CONNECTOME-SCALE FUNCTIONAL INTRINSIC CONNECTIVITY NETWORKS IN MACAQUES

• Authors: Wei Zhang, University of Georgia; Xi Jiang, University of Georgia; Shu Zhang, University of Georgia; Brittany R Howell, Emory University; Yu Zhao, University of Georgia; Tuo Zhang, University of Georgia; Lei Guo, Northwestern Polytechnical University; Maria Sanchez, Emory University; Xiaoping Hu, Emory University; Tianming Liu, University of Georgia
• Journal Title: Neuroscience
• Volume: Volume 364
• Publisher: Elsevier: 12 months | 2017-11-19, Pages 1-14
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.neuroscience.2017.08.022
• Subjects: Biology, Neuroscience
• Keywords: Science & Technology; Life Sciences & Biomedicine; Neurosciences; Neurosciences & Neurology; brain function; intrinsic connectivity network; rhesus monkey; resting-state fMRI; RESTING-STATE NETWORKS; VISUAL-CORTEX; INTRAPARIETAL SULCUS; RHESUS-MONKEY; HUMAN BRAIN; SPARSE REPRESENTATION; TEMPORAL CORTEX; PARIETAL CORTEX; CEREBRAL-CORTEX; DEFAULT MODE

Abstract

There have been extensive studies of intrinsic connectivity networks (ICNs) in the human brains using resting-state functional magnetic resonance imaging (fMRI) in the literature. However, the functional organization of ICNs in macaque brains has been less explored so far, despite growing interests in the field. In this work, we propose a computational framework to identify connectome-scale group-wise consistent ICNs in macaques via sparse representation of whole-brain resting-state fMRI data. Experimental results demonstrate that 70 group-wise consistent ICNs are successfully identified in macaque brains via the proposed framework. These 70 ICNs are interpreted based on two publicly available parcellation maps of macaque brains and our work significantly expand currently known macaque ICNs already reported in the literature. In general, this set of connectome-scale group-wise consistent ICNs can potentially benefit a variety of studies in the neuroscience and brain-mapping fields, and they provide a foundation to better understand brain evolution in the future.