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Jiayu Chen
The Mind Research Network
PsychologyPattern recognitionComputer scienceIndependent component analysisSchizophrenia
67Publications
16H-index
1,033Citations
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Publications 67
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#1Jiayu ChenH-Index: 16
#2Gunter SchumannH-Index: 51
Last. Jingyu LiuH-Index: 30
view all 9 authors...
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#1Kuaikuai DuanH-Index: 1
#2Jiayu ChenH-Index: 16
Last. Jingyu LiuH-Index: 30
view all 11 authors...
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#1Jason P. WeickH-Index: 14
#2Amber ZimmermanH-Index: 1
Last. Nikolaos MelliosH-Index: 11
view all 20 authors...
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#1Qingbao Yu (The Mind Research Network)H-Index: 20
#2Jiayu Chen (The Mind Research Network)H-Index: 16
Last. Vince D. Calhoun (Yale University)H-Index: 93
view all 17 authors...
Abstract It has been widely shown that genomic factors influence both risk for schizophrenia and variation in functional brain connectivity. Moreover, schizophrenia is characterized by disrupted brain connectivity. In this work, we proposed a genome-connectome bipartite graph model to perform imaging genomic analysis. Functional network connectivity (FNC) was estimated after decomposing resting state functional magnetic resonance imaging data from both healthy controls (HC) and patients with sch...
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#1Jiayu Chen (The Mind Research Network)H-Index: 16
#2Jingyu Liu (The Mind Research Network)H-Index: 30
Last. Vince D. Calhoun (The Mind Research Network)H-Index: 93
view all 3 authors...
Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives: 1) toward reliab...
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#1Zening Fu (The Mind Research Network)H-Index: 8
#2Arvind Caprihan (The Mind Research Network)H-Index: 30
Last. Vince D. Calhoun (UNM: University of New Mexico)H-Index: 93
view all 8 authors...
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#1Kuaikuai Duan (UNM: University of New Mexico)H-Index: 1
#2Rogers F. Silva (The Mind Research Network)H-Index: 9
Last. Jingyu Liu (UNM: University of New Mexico)H-Index: 30
view all 6 authors...
Independent component analysis has been widely applied to brain imaging and genetic data analyses for its ability to identify interpretable latent sources. Nevertheless, leveraging source sparsity in a more granular way may further improve its ability to optimize the solution for certain data types. For this purpose, we propose a sparse infomax algorithm based on nonlinear Hoyer projection, leveraging both sparsity and statistical independence of latent sources. The proposed algorithm iterativel...
1 CitationsSource
#1Wenhao JiangH-Index: 1
#2Kuaikuai DuanH-Index: 1
Last. Jessica A. TurnerH-Index: 53
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#1Mustafa S. Salman (UNM: University of New Mexico)H-Index: 3
#2Yuhui Du (Shanxi University)H-Index: 15
Last. Vince D. Calhoun (UNM: University of New Mexico)H-Index: 93
view all 14 authors...
Abstract Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, s...
3 CitationsSource
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