Background and Hypothesis The brain-predicted age difference (brain-PAD) may serve as a biomarker for neurodegeneration. We investigated the brain-PAD in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging (sMRI). Study Design We employed a convolutional network-based regression (SFCNR), and compared its performance with models based on three machine learning (ML) algorithms. We pretrained the SFCNR with sMRI data of 7590 healthy controls (HCs) selected from the UK Biobank. The parameters of the pretrained model were transferred to the next training phase with a new set of HCs (n = 541). The brain-PAD was analyzed in independent HCs (n = 209) and patients (n = 233). Correlations between the brain-PAD and clinical measures were investigated. Study Results The SFCNR model outperformed three commonly used ML models. Advanced brain aging was observed in patients with SCZ, FE-SSDs, and TRS compared to HCs. A significant difference in brain-PAD was observed between FE-SSDs and TRS with ridge regression but not with the SFCNR model. Chlorpromazine equivalent dose and cognitive function were correlated with the brain-PAD in SCZ and FE-SSDs. Conclusions Our findings indicate that there is advanced brain aging in patients with SCZ and higher brain-PAD in SCZ can be used as a surrogate marker for cognitive dysfunction. These findings warrant further investigations on the causes of advanced brain age in SCZ. In addition, possible psychosocial and pharmacological interventions targeting brain health should be considered in early-stage SCZ patients with advanced brain age.

中文翻译：

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基于深度学习的精神分裂症谱系障碍患者脑年龄预测


背景和假设 大脑预测的年龄差异（brain-PAD）可以作为神经退行性疾病的生物标志物。我们使用结构磁共振成像 (sMRI) 研究了精神分裂症 (SCZ)、首发精神分裂症谱系障碍 (FE-SSD) 和难治性精神分裂症 (TRS) 患者的脑 PAD。研究设计我们采用了基于卷积网络的回归（SFCNR），并将其性能与基于三种机器学习（ML）算法的模型进行了比较。我们使用从英国生物库中选出的 7590 名健康对照 (HC) 的 sMRI 数据对 SFCNR 进行预训练。使用一组新的 HC (n = 541) 将预训练模型的参数转移到下一个训练阶段。对独立 HC (n = 209) 和患者 (n = 233) 的 Brain-PAD 进行了分析。研究了 Brain-PAD 和临床测量之间的相关性。研究结果 SFCNR 模型的性能优于三种常用的 ML 模型。与 HC 患者相比，SCZ、FE-SSD 和 TRS 患者的大脑老化程度更高。通过岭回归，在 FE-SSD 和 TRS 之间观察到 Brain-PAD 存在显着差异，但在 SFCNR 模型中则没有显着差异。氯丙嗪当量剂量和认知功能与 SCZ 和 FE-SSD 中的脑 PAD 相关。结论 我们的研究结果表明，SCZ 患者大脑老化较严重，SCZ 中较高的 Brain-PAD 可以作为认知功能障碍的替代标志物。这些发现值得进一步研究 SCZ 脑龄提前的原因。此外，对于脑龄较高的早期 SCZ 患者，应考虑针对大脑健康的可能的心理社会和药物干预措施。