Neuroscience and Mental Health Programme, Division of Neurology, Hospital for Sick Children Toronto, ON, Canada ; Institute of Medical Science and Department of Paediatrics, Brain and Behaviour Centre, University of Toronto Toronto, ON, Canada.
Department of Neurosciences, School of Medicine, Case Western Reserve University Cleveland, OH, USA.
Front Neuroinform. 2013 Dec 24;7:37. doi: 10.3389/fninf.2013.00037. eCollection 2013.
Along with the study of brain activity evoked by external stimuli, an increased interest in the research of background, "noisy" brain activity is fast developing in current neuroscience. It is becoming apparent that this "resting-state" activity is a major factor determining other, more particular, responses to stimuli and hence it can be argued that background activity carries important information used by the nervous systems for adaptive behaviors. In this context, we investigated the generation of information in ongoing brain activity recorded with magnetoencephalography (MEG) in children with autism spectrum disorder (ASD) and non-autistic children. Using a stochastic dynamical model of brain dynamics, we were able to resolve not only the deterministic interactions between brain regions, i.e., the brain's functional connectivity, but also the stochastic inputs to the brain in the resting state; an important component of large-scale neural dynamics that no other method can resolve to date. We then computed the Kullback-Leibler (KLD) divergence, also known as information gain or relative entropy, between the stochastic inputs and the brain activity at different locations (outputs) in children with ASD compared to controls. The divergence between the input noise and the brain's ongoing activity extracted from our stochastic model was significantly higher in autistic relative to non-autistic children. This suggests that brains of subjects with autism create more information at rest. We propose that the excessive production of information in the absence of relevant sensory stimuli or attention to external cues underlies the cognitive differences between individuals with and without autism. We conclude that the information gain in the brain's resting state provides quantitative evidence for perhaps the most typical characteristic in autism: withdrawal into one's inner world.
随着对外界刺激引起的大脑活动的研究不断深入，当前神经科学领域对背景“嘈杂”大脑活动的研究也越来越感兴趣。越来越明显的是，这种“静息状态”活动是决定对刺激做出其他、更特殊反应的主要因素，因此可以说背景活动携带了神经系统用于适应性行为的重要信息。在这种情况下，我们调查了自闭症谱系障碍（ASD）儿童和非自闭症儿童在进行脑磁图（MEG）记录时大脑活动中信息的产生。我们使用大脑动力学的随机动力学模型，不仅能够解析大脑区域之间的确定性相互作用，即大脑的功能连接，还能够解析静息状态下大脑的随机输入；这是目前尚无其他方法能够解析的大规模神经动力学的重要组成部分。然后，我们计算了自闭症儿童与对照组相比，随机输入和大脑活动在不同位置（输出）之间的 Kullback-Leibler（KLD）散度，也称为信息增益或相对熵。与非自闭症儿童相比，自闭症儿童的输入噪声与从我们的随机模型中提取的大脑持续活动之间的散度明显更高。这表明自闭症患者的大脑在静息状态下产生更多的信息。我们提出，在没有相关感官刺激或对外界线索的关注的情况下，大脑过度产生信息，这是自闭症患者和非自闭症患者之间认知差异的基础。我们得出结论，大脑静息状态下的信息增益为自闭症最典型的特征之一提供了定量证据：即退回到自己的内心世界。
