A high performance 3D cluster-based test of unsmoothed fMRI data.
Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity. Recently, a threshold-free cluster enhancement technique was proposed which does not require spatial smoothing, but this method can be used only for group level analysis. Advances in imaging technology now yield high quality high spatial resolution imaging data in single subjects and an inference approach that retains the benefits of greater spatial resolution is called for. In this work, we present a new CST with a correction for voxelation to address this problem. The theoretical formulation of the new approach based on Gaussian random fields is developed to estimate statistical significance using 3D statistical parametric maps without assuming spatial smoothness. Simulated phantom and resting-state fMRI experimental data are then used to compare the voxelation-corrected procedure to the widely used standard random field theory. Unlike standard random field theory approaches, which require heavy spatial smoothing, the new approach has a higher sensitivity for localizing activation regions without the requirement of spatial smoothness.
Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Huanjie
H
Li
McLean Imaging Center, McLean Hospital/Harvard Medical School, Belmont, MA, USA.
Lisa D
LD
Nickerson
Department of Radiology, University of Iowa, Iowa City, IA, USA.
Jinhu
J
Xiong
Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Qihong
Q
Zou
Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Yang
Y
Fan
Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Yajun
Y
Ma
College of Medicine, University of Illinois, Chicago, IL, USA.
Tingqi
T
Shi
Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Jianqiao
J
Ge
Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China; McGovern Institute for Brain Research, Peking University, Beijing, China. Electronic address: jgao@pku.edu.cn.
Jia-Hong
JH
Gao
D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical
3D cluster analysis; Gaussian field; Smoothness