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首页> 外文期刊>Psychiatry Research. Neuroimaging >Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?
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Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

机译：精神分裂症脑年龄预测：机器学习算法的选择是否存在？

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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.
机译：Brain predicted age difference（brainPAD）已用于精神分裂症患者，以评估患者生物年龄的个体水平偏差？大脑（即大脑年龄）来自标准参考大脑结构数据集。精神分裂症患者的大脑垫存在显著的研究间差异，这通常归因于样本异质性。然而，用于大脑年龄估计的不同机器学习算法的潜在贡献尚未得到系统评估。在这里，我们的目的是评估六种常用算法（普通最小二乘回归、岭回归、最小绝对收缩和选择算子回归、弹性净回归、线性支持向量回归和相关向量回归）在应用于同一样本的相同脑结构特征时估计的脑年龄变化。为了评估再现性，我们使用了两个公开的健康个体样本（n=1092和n=492）和另外两个样本的数据，分别来自西奈山伊坎医学院（ISMMS）和生物医学研究卓越中心（COBRE），包括精神分裂症患者（n=90和n=76）和健康个体（n=200和n=87）。使用相关分析和层次聚类法，在每个样本中比较不同算法的性能相似性。在所有样本中，唯一没有惩罚项的算法普通最小二乘回归的表现明显较差。所有其他算法都显示出类似的性能，但尽管应用于相同的数据，它们仍然产生了不同的脑年龄估计值。尽管精神分裂症患者的大脑垫始终较高，但在ISMMS样本中，大脑垫的使用时间从3.8年到5.2年不等，在COBRE样本中，大脑垫的使用时间从4.5年到11.7年不等。算法选择会导致大脑年龄的变化，在评估精神分裂症患者的大脑垫时，可能会混淆研究间的比较。

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