Deleting<i>Mecp2</i>from the entire cerebellum rather than its neuronal subtypes causes a delay in motor learning in mice

Achilly, Nathan P.; He, Lingfeng; Kim, Olivia A.; Ohmae, Shogo; Wojaczynski, Gregory J; Lin, Tao; Sillitoe, Roy V.; Medina, Javier F.; Zoghbi, Huda Y

doi:10.1101/2020.11.12.380162

Cited by 2 publications

(2 citation statements)

References 65 publications

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“…The voltage signal was acquired at a 24,414-Hz sampling rate, and band-pass filtered between 100 - 10k Hz (AP channel) and between 2 - 300 Hz (LFP channel) using an integrated Tucker-Davis Technologies and MATLAB system (TDT RZ5, medusa, RPVdsEx) running custom code (github.com/blinklab/neuroblinks). The data include Purkinje cells from a previously collected dataset (Achilly et al, 2020; Ohmae & Medina, 2015).…”

Section: Methodsmentioning

confidence: 99%

P-sort: an open-source software for cerebellar neurophysiology

Sedaghat-Nejad

Fakharian

et al. 2021

Preprint

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Analysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents challenges for spike detection. Complex spikes have waveforms that vary significantly from one event to the next, raising the problem of misidentification. Even when complex spikes are detected correctly, the simple spikes may belong to a different P-cell, raising the danger of misattribution. Here, we analyzed data from over 300 P-cells in marmosets, macaques, and mice, using an open-source, semi-automated software called P-sort that addresses the spike identification and attribution problems. Like other sorting software, P-sort relies on nonlinear dimensionality reduction to cluster spikes. However, it also uses the statistical relationship between simple and complex spikes to merge seemingly disparate clusters, or split a single cluster. In comparison with expert manual curation, occasionally P-sort identified significantly more complex spikes, as well as prevented misattribution of clusters. Three existing automatic sorters performed less well, particularly for identification of complex spikes. To improve development of analysis tools for the cerebellum, we provide labeled data for 313 recording sessions, as well as statistical characteristics of waveforms and firing patterns.

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Section: Methodsmentioning

confidence: 99%

P-sort: an open-source software for cerebellar neurophysiology

Sedaghat-Nejad

Fakharian

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The voltage signal was acquired at a 24,414-Hz sampling rate, and bandpass filtered between 100 -10k Hz (AP channel) and between 2 -300 Hz (LFP channel) using an integrated Tucker-Davis Technologies and MATLAB system (TDT RZ5, medusa, RPVdsEx) running custom code (github.com/blinklab/neuroblinks). The data include Purkinje cells from a previously collected dataset [16], [17].…”

Section: Mice Data Acquisitionmentioning

confidence: 99%

P-sort: an open-source software for cerebellar neurophysiology

Sedaghat-Nejad

Fakharian

et al. 2021

Journal of Neurophysiology

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Analysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents unique challenges to spike sorting. Complex spikes have waveforms that vary significantly from one event to the next, raising the problem of misidentification. Even when complex spikes are detected correctly, the simple spikes may belong to a different P-cell, raising the danger of misattribution. To address these identification and attribution problems, we wrote an open-source, semi-automated software called P-sort, and then tested it by analyzing data from P-cells recorded in three species: marmosets, macaques, and mice. Like other sorting software, P-sort relies on nonlinear dimensionality reduction to cluster spikes. However, it also uses the statistical relationship between simple and complex spikes to merge disparate clusters and split a single cluster. In comparison with expert manual curation, occasionally P-sort identified significantly more complex spikes, as well as prevented misattribution of clusters. Three existing automatic sorters performed less well, particularly for identification of complex spikes. To improve development of analysis tools for the cerebellum, we provide labeled data for 313 recording sessions, as well as statistical characteristics of waveforms and firing patterns of P-cells in three species.

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DeletingMecp2from the entire cerebellum rather than its neuronal subtypes causes a delay in motor learning in mice

Cited by 2 publications

References 65 publications

P-sort: an open-source software for cerebellar neurophysiology

P-sort: an open-source software for cerebellar neurophysiology

P-sort: an open-source software for cerebellar neurophysiology

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