Model of low-pass filtering of local field potentials in brain tissue

Bédard, Claude; Kröger, H.; Destexhe, Alain

doi:10.1103/physreve.73.051911

Cited by 140 publications

(127 citation statements)

References 19 publications

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“…measurements. A previously suggested mechanism for the relatively small low-pass filter effect observed in vivo is the polarization of cell membranes (11). With the disintegration of cell membranes after death, it is conceivable that the filtering effect is further diminished, leading to the flattened frequency response.…”

Section: Discussionmentioning

confidence: 99%

Limitations of ex vivo measurements for in vivo neuroscience

Opitz

Falchier

Linn

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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A long history of postmortem studies has provided significant insight into human brain structure and organization. Cadavers have also proven instrumental for the measurement of artifacts and nonneural effects in functional imaging, and more recently, the study of biophysical properties critical to brain stimulation. However, death produces significant changes in the biophysical properties of brain tissues, making an ex vivo to in vivo comparison complex, and even questionable. This study directly compares biophysical properties of electric fields arising from transcranial electric stimulation (TES) in a nonhuman primate brain pre-and postmortem. We show that pre-vs. postmortem, TES-induced intracranial electric fields differ significantly in both strength and frequency response dynamics, even while controlling for confounding factors such as body temperature. Our results clearly indicate that ex vivo cadaver and in vivo measurements are not easily equitable. In vivo examinations remain essential to establishing an adequate understanding of even basic biophysical phenomena in vivo.biophysical | electric field | nonhuman primate T he use of cadavers to study human anatomy has a long history in medicine and science. For the neurosciences, centuries of postmortem examination have provided a fundamental understanding of human brain structure and organization, as well as the effect of a range of disease processes (1). With the advent of powerful in vivo imaging methods (2), the study of cadavers has become less important in modern-day neuroscience research. However, cadavers are still regularly used to examine possible methodological artifacts in brain imaging studies because of the complete absence of neuronal activity with largely preserved anatomical structures (3, 4). Numerous studies have relied on cadavers to establish the conductivity of differing tissue types, with notable discrepancies compared with in vivo measurements (5-8). Similarly, in developing noninvasive transcranial electric stimulation (TES) procedures, some have suggested the use of cadavers for measuring electric fields generated in the brain during stimulation. Such knowledge is crucial to efforts aiming to optimize brain stimulation, whether focusing on the spatial accuracy of stimulation, the magnitude of a dose actually delivered to an individual, or the possible variations in delivery across individuals. The most recent of these gained widespread attention because of its conclusion that TES-induced currents have poor penetration through the scalp and skull (9). These findings seem to reinforce concerns about the effectiveness of current dosing levels (10); however, to understand their implications, it is first important to determine how well cadaver-based conductivity measurements approximate in vivo conditions.Death initiates a cascade of biochemical processes affecting the biophysical properties of body and brain tissues, which can make the generalization from ex vivo results to the in vivo case problematic. However, it is not clear how in...

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Section: Discussionmentioning

confidence: 99%

Limitations of ex vivo measurements for in vivo neuroscience

Opitz

Falchier

Linn

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…In contrast to most previous studies, however, we found that low-frequency LFPs (in the 1-8 Hz range) were equally highly informative about the movie. The previously reported lack of visual stimulus selectivity at low LFP frequencies was commonly ascribed to the hypothesis that cortex acts as capacitive filter, spatially blurring low-frequency signals, which travel longer distances than those at high frequencies (Bedard et al, 2006). The hypothesis of capacitive filtering proved wrong according to recent detailed measurements of the cortical impedance spectrum (Logothetis et al, 2007).…”

Section: Temporal Scale Of the Lfp And Spiking Signalmentioning

confidence: 99%

Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information

Belitski¹,

Gretton²,

Magri³

et al. 2008

J. Neurosci.

405

454

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Local field potentials (LFPs) reflect subthreshold integrative processes that complement spike train measures. However, little is yet known about the differences between how LFPs and spikes encode rich naturalistic sensory stimuli. We addressed this question by recording LFPs and spikes from the primary visual cortex of anesthetized macaques while presenting a color movie. We then determined how the power of LFPs and spikes at different frequencies represents the visual features in the movie. We found that the most informative LFP frequency ranges were 1-8 and 60 -100 Hz. LFPs in the range of 12-40 Hz carried little information about the stimulus, and may primarily reflect neuromodulatory inputs. Spike power was informative only at frequencies Ͻ12 Hz. We further quantified "signal correlations" (correlations in the trial-averaged power response to different stimuli) and "noise correlations" (trial-by-trial correlations in the fluctuations around the average) of LFPs and spikes recorded from the same electrode. We found positive signal correlation between high-gamma LFPs (60 -100 Hz) and spikes, as well as strong positive signal correlation within high-gamma LFPs, suggesting that high-gamma LFPs and spikes are generated within the same network. LFPs Ͻ24 Hz shared strong positive noise correlations, indicating that they are influenced by a common source, such as a diffuse neuromodulatory input. LFPs Ͻ40 Hz showed very little signal and noise correlations with LFPs Ͼ40 Hz and with spikes, suggesting that low-frequency LFPs reflect neural processes that in natural conditions are fully decoupled from those giving rise to spikes and to high-gamma LFPs.

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“…However, it has been shown by Molden et al (2013) that to obtain an accurate action potential waveform, it is imperative to record signals with frequencies as low as 12.5Hz. Further, for local field potentials -which are extracted by low-pass filtering below 250Hz (Buzsáki et al, 2012;Oweiss, 2010;Bedard et al, 2006) -this approach completely fails in removing powerline noise. Band-stop filters have been used to attenuate powerline noise to inconspicuous levels.…”

Section: Introductionmentioning

confidence: 99%

Powerline noise elimination in neural signals via blind source separation and wavelet analysis

Akwei-Sekyere

2014

Preprint

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The distortion of neural signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of neural data. State of the art electrophysiology employs band-stop filters with which powerline noise are attenuated to low-amplitudes. Due to the instability of neural signals, the distribution of signals filtered out may not be centered at \(50/60Hz\). As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw electrophysiological recording and subtract it from the raw data in order to obtain neural data. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a band-stop finite impulse response filter. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned band-stop finite impulse response filter, especially at low signal-to-noise ratio. ABSTRACTThe distortion of neural signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of neural data. State of the art electrophysiology employs band-stop filters with which powerline noise are attenuated to low-amplitudes. Due to the instability of neural signals, the distribution of signals filtered out may not be centered at 50/60Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw electrophysiological recording and subtract it from the raw data in order to obtain neural data. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a band-stop finite impulse response filter. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned band-stop finite impulse response filter, especially at low signal-to-noise ratio.

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Model of low-pass filtering of local field potentials in brain tissue

Cited by 140 publications

References 19 publications

Limitations of ex vivo measurements for in vivo neuroscience

Limitations of ex vivo measurements for in vivo neuroscience

Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information

Powerline noise elimination in neural signals via blind source separation and wavelet analysis

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