| Summary: | 碩士 === 臺北醫學大學 === 醫學資訊研究所 === 94 === Abstract
Title of Thesis: Assessing rCBF Changes in Parkinson’s Disease Using Independent Component Analysis on SPECT Image
Author: Jung Lung Hsu
Thesis advised by : Chien-Yeh Hsu
Taipei Medical University,
Graduate Institute of Medical Informatics
Key words: Medical Image, Independent Component Analysis, ICA, Parkinson’s disease, Cognition, SPECT
Background: The emergence of non-invasive recording during the current decade is one of the most important developments in biomedical sciences. As technologies continue to push the boundaries of spatial-temporal resolutions of bio-signal recordings, analytic tools need to keep pace with these advances. However, analytic tools for exploring and modeling the wealth of data collected during functional imaging experiments do not yet capture or model the rapidly shifting dynamics of brain systems during complex cognitive activity. Methods are needed to analyze this wealth of data and to separate out machine noise and physiological artifacts to examine functionally independent physiological systems. Based on a recently developed signal-processing tool, Independent Component Analysis, we can implement methods for linear separation of activity originating in functionally distinct physiological systems by using the relative temporal independence of these activities across sufficient recording time and experimental conditions. This approach has resulted in very promising results in analyzing electroencephalogram (EEG), electrocardiogram (EKG), electromyography (EMG), or functional magnetic resonance imaging (fMRI) data. Materials and Methods: Despite extensive studies in Parkinson’s disease (PD) in recent decades, the neural mechanisms of this common neurodegenerative disease remain incompletely understood. Functional brain imaging technique such as single photon emission computerized tomography has emerged as a tool to help us understand the disease pathophysiology by assessing regional cerebral blood flow (rCBF) changes. We suggest that tools based on decomposition of biomedical time series data into a mixture of temporally or spatially independent components can further provide us more information in the analysis of biomedical image signals. In present study, we collected 27 PD patients in various stage of disease and 24 health controls. Clinical staging and motor symptoms in PD were measured by UPDRS scores (United Kingdom of Parkinson’s Disease Rating Scale) and Hoehn and Yahr stage. 99mTc-HMPAO SPECT (single photon emission computerized tomography) image was arranged for both patients and controls. We applied Independent Component Analysis (ICA) to assess the difference in rCBF between PD patients and healthy controls to identify brain regions involving in PD. Finally, statistic parametric mapping (SPM) tool was use to identified statistic significant regions between PD and controls. We also applied motor part UPDRS score to correlate with these significant regions and find brain areas responsible for clinical scores.
Results: After ICA decomposition, 9 independent components were classified as “disease related” subset and 42 as “non-disease related” subset. In “disease-related” subset, SPM revealed many brain areas identified by ICA included the basal ganglia, the brainstem, the cerebellum, and the cerebral cortex. Some of the regions have been largely overlooked in neuroimaging studies using region-of-interest approaches, yet they are consistent with previous pathophysiological reports. Besides, rCBF in limbic system included cingulated gyrus and limbic lobe had demonstrated not only had significant difference between PD and controls, but also had significant correlated with disease symptoms. These had not been reported in previously literatures.
Conclusions: Our study had showed that use ICA as image preprocessing step followed by SPM statistic analysis could significant improve image analysis results. However, our patients were on medication and patient number was not large, which should be caution in further interpretation our results. Since ICA has the ability to solve the blind source separation problem of recovering independent source signals after they are linearly mixed by an unknown matrix, we expect that ICA might be valuable to suggest a new alternative and more comprehensive disease and brain circuit models in PD.
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