Decoding Brain Network Complexity: General Concepts and Opportunities

The brain is one of the most fascinating organs, enabling our perceptions of the world, handling our emotions and decisions, and shaping our individual and social behaviors. Traditionally, the brain was considered a collection of independent elements, each corresponding to a specific function. However, network analysis, a prevalent approach in neuroscience, allows us to view the brain as an interconnected system. This enhances our understanding of both lower and higher-level brain and cognitive functions. Common graph theory measures are used to depict neural integration and functional segregation in the brain, assisting in differentiating between brain states and neurological disorders. Energy-based approaches, grounded in statistical mechanics, are gaining the attention of neuroscientists. These approaches enable the design of an energy landscape for the brain and the tracking of transitions between neural states. Exploring higher-order interactions, such as frustration, provides an opportunity for a deeper understanding of key elements of the brain and their impact on brain dynamics. As we move forward in brain science, the application of cutting-edge complex network techniques offers a promising to enhance our interpretation of brain functions. In this seminar, we will introduce complex brain networks and explore some potential approaches in this field, which will be interesting for any complex network scientist.

Impact of physicality on network structure

The emergence of detailed maps of physical networks, such as the brain connectome, vascular networks or composite networks in metamaterials, whose nodes and links are physical entities, has demonstrated the limits of the current network science toolset. Link physicality imposes a non-crossing condition that affects both the evolution and the structure of a network, in a way that the adjacency matrix alone—the starting point of all graph-based approaches—cannot capture. Here, we introduce a meta-graph that helps us to discover an exact mapping between linear physical networks and independent sets, which is a central concept in graph theory. The mapping allows us to analytically derive both the onset of physical effects and the emergence of a jamming transition, and to show that physicality affects the network structure even when the total volume of the links is negligible. Finally, we construct the meta-graphs of several real physical networks, which allows us to predict functional features, such as synapse formation in the brain connectome, that agree with empirical data. Overall, our results show that, to understand the evolution and behaviour of real complex networks, the role of physicality must be fully quantified.…

Is our brain chaotic?

  Chaos Theory provides a theoretical framework for better understanding deterministic systems. Deterministic systems are those in which current conditions and dynamics determine their future states. Chaos in a system means that small changes in the system’s state spread exponentially. Naturally, for reliable sensory information processing, the dynamics governing our brain should not be chaotic. In this seminar, I will first demonstrate that the activity of cells in the cerebral cortex is deterministic. Then, by obtaining a model of these cells (Rapid Theta Neuron model), we will attempt to investigate chaos in the collective processing of sensory information in this system.