The impact of input node placement in the controllability of structural brain networks

Network controllability refers to the ability to steer the state of a network towards a target state by driving certain nodes, known as input nodes. This concept can be applied to brain networks for studying brain function and its relation to the structure, which has numerous practical applications. Brain network controllability involves using external signals such as electrical stimulation to drive specific brain regions and navigate the neurophysiological activity level of the brain around the state space. Although controllability is mainly theoretical, the energy required for control is critical in real-world implementations. With a focus on the structural brain networks, this study explores the impact of white matter fiber architecture on the control energy in brain networks using the theory of how input node placement affects the LCC (the longest distance between inputs and other network nodes). Initially, we use a single input node as it is theoretically possible to control brain networks with just one input. We show that highly connected brain regions that lead to lower LCCs are more energy-efficient as a single input node. However, there may still be a need for a significant amount of control energy with one input, and achieving controllability with less energy could be of interest. We identify the minimum number of input nodes required to control brain networks with smaller LCCs, demonstrating that reducing the LCC can significantly decrease the control energy in brain networks. Our results show that relying solely on highly connected nodes is not effective in controlling brain networks with lower energy by using multiple inputs because of densely interconnected brain network hubs. Instead, a combination of low and high-degree nodes is necessary.

Functional control of electrophysiological network architecture using direct neurostimulation in humans

Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.

Brain stimulation devices capable of perturbing the physiological state of neural systems are rapidly gaining popularity for their potential to treat neurological and psychiatric disease. A root problem is that underlying dysfunction spans a large-scale network of brain regions, requiring the ability to control the complex interactions between multiple brain areas. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. We demonstrate the ability to predictably reconfigure patterns of interactions between functional brain areas by modulating the strength and location of stimulation. Our findings have high significance for designing stimulation protocols capable of modulating distributed neural circuits in the human brain.

Topology, robustness, and structural controllability of the Brazilian Federal Police criminal intelligence network

Law enforcement and intelligence agencies worldwide struggle to find effective ways to fight organized crime and reduce criminality. However, illegal networks operate outside the law and much of the data collected is classified. Therefore, little is known about the structure, topological weaknesses, and control of criminal networks. We fill this gap by presenting a unique criminal intelligence network built directly by the Brazilian Federal Police for intelligence and investigative purposes. We study its structure, its response to different attack strategies, and its structural controllability. Surprisingly, the network composed of individuals involved in multiple crimes of federal jurisdiction in Brazil has a giant component enclosing more than half of all its edges. We focus on the largest connected cluster of this network and show it has many social network features, such as small-worldness and heavy-tail degree distribution. However, it is less dense and less efficient than typical social networks. The giant component also shows a high degree cutoff that is associated with the lack of trust among individuals belonging to clandestine networks. The giant component of the network is also highly modular (Q=0.96) and thence fragile to module-based attacks. The targets in such attacks, i.e. the nodes connecting distinct communities, may be interpreted as individuals with bridging clandestine activities such as accountants, lawyers, or money changers. The network can be disrupted by the removal of approximately 2% of either its nodes or edges, the negligible difference between both approaches being due to low graph density. Finally, we show that 20% of driver nodes can control dynamic variables acting on the whole network, suggesting that non-repressive strategies such as access to basic education or sanitation can be effective in reducing criminality by changing the perception of driver individuals to norm compliance.