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. 2021 Apr 30:310:111270.
doi: 10.1016/j.pscychresns.2021.111270. Epub 2021 Mar 5.

Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

Won Hee Lee  1 , Mathilde Antoniades  2 , Hugo G Schnack  3 , Rene S Kahn  2 , Sophia Frangou  4
Affiliations

Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

Won Hee Lee et al. Psychiatry Res Neuroimaging. .

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.

Keywords: Brain age prediction; Machine learning; Regression; Schizophrenia; Structural MRI.

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Conflict of interest statement

Conflict of interest: The authors have declared that there are no conflicts of interest in relation to the subject of this study.

Figures

Figure 1.
Figure 1.. Similarity in predicted brain age in healthy individuals in the HCP and Cam-CAN samples across algorithms.
(A) HCP: Similarity matrix representing between-algorithm correlations of individual predicted brain-ages in healthy individuals; (B) HCP: Distance matrix and dendrogram resulting from hierarchical clustering of the individual brain-age results of the six algorithms; (C) Cam-CAN: Similarity matrix representing between-algorithm correlations of individual predicted brain-ages in healthy individuals; (D) Cam-CAN: Distance matrix and dendrogram resulting from hierarchical clustering of the individual brain-age results of the six algorithms. OLS: Ordinary least squares regression; Lasso: Least absolute shrinkage and selection operator; SVR: Support vector regression; RVR: Relevance vector regression; HCP: Human Connectome Project; Cam-CAN: Cambridge Centre for Ageing and Neuroscience Project.
Figure 2.
Figure 2.. Similarity in predicted brain age in healthy individuals in the ISMMS and COBRE samples across algorithms.
(A) ISMMS: Similarity matrix representing between-algorithm correlations of individual predicted brain-ages in healthy individuals; (B) ISMMS: Distance matrix and dendrogram resulting from hierarchical clustering of the individual brain-age results of the six algorithms in healthy individuals; (C) COBRE: Similarity matrix representing between-algorithm correlations of individual predicted brain-ages in healthy individuals; (D) COBRE: Distance matrix and dendrogram resulting from hierarchical clustering of the individual brain-age results of the six algorithms in healthy individuals;. OLS: Ordinary least squares regression; Lasso: Least absolute shrinkage and selection operator; SVR: Support vector regression; RVR: Relevance vector regression. COBRE: Center of Biomedical Research Excellence; ISMMS: Icahn School of Medicine at Mount Sinai.
Figure 3.
Figure 3.. Brain-predicted age difference the ISMMS and COBRE samples
Violin plots showing the distribution of individual brain-predicted age difference (brainPAD) scores in patients with schizophrenia in the ISMMS sample (top panel) and the COBRE sample (lower panel). Horizontal line within each violin plot represents the mean and the white circle the median values. OLS: Ordinary least squares regression; Lasso: Least absolute shrinkage and selection operator; SVR: Support vector regression; RVR: Relevance vector regression. COBRE: Center of Biomedical Research Excellence; ISMMS: Icahn School of Medicine at Mount Sinai.
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