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Chen Y, Chien J, Dai B, Lin D, Chen ZS. Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network. J Neural Eng 2024; 21:036051. [PMID: 38861996 DOI: 10.1088/1741-2552/ad5702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
Objective.Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors.Approach:We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system and a discrete-state hidden semi-Markov model. We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively.Main results:Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth.Significance:Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.
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Affiliation(s)
- Yibo Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, United States of America
- Program in Artificial Intelligence, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
| | - Jonathan Chien
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, United States of America
| | - Bing Dai
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States of America
| | - Dayu Lin
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, United States of America
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States of America
- Center for Neural Science, New York University, New York, NY, United States of America
| | - Zhe Sage Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, United States of America
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States of America
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States of America
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2
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Zhang L, Sun Y, Wang J, Zhang M, Wang Q, Xie B, Yu F, Wen D, Ma C. Dopaminergic dominance in the ventral medial hypothalamus: A pivotal regulator for methamphetamine-induced pathological aggression. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110971. [PMID: 38365104 DOI: 10.1016/j.pnpbp.2024.110971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/05/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024]
Abstract
Methamphetamine (METH) abuse is associated with a spectrum of behavioral consequences, among which heightened aggression presents a significant challenge. However, the causal role of METH's impact in aggression and its target circuit mechanisms remains largely unknown. We established an acute METH exposure-aggression mouse model to investigate the role of ventral tegmental area (VTA) dopaminergic neurons and ventral medial hypothalamus VMH glutamatergic neuron. Our findings revealed that METH-induced VTA dopamine excitability activates the ventromedial hypothalamus (VMH) glutamatergic neurons, contributing to pathological aggression. Notably, we uncovered a dopaminergic transmission within the VTA-VMH circuit that exclusively functioned under METH influence. This dopaminergic pathway emerged as a potential key player in enabling dopamine-related pathological aggression, with heightened dopaminergic excitability implicated in various psychiatric symptoms. Also, the modulatory function of this pathway opens new possibilities for targeted therapeutic strategies for intervention to improve treatment in METH abuse and may have broader implications for addressing pathological aggression syndromes.
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Affiliation(s)
- Ludi Zhang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Identification Center of Forensic Medicine, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Key Laboratory of Neural and Vascular Biology, Ministry of Education, 050017 Shijiazhuang, Hebei, PR China; Hebei Medical University Postdoctoral Research Station, 050017, Shijiazhuang, Hebei, PR China
| | - Yufei Sun
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China
| | - Jian Wang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Identification Center of Forensic Medicine, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China
| | - Minglong Zhang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China
| | - Qingwu Wang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Identification Center of Forensic Medicine, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China
| | - Bing Xie
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Identification Center of Forensic Medicine, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China
| | - Feng Yu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Identification Center of Forensic Medicine, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China
| | - Di Wen
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Identification Center of Forensic Medicine, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Key Laboratory of Neural and Vascular Biology, Ministry of Education, 050017 Shijiazhuang, Hebei, PR China.
| | - Chunling Ma
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Identification Center of Forensic Medicine, Hebei Medical University, 050017 Shijiazhuang, Hebei, PR China; Key Laboratory of Neural and Vascular Biology, Ministry of Education, 050017 Shijiazhuang, Hebei, PR China.
