Literature DB >> 35332916

Modeling functional difference between gyri and sulci within intrinsic connectivity networks.

Qiyu Wang1, Shijie Zhao1, Zhibin He1, Shu Zhang2, Xi Jiang3, Tuo Zhang1, Tianming Liu4, Cirong Liu5, Junwei Han1.   

Abstract

Recently, the functional roles of the human cortical folding patterns have attracted increasing interest in the neuroimaging community. However, most existing studies have focused on the gyro-sulcal functional relationship on a whole-brain scale but possibly overlooked the localized and subtle functional differences of brain networks. Actually, accumulating evidences suggest that functional brain networks are the basic unit to realize the brain function; thus, the functional relationships between gyri and sulci still need to be further explored within different functional brain networks. Inspired by these evidences, we proposed a novel intrinsic connectivity network (ICN)-guided pooling-trimmed convolutional neural network (I-ptFCN) to revisit the functional difference between gyri and sulci. By testing the proposed model on the task functional magnetic resonance imaging (fMRI) datasets of the Human Connectome Project, we found that the classification accuracy of gyral and sulcal fMRI signals varied significantly for different ICNs, indicating functional heterogeneity of cortical folding patterns in different brain networks. The heterogeneity may be contributed by sulci, as only sulcal signals show heterogeneous frequency features across different ICNs, whereas the frequency features of gyri are homogeneous. These results offer novel insights into the functional difference between gyri and sulci and enlighten the functional roles of cortical folding patterns.
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  cerebral cortex; cortical folding complexity; fully convolutional neural network; functional brain networks

Year:  2022        PMID: 35332916     DOI: 10.1093/cercor/bhac111

Source DB:  PubMed          Journal:  Cereb Cortex        ISSN: 1047-3211            Impact factor:   5.357


  1 in total

1.  The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification.

Authors:  Ke Liu; Qing Li; Li Yao; Xiaojuan Guo
Journal:  Front Neurosci       Date:  2022-06-03       Impact factor: 5.152

  1 in total

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