably. These two studies show they should not be. Finally, Study 4 built models from cognitive measures to predict AD pathology. The performance of these models was moderate showing that even with novel cognitive measures, further work is needed to link the clinical and amyloid related biological presentations of AD. Bridging the gap between clinical presentation and pathology of AD using clinical and cognitive markers alone is not possible. Even when using a novel measure of discrepancy score. The discrepancy measure shows promise but was limited due to the inability of the MMSE to measure verbal ability. Conceptually a discrepancy score remains a promising avenue of research for screening, but broader language measures, as well as other AD biomarkers are needed to further test the construct validity of this measure

Data Fusion and Systems Engineering Approaches for Quality and Performance Improvement of Health Care Systems: From Diagnosis to Care to System-level Decision-making

abstract: Technology advancements in diagnostic imaging, smart sensing, and health information systems have resulted in a data-rich environment in health care, which offers a great opportunity for Precision Medicine. The objective of my research is to develop data fusion and system informatics approaches for quality and performance improvement of health care. In my dissertation, I focus on three emerging problems in health care and develop novel statistical models and machine learning algorithms to tackle these problems from diagnosis to care to system-level decision-making. The first topic is diagnosis/subtyping of migraine to customize effective treatment to different subtypes of patients. Existing clinical definitions of subtypes use somewhat arbitrary boundaries primarily based on patient self-reported symptoms, which are subjective and error-prone. My research develops a novel Multimodality Factor Mixture Model that discovers subtypes of migraine from multimodality imaging MRI data, which provides complementary accurate measurements of the disease. Patients in the different subtypes show significantly different clinical characteristics of the disease. Treatment tailored and optimized for patients of the same subtype paves the road toward Precision Medicine. The second topic focuses on coordinated patient care. Care coordination between nurses and with other health care team members is important for providing high-quality and efficient care to patients. The recently developed Nurse Care Coordination Instrument (NCCI) is the first of its kind that enables large-scale quantitative data to be collected. My research develops a novel Multi-response Multi-level Model (M3) that enables transfer learning in NCCI data fusion. M3 identifies key factors that contribute to improving care coordination, and facilitates the design and optimization of nurses’ training, workload assignment, and practice environment, which leads to improved patient outcomes. The last topic is about system-level decision-making for Alzheimer’s disease early detection at the early stage of Mild Cognitive Impairment (MCI), by predicting each MCI patient’s risk of converting to AD using imaging and proteomic biomarkers. My research proposes a systems engineering approach that integrates the multi-perspectives, including prediction accuracy, biomarker cost/availability, patient heterogeneity and diagnostic efficiency, and allows for system-wide optimized decision regarding the biomarker testing process for prediction of MCI conversion.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Cognitive-Motor Integration In Normal Aging And Preclinical Alzheimer's Disease: Neural Correlates And Early Detection

The objectives of the studies included in this dissertation were to characterize how the ability to integrate cognition into action is disrupted by both normal and pathological aging, to evaluate the effectiveness of kinematic measures in discriminating between individuals who are and are not at increased Alzheimer’s disease (AD) risk, and to examine the structural and functional neural correlates of cognitive-motor impairment in individuals at increased AD risk. The underlying hypothesis, based on previous research, is that measuring visuomotor integration under conditions that place demands on visual-spatial and cognitive-motor processing may provide an effective behavioural means for the early detection of brain alterations associated with AD risk. To this end, the first study involved testing participants both with and without AD risk factors on visuomotor tasks using a dual-touchscreen tablet. Comparisons between high AD risk participants and both young and old healthy control groups revealed significant performance disruptions in at-risk participants in the most cognitively demanding task. Furthermore, a stepwise discriminant analysis was able to distinguish between high and low AD risk participants with a classification accuracy of 86.4%. Based on the prediction that the impairments observed in high AD risk participants reflect disruption to the intricate reciprocal communication between hippocampal, parietal, and frontal brain regions required to successfully prepare and update complex reaching movements, the second and third studies were designed to examine the underlying structural and functional connectivity associated with cognitive-motor performance. Young adult and both low AD risk and high AD risk older adult participants underwent anatomical, diffusion-weighted, and resting-state functional connectivity scans. These data revealed significant age-related declines in white matter integrity that were more pronounced in the high AD risk group. Decreased functional connectivity in the default mode network (DMN) was also found in high AD risk participants. Furthermore, measures of white matter integrity and resting-state functional connectivity with DMN seed-regions were significantly correlated with task performance. These data support our hypothesis that disease-related disruptions in visuomotor control are associated with identifiable brain alterations, and thus behavioural assessments incorporating both cognition and action together may be useful in identifying individuals at increased AD risk

