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

Spectroscopic detection of pathological severity in Alzheimer's disease

Alzheimer’s disease (AD) has emerged as one of the most widespread and devastating forms of dementia. Over the past few decades, AD has consistently increased in prevalence worldwide due to the rising proportion of elderly individuals and lack of effective screening and treatment modalities. To date, few economically viable and widely applicable tools exist to make definitive, early diagnoses of the disease. Therefore, there is a clear need for interventions that facilitate accurate diagnoses, monitoring, and therapeutic treatment of AD. In the course of AD, cognitive impairment is preceded by physiological changes to the central nervous system (CNS). This includes neuronal atrophy, synaptic dysfunction, and the abnormal post-translational modification of the proteins tau and beta-amyloid (A), which contributes to the deposition of intracellular neurofibrillary tangles (NFTs) and extracellular neuritic plaques (NPs). The pathological cellular changes in AD occur long before the clinical course of the disease, and biomarkers for these changes can be detected prior to measurable cognitive decline. Because the biochemical changes associated with AD are irreversible, effective tools for diagnosis must detect the presence and severity of molecular pathology during the preliminary stages of the disease’s insidious onset. Biomarkers of AD can be detected by neuroimaging technologies, including magnetic resonance imaging (MRI), positron emission tomography (PET), and blood or cerebrospinal fluid (CSF) analyses. However, these methods are not currently suited to diagnose and monitor the unique pathogenesis of AD prior to cognitive decline. An ideal instrument for widespread AD screening, diagnosis, and monitoring must be noninvasive, inexpensive, portable, and accommodating to the cognitive sensitivities of patients on a spectrum from mild cognitive impairment (MCI) to full-blown dementia. Recently, several spectroscopic methods of assessing AD pathology have met these criteria and may be better suited for widespread clinical application. The objective of this thesis is to evaluate the use of near-infrared optical spectroscopy (NIRS) to detect pathological severity in human AD. Near-infrared (NIR) light is poorly absorbed by biological tissue, and can safely penetrate bone, skin, vasculature, and neuronal tissue. NIRS has traditionally been used in biomedical contexts to evaluate cerebral oxygenation changes, however the dense protein aggregates NFTs and NPs in AD tissue have recently been shown to characteristically affect several optical parameters of a NIR signal, including fluorescence and particle path (scattering). To date, applications of NIRS have been used to differentiate AD brains from non-AD controls in vitro, and further identify MCI patients in vivo, suggesting the NIR signal can identify molecular changes in AD. Severe AD cases are characterized by increased involvement of NFTs and NPs in the cerebral cortex, which would be expected to further affect the extent of NIR scatter. The current study aims to quantify AD-related pathology for investigation into whether the extent of optical scattering is correlated with the severity of amyloid plaque load and NFT density in the temporal cortex. Quantification of these lesions was accomplished using immunohistochemistry (IHC) and stereological analyses. Preliminary results show that the severity of AD pathology detected via IHC can be correlated with measured parameters of an in vitro near-infrared signal. Future studies aim to further characterize the relationship between scattering intensity and pathological severity, as well as evaluate the in vivo potential of this technology in predicting the clinical outcome and cognitive status of individuals in different stages of AD

Neurocognitive findings in adults who played youth football

Chronic Traumatic Encephalopathy (CTE) has been linked to contact sports, most notably boxing and American football, due to their propensity for repetitive head impacts. Concerns in the community for the safety of athletes in all contact sports has driven a significant amount of research into concussions, their long term effects, and strategies for treatment and prevention. Knowledge of long term brain health in response to neurotrauma is limited, a gap especially noticeable in the literature on non-catastrophic brain injuries sustained as a child. Concussion is a common injury that is often self-resolving with no lasting neurologic or cognitive deficits. Although repetitive brain trauma is hypothesized to be necessary and sufficient to lead to CTE, no human or animal models have definitively demonstrated the pathophysiologic connection or confirmed the mechanism of symptoms. The research to date has been case based, lacking prospective cohorts, with data complicated by convenience sampling. These factors limit the generalizability of conclusions. CTE is neuropathologically defined with variable symptoms; however, it is only diagnosable at postmortem autopsy making the etiology and prevalence difficult to understand. As more research is published to understand if there is an association between a neurocognitive degenerative disease and contact sports, the concentration is on professional athletes. Yet professional athletes do not represent the overwhelming majority of all contact sport participants. The proposed study will compare adults who participated in youth football, but not beyond the high school level, to a control group of adults who did not play contact sports. Evaluating their cognitive function with an online assessment, the Behavior Rating Inventory of Executive Function – Adult Version (BRIEF-A), data will be analyzed for signs of clinical cognitive impairment. The objective is to measure adults who represent the high percentage of youth football players who do not continue to the advanced levels. Data obtained from this study will help communities make informed decisions, and create the foundation for future studies on long term benefits and risks of contact sports for children

A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography

Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. However, the current literature lacks an extensive survey on the diagnosis of Alzheimer's disease or cognitive impairment using OCT or OCTA. This has motivated us to do a comprehensive survey aimed at machine/deep learning scientists or practitioners who require an introduction to the problem. The paper contains 1) an introduction to the medical background of Alzheimer's Disease and Cognitive Impairment and their diagnosis using OCT and OCTA imaging modalities, 2) a review of various technical proposals for the problem and the sub-problems from an automated analysis perspective, 3) a systematic review of the recent deep learning studies and available OCT/OCTA datasets directly aimed at the diagnosis of Alzheimer's Disease and Cognitive Impairment. For the latter, we used Publish or Perish Software to search for the relevant studies from various sources such as Scopus, PubMed, and Web of Science. We followed the PRISMA approach to screen an initial pool of 3073 references and determined ten relevant studies (N=10, out of 3073) that directly targeted AD diagnosis. We identified the lack of open OCT/OCTA datasets (about Alzheimer's disease) as the main issue that is impeding the progress in the field.Comment: Submitted to Computerized Medical Imaging and Graphics. Concept, methodology, invest, data curation, and writing org.draft by Yasemin Turkan. Concept, method, writing review editing, and supervision by F. Boray Te