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3
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Vinograd A, Nair A, Linderman SW, Anderson DJ. Intrinsic Dynamics and Neural Implementation of a Hypothalamic Line Attractor Encoding an Internal Behavioral State. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.595051. [PMID: 38826298 PMCID: PMC11142118 DOI: 10.1101/2024.05.21.595051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Line attractors are emergent population dynamics hypothesized to encode continuous variables such as head direction and internal states. In mammals, direct evidence of neural implementation of a line attractor has been hindered by the challenge of targeting perturbations to specific neurons within contributing ensembles. Estrogen receptor type 1 (Esr1)-expressing neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) show line attractor dynamics in male mice during fighting. We hypothesized that these dynamics may encode continuous variation in the intensity of an internal aggressive state. Here, we report that these neurons also show line attractor dynamics in head-fixed mice observing aggression. We exploit this finding to identify and perturb line attractor-contributing neurons using 2-photon calcium imaging and holographic optogenetic perturbations. On-manifold perturbations demonstrate that integration and persistent activity are intrinsic properties of these neurons which drive the system along the line attractor, while transient off-manifold perturbations reveal rapid relaxation back into the attractor. Furthermore, stimulation and imaging reveal selective functional connectivity among attractor-contributing neurons. Intriguingly, individual differences among mice in line attractor stability were correlated with the degree of functional connectivity among contributing neurons. Mechanistic modelling indicates that dense subnetwork connectivity and slow neurotransmission are required to explain our empirical findings. Our work bridges circuit and manifold paradigms, shedding light on the intrinsic and operational dynamics of a behaviorally relevant mammalian line attractor.
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Affiliation(s)
- Amit Vinograd
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, USA
- Tianqiao and Chrissy Chen Institute for Neuroscience Caltech; Pasadena, USA
- Howard Hughes Medical Institute; Chevy Chase, USA
| | - Aditya Nair
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, USA
- Tianqiao and Chrissy Chen Institute for Neuroscience Caltech; Pasadena, USA
- Howard Hughes Medical Institute; Chevy Chase, USA
| | - Scott W. Linderman
- Department of Statistics, Stanford University, Stanford, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, USA
| | - David J. Anderson
- Division of Biology and Biological Engineering, California Institute of Technology; Pasadena, USA
- Tianqiao and Chrissy Chen Institute for Neuroscience Caltech; Pasadena, USA
- Howard Hughes Medical Institute; Chevy Chase, USA
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Lin A, Akafia C, Dal Monte O, Fan S, Fagan N, Putnam P, Tye KM, Chang S, Ba D, Allsop AZAS. An unbiased method to partition diverse neuronal responses into functional ensembles reveals interpretable population dynamics during innate social behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593229. [PMID: 38766234 PMCID: PMC11100741 DOI: 10.1101/2024.05.08.593229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
In neuroscience, understanding how single-neuron firing contributes to distributed neural ensembles is crucial. Traditional methods of analysis have been limited to descriptions of whole population activity, or, when analyzing individual neurons, criteria for response categorization varied significantly across experiments. Current methods lack scalability for large datasets, fail to capture temporal changes and rely on parametric assumptions. There's a need for a robust, scalable, and non-parametric functional clustering approach to capture interpretable dynamics. To address this challenge, we developed a model-based, statistical framework for unsupervised clustering of multiple time series datasets that exhibit nonlinear dynamics into an a-priori-unknown number of parameterized ensembles called Functional Encoding Units (FEUs). FEU outperforms existing techniques in accuracy and benchmark scores. Here, we apply this FEU formalism to single-unit recordings collected during social behaviors in rodents and primates and demonstrate its hypothesis-generating and testing capacities. This novel pipeline serves as an analytic bridge, translating neural ensemble codes across model systems.
