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Antin B, Sadahiro M, Gajowa M, Triplett MA, Adesnik H, Paninski L. Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization. PLoS Comput Biol 2024; 20:e1012053. [PMID: 38709828 DOI: 10.1371/journal.pcbi.1012053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 04/03/2024] [Indexed: 05/08/2024] Open
Abstract
Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recording, enables large-scale mapping of physiological circuit parameters. In this experimental setup, recorded postsynaptic currents are used to infer the presence and strength of connections. For many cell types, nearby connections are those we expect to be strongest. However, when the postsynaptic cell expresses opsin, optical excitation of nearby cells can induce direct photocurrents in the postsynaptic cell. These photocurrent artifacts contaminate synaptic currents, making it difficult or impossible to probe connectivity for nearby cells. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are less variable than those of synaptic currents. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available as open source software.
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Affiliation(s)
- Benjamin Antin
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Masato Sadahiro
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Marta Gajowa
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Marcus A Triplett
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Grossman Center for the Statistics of Mind, and Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
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Triplett MA, Gajowa M, Adesnik H, Paninski L. Bayesian target optimisation for high-precision holographic optogenetics. bioRxiv 2023:2023.05.25.542307. [PMID: 37292661 PMCID: PMC10246014 DOI: 10.1101/2023.05.25.542307] [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: 06/10/2023]
Abstract
Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of nearby non-target neurons due to imperfect confinement of light onto target neurons. Here we propose a novel computational approach to this problem called Bayesian target optimisation. Our approach uses nonparametric Bayesian inference to model neural responses to optogenetic stimulation, and then optimises the laser powers and optical target locations needed to achieve a desired activity pattern with minimal OTS. We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. Together, these results establish our ability to overcome OTS, enabling optogenetic stimulation with substantially improved precision.
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Affiliation(s)
- Marcus A. Triplett
- Department of Statistics, Columbia University
- Zuckerman Mind Brain Behavior Institute, Columbia University
| | - Marta Gajowa
- Department of Molecular and Cell Biology, UC Berkeley
| | | | - Liam Paninski
- Department of Statistics, Columbia University
- Zuckerman Mind Brain Behavior Institute, Columbia University
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Triplett MA, Goodhill GJ. Inference of Multiplicative Factors Underlying Neural Variability in Calcium Imaging Data. Neural Comput 2022; 34:1143-1169. [PMID: 35344990 DOI: 10.1162/neco_a_01492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/11/2022] [Indexed: 11/04/2022]
Abstract
Understanding brain function requires disentangling the high-dimensional activity of populations of neurons. Calcium imaging is an increasingly popular technique for monitoring such neural activity, but computational tools for interpreting extracted calcium signals are lacking. While there has been a substantial development of factor-analysis-type methods for neural spike train analysis, similar methods targeted at calcium imaging data are only beginning to emerge. Here we develop a flexible modeling framework that identifies low-dimensional latent factors in calcium imaging data with distinct additive and multiplicative modulatory effects. Our model includes spike-and-slab sparse priors that regularize additive factor activity and gaussian process priors that constrain multiplicative effects to vary only gradually, allowing for the identification of smooth and interpretable changes in multiplicative gain. These factors are estimated from the data using a variational expectation-maximization algorithm that requires a differentiable reparameterization of both continuous and discrete latent variables. After demonstrating our method on simulated data, we apply it to experimental data from the zebrafish optic tectum, uncovering low-dimensional fluctuations in multiplicative excitability that govern trial-to-trial variation in evoked responses.
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Affiliation(s)
- Marcus A Triplett
- Queensland Brain Institute and School of Mathematics and Physics, University of Queensland, St Lucia, QLD 4072, Australia
| | - Geoffrey J Goodhill
- Queensland Brain Institute and School of Mathematics and Physics, University of Queensland, St Lucia, QLD 4072, Australia
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Triplett MA, Pujic Z, Sun B, Avitan L, Goodhill GJ. Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data. PLoS Comput Biol 2020; 16:e1008330. [PMID: 33253161 PMCID: PMC7728401 DOI: 10.1371/journal.pcbi.1008330] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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: 06/30/2020] [Revised: 12/10/2020] [Accepted: 09/10/2020] [Indexed: 11/19/2022] Open
Abstract
The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.
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Affiliation(s)
- Marcus A. Triplett
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
- School of Mathematics and Physics, The University of Queensland, St Lucia, Australia
| | - Zac Pujic
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
| | - Biao Sun
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
| | - Lilach Avitan
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
| | - Geoffrey J. Goodhill
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia
- School of Mathematics and Physics, The University of Queensland, St Lucia, Australia
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Abstract
A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis. Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity. In particular, we discuss methods for estimating receptive fields, modeling neural population dynamics, and inferring low dimensional latent structure from a population of neurons, in the context of both electrophysiology and calcium imaging data.
