Developmental Neurocognition Lab, Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom.
Psychol Rev. 2011 Oct;118(4):637-54. doi: 10.1037/a0025234.
Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by overaggressive synaptic pruning and identifying the mechanisms involved. We used a novel population-modeling technique to investigate developmental deficits, in which both neurocomputational parameters and the learning environment were varied across a large number of simulated individuals. Regression was generated by the atypical setting of a single pruning-related parameter. We observed a probabilistic relationship between the atypical pruning parameter and the presence of regression, as well as variability in the onset, severity, behavioral specificity, and recovery from regression. Other neurocomputational parameters that varied across the population modulated the risk that an individual would show regression. We considered a further hypothesis that behavioral regression may index an underlying anomaly characterizing the broader autism phenotype. If this is the case, we show how the model also accounts for several additional findings: shared gene variants between autism and language impairment (Vernes et al., 2008); larger brain size in autism but only in early development (Redcay & Courchesne, 2005); and the possibility of quasi-autism, caused by extreme environmental deprivation (Rutter et al., 1999). We make a novel prediction that the earliest developmental symptoms in the emergence of autism should be sensory and motor rather than social and review empirical data offering preliminary support for this prediction.
儿童早期已建立行为的丧失构成了一种非常典型的发展轨迹。这种情况几乎只在自闭症中发现，其原因目前尚不清楚（Baird 等人，2008 年）。我们提出了一种发展退化的人工神经网络模型，探索了退化是由过度激进的突触修剪引起的假设，并确定了所涉及的机制。我们使用一种新的群体建模技术来研究发育缺陷，在该技术中，神经计算参数和学习环境在大量模拟个体中都有所变化。退化是通过单个与修剪相关的参数的异常设置产生的。我们观察到异常修剪参数与退化的存在之间存在概率关系，以及退化的发作、严重程度、行为特异性和从退化中恢复的可变性。群体中变化的其他神经计算参数会影响个体出现退化的风险。我们考虑了另一个假设，即行为退化可能是表征更广泛自闭症表型的潜在异常的指标。如果是这样，我们将展示模型如何还解释了其他几个发现：自闭症和语言障碍之间的共享基因变异（Vernes 等人，2008 年）；自闭症患者的大脑更大，但仅在早期发育中（Redcay 和 Courchesne，2005 年）；以及由极端环境剥夺引起的准自闭症的可能性（Rutter 等人，1999 年）。我们提出了一个新的预测，即自闭症出现的最早发育症状应该是感觉和运动方面的，而不是社交方面的，并回顾了初步支持这一预测的实证数据。