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Affiliation(s)
- Alexander Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Cyril Akafia
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
| | - Olga Dal Monte
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Siqi Fan
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Nicholas Fagan
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Philip Putnam
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Kay M. Tye
- Salk Institute for Biological Studies, La Jolla, California, USA
- Howard Hughes Medical Institute, La Jolla, California, USA
- Kavli Institute for the Brain and Mind, La Jolla, California, USA
| | - Steve Chang
- Department of Psychology, Yale University, New Haven, Connecticut, USA
| | - Demba Ba
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
- Center for Brain Sciences, Harvard University, Cambridge, Massachusetts, USA
- Kempner Institute for the Study of Artificial and Natural Intelligence, Harvard University, Cambridge, Massachusetts, USA
| | - AZA Stephen Allsop
- Center for Collective Healing, Department of Psychiatry and Behavioral Sciences, Howard University, Washington DC, USA
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
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5
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Fine JM, Yoo SBM, Hayden BY. Control over a mixture of policies determines change of mind topology during continuous choice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590154. [PMID: 38712284 PMCID: PMC11071291 DOI: 10.1101/2024.04.18.590154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Behavior is naturally organized into categorically distinct states with corresponding patterns of neural activity; how does the brain control those states? We propose that states are regulated by specific neural processes that implement meta-control that can blend simpler control processes. To test this hypothesis, we recorded from neurons in the dorsal anterior cingulate cortex (dACC) and dorsal premotor cortex (PMd) while macaques performed a continuous pursuit task with two moving prey that followed evasive strategies. We used a novel control theoretic approach to infer subjects' moment-to-moment latent control variables, which in turn dictated their blend of distinct identifiable control processes. We identified low-dimensional subspaces in neuronal responses that reflected the current strategy, the value of the pursued target, and the relative value of the two targets. The top two principal components of activity tracked changes of mind in abstract and change-type-specific formats, respectively. These results indicate that control of behavioral state reflects the interaction of brain processes found in dorsal prefrontal regions that implement a mixture over low-level control policies.
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6
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Nghiem TAE, Lee B, Chao THH, Branigan NK, Mistry PK, Shih YYI, Menon V. Space wandering in the rodent default mode network. Proc Natl Acad Sci U S A 2024; 121:e2315167121. [PMID: 38557177 PMCID: PMC11009630 DOI: 10.1073/pnas.2315167121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 01/17/2024] [Indexed: 04/04/2024] Open
Abstract
The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes-the retrosplenial cortex, cingulate cortex, and prelimbic cortex-as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and found that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.
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Affiliation(s)
| | - Byeongwook Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Nicholas K. Branigan
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
| | - Percy K. Mistry
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
| | - Yen-Yu Ian Shih
- Center for Animal MRI, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC27514
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University, Palo Alto, CA94304
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA94304
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA94305
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7
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Minakuchi T, Guthman EM, Acharya P, Hinson J, Fleming W, Witten IB, Oline SN, Falkner AL. Independent inhibitory control mechanisms for aggressive motivation and action. Nat Neurosci 2024; 27:702-715. [PMID: 38347201 DOI: 10.1038/s41593-023-01563-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/19/2023] [Indexed: 04/10/2024]
Abstract
Social behaviors often consist of a motivational phase followed by action. Here we show that neurons in the ventromedial hypothalamus ventrolateral area (VMHvl) of mice encode the temporal sequence of aggressive motivation to action. The VMHvl receives local inhibitory input (VMHvl shell) and long-range input from the medial preoptic area (MPO) with functional coupling to neurons with specific temporal profiles. Encoding models reveal that during aggression, VMHvl shellvgat+ activity peaks at the start of an attack, whereas activity from the MPO-VMHvlvgat+ input peaks at specific interaction endpoints. Activation of the MPO-VMHvlvgat+ input promotes and prolongs a low motivation state, whereas activation of VMHvl shellvgat+ results in action-related deficits, acutely terminating attack. Moreover, stimulation of MPO-VMHvlvgat+ input is positively valenced and anxiolytic. Together, these data demonstrate how distinct inhibitory inputs to the hypothalamus can independently gate the motivational and action phases of aggression through a single locus of control.
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Affiliation(s)
| | | | | | - Justin Hinson
- Princeton Neuroscience Institute, Princeton, NJ, USA
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8
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Bell AM. The evolution of decision-making mechanisms under competing demands. Trends Ecol Evol 2024; 39:141-151. [PMID: 37783626 PMCID: PMC10922085 DOI: 10.1016/j.tree.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 10/04/2023]
Abstract
Animals in nature are constantly managing multiple demands, and decisions about how to adjust behavior in response to ecologically relevant demands is critical for fitness. Evidence for behavioral correlations across functional contexts (behavioral syndromes) and growing appreciation for shared proximate substrates of behavior prompts novel questions about the existence of distinct neural, molecular, and genetic mechanisms involved in decision-making. Those proximate mechanisms are likely to be an important target of selection, but little is known about how they evolve, their evolutionary history, or where they harbor genetic variation. Herein I provide a conceptual framework for understanding the evolution of mechanisms for decision-making, highlighting insights on decision-making in humans and model organisms, and sketch an emerging synthesis.
