Figure 1. Brain states encoded in network activity and local circuitry.
(a) The common strategy to use whole-brain regression analysis under various conditions and behavioral recordings often reveals widespread sensory (teal) and motor (purple) related signals. After task relevant neurons are located, most studies progress with local circuit analysis to refine response types, connectivity and anatomy, using molecular identity, and through the use of a combination of single cell electroporation, transgenic lines, and causal perturbations, including laser ablations of single neurons or optogenetic activation in concert with behavioral testing. These results are often synthesized into models that range from simple activity maps, suggesting circuit diagrams, to realistic quantitative network simulations using recorded activity dynamics in recurrent neural network replicating biological effective connectivity. Other approaches include probabilistic models linking sensory input to behavioral output and comparison to artificial neural networks.
(b) In a recent study ••[24], focus shifted towards dynamic brain state transitions rather than correlational maps. Left, example behavior trajectories of zebrafish in exploitation (red) and exploration (blue) states. Right, locations of all neurons encoding these brain states, projected across 17 registered fish recorded with a tracking microscope, revealing brain state switches triggered by serotonergic neurons (dashed box). Ro, rostral; C, caudal; L, left; R, right.
(c) PCA trajectory of whole-brain activity (104,142 neurons) from a representative animal, color-coded by the activity of exploitation-state-encoding neurons.
(d) Average activity (mean ± s.d.) of exploitation-state-encoding (red) and exploration-state-encoding (blue) neurons across the transition from exploration to exploitation (top). These dynamics are likely key to understand brain wide states that modulating other processes.
(e) Lin et al. ••[25]. used an operant conditioning assay combined with whole-brain calcium imaging to investigate how decision brain states develop over time. Head-fixed larval zebrafish receive a mildly aversive heat stimulus by an infrared laser (red trapezoid) at the beginning of a trial. The laser is turned off if the fish makes a tail movement in the reward direction and remains on otherwise. In the second training block the reward direction is switched, with each block consisting of 20–25 trials
(f) The learning progress of an example learner fish. Black traces indicate tail positions over time, magenta and green rectangles indicate the duration of the heat stimulus for each incorrect and correct trial, respectively.
(g) Temporal evolution of brain states reveals distributed brain activity and bihemispheric preparatory activity. ARTR, anterior rhombencephalic turning region; Cb, cerebellum; HB, habenula; Te, telencephalon.
(h) Performance-dependent bifurcation of brain states before turn initiation. Left, after heat onset, brain states exhibit similarity along the “Heat ON” dimension (vertical axis), followed by a pre-turn bifurcation toward the correct or incorrect state. Similarity is measured by partial correlation between a given brain state and the average correct, incorrect, or “Heat ON” state. Right, representation of single-trial bifurcation process. Individual correct and incorrect trials are highlighted during the pre-motor period (black diamonds: heat onset, green and magenta dots: turn initiations for correct and incorrect turns, respectively).
(i) 2D representation of single-trial bifurcation process. Individual correct and incorrect trials are highlighted during the pre-motor period (black diamonds: heat onset, green and magenta dots: turn initiations for correct and incorrect turns, respectively).