title: Group-wise FMRI activation detection on corresponding cortical landmarks.
abstract: Group-wise activation detection in task-based fMRI has been widely used because of its robustness to noises and statistical power to deal with variability of individual brains. However, current group-wise fMRI activation detection methods typically rely on the spatial alignment established by coregistration of individual brains' fMRI images into the same template space, which has difficulty in dealing with the remarkable anatomic variation of different brains. As a consequence, the resulted misalignment among multiple brains could substantially degrade the accuracy and specificity of group-wise fMRI activation detection. To address these challenges, this paper presents a novel methodology to detect group-wise fMRI activation based on a publicly released dense map of DTI-derived structural cortical landmarks, which possess intrinsic correspondences across individuals and populations. The basic idea here is that a first-level general linear model (GLM) analysis is performed on fMRI signals of each corresponding cortical landmark in each individual brain's own space, and then the single-subject effect size of the same landmark from a group of subjects are statistically integrated and assessed at the group level using the mixed-effects model. As a result, the consistently activated cortical landmarks are determined and declared group-wisely in response to external block-based stimuli. Our experimental results demonstrated that the proposed approach can map meaningful group-wise activation patterns on the atlas of cortical landmarks without image registration between subjects and spatial smoothing.
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Jinglei
authors.initials: J
authors.lastname: Lv
authors.affiliation: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Xintao
authors.initials: X
authors.lastname: Hu
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Tuo
authors.initials: T
authors.lastname: Zhang
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.affiliation: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000033:anatomy & histology / Q000502:physiology*; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity