1
|
Kim JH, Daie K, Li N. A combinatorial neural code for long-term motor memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.05.597627. [PMID: 38895416 PMCID: PMC11185691 DOI: 10.1101/2024.06.05.597627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Motor skill repertoire can be stably retained over long periods, but the neural mechanism underlying stable memory storage remains poorly understood. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of a mouse's lifespan, and we show that learned actions are stably retained in motor memory in combination with context, which protects existing memories from erasure during new motor learning. We used automated home-cage training to establish a continual learning paradigm in which mice learned to perform directional licking in different task contexts. We combined this paradigm with chronic two-photon imaging of motor cortex activity for up to 6 months. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories while retaining the previous memories. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. At the same time, continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning. Learning in new contexts produces parallel new representations instead of modifying existing representations, thus protecting existing motor repertoire from erasure.
Collapse
|
2
|
Rich PD, Thiberge SY, Scott BB, Guo C, Tervo DGR, Brody CD, Karpova AY, Daw ND, Tank DW. Magnetic voluntary head-fixation in transgenic rats enables lifespan imaging of hippocampal neurons. Nat Commun 2024; 15:4154. [PMID: 38755205 PMCID: PMC11099169 DOI: 10.1038/s41467-024-48505-9] [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: 08/16/2023] [Accepted: 05/01/2024] [Indexed: 05/18/2024] Open
Abstract
The precise neural mechanisms within the brain that contribute to the remarkable lifetime persistence of memory are not fully understood. Two-photon calcium imaging allows the activity of individual cells to be followed across long periods, but conventional approaches require head-fixation, which limits the type of behavior that can be studied. We present a magnetic voluntary head-fixation system that provides stable optical access to the brain during complex behavior. Compared to previous systems that used mechanical restraint, there are no moving parts and animals can engage and disengage entirely at will. This system is failsafe, easy for animals to use and reliable enough to allow long-term experiments to be routinely performed. Animals completed hundreds of trials per session of an odor discrimination task that required 2-4 s fixations. Together with a reflectance fluorescence collection scheme that increases two-photon signal and a transgenic Thy1-GCaMP6f rat line, we are able to reliably image the cellular activity in the hippocampus during behavior over long periods (median 6 months), allowing us track the same neurons over a large fraction of animals' lives (up to 19 months).
Collapse
Affiliation(s)
- P Dylan Rich
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
| | | | - Benjamin B Scott
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
| | - Caiying Guo
- Janelia Research Campus, Ashburn, VA, USA
- Howard Hughes Medical Institute, Ashburn, VA, USA
| | - D Gowanlock R Tervo
- Janelia Research Campus, Ashburn, VA, USA
- Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Carlos D Brody
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Howard Hughes Medical Institute, Princeton University, Princeton, NJ, USA
| | - Alla Y Karpova
- Janelia Research Campus, Ashburn, VA, USA
- Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nathaniel D Daw
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Department of Psychology, Princeton University, Princeton, NJ, USA
| | - David W Tank
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Bezos Center for Neural Circuit Dynamics, Princeton University, Princeton, NJ, USA.
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
| |
Collapse
|
3
|
Catto A, O’Connor R, Braunscheidel KM, Kenny PJ, Shen L. FABEL: Forecasting Animal Behavioral Events with Deep Learning-Based Computer Vision. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.584610. [PMID: 38559273 PMCID: PMC10980057 DOI: 10.1101/2024.03.15.584610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Behavioral neuroscience aims to provide a connection between neural phenomena and emergent organism-level behaviors. This requires perturbing the nervous system and observing behavioral outcomes, and comparing observed post-perturbation behavior with predicted counterfactual behavior and therefore accurate behavioral forecasts. In this study we present FABEL, a deep learning method for forecasting future animal behaviors and locomotion trajectories from historical locomotion alone. We train an offline pose estimation network to predict animal body-part locations in behavioral video; then sequences of pose vectors are input to deep learning time-series forecasting models. Specifically, we train an LSTM network that predicts a future food interaction event in a specified time window, and a Temporal Fusion Transformer that predicts future trajectories of animal body-parts, which are then converted into probabilistic label forecasts. Importantly, accurate prediction of food interaction provides a basis for neurobehavioral intervention in the context of compulsive eating. We show promising results on forecasting tasks between 100 milliseconds and 5 seconds timescales. Because the model takes only behavioral video as input, it can be adapted to any behavioral task and does not require specific physiological readouts. Simultaneously, these deep learning models may serve as extensible modules that can accommodate diverse signals, such as in-vivo fluorescence imaging and electrophysiology, which may improve behavior forecasts and elucidate invervention targets for desired behavioral change.
