Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?
Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?
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DOI：
10.1016/j.pscychresns.2021.111270
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发表时间：
2021-04-30
期刊：
影响因子：
--
通讯作者：
Frangou S
中科院分区：
文献类型：
--
作者：
Lee WH;Antoniades M;Schnack HG;Kahn RS;Frangou S
脑预测年龄差（Brain-predicted age difference，brainPAD）已被用于精神分裂症，以评估患者大脑生物学年龄的个体水平偏差（即，脑年龄）。精神分裂症患者脑PAD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，我们旨在评估六种常用算法[普通最小二乘回归，岭回归，最小绝对收缩和选择算子回归，弹性网络回归，线性支持向量回归和相关向量回归]在应用于相同样本的相同大脑结构特征时估计的脑年龄变化。为了评估可重复性，我们使用了来自两个临床可用的健康个体样本（n=1092和n=492）和另外两个样本的数据，这些样本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）。使用相关性分析和分层聚类来比较每个样本内算法之间的性能相似性。在所有样本中，普通最小二乘回归，唯一没有惩罚项的算法，表现明显较差。所有其他算法都表现出相当的性能，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。虽然精神分裂症患者的brainPAD始终较高，但ISMMS样本中的算法从3.8到5.2年不等，COBRE样本中的算法从4.5到11.7年不等。算法选择引入了脑年龄的变化，并且在评估精神分裂症患者的脑PAD时可能混淆研究间比较。
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.
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