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۱Detecting Functional Connectivity in the Resting Brain using Independent Component Analysis
نویسنده(ها): ،
اطلاعات انتشار: شانزدهمین کنفرانس مهندسی پزشکی ایران، سال
تعداد صفحات: ۶
The functional network of the human brain is altered in many neurological and psychiatric disorders. Aim of this study is to assess the fuctional connectivity from resting state functional magnetic resonance imging (Fmri) data. Two different spatial independent Component Analysis (sICA) ALGORITHMS(THE infomax and the fixed – point ICA) were applied to the simulated and experimental fMRI data acquired from a resting healthy subject to fing functionally connected brain regions . Simlated data were used to investigate the influences of the noise level and threshold on the performances of the two algorithms In order to enhance the performance of the results , a variety of data pre and post processing steps , including data normalization , outlier removal, estimation of optimal number of independent components (ICs) using Minimum Description Length (MDL)principle , dimensionality reduction using principal component Analysis (PCA)and cluster filtering were employed . The proposed apporaches were compared to some well – known algorithms such as the Cross Correlation Analysis (CCA) and Eigenimage analysis . Results reveal that the performance of infomax algorithm is superior . I n addition , careful pre and post processing of the data are important factors and have significant enhancing effects on overall results.<\div>

۲ESTIMATING COMPONENTS OF FUNCTIONAL MAGMETIC RESONANCE IMAGING (FMRI)DATA IN A TIME – DEPENDENT ORDER BY MODIFYING ICA ALGORITHMS
نویسنده(ها): ،
اطلاعات انتشار: شانزدهمین کنفرانس مهندسی پزشکی ایران، سال
تعداد صفحات: ۶
In this paper, our aim is analyzing functional magnetic resonance imaging (FMRI) data by independent component analysis (ICA)in order to find regions of brain which were activates by neural activity in human brain . Usually by applying ICA algorithm for whole dataset , independent components can be estimated but we cant understand the procedure of activation. Here, we propose a method to detect active components in different time intervals . Spatial ICA is applies in sliding time windows . we find active components in each window be applying a criteria which measure two kind of cross – correlation coefficients. The correlation between components in each window and reference function in that time interval and the correlation between components in adjacent windows. Finally we detect active regions of active components in each window . In order to investing the advantage of using this method , we perform some experiments for simulated and experimental fMRI datasets and show the results . Receiver operating characteristic (ROC)curve shows the performance of this method.<\div>
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