Collapse
Affiliation(s)
- Adam Catto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Richard O’Connor
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kevin M. Braunscheidel
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Paul J. Kenny
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Li Shen
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| |
Collapse
|
4
|
Badrulhisham F, Pogatzki-Zahn E, Segelcke D, Spisak T, Vollert J. Machine learning and artificial intelligence in neuroscience: A primer for researchers. Brain Behav Immun 2024; 115:470-479. [PMID: 37972877 DOI: 10.1016/j.bbi.2023.11.005] [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: 04/26/2023] [Revised: 10/16/2023] [Accepted: 11/08/2023] [Indexed: 11/19/2023] Open
Abstract
Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.
Collapse
Affiliation(s)
| | - Esther Pogatzki-Zahn
- Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany
| | - Daniel Segelcke
- Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany
| | - Tamas Spisak
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany; Center for Translational Neuro- and Behavioral Sciences, Department of Neurology, University Medicine Essen, Essen, Germany
| | - Jan Vollert
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom; Pain Research, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
| |
Collapse
|
5
|
Thomas A, Yang W, Wang C, Tipparaju SL, Chen G, Sullivan B, Swiekatowski K, Tatam M, Gerfen C, Li N. Superior colliculus bidirectionally modulates choice activity in frontal cortex. Nat Commun 2023; 14:7358. [PMID: 37963894 PMCID: PMC10645979 DOI: 10.1038/s41467-023-43252-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 11/03/2023] [Indexed: 11/16/2023] Open
Abstract
Action selection occurs through competition between potential choice options. Neural correlates of choice competition are observed across frontal cortex and downstream superior colliculus (SC) during decision-making, yet how these regions interact to mediate choice competition remains unresolved. Here we report that SC can bidirectionally modulate choice competition and drive choice activity in frontal cortex. In the mouse, topographically matched regions of frontal cortex and SC formed a descending motor pathway for directional licking and a re-entrant loop via the thalamus. During decision-making, distinct neuronal populations in both frontal cortex and SC encoded opposing lick directions and exhibited competitive interactions. SC GABAergic neurons encoded ipsilateral choice and locally inhibited glutamatergic neurons that encoded contralateral choice. Activating or suppressing these cell types could bidirectionally drive choice activity in frontal cortex. These results thus identify SC as a major locus to modulate choice competition within the broader action selection network.
Collapse
Affiliation(s)
- Alyse Thomas
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Weiguo Yang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Catherine Wang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | | | - Guang Chen
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Brennan Sullivan
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Kylie Swiekatowski
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Mahima Tatam
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Charles Gerfen
- Section on Neuroanatomy, National Institute of Mental Health, Bethesda, MD, USA
| | - Nuo Li
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
| |
Collapse
|
6
|
Zhu J, Hasanbegović H, Liu LD, Gao Z, Li N. Activity map of a cortico-cerebellar loop underlying motor planning. Nat Neurosci 2023; 26:1916-1928. [PMID: 37814026 PMCID: PMC10620095 DOI: 10.1038/s41593-023-01453-x] [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: 08/29/2022] [Accepted: 09/06/2023] [Indexed: 10/11/2023]
Abstract
The neocortex and cerebellum interact to mediate cognitive functions. It remains unknown how the two structures organize into functional networks to mediate specific behaviors. Here we delineate activity supporting motor planning in relation to the mesoscale cortico-cerebellar connectome. In mice planning directional licking based on short-term memory, preparatory activity instructing future movement depends on the anterior lateral motor cortex (ALM) and the cerebellum. Transneuronal tracing revealed divergent and largely open-loop connectivity between the ALM and distributed regions of the cerebellum. A cerebellum-wide survey of neuronal activity revealed enriched preparatory activity in hotspot regions with conjunctive input-output connectivity to the ALM. Perturbation experiments show that the conjunction regions were required for maintaining preparatory activity and correct subsequent movement. Other cerebellar regions contributed little to motor planning despite input or output connectivity to the ALM. These results identify a functional cortico-cerebellar loop and suggest the cerebellar cortex selectively establishes reciprocal cortico-cerebellar communications to orchestrate motor planning.
