1
|
Beau M, Herzfeld DJ, Naveros F, Hemelt ME, D'Agostino F, Oostland M, Sánchez-López A, Chung YY, Maibach M, Kyranakis S, Stabb HN, Martínez Lopera MG, Lajko A, Zedler M, Ohmae S, Hall NJ, Clark BA, Cohen D, Lisberger SG, Kostadinov D, Hull C, Häusser M, Medina JF. A deep-learning strategy to identify cell types across species from high-density extracellular recordings. bioRxiv 2024:2024.01.30.577845. [PMID: 38352514 PMCID: PMC10862837 DOI: 10.1101/2024.01.30.577845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.
Collapse
|
2
|
Carbonero D, Noueihed J, Kramer MA, White JA. Non-Negative Matrix Factorization for Analyzing State Dependent Neuronal Network Dynamics in Calcium Recordings. bioRxiv 2024:2023.10.11.561797. [PMID: 37905071 PMCID: PMC10614735 DOI: 10.1101/2023.10.11.561797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Non-negative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.
Collapse
Affiliation(s)
- Daniel Carbonero
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - Jad Noueihed
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| | - Mark A. Kramer
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
| | - John A. White
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States of America
| |
Collapse
|
3
|
Lavaud S, Bichara C, D'Andola M, Yeh SH, Takeoka A. Two inhibitory neuronal classes govern acquisition and recall of spinal sensorimotor adaptation. Science 2024; 384:194-201. [PMID: 38603479 DOI: 10.1126/science.adf6801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 03/06/2024] [Indexed: 04/13/2024]
Abstract
Spinal circuits are central to movement adaptation, yet the mechanisms within the spinal cord responsible for acquiring and retaining behavior upon experience remain unclear. Using a simple conditioning paradigm, we found that dorsal inhibitory neurons are indispensable for adapting protective limb-withdrawal behavior by regulating the transmission of a specific set of somatosensory information to enhance the saliency of conditioning cues associated with limb position. By contrast, maintaining previously acquired motor adaptation required the ventral inhibitory Renshaw cells. Manipulating Renshaw cells does not affect the adaptation itself but flexibly alters the expression of adaptive behavior. These findings identify a circuit basis involving two distinct populations of spinal inhibitory neurons, which enables lasting sensorimotor adaptation independently from the brain.
Collapse
Affiliation(s)
- Simon Lavaud
- VIB-Neuroelectronics Research Flanders (NERF), 3001 Leuven, Belgium
- KU Leuven, Department of Neuroscience and Leuven Brain Institute, 3000 Leuven, Belgium
| | - Charlotte Bichara
- VIB-Neuroelectronics Research Flanders (NERF), 3001 Leuven, Belgium
- KU Leuven, Department of Neuroscience and Leuven Brain Institute, 3000 Leuven, Belgium
| | - Mattia D'Andola
- VIB-Neuroelectronics Research Flanders (NERF), 3001 Leuven, Belgium
- KU Leuven, Department of Neuroscience and Leuven Brain Institute, 3000 Leuven, Belgium
| | - Shu-Hao Yeh
- VIB-Neuroelectronics Research Flanders (NERF), 3001 Leuven, Belgium
- KU Leuven, Department of Neuroscience and Leuven Brain Institute, 3000 Leuven, Belgium
| | - Aya Takeoka
- VIB-Neuroelectronics Research Flanders (NERF), 3001 Leuven, Belgium
- KU Leuven, Department of Neuroscience and Leuven Brain Institute, 3000 Leuven, Belgium
- IMEC, 3001 Leuven, Belgium
- RIKEN Center for Brain Science, Laboratory for Motor Circuit Plasticity, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| |
Collapse
|
4
|
Ye Z, Shelton AM, Shaker JR, Boussard J, Colonell J, Birman D, Manavi S, Chen S, Windolf C, Hurwitz C, Namima T, Pedraja F, Weiss S, Raducanu B, Ness TV, Jia X, Mastroberardino G, Rossi LF, Carandini M, Häusser M, Einevoll GT, Laurent G, Sawtell NB, Bair W, Pasupathy A, Lopez CM, Dutta B, Paninski L, Siegle JH, Koch C, Olsen SR, Harris TD, Steinmetz NA. Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings. bioRxiv 2024:2023.08.23.554527. [PMID: 37662298 PMCID: PMC10473688 DOI: 10.1101/2023.08.23.554527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
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'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.