Modelling prognostic trajectories in Alzheimer’s disease

Progression to dementia due to Alzheimer’s Disease (AD) is a long and protracted process that involves multiple pathways of disease pathophysiology. Predicting these dynamic changes has major implications for timely and effective clinical management in AD. There are two reasons why at present we lack appropriate tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread neurodegeneration. Current models fail to capture these relationships because they are unable to successfully reduce the high dimensionality of biomarkers while exploiting informative multivariate relationships. Second, current models focus on simply predicting in a binary manner whether an individual will develop dementia due to AD or not, without informing clinicians about their predicted disease trajectory. This can result in administering inefficient treatment plans and hindering appropriate stratification for clinical trials. In this thesis, we overcome these challenges by using applied machine learning to build predictive models of patient disease trajectories in the earliest stages of AD. Specifically, to exploit the multi-dimensionality of biomarker data, we used a novel feature generation methodology Partial Least Squares regression with recursive feature elimination (PLSr-RFE). This method applies a hybrid-feature selection and feature construction method that captures co-morbidities in cognition and pathophysiology, resulting in an index of Alzheimer’s disease atrophy from structural MRI. We validated our choice of biomarker and the efficacy of our methodology by showing that the learnt pattern of grey matter atrophy is highly predictive of tau accumulation in an independent sample. Next, to go beyond predicting binary outcomes to deriving individualised prognostic scores of cognitive decline due to AD, we used a novel trajectory modelling approach (Generalised Metric Learning Vector Quantization – Scalar projection) that mines multimodal data from large AD research cohorts. Using this approach, we derive individualised prognostic scores of cognitive decline due to AD, revealing interactive cognitive, and biological factors that improve prediction accuracy. Next, we extended our machine learning framework to classify and stage early AD individuals based on future pathological tau accumulation. Our results show that the characteristic spreading pattern of tau in early AD can be predicted by baseline biomarkers, particularly when stratifying groups using multimodal data. Further, we showed that our prognostic index predicts individualised rates of future tau accumulation with high accuracy and regional specificity in an independent sample of cognitively unimpaired individuals. Overall, our work used machine learning to combine continuous information from AD biomarkers predicting pathophysiological changes at different stages in the AD cascade. The approaches presented in this thesis provide an excellent framework to support personalised clinical interventions and guide effective drug discovery trials

Efficacy of memantine on cognitive functions of patients with moderate vascular dementia

Introduction: Vascular dementia is a common condition for which there is no effective approved pharmacological treatment available. Absence of effective treatment creates a difficult situation for those suffering from the disease, their caregivers, and healthcare providers. The objective of this study is todetermine the Efficacy of Memantine on cognitive functions in patients with Moderate vascular dementia. Material and methods: This Case series study was carried out inthe Neurology Section of department of Medicine Ziauddin University and Hospitals North Nazimabad campus, Karachifrom 12th March 2010 to 11th March 2011.90 patients were included, who fulfilled the inclusion criteria after taking an informed consent. The SPSS version 11 was applied to the data. Results: There were 55 (59.1%) males and 38 (40.9%) females. Mean ± standard deviation age 69.7 ± 6.6 years, mean duration of symptoms was 1.76 ± 1.1 years. Baseline minimental examination score was 15.14 ± 3.1. Minimental score after 24 weeks of drug therapy was 17.14 ± 4.1. Efficiency was found in 62 (66.7%) patients. Conclusion: Memantine produced only a small benefit in cognition (of uncertain clinical significance) in patients with moderate vascular dementia. Data is insufficient to support widespread use of this drug in vascular dementia. Individual patient analysis is needed to identify subgroups of patients with vascular dementia who might benefit

Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood

The progressive aging of the world’s population makes a higher prevalence of neurodegenerative diseases inevitable. The necessity for an accurate, but at the same time, inexpensive and minimally invasive, diagnostic test is urgently required, not only to confirm the presence of the disease but also to discriminate between different types of dementia to provide the appropriate management and treatment. In this study, attenuated total reflection FTIR (ATR-FTIR) spectroscopy combined with chemometric techniques were used to analyze blood plasma samples from our cohort. Blood samples are easily collected by conventional venepuncture, permitting repeated measurements from the same individuals to monitor their progression throughout the years or evaluate any tested drugs. We included 549 individuals: 347 with various neurodegenerative diseases and 202 age-matched healthy individuals. Alzheimer’s disease (AD; n = 164) was identified with 70% sensitivity and specificity, which after the incorporation of apolipoprotein ε4 genotype (APOE ε4) information, increased to 86% when individuals carried one or two alleles of ε4, and to 72% sensitivity and 77% specificity when individuals did not carry ε4 alleles. Early AD cases (n = 14) were identified with 80% sensitivity and 74% specificity. Segregation of AD from dementia with Lewy bodies (DLB; n = 34) was achieved with 90% sensitivity and specificity. Other neurodegenerative diseases, such as frontotemporal dementia (FTD; n = 30), Parkinson’s disease (PD; n = 32), and progressive supranuclear palsy (PSP; n = 31), were included in our cohort for diagnostic purposes. Our method allows for both rapid and robust diagnosis of neurodegeneration and segregation between different dementias