Phase Transitions in Brain
Networks
Phase
transitions occur in a variety of physical and biological systems, including
brain networks. They are characterized by instabilities that generate special dynamical
properties, which are associated with dynamical and functional benefits. Phase
transition is also an appealing framework that has been utilised to explain
pathological brain activity. Historically, the idea of phase transitions in the
brain is not new and has sparked some controversy over the years. In this half-day
workshop, a number of current world-class experts will revisit the possible
roles of phase transitions in the brain. They will discuss the recent progress
in the field and the relevance and limitations of this framework to
computational neuroscience. In order to further exploit and explicitize
contemporary viewpoints on the promises and pitfalls of phase transitions in
the brain, we will end this workshop with a roundtable discussion in which the
speakers and the audience will be invited to participate. When: July 16. Schedule: 9:55-10:20 Anna Levina (MPI): Influence of spatial structure on data processing and phase transitions in neuronal networks 10:20-10:45 Jonathan Touboul (Brandeis University): Power-law scalings in neuronal data: proofs for criticality? 10:45-11:15 Coffee Break 11:15-11:40 Etienne Hugues (Université Grenoble Alpes): Hallmarks of spontaneous and stimulation-induced activity are reproduced in a scale-invariant avalanche regime 11:40-12:05 Fernando Santos (UFPE): Topological phase transitions in functional brain networks 12:05-12:30 James Roberts (QIMRB): Geometry and fragility of the human connectome 12:30-13:00 Roundtable discussions Abstracts Linda Douw (Amsterdam UMC) A historical perspective on phase transitions in the brain The
brain has been seen as either a distributed collection of localized
centers of function, or as an integrated, dynamic system. With the
latter view come the concepts of criticality from physics and phase
transitions from thermodynamics, which have been amply applied to the
brain over the past decades. In this historical overview, some
background on the investigation of phase transitions in the brain will
be provided, as well as the controversies that have sparked over it. Anna Levina (MPI) Influence of spatial structure on data processing and phase transitions in neuronal networks Networks are backbones of the complex brain activity. Modern methods allow extracting more and more reliable functional and structural networks on different scales. One of the major challenges is to understand the relationship between the structure of the network and the properties of its dynamics. Using simple models and data analysis I am going to discuss, on the one hand, how the features of the networks are reflected in the dynamics of single units. And on the other hand, how the system's structure changes the nature of the phase transition in its dynamics.Jonathan Touboul (Brandeis University) Power-law scalings in neuronal data: proofs for criticality? Etienne Hugues (Université Grenoble Alpes) Hallmarks of spontaneous and stimulation-induced activity are reproduced in a scale-invariant avalanche regime As neurons are spontaneously active, a global state emerges on the brain network. This spontaneous state is of utmost importance as sensory stimulation for example, activating a relatively small subset of all neurons, can be seen as a perturbation of this state. In other words, cognition inherits from the resting state. The resting state has been the focus of intense study in the last two decades, uncovering its spatial and temporal organization with functional connectivity (FC) and scale-invariant neuronal avalanches. Under stimulation, increased neural activity propagates on the brain network, and the firing variability across trials generally decreases. What properties should have the spontaneous state to exhibit such hallmarks is unknown. Mesoscopic large-scale modeling, where the full neuronal network of the brain is coarse-grained at the local neural network level, has been essentially focused on reproducing resting state FC. The main proposed dynamical scenario of the spontaneous state -denoted here the fluctuation scenario, corresponds to neural activity wandering around a stable global fixed point with low firing rate, corresponding to a global asynchronous state. Despite its ability to reproduce FC, this scenario is unable to exhibit scale-invariant neuronal avalanches and stimulation-induced propagation of activity. In this talk, I will show why the fluctuation scenario fails. I will also show how known biophysics allows to introduce a new dynamical scenario, in which a regime of scale-invariant neuronal avalanches reproduces the hallmarks of the spontaneous state and during stimulation. Fernando Santos (UFPE) Topological phase transitions in functional brain networks Functional brain networks are often constructed by quantifying correlations among brain regions. Their topological structure includes nodes, edges, triangles and even higher-dimensional objects. Topological data analysis (TDA) is the emerging framework to process datasets under this perspective. Here we report the discovery of topological phase transitions in functional brain networks by merging concepts from TDA, topology, geometry, physics, and network theory. We show that topological phase transitions occur when the Euler entropy has a singularity, which remarkably coincides with the emergence of multidimensional topological holes in the brain network. Our results suggest that a major alteration in the pattern of brain correlations can modify the signature of such transitions, and may point to suboptimal brain functioning. Due to the universal character of phase transitions and noise robustness of TDA, our findings open perspectives towards establishing reliable topological and geometrical biomarkers of individual and group differences in functional brain network organization.James Roberts (QIMRB) Geometry and fragility of the human connectome The human connectome is a topologically complex, spatially embedded network. While its topological properties have been richly characterized, the constraints imposed by its spatial embedding are poorly understood. In this talk I will present a recent novel resampling method that enables randomization of a network while preserving its spatial embedding. Applying this method to tractography data reveals that the brain's spatial embedding – its geometry – makes a major contribution to the topology of the human connectome. For example, geometry accounts for much of the brain's modularity. But geometry is not the sole determinant: the brain's structural hubs would be positioned closer to the geometric center of the brain if geometry was the only source of topology. Closer analysis of the brain's hubs under weaker randomization reveals that the brain sits at a local minimum in wiring cost, and that progressive randomization leads to a topologically unstable regime consistent with a phase transition. Moreover, prefrontal hubs are particularly fragile to perturbations, correlating with the pattern of acceleration of grey matter loss in schizophrenia. This suggests that fragile prefrontal hub connections and topological volatility act as evolutionary influences on complex brain networks, whose set point may be perturbed in neurological and psychiatric disorders. Organizers: Leonardo Gollo (QIMRB) Linda Douw (Amsterdam UMC) Past events: |