RT Journal Article SR Electronic T1 Reproducibility of in-vivo electrophysiological measurements in mice JF bioRxiv FD Cold Spring Harbor Laboratory SP 2022.05.09.491042 DO 10.1101/2022.05.09.491042 A1 International Brain Laboratory A1 Kush Banga A1 Julius Benson A1 Niccolò Bonacchi A1 Sebastian A Bruijns A1 Rob Campbell A1 Gaëlle A Chapuis A1 Anne K Churchland A1 M Felicia Davatolhagh A1 Hyun Dong Lee A1 Mayo Faulkner A1 Fei Hu A1 Julia Hunterberg A1 Anup Khanal A1 Christopher Krasniak A1 Guido T Meijer A1 Nathaniel J Miska A1 Zeinab Mohammadi A1 Jean-Paul Noel A1 Liam Paninski A1 Alejandro Pan-Vazquez A1 Noam Roth A1 Michael Schartner A1 Karolina Socha A1 Nicholas A Steinmetz A1 Marsa Taheri A1 Anne E Urai A1 Miles Wells A1 Steven J West A1 Matthew R Whiteway A1 Olivier Winter YR 2022 UL http://biorxiv.org/content/early/2022/05/09/2022.05.09.491042.1.abstract AB Understanding whole-brain-scale electrophysiological recordings will rely on the collective work of multiple labs. Because two labs recording from the same brain area often reach different conclusions, it is critical to quantify and control for features that decrease reproducibility. To address these issues, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. We repeatedly inserted Neuropixels multi-electrode probes targeting the same brain locations (including posterior parietal cortex, hippocampus, and thalamus) in mice performing the behavioral task. We gathered data across 9 labs and developed a common histological and data processing pipeline to analyze the resulting large datasets. After applying stringent behavioral, histological, and electrophysiological quality-control criteria, we found that neuronal yield, firing rates, spike amplitudes, and task-modulated neuronal activity were reproducible across laboratories. To quantify variance in neural activity explained by task variables (e.g., stimulus onset time), behavioral variables (timing of licks/paw movements), and other variables (e.g., spatial location in the brain or the lab ID), we developed a multi-task neural network encoding model that extends common, simpler regression approaches by allowing nonlinear interactions between variables. We found that within-lab random effects captured by this model were comparable to between-lab random effects. Taken together, these results demonstrate that across-lab standardization of electrophysiological procedures can lead to reproducible results across labs. Moreover, our protocols to achieve reproducibility, along with our analyses to evaluate it are openly accessible to the scientific community, along with our extensive electrophysiological dataset with corresponding behavior and open-source analysis code.Competing Interest StatementThe authors have declared no competing interest.