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Zhukovskaya A, Christopher Z, Willmore L, Pan Vazquez A, Janarthanan S, Falkner A, Witten I. Heightened lateral habenula activity during stress produces brainwide and behavioral substrates of susceptibility. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.06.565681. [PMID: 39005438 PMCID: PMC11244933 DOI: 10.1101/2023.11.06.565681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Some individuals are susceptible to the experience of chronic stress and others are more resilient. While many brain regions implicated in learning are dysregulated after stress, little is known about whether and how neural teaching signals during stress differ between susceptible and resilient individuals. Here, we seek to determine if activity in the lateral habenula (LHb), which encodes a negative teaching signal, differs between susceptible and resilient mice during stress to produce different outcomes. After, but not before, chronic social defeat stress (CSDS), the LHb is active when susceptible mice are in the proximity of the aggressor strain. During stress itself, LHb activity is higher in susceptible mice during aggressor proximity, and activation of the LHb during stress biases mice towards susceptibility. This manipulation generates a persistent and widespread increase in the balance of subcortical versus cortical activity in susceptible mice. Taken together, our results indicate that heightened activity in the LHb during stress produces lasting brainwide and behavioral substrates of susceptibility.
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Caznok Silveira AC, Antunes ASLM, Athié MCP, da Silva BF, Ribeiro dos Santos JV, Canateli C, Fontoura MA, Pinto A, Pimentel-Silva LR, Avansini SH, de Carvalho M. Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders. Front Neurosci 2024; 18:1340345. [PMID: 38445254 PMCID: PMC10912403 DOI: 10.3389/fnins.2024.1340345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/29/2024] [Indexed: 03/07/2024] Open
Abstract
The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.
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Affiliation(s)
- Ana Clara Caznok Silveira
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | | | - Maria Carolina Pedro Athié
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Bárbara Filomena da Silva
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Camila Canateli
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Marina Alves Fontoura
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Allan Pinto
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Simoni Helena Avansini
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Murilo de Carvalho
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
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Engel EA, Card JP, Enquist LW. Transneuronal Circuit Analysis with Pseudorabies Viruses. Curr Protoc 2023; 3:e841. [PMID: 37486157 PMCID: PMC10664030 DOI: 10.1002/cpz1.841] [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] [Indexed: 07/25/2023]
Abstract
Our ability to understand the function of the nervous system is dependent upon defining the connections of its constituent neurons. Development of methods to define connections within neural networks has always been a growth industry in the neurosciences. Transneuronal spread of neurotropic viruses currently represents the best means of defining synaptic connections within neural networks. The method exploits the ability of viruses to invade neurons, replicate, and spread through the intimate synaptic connections that enable communication among neurons. Since the method was first introduced in the 1970s, it has benefited from an increased understanding of the virus life cycle, the function of viral genomes, and the ability to manipulate the viral genome in support of directional spread of virus and the expression of transgenes. In this article, we review these advances in viral tracing technology and the ways in which they may be applied for functional dissection of neural networks. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Retrograde infection of CNS circuits by peripheral injection of virus Basic Protocol 2: Transneuronal analysis by intracerebral injection Alternate Protocol 1: Transneuronal analysis with multiple recombinant strains Alternate Protocol 2: Conditional replication and spread of PRV Alternate Protocol 3: Conditional reporters of PRV infection and spread Alternate Protocol 4: Reporters of neural activity in polysynaptic circuits Support Protocol 1: Growing and titering a PRV viral stock Support Protocol 2: Immunohistochemical processing and detection Support Protocol 3: Dual-immunofluorescence localization.
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Affiliation(s)
- Esteban A Engel
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey
- Current address: Spark Therapeutics, Philadelphia, PA, 19104
| | - J Patrick Card
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Lynn W Enquist
- Department of Molecular Biology, Princeton University, Princeton, New Jersey
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Verpeut JL, Bergeler S, Kislin M, William Townes F, Klibaite U, Dhanerawala ZM, Hoag A, Janarthanan S, Jung C, Lee J, Pisano TJ, Seagraves KM, Shaevitz JW, Wang SSH. Cerebellar contributions to a brainwide network for flexible behavior in mice. Commun Biol 2023; 6:605. [PMID: 37277453 PMCID: PMC10241932 DOI: 10.1038/s42003-023-04920-0] [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: 11/10/2022] [Accepted: 05/05/2023] [Indexed: 06/07/2023] Open
Abstract
The cerebellum regulates nonmotor behavior, but the routes of influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task through a network of diencephalic and neocortical structures, and in flexibility of free behavior. After chemogenetic inhibition of lobule VI vermis or hemispheric crus I Purkinje cells, mice could learn a water Y-maze but were impaired in ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of whole-body movement revealed deficiencies in across-day behavioral habituation to an open-field environment. Taken together, these experiments reveal brainwide systems for cerebellar influence that affect multiple flexible responses.
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Affiliation(s)
- Jessica L Verpeut
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA.
| | - Silke Bergeler
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA
- Department of Physics, Princeton University, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Mikhail Kislin
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA
| | - F William Townes
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Ugne Klibaite
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 01451, USA
| | - Zahra M Dhanerawala
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA
| | - Austin Hoag
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA
| | - Sanjeev Janarthanan
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA
| | - Caroline Jung
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA
| | - Junuk Lee
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA
| | - Thomas J Pisano
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA
| | - Kelly M Seagraves
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA
| | - Joshua W Shaevitz
- Department of Physics, Princeton University, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Samuel S-H Wang
- Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ, 08544, USA.
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