9 research outputs found

    Assessment of White Matter Hyperintensity, Cerebral Blood Flow, and Cerebral Oxygenation in Older Subjects Stratified by Cerebrovascular Risk

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    Objective: Cerebrovascular disease (CVD) is the fifth most common cause of mortality in the United States. Diagnosis of CVD at an early stage is critical for optimal intervention designed to prevent ongoing and future brain injury. CVD is commonly associated with abnormalities of the cerebral microvasculature leading to tissue dysfunction, neuronal injury and death, and resultant clinical symptoms, which in turn, further impacts cerebral autoregulation (CA). This series of studies aims to test the hypothesis that white matter hyperintensities (WMH) and cerebral hemodynamics (quantified by magnetic resonance imaging (MRI) and an by innovative hybrid near-infrared diffuse optical instrument) can be used as biomarkers to distinguish cognitively healthy older subjects with high or low risk for developing CVD. Methods: Using functional MRI, WMH and cerebral blood flow (CBF) were quantified in 26 cognitively healthy older subjects (age: 77.8 ± 6.8 years). In a follow-up study, significant variability in WMH quantification methodology was addressed, with sources of variability identified in selecting image center of gravity, software compatibility, thresholding techniques, and manual editing procedures. Accordingly, post-acquisition processing methods were optimized to develop a standardized protocol with less than 0.5% inter-rater variance. Using a novel laboratory-made hybrid near-infrared spectroscopy/diffuse correlation spectroscopy (NIRS/DCS) and a finger plethysmograph, low-frequency oscillations (LFOs) of CBF, cerebral oxygenation, and main arterial pressure (MAP) were simultaneously measured before, during, and after 70° head-up-tilting (HUT). Gains (associated with CAs) to magnify LFOs were determined by transfer function analyses with MAP as the input and cerebral hemodynamic parameters as the outputs. In a follow-up study, a fast software correlator for DCS and a parallel detection technique for NIRS/DCS were adapted to improve the sampling rate of hybrid optical measurements. In addition, a new DCS probe was developed to measure CBF at the occipital lobe, which represents a novel application of the NIRS/DCS technique. Results: MRI measurements demonstrate that deep WMH (dWMH) and periventricular WMH (pWMH) volumetric measures are associated with reduced regional cortical CBF in patients at high-risk of CVD. Moreover, CBF in white matter (WM) was reduced in regions demonstrating both pWMH and dWMHs. NIRS/DCS optical measurements demonstrate that at resting baseline, LFO gains in the high-risk group were relatively lower compared to the low-risk group. The lower baseline gains in the high-risk group may be attributed to compensatory mechanisms that allow the maintenance of a stronger steady-state CA. However, HUT resulted in smaller gain reductions in the high-risk group compared to the low-risk group, suggesting weaker dynamic CA in association with increased CVD risks. A noteworthy finding in these experiments was that CVD risk more strongly influenced CBF than cerebral oxygenation. Conclusions: Regional WMH volumes, cortical and WM CBF values, and LFO gains of cerebral hemodynamics demonstrate specific associations with CA and may serve as important potential biomarkers for early diagnosis of CVD. The high spatial resolution, large penetration depth, and variety of imaging-sequences afforded by MRI make it an appealing imaging modality for evaluation of CVD, although MRI is costly, time-limited, and requires transfer of subjects from bed to imaging facility. In contrast, low-cost, portable, mobile diffuse optical technologies provide a complementary alternative for early screening of CVD, that can further allow continuous monitoring of disease attenuation or progression at the subject’s bedside. Thus, development of both methodologies is essential for progress in our future understanding of CVD as a major contributor to the morbidity and mortality associated with CVD today

    Proceedings of ICMMB2014

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    Methodology of optical topography measurements for functional brain imaging and the development and implementation of functional optical signal analysis software.

