NDLI: Group-Wise FMRI Activation Detection on DICCCOL Landmarks

Content Provider	Springer Nature Link
Author	Lv, Jinglei Guo, Lei Zhu, Dajiang Zhang, Tuo Hu, Xintao Han, Junwei Liu, Tianming
Copyright Year	2014
Abstract	Group-wise activation detection in task-based fMRI has been widely used because of its robustness to noises and its capacity to deal with variability of individual brains. However, current group-wise fMRI activation detection methods typically rely on the co-registration of individual brains’ fMRI images, which has difficulty in dealing with the remarkable anatomic variation of different brains. As a consequence, the resulted misalignments could significantly degrade the required inter-subject correspondences, thus substantially reducing the sensitivity and specificity of group-wise fMRI activation detection. To deal with these challenges, this paper presents a novel approach to detecting group-wise fMRI activation on our recently developed and validated Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL). The basic idea here is that the first-level general linear model (GLM) analysis is first performed on the fMRI signal of each corresponding DICCCOL landmark in individual brain’s own space, and then the estimated effect sizes of the same landmark from a group of subjects are statistically assessed with the mixed-effect model at the group level. Finally, the consistently activated DICCCOL landmarks are determined and declared in a group-wise fashion in response to external block-based stimuli. Our experimental results have demonstrated that the proposed approach can detect meaningful activations.
Starting Page	513
Ending Page	534
Page Count	22
File Format	PDF
ISSN	15392791
Journal	Neuroinformatics
Volume Number	12
Issue Number	4
e-ISSN	15590089
Language	English
Publisher	Springer US
Publisher Date	2014-04-29
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	DTI fMRI Group-wise activation detection DICCCOL Neurosciences Bioinformatics Computational Biology/Bioinformatics Computer Application in Life Sciences Neurology
Content Type	Text
Resource Type	Article
Subject	Neuroscience Information Systems Software

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