In this webinar, Andrea Benucci, PhD will discuss a setup developed in his laboratory for high-throughput behavioral training of mice based on voluntary head fixation. He will describe its flexible use for behavioral training and concurrent neural recordings, delving into some technical considerations related to user-specific customizations as well. In Andrea’s lab, they study the neural substrate of visual processing and vision-based decision making. To this end, they aim to define a research framework capable of linking neural architectures to the underlying computations. The solution they have developed is to integrate experimental methods for all-optical dissection of neuronal circuits with large-scale dynamical network models based on artificial neural networks (aNNs). The connectivity architecture of aNNs closely mirror that of biological neural networks, thus representing an effective theoretical framework to unify computational, algorithmic, and implementation levels of analysis. Finally, Andrea will present some examples of unique research achievements made possible by the use of this setup.

1. Andrea Benucci, PhD
RIKEN Center for Brain Science
Wakoshi, Saitama, Japan
Copyright 2021 A. Benucci and InsideScientific. All Rights Reserved.
Self Head Fixation Training
for the Study of Perceptual
Decisions in Mice

2. An expert presents the development and
assessment of a voluntary head fixation
system in his laboratory.
Self Head Fixation Training
for the Study of Perceptual
Decisions in Mice

3. The mouse is a convenient model organism in view of the unmatched
set of experimental toolboxes available to study brain functions at
multiple levels of analysis.
Depending on your research interests, you might be facing one of the
following experimental requirements:
Motivations:

4. Train mice in a very complex task , but relatively few animals are
needed (about n ≈ 10)
Primates can solve this, how about rodents?
Complex tasks in rodents need the exploration of a large parameters’ space; just “copy-
paste” from primate experiments might not work.

5. Train mice in a very simple task , but many animals are needed (about
n ≈ 100)—and quickly…
(e.g., associative task: tone-reward)

6. Complex task, few animals (but explore a large parameters’ space)
OR
Simple task, many animals
High Throughput

7. Easy to modify
(e.g., multisensory visuo-auditory task)

8. The setup should be compatible with your favorite experimental tools
• ePhys
• Two-photon
• “Macro”scopy
• GRIN lenses
• Opto/chemo-genetics
…

9. Importantly, you do not want to redesign the setup from scratch if in
need to adopt a new experimental tool.

10. Train with head fixation
If animals are trained in freely moving conditions, then forcing head fixation
during brain recordings (if that is what you need to do) will likely disrupt the
learned behavior, requiring retraining with head fixation.

11. Finally, it is convenient to minimize the human effort and involvement in behavioral
training: ideally 1 person (e.g., one lab technician), part-time.
Automation

12. How do we achieve all of this?

18. Habituation system used in the home-cage

19. Habituation system used in the home-cage
- Before chamber implantation we keep animals with littermates in enriched cages.
- In principle you can habituate more than 1 mouse in the same home-cage.
- Habituate for 1-2 weeks, until sure the animal goes in and out without any hesitation.
- Keep on monitoring the weight.

20. Easy to customize, e.g., dispenser box for food pellets.

22. First time in the main setup
- Avoid cheering or staring! Best if you observe
via webcam.
- First full latching, reward abundantly.
- Keep the first few sessions brief.

23. Latching Unlatching

25. Latching unit

26. Dual latching system

27. Tilt for recordings
Leveled post for
latching railings

28. Typical schedule
10:00 12:00 14:00 16:00
cage1 cage2 cage3
21:00 23:00 2:00 4:00
cage4
about 30 min / session
Replace cages
night group
Replace cages
day group
cage1 cage2 cage3 cage4
Day group: 2 mice 2 sessions each Night group: 2 mice 2 sessions each
2h

29. High throughput
• 1 setup trains 4 animals in 24 hours, 2 sessions/animal → 1000 trials.
• 12 setups → 48 animals/day → 12,000 trials/day.
1 technical staff:
- Replacing day/night groups: 30 min.
- Cleaning cages: 30 min.

31. Possible schedule optimization
cage1 cage2
2h
Unlatch
Door opens
Mouse latching Back to home-cage
More sessions/day: 2 hours is a generous overestimate…

32. Possible throughput optimization
More cages to 1 setup
cage1 cage2
cage3 cage4

33. Safety
- Accidents: 1 detached headplate (necessitating culling) over >200 animals
trained in 4 years.
- Check with your animal-unit manager & safety division.
But we had “escapers”…

34. Caught by the IR camera, escaping in the middle of the night…

35. How to use it
for brain
recordings
• compatibility with
your favorite
experimental tools:
Unit for physiology
Same restraining and
latching as in the main
setup

37. We have used the latching unit for physiology with:
- Two photon microscopy
- Optogenetic setup using a digital micromirror device (DMD)
- Macroscope for widefield imaging
Depending on the animal (and on the experimenter) it typically takes 1-5
sessions to reach back peak performance in the new setup.

38. Make training and physiology setups as
similar as possible
• Minimize the differences in used equipment.
• Type of monitor
• Monitor distance
• Position of eye-tracking camera
• Spout & wheel adjustments
• …

39. Make training and physiology setups as
similar as possible
• Minimize the differences in used equipment.
• Use the same software.

40. Pseudo Code

41. Applications

42. My lab studies visual perception and perceptual
decision making.
Therefore, we use the automated setups to train mice
in visual tasks.

43. Invariant orientation discrimination task

44. Lyamzin et al., bioRxiv 2021; Abdolrahmani et al., bioRxiv 2020; Orlandi et al., bioRxiv 2021

45. L/R choice probability surface
Lyamzin et al., bioRxiv 2021
Choice gradients
6° sensitivity
History-dependent strategies
N = 40 mice; 1,313,355 trials

46. Orlandi et al., bioRxiv 2021
Distributed context-dependent choice information in mouse dorsal-parietal cortex

47. Simultaneous two-photon imaging and patterned optogenetics
GCaMP8 + Chrimson

48. DeepLabCut for
eye tracking
&
Wheel for paw
tracking
(Burgess et al., Cell Rep. 2017)
https://www.internationalbrainlab.com/

49. Paw tracking
Wheel
L R
B
DLC-setup realtime close-loop

50. Sanders and Kepecs, J. Neurophysiol. 2012
Customize for VR and navigation tasks

51. Take advantage of the
automation and web-based
accessibility for domestic and
international collaborations.

52. Lab 1
Lab 2
Lab 3
- Custom hardware: a few
implementations.
- Web interface for:
- Operating software
- Scheduling/booking
- Data format
- Cloud-based data collection.
- Within-center delivery of trained
animals.
Core behavioral training facility

53. Distributed facilities across
Centers, domestic and
international:
- Improve reproducibility
across studies.
- Create a large amount of
sharable behavioral
data.
- Across-centers delivery
of trained animals?

54. Use this setup for your lab, but consider it also as
a means to boost intra- and extra-mural
collaborative research.

55. Thank you for your attention
Funding agencies:
RIKEN CBS – intramural research grant.
MEXT-JSPS Research Grants: 26290011,
17H06037, C0219129.
Fujitsu collaborative grant.

56. Andrea Benucci, PhD
RIKEN Center for Brain Science
Wakoshi, Saitama, Japan
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