Real-time neural feedback of mesoscale cortical GCAMP6 signals for training mice

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Mice can learn to control specific neuronal ensembles using sensory (eg. auditory) cues (Clancy et al. 2014) or even artificial optogenetic stimulation (Prsa et al. 2017). In the present work, we measure mesoscale cortical activity with GCaMP6s and provide graded auditory feedback (within ~100 ms after GCaMP fluorescence) based on changes in dorsal-cortical activation within specified regions of interest (ROI)s with a specified rule. We define a compact, low-cost optical brain-machine-interface (BMI) capable of image acquisition, processing, and conducting closed-loop auditory feedback and rewards, using a Raspberry Pi (Fig. 1). The changes in fluorescence activity (ΔF/F) are calculated based on a running baseline (eg. 5 sec.). Two ROIs (R1, R2) on the dorsal cortical map were selected as targets. We started with a rule of ‘R1-R2’ (ΔF/F of R1 minus ΔF/F of R2) where the activity of R1 relative to R2 was mapped to frequency of the audio feedback (Fig. 1D) and if it were to cross a set threshold, a water drop reward is generated. To investigate learning in this context, water-deprived tetO-GCaMP6s mice (N=8) were trained for 30-minutes every day on the system for several days, with a task to increase audio frequency leading to reward. We found that mice could modulate activity in the rule-specific target ROIs to get an increasing number of rewards over days (Figure 2C). Analysis of the reward-triggered ΔF/F over time indicated that mice progressively learned to activate the cortical ROI to a greater extent (Figure 2B, 2A). In conclusion, we developed an open-source system (to-be released) for closed-loop feedback that can be added to experimental scenarios for brain activity training and could be possibly effective in inducing neuroplasticity.

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