Journal
CEREBRAL CORTEX
Volume 29, Issue 10, Pages 4238-4252Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhy305
Keywords
cerebral cortex; convolutional neural network; gyri; sulci; wavelet entropy
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Funding
- China National Science Foundation (NSFC) [61 703 073]
- China Special Fund for Basic Scientific Research of Central Colleges [ZYGX2017KYQD165]
- National Institutes of Health [DA-033393, AG-042599]
- National Science Foundation (NSF CAREER Award) [IIS-1149260, CBET-1302089, BCS-1439051, DBI-1564736]
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The human cerebral cortex is highly folded into diverse gyri and sulci. Accumulating evidences suggest that gyri and sulci exhibit anatomical, morphological, and connectional differences. Inspired by these evidences, we performed a series of experiments to explore the frequency-specific differences between gyral and sulcal neural activities from resting-state and task-based functional magnetic resonance imaging (fMRI) data. Specifically, we designed a convolutional neural network (CNN) based classifier, which can differentiate gyral and sulcal fMRI signals with reasonable accuracies. Further investigations of learned CNN models imply that sulcal fMRI signals are more diverse and more high frequency than gyral signals, suggesting that gyri and sulci truly play different functional roles. These differences are significantly associated with axonal fiber wiring and cortical thickness patterns, suggesting that these differences might be deeply rooted in their structural and cellular underpinnings. Further wavelet entropy analyses demonstrated the validity of CNN-based findings. In general, our collective observations support a new concept that the cerebral cortex is bisectionally segregated into 2 functionally different units of gyri and sulci.
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