Department of Psychiatry and Imaging Research Center, University of California Davis School of Medicine, Sacramento CA, USA.
Schizophr Res. 2012 Mar;135(1-3):28-33. doi: 10.1016/j.schres.2012.01.001. Epub 2012 Jan 25.
The establishment of a neurobiologically based nosological system is one of the ultimate goals of modern biological psychiatry research. Developments in neuroimaging and statistical/machine learning have provided useful basic tools for these efforts. Recent studies have demonstrated the utility of fMRI as input data for the classification of schizophrenia, but none, to date, has used fMRI of cognitive control for this purpose. In this study, we evaluated the accuracy of an unbiased classification method on fMRI data from a large cohort of subjects with first episode schizophrenia and a cohort of age matched healthy control subjects while they completed the AX version of the Continuous Performance Task (AX-CPT). We compared these results to classifications based on AX-CPT behavioral data. Classification accuracy for DSM-IV defined schizophrenia using fMRI data was modest and comparable to classifications conducted with behavioral data. Interestingly fMRI classifications did however identify a distinct subgroup of patients with greater behavioral disorganization, whereas behavioral data classifications did not. These results suggest that fMRI-based classification could be a useful tool in defining a neurobiologically distinct subgroup within the clinically defined syndrome of schizophrenia, reflecting alterations in discrete neural circuits. Independent validation of classification-based phenotypes using other biological data such as genetics would provide a strong test of this hypothesis.
建立一个基于神经生物学的分类系统是现代生物精神病学研究的最终目标之一。神经影像学和统计/机器学习的发展为这些努力提供了有用的基本工具。最近的研究表明 fMRI 可作为分类精神分裂症的输入数据，但迄今为止，尚无研究将认知控制的 fMRI 用于此目的。在这项研究中，我们评估了一种无偏分类方法在来自首发精神分裂症大样本队列和年龄匹配的健康对照组队列的 fMRI 数据上的准确性，这些患者在完成 AX 版本的连续作业任务（AX-CPT）时进行了 fMRI 扫描。我们将这些结果与基于 AX-CPT 行为数据的分类进行了比较。使用 fMRI 数据对 DSM-IV 定义的精神分裂症进行分类的准确性适中，与使用行为数据进行的分类相当。有趣的是，fMRI 分类确实可以识别出具有更大行为紊乱的患者的一个独特亚组，而行为数据分类则不能。这些结果表明，基于 fMRI 的分类可能是在精神分裂症的临床定义综合征中定义具有神经生物学差异的亚组的有用工具，反映了离散神经回路的改变。使用其他生物学数据（如遗传学）对基于分类的表型进行独立验证，将为这一假设提供强有力的检验。
