Center for Advanced Brain Imaging, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962, USA.
Hum Brain Mapp. 2011 Jan;32(1):1-9. doi: 10.1002/hbm.20995.
The objective of this research was to determine whether fractional anisotropy (FA) and mean diffusivity (MD) maps derived from diffusion tensor imaging (DTI) of the brain are able to reliably differentiate patients with schizophrenia from healthy volunteers. DTI and high resolution structural magnetic resonance scans were acquired in 50 patients with schizophrenia and 50 age- and sex-matched healthy volunteers. FA and MD maps were estimated from the DTI data and spatially normalized to the Montreal Neurologic Institute standard stereotactic space. Individuals were divided randomly into two groups of 50, a training set, and a test set, each comprising 25 patients and 25 healthy volunteers. A pattern classifier was designed using Fisher's linear discriminant analysis (LDA) based on the training set of images to categorize individuals in the test set as either patients or healthy volunteers. Using the FA maps, the classifier correctly identified 94% of the cases in the test set (96% sensitivity and 92% specificity). The classifier achieved 98% accuracy (96% sensitivity and 100% specificity) when using the MD maps as inputs to distinguish schizophrenia patients from healthy volunteers in the test dataset. Utilizing FA and MD data in combination did not significantly alter the accuracy (96% sensitivity and specificity). Patterns of water self-diffusion in the brain as estimated by DTI can be used in conjunction with automated pattern recognition algorithms to reliably distinguish between patients with schizophrenia and normal control subjects.
本研究旨在确定大脑弥散张量成像（DTI）所得的各向异性分数（FA）和平均弥散度（MD）图是否能够可靠地区分精神分裂症患者和健康志愿者。在 50 例精神分裂症患者和 50 例年龄和性别匹配的健康志愿者中采集了 DTI 和高分辨率结构磁共振扫描。从 DTI 数据中估计 FA 和 MD 图，并将其空间归一化为蒙特利尔神经学研究所标准立体定向空间。将个体随机分为两组，每组 50 例，一组为训练集，另一组为测试集，每组包含 25 例患者和 25 例健康志愿者。使用基于训练集图像的 Fisher 线性判别分析（LDA）设计模式分类器，以将测试集中的个体分类为患者或健康志愿者。使用 FA 图，分类器正确识别了测试集中 94%的病例（96%的敏感性和 92%的特异性）。当使用 MD 图作为输入时，分类器在测试数据集区分精神分裂症患者和健康志愿者时的准确率达到 98%（96%的敏感性和 100%的特异性）。FA 和 MD 数据的联合使用并未显著改变准确率（96%的敏感性和特异性）。DTI 估计的脑内水自扩散模式可与自动模式识别算法结合使用，以可靠地区分精神分裂症患者和正常对照者。
