An automated method for identifying an independent componentanalysis-based language-related resting-state network in brain tumorsubjects for surgical planning

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/474530.do
DOI
10.1038/s41598-017-14248-5
Title
An automated method for identifying an independent componentanalysis-based language-related resting-state network in brain tumorsubjects for surgical planning
Description
The authors would like to thank our neuropsychologist (Yan Zhou) for thelanguage assessment; the nurses (Qiuyue Wu, Chunmei Li, Ye Wang) fortheir contribution; the MRI technician (Zhong Yang) for collecting thedata, and Jianbing Shi, Geng Xu for technical support in neuronavigationand electrophysiological monitoring. This work was supported by theNational Natural Science Foundation of China (No. 81401395) and theNational Key Technology R&D Program of China (No. 2014BAI04B05). Thiswork was supported in part by National Institutes of Health (NIH) grants(EB022880, AG041721, AG049371, AG042599).
abstract
As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.
provenance
Made available in Cube on 2018-09-28T10:48:29Z (GMT). No. of bitstreams: 0
language
English
author
Lu, Junfeng
Zhang, Han
Hameed, N. U. Farrukh
Zhang, Jie
Yuan, Shiwen
Qiu, Tianming
Shen, Dinggang
Wu, Jinsong
orcid
Hameed, N. U. Farrukh/0000-0001-6112-6238
accessioned
2018-09-28T10:48:29Z
available
2018-09-28T10:48:29Z
issued
2017
citation
SCIENTIFIC REPORTS(7)
issn
2045-2322
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/474530.do
Funder
교육부
Funding Program
BK21플러스사업(0.5)
Project ID
1345273936
Jurisdiction
Rep.of Korea
Project Name
Global Leader Development Division in Brain Engineering
rights
openAccess
type
article


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