Collapse
Affiliation(s)
- Zhiwen Ye
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Andrew M. Shelton
- MindScope Program, Allen Institute, Seattle, WA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Jordan R. Shaker
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Julien Boussard
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | | | - Daniel Birman
- Department of Biological Structure, University of Washington, Seattle, WA, USA
| | - Sahar Manavi
- MindScope Program, Allen Institute, Seattle, WA, USA
| | - Susu Chen
- Janelia Research Campus, Ashburn, VA, USA
| | - Charlie Windolf
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Cole Hurwitz
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Tomoyuki Namima
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Washington National Primate Research Center, Seattle, WA, USA
| | - Federico Pedraja
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Shahaf Weiss
- Max Planck Institute for Brain Research, Frankfurt, Germany
| | | | | | - Xiaoxuan Jia
- Center for Life Sciences & IDG/McGovern Institute for Brain Research, Tsinghua University, China
| | - Giulia Mastroberardino
- UCL Institute of Ophthalmology, University College London, London, UK
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - L. Federico Rossi
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London, UK
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Gaute T. Einevoll
- Norwegian University of Life Sciences, Ås, Norway
- University of Oslo, Oslo, Norway
| | - Gilles Laurent
- Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Nathaniel B. Sawtell
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Wyeth Bair
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Washington National Primate Research Center, Seattle, WA, USA
| | - Anitha Pasupathy
- Department of Biological Structure, University of Washington, Seattle, WA, USA
- Washington National Primate Research Center, Seattle, WA, USA
| | | | | | - Liam Paninski
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | | | - Christof Koch
- MindScope Program, Allen Institute, Seattle, WA, USA
| | - Shawn R. Olsen
- MindScope Program, Allen Institute, Seattle, WA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Timothy D. Harris
- Janelia Research Campus, Ashburn, VA, USA
- Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD, USA
| | | |
Collapse
|
5
|
Hornauer P, Prack G, Anastasi N, Ronchi S, Kim T, Donner C, Fiscella M, Borgwardt K, Taylor V, Jagasia R, Roqueiro D, Hierlemann A, Schröter M. DeePhys: A machine learning-assisted platform for electrophysiological phenotyping of human neuronal networks. Stem Cell Reports 2024; 19:285-298. [PMID: 38278155 PMCID: PMC10874850 DOI: 10.1016/j.stemcr.2023.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/28/2024] Open
Abstract
Reproducible functional assays to study in vitro neuronal networks represent an important cornerstone in the quest to develop physiologically relevant cellular models of human diseases. Here, we introduce DeePhys, a MATLAB-based analysis tool for data-driven functional phenotyping of in vitro neuronal cultures recorded by high-density microelectrode arrays. DeePhys is a modular workflow that offers a range of techniques to extract features from spike-sorted data, allowing for the examination of functional phenotypes both at the individual cell and network levels, as well as across development. In addition, DeePhys incorporates the capability to integrate novel features and to use machine-learning-assisted approaches, which facilitates a comprehensive evaluation of pharmacological interventions. To illustrate its practical application, we apply DeePhys to human induced pluripotent stem cell-derived dopaminergic neurons obtained from both patients and healthy individuals and showcase how DeePhys enables phenotypic screenings.
Collapse
Affiliation(s)
- Philipp Hornauer
- Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland.
| | - Gustavo Prack
- Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland
| | - Nadia Anastasi
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche, 4070 Basel, Switzerland
| | - Silvia Ronchi
- Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland
| | - Taehoon Kim
- Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland
| | | | - Michele Fiscella
- Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland; MaxWell Biosystems AG, 8047 Zürich, Switzerland
| | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Verdon Taylor
- Department of Biomedicine, University of Basel, 4031 Basel, Switzerland
| | - Ravi Jagasia
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche, 4070 Basel, Switzerland
| | - Damian Roqueiro
- Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland; Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche, 4070 Basel, Switzerland
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland
| | - Manuel Schröter
- Department of Biosystems Science and Engineering, ETH Zürich, 4056 Basel, Switzerland
| |
Collapse
|
6
|
Vincent JP, Economo MN. Assessing cross-contamination in spike-sorted electrophysiology data. bioRxiv 2023:2023.12.21.572882. [PMID: 38187738 PMCID: PMC10769346 DOI: 10.1101/2023.12.21.572882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike sorting accuracy. One longstanding method of assessing the false discovery rate (FDR) of spike sorting - the rate at which spikes are misassigned to the wrong cluster - has been the rate of inter-spike-interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remain limited. Here, we describe an analytical solution that can be used to predict FDR from ISI violation rate. We test this model in silico through Monte Carlo simulation, and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISI violation rate and FDR is highly nonlinear, with additional dependencies on firing rate, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets were found to range from 3.1% to 50.0%. We find that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence, but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike sorting accuracy in a principled manner.
Collapse
Affiliation(s)
- Jack P. Vincent
- Department of Biomedical Engineering, Boston University, Boston, MA
- Center for Neurophotonics, Boston University, Boston, MA
| | - Michael N. Economo
- Department of Biomedical Engineering, Boston University, Boston, MA
- Center for Neurophotonics, Boston University, Boston, MA
- Center for Systems Neuroscience, Boston University, Boston, MA
| |
Collapse
|
7
|
Ceccarelli F, Ferrucci L, Londei F, Ramawat S, Brunamonti E, Genovesio A. Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex. Nat Commun 2023; 14:8325. [PMID: 38097560 PMCID: PMC10721651 DOI: 10.1038/s41467-023-43712-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023] Open
Abstract
The prefrontal cortex maintains information in memory through static or dynamic population codes depending on task demands, but whether the population coding schemes used are learning-dependent and differ between cell types is currently unknown. We investigate the population coding properties and temporal stability of neurons recorded from male macaques in two mapping tasks during and after stimulus-response associative learning, and then we use a Strategy task with the same stimuli and responses as control. We identify a heterogeneous population coding for stimuli, responses, and novel associations: static for putative pyramidal cells and dynamic for putative interneurons that show the strongest selectivity for all the variables. The population coding of learned associations shows overall the highest stability driven by cell types, with interneurons changing from dynamic to static coding after successful learning. The results support that prefrontal microcircuitry expresses mixed population coding governed by cell types and changes its stability during associative learning.
