Data analysis on MRI data using ICA :from single to group dataset

• Main Authors: Chia-Ju Chen, 陳佳如
• Other Authors: Keh-Shih Chuang
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/67643489686347187416

博士 === 國立清華大學 === 生醫工程與環境科學系 === 96 === Independent component analysis (ICA) decomposes mixing signal into their constituent components and is commonly used in the research of signal segmentation and functional localization in magnetic resonance imaging (MRI) field. Recently, analysis on MRI data show that the interesting source signals attributing to brain activity can be regarded as independent. In perfusion study, ICA utilizes the temporal-spatial independence to segment the tissue into gray, white matter and the tissue surrounding vessel which is affected by the local field inhomogeneity during the contrast agent passage. Besides the perfusion study, ICA is also applied to locate the brain activity region by the level of blood-oxygen dependence under task delivery in function study. Determination of the data dimension in data analysis could reduce the computer loading and increase the computation speed. However, the traditional estimation methods (i.e. Akaike information criterion (AIC), Bayesian information criterion (BIC) and minimum description length (MDL)) over-estimate the dimension number due to the variation of between- and within-subject. This over-estimated situation can be decreased by a conservative method: the fitting of auto-regression model with first order, acronymic in autoregressive model of order one (AR (1)). It estimates the dimension of data by fitting the noise part of data because it assumes that the noise contribution to data is colored. In this work, ICA combined and the extended AR(1) method is applied to a series of MRI data from single dataset, single-group dataset to multi-group dataset. In the first part, ICA is used to segment the tissue around vessel in perfusion MRI study. In the second part, extended ICA is applied to single group dataset for drug abuse investigation. The resultant performance is compared with the traditional model based method. In the third part, an extended AR(1) method is used to assess the data dimension in order to handle the analysis of a giant dataset with multi-groups. In the final part, an application to the brain default system is involved to study the brain function consistency across time series. Preliminary result showed that the ICA performed excellent signal decomposition on the data. The partial volume problem for the region around the vessel is alleviated by ICA and a better arterial input function (AIF) for quantifying physiological parameters can be achieved. Subsequently, ICA utilize a non-parametric model of drug effect as compared with the traditional parametric model and it also provides extra information for the drug study such as behavior task, physiology test during scan delivery. With the same concept, ICA also helps the analysis on the brain function under resting state. The results showed that the brain default function is consistent existing across time frame and it is also exposed that ICA combined with our home-made dimension estimation method could alleviate the overestimation of dimension caused by the variation of within- and between-subject. In conclusion, ICA is a powerful tool in analyzing data. The relative research such as brain connectivity under brain resting state and the extra information involved to ICA is worth investigation and development.