Unambiguous reconstruction of network structure using avalanche dynamics

Modular co-organization of functional connectivity and scale-free dynamics in the human brain

Scale-free neuronal dynamics and interareal correlations are emergent characteristics of spontaneous brain activity. How such dynamics and the anatomical patterns of neuronal connectivity are mutually related in brain networks has, however, remained unclear. We addressed this relationship by quantifying the network colocalization of scale-free neuronal activity-both neuronal avalanches and long-range temporal correlations (LRTCs)-and functional connectivity (FC) by means of intracranial and noninvasive human resting-state electrophysiological recordings. We found frequency-specific colocalization of scale-free dynamics and FC so that the interareal couplings of LRTCs and the propagation of neuronal avalanches were most pronounced in the predominant pathways of FC. Several control analyses and the frequency specificity of network colocalization showed that the results were not trivial by-products of either brain dynamics or our analysis approach. Crucially, scale-free neuronal dynamics and connectivity also had colocalized modular structures at multiple levels of network organization, suggesting that modules of FC would be endowed with partially independent dynamic states. These findings thus suggest that FC and scale-free dynamics-hence, putatively, neuronal criticality as well-coemerge in a hierarchically modular structure in which the modules are characterized by dense connectivity, avalanche propagation, and shared dynamic states.

Modulation of Context-Dependent Spatiotemporal Patterns within Packets of Spiking Activity

Recent experiments have shown that stereotypical spatiotemporal patterns occur during brief packets of spiking activity in the cortex, and it has been suggested that top-down inputs can modulate these patterns according to the context. We propose a simple model that may explain important features of these experimental observations and is analytically tractable. The key mechanism underlying this model is that context-dependent top-down inputs can modulate the effective connection strengths between neurons because of short-term synaptic depression. As a result, the degree of synchrony and, in turn, the spatiotemporal patterns of spiking activity that occur during packets are modulated by the top-down inputs. This is shown using an analytical framework, based on avalanche dynamics, that allows calculating the probability that a given neuron spikes during a packet and numerical simulations. Finally, we show that the spatiotemporal patterns that replay previously experienced sequential stimuli and their binding with their corresponding context can be learned because of spike-timing-dependent plasticity.