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Brain activity is characterized by brain-wide spatiotemporal patterns which emerge from synapse-mediated interactions between individual neurons. Calcium imaging provides access to in vivo recordings of whole-brain activity at single-neuron resolution and, therefore, allows the study of how large-scale brain dynamics emerge from local activity. In this study, we used a statistical mechanics approach - the pairwise maximum entropy model (MEM) - to infer microscopic network features from collective patterns of activity in the larval zebrafish brain, and relate these features to the emergence of observed whole-brain dynamics. Our findings indicate that the pairwise interactions between neural populations and their intrinsic activity states are sufficient to explain observed whole-brain dynamics. In fact, the pairwise relationships between neuronal populations estimated with the MEM strongly correspond to observed structural connectivity patterns. Model simulations also demonstrated how tuning pairwise neuronal interactions drives transitions between critical and pathologically hyper-excitable whole-brain regimes. Finally, we use virtual resection to identify the brain structures that are important for maintaining the brain in a critical regime. Together, our results indicate that whole-brain activity emerges out of a complex dynamical system that transitions between basins of attraction whose strength and topology depend on the connectivity between brain areas., Comment: 14 pages, 4 figures; additionaly supplementary material

We consider the theoretical constraints on interactions between coupled cortical columns. Each column comprises a set of neural populations, where each population is modelled as a neural mass. The existence of semi-stable states within a cortical column has been shown to be dependent on the type of interaction between the constituent neuronal subpopulations, i.e., the form of the implicit synaptic convolution kernels. Current-to-current coupling has been shown, in contrast to potential-to-current coupling, to create semi-stable states within a cortical column. In this analytic and numerical study, the interaction between semi-stable states is characterized by equations of motion for ensemble activity. We show that for small excitations, the dynamics follow the Kuramoto model. However, in contrast to previous work, we derive coupled equations between phase and amplitude dynamics. This affords the possibility of defining connectivity as a dynamic variable. The turbulent flow of phase dynamics found in networks of Kuramoto oscillators indicate turbulent changes in dynamic connectivity for coupled cortical columns. Crucially, this is something that has been recorded in epileptic seizures. We used the results we derived to estimate a seizure propagation model, which allowed for relatively straightforward inversions using variational Laplace (a.k.a., Dynamic Causal Modelling). The face validity of the ensuing seizure propagation model was established using simulated data as a prelude to future work; which will investigate dynamic connectivity from empirical data. This model also allows predictions of seizure evolution, induced by virtual lesions to synaptic connectivity: something that could be of clinical use, when applied to epilepsy surgical cases.