Collapse
Affiliation(s)
- Francesco Ceccarelli
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Fabrizio Londei
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
- PhD program in Behavioral Neuroscience, Sapienza University, Rome, Italy
| | - Surabhi Ramawat
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Emiliano Brunamonti
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy.
| |
Collapse
|
8
|
Abbaspoor S, Hoffman KL. State-dependent circuit dynamics of superficial and deep CA1 pyramidal cells in macaques. bioRxiv 2023:2023.12.06.570369. [PMID: 38106053 PMCID: PMC10723348 DOI: 10.1101/2023.12.06.570369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Diverse neuron classes in hippocampal CA1 have been identified through their heterogeneity in cellular/molecular composition. How these classes relate to the hippocampal function and network dynamics that support cognition in primates remains unclear. Here we report functional cell groups in CA1 of freely-behaving macaques that show a range of spectral phase preferences and varied responses to local network states like the sharp-wave ripple. Segregating superficial- from deep-layer pyramidal cells uncovered differences in firing rate, burstiness, and sharp-wave ripple associated firing. They also showed strata-specific interactions with the inhibitory cell groups. CA1 ensemble recordings revealed the first observations of cell assemblies in the macaque hippocampus, and show that the stratification of pyramidal cells in primates is a major organizing principle of assemblies. These results suggest a sublayer-specific circuit organization in hippocampal CA1 of the freely-behaving macaque that may support dissociable contributions across cognitive and behavioral processes in the primate.
Collapse
Affiliation(s)
- S Abbaspoor
- Department of Psychology, Vanderbilt Vision Research Center, Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee
| | - K L Hoffman
- Department of Psychology, Vanderbilt Vision Research Center, Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| |
Collapse
|
9
|
Wang X, Nandy AS, Jadi MP. Laminar compartmentalization of attention modulation in area V4 aligns with the demands of visual processing hierarchy in the cortex. Sci Rep 2023; 13:19558. [PMID: 37945642 PMCID: PMC10636153 DOI: 10.1038/s41598-023-46722-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023] Open
Abstract
Attention selectively enhances neural responses to low contrast stimuli in visual area V4, a critical hub that sends projections both up and down the visual hierarchy. Veridical encoding of contrast information is a key computation in early visual areas, while later stages encoding higher level features benefit from improved sensitivity to low contrast. How area V4 meets these distinct information processing demands in the attentive state is unknown. We found that attentional modulation in V4 is cortical layer and cell-class specific. Putative excitatory neurons in the superficial layers show enhanced boosting of low contrast information, while those of deep layers exhibit contrast-independent scaling. Computational modeling suggested the extent of spatial integration of inhibitory neurons as the mechanism behind such laminar differences. Considering that superficial neurons are known to project to higher areas and deep layers to early visual areas, our findings suggest that the interactions between attention and contrast in V4 are compartmentalized, in alignment with the demands of the visual processing hierarchy.
Collapse
Affiliation(s)
- Xiang Wang
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA
| | - Anirvan S Nandy
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA
- Department of Neuroscience, Yale University, New Haven, CT, 06511, USA
| | - Monika P Jadi
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA.
- Department of Psychiatry, Yale University, New Haven, CT, 06511, USA.
- Department of Neuroscience, Yale University, New Haven, CT, 06511, USA.
| |
Collapse
|
10
|
Wu EG, Rudzite AM, Bohlen MO, Li PH, Kling A, Cooler S, Rhoades C, Brackbill N, Gogliettino AR, Shah NP, Madugula SS, Sher A, Litke AM, Field GD, Chichilnisky E. Decomposition of retinal ganglion cell electrical images for cell type and functional inference. bioRxiv 2023:2023.11.06.565889. [PMID: 37986895 PMCID: PMC10659265 DOI: 10.1101/2023.11.06.565889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. Large-scale multi-electrode recordings from the macaque retina were used to test the effectiveness of the decomposition. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells), a substantial advance. Together, these findings may contribute to more accurate inference of RGC types and their original light responses in the degenerated retina, with possible implications for other electrical imaging applications.
Collapse
Affiliation(s)
- Eric G. Wu
- Department of Electrical Engineering, Stanford University
| | | | | | - Peter H. Li
- Department of Neurosurgery, Stanford University
- Department of Ophthalmology, Stanford University
- Hansen Experimental Physics Laboratory, Stanford University
| | - Alexandra Kling
- Department of Neurosurgery, Stanford University
- Department of Ophthalmology, Stanford University
- Hansen Experimental Physics Laboratory, Stanford University
| | - Sam Cooler
- Department of Neurosurgery, Stanford University
| | | | | | | | - Nishal P. Shah
- Department of Electrical Engineering, Stanford University
- Department of Neurosurgery, Stanford University
| | - Sasidhar S. Madugula
- Neurosciences PhD Program, Stanford University
- Stanford School of Medicine, Stanford University
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz
| | - Alan M. Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz
| | - Greg D. Field
- Department of Neurobiology, Duke University
- Stein Eye Institute, Department of Ophthalmology, University of California, Los Angeles
| | - E.J. Chichilnisky
- Department of Neurosurgery, Stanford University
- Department of Ophthalmology, Stanford University
- Hansen Experimental Physics Laboratory, Stanford University
| |
Collapse
|
11
|
Lv S, He E, Luo J, Liu Y, Liang W, Xu S, Zhang K, Yang Y, Wang M, Song Y, Wu Y, Cai X. Using Human-Induced Pluripotent Stem Cell Derived Neurons on Microelectrode Arrays to Model Neurological Disease: A Review. Adv Sci (Weinh) 2023; 10:e2301828. [PMID: 37863819 PMCID: PMC10667858 DOI: 10.1002/advs.202301828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/04/2023] [Indexed: 10/22/2023]
Abstract
In situ physiological signals of in vitro neural disease models are essential for studying pathogenesis and drug screening. Currently, an increasing number of in vitro neural disease models are established using human-induced pluripotent stem cell (hiPSC) derived neurons (hiPSC-DNs) to overcome interspecific gene expression differences. Microelectrode arrays (MEAs) can be readily interfaced with two-dimensional (2D), and more recently, three-dimensional (3D) neural stem cell-derived in vitro models of the human brain to monitor their physiological activity in real time. Therefore, MEAs are emerging and useful tools to model neurological disorders and disease in vitro using human iPSCs. This is enabling a real-time window into neuronal signaling at the network scale from patient derived. This paper provides a comprehensive review of MEA's role in analyzing neural disease models established by hiPSC-DNs. It covers the significance of MEA fabrication, surface structure and modification schemes for hiPSC-DNs culturing and signal detection. Additionally, this review discusses advances in the development and use of MEA technology to study in vitro neural disease models, including epilepsy, autism spectrum developmental disorder (ASD), and others established using hiPSC-DNs. The paper also highlights the application of MEAs combined with hiPSC-DNs in detecting in vitro neurotoxic substances. Finally, the future development and outlook of multifunctional and integrated devices for in vitro medical diagnostics and treatment are discussed.
