The autism dataset is studied to identify the differences between autistic and healthy groups. For this, the resting-state Functional Magnetic Resonance Imaging (rs-fMRI) data of the two groups are analyzed, and networks of connections between brain regions were created. Several classification frameworks are developed to distinguish the connectivity patterns between the groups. The best models for statistical inference and precision were compared, and the tradeoff between precision and model interpretability was analyzed. Finally, the classification accuracy measures were reported to justify the performance of our framework. Our best model can classify autistic and healthy patients on the multisite ABIDE I data with 71% accuracy.
翻译:研究自闭症数据集是为了确定自闭症与健康群体之间的差异。 为此,分析了两组的休息状态功能磁共振成像(rs-fMRI)数据,建立了脑区域连接网络。开发了若干分类框架,以区分各组之间的连接模式。比较了统计推理和精确度的最佳模型,分析了精确度与模型可解释性之间的取舍。最后,报告了分类精确度衡量标准,以证明我们框架的性能。我们的最佳模型可以将多站ABIDE I数据的自闭症和健康病人分类为71%的准确度。