Summary: Researchers have identified how different parts of the brain interact with each other at different times in order to discover how intellect works.
Source: University of Warwick.
Human intelligence is being defined and measured for the first time ever, by researchers at the University of Warwick.
Led by Professor Jianfeng Feng in the Department of Computer Science, studies at Warwick and in China have been recently undertaken to quantify the brain’s dynamic functions, and identify how different parts of the brain interact with each other at different times – namely, to discover how intellect works.
Professor Jianfeng finds that the more variable a brain is, and the more its different parts frequently connect with each other, the higher a person’s IQ and creativity are.
More accurate understanding of human intelligence could lead to future developments in artificial intelligence (AI). Currently, AI systems do not process the variability and adaptability that is vital, as evidenced by Professor Jianfeng’s research, to the human brain for growth and learning. This discovery of dynamic functions inside the brain could be applied to the construction of advanced artificial neural networks for computers, with the ability to learn, grow and adapt.
This study may also have implications for a deeper understanding of another largely misunderstood field: mental health. Altered patterns of variability were observed in the brain’s default network with schizophrenia, autism and Attention Deficit Hyperactivity Disorder (ADHD) patients. Knowing the root cause of mental health defects brings scientists exponentially closer to treating and preventing them in the future.
Using resting-state MRI analysis on thousands of people’s brains around the world, the research has found that the areas of the brain which are associated with learning and development show high levels of variability, meaning that they change their neural connections with other parts of the brain more frequently, over a matter of minutes or seconds. On the other hand, regions of the brain which aren’t associated with intelligence – the visual, auditory, and sensory-motor areas – show small variability and adaptability.
Professor Jianfeng Feng commented that new technology has made it possible to conduct this trail-blazing study: “human intelligence is a widely and hotly debated topic and only recently have advanced brain imaging techniques, such as those used in our current study, given us the opportunity to gain sufficient insights to resolve this and inform developments in artificial intelligence, as well as help establish the basis for understanding and diagnosis of debilitating human mental disorders such as schizophrenia and depression.”
Source: Luke Walton – University of Warwick
Image Source: This NeuroscienceNews.com image is in the public domain.
Original Research: Abstract for “Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders” by Jie Zhang, Wei Cheng, Zhaowen Liu, Kai Zhang, Xu Lei, Ye Yao, Benjamin Becker, Yicen Liu, Keith M. Kendrick, Guangming Lu, and Jianfeng Feng in Brain. Published online July 14 2016 doi:10.1093/brain/aww143
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.
“Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders” by Jie Zhang, Wei Cheng, Zhaowen Liu, Kai Zhang, Xu Lei, Ye Yao, Benjamin Becker, Yicen Liu, Keith M. Kendrick, Guangming Lu, and Jianfeng Feng in Brain. Published online July 14 2016 doi:10.1093/brain/aww143