Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

Bhuvaneshwari Bhaskaran (Centre for Healthcare Technologies, Department of Biomedical Engineering, SSN College of Engineering, Chennai, India) and Kavitha Anandan (Centre for Healthcare Technologies, Department of Biomedical Engineering, SSN College of Engineering, Chennai, India)
DOI: 10.4018/IJSSCI.2017100104
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Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.
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1. Introduction

Alzheimer’s disease (AD) is a prominent cause of dementia that gradually eradicates memory and other cognitive functions (Ballard, Gauthier, Corbett, Brayne & Aarsland, 2011; Brookmeyer, Johnson, Ziegler-Graham & Arrighi, 2007). In its early stages, loss of, episodic memory, motor and language skills are recognized symptoms. Mild cognitive impairment (MCI) is the preliminary stage of AD, where the memory deterioration is greater than the normal cognitive subjects but not worse than the AD subjects. Also, the probability of becoming cognitively impaired increases with age (Huang, Matsushita, Kawagoe & Yasuda, 2016). As the disease progresses cognitive deficiency manifest leading to death eventually. Thus, Alzheimer's disease represents a most important public health apprehension and has been recognized as a research priority (Matthews, Filippini & Douaud, 2013; Carr, Goate, Phil & Morris, 1997).

Neuroimaging techniques provide promising potentials in diagnosis of Alzheimer’s disease (AD) (Huang et al., 2009). The various imaging modalities in analysing AD include magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). Structural imaging studies have shown that the specific brain regions such as hippocampus of people with AD shrink significantly as the disease progresses. The micro-anatomy integrity of white matter tracts has been analysed using DTI (Nestor, Scheltens & Hodges, 2004). MRS provides crucial information about AD pathology due to induced alteration in various neurochemical levels. MRS demonstrates changes in neurochemistry due to increased oxidative stress and altered lipid metabolism with the progression of the disease (Mandal, 2007). In order to identify brain genes related to Late-Onset Alzheimer’s Disease (LOAD) Classification models have been identified with statistical accuracy (Coelho, Goertzel, Pennachin & Heward, 2009). Cognitive methods have been successful in investigation AD related genes using microarray data (Nishiwaki, Kanamori & Ohwada, 2017).

Functional magnetic resonance imaging (fMRI) procedure has been used to evaluate the functional integrity of brain networks involving memory regions through the stages of AD. It is a non-invasive practice that permits the indirect measurement of neuronal activity and imaging of activated cortical regions. Mathews measured function and connectivity using MRI, for classification of genetic aids to determine the neuropathology of AD (Matthews, Filippini & Douaud, 2013) fMRI has also been used to observe spontaneous inherent activity during rest and constant performance of a segmented two-back working memory test (Fransson, 2006).

The existence of spontaneous intrinsic low frequency BOLD (blood oxygen level dependent) signal fluctuations during rest have attracted attention in neuroscience community in recent times. An automated technique that helps in the diagnosis of Alzheimer’s disease has been developed by Tripoliti (Tripoliti, Fotiadis, Argyropoulou & Manis, 2010). The method also supports in observing the progression of the disease. fMRI and multivariate analytic procedures have been employed to examine memory related fMRI activity among the normal ageing, mild cognitive impairment (MCI) and mild AD subjects. Independent component analysis showed exact memory related networks that activated or deactivated throughout an associate memory paradigm. As the cognitive task may not be effectively performed by AD patients; resting state fMRI study has been identified to be more feasible (Tie et al., 2014).

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