Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review

Renato de Filippis,1,* Elvira Anna Carbone,1,* Raffaele Gaetano,1 Antonella Bruni,1 Valentina Pugliese,1 Cristina Segura-Garcia,2 Pasquale De Fazio11Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy; 2Department of Medical and Surgical Sciences, University...

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Autores principales:	de Filippis R, Carbone EA, Gaetano R, Bruni A, Pugliese V, Segura-Garcia C, De Fazio P
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2019
Materias:	machine learning schizophrenia support vector machine resting-state fMRI sMRI multivariate pattern analysis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/e96672ad6d604000957d1df2aa3be27c
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spelling	oai:doaj.org-article:e96672ad6d604000957d1df2aa3be27c2021-12-02T04:34:51ZMachine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review1178-2021https://doaj.org/article/e96672ad6d604000957d1df2aa3be27c2019-06-01T00:00:00Zhttps://www.dovepress.com/machine-learning-techniques-in-a-structural-and-functional-mri-diagnos-peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Renato de Filippis,1,* Elvira Anna Carbone,1,* Raffaele Gaetano,1 Antonella Bruni,1 Valentina Pugliese,1 Cristina Segura-Garcia,2 Pasquale De Fazio11Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy; 2Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy*These authors contributed equally to this work Background: Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders. Objectives: A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls. Methods: We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75–90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors. Results: Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network). Conclusion: The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.Keywords: machine learning, schizophrenia, support vector machine, resting-state fMRI, sMRI, multivariate pattern analysisde Filippis RCarbone EAGaetano RBruni APugliese VSegura-Garcia CDe Fazio PDove Medical Pressarticlemachine learningschizophreniasupport vector machineresting-state fMRIsMRImultivariate pattern analysisNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 15, Pp 1605-1627 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning schizophrenia support vector machine resting-state fMRI sMRI multivariate pattern analysis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	machine learning schizophrenia support vector machine resting-state fMRI sMRI multivariate pattern analysis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 de Filippis R Carbone EA Gaetano R Bruni A Pugliese V Segura-Garcia C De Fazio P Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review
description	Renato de Filippis,1,* Elvira Anna Carbone,1,* Raffaele Gaetano,1 Antonella Bruni,1 Valentina Pugliese,1 Cristina Segura-Garcia,2 Pasquale De Fazio11Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy; 2Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy*These authors contributed equally to this work Background: Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders. Objectives: A systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls. Methods: We systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75–90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors. Results: Support vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network). Conclusion: The greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.Keywords: machine learning, schizophrenia, support vector machine, resting-state fMRI, sMRI, multivariate pattern analysis
format	article
author	de Filippis R Carbone EA Gaetano R Bruni A Pugliese V Segura-Garcia C De Fazio P
author_facet	de Filippis R Carbone EA Gaetano R Bruni A Pugliese V Segura-Garcia C De Fazio P
author_sort	de Filippis R
title	Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review
title_short	Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review
title_full	Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review
title_fullStr	Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review
title_full_unstemmed	Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review
title_sort	machine learning techniques in a structural and functional mri diagnostic approach in schizophrenia: a systematic review
publisher	Dove Medical Press
publishDate	2019
url	https://doaj.org/article/e96672ad6d604000957d1df2aa3be27c
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Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review

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