The Characterization of Alzheimer’s Disease and the Development of Early Detection Paradigms: Insights from Nosology, Biomarkers and Machine Learning

Alzheimer’s Disease (AD) is the only condition in the top ten leading causes of death for which we do not have an effective treatment that prevents, slows, or stops its progression. Our ability to design useful interventions relies on (a) increasing our understanding of the pathological process of AD and (b) improving our ability for its early detection. These goals are impeded by our current reliance on the clinical symptoms of AD for its diagnosis. This characterizations of AD often falsely assumes a unified, underlying AD-specific pathology for similar presentations of dementia that leads to inconsistent diagnoses. It also hinges on postmortem verification, and so is not a helpful method for identifying patients and research subjects in the beginning phases of the pathophysiological process. Instead, a new biomarker-based approach provides a more biological understanding of the disease and can detect pathological changes up to 20 years before the clinical symptoms emerge. Subjects are assigned a profile according to their biomarker measures of amyloidosis (A), tauopathy (T) and neurodegeneration (N) that reflects their underlying pathology in vivo. AD is confirmed as the underlying pathology when subjects have abnormal values of both amyloid and tauopathy biomarkers, and so have a biomarker profile of A+T+(N)- or A+T+(N)+. This new biomarker based characterization of AD can be combined with machine learning techniques in multimodal classification studies to shed light on the elements of the AD pathological process and develop early detection paradigms. A guiding research framework is proposed for the development of reliable, biologically-valid and interpretable multimodal classification models

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification: Weighted Sparse Group Model for MCI Classification

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method

Deep Interpretability Methods for Neuroimaging

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Nevertheless, the difficulty of reliable training on high-dimensional but small-sample datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this dissertation, we address these challenges by proposing a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. The developed model is pre-trainable and alleviates the need to collect an enormous amount of neuroimaging samples to achieve optimal training. We also provide a quantitative validation module, Retain and Retrain (RAR), that can objectively verify the higher predictability of the dynamics learned by the model. Results successfully demonstrate that the proposed framework enables learning the fMRI dynamics directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction. We also comprehensively reviewed deep interpretability literature in the neuroimaging domain. Our analysis reveals the ongoing trend of interpretability practices in neuroimaging studies and identifies the gaps that should be addressed for effective human-machine collaboration in this domain. This dissertation also proposed a post hoc interpretability method, Geometrically Guided Integrated Gradients (GGIG), that leverages geometric properties of the functional space as learned by a deep learning model. With extensive experiments and quantitative validation on MNIST and ImageNet datasets, we demonstrate that GGIG outperforms integrated gradients (IG), which is considered to be a popular interpretability method in the literature. As GGIG is able to identify the contours of the discriminative regions in the input space, GGIG may be useful in various medical imaging tasks where fine-grained localization as an explanation is beneficial

Timecourse of Cognitive and Brain Adaptation to Cognitive Training in At-risk Elderly