Collapse
Affiliation(s)
- Shiya Lv
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Enhui He
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
- The State Key Lab of Brain‐Machine IntelligenceZhejiang UniversityHangzhou321100China
| | - Jinping Luo
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yaoyao Liu
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Wei Liang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Shihong Xu
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Kui Zhang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yan Yang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Mixia Wang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yilin Song
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yirong Wu
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Xinxia Cai
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| |
Collapse
|
12
|
Someck S, Levi A, Sloin HE, Spivak L, Gattegno R, Stark E. Positive and biphasic extracellular waveforms correspond to return currents and axonal spikes. Commun Biol 2023; 6:950. [PMID: 37723241 PMCID: PMC10507124 DOI: 10.1038/s42003-023-05328-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/06/2023] [Indexed: 09/20/2023] Open
Abstract
Multiple biophysical mechanisms may generate non-negative extracellular waveforms during action potentials, but the origin and prevalence of positive spikes and biphasic spikes in the intact brain are unknown. Using extracellular recordings from densely-connected cortical networks in freely-moving mice, we find that a tenth of the waveforms are non-negative. Positive phases of non-negative spikes occur in synchrony or just before wider same-unit negative spikes. Narrow positive spikes occur in isolation in the white matter. Isolated biphasic spikes are narrower than negative spikes, occurring right after spikes of verified inhibitory units. In CA1, units with dominant non-negative spikes exhibit place fields, phase precession, and phase-locking to ripples. Thus, near-somatic narrow positive extracellular potentials correspond to return currents, and isolated non-negative spikes correspond to axonal potentials. Identifying non-negative extracellular waveforms that correspond to non-somatic compartments during spikes can enhance the understanding of physiological and pathological neural mechanisms in intact animals.
Collapse
Affiliation(s)
- Shirly Someck
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Amir Levi
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Hadas E Sloin
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Lidor Spivak
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Roni Gattegno
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Eran Stark
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel.
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel.
- Sagol Department of Neurobiology, Haifa University, Haifa, 3103301, Israel.
| |
Collapse
|
13
|
Zaidi M, Aggarwal G, Shah NP, Karniol-Tambour O, Goetz G, Madugula SS, Gogliettino AR, Wu EG, Kling A, Brackbill N, Sher A, Litke AM, Chichilnisky EJ. Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures. J Neural Eng 2023; 20:10.1088/1741-2552/ace657. [PMID: 37433293 PMCID: PMC11067857 DOI: 10.1088/1741-2552/ace657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 07/11/2023] [Indexed: 07/13/2023]
Abstract
Objective. Retinal implants are designed to stimulate retinal ganglion cells (RGCs) in a way that restores sight to individuals blinded by photoreceptor degeneration. Reproducing high-acuity vision with these devices will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without being able to measure them directly. Here we demonstrate an inference approach that exploits intrinsic electrophysiological features of primate RGCs.Approach.First, ON-parasol and OFF-parasol RGC types were identified using their intrinsic electrical features in large-scale multi-electrode recordings from macaque retina. Then, the electrically inferred somatic location, inferred cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. The accuracy of the cell type classification and of reproducing measured light responses with the model were evaluated.Main results.A cell-type classifier trained on 246 large-scale multi-electrode recordings from 148 retinas achieved 95% mean accuracy on 29 test retinas. In five retinas tested, the inferred models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses (an upper bound). Linear decoding of natural images from predicted RGC activity in one retina showed a mean correlation of 0.55 between decoded and true images, compared to an upper bound of 0.81 using models fitted to light response data.Significance.These results suggest that inference of RGC light response properties from intrinsic features of their electrical activity may be a useful approach for high-fidelity sight restoration. The overall strategy of first inferring cell type from electrical features and then exploiting cell type to help infer natural cell function may also prove broadly useful to neural interfaces.