Collapse
Affiliation(s)
- Jia Zhu
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | | | - Liu D Liu
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Zhenyu Gao
- Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.
| | - Nuo Li
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
| |
Collapse
|
7
|
Yang F, Wang F, Ma X, Zhou M, Jiang S, Xu W. Longitudinal optogenetic mapping reveals enhanced motor control by the contralesional cortex after traumatic brain injury in mice. Exp Neurol 2023; 369:114546. [PMID: 37751813 DOI: 10.1016/j.expneurol.2023.114546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/13/2023] [Accepted: 09/21/2023] [Indexed: 09/28/2023]
Abstract
Traumatic brain injury (TBI) is a significant cause of human disability, and understanding its spontaneous recovery pattern after injury is critical for potential treatments. However, studies on the function of the contralesional cortex after TBI have mostly focused on acute-phase changes, and long-term dynamic changes in the control of the affected limb by the contralesional cortex are less understood. To unravel long-term adaptations in the contralesional cortex, we developed a mouse model of TBI and used longitudinal optogenetic motor mapping to observe the function of contralesional corticospinal neurons (CSNs) projecting to the unilateral seventh cervical (C7) segment of the spinal cord. We injected a retrograde adeno-associated virus (AAV) expressing channelrhodopsin-2 to optogenetically stimulate and map the functional connections of the motor-sensory cortex. We validated the effectiveness of transcranial optogenetic stimulation for functional mapping and observed a general increase in the control of the affected limb by the contralesional cortex over time. Using retrograde labeling techniques, we showed that TBI does not affect the distribution of C7-CSNs but alters their function, and the labeled CSNs are concentrated in the caudal and rostral forelimb areas. Our findings provide new insights into harnessing contralesional cortical plasticity to improve treatment for affected limbs. This study sheds light on the long-term adaptations in the contralesional cortex after TBI, paving the way for potential clinical applications of optogenetic stimulation to improve motor control and rehabilitation outcomes.
Collapse
Affiliation(s)
- Fangjing Yang
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Fei Wang
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China; The National Clinical Research Center for Aging and Medicine, Fudan University, Shanghai, China; Department of Hand and Upper Extremity Surgery, Jing'an District Central Hospital, Fudan University, Shanghai, China
| | - Xingyi Ma
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Mingjie Zhou
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Su Jiang
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wendong Xu
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China; The National Clinical Research Center for Aging and Medicine, Fudan University, Shanghai, China; Department of Hand and Upper Extremity Surgery, Jing'an District Central Hospital, Fudan University, Shanghai, China; Institutes of Brain Science, Fudan University, Shanghai, China; State Key Laboratory of Medical Neurobiology, Collaborative Innovation Center of Brain Science, Fudan University, Shanghai, China; Co-innovation Center of Neuroregeneration, Nantong University,226000 Nantong, China; Research Unit of Synergistic Reconstruction of Upper and Lower Limbs After Brain Injury, Chinese Academy of Medical Sciences, Beijing, China.