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Affiliation(s)
- Alison M Bell
- Department of Evolution, Ecology and Behavior, 505 S. Goodwin Ave, Urbana, IL 61801, USA.
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9
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Chen Y, Chien J, Dai B, Lin D, Chen ZS. Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.25.573308. [PMID: 38234793 PMCID: PMC10793434 DOI: 10.1101/2023.12.25.573308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Distributed hypothalamic-midbrain neural circuits orchestrate complex behavioral responses during social interactions. How population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include continuous-state linear dynamical system (LDS) and discrete-state hidden semi-Markov model (HSMM). We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states. Overall, these analysis approaches provide an unbiased strategy to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.
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Affiliation(s)
- Yibo Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
- Program in Artificial Intelligence, University of Science and Technology of China, Hefei, Anhui, China
| | - Jonathan Chien
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
| | - Bing Dai
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
| | - Dayu Lin
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
| | - Zhe Sage Chen
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA
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10
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Li M, Chen DS, Junker IP, Szorenyi F, Chen GH, Berger AJ, Comeault AA, Matute DR, Ding Y. Ancestral neural circuits potentiate the origin of a female sexual behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.570174. [PMID: 38106147 PMCID: PMC10723342 DOI: 10.1101/2023.12.05.570174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Courtship interactions are remarkably diverse in form and complexity among species. How neural circuits evolve to encode new behaviors that are functionally integrated into these dynamic social interactions is unknown. Here we report a recently originated female sexual behavior in the island endemic Drosophila species D. santomea, where females signal receptivity to male courtship songs by spreading their wings, which in turn promotes prolonged songs in courting males. Copulation success depends on this female signal and correlates with males' ability to adjust his singing in such a social feedback loop. Functional comparison of sexual circuitry across species suggests that a pair of descending neurons, which integrates male song stimuli and female internal state to control a conserved female abdominal behavior, drives wing spreading in D. santomea. This co-option occurred through the refinement of a pre-existing, plastic circuit that can be optogenetically activated in an outgroup species. Combined, our results show that the ancestral potential of a socially-tuned key circuit node to engage the wing motor program facilitates the expression of a new female behavior in appropriate sensory and motivational contexts. More broadly, our work provides insights into the evolution of social behaviors, particularly female behaviors, and the underlying neural mechanisms.
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Affiliation(s)
- Minhao Li
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Dawn S Chen
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian P Junker
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Fabianna Szorenyi
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Guan Hao Chen
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Arnold J Berger
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Aaron A Comeault
- Department of Biology, University of North Carolina, Chapel Hill, NC, USA
- Current address: School of Environmental and Natural Sciences, Bangor University, Bangor, UK
| | - Daniel R Matute
- Department of Biology, University of North Carolina, Chapel Hill, NC, USA
| | - Yun Ding
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
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11
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Stagkourakis S, Spigolon G, Marks M, Feyder M, Kim J, Perona P, Pachitariu M, Anderson DJ. Anatomically distributed neural representations of instincts in the hypothalamus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.21.568163. [PMID: 38045312 PMCID: PMC10690204 DOI: 10.1101/2023.11.21.568163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.
