Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands.
Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands.
Neurosci Biobehav Rev. 2019 Sep;104:240-254. doi: 10.1016/j.neubiorev.2019.07.010. Epub 2019 Jul 19.
Pattern classification and stratification approaches have increasingly been used in research on Autism Spectrum Disorder (ASD) over the last ten years with the goal of translation towards clinical applicability. Here, we present an extensive scoping literature review on those two approaches. We screened a total of 635 studies, of which 57 pattern classification and 19 stratification studies were included. We observed large variance across pattern classification studies in terms of predictive performance from about 60% to 98% accuracy, which is among other factors likely linked to sampling bias, different validation procedures across studies, the heterogeneity of ASD and differences in data quality. Stratification studies were less prevalent with only two studies reporting replications and just a few showing external validation. While some identified strata based on cognition and intelligence reappear across studies, biology as a stratification marker is clearly underexplored. In summary, mapping biological differences at the level of the individual with ASD is a major challenge for the field now. Conceptualizing those mappings and individual trajectories that lead to the diagnosis of ASD, will become a major challenge in the near future.
在过去十年中,模式分类和分层方法在自闭症谱系障碍(ASD)的研究中越来越多地被使用,目的是转化为临床适用性。在这里,我们对这两种方法进行了广泛的文献综述。我们总共筛选了 635 项研究,其中包括 57 项模式分类研究和 19 项分层研究。我们观察到模式分类研究在预测性能方面存在很大差异,准确性从约 60%到 98%不等,这可能与采样偏差、不同研究之间的验证程序、ASD 的异质性以及数据质量的差异有关。分层研究则不太常见,只有两项研究报告了重复,只有少数研究显示了外部验证。虽然一些研究根据认知和智力确定了重复出现的分层,但生物标志物作为分层标记显然还没有得到充分探索。总之,在个体层面上绘制自闭症的生物学差异是该领域目前面临的一个主要挑战。将这些映射和导致自闭症诊断的个体轨迹概念化,将成为不久的将来的一个主要挑战。