Engaged decision-makers align spontaneous movements to stereotyped task demands.

Neural activity during sensory-guided decision-making is strongly modulated by animal movements. Although the impact of movements on neural activity is now well-documented, the relationship between these movements and behavioral performance remains unclear. To understand this relationship, we first tested whether the magnitude of animal movements (assessed with posture analysis of 28 individual body parts) was correlated with performance on a perceptual decision-making task. No strong relationship was present, suggesting that task performance is not affected by the magnitude of movements. We then tested if performance instead depends on movement timing and trajectory. We partitioned the movements into two groups: task-aligned movements that were well predicted by task events (such as the onset of the sensory stimulus or choice) and task independent movement (TIM) that occurred independently of task events. TIM had a reliable, inverse correlation with performance in head-restrained mice and freely moving rats. This argues that certain movements, defined by their timing and trajectories relative to task events, might indicate periods of engagement or disengagement in the task. To confirm this, we compared TIM to the latent behavioral states recovered by a hidden Markov model with Bernoulli generalized linear model observations (GLM-HMM) and found these, again, to be inversely correlated. Finally, we examined the impact of these behavioral states on neural activity measured with widefield calcium imaging. The engaged state was associated with widespread increased activity, particularly during the delay period. However, a linear encoding model could account for more overall variance in neural activity in the disengaged state. Our analyses demonstrate that this is likely because uninstructed movements had a greater impact on neural activity during disengagement. Taken together, these findings suggest that TIM is informative about the internal state of engagement, and that movements and state together have a major impact on neural activity.

Fast calcium sensor proteins for monitoring neural activity.

A major goal of the BRAIN Initiative is the development of technologies to monitor neuronal network activity during active information processing. Toward this goal, genetically encoded calcium indicator proteins have become widely used for reporting activity in preparations ranging from invertebrates to awake mammals. However, slow response times, the narrow sensitivity range of Ca2+ and in some cases, poor signal-to-noise ratio still limit their usefulness. Here, we review recent improvements in the field of neural activity-sensitive probe design with a focus on the GCaMP family of calcium indicator proteins. In this context, we present our newly developed Fast-GCaMPs, which have up to 4-fold accelerated off-responses compared with the next-fastest GCaMP, GCaMP6f. Fast-GCaMPs were designed by destabilizing the association of the hydrophobic pocket of calcium-bound calmodulin with the RS20 binding domain, an intramolecular interaction that protects the green fluorescent protein chromophore. Fast-GCaMP6f-RS06 and Fast-GCaMP6f-RS09 have rapid off-responses in stopped-flow fluorimetry, in neocortical brain slices, and in the intact cerebellum in vivo. Fast-GCaMP6f variants should be useful for tracking action potentials closely spaced in time, and for following neural activity in fast-changing compartments, such as axons and dendrites. Finally, we discuss strategies that may allow tracking of a wider range of neuronal firing rates and improve spike detection.

Optimization of a GCaMP calcium indicator for neural activity imaging.

Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Recent efforts in protein engineering have significantly increased the performance of GECIs. The state-of-the art single-wavelength GECI, GCaMP3, has been deployed in a number of model organisms and can reliably detect three or more action potentials in short bursts in several systems in vivo. Through protein structure determination, targeted mutagenesis, high-throughput screening, and a battery of in vitro assays, we have increased the dynamic range of GCaMP3 by severalfold, creating a family of "GCaMP5" sensors. We tested GCaMP5s in several systems: cultured neurons and astrocytes, mouse retina, and in vivo in Caenorhabditis chemosensory neurons, Drosophila larval neuromuscular junction and adult antennal lobe, zebrafish retina and tectum, and mouse visual cortex. Signal-to-noise ratio was improved by at least 2- to 3-fold. In the visual cortex, two GCaMP5 variants detected twice as many visual stimulus-responsive cells as GCaMP3. By combining in vivo imaging with electrophysiology we show that GCaMP5 fluorescence provides a more reliable measure of neuronal activity than its predecessor GCaMP3. GCaMP5 allows more sensitive detection of neural activity in vivo and may find widespread applications for cellular imaging in general.