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Connectome-scale functional intrinsic connectivity networks in macaques
Wei Zhang1, Xi Jiang1, Shu Zhang1
1Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Neuroscience
|August 27, 2017
Summary
This study introduces a new computational framework to identify intrinsic connectivity networks (ICNs) in macaque brains using resting-state functional MRI (fMRI). Researchers successfully mapped 70 consistent ICNs, expanding our understanding of macaque brain organization.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Brain Imaging
Background:
- Extensive research exists on human intrinsic connectivity networks (ICNs) using resting-state functional MRI (fMRI).
- The functional organization of ICNs in macaque brains remains less explored, despite increasing scientific interest.
Purpose of the Study:
- To develop a computational framework for identifying connectome-scale, group-wise consistent ICNs in macaque brains.
- To expand the known repertoire of macaque ICNs and provide a foundation for future neuroscience and brain-mapping studies.
Main Methods:
- Utilized sparse representation of whole-brain resting-state fMRI data.
- Developed a novel computational framework for macaque ICN identification.
Main Results:
- Successfully identified 70 group-wise consistent ICNs in macaque brains.
- Interpreted the identified ICNs using two publicly available macaque brain parcellation maps.
- Significantly expanded the number of known macaque ICNs compared to previous literature.
Conclusions:
- The proposed framework effectively identifies connectome-scale ICNs in macaque brains.
- The discovered ICNs offer a valuable resource for neuroscience research, brain mapping, and understanding brain evolution.
