Brain：科学家首次成功测定人类的智力
2016/8/1 6:48:43 世界医疗科技资讯

     导读

     近日，刊登在国际杂志Brain上的一篇研究报告中，来自沃威克大学的研究人员通过研究确定并且测定了人类的智力。文章中研究者通过定量大脑的动态功能，鉴别出了在不同时间里大脑不同部位彼此相互作用的方式，从而就为揭示大脑智力工作的机制提供了一定思路。

    

     研究者Jianfeng Feng教授说道，大脑的变化性越大，其不同部位彼此相互连接作用的频率就越大，而且个体的IQ及创造力水平就越高。准确理解人类大脑的智力或可帮助未来科学家们开发人工智能(AI)；当前人工智能系统并不能够处理可变性和自适应性，而这两种特性对于大脑生长和学习非常重要，研究者认为，大脑内部动态功能或被应用于构建先进的人工神经网络计算机，从而实现有能力去学习、生长以及不断适应环境变化。

     本文研究同时对于理解另外一个常被“误解”的领域也有一定帮助，即心理健康，研究者通常会在精神分裂症、自闭症及注意缺陷多动障碍(ADHD)患者的大脑错误网络中发现大脑可变性模式的改变，而揭示心理健康缺陷发生的原因或可帮助科学家更加深入地研究开发治疗或者抑制相关疾病的新型疗法。

     文章中，利用休眠状态的MRI分析对全球成千上万名个体的大脑进行扫描分析，研究者发现，参与者大脑中和学习和发育相关的区域的可变性水平较高，这就意味着这些个体可以在几分钟或者几秒钟内频繁改变其大脑中的神经连接；从另一方面来讲，大脑中和智力不相关的区域的可变性和适应性水平或许较低，这些区域包括视觉、听觉和感觉-运动区域。

     研究者认为，利用MRI技术可以帮助我们进行这项前驱性研究，长期以来，人类的智力一直是广泛且热点的讨论话题，而且近年来科学家们利用了多种先进的大脑成像技术对人类智力进行了深入的研究，为科学家们获得足够数据来开发人工智能提供了一定帮助，研究者认为随着后期更加广泛的研究，他们或许可以更加深入地理解或诊断一些比较棘手的人类精神性障碍，比如精神分裂症和抑郁症等。

     原文阅读

    

    

     Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

     Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be potentially useful as a predictor for learning and neural rehabilitation.

     http://brain.oxfordjournals.org/content/early/2016/07/13/brain.aww143

    

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