Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland OR, 97239, USA.
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA.
Neuroimage. 2018 May 15;172:674-688. doi: 10.1016/j.neuroimage.2017.12.044. Epub 2017 Dec 21.
DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple cognitive mechanisms. This study attempts to identify and characterize cognitive subtypes within the ASD population using our Functional Random Forest (FRF) machine learning classification model. This model trained a traditional random forest model on measures from seven tasks that reflect multiple levels of information processing. 47 ASD diagnosed and 58 typically developing (TD) children between the ages of 9 and 13 participated in this study. Our RF model was 72.7% accurate, with 80.7% specificity and 63.1% sensitivity. Using the random forest model, the FRF then measures the proximity of each subject to every other subject, generating a distance matrix between participants. This matrix is then used in a community detection algorithm to identify subgroups within the ASD and TD groups, and revealed 3 ASD and 4 TD putative subgroups with unique behavioral profiles. We then examined differences in functional brain systems between diagnostic groups and putative subgroups using resting-state functional connectivity magnetic resonance imaging (rsfcMRI). Chi-square tests revealed a significantly greater number of between group differences (p < .05) within the cingulo-opercular, visual, and default systems as well as differences in inter-system connections in the somato-motor, dorsal attention, and subcortical systems. Many of these differences were primarily driven by specific subgroups suggesting that our method could potentially parse the variation in brain mechanisms affected by ASD.
DSM-5 自闭症谱系障碍 (ASD) 包括一组神经发育障碍，其特征是社交沟通和互动以及重复行为或受限兴趣方面的缺陷，并且可能同时受到多种认知机制的影响和影响。本研究试图使用我们的功能随机森林 (FRF) 机器学习分类模型在 ASD 人群中识别和描述认知亚型。该模型在反映多个信息处理层次的七个任务的测量值上训练了一个传统的随机森林模型。本研究纳入了 47 名被诊断为 ASD 和 58 名年龄在 9 至 13 岁之间的典型发育 (TD) 儿童。我们的 RF 模型的准确率为 72.7%，特异性为 80.7%，敏感性为 63.1%。使用随机森林模型，FRF 然后测量每个受试者与其他每个受试者的接近程度，生成参与者之间的距离矩阵。然后，该矩阵用于社区检测算法，以识别 ASD 和 TD 组内的亚组，并揭示了 3 个 ASD 和 4 个 TD 可能的亚组，具有独特的行为特征。然后，我们使用静息状态功能磁共振成像 (rsfcMRI) 检查诊断组和假定亚组之间功能大脑系统的差异。卡方检验显示，在扣带-顶叶、视觉和默认系统中以及在躯体运动、背侧注意和皮质下系统中的系统间连接差异方面，组间差异的数量显著增加 (p <.05)。许多这些差异主要是由特定的亚组驱动的，这表明我们的方法可能能够解析受 ASD 影响的大脑机制的变化。