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Affiliation(s)
- Stefanos Stagkourakis
- Division of Biology and Biological Engineering 156-29, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, California 91125, USA
| | - Giada Spigolon
- Biological Imaging Facility, California Institute of Technology, Pasadena, California 91125, USA
| | - Markus Marks
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, USA
| | - Michael Feyder
- Division of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California 94305, USA
| | - Joseph Kim
- Division of Biology and Biological Engineering 156-29, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, California 91125, USA
| | - Pietro Perona
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, USA
| | - Marius Pachitariu
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
| | - David J. Anderson
- Division of Biology and Biological Engineering 156-29, Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, California 91125, USA
- Howard Hughes Medical Institute, California Institute of Technology, 1200 East California Blvd, Pasadena, California 91125, USA
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12
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Mountoufaris G, Nair A, Yang B, Kim DW, Anderson DJ. Neuropeptide Signaling is Required to Implement a Line Attractor Encoding a Persistent Internal Behavioral State. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565073. [PMID: 37961374 PMCID: PMC10635056 DOI: 10.1101/2023.11.01.565073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Internal states drive survival behaviors, but their neural implementation is not well understood. Recently we identified a line attractor in the ventromedial hypothalamus (VMH) that represents an internal state of aggressiveness. Line attractors can be implemented by recurrent connectivity and/or neuromodulatory signaling, but evidence for the latter is scant. Here we show that neuropeptidergic signaling is necessary for line attractor dynamics in this system, using a novel approach that integrates cell type-specific, anatomically restricted CRISPR/Cas9-based gene editing with microendoscopic calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1 + neurons that control aggression suppressed attack, reduced persistent neural activity and eliminated line attractor dynamics, while only modestly impacting neural activity and sex- or behavior-tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.
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13
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Wang S, Falcone R, Richmond B, Averbeck BB. Attractor dynamics reflect decision confidence in macaque prefrontal cortex. Nat Neurosci 2023; 26:1970-1980. [PMID: 37798412 DOI: 10.1038/s41593-023-01445-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 08/31/2023] [Indexed: 10/07/2023]
Abstract
Decisions are made with different degrees of consistency, and this consistency can be linked to the confidence that the best choice has been made. Theoretical work suggests that attractor dynamics in networks can account for choice consistency, but how this is implemented in the brain remains unclear. Here we provide evidence that the energy landscape around attractor basins in population neural activity in the prefrontal cortex reflects choice consistency. We trained two rhesus monkeys to make accept/reject decisions based on pretrained visual cues that signaled reward offers with different magnitudes and delays to reward. Monkeys made consistent decisions for very good and very bad offers, but decisions were less consistent for intermediate offers. Analysis of neural data showed that the attractor basins around patterns of activity reflecting decisions had steeper landscapes for offers that led to consistent decisions. Therefore, we provide neural evidence that energy landscapes predict decision consistency, which reflects decision confidence.
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Affiliation(s)
- Siyu Wang
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Rossella Falcone
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Leo M. Davidoff Department of Neurological Surgery, Albert Einstein College of Medicine Montefiore Medical Center, Bronx, NY, USA
| | - Barry Richmond
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Bruno B Averbeck
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
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14
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Durstewitz D, Koppe G, Thurm MI. Reconstructing computational system dynamics from neural data with recurrent neural networks. Nat Rev Neurosci 2023; 24:693-710. [PMID: 37794121 DOI: 10.1038/s41583-023-00740-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/18/2023] [Indexed: 10/06/2023]
Abstract
Computational models in neuroscience usually take the form of systems of differential equations. The behaviour of such systems is the subject of dynamical systems theory. Dynamical systems theory provides a powerful mathematical toolbox for analysing neurobiological processes and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) have become a popular machine learning tool for studying the non-linear dynamics of neural and behavioural processes by emulating an underlying system of differential equations. RNNs have been routinely trained on similar behavioural tasks to those used for animal subjects to generate hypotheses about the underlying computational mechanisms. By contrast, RNNs can also be trained on the measured physiological and behavioural data, thereby directly inheriting their temporal and geometrical properties. In this way they become a formal surrogate for the experimentally probed system that can be further analysed, perturbed and simulated. This powerful approach is called dynamical system reconstruction. In this Perspective, we focus on recent trends in artificial intelligence and machine learning in this exciting and rapidly expanding field, which may be less well known in neuroscience. We discuss formal prerequisites, different model architectures and training approaches for RNN-based dynamical system reconstructions, ways to evaluate and validate model performance, how to interpret trained models in a neuroscience context, and current challenges.
