Adaptive integration of local region information to detect fine-scale brain activity patterns |
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Authors: | ZongLei Zhen Jie Tian Hui Zhang |
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Affiliation: | (1) Medical Image Processing Group, Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, China;(2) Graduate University of the Chinese Academy of Sciences, Beijing, 100039, China |
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Abstract: | With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data
is continuously growing. This provides us the possibility to detect the fine-scale patterns of brain activities. The established
univariate and multivariate methods to analyze fMRI data mostly focus on detecting the activation blobs without considering
the distributed fine-scale patterns within the blobs. To improve the sensitivity of the activation detection, in this paper,
multivariate statistical method and univariate statistical method are combined to discover the fine-grained activity patterns.
For one voxel in the brain, a local homogenous region is constructed. Then, time courses from the local homogenous region
are integrated with multivariate statistical method. Univariate statistical method is finally used to construct the interests
of statistic for that voxel. The approach has explicitly taken into account the structures of both activity patterns and existing
noise of local brain regions. Therefore, it could highlight the fine-scale activity patterns of the local regions. Experiments
with simulated and real fMRI data demonstrate that the proposed method dramatically increases the sensitivity of detection
of fine-scale brain activity patterns which contain the subtle information about experimental conditions.
Supported by Chair Professors of Changjiang Scholars Program and CAS Hundred Talents Program, National Program on Key Basic
Research Projects (Grant No. 2006CB705700), National High-Tech R&D Program of China (Grant No.2006AA04Z216), National Key
Technology R&D Program (Grant No. 2006BAH02A25), Joint Research Fund for Overseas Chinese Young Scholars (Grant No.30528027),
National Natural Science Foundation of China (Grant Nos.30600151, 30500131 and 60532050), and Natural Science Foundation of
Beijing (Grant Nos. 4051002 and 4071003) |
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Keywords: | functional magnetic resonance imaging (fMRI) principal component analysis general linear model local region fine-scale activity patterns |
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