From the Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Centre Utrecht, Utrecht.
Am J Psychiatry. 2016 Jun 1;173(6):607-16. doi: 10.1176/appi.ajp.2015.15070922. Epub 2016 Feb 26.
Despite the multitude of longitudinal neuroimaging studies that have been published, a basic question on the progressive brain loss in schizophrenia remains unaddressed: Does it reflect accelerated aging of the brain, or is it caused by a fundamentally different process? The authors used support vector regression, a supervised machine learning technique, to address this question.
In a longitudinal sample of 341 schizophrenia patients and 386 healthy subjects with one or more structural MRI scans (1,197 in total), machine learning algorithms were used to build models to predict the age of the brain and the presence of schizophrenia ("schizophrenia score"), based on the gray matter density maps. Age at baseline ranged from 16 to 67 years, and follow-up scans were acquired between 1 and 13 years after the baseline scan. Differences between brain age and chronological age ("brain age gap") and between schizophrenia score and healthy reference score ("schizophrenia gap") were calculated. Accelerated brain aging was calculated from changes in brain age gap between two consecutive measurements. The age prediction model was validated in an independent sample.
In schizophrenia patients, brain age was significantly greater than chronological age at baseline (+3.36 years) and progressively increased during follow-up (+1.24 years in addition to the baseline gap). The acceleration of brain aging was not constant: it decreased from 2.5 years/year just after illness onset to about the normal rate (1 year/year) approximately 5 years after illness onset. The schizophrenia gap also increased during follow-up, but more pronounced variability in brain abnormalities at follow-up rendered this increase nonsignificant.
The progressive brain loss in schizophrenia appears to reflect two different processes: one relatively homogeneous, reflecting accelerated aging of the brain and related to various measures of outcome, and a more variable one, possibly reflecting individual variation and medication use. Differentiating between these two processes may not only elucidate the various factors influencing brain loss in schizophrenia, but also assist in individualizing treatment.
尽管已经发表了大量的纵向神经影像学研究，但精神分裂症患者大脑逐渐丧失的一个基本问题仍未得到解决：这是否反映了大脑的加速老化，还是由根本不同的过程引起的？作者使用支持向量回归（一种监督机器学习技术）来解决这个问题。
在一个 341 名精神分裂症患者和 386 名健康受试者的纵向样本中，这些受试者有一次或多次结构 MRI 扫描（总共 1197 次），机器学习算法被用于构建基于灰质密度图来预测大脑年龄和精神分裂症（“精神分裂症评分”）的模型。基线时的年龄范围为 16 至 67 岁，基线扫描后 1 至 13 年内进行了随访扫描。计算大脑年龄与实际年龄之间的差异（“大脑年龄差距”）以及精神分裂症评分与健康参考评分之间的差异（“精神分裂症差距”）。通过两次连续测量之间的大脑年龄差距变化来计算大脑衰老的加速。在独立样本中验证了年龄预测模型。
在精神分裂症患者中，基线时大脑年龄明显大于实际年龄（大 3.36 岁），并在随访期间逐渐增加（在基线差距的基础上增加 1.24 岁）。大脑衰老的加速并非恒定不变：它从发病后不久的 2.5 年/年下降到发病后约 5 年的正常速度（1 年/年）。随访期间精神分裂症差距也有所增加，但由于随访时大脑异常的变异性更大，这种增加变得无统计学意义。
精神分裂症患者的大脑逐渐丧失似乎反映了两种不同的过程：一种相对均匀，反映了大脑的加速老化，与各种预后测量有关；另一种则更为多变，可能反映了个体差异和药物使用。区分这两种过程不仅可以阐明影响精神分裂症患者大脑丧失的各种因素，还可以帮助个体化治疗。