Collapse
Affiliation(s)
- Moosa Zaidi
- Stanford University School of Medicine, Stanford University, Stanford, CA, United States of America
- Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Gorish Aggarwal
- Neurosurgery, Stanford University, Stanford, CA, United States of America
- Electrical Engineering, Stanford University, Stanford, CA, United States of America
| | - Nishal P Shah
- Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Orren Karniol-Tambour
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
| | - Georges Goetz
- Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Sasidhar S Madugula
- Stanford University School of Medicine, Stanford University, Stanford, CA, United States of America
- Neurosciences, Stanford University, Stanford, CA, United States of America
| | - Alex R Gogliettino
- Neurosciences, Stanford University, Stanford, CA, United States of America
| | - Eric G Wu
- Electrical Engineering, Stanford University, Stanford, CA, United States of America
| | - Alexandra Kling
- Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Nora Brackbill
- Physics, Stanford University, Stanford, CA, United States of America
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, United States of America
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, United States of America
| | - E J Chichilnisky
- Neurosurgery, Stanford University, Stanford, CA, United States of America
- Ophthalmology, Stanford University, Stanford, CA, United States of America
| |
Collapse
|
14
|
Lee K, Carr N, Perliss A, Chandrasekaran C. WaveMAP for identifying putative cell types from in vivo electrophysiology. STAR Protoc 2023; 4:102320. [PMID: 37220000 DOI: 10.1016/j.xpro.2023.102320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/28/2023] [Accepted: 04/27/2023] [Indexed: 05/25/2023] Open
Abstract
Action potential spike widths are used to classify cell types as either excitatory or inhibitory; however, this approach obscures other differences in waveform shape useful for identifying more fine-grained cell types. Here, we present a protocol for using WaveMAP to generate nuanced average waveform clusters more closely linked to underlying cell types. We describe steps for installing WaveMAP, preprocessing data, and clustering waveform into putative cell types. We also detail cluster evaluation for functional differences and interpretation of WaveMAP output. For complete details on the use and execution of this protocol, please refer to Lee et al. (2021).1.
Collapse
Affiliation(s)
- Kenji Lee
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA.
| | - Nicole Carr
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Alec Perliss
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Chandramouli Chandrasekaran
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian, School of Medicine, Boston, MA 02118, USA; Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA.
| |
Collapse
|
15
|
Trautmann EM, Hesse JK, Stine GM, Xia R, Zhu S, O'Shea DJ, Karsh B, Colonell J, Lanfranchi FF, Vyas S, Zimnik A, Steinmann NA, Wagenaar DA, Andrei A, Lopez CM, O'Callaghan J, Putzeys J, Raducanu BC, Welkenhuysen M, Churchland M, Moore T, Shadlen M, Shenoy K, Tsao D, Dutta B, Harris T. Large-scale high-density brain-wide neural recording in nonhuman primates. bioRxiv 2023:2023.02.01.526664. [PMID: 37205406 PMCID: PMC10187172 DOI: 10.1101/2023.02.01.526664] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
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 programmatically 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.
Collapse
|
16
|
Yun R, Mishler JH, Perlmutter SI, Rao RPN, Fetz EE. Responses of Cortical Neurons to Intracortical Microstimulation in Awake Primates. eNeuro 2023; 10:ENEURO.0336-22.2023. [PMID: 37037604 PMCID: PMC10135083 DOI: 10.1523/eneuro.0336-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 03/19/2023] [Accepted: 03/31/2023] [Indexed: 04/12/2023] Open
Abstract
Intracortical microstimulation (ICMS) is commonly used in many experimental and clinical paradigms; however, its effects on the activation of neurons are still not completely understood. To document the responses of cortical neurons in awake nonhuman primates to stimulation, we recorded single-unit activity while delivering single-pulse stimulation via Utah arrays implanted in primary motor cortex (M1) of three macaque monkeys. Stimuli between 5 and 50 μA delivered to single channels reliably evoked spikes in neurons recorded throughout the array with delays of up to 12 ms. ICMS pulses also induced a period of inhibition lasting up to 150 ms that typically followed the initial excitatory response. Higher current amplitudes led to a greater probability of evoking a spike and extended the duration of inhibition. The likelihood of evoking a spike in a neuron was dependent on the spontaneous firing rate as well as the delay between its most recent spike time and stimulus onset. Tonic repetitive stimulation between 2 and 20 Hz often modulated both the probability of evoking spikes and the duration of inhibition; high-frequency stimulation was more likely to change both responses. On a trial-by-trial basis, whether a stimulus evoked a spike did not affect the subsequent inhibitory response; however, their changes over time were often positively or negatively correlated. Our results document the complex dynamics of cortical neural responses to electrical stimulation that need to be considered when using ICMS for scientific and clinical applications.
Collapse
Affiliation(s)
- Richy Yun
- Departments of Bioengineering
- Center for Neurotechnology
- Washington National Primate Research Center, University of Washington, Seattle, Washington 98195
| | - Jonathan H Mishler
- Departments of Bioengineering
- Center for Neurotechnology
- Washington National Primate Research Center, University of Washington, Seattle, Washington 98195
| | - Steve I Perlmutter
- Physiology and Biophysics
- Center for Neurotechnology
- Washington National Primate Research Center, University of Washington, Seattle, Washington 98195
| | - Rajesh P N Rao
- Allen School for Computer Science and Engineering
- Center for Neurotechnology
| | - Eberhard E Fetz
- Departments of Bioengineering
- Physiology and Biophysics
- Center for Neurotechnology
- Washington National Primate Research Center, University of Washington, Seattle, Washington 98195
| |
Collapse
|
17
|
Noichl M. How localized are computational templates? A machine learning approach. Synthese 2023; 201:107. [PMID: 36936886 PMCID: PMC10009358 DOI: 10.1007/s11229-023-04057-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
A commonly held background assumption about the sciences is that they connect along borders characterized by ontological or explanatory relationships, usually given in the order of mathematics, physics, chemistry, biology, psychology, and the social sciences. Interdisciplinary work, in this picture, arises in the connecting regions of adjacent disciplines. Philosophical research into interdisciplinary model transfer has increasingly complicated this picture by highlighting additional connections orthogonal to it. But most of these works have been done through case studies, which due to their strong focus struggle to provide foundations for claims about large-scale relations between multiple scientific disciplines. As a supplement, in this contribution, we propose to philosophers of science the use of modern science mapping techniques to trace connections between modeling techniques in large literature samples. We explain in detail how these techniques work, and apply them to a large, contemporary, and multidisciplinary data set (n=383.961 articles). Through the comparison of textual to mathematical representations, we suggest formulaic structures that are particularly common among different disciplines and produce first results indicating the general strength and commonality of such relationships.
