BMC NeuroscienceNeurosciencePeer-reviewed journal article
Detection of neuronal OFF periods as low amplitude neural activity segments
Christian D. HardingiD, Mathilde C. C. GuillauminiD, Lukas B. KroneiD, Martin C. KahniD, Cristina Blanco‐DuqueiD, Christian Alexander MikuttaiD, Vladyslav V. VyazovskiyiD
Abstract
During non-rapid eye movement sleep (NREM), synchronised neuronal activity is reflected in a specific neural oscillation observed in neocortical electrophysiological signals: a low frequency component characterised by depth-positive/surface-negative potentials known as slow waves, corresponding to alternating periods of high (ON period) and low (OFF period) spiking activity. Often overlooked in favour of slow waves, there is an interest in understanding how neuronal silencing during OFF periods leads to the generation of slow waves and whether this relationship changes between cortical layers. The foremost issue in detecting population OFF periods is the absence of a formal, widely adopted definition. Here, we grouped segments of high frequency neural activity containing spikes, recorded from the neocortex, on the basis of amplitude and asked whether the population of low amplitude (LA) segments displayed the expected characteristics of OFF periods. We corroborate previous studies showing that LA segments in neural activity signals are a uniquely identifiable structure with distinct characteristics from the surrounding signal that identify them as OFF periods including NREM sleep predominance and association with a local field potential (LFP) slow wave. In addition, we attribute new characteristics to these segments not previously associated with OFF periods: vigilance-state-dependent duration and duration-dependent homeostatic response. This could suggest that ON/OFF periods are currently underdefined, and their appearance is less binary than previously considered, instead representing a continuum.
- organism: mouse
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 9
- FWCI
- 1.20
- Field percentile
- top 26%
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