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|Title:||White matter connectivity for detection of Alzheimer's disease||Authors:||Huang, Dajing||Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2017||Abstract:||Alzheimer’s disease (AD) is the most common type of dementia, characterized by progressive neurodegeneration and cognitive impairment. Patients typically experience progressive loss of cognitive abilities which eventually becomes serious enough to interfere with one’s daily life. Alzheimer’s disease is considered as a grey matter disease since neuron loss in hippocampus and cortical atrophy in temporal lobe were consistently discovered in earlier structural Magnetic Resonance Imaging (MRI) studies. With the development of neuroimaging technique, diffusion tensor imaging (DTI) has offered researchers a new window into the white matter integrity and fibre organizations in the human brain. Recent studies suggest that white matter impairment also contribute to AD pathology. DTI may be able to contribute to early diagnosis of AD and individual cognition prediction. Hence, this project was designed to construct structural connectome (i.e. fibre organization) of the brain, which were further employed to make individual classification and cognition scores prediction for participants cross the disease stages, including Normal (NC), Significant Memory Concern (SMC), Early Mid Cognitive Impairment (EMCI), Late Mid Cognitive Impairment (LMCI) and Alzheimer's Disease (AD). 953 sets of MRI scans of 283 subjects was used for this project. Each set of scans consist of a DTI scan and structural MRI scan. For structural network construction, DTI tractography was first used to detect fibre organization of the brain, which were further combined with structural MRI data to construct the connectivity matrices which represents fibre organisation of the brain. For individual classification of disease stages, the matrices were used as features to feed the SVM classifier. For individual prediction of cognition scores, the matrices were employed as features to feed the SVM regressor to predict the composite memory (ADNI-MEM), the composite executive function (ADNI-EF) and Mini–Mental State Examination (MMSE). The structural connectome based on DTI achieved high accuracy for individual prediction of disease stages and cognitive scores. The 5-class classification tasks achieved an average F1-score of 0.89 (p<0.05). The ADNI-MEM prediction achieved a Mean Squared Error (MSE) of 0.32 and coefficient of determination (R2) of 0.55 (p<0.05). The prediction of ADNI-EF achieved MSE of 0.36 and R2 of 0.53 (p<0.05) and the prediction of MMSE achieved MSE of 4.02 and R2 of 0.55 (p<0.05).||URI:||http://hdl.handle.net/10356/72839||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
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