Collapse
Affiliation(s)
- Maximilian Noichl
- Faculty of Philosophy and Education, University of Vienna, Universitätsstraße 7, 1010 Vienna, Austria
- Faculty for Social Sciences and Economics, University of Bamberg, Feldkirchenstraße 21, 96045 Bamberg, Germany
| |
Collapse
|
18
|
Zhao S, Tang X, Tian W, Partarrieu S, Liu R, Shen H, Lee J, Guo S, Lin Z, Liu J. Tracking neural activity from the same cells during the entire adult life of mice. Nat Neurosci 2023. [PMID: 36804648 DOI: 10.1038/s41593-023-01267-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/19/2023] [Indexed: 02/22/2023]
Abstract
Stably recording the electrical activity of the same neurons over the adult life of an animal is important to neuroscience research and biomedical applications. Current implantable devices cannot provide stable recording on this timescale. Here, we introduce a method to precisely implant electronics with an open, unfolded mesh structure across multiple brain regions in the mouse. The open mesh structure forms a stable interwoven structure with the neural network, preventing probe drifting and showing no immune response and neuron loss during the year-long implantation. Rigorous statistical analysis, visual stimulus-dependent measurement and unbiased, machine-learning-based analysis demonstrated that single-unit action potentials have been recorded from the same neurons of behaving mice in a very long-term stable manner. Leveraging this stable structure, we demonstrated that the same neurons can be recorded over the entire adult life of the mouse, revealing the aging-associated evolution of single-neuron activities.
Collapse
|
19
|
Kim T, Chen D, Hornauer P, Emmenegger V, Bartram J, Ronchi S, Hierlemann A, Schröter M, Roqueiro D. Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks. Front Neuroinform 2023; 16:1032538. [PMID: 36713289 PMCID: PMC9874697 DOI: 10.3389/fninf.2022.1032538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/13/2022] [Indexed: 01/12/2023] Open
Abstract
Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA A receptor antagonist. Our results suggest that the joint representation of node features and functional connectivity, extracted from a baseline recording, was informative for predicting firing rate changes of individual neurons after the perturbation. Specifically, our implementation of a GNN model with inductive learning capability (GraphSAGE) outperformed other prediction models that relied only on single-neuron features. We tested the generalizability of the results on two additional datasets of HD-MEA recordings-a second dataset with cultures perturbed with Bicuculline and a dataset perturbed with the GABA A receptor antagonist Gabazine. GraphSAGE models showed improved prediction accuracy over other prediction models. Our results demonstrate the added value of taking into account the functional connectivity between neurons and the potential of GNNs to study complex interactions between neurons.
Collapse
Affiliation(s)
- Taehoon Kim
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Dexiong Chen
- Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Philipp Hornauer
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Vishalini Emmenegger
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Julian Bartram
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Silvia Ronchi
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Andreas Hierlemann
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Manuel Schröter
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Damian Roqueiro
- Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| |
Collapse
|
20
|
Mitchell-Heggs R, Prado S, Gava GP, Go MA, Schultz SR. Neural manifold analysis of brain circuit dynamics in health and disease. J Comput Neurosci 2023; 51:1-21. [PMID: 36522604 DOI: 10.1007/s10827-022-00839-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/30/2022] [Accepted: 10/29/2022] [Indexed: 12/23/2022]
Abstract
Recent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands of neurons. However, the development of analysis approaches for such large-scale neural recordings have been slower than those applicable to single-cell experiments. One approach that has gained recent popularity is neural manifold learning. This approach takes advantage of the fact that often, even though neural datasets may be very high dimensional, the dynamics of neural activity tends to traverse a much lower-dimensional space. The topological structures formed by these low-dimensional neural subspaces are referred to as "neural manifolds", and may potentially provide insight linking neural circuit dynamics with cognitive function and behavioral performance. In this paper we review a number of linear and non-linear approaches to neural manifold learning, including principal component analysis (PCA), multi-dimensional scaling (MDS), Isomap, locally linear embedding (LLE), Laplacian eigenmaps (LEM), t-SNE, and uniform manifold approximation and projection (UMAP). We outline these methods under a common mathematical nomenclature, and compare their advantages and disadvantages with respect to their use for neural data analysis. We apply them to a number of datasets from published literature, comparing the manifolds that result from their application to hippocampal place cells, motor cortical neurons during a reaching task, and prefrontal cortical neurons during a multi-behavior task. We find that in many circumstances linear algorithms produce similar results to non-linear methods, although in particular cases where the behavioral complexity is greater, non-linear methods tend to find lower-dimensional manifolds, at the possible expense of interpretability. We demonstrate that these methods are applicable to the study of neurological disorders through simulation of a mouse model of Alzheimer's Disease, and speculate that neural manifold analysis may help us to understand the circuit-level consequences of molecular and cellular neuropathology.
