Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut.
Methods of Plasticity Research, Department of Psychology, University of Zürich, Zurich, Switzerland; Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands.
Biol Psychiatry. 2022 Oct 15;92(8):626-642. doi: 10.1016/j.biopsych.2022.04.008. Epub 2022 Apr 25.
Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.
自闭症是一种异质性的神经发育障碍，基于功能磁共振成像的研究有助于增进我们对其对大脑网络活动影响的理解。我们回顾了预测模型如何利用功能连接和症状的测量来帮助揭示自闭症的关键见解。我们讨论了不同的预测框架如何进一步了解构成复杂自闭症症状基础的大脑特征，并考虑预测模型如何在临床环境中使用。在整个过程中，我们强调了研究解释的各个方面，例如数据衰减和采样偏差，这些方面需要在自闭症的背景下加以考虑。最后，我们提出了自闭症预测建模的一些令人兴奋的未来方向。