Maintaining cognitive ability in the elderly is a global priority. Computerised cognitive training (CCT) is among the few effective interventions but the boundaries and mechanisms underlying its effectiveness are largely unknown. Chapter 2 is the first systematic review and meta-analysis of 37 randomised controlled trials (RCTs) of CCT in healthy elderly, encompassing a total of 4,310 participants. CCT was effective on all the cognitive domains except for executive functions. Type of training program, mode of delivery, session length and training frequency were found to moderate CCT efficacy. The Timecourse Trial (Chapter 3) was a randomized, double-blind, active controlled longitudinal trial of CCT in 80 healthy elderly. Significant effects were found on global cognition, memory and processing speed, and dose-response curves differed across domains. These domain-specific gains also followed different decay curves after training cessation throughout the 12 months follow-up period. Chapter 4 investigates the neural underpinnings of gains in global cognition. Modification of resting-state functional connectivity was found to predict subsequent cognitive gains, gains that were also correlated to structural cortical plasticity. CCT is an effective intervention in the elderly. The field may do well to now focus on improving standards, large-scale trials and a further understanding of biological mechanisms

A Neuroimaging Web Interface for Data Acquisition, Processing and Visualization of Multimodal Brain Images

Structural and functional brain images are generated as essential modalities for medical experts to learn about the different functions of the brain. These images are typically visually inspected by experts. Many software packages are available to process medical images, but they are complex and difficult to use. The software packages are also hardware intensive. As a consequence, this dissertation proposes a novel Neuroimaging Web Services Interface (NWSI) as a series of processing pipelines for a common platform to store, process, visualize and share data. The NWSI system is made up of password-protected interconnected servers accessible through a web interface. The web-interface driving the NWSI is based on Drupal, a popular open source content management system. Drupal provides a user-based platform, in which the core code for the security and design tools are updated and patched frequently. New features can be added via modules, while maintaining the core software secure and intact. The webserver architecture allows for the visualization of results and the downloading of tabulated data. Several forms are ix available to capture clinical data. The processing pipeline starts with a FreeSurfer (FS) reconstruction of T1-weighted MRI images. Subsequently, PET, DTI, and fMRI images can be uploaded. The Webserver captures uploaded images and performs essential functionalities, while processing occurs in supporting servers. The computational platform is responsive and scalable. The current pipeline for PET processing calculates all regional Standardized Uptake Value ratios (SUVRs). The FS and SUVR calculations have been validated using Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) results posted at Laboratory of Neuro Imaging (LONI). The NWSI system provides access to a calibration process through the centiloid scale, consolidating Florbetapir and Florbetaben tracers in amyloid PET images. The interface also offers onsite access to machine learning algorithms, and introduces new heat maps that augment expert visual rating of PET images. NWSI has been piloted using data and expertise from Mount Sinai Medical Center, the 1Florida Alzheimer’s Disease Research Center (ADRC), Baptist Health South Florida, Nicklaus Children\u27s Hospital, and the University of Miami. All results were obtained using our processing servers in order to maintain data validity, consistency, and minimal processing bias

Factors affecting assessment, uptake and adherence to physical activities in people with dementia: an inclusive approach