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    Near-infrared spectroscopy (N1RS) has been used extensively in recent years as a non invasive tool for investigating cerebral hemodynamics and oxygenation. The technique exploits the different optical absorption of oxy-haemoglobin and deoxy-haemoglobin in the near infrared region to measure changes in their concentrations in tissue. By making multiple NIRS measurement simultaneously, optical topography (OT) provides spatial maps of the changes in haemoglobin concentration levels from specific regions of the cerebral cortex. The thesis describes several key developments in optical topography studies of functional brain activation. These include the development of a novel data analysis software to process the experimental data and a new statistical methodology for examining the spatial and temporal variance of OT data. The experimental work involved the design of a cognitive task to measure the haemodynamic response using a 24-channeI Hitachi ETG-100 OT system. Following a series of pilot studies, a study on twins with opposite handedness was conducted to study the functional changes in the parietal region of the brain. Changes in systemic variables were also investigated. A dynamic phantom with optical properties similar to those of biological tissues was developed with the use of liquid crystals to simulate spatially varying changes in haemodynamics. A new software tool was developed to provide a flexible processing approach with real time analysis of the optical signals and advanced statistical analysis. Unlike conventional statistical measures which compare a pre-defined activation and task periods, the thesis describes the incorporation of a Statistical Parametric Mapping toolbox which enables statistical inference about the spatially-resolved topographic data to be made. The use of the general linear model computes the temporal correlations between the defined model and optical signals but also corrects for the spatial correlations between neighbouring measurement points. The issues related to collecting functional activation data using optical topography are fully discussed with a view that the work presented in this thesis will extend the applicability of this technology

    A Machine Learning Classification Framework for Early Prediction of Alzheimer’s Disease

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    People today, in addition to their concerns about getting old and having to go through watching themselves grow weak and wrinkly, are facing an increasing fear of dementia. There are around 47 million people affected by dementia worldwide and the cost associated with providing them health and social care support is estimated to reach 2 trillion by 2030 which is almost equivalent to the 18th largest economy in the world. The most common form of dementia with the highest costs in health and social care is Alzheimer’s disease, which gradually kills neurons and causes patients to lose loving memories, the ability to recognise family members, childhood memories, and even the ability to follow simple instructions. Alzheimer’s disease is irreversible, unstoppable and has no known cure. Besides being a calamity to affected patients, it is a great financial burden on health providers. Health care providers also face a challenge in diagnosing the disease as current methods used to diagnose Alzheimer’s disease rely on manual evaluations of a patient’s medical history and mental examinations such as the Mini-Mental State Examination. These diagnostic methods often give a false diagnosis and were designed to identify Alzheimer’s after stage two when the part of all symptoms are evident. The problem is that clinicians are unable to stop or control the progress of Alzheimer’s disease, because of a lack of knowledge on the patterns that triggered the development of the disease. In this thesis, we explored and investigated Alzheimer’s disease from a computational perspective to uncover different risk factors and present a strategic framework called Early Prediction of Alzheimer’s Disease Framework (EPADf) that would give a future prediction of early-onset Alzheimer’s disease. Following extensive background research that resulted in the formalisation of the framework concept, prediction approaches, and the concept of ranking the risk factors based on clinical instinct, knowledge and experience using mathematical reasoning, we carried out experiments to get further insight and investigate the disease further using machine learning models. In this study, we used machine learning models and conducted two classification experiments for early prediction of Alzheimer’s disease, and one ranking experiment to rank its risk factors by importance. Besides these experiments, we also presented two logical approaches to search for patterns in an Alzheimer’s dataset, and a ranking algorithm to rank Alzheimer’s disease risk factors based on clinical evaluation. For the classification experiments we used five different Machine Learning models; Random Forest (RF), Random Oracle Model (ROM), a hybrid model combined of Levenberg-Marquardt neural network and Random Forest, combined using Fischer discriminate analysis (H2), Linear Neural Networks (LNN), and Multi-layer Perceptron Model (MLP). These models were deployed on a de-identified multivariable patient’s data, provided by the ADNI (Alzheimer’s disease Neuroimaging Initiative), to illustrate the effective use of data analysis to investigate Alzheimer’s disease biological and behavioural risk factors. We found that the continues enhancement of patient’s data and the use of combined machine learning models can provide an early cost-effective prediction of Alzheimer’s disease, and help in extracting insightful information on the risk factors of the disease. Based on this work and findings we have developed the strategic framework (EPADf) which is discussed in more depth in this thesis
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