Electrophysiology provides a direct readout of neuronal activity at a temporal precision only limited by the sampling rate. However, interrogating deep brain structures, implanting multiple targets or aiming at unusual angles still poses significant challenges for operators, and errors are only discovered by post-hoc histological reconstruction. Here, we propose a method combining the high-resolution information about bone landmarks provided by micro-CT scanning with the soft tissue contrast of the MRI, which allowed us to precisely localize electrodes and optic fibers in mice in vivo. This enables arbitrating the success of implantation directly after surgery with a precision comparable to gold standard histology. Adjustment of the recording depth with micro-drives or early termination of unsuccessful experiments saves many working hours, and fast 3-dimensional feedback helps surgeons avoid systematic errors. Increased aiming precision enables more precise targeting of small or deep brain nuclei and multiple targeting of specific cortical or hippocampal layers.
Experiments aiming to understand sensory-motor systems, cognition and behavior necessitate training animals to perform complex tasks. Traditional training protocols require lab personnel to move the animals between home cages and training chambers, to start and end training sessions, and in some cases, to hand-control each training trial. Human labor not only limits the amount of training per day, but also introduces several sources of variability and may increase animal stress. Here we present an automated training system for the 5-choice serial reaction time task (5CSRTT), a classic rodent task often used to test sensory detection, sustained attention and impulsivity. We found that full automation without human intervention allowed rapid, cost-efficient training, and decreased stress as measured by corticosterone levels. Training breaks introduced only a transient drop in performance, and mice readily generalized across training systems when transferred from automated to manual protocols. We further validated our automated training system with wireless optogenetics and pharmacology experiments, expanding the breadth of experimental needs our system may fulfill. Our automated 5CSRTT system can serve as a prototype for fully automated behavioral training, with methods and principles transferrable to a range of rodent tasks.
11Experiments aiming to understand sensory-motor systems, cognition and behavior often require animals 12 trained to perform complex tasks. Traditional training protocols require lab personnel to move the animals 13 between home cages and training chambers, to start and end training sessions, and in some cases, to 14 hand-control each training trial. Human labor not only limits the amount of training per day, but also 15 introduces several sources of variability and may increase animal stress. Here we present an automated 16 training system for the 5-choice serial reaction time task (5CSRTT), a classic rodent task often used to test 17 sensory detection, sustained attention and impulsivity. We found that fully automated training without 18 human intervention greatly increased the speed and efficiency of learning, and decreased stress as 19 measured by corticosterone levels. Introducing training breaks did not cancel these beneficial effects of 20 automated training, and mice readily generalized across training systems when transferred from 21 automated to manual protocols. Additionally, we validated our automated training system with mice 22 implanted with wireless optogenetic stimulators, expanding the breadth of experimental needs our 23 system may fulfill. Our automated 5CSRTT system can serve as a prototype for fully automated behavioral 24 training, with methods and principles transferrable to a range of rodent tasks. 25 A few automated training systems have been developed for rodent behavioral tasks 8-15 , including 5-choice 40 serial reaction time task (5CSRTT) 16,17 , in order to standardize the training and reduce the effects of human 41 factors and other random variables. While these systems provide means for large capacity automated 42 training of rodents, most of them are customized to train a specific task variant, and/or contain expensive, 43proprietary components. For these reasons, automated behavioral training of the 5CSRTT task has not yet 44 become widespread. Here we developed an affordable, open source, high-throughput automated training 45 system for mice and demonstrate its use on an automated protocol of the widely used 5CSRTT assay 18-21 . 46 We show that use of this Automated Training System (ATS) allows faster training of mice, and that 47
14Electrophysiology provides a direct readout of neuronal activity at a temporal precision only limited by 15 the sampling rate. However, interrogating deep brain structures, implanting multiple targets or aiming 16 at unusual angles still poses significant challenges even for expert operators, and errors are only 17 discovered by post-hoc histological reconstruction. Here, we propose a method combining the high-18resolution information about bone landmarks provided by micro-CT scanning with the soft tissue 19 contrast of the MRI, which allowed us to precisely localize electrodes and optic fibers in mice in vivo. 20 This enables arbitrating the success of implantation directly after surgery with a precision comparable 21 to the gold standard histological reconstruction. Adjustment of the recording depth with electrode 22 microdrives or early termination of unsuccessful experiments saves many working hours, while fast 3-23 dimensional feedback helps surgeons to avoid systematic errors. Increased aiming precision will allow 24 more precise targeting of small or deep brain nuclei and multiple targeting of specific cortical layers. 25
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