Dementia is a growing problem worldwide. There is no available long term effective treatment and many cases of dementia remain undiagnosed. Within this context, appropriate, accurate and reliable cognitive assessments are important in informing the process of diagnosing dementia, and monitoring the effects of subsequent interventions. Previous research has often researched the journey of dementia in stages. This thesis, however, was guided by inclusivity, a concept applied to encapsulate the need for the inclusion of all individuals across the whole journey of dementia. Assessments utilised during diagnostics should be cross-culturally applicable, easy and quick to administer, inexpensive, non-invasive and able to identify changes in cognitive functioning. Little research has explored cognitive assessments for people with intellectual disabilities, a growing group at high risk for experiencing dementia at a younger age. Moreover, physical activity could be a key intervention for people with dementia, with the potential to slow cognitive symptoms and promote independence. However, meta-analyses show mixed outcomes for the success of physical activity interventions. This may partly be due to low levels of engagement and adherence. Therefore, both cognitive assessments and physical activity, including factors influencing adherence, are important aspects of the journey of dementia, which require more research with an inclusive approach. This thesis was divided into 2 parts to reflect the underpinning paradigms that informed the investigations in each part. Hence, a mixed methods approach is used to investigate more inclusive practices in dementia diagnostics, intervention assessment and delivery of physical activity. Applied quantitative methods were used in part 1 to assess the accuracy of a battery of cognitive assessments (Mini Mental State Examination or MMSE, Hopkins Verbal Learning Test or HVLT, Verbal Fluency or VF, and the novel: Cognitive Computerized Test Battery for Individuals with Intellectual Disabilities or CCIID) in informing dementia diagnostics for individuals with (n=30) and without (n=25) intellectual disabilities (chapters 4 and 5). The same cognitive tests were then utilised to assess the acute effects of a physical activity intervention compared to a psychosocial control activity using a cross-over design involving people with dementia (chapter 6). The second part of the thesis informed by critical realism, but continuing the inclusive approach began by exploring the barriers and facilitators to physical activity for people with dementia (chapter 7). Novel mobile methods of interviewing were applied to explore the perspectives of people with dementia towards physical activity (chapter 8). These walking interviews were also discussed in comparison to more traditional seated interviews for their application in understanding the perspectives of people with demenita. This was only the second study to conduct walking interviews with people who have dementia, but the first to discuss physical activity within this context. Chapter 9 then sought to investigate the perspectives of professionals who work to provide physical activity for and with people who have dementia. This study investigated how professionals navigate barriers and facilitate adherence to physical activity for people with dementia within the community, and hence offers a discussion of practical solutions to barriers identified in the literature and from interviews with people with dementia. The findings from the initial investigations in this thesis showed that participants with and without a pre-existing cognitive impairment who had dementia scored significantly lower on all included cognitive assessments (MMSE, VF, HVLT, Series and Jigsaw subtests and total CCIID) than their age-matched counterparts. Receiver Operating Characteristic analysis revealed that all included assessments significantly classified those who had dementia, with a high accuracy of above 0.80 for all assessments with all populations. Assessments were well tolerated by all participants, including those with an intellectual disability. Acute cognitive benefits of physical activity were demonstrated over and above a psychosocial control using an order balanced cross-over design. An increase in cognitive scores was visible on the MMSE, VF, HVLT, Series and Jigsaw subtests and total CCIID after engaging in a short bout of resistance band physical activity versus a bingo (psychosocial) activity. This study confirms earlier research with resistance band physical activities in promoting memory in older people with and without dementia, but adds another new sensitive planning and logical reasoning test (CCIID) which could be important for early stages- or different types- of dementia. This study shows that the same well tolerated cognitive tests can be used for the initial screening and subsequent assessment of interventions.Systematic literature review (chapter 7) revealed that people with dementia have problems adhering to regular physical activity. The following thematic analysis of walking interview data with people who have dementia in chapter 8 revealed four key themes as to why this might be. The themes were: i) competition, ii) physical activity across the lifespan, iii) injury and decline; and iv) barriers to physical activity. The themes indicated that competitive aspects of physical activities can be encouraging or discouraging depending upon the individual participating, by giving the activity purpose, whether this is through competition or an activity goal, more people with dementia are interested in repeatedly engaging. Furthermore, injuries and decline in physical functioning frequently impacted participants’ ability to enjoy physical activity. This often led to adapted physical activities rather than traditional sports that participants described enjoying earlier on in their lives. Each participant also discussed different logistical barriers outside of physical capabilities that limited their consistent participation in physical activity. The final study of the thesis, in chapter 9, analysed interviews with professionals, and offered methods of navigating the barriers highlighted by people with dementia; and discussed the potential for professional engagement with dementia care to increase physical activity participation and inclusively deliver interventions. This often meant providing a personalised activity that includes social interaction for the participants to further engage with, and benefit from. The professionals discussed the structure of the context in which physical activity is provided for people with dementia. Overall, this thesis argues for inclusive practices for people with dementia regardless of pre-existing cognitive ability, from diagnosis through to strategies for sustaining interventions that could offer substantial benefits. The empirical chapters are potentially limited by the small numbers of participants per study (n=9-25). However, this also allowed for in-depth analyses. The findings demonstrate the need for increased communication between healthcare professionals and people with dementia to offer more inclusive practices that can give greater insight into our understanding of dementia, as well as offer better care throughout the journey of dementia for all individuals.</div