Spike decontamination in local field potential signals from the primate superior colliculus

Megan Blacka, Clara Bourrellyb, Neeraj Gandhib , Ahmed Dallala

aDepartment of Electrical and Computer Engineering, bDepartment of Bioengineering

Megan Black Megan is a senior from McMurray, PA. She is studying electrical engineering with a minor in mechanical engineering. After graduation, she plans to pursue a PhD program with research interests in signal processing and control.

Ahmed Dallal, Ph.D. Dr. Dallal primary focus is on education development and innovation. His research interests include biomedical signal processing, biomedical image analysis, and computer vision, as well as machine learning, networked control systems, and human-machine learning.

Significance Statement

Spiking activity in the superior colliculus of primates causes contamination in the local field potential signals. This work focuses on removing spike bleed from LFP recordings using an adaptive method for spike removal.

Category: Methods

Keywords: spike contamination, signal processing,

local field potential

Abbreviations: local field potential (LFP), superior

colliculus (SC), adaptive spike removal (ASR)

Abstract

Communication inside the superior colliculus (SC) of primates depends largely on neuronal spiking activity and local field potentials (LFP). While the monkey is performing a visuomotor task, spiking activity for neurons align with the LFP in the SC. Measured LFP signals contain transmembrane voltage changes and contamination from spiking activity. By removing spike bleed contamination, LFP signals can provide insight into communication in the SC. This project focuses on an adaptive method to remove contamination in lower frequencies as low frequencies contain valuable information about the LFP response to stimulus during a task. The adaptive spike removal (ASR) method tunes spike bleed removal based on the Fourier components of the LFP signal. Analysis in the time and frequency domains revealed key patterns in the deviation between spike removed LFP and spike contaminated LFP signals. Further investigation based on neuron types is proposed for future research into spike bleed removal to tune contamination removal based on the spike waveform for each neuron type.

1. Introduction

Analyses of neural communication in the superior colliculus (SC) during the sensorimotor transformation are mainly based on the analyses of local field potential (LFP) and spiking activity. During a visuomotor task, various neurons in the SC synchronize spiking activity to LFP. Investigating this relationship between spikes and LFP provides insight into how the SC processes information.

Rather than reflecting transmembrane voltage changes alone, LFP signals can be contaminated by spiking activity occurring in neurons in close proximity, within ~200 μm [1]. One solution to spike bleeds might be to move LFP electrode tips further apart, > 200 μm, however, this creates new issues as the LFP is not homogeneous over that distance. These limitations necessitate a post-recording process in order to decontaminate LFP signals and filter out spike bleeds. Current methods [2] focus on removing the average contribution of a neuron during a spike. These approaches rely on estimating the spike, designing a filter to predict the influence of spikes on LFP, and altering data to remove spikes and interpolating. These methods are limited in their ability to remove the contamination of spikes, often leaving contamination in lower frequencies.

Unlike existing methods, this project explored an adaptive method that allows removal to be tuned based on the Fourier components in time segments surrounding each spike. This method aims to limit the effects of spike bleeds in lower frequencies, 25 -200 Hz, as these lower frequencies contain important modulations that provide info about communication in the SC. This project focuses on implementing an adaptive method to remove spike contamination in recorded LFP signals from the primate superior colliculus.

2. Methods

2.1 Data acquisition from SC of primates

Laminar probes (16 or 24 channels) were used to record neural activity across different layers of the SC while monkeys performed the well-known delayed saccade task [3], which facilitates analysis of visuomotor transformation.

This task consists of several steps that create distinct modulation of the LFP signals. First, the monkey fixates on a central fixation point presented on a screen. Next, a peripheral target appears, while the monkey continues to fixate on the central fixation point. After a variable delay period, the monkey is given a go cue by the disappearance of the central fixation point. This go cue allows the monkey to make a saccade, characterized by a rapid eye movement toward the peripheral target. To allow for continuity between trials, segments of LFP can be aligned based on target onset or saccade onset. 2.2 LFP signal processing and analysis

Processing of the recorded LFP signals in MATLAB includes four stages: (i) boundary spike rejection, (ii) adaptive spike removal [4], (iii) filtration and downsampling, and (iv) data alignment. Figure 1 illustrates this process for removing spike contamination from a schematic LFP signal. The LFP signal is shown in blue for the duration of a trial with individual spikes shown as red tick marks. The major trial landmarks are indicated along the time axis. For comparison, original datasets are processed using only stages iii and iv to reflect the spike contaminated LFP signals.

Figure 1: Process for removing spike bleed

2.2.1 Boundary spike rejection

Each spike occurrence requires 250 ms of LFP signal centered around the spike timestamp to successfully remove contamination. This necessitates that spikes occurring in the first and last 250 ms of each trial be ignored and excluded from further analysis. Ignored spikes are shown in purple tick marks in Figure 1, Stage i.

