Reproducibility of in vivo electrophysiological measurements in mice.

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作者:Banga Kush, Benson Julius, Bhagat Jai, Biderman Dan, Birman Daniel, Bonacchi Niccolò, Bruijns Sebastian A, Buchanan Kelly, Campbell Robert A A, Carandini Matteo, Chapuis Gaelle A, Churchland Anne K, Davatolhagh M Felicia, Lee Hyun Dong, Faulkner Mayo, Gerçek Berk, Hu Fei, Huntenburg Julia, Hurwitz Cole Lincoln, Khanal Anup, Krasniak Christopher, Lau Petrina, Langfield Christopher, Mackenzie Nancy, Meijer Guido T, Miska Nathaniel J, Mohammadi Zeinab, Noel Jean-Paul, Paninski Liam, Pan-Vazquez Alejandro, Rossant Cyrille, Roth Noam, Schartner Michael, Socha Karolina Z, Steinmetz Nicholas A, Svoboda Karel, Taheri Marsa, Urai Anne E, Wang Shuqi, Wells Miles, West Steven J, Whiteway Matthew R, Winter Olivier, Witten Ilana B, Zhang Yizi
Understanding brain function relies on the collective work of many labs generating reproducible results. However, reproducibility has not been systematically assessed within the context of electrophysiological recordings during cognitive behaviors. To address this, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. Experimenters in 10 laboratories repeatedly targeted Neuropixels probes to the same location (spanning secondary visual areas, hippocampus, and thalamus) in mice making decisions; this generated a total of 121 experimental replicates, a unique dataset for evaluating reproducibility of electrophysiology experiments. Despite standardizing both behavioral and electrophysiological procedures, some experimental outcomes were highly variable. A closer analysis uncovered that variability in electrode targeting hindered reproducibility, as did the limited statistical power of some routinely used electrophysiological analyses, such as single-neuron tests of modulation by individual task parameters. Reproducibility was enhanced by histological and electrophysiological quality-control criteria. Our observations suggest that data from systems neuroscience is vulnerable to a lack of reproducibility, but that across-lab standardization, including metrics we propose, can serve to mitigate this.

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