| |
Collapse
|
8
|
Kim SJ, Affan RO, Frostig H, Scott BB, Alexander AS. Advances in cellular resolution microscopy for brain imaging in rats. NEUROPHOTONICS 2023; 10:044304. [PMID: 38076724 PMCID: PMC10704261 DOI: 10.1117/1.nph.10.4.044304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/23/2023] [Accepted: 11/07/2023] [Indexed: 02/12/2024]
Abstract
Rats are used in neuroscience research because of their physiological similarities with humans and accessibility as model organisms, trainability, and behavioral repertoire. In particular, rats perform a wide range of sophisticated social, cognitive, motor, and learning behaviors within the contexts of both naturalistic and laboratory environments. Further progress in neuroscience can be facilitated by using advanced imaging methods to measure the complex neural and physiological processes during behavior in rats. However, compared with the mouse, the rat nervous system offers a set of challenges, such as larger brain size, decreased neuron density, and difficulty with head restraint. Here, we review recent advances in in vivo imaging techniques in rats with a special focus on open-source solutions for calcium imaging. Finally, we provide suggestions for both users and developers of in vivo imaging systems for rats.
Collapse
Affiliation(s)
- Su Jin Kim
- Johns Hopkins University, Department of Psychological and Brain Sciences, Baltimore, Maryland, United States
| | - Rifqi O. Affan
- Boston University, Center for Systems Neuroscience, Department of Psychological and Brain Sciences, Boston, Massachusetts, United States
- Boston University, Graduate Program in Neuroscience, Boston, Massachusetts, United States
| | - Hadas Frostig
- Boston University, Center for Systems Neuroscience, Department of Psychological and Brain Sciences, Boston, Massachusetts, United States
| | - Benjamin B. Scott
- Boston University, Center for Systems Neuroscience, Department of Psychological and Brain Sciences, Boston, Massachusetts, United States
- Boston University, Neurophotonics Center and Photonics Center, Boston, Massachusetts, United States
| | - Andrew S. Alexander
- University of California Santa Barbara, Department of Psychological and Brain Sciences, Santa Barbara, California, United States
| |
Collapse
|
9
|
Mangin EN, Chen J, Lin J, Li N. Behavioral measurements of motor readiness in mice. Curr Biol 2023; 33:3610-3624.e4. [PMID: 37582373 PMCID: PMC10529875 DOI: 10.1016/j.cub.2023.07.029] [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: 02/26/2023] [Revised: 05/09/2023] [Accepted: 07/18/2023] [Indexed: 08/17/2023]
Abstract
Motor planning facilitates rapid and precise execution of volitional movements. Although motor planning has been classically studied in humans and monkeys, the mouse has become an increasingly popular model system to study neural mechanisms of motor planning. It remains yet untested whether mice and primates share common behavioral features of motor planning. We combined videography and a delayed response task paradigm in an autonomous behavioral system to measure motor planning in non-body-restrained mice. Motor planning resulted in both reaction time (RT) savings and increased movement accuracy, replicating classic effects in primates. We found that motor planning was reflected in task-relevant body features. Both the specific actions prepared and the degree of motor readiness could be read out online during motor planning. The online readout further revealed behavioral evidence of simultaneous preparation for multiple actions under uncertain conditions. These results validate the mouse as a model to study motor planning, demonstrate body feature movements as a powerful real-time readout of motor readiness, and offer behavioral evidence that motor planning can be a parallel process that permits rapid selection of multiple prepared actions.
Collapse
Affiliation(s)
- Elise N Mangin
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jian Chen
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jing Lin
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nuo Li
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA.
| |
Collapse
|
10
|
Coen P, Sit TPH, Wells MJ, Carandini M, Harris KD. Mouse frontal cortex mediates additive multisensory decisions. Neuron 2023; 111:2432-2447.e13. [PMID: 37295419 PMCID: PMC10957398 DOI: 10.1016/j.neuron.2023.05.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/02/2022] [Accepted: 05/10/2023] [Indexed: 06/12/2023]
Abstract
The brain can combine auditory and visual information to localize objects. However, the cortical substrates underlying audiovisual integration remain uncertain. Here, we show that mouse frontal cortex combines auditory and visual evidence; that this combination is additive, mirroring behavior; and that it evolves with learning. We trained mice in an audiovisual localization task. Inactivating frontal cortex impaired responses to either sensory modality, while inactivating visual or parietal cortex affected only visual stimuli. Recordings from >14,000 neurons indicated that after task learning, activity in the anterior part of frontal area MOs (secondary motor cortex) additively encodes visual and auditory signals, consistent with the mice's behavioral strategy. An accumulator model applied to these sensory representations reproduced the observed choices and reaction times. These results suggest that frontal cortex adapts through learning to combine evidence across sensory cortices, providing a signal that is transformed into a binary decision by a downstream accumulator.
