Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?. (30th April 2021)

• Record Type: Journal Article
• Authors: Lee, Won Hee; Antoniades, Mathilde; Schnack, Hugo G; Kahn, Rene S.; Frangou, Sophia
• Is Part Of: Psychiatry research. Volume 310(2021)
• Publication Date: 2021-04-30
• Subjects: Brain age prediction -- Schizophrenia -- Structural MRI -- Machine learning -- Regression; Psychiatry -- Periodicals; Brain -- Imaging -- Periodicals; Psychiatry -- Periodicals; Diagnostic Imaging -- Periodicals; Psychiatrie -- Périodiques; Cerveau -- Imagerie pour le diagnostic -- Périodiques; 616.890754
• DOI: 10.1016/j.pscychresns.2021.111270
• Languages: English
• ISSNs: 0925-4927

Abstract

Highlights: Different machine learning algorithms have been used to compute brain-predicted-age-difference (brainPAD) in schizophrenia. Inter-study BrainPAD variability is typically attributed to schizophrenia-related heterogeneity rather than the algorithms used. Comparison of 6 different algorithms in 4 independent samples yielded consistent differences in their performance. BrainPAD estimates in the same sample of patients with schizophrenia differed by up to 10 years depending on the algorithm used. Abstract: Brain-predicted age difference (brainPAD) has been used in schizophrenia to assess individual-level deviation in the biological age of the patients' brain (i.e., brain-age) from normative reference brain structural datasets. There is marked inter-study variation in brainPAD in schizophrenia which is commonly attributed to sample heterogeneity. However, the potential contribution of the different machine learning algorithms used for brain-age estimation has not been systematically evaluated. Here, we aimed to assess variation in brain-age estimated by six commonly used algorithms [ordinary least squares regression, ridge regression, least absolute shrinkage and selection operator regression, elastic-net regression, linear support vector regression, and relevance vector regression] when applied to the same brain structural features from the same sample. To assess reproducibility we used data from two publically available samples of healthy individuals ( n = 1092 and n = 492) and two further samples, from the Icahn School of Medicine at Mount Sinai (ISMMS) and the Center of Biomedical Research Excellence (COBRE), comprising both patients with schizophrenia ( n = 90 and n = 76) and healthy individuals ( n = 200 and n = 87). Performance similarity across algorithms was compared within each sample using correlation analyses and hierarchical clustering. Across all samples ordinary least squares regression, the only algorithm without a penalty term, performed markedly worse. All other algorithms showed comparable performance but they still yielded variable brain-age estimates despite being applied to the same data. Although brainPAD was consistently higher in patients with schizophrenia, it varied by algorithm from 3.8 to 5.2 years in the ISMMS sample and from to 4.5 to 11.7 years in the COBRE sample. Algorithm choice introduces variations in brain-age and may confound inter-study comparisons when assessing brainPAD in schizophrenia.