Collapse
|
21
|
Trepka EB, Zhu S, Xia R, Chen X, Moore T. Functional interactions among neurons within single columns of macaque V1. eLife 2022; 11:e79322. [PMID: 36321687 PMCID: PMC9662816 DOI: 10.7554/elife.79322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022] Open
Abstract
Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.
Collapse
Affiliation(s)
- Ethan B Trepka
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
- Neurosciences Program, Stanford UniversityStanfordUnited States
| | - Shude Zhu
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Ruobing Xia
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Xiaomo Chen
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
- Center for Neuroscience, Department of Neurobiology, Physiology, and Behavior, University of California, DavisDavisUnited States
| | - Tirin Moore
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| |
Collapse
|
22
|
Sibille J, Kremkow J, Koch U. Absence of the Fragile X messenger ribonucleoprotein alters response patterns to sounds in the auditory midbrain. Front Neurosci 2022; 16:987939. [PMID: 36188480 PMCID: PMC9523263 DOI: 10.3389/fnins.2022.987939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/26/2022] [Indexed: 11/13/2022] Open
Abstract
Among the different autism spectrum disorders, Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability. Sensory and especially auditory hypersensitivity is a key symptom in patients, which is well mimicked in the Fmr1 -/- mouse model. However, the physiological mechanisms underlying FXS’s acoustic hypersensitivity in particular remain poorly understood. Here, we categorized spike response patterns to pure tones of different frequencies and intensities from neurons in the inferior colliculus (IC), a central integrator in the ascending auditory pathway. Based on this categorization we analyzed differences in response patterns between IC neurons of wild-type (WT) and Fmr1 -/- mice. Our results report broadening of frequency tuning, an increased firing in response to monaural as well as binaural stimuli, an altered balance of excitation-inhibition, and reduced response latencies, all expected features of acoustic hypersensitivity. Furthermore, we noticed that all neuronal response types in Fmr1 -/- mice displayed enhanced offset-rebound activity outside their excitatory frequency response area. These results provide evidence that the loss of Fmr1 not only increases spike responses in IC neurons similar to auditory brainstem neurons, but also changes response patterns such as offset spiking. One can speculate this to be an underlying aspect of the receptive language problems associated with Fragile X syndrome.
Collapse
Affiliation(s)
- Jérémie Sibille
- Institute for Biology, Freie Universität Berlin, Berlin, Germany
- Neuroscience Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Berlin, Germany
- *Correspondence: Jérémie Sibille, ,
| | - Jens Kremkow
- Neuroscience Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Berlin, Germany
| | - Ursula Koch
- Institute for Biology, Freie Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Berlin, Germany
- Ursula Koch,
| |
Collapse
|
23
|
Schröter M, Wang C, Terrigno M, Hornauer P, Huang Z, Jagasia R, Hierlemann A. Functional imaging of brain organoids using high-density microelectrode arrays. MRS Bull 2022; 47:530-544. [PMID: 36120104 PMCID: PMC9474390 DOI: 10.1557/s43577-022-00282-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/02/2022] [Indexed: 05/31/2023]
Abstract
ABSTRACT Studies have provided evidence that human cerebral organoids (hCOs) recapitulate fundamental milestones of early brain development, but many important questions regarding their functionality and electrophysiological properties persist. High-density microelectrode arrays (HD-MEAs) represent an attractive analysis platform to perform functional studies of neuronal networks at the cellular and network scale. Here, we use HD-MEAs to derive large-scale electrophysiological recordings from sliced hCOs. We record the activity of hCO slices over several weeks and probe observed neuronal dynamics pharmacologically. Moreover, we present results on how the obtained recordings can be spike-sorted and subsequently studied across scales. For example, we show how to track single neurons across several days on the HD-MEA and how to infer axonal action potential velocities. We also infer putative functional connectivity from hCO recordings. The introduced methodology will contribute to a better understanding of developing neuronal networks in brain organoids and provide new means for their functional characterization. IMPACT STATEMENT Human cerebral organoids (hCOs) represent an attractive in vitro model system to study key physiological mechanisms underlying early neuronal network formation in tissue with healthy or disease-related genetic backgrounds. Despite remarkable advances in the generation of brain organoids, knowledge on the functionality of their neuronal circuits is still scarce. Here, we used complementary metal-oxide-semiconductor (CMOS)-based high-density microelectrode arrays (HD-MEAs) to perform large-scale recordings from sliced hCOs over several weeks and quantified their activity across scales. Using single-cell and network metrics, we were able to probe aspects of hCO neurophysiology that are more difficult to obtain with other techniques, such as patch clamping (lower yield) and calcium imaging (lower temporal resolution). These metrics included, for example, extracellular action potential (AP) waveform features and axonal AP velocity at the cellular level, as well as functional connectivity at the network level. Analysis was enabled by the large sensing area and the high spatiotemporal resolution provided by HD-MEAs, which allowed recordings from hundreds of neurons and spike sorting of their activity. Our results demonstrate that HD-MEAs provide a multi-purpose platform for the functional characterization of hCOs, which will be key in improving our understanding of this model system and assessing its relevance for translational research. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1557/s43577-022-00282-w.
