Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms

Neuroimaging: Brain connectivity pattern predicts symptom severity Brain network analyses from functional magnetic resonance imaging (fMRI) data may help diagnose schizophrenia and predict symptom severity. Detecting neuroimaging patterns requires large-scale analysis across multiple data sets. Mina...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mina Gheiratmand, Irina Rish, Guillermo A. Cecchi, Matthew R. G. Brown, Russell Greiner, Pablo I. Polosecki, Pouya Bashivan, Andrew J. Greenshaw, Rajamannar Ramasubbu, Serdar M. Dursun
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Psychiatry RC435-571
Acceso en línea:	https://doaj.org/article/c2d7e64345e8436c975666a48119346f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c2d7e64345e8436c975666a48119346f
record_format	dspace
spelling	oai:doaj.org-article:c2d7e64345e8436c975666a48119346f2021-12-02T16:19:50ZLearning stable and predictive network-based patterns of schizophrenia and its clinical symptoms10.1038/s41537-017-0022-82334-265Xhttps://doaj.org/article/c2d7e64345e8436c975666a48119346f2017-05-01T00:00:00Zhttps://doi.org/10.1038/s41537-017-0022-8https://doaj.org/toc/2334-265XNeuroimaging: Brain connectivity pattern predicts symptom severity Brain network analyses from functional magnetic resonance imaging (fMRI) data may help diagnose schizophrenia and predict symptom severity. Detecting neuroimaging patterns requires large-scale analysis across multiple data sets. Mina Gheiratmand and colleagues from the University of Alberta, along with researchers at the IBM T.J. Watson Research Center analyzed brain imaging data from the Function Biomedical Informatics Research Network, a study designed to test the reproducibility of brain scan results taken on different fMRI machines from people with schizophrenia and schizoaffective disorders, as well as healthy controls. They studied brain networks at different levels of resolution from data gathered while study participants conducted a common auditory test. The researchers showed that they could discriminate between patients with schizophrenia and controls with 74% accuracy across multiple neuroimaging sites using the strength of connection in a functional network. They observed the most robust and discriminative connectivity differences between the thalamus and primary motor and sensory cortices as well as between the precuneus and other brain regions. Moreover, they could determine symptom severity based on the connectivity changes involving these areas. This new approach towards finding objective, reliable neuroimaging biomarkers for schizophrenia and its severity could be used for diagnosis and to assess disease progression and therapeutic efficacy.Mina GheiratmandIrina RishGuillermo A. CecchiMatthew R. G. BrownRussell GreinerPablo I. PoloseckiPouya BashivanAndrew J. GreenshawRajamannar RamasubbuSerdar M. DursunNature PortfolioarticlePsychiatryRC435-571ENnpj Schizophrenia, Vol 3, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Psychiatry RC435-571
spellingShingle	Psychiatry RC435-571 Mina Gheiratmand Irina Rish Guillermo A. Cecchi Matthew R. G. Brown Russell Greiner Pablo I. Polosecki Pouya Bashivan Andrew J. Greenshaw Rajamannar Ramasubbu Serdar M. Dursun Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms
description	Neuroimaging: Brain connectivity pattern predicts symptom severity Brain network analyses from functional magnetic resonance imaging (fMRI) data may help diagnose schizophrenia and predict symptom severity. Detecting neuroimaging patterns requires large-scale analysis across multiple data sets. Mina Gheiratmand and colleagues from the University of Alberta, along with researchers at the IBM T.J. Watson Research Center analyzed brain imaging data from the Function Biomedical Informatics Research Network, a study designed to test the reproducibility of brain scan results taken on different fMRI machines from people with schizophrenia and schizoaffective disorders, as well as healthy controls. They studied brain networks at different levels of resolution from data gathered while study participants conducted a common auditory test. The researchers showed that they could discriminate between patients with schizophrenia and controls with 74% accuracy across multiple neuroimaging sites using the strength of connection in a functional network. They observed the most robust and discriminative connectivity differences between the thalamus and primary motor and sensory cortices as well as between the precuneus and other brain regions. Moreover, they could determine symptom severity based on the connectivity changes involving these areas. This new approach towards finding objective, reliable neuroimaging biomarkers for schizophrenia and its severity could be used for diagnosis and to assess disease progression and therapeutic efficacy.
format	article
author	Mina Gheiratmand Irina Rish Guillermo A. Cecchi Matthew R. G. Brown Russell Greiner Pablo I. Polosecki Pouya Bashivan Andrew J. Greenshaw Rajamannar Ramasubbu Serdar M. Dursun
author_facet	Mina Gheiratmand Irina Rish Guillermo A. Cecchi Matthew R. G. Brown Russell Greiner Pablo I. Polosecki Pouya Bashivan Andrew J. Greenshaw Rajamannar Ramasubbu Serdar M. Dursun
author_sort	Mina Gheiratmand
title	Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms
title_short	Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms
title_full	Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms
title_fullStr	Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms
title_full_unstemmed	Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms
title_sort	learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/c2d7e64345e8436c975666a48119346f
work_keys_str_mv	AT minagheiratmand learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms AT irinarish learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms AT guillermoacecchi learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms AT matthewrgbrown learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms AT russellgreiner learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms AT pabloipolosecki learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms AT pouyabashivan learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms AT andrewjgreenshaw learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms AT rajamannarramasubbu learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms AT serdarmdursun learningstableandpredictivenetworkbasedpatternsofschizophreniaanditsclinicalsymptoms
_version_	1718384197852200960

Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms

Ejemplares similares