Collapse
Affiliation(s)
- Philip Coen
- UCL Queen Square Institute of Neurology, University College London, London, UK; UCL Institute of Ophthalmology, University College London, London, UK.
| | - Timothy P H Sit
- Sainsbury-Wellcome Center, University College London, London, UK
| | - Miles J Wells
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London, UK
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London, UK
| |
Collapse
|
11
|
Thomas A, Yang W, Wang C, Tipparaju SL, Chen G, Sullivan B, Swiekatowski K, Tatam M, Gerfen C, Li N. Superior colliculus cell types bidirectionally modulate choice activity in frontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.22.537884. [PMID: 37162880 PMCID: PMC10168218 DOI: 10.1101/2023.04.22.537884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Action selection occurs through competition between potential choice options. Neural correlates of choice competition are observed across frontal cortex and downstream superior colliculus (SC) during decision-making, yet how these regions interact to mediate choice competition remains unresolved. Here we report that cell types within SC can bidirectionally modulate choice competition and drive choice activity in frontal cortex. In the mouse, topographically matched regions of frontal cortex and SC formed a descending motor pathway for directional licking and a re-entrant loop via the thalamus. During decision-making, distinct neuronal populations in both frontal cortex and SC encoded opposing lick directions and exhibited push-pull dynamics. SC GABAergic neurons encoded ipsilateral choice and glutamatergic neurons encoded contralateral choice, and activating or suppressing these cell types could bidirectionally drive push-pull choice activity in frontal cortex. These results thus identify SC as a major locus to modulate choice competition within the broader action selection network.
Collapse
Affiliation(s)
- Alyse Thomas
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
| | - Weiguo Yang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
| | - Catherine Wang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
| | | | - Guang Chen
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
| | - Brennan Sullivan
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
| | | | - Mahima Tatam
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
| | - Charles Gerfen
- Section on Neuroanatomy, National Institute of Mental Health, Bethesda, MD
| | - Nuo Li
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
| |
Collapse
|
12
|
Mangin EN, Chen J, Lin J, Li N. Behavioral measurements of motor readiness in mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.03.527054. [PMID: 36778494 PMCID: PMC9915731 DOI: 10.1101/2023.02.03.527054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Motor planning facilitates rapid and precise execution of volitional movements. Although motor planning has been classically studied in humans and monkeys, the mouse has become an increasingly popular model system to study neural mechanisms of motor planning. It remains yet untested whether mice and primates share common behavioral features of motor planning. We combined videography and a delayed response task paradigm in an autonomous behavioral system to measure motor planning in non-body- restrained mice. Motor planning resulted in both reaction time savings and increased movement accuracy, replicating classic effects in primates. We found that motor planning was reflected in task-relevant body features. Both the specific actions prepared and the degree of motor readiness could be read out online during motor planning. The online readout further revealed behavioral evidence of simultaneous preparation for multiple actions under uncertain conditions. These results validate the mouse as a model to study motor planning, demonstrate body feature movements as a powerful real-time readout of motor readiness, and offer behavioral evidence that motor planning can be a parallel process that permits rapid selection of multiple prepared actions.
