Cited 7 times in

Geometric learning of functional brain network on the correlation manifold

Authors
 Kisung You  ;  Hae-Jeong Park 
Citation
 SCIENTIFIC REPORTS, Vol.12(1) : 17752, 2022-10 
Journal Title
SCIENTIFIC REPORTS
Issue Date
2022-10
MeSH
Algorithms* ; Brain* / diagnostic imaging ; Computer Simulation
Abstract
The correlation matrix is a typical representation of node interactions in functional brain network analysis. The analysis of the correlation matrix to characterize brain networks observed in several neuroimaging modalities has been conducted predominantly in the Euclidean space by assuming that pairwise interactions are mutually independent. One way to take account of all interactions in the network as a whole is to analyze the correlation matrix under some geometric structure. Recent studies have focused on the space of correlation matrices as a strict subset of symmetric positive definite (SPD) matrices, which form a unique mathematical structure known as the Riemannian manifold. However, mathematical operations of the correlation matrix under the SPD geometry may not necessarily be coherent (i.e., the structure of the correlation matrix may not be preserved), necessitating a post-hoc normalization. The contribution of the current paper is twofold: (1) to devise a set of inferential methods on the correlation manifold and (2) to demonstrate its applicability in functional network analysis. We present several algorithms on the correlation manifold, including measures of central tendency, cluster analysis, hypothesis testing, and low-dimensional embedding. Simulation and real data analysis support the application of the proposed framework for brain network analysis.
Files in This Item:
T202204848.pdf Download
DOI
10.1038/s41598-022-21376-0
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers
Yonsei Authors
Park, Hae Jeong(박해정) ORCID logo https://orcid.org/0000-0002-4633-0756
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/192259
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links