Kernel current source density method

Potworowski, Jan; Jakuczun, Wit; Łęski, Szymon; Wójcik, Daniel K.

doi:10.1186/1471-2202-12-s1-p375

“…Next, we further exploited our platform to pinpoint the high spatiotemporal resolution of local field generators (i.e., sources and sinks) in the inter-bulbar circuitry as emerged from the oscillatory activity of OB neuronal ensembles. Thus, we constructed bidimensional maps employing the kCSD method 58 to estimate the average transmembrane currents extracted from the field activity recordings ( Figure 5b, and Supplementary Movie 3 ). At the beginning of the LFP activation ( t=187.5 ms ), the KCSD maps ( Figure 5b, bottom ) showed defined topographic activation features indicated by stronger and focused source generators (red color-coded in the GL and GCL layers with defined cellular identity (i.e., the microelectrodes underlying the transmembrane dipole).…”

Section: Resultsmentioning

confidence: 99%

“…To estimate the current sources generating the extracellular low-frequency potentials recorded in all OB layers, we used the Kernel Current Source Density (kCSD) Analysis 58 , described in the kCSD-python package and available on GitHub (https://github.com/Neuroinflab/kCSD-python).…”

Section: Methodsmentioning

confidence: 99%

Revealing Spatiotemporal Circuit Information of Olfactory Bulb in Large-scale Neural Recordings

Hu

¹

,

Klütsch

²

,

Amin

³

et al. 2021

Preprint

0

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Large-scale multi-site biosensors are essential to probe the olfactory bulb (OB) circuitry for understanding the spatiotemporal dynamics of simultaneous discharge patterns. Current ex-vivo electrophysiological techniques are limited to recording a small set of neurons and cannot provide an inadequate resolution, which hinders revealing the fast dynamic underlying the information coding mechanisms in the OB circuit. Here, we demonstrate a novel biohybrid OB-CMOS platform to decipher the cross-scale dynamics of OB electrogenesis and quantify the distinct neuronal coding properties. The approach with 4096-microelectrodes offers a non-invasive, label-free, bioelectrical imaging to decode simultaneous firing patterns from thousands of connected neuronal ensembles in acute OB slices. The platform can measure spontaneous and drug-induced extracellular field potential activity. We employ our OB-CMOS recordings to perform multidimensional analysis to instantiate specific neurophysiological metrics underlying the olfactory spatiotemporal coding that emerged from the OB interconnected layers. Our results delineate the computational implications of large-scale activity patterns in functional olfactory processing. The high-content characterization of the olfactory circuit could benefit better functional interrogations of the olfactory spatiotemporal coding, connectivity mapping, and, further, the designing of reliable and advanced olfactory cell-based biosensors for diagnostic biomarkers and drug discovery.

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“…This implies that this framework is suitable for the analysis of recordings that are extremely sensitive to amplitude changes. With neuroinformatic algorithms such as spike sorting (Oweiss et al, 2013;Quiroga et al, 2004;Vargas-Irwin and Donoghue, 2007;Lewicki, 1998) and current source density analysis (Freeman and Nicholson, 1975;Potworowski et al, 2012), an accretion of minor fluctuations may go a long way in rendering the interpretations of neural signals erroneous. …”

Section: Performance Of Proposed Approach Relative To a Finite Impulsmentioning

confidence: 99%

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

Akwei-Sekyere

¹

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.

show abstract

“…This implies that this framework is suitable for the analysis of recordings that are extremely sensitive to amplitude changes. With neuroinformatic algorithms such as spike sorting (Oweiss et al, 2013;Quiroga et al, 2004;Vargas-Irwin and Donoghue, 2007;Lewicki, 1998) and current source density analysis (Freeman and Nicholson, 1975;Potworowski et al, 2012), an accretion of minor fluctuations may go a long way in rendering the interpretations of neural signals erroneous.…”

Section: Performance Of Proposed Approach Relative To a Finite Impulsmentioning

confidence: 99%

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

Akwei-Sekyere

¹

2014

Preprint

0

View full text Add to dashboard Cite

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.

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Kernel current source density method

Cited by 9 publications

References 3 publications

Revealing Spatiotemporal Circuit Information of Olfactory Bulb in Large-scale Neural Recordings

Revealing Spatiotemporal Circuit Information of Olfactory Bulb in Large-scale Neural Recordings

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

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

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