Collapse
Affiliation(s)
| | - Jian Chen
- Department of Neuroscience, Baylor College of Medicine
| | - Jing Lin
- Department of Neuroscience, Baylor College of Medicine
| | - Nuo Li
- Department of Neuroscience, Baylor College of Medicine
| |
Collapse
|
13
|
Yang W, Tipparaju SL, Chen G, Li N. Thalamus-driven functional populations in frontal cortex support decision-making. Nat Neurosci 2022; 25:1339-1352. [PMID: 36171427 PMCID: PMC9534763 DOI: 10.1038/s41593-022-01171-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 08/18/2022] [Indexed: 12/02/2022]
Abstract
Neurons in frontal cortex exhibit diverse selectivity representing sensory, motor and cognitive variables during decision-making. The neural circuit basis for this complex selectivity remains unclear. We examined activity mediating a tactile decision in mouse anterior lateral motor cortex in relation to the underlying circuits. Contrary to the notion of randomly mixed selectivity, an analysis of 20,000 neurons revealed organized activity coding behavior. Individual neurons exhibited prototypical response profiles that were repeatable across mice. Stimulus, choice and action were coded nonrandomly by distinct neuronal populations that could be delineated by their response profiles. We related distinct selectivity to long-range inputs from somatosensory cortex, contralateral anterior lateral motor cortex and thalamus. Each input connects to all functional populations but with differing strength. Task selectivity was more strongly dependent on thalamic inputs than cortico-cortical inputs. Our results suggest that the thalamus drives subnetworks within frontal cortex coding distinct features of decision-making. Frontal cortex contains a complex mixture of signals reflecting distinct behavioral and cognitive processes. An analysis of 20,000 neurons during decision-making revealed distinct functional clusters and their activities are driven by the thalamus.
Collapse
Affiliation(s)
- Weiguo Yang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | | | - Guang Chen
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Nuo Li
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
| |
Collapse
|
14
|
Wheatcroft T, Saleem AB, Solomon SG. Functional Organisation of the Mouse Superior Colliculus. Front Neural Circuits 2022; 16:792959. [PMID: 35601532 PMCID: PMC9118347 DOI: 10.3389/fncir.2022.792959] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 03/07/2022] [Indexed: 11/30/2022] Open
Abstract
The superior colliculus (SC) is a highly conserved area of the mammalian midbrain that is widely implicated in the organisation and control of behaviour. SC receives input from a large number of brain areas, and provides outputs to a large number of areas. The convergence and divergence of anatomical connections with different areas and systems provides challenges for understanding how SC contributes to behaviour. Recent work in mouse has provided large anatomical datasets, and a wealth of new data from experiments that identify and manipulate different cells within SC, and their inputs and outputs, during simple behaviours. These data offer an opportunity to better understand the roles that SC plays in these behaviours. However, some of the observations appear, at first sight, to be contradictory. Here we review this recent work and hypothesise a simple framework which can capture the observations, that requires only a small change to previous models. Specifically, the functional organisation of SC can be explained by supposing that three largely distinct circuits support three largely distinct classes of simple behaviours-arrest, turning towards, and the triggering of escape or capture. These behaviours are hypothesised to be supported by the optic, intermediate and deep layers, respectively.
Collapse
Affiliation(s)
| | | | - Samuel G. Solomon
- Institute of Behavioural Neuroscience, University College London, London, United Kingdom
| |
Collapse
|
15
|
A miniature kinematic coupling device for mouse head fixation. J Neurosci Methods 2022; 372:109549. [DOI: 10.1016/j.jneumeth.2022.109549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/22/2022] [Accepted: 02/26/2022] [Indexed: 11/18/2022]
|
16
|
Xiao D, Forys BJ, Vanni MP, Murphy TH. MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning. Nat Commun 2021; 12:5992. [PMID: 34645817 PMCID: PMC8514445 DOI: 10.1038/s41467-021-26255-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 09/23/2021] [Indexed: 01/17/2023] Open
Abstract
Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis.
Collapse
Affiliation(s)
- Dongsheng Xiao
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, V6T 1Z3, British Columbia, Canada
| | - Brandon J Forys
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, V6T 1Z3, British Columbia, Canada
- Department of Psychology, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matthieu P Vanni
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, V6T 1Z3, British Columbia, Canada
- Université de Montréal, École d'Optométrie, 3744 Jean Brillant H3T 1P1, Montréal, Québec, Canada
| | - Timothy H Murphy
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, V6T 1Z3, British Columbia, Canada.
| |
Collapse
|
17
|
Nomura H. [Autonomous mouse behavioral experiments]. Nihon Yakurigaku Zasshi 2021; 156:312. [PMID: 34470937 DOI: 10.1254/fpj.21048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|