How different are the local field potentials and spiking activities? Insights from multi-electrodes arrays

Gaucher, Quentin; Edeline, Jean‐Marc; Gourévitch, Boris

doi:10.1016/j.jphysparis.2011.09.006

Cited by 45 publications

(46 citation statements)

References 95 publications

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“…In the present study, we found a mismatch between auditory evoked firing and auditory evoked potentials, with the latter being increased or decreased by the concurrent electrical stimulation (see, Gaucher et al 2012). Our findings for the AEPs are consistent with the findings of Happel and colleagues (2014) that mimicking the effects of activating the ventral tegmental area by application of a D1-receptor agonist increased AEPs and synaptic currents in primary auditory cortex of rodents.…”

Section: Changes Of Evoked Activity By the Dopaminergic Ventral Midbrainsupporting

confidence: 92%

Tonic effects of the dopaminergic ventral midbrain on the auditory cortex of awake macaque monkeys

et al. 2014

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This study shows that ongoing electrical stimulation of the dopaminergic ventral midbrain can modify neuronal activity in the auditory cortex of awake primates for several seconds. This was reflected in a decrease of the spontaneous firing and in a bidirectional modification of the power of auditory evoked potentials. We consider that both effects are due to an increase in the dopamine tone in auditory cortex induced by the electrical stimulation. Thus, the dopaminergic ventral midbrain may contribute to the tonic activity in auditory cortex that has been proposed to be involved in associating events of auditory tasks (Brosch et al. Hear Res 271:66-73, 2011) and may modulate the signal-to-noise ratio of the responses to auditory stimuli.

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Section: Changes Of Evoked Activity By the Dopaminergic Ventral Midbrainsupporting

confidence: 92%

Tonic effects of the dopaminergic ventral midbrain on the auditory cortex of awake macaque monkeys

et al. 2014

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“…A particular case that has attracted much attention is the discrepant expanse of sensory receptive fields in the cortex measured by LFPs or spikes, which range from a few hundred microns to several millimeters (Gieselmann and Thiele, 2008; Katzner et al, 2009; Denker et al, 2011; Eggermont et al, 2011; Kajikawa and Schroeder, 2011; Gaucher et al, 2012; Liu et al, 2015). Several explanations have been proposed for this phenomenon, such as the size of the brain or the very nature of the LFPs.…”

Section: Common Misconceptions Around Local Field Potentialsmentioning

confidence: 99%

Local Field Potentials: Myths and Misunderstandings

Herreras

2016

Front. Neural Circuits

266

244

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The intracerebral local field potential (LFP) is a measure of brain activity that reflects the highly dynamic flow of information across neural networks. This is a composite signal that receives contributions from multiple neural sources, yet interpreting its nature and significance may be hindered by several confounding factors and technical limitations. By and large, the main factor defining the amplitude of LFPs is the geometry of the current sources, over and above the degree of synchronization or the properties of the media. As such, similar levels of activity may result in potentials that differ in several orders of magnitude in different populations. The geometry of these sources has been experimentally inaccessible until intracerebral high density recordings enabled the co-activating sources to be revealed. Without this information, it has proven difficult to interpret a century's worth of recordings that used temporal cues alone, such as event or spike related potentials and frequency bands. Meanwhile, a collection of biophysically ill-founded concepts have been considered legitimate, which can now be corrected in the light of recent advances. The relationship of LFPs to their sources is often counterintuitive. For instance, most LFP activity is not local but remote, it may be larger further from rather than close to the source, the polarity does not define its excitatory or inhibitory nature, and the amplitude may increase when source's activity is reduced. As technological developments foster the use of LFPs, the time is now ripe to raise awareness of the need to take into account spatial aspects of these signals and of the errors derived from neglecting to do so.

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“…Using electrodes we can measure the activity of few cells (spiking activity -SA) or instead sense the activity of a larger group of cells (local field potentials -LFP) [51 ]. In this study we have worked with LFP from the STN.The LFP is a massed neuronal signal obtained using a two step procedure.…”

Section: Patient Datasetmentioning

confidence: 99%

Resting tremor classification and detection in Parkinson's disease patients

Cámara

Isasi

Warwick

et al. 2015

Biomedical Signal Processing and Control

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Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients. Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly.Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rectification. Then, feature extraction was conducted through a multi-level decomposition via a wavelet transform. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified.Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

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How different are the local field potentials and spiking activities? Insights from multi-electrodes arrays

Cited by 45 publications

References 95 publications

Tonic effects of the dopaminergic ventral midbrain on the auditory cortex of awake macaque monkeys

Tonic effects of the dopaminergic ventral midbrain on the auditory cortex of awake macaque monkeys

Local Field Potentials: Myths and Misunderstandings

Resting tremor classification and detection in Parkinson's disease patients

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