@article {68936, title = {Multi-day neuron tracking in high-density electrophysiology recordings using earth mover{\textquoteright}s distance}, journal = {eLife}, year = {2024}, month = {2024-07-10}, abstract = {

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84\% average recovery rate.

}, doi = {10.7554/eLife.92495.3}, url = {https://doi.org/10.7554/eLife.92495.3}, author = {Augustine Xiaoran Yuan and Jennifer Colonell and Anna Lebedeva and Adam Charles and Timothy Harris} } @article {69350, title = {Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings}, journal = {bioRxiv}, year = {2024}, month = {2024 Apr 10}, abstract = {

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density ( 10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron{\textquoteright}s electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to 3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75\% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

}, doi = {10.1101/2023.08.23.554527}, url = {https://www.biorxiv.org/content/early/2024/04/10/2023.08.23.554527}, author = {Zhiwen Ye and Andrew M Shelton and Jordan R Shaker and Julien M Boussard and Jennifer Colonell and Daniel Birman and Sahar Manavi and Susu Chen and Charlie Windolf and Cole Hurwitz and Tomoyuki Namima and Frederico Pedraja and Shahaf Weiss and Bogdan Raducanu and Torbj{\o}rn Ness and Xiaoxuan Jia and Giulia Mastroberardino and L. Federico Rossi and Matteo Carandini and Michael Hausser and Gaute T Einevoll and Gilles Laurent and Nathaniel B Sawtell and Wyeth Bair and Anitha Pasupathy and Carolina Mora-Lopez and Barun Dutta and Liam Paninski and Joshua H Siegle and Christof Koch and Shawn R Olsen and Timothy D Harris and Nicholas A Steinmetz} } @article {68647, title = {Brain-wide neural activity underlying memory-guided movement}, journal = {bioRxiv}, year = {2023}, month = {2023 Mar 02}, abstract = {

Behavior requires neural activity across the brain, but most experiments probe neurons in a single area at a time. Here we used multiple Neuropixels probes to record neural activity simultaneously in brain-wide circuits, in mice performing a memory-guided directional licking task. We targeted brain areas that form multi-regional loops with anterior lateral motor cortex (ALM), a key circuit node mediating the behavior. Neurons encoding sensory stimuli, choice, and actions were distributed across the brain. However, in addition to ALM, coding of choice was concentrated in subcortical areas receiving input from ALM, in an ALM-dependent manner. Choice signals were first detected in ALM and the midbrain, followed by the thalamus, and other brain areas. At the time of movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

}, doi = {10.1101/2023.03.01.530520}, url = {https://doi.org/10.1101/2023.03.01.530520}, author = {Susu Chen and Yi Liu and Ziyue Wang and Jennifer Colonell and Liu D. Liu and Han Hou and Nai-Wen Tien and Tim Wang and Timothy Harris and Shaul Druckmann and Nuo Li and Karel Svoboda} } @article {68607, title = {Large-scale brain-wide neural recording in nonhuman primates}, journal = {bioRxiv}, year = {2023}, month = {2023 Feb 04}, abstract = {

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.

}, doi = {10.1101/2023.02.01.526664}, url = {https://doi.org/10.1101/2023.02.01.526664}, author = {Eric M. Trautmann and Janis K. Hesse and Gabriel M. Stine and Ruobing Xia and Shude Zhu and Daniel J. O{\textquoteright}Shea and Bill Karsh and Jennifer Colonell and Frank F. Lanfranchi and Saurabh Vyas and Andrew Zimnik and Natalie A. Steinmann and Daniel A. Wagenaar and Alexandru Andrei and Carolina Mora Lopez and John O{\textquoteright}Callaghan and Jan Putzeys and Bogdan C. Raducanu and Marleen Welkenhuysen and Mark Churchland and Tirin Moore and Michael Shadlen and Krishna Shenoy and Doris Tsao and Barundeb Dutta and Timothy Harris} } @article {69009, title = {Low-latency extracellular spike assignment for high-density electrodes at single-neuron resolution}, journal = {bioRxiv}, year = {2023}, month = {2023 Sep 15}, abstract = {

Real-time neural signal processing is essential for brain-machine interfaces and closed-loop neuronal perturbations. However, most existing applications sacrifice cell-specific identity and temporal spiking information for speed. We developed a hybrid hardware-software system that utilizes a Field Programmable Gate Array (FPGA) chip to acquire and process data in parallel, enabling individual spikes from many simultaneously recorded neurons to be assigned single-neuron identities with 1-millisecond latency. The FPGA assigns labels, validated with ground-truth data, by comparing multichannel spike waveforms from tetrode or silicon probe recordings to a spike-sorted model generated offline in software. This platform allowed us to rapidly inactivate a region in vivo based on spikes from an upstream neuron before these spikes could excite the downstream region. Furthermore, we could decode animal location within 3 ms using data from a population of individual hippocampal neurons. These results demonstrate our system{\textquoteright}s suitability for a broad spectrum of research and clinical applications.

}, doi = {10.1101/2023.09.14.557854}, url = {https://doi.org/10.1101/2023.09.14.557854}, author = {Chongxi Lai and Dohoung Kim and Brian Lustig and Shinsuke Tanaka and Brian Barbarits and Lakshmi Narayan and Jennifer Colonell and Ole Paulsen and Albert K. Lee and Timothy D. Harris} } @article {67849, title = {Rapid reconstruction of neural circuits using tissue expansion and lattice light sheet microscopy}, journal = {eLife}, year = {2022}, month = {2022 Oct 26}, abstract = {

Electron microscopy (EM) allows for the reconstruction of dense neuronal connectomes but suffers from low throughput, limiting its application to small numbers of reference specimens. We developed a protocol and analysis pipeline using tissue expansion and lattice light-sheet microscopy (ExLLSM) to rapidly reconstruct selected circuits across many samples with single synapse resolution and molecular contrast. We validate this approach in\ Drosophila, demonstrating that it yields synaptic counts similar to those obtained by EM, can be used to compare counts across sex and experience, and to correlate structural connectivity with functional connectivity. This approach fills a critical methodological gap in studying variability in the structure and function of neural circuits across individuals within and between species.

}, doi = {10.7554/eLife.81248}, url = {https://doi.org/10.7554/eLife.81248}, author = {Joshua L. Lillvis and Hideo Otsuna and Xiaoyu Ding and Igor Pisarev and Takashi Kawase and Jennifer Colonell and Konrad Rokicki and Cristian Goina and Ruixuan Gao and Amy Hu and Kaiyu Wang and John Bogovic and Daniel E. Milkie and Edward S. Boyden and Stephan Saalfeld and Paul W. Tillberg and Barry J. Dickson} }