2.2.2 Adaptive spike removal

The adaptive spike removal (ASR) method published previously in the Journal of Neuroscience Methods [4] was employed. Each spike is analyzed in a ±250 ms window centered around the location of the spike. The LFP signal is decomposed into its Fourier components to remove spike bleed contributions. This method tunes removal based on the frequency of highest peak in the normalized power spectral density for frequencies between 2-200 Hz. Signals are processed based on this frequency to filter and remove the effects of spike contamination. Figure 1, Stage ii illustrates the removal of spike contamination by the removal of red tick marks.

2.2.3 Filtration and downsampling

The LFP waveforms, originally sampled at 30 kHz, were downsampled to 1 kHz after applying the ASR method. Our analysis focuses on the lower frequency components. Data was then passed through a low pass, fourth order Butterworth filter at cut-off frequency of 250 Hz. Fourth order notch filters were also used to eliminate the power line interference at 60, 120, and 180 Hz. Figure 1, Stage iii shows the filtered signal as a smoother blue line.

2.2.4 Data alignment

Each trial is separated into 1-sec segments aligned on target onset and saccade onset, shown in Figure 1, Stage iv. For segments aligned on target onset, the target is presented at 0.4 sec. For segments aligned on saccade onset, the saccade begins at 0.6 sec. LFP data is averaged across trials for each channel on the probe. This allows analysis of how the average trends change before and after spike removal. Average LFP signals before and after spike removal are plotted, along with spiking activity, to compare the spike bleed free and spike contaminated LFP signals.

3. Results

Specific channels were isolated for analysis as the LFP response varied depending on channel depth. Figure 2 shows average LFP signals for before (blue trace) and after (red trace) removal of spike bleed, and the spike train showing average spiking activity (black trace, note that for visualization, the spiking activity is flipped and rescaled to overlap the LFP signal). Figure 2a shows signals aligned on target onset (400 ms) and Figure 2b shows signals aligned on saccade onset (600 ms). During periods of increased spiking activity, the spike bleed removed signal (red trace) deviates from the contaminated LFP signal (blue trace). This indicates removal of spikes and contamination. When spiking activity is low (around zero), the spike bleed removed (red trace) and contaminated (blue trace) signals show limited differences. Conversely, when spiking activity increases (most negative values of the black trace), the spike bleed removed signal is very distinguishable from the filtered signal.

Figure 2: Average LFP signal before and after ASR and spike train aligned on target onset (a) and saccade onset (b). Note that for visualization, the spiking activity is flipped and rescaled on the right axis to overlap to LFP signal.

Figure 3 shows the spectrogram for average LFP signals before and after spike bleed removal. In Figure 3a, there is a change in the magnitude of power, as shown by intensity of the color, between 500 and 600 ms. This aligns with period of high average spiking activity after target onset (400 ms). Figure 3b shows the spectrogram for average LFP signals aligned on saccade onset (600 ms). At the time of saccade onset, an artifact present in the spike contaminated average LFP signal is eliminated after ASR. These major differences in power for LFP data before and after spike bleed removal occur at the same time points as deviations in the LFP analyzed in the time domain caused by periods of high spiking activity. As observed in Figure 2, Figure 3 shows periods of low differences in power caused by low spiking activity.

Figure 3: Spectrogram of average LFP signal before (1) and after (2) ASR aligned on target onset (a) and saccade onset (b)

4. Discussion

The success of ASR method in removing spike artifacts from the LFP signals could be appreciated by examining the before and after frequency spectra. High frequency components, associated with the spike itself, were removed while low-frequency components remained uncompromised. This effect was best appreciated when spikes occurred in close succession, as a burst of action potentials. The repetitive filter aspect of ASR was able to remove the compound contamination, as shown in the distinct differences in LFP signal after ASR.

For trials aligned on target onset, the periods of high spiking activity and strong downward deflection in the LFP are nearly simultaneous and reflect sensory processing of the sensory stimulus in the visual world. This modulation causes neurons to spike, creating the spike bleed contamination in the signal. Removal of spike contamination yields a more veridical LFP signal and potentially better insight into neuronal communication inside the brain. Specifically, insight into how the presence of a peripheral target is communicated in the SC can be explored. The analysis performed on data aligned on saccade onset yielded similar findings, which may help in deciphering communication during movement generation.

Frequency domain analysis can provide insight into how spike removal affects frequency components in the LFP signal. The low frequencies analyzed in this project contain valuable information to aide in understanding how spiking activity and LFP interact in the SC.

5. Conclusion

Investigation into removing spike bleed in LFP signals can provide insight into the relationship between neuronal spiking activity and LFP. This project focused on applying a process for removing spike contamination from LFP signals and showed deviations from contaminated LFP signals during periods of high spiking activity. This spike bleed removal process was applied to all spikes, however, spikes recorded from a particular channel can come from various cells creating distinct waveforms. Further research could include sorting spikes based on its cell and removing spike bleed on the different spike populations. This analysis would include spike detection and sorting software [5] to confirm the identification of spikes and separate into clusters.

6. Acknowledgements

The Swanson School of Engineering and the Office of the Provost for jointly funding this project. Dr. Dallal, Dr. Gandhi, and Dr. Bourrelly for their mentorship.

7. References

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