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Affiliation(s)
- Daniel Durstewitz
- Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany.
- Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany.
| | - Georgia Koppe
- Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Dept. of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Max Ingo Thurm
- Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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15
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Adamantidis AR, de Lecea L. Sleep and the hypothalamus. Science 2023; 382:405-412. [PMID: 37883555 DOI: 10.1126/science.adh8285] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 09/08/2023] [Indexed: 10/28/2023]
Abstract
Neural substrates of wakefulness, rapid eye movement sleep (REMS), and non-REMS (NREMS) in the mammalian hypothalamus overlap both anatomically and functionally with cellular networks that support physiological and behavioral homeostasis. Here, we review the roles of sleep neurons of the hypothalamus in the homeostatic control of thermoregulation or goal-oriented behaviors during wakefulness. We address how hypothalamic circuits involved in opposing behaviors such as core body temperature and sleep compute conflicting information and provide a coherent vigilance state. Finally, we highlight some of the key unresolved questions and challenges, and the promise of a more granular view of the cellular and molecular diversity underlying the integrative role of the hypothalamus in physiological and behavioral homeostasis.
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Affiliation(s)
- Antoine R Adamantidis
- Zentrum für Experimentelle Neurologie, Department of Neurology, Inselspital University Hospital Bern, Bern, Switzerland
- Department of Biomedical Research, University of Bern, Bern, Switzerland
| | - Luis de Lecea
- Department of Psychiatry and Behavioural Sciences, Stanford, CA, USA
- Wu Tsai Neurosciences Institute Stanford University School of Medicine, Stanford, CA, USA
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16
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Migliaro M, Ruiz-Contreras AE, Herrera-Solís A, Méndez-Díaz M, Prospéro-García OE. Endocannabinoid system and aggression across animal species. Neurosci Biobehav Rev 2023; 153:105375. [PMID: 37643683 DOI: 10.1016/j.neubiorev.2023.105375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 08/31/2023]
Abstract
This narrative review article summarizes the current state of knowledge regarding the relationship between the endocannabinoid system (ECS) and aggression across multiple vertebrate species. Experimental evidence indicates that acute administration of phytocannabinoids, synthetic cannabinoids, and the pharmacological enhancement of endocannabinoid signaling decreases aggressive behavior in several animal models. However, research on the chronic effects of cannabinoids on animal aggression has yielded inconsistent findings, indicating a need for further investigation. Cannabinoid receptors, particularly cannabinoid receptor type 1, appear to be an important part of the endogenous mechanism involved in the dampening of aggressive behavior. Overall, this review underscores the importance of the ECS in regulating aggressive behavior and provides a foundation for future research in this area.
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Affiliation(s)
- Martin Migliaro
- Grupo de Neurociencias: Laboratorio de Cannabinoides, Departamento de Fisiología, Facultad de Medicina, UNAM, Mexico.
| | - Alejandra E Ruiz-Contreras
- Grupo de Neurociencias: Laboratorio de Neurogenómica Cognitiva, Unidad de Investigación en Psicobiología y Neurociencias, Coordinación de Psicobiología y Neurociencias, Facultad de Psicología, UNAM, Mexico
| | - Andrea Herrera-Solís
- Grupo de Neurociencias: Laboratorio de Efectos Terapéuticos de los Cannabinoides, Hospital General Dr. Manuel Gea González, Secretaría de Salud, Mexico
| | - Mónica Méndez-Díaz
- Grupo de Neurociencias: Laboratorio de Cannabinoides, Departamento de Fisiología, Facultad de Medicina, UNAM, Mexico
| | - Oscar E Prospéro-García
- Grupo de Neurociencias: Laboratorio de Cannabinoides, Departamento de Fisiología, Facultad de Medicina, UNAM, Mexico
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17
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Wang S, Falcone R, Richmond B, Averbeck BB. Attractor dynamics reflect decision confidence in macaque prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.17.558139. [PMID: 37886489 PMCID: PMC10602028 DOI: 10.1101/2023.09.17.558139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Decisions are made with different degrees of consistency, and this consistency can be linked to the confidence that the best choice has been made. Theoretical work suggests that attractor dynamics in networks can account for choice consistency, but how this is implemented in the brain remains unclear. Here, we provide evidence that the energy landscape around attractor basins in population neural activity in prefrontal cortex reflects choice consistency. We trained two rhesus monkeys to make accept/reject decisions based on pretrained visual cues that signaled reward offers with different magnitudes and delays-to-reward. Monkeys made consistent decisions for very good and very bad offers, but decisions were less consistent for intermediate offers. Analysis of neural data showed that the attractor basins around patterns of activity reflecting decisions had steeper landscapes for offers that led to consistent decisions. Therefore, we provide neural evidence that energy landscapes predict decision consistency, which reflects decision confidence.
