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Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks.
Chan, Yi Hao; Yew, Wei Chee; Chew, Qian Hui; Sim, Kang; Rajapakse, Jagath C.
Afiliação
  • Chan YH; School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
  • Yew WC; School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
  • Chew QH; Research Division, Institute of Mental Health (IMH), Singapore, Singapore.
  • Sim K; Research Division, Institute of Mental Health (IMH), Singapore, Singapore.
  • Rajapakse JC; West Region, Institute of Mental Health (IMH), Singapore, Singapore.
Sci Rep ; 13(1): 21047, 2023 11 29.
Article em En | MEDLINE | ID: mdl-38030699
Schizophrenia is a highly heterogeneous disorder and salient functional connectivity (FC) features have been observed to vary across study sites, warranting the need for methods that can differentiate between site-invariant FC biomarkers and site-specific salient FC features. We propose a technique named Semi-supervised learning with data HaRmonisation via Encoder-Decoder-classifier (SHRED) to examine these features from resting state functional magnetic resonance imaging scans gathered from four sites. Our approach involves an encoder-decoder-classifier architecture that simultaneously performs data harmonisation and semi-supervised learning (SSL) to deal with site differences and labelling inconsistencies across sites respectively. The minimisation of reconstruction loss from SSL was shown to improve model performance even within small datasets whilst data harmonisation often led to lower model generalisability, which was unaffected using the SHRED technique. We show that our proposed model produces site-invariant biomarkers, most notably the connection between transverse temporal gyrus and paracentral lobule. Site-specific salient FC features were also elucidated, especially implicating the paracentral lobule for our local dataset. Our examination of these salient FC features demonstrates how site-specific features and site-invariant biomarkers can be differentiated, which can deepen our understanding of the neurobiology of schizophrenia.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Esquizofrenia Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Esquizofrenia Idioma: En Ano de publicação: 2023 Tipo de documento: Article