  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.
