Literature DB >> 31433725

LFP clustering in cortex reveals a taxonomy of Up states and near-millisecond, ordered phase-locking in cortical neurons.

Catalin C Mitelut1,2,3,4, Martin A Spacek4,5, Allen W Chan6, Tim H Murphy2,3, Nicholas V Swindale4.   

Abstract

During slow-wave sleep and anesthesia, mammalian cortex exhibits a synchronized state during which neurons shift from a largely nonfiring to a firing state, known as an Up-state transition. Up-state transitions may constitute the default activity pattern of the entire cortex (Neske GT. Front Neural Circuits 9: 88, 2016) and could be critical to understanding cortical function, yet the genesis of such transitions and their interaction with single neurons is not well understood. It was recently shown that neurons firing at rates >2 Hz fire spikes in a stereotyped order during Up-state transitions (Luczak A, McNaughton BL, Harris KD. Nat Rev Neurosci 16: 745-755, 2015), yet it is still unknown if Up states are homogeneous and whether spiking order is present in neurons with rates <2 Hz (the majority). Using extracellular recordings from anesthetized cats and mice and from naturally sleeping rats, we show for the first time that Up-state transitions can be classified into several types based on the shape of the local field potential (LFP) during each transition. Individual LFP events could be localized in time to within 1-4 ms, more than an order of magnitude less than in previous studies. The majority of recorded neurons synchronized their firing to within ±5-15 ms relative to each Up-state transition. Simultaneous electrophysiology and wide-field imaging in mouse confirmed that LFP event clusters are cortex-wide phenomena. Our findings show that Up states are of different types and point to the potential importance of temporal order and millisecond-scale signaling by cortical neurons.NEW & NOTEWORTHY During cortical Up-state transitions in sleep and anesthesia, neurons undergo brief periods of increased firing in an order similar to that occurring in awake states. We show that these transitions can be classified into distinct types based on the shape of the local field potential. Transition times can be defined to <5 ms. Most neurons synchronize their firing to within ±5-15 ms of the transitions and fire in a consistent order.

Entities:  

Keywords:  Up states; clustering; local field potential; single-neuron recording; spike order

Mesh:

Year:  2019        PMID: 31433725      PMCID: PMC6843104          DOI: 10.1152/jn.00456.2019

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  51 in total

1.  Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

Authors:  R Quian Quiroga; Z Nadasdy; Y Ben-Shaul
Journal:  Neural Comput       Date:  2004-08       Impact factor: 2.026

2.  Field potential signature of distinct multicellular activity patterns in the mouse hippocampus.

Authors:  Susanne Reichinnek; Thomas Künsting; Andreas Draguhn; Martin Both
Journal:  J Neurosci       Date:  2010-11-17       Impact factor: 6.167

Review 3.  Cortical state and attention.

Authors:  Kenneth D Harris; Alexander Thiele
Journal:  Nat Rev Neurosci       Date:  2011-08-10       Impact factor: 34.870

4.  Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states.

Authors:  A Destexhe; D Contreras; M Steriade
Journal:  J Neurosci       Date:  1999-06-01       Impact factor: 6.167

Review 5.  The slow (<1 Hz) rhythm of non-REM sleep: a dialogue between three cardinal oscillators.

Authors:  Vincenzo Crunelli; Stuart W Hughes
Journal:  Nat Neurosci       Date:  2009-12-06       Impact factor: 24.884

6.  Spontaneous events outline the realm of possible sensory responses in neocortical populations.

Authors:  Artur Luczak; Peter Barthó; Kenneth D Harris
Journal:  Neuron       Date:  2009-05-14       Impact factor: 17.173

7.  Origin of active states in local neocortical networks during slow sleep oscillation.

Authors:  Sylvain Chauvette; Maxim Volgushev; Igor Timofeev
Journal:  Cereb Cortex       Date:  2010-03-03       Impact factor: 5.357

8.  Robust off- and online separation of intracellularly recorded up and down cortical states.

Authors:  Yamina Seamari; José A Narváez; Francisco J Vico; Daniel Lobo; Maria V Sanchez-Vives
Journal:  PLoS One       Date:  2007-09-12       Impact factor: 3.240

9.  Spike sorting for polytrodes: a divide and conquer approach.

Authors:  Nicholas V Swindale; Martin A Spacek
Journal:  Front Syst Neurosci       Date:  2014-02-10

Review 10.  The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions.

Authors:  Garrett T Neske
Journal:  Front Neural Circuits       Date:  2016-01-14       Impact factor: 3.492

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