BACKGROUND: Atlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently, it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous, in particular for understanding how different regions share information. However, functional atlases so far proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that each region in one hemisphere has a homologue in the other hemisphere. NEW METHOD: We present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1-homogeneity of its constituting voxels intrinsic activity, and 2-a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in 4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independent components, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopic regional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling. RESULTS: AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 gray nuclei). As an application, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity patterns at different sparsities. COMPARISON WITH EXISTING METHOD: ROI functional homogeneity was higher for AICHA than for anatomical ROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy. CONCLUSION: AICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigating brain hemispheric specialization.
BACKGROUND: Atlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently, it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous, in particular for understanding how different regions share information. However, functional atlases so far proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that each region in one hemisphere has a homologue in the other hemisphere. NEW METHOD: We present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1-homogeneity of its constituting voxels intrinsic activity, and 2-a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in 4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independent components, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopic regional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling. RESULTS: AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 gray nuclei). As an application, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity patterns at different sparsities. COMPARISON WITH EXISTING METHOD: ROI functional homogeneity was higher for AICHA than for anatomical ROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy. CONCLUSION: AICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigating brain hemispheric specialization.
Authors: Gaelle E Doucet; Xiaosong He; Michael R Sperling; Ashwini Sharan; Joseph I Tracy Journal: Hum Brain Mapp Date: 2017-02-14 Impact factor: 5.038
Authors: Kevin Kim; Luke Adams; Lynsey M Keator; Shannon M Sheppard; Bonnie L Breining; Chris Rorden; Julius Fridriksson; Leonardo Bonilha; Corianne Rogalsky; Tracy Love; Gregory Hickok; Argye E Hillis Journal: Brain Lang Date: 2019-08-20 Impact factor: 2.381
Authors: Andreas Horn; Ningfei Li; Till A Dembek; Ari Kappel; Chadwick Boulay; Siobhan Ewert; Anna Tietze; Andreas Husch; Thushara Perera; Wolf-Julian Neumann; Marco Reisert; Hang Si; Robert Oostenveld; Christopher Rorden; Fang-Cheng Yeh; Qianqian Fang; Todd M Herrington; Johannes Vorwerk; Andrea A Kühn Journal: Neuroimage Date: 2018-09-01 Impact factor: 6.556