Abstract：
BACKGROUND: Brain imaging studies investigating grey matter in functional neurological disorder (FND) have used univariate approaches to report group-level differences compared with healthy controls (HCs). However, these findings have limited translatability because they do not differentiate patients from controls at the individual-level.
METHODS: 183 participants were prospectively recruited across three groups: 61 patients with mixed FND (FND-mixed), 61 age-matched and sex-matched HCs and 61 age, sex, depression and anxiety-matched psychiatric controls (PCs). Radial basis function support vector machine classifiers with cross-validation were used to distinguish individuals with FND from HCs and PCs using 134 FreeSurfer-derived grey matter MRI features.
RESULTS: Patients with FND-mixed were differentiated from HCs with an accuracy of 0.66 (p=0.005; area under the receiving operating characteristic (AUROC)=0.74); this sample was also distinguished from PCs with an accuracy of 0.60 (p=0.038; AUROC=0.56). When focusing on the functional motor disorder subtype (FND-motor, n=46), a classifier robustly differentiated these patients from HCs (accuracy=0.72; p=0.002; AUROC=0.80). FND-motor could not be distinguished from PCs, and the functional seizures subtype (n=23) could not be classified against either control group. Important regions contributing to statistically significant multivariate classifications included the cingulate gyrus, hippocampal subfields and amygdalar nuclei. Correctly versus incorrectly classified participants did not differ across a range of tested psychometric variables.
CONCLUSIONS: These findings underscore the interconnection of brain structure and function in the pathophysiology of FND and demonstrate the feasibility of using structural MRI to classify the disorder. Out-of-sample replication and larger-scale classifier efforts incorporating psychiatric and neurological controls are needed.
METHODS: 183 participants were prospectively recruited across three groups: 61 patients with mixed FND (FND-mixed), 61 age-matched and sex-matched HCs and 61 age, sex, depression and anxiety-matched psychiatric controls (PCs). Radial basis function support vector machine classifiers with cross-validation were used to distinguish individuals with FND from HCs and PCs using 134 FreeSurfer-derived grey matter MRI features.
RESULTS: Patients with FND-mixed were differentiated from HCs with an accuracy of 0.66 (p=0.005; area under the receiving operating characteristic (AUROC)=0.74); this sample was also distinguished from PCs with an accuracy of 0.60 (p=0.038; AUROC=0.56). When focusing on the functional motor disorder subtype (FND-motor, n=46), a classifier robustly differentiated these patients from HCs (accuracy=0.72; p=0.002; AUROC=0.80). FND-motor could not be distinguished from PCs, and the functional seizures subtype (n=23) could not be classified against either control group. Important regions contributing to statistically significant multivariate classifications included the cingulate gyrus, hippocampal subfields and amygdalar nuclei. Correctly versus incorrectly classified participants did not differ across a range of tested psychometric variables.
CONCLUSIONS: These findings underscore the interconnection of brain structure and function in the pathophysiology of FND and demonstrate the feasibility of using structural MRI to classify the disorder. Out-of-sample replication and larger-scale classifier efforts incorporating psychiatric and neurological controls are needed.
摘要：
背景：研究功能性神经系统疾病（FND）中灰质的脑成像研究使用单变量方法来报告与健康对照（HC）相比的组水平差异。然而,这些发现的可译性有限,因为它们不能在个体水平上区分患者和对照组.
方法：183名参与者被前瞻性地纳入三组：61名混合FND患者（混合FND），61个年龄匹配和性别匹配的HCs和61个年龄，性别,抑郁和焦虑匹配的精神病对照（PC）。使用具有交叉验证的径向基函数支持向量机分类器，使用134FreeSurfer衍生的灰质MRI特征将FND个体与HC和PC区分开。
结果：将FND混合患者与HC区分开来，准确度为0.66（p=0.005；接受操作特征下面积（AUROC）=0.74）；该样本也与PC区分开来，准确度为0.60（p=0.038；AUROC=0.56）。当关注功能性运动障碍亚型(FND-motor，n=46),分类器可以将这些患者与HC进行稳健区分(准确度=0.72;p=0.002;AUROC=0.80).FND-motor无法与PC区分开，功能性癫痫发作亚型（n=23）无法与任一对照组进行分类。有助于统计上显着的多变量分类的重要区域包括扣带回，海马亚野和杏仁核。正确分类的参与者与错误分类的参与者在一系列测试的心理测量变量中没有差异。
结论：这些发现强调了大脑结构和功能在FND病理生理学中的相互联系，并证明了使用结构MRI对疾病进行分类的可行性。需要样本外复制和包含精神病学和神经学对照的大规模分类器努力。
方法：183名参与者被前瞻性地纳入三组：61名混合FND患者（混合FND），61个年龄匹配和性别匹配的HCs和61个年龄，性别,抑郁和焦虑匹配的精神病对照（PC）。使用具有交叉验证的径向基函数支持向量机分类器，使用134FreeSurfer衍生的灰质MRI特征将FND个体与HC和PC区分开。
结果：将FND混合患者与HC区分开来，准确度为0.66（p=0.005；接受操作特征下面积（AUROC）=0.74）；该样本也与PC区分开来，准确度为0.60（p=0.038；AUROC=0.56）。当关注功能性运动障碍亚型(FND-motor，n=46),分类器可以将这些患者与HC进行稳健区分(准确度=0.72;p=0.002;AUROC=0.80).FND-motor无法与PC区分开，功能性癫痫发作亚型（n=23）无法与任一对照组进行分类。有助于统计上显着的多变量分类的重要区域包括扣带回，海马亚野和杏仁核。正确分类的参与者与错误分类的参与者在一系列测试的心理测量变量中没有差异。
结论：这些发现强调了大脑结构和功能在FND病理生理学中的相互联系，并证明了使用结构MRI对疾病进行分类的可行性。需要样本外复制和包含精神病学和神经学对照的大规模分类器努力。