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18
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Nghiem TAE, Lee B, Chao THH, Branigan NK, Mistry PK, Shih YYI, Menon V. Space wandering in the rodent default mode network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.31.555793. [PMID: 37693501 PMCID: PMC10491169 DOI: 10.1101/2023.08.31.555793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here we use multisite GCaMP fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes- the retrosplenial cortex, cingulate cortex, and prelimbic cortex- as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and discovered that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.
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Affiliation(s)
| | - Byeongwook Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, University of North Carolina at Chapel Hill
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill
- Department of Neurology, University of North Carolina at Chapel Hill
| | | | - Percy K. Mistry
- Department of Psychiatry & Behavioral Sciences, Stanford University
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina at Chapel Hill
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill
- Department of Neurology, University of North Carolina at Chapel Hill
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University
- Department of Neurology & Neurological Sciences, Stanford University
- Wu Tsai Neurosciences Institute, Stanford University
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19
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Ma Z. Bridging network structures and dynamics: Comment on "Structure and function in artificial, zebrafish and human neural networks" by Ji et al. Phys Life Rev 2023; 46:245-247. [PMID: 37506591 DOI: 10.1016/j.plrev.2023.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023]
Affiliation(s)
- Zhengyu Ma
- Peng Cheng Laboratory, Shenzhen 518000, China.
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20
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He M, Das P, Hotan G, Purdon PL. Switching state-space modeling of neural signal dynamics. PLoS Comput Biol 2023; 19:e1011395. [PMID: 37639391 PMCID: PMC10491408 DOI: 10.1371/journal.pcbi.1011395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 09/08/2023] [Accepted: 07/28/2023] [Indexed: 08/31/2023] Open
Abstract
Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting rapid changes in dynamics, with transient activity that is often the key feature of interest in the data. Stationary methods can be adapted to time-varying scenarios by employing fixed-duration windows under an assumption of quasi-stationarity. But time-varying dynamics can be explicitly modeled by switching state-space models, i.e., by using a pool of state-space models with different dynamics selected by a probabilistic switching process. Unfortunately, exact solutions for state inference and parameter learning with switching state-space models are intractable. Here we revisit a switching state-space model inference approach first proposed by Ghahramani and Hinton. We provide explicit derivations for solving the inference problem iteratively after applying a variational approximation on the joint posterior of the hidden states and the switching process. We introduce a novel initialization procedure using an efficient leave-one-out strategy to compare among candidate models, which significantly improves performance compared to the existing method that relies on deterministic annealing. We then utilize this state inference solution within a generalized expectation-maximization algorithm to estimate model parameters of the switching process and the linear state-space models with dynamics potentially shared among candidate models. We perform extensive simulations under different settings to benchmark performance against existing switching inference methods and further validate the robustness of our switching inference solution outside the generative switching model class. Finally, we demonstrate the utility of our method for sleep spindle detection in real recordings, showing how switching state-space models can be used to detect and extract transient spindles from human sleep electroencephalograms in an unsupervised manner.
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Affiliation(s)
- Mingjian He
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Proloy Das
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Anesthesia, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Gladia Hotan
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Anesthesia, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States of America
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