scholarly journals Olfactory Bulb Field Potentials and Respiration in Sleep-Wake States of Mice

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jakob Jessberger ◽  
Weiwei Zhong ◽  
Jurij Brankačk ◽  
Andreas Draguhn
Keyword(s):  
Olfactory Bulb ◽  
Local Field ◽  
Respiratory Rhythm ◽  
Local Field Potentials ◽  
Nrem Sleep ◽  
Network Activity ◽  
Whole Body ◽  
Field Potentials ◽  
Biophysical Parameters ◽  
Similar Frequency

It is well established that local field potentials (LFP) in the rodent olfactory bulb (OB) follow respiration. This respiration-related rhythm (RR) in OB depends on nasal air flow, indicating that it is conveyed by sensory inputs from the nasal epithelium. Recently RR was found outside the olfactory system, suggesting that it plays a role in organizing distributed network activity. It is therefore important to measure RR and to delineate it from endogenous electrical rhythms like theta which cover similar frequency bands in small rodents. In order to validate such measurements in freely behaving mice, we compared rhythmic LFP in the OB with two respiration-related biophysical parameters: whole-body plethysmography (PG) and nasal temperature (thermocouple; TC). During waking, all three signals reflected respiration with similar reliability. Peak power of RR in OB decreased with increasing respiration rate whereas power of PG increased. During NREM sleep, respiration-related TC signals disappeared and large amplitude slow waves frequently concealed RR in OB. In this situation, PG provided a reliable signal while breathing-related rhythms in TC and OB returned only during microarousals. In summary, local field potentials in the olfactory bulb do reliably reflect respiratory rhythm during wakefulness and REM sleep but not during NREM sleep.

Brain-state dependent uncoupling of BOLD and local field potentials in laminar olfactory bulb

2014 ◽  
Vol 580 ◽  
pp. 1-6 ◽  
Author(s):  
Ling Gong ◽  
Bo Li ◽  
Ruiqi Wu ◽  
Anan Li ◽  
Fuqiang Xu
Keyword(s):  
Olfactory Bulb ◽  
Local Field ◽  
Local Field Potentials ◽  
Brain State ◽  
Field Potentials ◽  
State Dependent

scholarly journals Changes in Local Network Activity Approximated by Reverse Spike-Triggered Local Field Potentials Predict the Focus of Attention

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdelrahman Sharafeldin ◽  
Vanessa L Mock ◽  
Stephen Meisenhelter ◽  
Jacqueline R Hembrook-Short ◽  
Farran Briggs
Keyword(s):  
Spatial Attention ◽  
Local Field ◽  
Local Field Potentials ◽  
Focus Of Attention ◽  
Neuronal Firing ◽  
Network Activity ◽  
Local Network ◽  
Field Potentials ◽  
Visual Spatial Attention ◽  
Visual Spatial

Abstract The effects of visual spatial attention on neuronal firing rates have been well characterized for neurons throughout the visual processing hierarchy. Interestingly, the mechanisms by which attention generates more or fewer spikes in response to a visual stimulus remain unknown. One possibility is that attention boosts the likelihood that synaptic inputs to a neuron result in spikes. We performed a novel analysis to measure local field potentials (LFPs) just prior to spikes, or reverse spike-triggered LFP “wavelets,” for neurons recorded in primary visual cortex (V1) of monkeys performing a contrast change detection task requiring covert shifts in visual spatial attention. We used dimensionality reduction to define LFP wavelet shapes with single numerical values, and we found that LFP wavelet shape changes correlated with changes in neuronal firing rate. We then tested whether a simple classifier could predict monkeys’ focus of attention from LFP wavelet shape. LFP wavelet shapes sampled in discrete windows were predictive of the locus of attention for some neuronal types. These findings suggest that LFP wavelets are a useful proxy for local network activity influencing spike generation, and changes in LFP wavelet shape are predictive of the focus of attention.

scholarly journals Local Field Potentials in a Pre-motor Region Predict Learned Vocal Sequences

2020 ◽  
Author(s):  
Daril E. Brown ◽  
Jairo I. Chavez ◽  
Derek H. Nguyen ◽  
Adam Kadwory ◽  
Bradley Voytek ◽  
...  
Keyword(s):  
Motor Control ◽  
Local Field ◽  
Local Field Potentials ◽  
Brain Regions ◽  
Network Activity ◽  
Field Potentials ◽  
Population Activity ◽  
Time Frequency ◽  
Useful Signal ◽  
Complex Motor

AbstractNeuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity typically focuses on patterns of sequential bursting in small carefully identified subsets of single neurons in the HVC population. Much less is known about population dynamics beyond the scale of individual neurons. There is a rich history of using local field potentials (LFP), to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time and have been used to study and decode complex motor behaviors, such as human speech. Here we characterize LFP signals in the putative HVC of freely behaving male zebra finches during song production, to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates that LFP is a useful signal for studying motor control in songbirds. Surprisingly, the time frequency structure of putative HVC LFP is qualitatively similar to well established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight to common computational principles for learning and/or generating complex motor-vocal behaviors.Author SummaryVocalizations, such as speech and song, are a motor process that requires the coordination of several muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing and it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity in putative HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Given the similarities of the songbird and human motor-vocal systems these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.

