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(A) 3D graphic of the home cage system with 8 mice. Two cages are connected via a tunnel with an RFID sensor underneath and designed to automatically monitor the weight of the traversing mice. (B) Side view of the imaging chamber with a head-fixed mouse. An RFID sensor identifies the specific mouse. The snout of the mouse breaking the IR beam initiates the servo motor to head-fix the mouse by using the mounted aluminum T-piece to press the head-bar against the metal reinforced end of the chamber. Imaging LEDs, brain camera, and body cameras which record the behavior (eye, face and body) of the head-fixed animal are then activated. The spout provides the water rewards and detects licks for the task via a capacitance sensor. The Raspberry Pi processes inputs from the lick sensor and controls task related electronics (water solenoid, stimulus buzzer, auditory feedback). (C) Photo of the assembled major cage components. The Raspberry Pi and a printed circuit board (PCB) containing transistor logic switches and servo driving hardware are covered in a metal case (upper right). Electronic devices are plugged into headers on the PCB breakout (Water solenoid, Servo Motor) or into the Raspberry Pi directly (HDMI, USB RFID Reader). Picam with triple band filter 69013 m Chroma was used for brain imaging. IR beam breakers, auditory feedback buzzer, LEDs, vibration motor and licking sensor adapters (need short wiring to reduce noise) can be plugged via unique cables to the breakout extension board and to the Raspberry Pi. The breakout also supplies several regulated supply voltages and contains a small breadboard for use in developing future behavioral tasks.
First, I was concerned that the authors didn't make sufficiently clear how this work is an advance on the Aoki, Benucci et al. approach that was published in 2017. The text states that, "While an advance for training, this work did not longitudinally gather brain imaging data in an autonomous manner, nor were the systems of a footprint or cost appropriate for running at scale." But in the Aoki paper, 2-photon data was collected for 21 days (Figure 5D). The measurements were not taken in the home cage; if the authors feel that is a critical difference, they should state why. I also wasn't sure how to compare the cost difference with that approach and the one presented here. The Aoki apparatus does appear to be commercially available ( -time.co.jp/wordpress/wp-content/uploads/SelfHead-restraintOperantTestSystem_Pamphlet2017.pdf), but I wasn't sure of its price or the total price of the setup in the current paper so it was hard to compare.
There was no mechanism to automatically release mice using force sensing as all trials were short in duration. Trial length was set by the investigator and generally fixed in the range of no more than 45 sec of headfixation. The trial lengths did vary as we added extra time to allow mice to finish a trial once they started.
We agree that it would be interesting to compare automatically headfixed and extended manual headfixation. However, this was not our intention. Any data regarding the performance in headfixed and non-headfixed cases was included as it might aid someone attempting this work in the future. We should stress that all analysis of mesoscale GCAMP dynamics and task performance is done using fully headfixed trials. We did not use non-headfixed trials in behavioral measures that are presented in figures (such as licking data in Figure 5), other than the comparison of task performance in non-headfixed states.
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