Robust Online Multiband Drift Estimation in Electrophysiology Data

Paulk, Angelique C.; Kfir, Yoav; Trautmann, Eric M.; Garcia, Samuel; Meszéna, Domokos; Muñoz, William; Caprara, Irene; Jamali, Mohsen; Boussard, Julien; Williams, Ziv; Cash, Sydney S.; Paninski, Liam; Varol, Erdem

doi:10.1101/2022.12.04.519043

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…The high spatial resolution offers a number of advantages over sparser sampling, including high quality single-unit isolation, automated drift correction (e.g., refs. 25,51 , Extended Data Fig. 4), and localizing the position and depth of the recording electrodes within a brain structure (e.g., inferring probe depth with respect to cortical lamina) using current source density or other features of the recording.…”

Section: Discussionmentioning

confidence: 99%

Large-scale high-density brain-wide neural recording in nonhuman primates

Trautmann

Hesse

Stine

et al. 2023

Preprint

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High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which offer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.0-NHP, a high channel count linear electrode array designed to enable large-scale simultaneous recording in superficial and deep structures within the macaque or other large animal brain. These devices were fabricated in two versions: 4416 electrodes along a 45 mm shank, and 2496 along a 25 mm shank. For both versions, users can programmably select 384 channels, enabling simultaneous multi-area recording with a single probe. We demonstrate recording from over 3000 single neurons within a session, and simultaneous recordings from over 1000 neurons using multiple probes. This technology represents a significant increase in recording access and scalability relative to existing technologies, and enables new classes of experiments involving fine-grained electrophysiological characterization of brain areas, functional connectivity between cells, and simultaneous brain-wide recording at scale.

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Section: Discussionmentioning

confidence: 99%

Large-scale high-density brain-wide neural recording in nonhuman primates

Trautmann

Hesse

Stine

et al. 2023

Preprint

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“…Then, we used a subtraction-based spike detection and denoising method described in Boussard et al 2023. After preprocessing, we computed the spike locations (Boussard et al 2021) to acquire spike features for decoding and then utilized registration techniques (Windolf et al 2022) to correct for motion drift in the recorded data. Further details about data preprocessing can be found in Supplement 4.…”

Section: Methodsmentioning

confidence: 99%

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes

Zhang,

He,

Boussard

et al. 2023

Preprint

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Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting.

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“…In this work, we introduce DREDge (Decentralized Registration of Electrophysiology Data). In contrast to previous global template-based methods, DREDge starts from the decentralized framework of Varol et al 30 ; Windolf et al 31 , which infers motion by modeling local relationships in the data, allowing for motion estimation from either time-binned spiking data or filtered local field potential recordings. This approach estimates the relative displacements of pairs of time bins via crosscorrelation 28 , and models these local relationships as arising from a latent motion trace, which can then be inferred through optimization.…”

Section: Introductionmentioning

confidence: 99%

DREDge: robust motion correction for high-density extracellular recordings across species

Windolf,

Yu,

Paulk

et al. 2023

Preprint

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High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.

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Robust Online Multiband Drift Estimation in Electrophysiology Data

Cited by 6 publications

References 23 publications

Large-scale high-density brain-wide neural recording in nonhuman primates

Large-scale high-density brain-wide neural recording in nonhuman primates

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes

DREDge: robust motion correction for high-density extracellular recordings across species

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