Noradrenaline-induced enhancement of oscillatory local field potentials in the mouse accessory olfactory bulb does not depend on disinhibition of mitral cells

2012 ◽  
Vol 35 (9) ◽  
pp. 1433-1445 ◽  
Author(s):  
Emilia Leszkowicz ◽  
Selina Khan ◽  
Stephanie Ng ◽  
Nikita Ved ◽  
Daniel L. Swallow ◽  
...  
Keyword(s):  
Olfactory Bulb ◽  
Local Field ◽  
Local Field Potentials ◽  
Mitral Cells ◽  
Accessory Olfactory Bulb ◽  
Field Potentials

Tracking of informative gamma frequency range in local field potentials of anesthetized rat olfactory bulb for odor discrimination

2022 ◽  
Vol 71 ◽  
pp. 103139
Author(s):  
Igor Shepelev ◽  
Valery Kiroy ◽  
Igor Scherban ◽  
Petr Kosenko ◽  
Alexey Smolikov ◽  
...  
Keyword(s):  
Olfactory Bulb ◽  
Local Field ◽  
Local Field Potentials ◽  
Odor Discrimination ◽  
Field Potentials ◽  
Frequency Range ◽  
Gamma Frequency

Removal of Spurious Correlations Between Spikes and Local Field Potentials

2011 ◽  
Vol 105 (1) ◽  
pp. 474-486 ◽  
Author(s):  
Theodoros P. Zanos ◽  
Patrick J. Mineault ◽  
Christopher C. Pack
Keyword(s):  
Local Field ◽  
Local Field Potentials ◽  
Ground Truth ◽  
Real Data ◽  
Network Activity ◽  
Mathematical Framework ◽  
Field Potentials ◽  
Sensory Stimuli ◽  
Electrophysiological Recordings ◽  
Low Pass

Single neurons carry out important sensory and motor functions related to the larger networks in which they are embedded. Understanding the relationships between single-neuron spiking and network activity is therefore of great importance and the latter can be readily estimated from low-frequency brain signals known as local field potentials (LFPs). In this work we examine a number of issues related to the estimation of spike and LFP signals. We show that spike trains and individual spikes contain power at the frequencies that are typically thought to be exclusively related to LFPs, such that simple frequency-domain filtering cannot be effectively used to separate the two signals. Ground-truth simulations indicate that the commonly used method of estimating the LFP signal by low-pass filtering the raw voltage signal leads to artifactual correlations between spikes and LFPs and that these correlations exert a powerful influence on popular metrics of spike–LFP synchronization. Similar artifactual results were seen in data obtained from electrophysiological recordings in macaque visual cortex, when low-pass filtering was used to estimate LFP signals. In contrast LFP tuning curves in response to sensory stimuli do not appear to be affected by spike contamination, either in simulations or in real data. To address the issue of spike contamination, we devised a novel Bayesian spike removal algorithm and confirmed its effectiveness in simulations and by applying it to the electrophysiological data. The algorithm, based on a rigorous mathematical framework, outperforms other methods of spike removal on most metrics of spike–LFP correlations. Following application of this spike removal algorithm, many of our electrophysiological recordings continued to exhibit spike–LFP correlations, confirming previous reports that such relationships are a genuine aspect of neuronal activity. Overall, these results show that careful preprocessing is necessary to remove spikes from LFP signals, but that when effective spike removal is used, spike–LFP correlations can potentially yield novel insights about brain function.

Combined measurements of cerebellar blood flow, local field potentials and glucose utilization during climbing fibres stimulation: Effect of postsynaptic excitatory activity

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S192-S192
Author(s):  
Aïcha Douhou ◽  
Kirsten Caesar ◽  
Nikolas Offenhauser ◽  
Martin Lauritzen ◽  
Gilles Bonvento
Keyword(s):  
Blood Flow ◽  
Local Field ◽  
Glucose Utilization ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Climbing Fibres ◽  
Excitatory Activity ◽  
Combined Measurements
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