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Affiliation(s)
| | - Geoffrey J. Goodhill
- Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
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Triplett MA, Avitan L, Goodhill GJ. Emergence of spontaneous assembly activity in developing neural networks without afferent input. PLoS Comput Biol 2018; 14:e1006421. [PMID: 30265665 PMCID: PMC6161857 DOI: 10.1371/journal.pcbi.1006421] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [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: 04/17/2018] [Accepted: 08/07/2018] [Indexed: 02/04/2023] Open
Abstract
Spontaneous activity is a fundamental characteristic of the developing nervous system. Intriguingly, it often takes the form of multiple structured assemblies of neurons. Such assemblies can form even in the absence of afferent input, for instance in the zebrafish optic tectum after bilateral enucleation early in life. While the development of neural assemblies based on structured afferent input has been theoretically well-studied, it is less clear how they could arise in systems without afferent input. Here we show that a recurrent network of binary threshold neurons with initially random weights can form neural assemblies based on a simple Hebbian learning rule. Over development the network becomes increasingly modular while being driven by initially unstructured spontaneous activity, leading to the emergence of neural assemblies. Surprisingly, the set of neurons making up each assembly then continues to evolve, despite the number of assemblies remaining roughly constant. In the mature network assembly activity builds over several timesteps before the activation of the full assembly, as recently observed in calcium-imaging experiments. Our results show that Hebbian learning is sufficient to explain the emergence of highly structured patterns of neural activity in the absence of structured input.
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Affiliation(s)
- Marcus A. Triplett
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
- School of Mathematics and Physics, University of Queensland, St Lucia, Queensland, Australia
| | - Lilach Avitan
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
| | - Geoffrey J. Goodhill
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
- School of Mathematics and Physics, University of Queensland, St Lucia, Queensland, Australia
- * E-mail:
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Abbas F, Triplett MA, Goodhill GJ, Meyer MP. A Three-Layer Network Model of Direction Selective Circuits in the Optic Tectum. Front Neural Circuits 2017; 11:88. [PMID: 29209178 PMCID: PMC5702351 DOI: 10.3389/fncir.2017.00088] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [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: 09/15/2017] [Accepted: 11/06/2017] [Indexed: 11/13/2022] Open
Abstract
The circuit mechanisms that give rise to direction selectivity in the retina have been studied extensively but how direction selectivity is established in retinorecipient areas of the brain is less well understood. Using functional imaging in larval zebrafish we examine how the direction of motion is encoded by populations of neurons at three layers of the optic tectum; retinal ganglion cell axons (RGCs), a layer of superficial inhibitory interneurons (SINs), and periventricular neurons (PVNs), which constitute the majority of neurons in the tectum. We show that the representation of motion direction is transformed at each layer. At the level of RGCs and SINs the direction of motion is encoded by three direction-selective (DS) subtypes tuned to upward, downward, and caudal-to-rostral motion. However, the tuning of SINs is significantly narrower and this leads to a conspicuous gap in the representation of motion in the rostral-to-caudal direction at the level of SINs. Consistent with previous findings we demonstrate that, at the level of PVNs the direction of motion is encoded by four DS cell types which include an additional DS PVN cell type tuned to rostral-to-caudal motion. Strikingly, the tuning profile of this emergent cell type overlaps with the gap in the representation of rostral-to-caudal motion at the level of SINs. Using our functional imaging data we constructed a simple computational model that demonstrates how the emergent population of PVNs is generated by the interactions of cells at each layer of the tectal network. The model predicts that PVNs tuned to rostral-to-caudal motion can be generated via convergence of DS RGCs tuned to upward and downward motion and feedforward tuned inhibition via SINs which suppresses responses to non-preferred directions. Thus, by reshaping directional tuning that is inherited from the retina inhibitory inputs from SINs can generate a novel subtype of DS PVN and in so doing enhance the encoding of directional stimuli.
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Affiliation(s)
- Fatima Abbas
- Centre for Developmental Neurobiology and MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Marcus A. Triplett
- Queensland Brain Institute and School of Mathematics and Physics, University of Queensland, St Lucia, QLD, Australia
| | - Geoffrey J. Goodhill
- Queensland Brain Institute and School of Mathematics and Physics, University of Queensland, St Lucia, QLD, Australia
| | - Martin P. Meyer
- Centre for Developmental Neurobiology and MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Robinson MR, English G, Moser G, Lloyd-Jones LR, Triplett MA, Zhu Z, Nolte IM, van Vliet-Ostaptchouk JV, Snieder H, Esko T, Milani L, Mägi R, Metspalu A, Magnusson PKE, Pedersen NL, Ingelsson E, Johannesson M, Yang J, Cesarini D, Visscher PM. Genotype-covariate interaction effects and the heritability of adult body mass index. Nat Genet 2017; 49:1174-1181. [PMID: 28692066 DOI: 10.1038/ng.3912] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 06/12/2017] [Indexed: 12/18/2022]
Abstract
Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype-covariate interaction effects at common SNP loci. We find evidence for genotype-age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10-18), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype-environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10-5 and LRT = 30.80, P = 1.42 × 10-8), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI.
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Affiliation(s)
- Matthew R Robinson
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Geoffrey English
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Gerhard Moser
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Luke R Lloyd-Jones
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Marcus A Triplett
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jana V van Vliet-Ostaptchouk
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Tonu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Division of Endocrinology, Boston Children's Hospital, Cambridge, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.,Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA
| | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia.,Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.,Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | | | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
| | - David Cesarini
- Center for Experimental Social Science, Department of Economics, New York University, New York, New York, USA
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
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