Collapse
Affiliation(s)
- Manuel Schröter
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Congwei Wang
- NRD, F. Hoffmann-La Roche Ltd., Roche Innovation Center Basel, Basel, Switzerland
| | - Marco Terrigno
- NRD, F. Hoffmann-La Roche Ltd., Roche Innovation Center Basel, Basel, Switzerland
| | - Philipp Hornauer
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Ziqiang Huang
- EMBL Imaging Centre, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Ravi Jagasia
- NRD, F. Hoffmann-La Roche Ltd., Roche Innovation Center Basel, Basel, Switzerland
| | - Andreas Hierlemann
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| |
Collapse
|
24
|
Paulk AC, Kfir Y, Khanna AR, Mustroph ML, Trautmann EM, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Richardson RM, Williams ZM, Cash SS. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat Neurosci 2022; 25:252-263. [PMID: 35102333 DOI: 10.1038/s41593-021-00997-0] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/07/2021] [Indexed: 12/20/2022]
Abstract
Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here we describe a new probe variant and set of techniques that enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single-unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread and modulation by LFP events such as inter-ictal discharges and burst suppression. Although some challenges remain in creating a turnkey recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution.
Collapse
Affiliation(s)
- Angelique C Paulk
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Arjun R Khanna
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York City, NY, USA
- Zuckerman Institute, Columbia University, New York City, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York City, NY, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Columbia University, New York City, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Sergey D Stavisky
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurological Surgery, University of California at Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - R Mark Richardson
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA.
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
25
|
Tsolias A, Medalla M. Muscarinic Acetylcholine Receptor Localization on Distinct Excitatory and Inhibitory Neurons Within the ACC and LPFC of the Rhesus Monkey. Front Neural Circuits 2022; 15:795325. [PMID: 35087381 PMCID: PMC8786743 DOI: 10.3389/fncir.2021.795325] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/09/2021] [Indexed: 12/14/2022] Open
Abstract
Acetylcholine (ACh) can act on pre- and post-synaptic muscarinic receptors (mAChR) in the cortex to influence a myriad of cognitive processes. Two functionally-distinct regions of the prefrontal cortex-the lateral prefrontal cortex (LPFC) and the anterior cingulate cortex (ACC)-are differentially innervated by ascending cholinergic pathways yet, the nature and organization of prefrontal-cholinergic circuitry in primates are not well understood. Using multi-channel immunohistochemical labeling and high-resolution microscopy, we found regional and laminar differences in the subcellular localization and the densities of excitatory and inhibitory subpopulations expressing m1 and m2 muscarinic receptors, the two predominant cortical mAChR subtypes, in the supragranular layers of LPFC and ACC in rhesus monkeys (Macaca mulatta). The subset of m1+/m2+ expressing SMI-32+ pyramidal neurons labeled in layer 3 (L3) was denser in LPFC than in ACC, while m1+/m2+ SMI-32+ neurons co-expressing the calcium-binding protein, calbindin (CB) was greater in ACC. Further, we found between-area differences in laminar m1+ dendritic expression, and m2+ presynaptic localization on cortico-cortical (VGLUT1+) and sub-cortical inputs (VGLUT2+), suggesting differential cholinergic modulation of top-down vs. bottom-up inputs in the two areas. While almost all inhibitory interneurons-identified by their expression of parvalbumin (PV+), CB+, and calretinin (CR+)-expressed m1+, the localization of m2+ differed by subtype and area. The ACC exhibited a greater proportion of m2+ inhibitory neurons compared to the LPFC and had a greater density of presynaptic m2+ localized on inhibitory (VGAT+) inputs targeting proximal somatodendritic compartments and axon initial segments of L3 pyramidal neurons. These data suggest a greater capacity for m2+-mediated cholinergic suppression of inhibition in the ACC compared to the LPFC. The anatomical localization of muscarinic receptors on ACC and LPFC micro-circuits shown here contributes to our understanding of diverse cholinergic neuromodulation of functionally-distinct prefrontal areas involved in goal-directed behavior, and how these interactions maybe disrupted in neuropsychiatric and neurological conditions.
Collapse
Affiliation(s)
- Alexandra Tsolias
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, United States
| | - Maria Medalla
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Center for Systems Neuroscience, Boston University, Boston, MA, United States
| |
Collapse
|
26
|
Whiteway MR, Biderman D, Friedman Y, Dipoppa M, Buchanan EK, Wu A, Zhou J, Bonacchi N, Miska NJ, Noel JP, Rodriguez E, Schartner M, Socha K, Urai AE, Salzman CD, Cunningham JP, Paninski L. Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. PLoS Comput Biol 2021; 17:e1009439. [PMID: 34550974 PMCID: PMC8489729 DOI: 10.1371/journal.pcbi.1009439] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 10/04/2021] [Accepted: 09/09/2021] [Indexed: 12/02/2022] Open
Abstract
Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.
Collapse
Affiliation(s)
- Matthew R. Whiteway
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - Dan Biderman
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - Yoni Friedman
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America
| | - Mario Dipoppa
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
| | - E. Kelly Buchanan
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - Anqi Wu
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | - John Zhou
- Department of Computer Science, Columbia University, New York, New York, United States of America
| | | | - Nathaniel J. Miska
- Sainsbury-Wellcome Centre for Neural Circuits and Behavior, University College London, London, United Kingdom
| | - Jean-Paul Noel
- Center for Neural Science, New York University, New York, New York, United States of America
| | - Erica Rodriguez
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| | | | - Karolina Socha
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Anne E. Urai
- Cognitive Psychology Unit, Leiden University, Leiden, The Netherlands
| | - C. Daniel Salzman
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Department of Psychiatry, Columbia University, New York, New York, United States of America
- New York State Psychiatric Institute, New York, New York, United States of America
- Kavli Institute for Brain Sciences, New York, New York, United States of America
| | | | - John P. Cunningham
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
| | - Liam Paninski
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
| |
Collapse
|