title: Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation.
abstract: Parcellation of the human brain into fine-grained units by grouping voxels into distinct clusters has been an effective approach for delineating specific brain regions and their subregions. Published neuroimaging studies employing coordinate-based meta-analyses have shown that the activation foci and their corresponding behavioral categories may contain useful information about the anatomical-functional organization of brain regions. Inspired by these developments, we proposed a new parcellation scheme called meta-analytic activation modeling-based parcellation (MAMP) that uses meta-analytically obtained information. The raw meta data, including the experiments and the reported activation coordinates related to a brain region of interest, were acquired from the Brainmap database. Using this data, we first obtained the "modeled activation" pattern by modeling the voxel-wise activation probability given spatial uncertainty for each experiment that featured at least one focus within the region of interest. Then, we processed these "modeled activation" patterns across the experiments with a K-means clustering algorithm to group the voxels into different subregions. In order to verify the reliability of the method, we employed our method to parcellate the amygdala and the left Brodmann area 44 (BA44). The parcellation results were quite consistent with previous cytoarchitectonic and in vivo neuroimaging findings. Therefore, the MAMP proposed in the current study could be a useful complement to other methods for uncovering the functional organization of the human brain.
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China.
authors: Yong
authors: Y
authors: Yang
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China.
authors: Lingzhong
authors: L
authors: Fan
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China.
authors: Congying
authors: C
authors: Chu
authors: Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, PR China.
authors: Junjie
authors: J
authors: Zhuo
authors: Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, PR China.
authors: Jiaojian
authors: J
authors: Wang
authors: Research Imaging Institute, University of Texas Health Science Center at San Antonio, TX, USA.
authors: Peter T
authors: PT
authors: Fox
authors: Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, 52425 Juelich, Germany.|Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany.
authors: Simon B
authors: SB
authors: Eickhoff
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China.|CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China.|Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, PR China.|The Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia.
authors: Tianzi
authors: T
authors: Jiang
mesh_terms: D000679:Amygdala / Q000033:anatomy & histology / Q000502:physiology; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D016208:Databases, Factual; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015201:Meta-Analysis as Topic; D015203:Reproducibility of Results
keywords: Activation modeling; Behavior domain; Meta-analysis; Neuroimaging; Parcellation