近日，刊登在國際雜誌Brain上的一篇研究報告中，來自沃威克大學的研究人員通過研究確定並且測定了人類的智力；文章中研究者通過定量大腦的動態功能，鑒別出了在不同時間裏大腦不同部位彼此相互作用的方式，從而就為揭示大腦智力工作的機製提供了一定思路。
研究者Jianfeng Feng教授說道，大腦的變化性越大，其不同部位彼此相互連接作用的頻率就越大，而且個體的IQ及創造力水平就越高。準確理解人類大腦的智力或可幫助未來科學家們開發人工智能（AI）；當前人工智能係統並不能夠處理可變性和自適應性，而這兩種特性對於大腦生長和學習非常重要，研究者認為，大腦內部動態功能或被應用於構建先進的人工神經網絡計算機，從而實現有能力去學習、生長以及不斷適應環境變化。
本文研究同時對於理解另外一個常被“誤解”的領域也有一定幫助，即心理健康，研究者通常會在精神分裂症、自閉症及注意缺陷多動障礙（ADHD）患者的大腦錯誤網絡中發現大腦可變性模式的改變，而揭示心理健康缺陷發生的原因或可幫助科學家更加深入地研究開發治療或者抑製相關疾病的新型療法。
文章中，利用休眠狀態的MRI分析對全球成千上萬名個體的大腦進行掃描分析，研究者發現，參與者大腦中和學習和發育相關的區域的可變性水平較高，這就意味著這些個體可以在幾分鍾或者幾秒鍾內頻繁改變其大腦中的神經連接；從另一方麵來講，大腦中和智力不相關的區域的可變性和適應性水平或許較低，這些區域包括視覺、聽覺和感覺-運動區域。
研究者認為，利用MRI技術可以幫助我們進行這項前驅性研究，長期以來，人類的智力一直是廣泛且熱點的討論話題，而且近年來科學家們利用了多種先進的大腦成像技術對人類智力進行了深入的研究，為科學家們獲得足夠數據來開發人工智能提供了一定幫助，研究者認為隨著後期更加廣泛的研究，他們或許可以更加深入地理解或診斷一些比較棘手的人類精神性障礙，比如精神分裂症和抑鬱症等。
doi:10.1093/brain/aww143
PMC:
PMID:
Neural, electrophysiological and anatomical basis of brain-networkvariabilityand its characteristic changes in mental disorders
Jie Zhang, Wei Cheng, Zhaowen Liu, Kai Zhang, Xu Lei, Ye Yao, Benjamin Becker, Yicen Liu, Keith M. Kendrick, Guangming Lu, Jianfeng Feng
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.
