Hull Jocelyn V, Dokovna Lisa B, Jacokes Zachary J, Torgerson Carinna M, Irimia Andrei, Van Horn John Darrell
Laboratory of Neuro Imaging (LONI), The Institute for Neuroimaging and Informatics (INI), Keck School of Medicine of USC, University of Southern California , Los Angeles, CA , USA.
Front Psychiatry. 2017 Jan 4;7:205. doi: 10.3389/fpsyt.2016.00205. eCollection 2016.
Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives.
静息态功能磁共振成像(fMRI)文献中,关于自闭症大脑中内在连接性如何改变存在持续的争论,有报道称存在普遍的过度连接、连接不足和/或两者兼有的情况。由于自闭症病情的异质性,使用大脑连接性对自闭症进行分类很复杂,这使得该疾病患者之间的连接模式可能存在很大差异。报告结果的进一步差异可能归因于所纳入参与者的年龄和性别、静息态扫描的设计以及用于评估数据的分析技术。本综述系统地审视了迄今为止静息态fMRI自闭症文献,并比较各项研究,试图得出目前颇具挑战性的总体结论。我们还提出了静息态fMRI未来的应用方向,用于对自闭症谱系障碍个体进行分类、作为一种可能的诊断工具以及最佳地利用数据共享计划。