Driving the brain towards creativity and intelligence: A network control theory analysis

The Hidden Control Architecture of Complex Brain Networks

The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to delineate a control architecture of a complex network that is compatible with the behavior of the network and applied the framework to structural brain networks and other complex networks. As a result, we revealed that the brain networks have a distributed and overlapping control architecture governed by a small number of control nodes, which may be responsible for the robust and efficient brain functions. Moreover, our artificial network evolution analysis showed that the distributed and overlapping control architecture of the brain network emerges when it evolves toward increasing both robustness and efficiency.

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We develop a framework to delineate the control architecture of brain networks

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The control architecture of brain networks is compared with other complex networks

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Brain networks have a distributed and overlapping control architecture

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Robust and efficient brain functions might be rooted in its control architecture

Investigating the Role of the Primary Motor Cortex in Musical Creativity: A Transcranial Direct Current Stimulation Study

Neuroscientific research has revealed interconnected brain networks implicated in musical creativity, such as the executive control network, the default mode network, and premotor cortices. The present study employed brain stimulation to evaluate the role of the primary motor cortex (M1) in creative and technically fluent jazz piano improvisations. We implemented transcranial direct current stimulation (tDCS) to alter the neural activation patterns of the left hemispheric M1 whilst pianists performed improvisations with their right hand. Two groups of expert jazz pianists (n = 8 per group) performed five improvisations in each of two blocks. In Block 1, they improvised in the absence of brain stimulation. In Block 2, one group received inhibitory tDCS and the second group received excitatory tDCS while performing five new improvisations. Three independent expert-musicians judged the 160 performances on creativity and technical fluency using a 10-point Likert scale. As the M1 is involved in the acquisition and consolidation of motor skills and the control of hand orientation and velocity, we predicted that excitatory tDCS would increase the quality of improvisations relative to inhibitory tDCS. Indeed, improvisations under conditions of excitatory tDCS were rated as significantly more creative than those under conditions of inhibitory tDCS. A music analysis indicated that excitatory tDCS elicited improvisations with greater pitch range and number/variety of notes. Ratings of technical fluency did not differ significantly between tDCS groups. We discuss plausible mechanisms by which the M1 region contributes to musical creativity.

A computational network control theory analysis of depression symptoms

Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

Control principles for complex biological networks

Networks have been widely used to model the structure of various biological systems. Currently, a series of approaches have been developed to construct reliable biological networks. However, the ultimate understanding of a biological system is to steer its states to the desired ones by imposing signals. The control process is dominated by the intrinsic structure and the dynamic propagation. To understand the underlying mechanisms behind the life process, the control theory can be applied to biological networks with specific target requirements. In this article, we first introduce the structural controllability of complex networks and discuss its advantages and disadvantages. Then, we review the effective control to meet the specific requirements for complex biological networks. Moreover, we summarize the existing methods for finding the unique minimum set of driver nodes via the optimal control for complex networks. Finally, we discuss the relationships between biological networks and structural controllability, effective control and optimal control. Moreover, potential applications of general control principles are pointed out.

Network Neuroscience of Creative Cognition: Mapping Cognitive Mechanisms and Individual Differences in the Creative Brain

Network neuroscience research is providing increasing specificity on the contribution of large-scale brain networks to creative cognition. Here, we summarize recent experimental work examining cognitive mechanisms of network interactions and correlational studies assessing network dynamics associated with individual creative abilities. Our review identifies three cognitive processes related to network interactions during creative performance: goal-directed memory retrieval, prepotent-response inhibition, and internally-focused attention. Correlational work using prediction modeling indicates that functional connectivity between networks-particularly the executive control and default networks-can reliably predict an individual's creative thinking ability. We discuss potential directions for future network neuroscience, including assessing creative performance in specific domains and using brain stimulation to test causal hypotheses regarding network interactions and cognitive mechanisms of creative thought.

Warnings and caveats in brain controllability

A recent article by Gu et al. (Nat. Commun. 6, 2015) proposed to characterize brain networks, quantified using anatomical diffusion imaging, in terms of their "controllability", drawing on concepts and methods of control theory. They reported that brain activity is controllable from a single node, and that the topology of brain networks provides an explanation for the types of control roles that different regions play in the brain. In this work, we first briefly review the framework of control theory applied to complex networks. We then show contrasting results on brain controllability through the analysis of five different datasets and numerical simulations. We find that brain networks are not controllable (in a statistical significant way) by one single region. Additionally, we show that random null models, with no biological resemblance to brain network architecture, produce the same type of relationship observed by Gu et al. between the average/modal controllability and weighted degree. Finally, we find that resting state networks defined with fMRI cannot be attributed specific control roles. In summary, our study highlights some warning and caveats in the brain controllability framework.

The association between visual creativity and cortical thickness in healthy adults

Creativity is necessary to human survival, human prosperity, civilization and well-being. Visual creativity is an important part of creativity and is the ability to create products of novel and useful visual forms, playing important role in many fields such as art, painting and sculpture. There have been several neuroimaging studies exploring the neural basis of visual creativity. However, to date, little is known about the relationship between cortical structure and visual creativity as measured by the Torrance Tests of Creative Thinking. Here, we investigated the association between cortical thickness and visual creativity in a large sample of 310 healthy adults. We used multiple regression to analyze the correlation between cortical thickness and visual creativity, adjusting for gender, age and general intelligence. The results showed that visual creativity was significantly negatively correlated with cortical thickness in the left middle frontal gyrus (MFG), right inferior frontal gyrus (IFG), right supplementary motor cortex (SMA) and the left insula. These observations have implications for understanding that a thinner prefrontal cortex (PFC) (e.g. IFG, MFG), SMA and insula correspond to higher visual creative performance, presumably due to their role in executive attention, cognitive control, motor planning and dynamic switching.