摘要
自闭症(ASD)的诊断一直以来是基于对患者行为层面的观察.近年来脑成像的研究表明,自闭症患者大脑存在结构及功能的改变,这些发现为基于成像的诊断提供了新的途径.准确的诊断需要基于对大脑异常特征的准确描述,多个脑生理参量的成像可以为自闭症患者的大脑特征提供更全面的信息.在现有的无损脑功能成像中,光学脑成像具有较高的时间分辨率及可接受的空间分辨率,测量时对头部晃动相对不敏感,适用于对儿童特别是自闭症儿童的研究.近红外光谱技术(NIRS)表征皮层的血氧代谢,而漫反射相关光谱技术(DCS)用于测量皮层的血流活动.这两类参量可以提供互补的皮层血液动力学信息,结合更为高效的分类算法,有望取得对自闭症患者大脑皮层活动特征进行更为准确的描述和区分.在广泛调研文献、结合作者的研究成果的基础上,对利用光学脑成像研究自闭症患者大脑活动特征、结合特征参量利用机器学习分类算法对自闭症的预测、利用多模态光学脑成像技术(即结合近红外光谱及漫反射相关光谱)研究自闭症的前景等问题进行了系统的回顾与展望,希望为自闭症相关领域的科研人员提供参考和借鉴.
Diagnosis for Autism Spectrum Disorder(ASD)has always relied on behavioral observations.However,recent studies with brain imaging show significant alterations in the brain structure and function associated with ASD,which opens a new channel for imaging-based diagnosis.Accurate diagnosis depends on accurate characteri-zation of the alterations.In this regard,multi-contrast imaging can provide more complete and complementary information for characterizing autistic brain.Among various brain imaging modalities,optical brain imaging has good temporal and acceptable spatial resolution.It is also less sensitive to head motion and thus very suitable for studies on children,in particular,children with ASD.Near-infrared spectroscopy(NIRS)probes cerebral blood oxygenation,while diffuse correlation spectroscopy(DCS)measures cerebral blood flow.These hemodynamic variables may provide complementary information about the cerebral hemodynamics.Therefore it is anticipated that,by using more efficient machine-learning classification algorithm,more accurate characterization and classification of ASD can be achieved.Based on extensive literature research and our on-going study,the progress in using optical brain imaging to investigate the characteristics of autistic brain and the classification of ASD with various machine-learning algorithms is systematically reviewed.The prospect of using multimodal optical imaging(combining NIRS with DCS)to study ASD is also discussed.This systematic review and prospect can be beneficial to scientists who are working in the ASD-related field.
作者
李军
程卉怡
LI Jun;CHENG Huiyi(South China Academy of Advanced Optoelectronics∥Key Lab for Behavioral Economic Science and Technology,South China Normal University, Guangzhou 510006, China)
出处
《华南师范大学学报（自然科学版）》
CAS
北大核心
2018年第5期1-13,共13页
Journal of South China Normal University(Natural Science Edition)
基金
国家自然科学基金项目(81771876)
华南师范大学研究生创新计划项目(033)
关键词
光学脑成像
自闭症
特征提取
机器学习分类算法
optical brain imaging
autism
feature extraction
machine-learning classification algorithms
