Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence

Autor(es):Mostrar menos -

Vieira, Sandra ^[1] ; Gong, Qi-Yong ^{[2, 3]} ; Pinaya, Walter H. L. ^{[4, 1]} ; Scarpazza, Cristina ^{[1, 5]} ; Tognin, Stefania ^[1] ; Crespo-Facorro, Benedicto ^{[6, 7]} ; Tordesillas-Gutierrez, Diana ^{[6, 8]} ; Ortiz-Garcia, Victor ^{[6, 7]} ; Setien-Suero, Esther ^{[6, 7]} ; Scheepers, Floortje E. ^[9] ; Van Haren, Neeltje E. M. ^[10] ; Marques, Tiago R. ^[1] ; Murray, Robin M. ^[1] ; David, Anthony ^[1] ; Dazzan, Paola ^[1] ; McGuire, Philip ^[1] ; Mechelli, Andrea ^[1]

Afiliação do(s) autor(es)

^[1] Kings Coll London, Inst Psychiat, Dept Psychosis Studies, London - England

^[2] Sichuan Univ, HMRRC, Dept Radiol, West China Hosp, Chengdu 610041 - Peoples R China

^[3] Chengdu Mental Hlth Ctr, Dept Psychoradiol, Chengdu - Peoples R China

^[4] Univ Fed ABC, Ctr Math Computat & Cognit, Sao Paulo - Brazil

^[5] Univ Padua, Dept Gen Psychol, Padua - Italy

^[6] Ctr Invest Biomed Red Salud Mental CIBERSAM, Madrid - Spain

^[7] Univ Cantabria IDIVAL, Univ Hosp Marques de Valdecilla, Sch Med, Dept Psychiat, Santander - Spain

^[8] Valdecilla Biomed Res Inst IDIVAL, Neuroimaging Unit, Technol Facil, Santander - Spain

^[9] Univ Med Ctr Utrecht, Dept Psychiat, Utrecht - Netherlands

^[10] Univ Med Ctr Utrecht, Brain Ctr Rudolf Magnus, Utrecht - Netherlands

• Tipo de documento: Artigo Científico
• Fonte: SCHIZOPHRENIA BULLETIN; v. 46, n. 1, p. 17-26, JAN 2020.
• Citações Web of Science: 6

Resumo

Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation. (AU)

• Processo FAPESP: 13/05168-7 - Desenvolvimento de Deep Belief Networks voltadas para o suporte ao diagnóstico de transtornos psiquiátricos
• Beneficiário: Walter Hugo Lopez Pinaya
• Modalidade de apoio: Bolsas no Brasil - Doutorado