1
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Sihn D, Chae S, Kim SP. A method to find temporal structure of neuronal coactivity patterns with across-trial correlations. J Neurosci Methods 2024; 408:110172. [PMID: 38782124 DOI: 10.1016/j.jneumeth.2024.110172] [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: 02/05/2024] [Revised: 05/08/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND The across-trial correlation of neurons' coactivity patterns emerges to be important for information coding, but methods for finding their temporal structures remain largely unexplored. NEW METHOD In the present study, we propose a method to find time clusters in which coactivity patterns of neurons are correlated across trials. We transform the multidimensional neural activity at each timing into a coactivity pattern of binary states, and predict the coactivity patterns at different timings. We devise a method suitable for these coactivity pattern predictions, call general event prediction. Cross-temporal prediction accuracy is then used to estimate across-trial correlations between coactivity patterns at two timings. We extract time clusters from the cross-temporal prediction accuracy by a modified k-means algorithm. RESULTS The feasibility of the proposed method is verified through simulations based on ground truth. We apply the proposed method to a calcium imaging dataset recorded from the motor cortex of mice, and demonstrate time clusters of motor cortical coactivity patterns during a motor task. COMPARISON WITH EXISTING METHODS While the existing cosine similarity method, which does not account for across-trial correlation, shows temporal structures only for contralateral neural responses, the proposed method reveals those for both contralateral and ipsilateral neural responses, demonstrating the effect of across-trial correlations. CONCLUSIONS This study introduces a novel method for measuring the temporal structure of neuronal ensemble activity.
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Affiliation(s)
- Duho Sihn
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, the Republic of Korea
| | - Soyoung Chae
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, the Republic of Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, the Republic of Korea.
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2
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Yan Y, Murphy TH. Decoding state-dependent cortical-cerebellar cellular functional connectivity in the mouse brain. Cell Rep 2024; 43:114348. [PMID: 38865245 DOI: 10.1016/j.celrep.2024.114348] [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: 09/28/2023] [Revised: 04/16/2024] [Accepted: 05/26/2024] [Indexed: 06/14/2024] Open
Abstract
The cortex and cerebellum form multi-synaptic reciprocal connections. We investigate the functional connectivity between single spiking cerebellar neurons and the population activity of the mouse dorsal cortex using mesoscale imaging. Cortical representations of individual cerebellar neurons vary significantly across different brain states but are drawn from a common set of cortical networks. These cortical-cerebellar connectivity features are observed in mossy fibers and Purkinje cells as well as neurons in different cerebellar lobules, albeit with variations across cell types and regions. Complex spikes of Purkinje cells preferably associate with the sensorimotor cortex, whereas simple spikes display more diverse cortical connectivity patterns. The spontaneous functional connectivity patterns align with cerebellar neurons' functional responses to external stimuli in a modality-specific manner. The tuning properties of subsets of cerebellar neurons differ between anesthesia and awake states, mirrored by state-dependent changes in their long-range functional connectivity patterns with mesoscale cortical activity.
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Affiliation(s)
- Yuhao Yan
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Timothy H Murphy
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.
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3
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Schilling A, Gerum R, Boehm C, Rasheed J, Metzner C, Maier A, Reindl C, Hamer H, Krauss P. Deep learning based decoding of single local field potential events. Neuroimage 2024; 297:120696. [PMID: 38909761 DOI: 10.1016/j.neuroimage.2024.120696] [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: 01/18/2023] [Revised: 06/12/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024] Open
Abstract
How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.
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Affiliation(s)
- Achim Schilling
- Neuroscience Lab, University Hospital Erlangen, Germany; Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany
| | - Richard Gerum
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany; Department of Physics and Center for Vision Research, York University, Toronto, Canada
| | - Claudia Boehm
- Neuroscience Lab, University Hospital Erlangen, Germany; Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany
| | - Jwan Rasheed
- Neuroscience Lab, University Hospital Erlangen, Germany; Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany
| | - Claus Metzner
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany; Pattern Recognition Lab, University Erlangen-Nürnberg, Germany
| | - Andreas Maier
- Pattern Recognition Lab, University Erlangen-Nürnberg, Germany
| | - Caroline Reindl
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Germany
| | - Hajo Hamer
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Germany
| | - Patrick Krauss
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany; Pattern Recognition Lab, University Erlangen-Nürnberg, Germany.
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4
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Pellegrino A, Stein H, Cayco-Gajic NA. Dimensionality reduction beyond neural subspaces with slice tensor component analysis. Nat Neurosci 2024; 27:1199-1210. [PMID: 38710876 DOI: 10.1038/s41593-024-01626-2] [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: 12/06/2022] [Accepted: 03/20/2024] [Indexed: 05/08/2024]
Abstract
Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.
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Affiliation(s)
- Arthur Pellegrino
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France.
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Heike Stein
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
| | - N Alex Cayco-Gajic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France.
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5
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Meyer J, Yu K, Luna-Figueroa E, Deneen B, Noebels J. Glioblastoma disrupts cortical network activity at multiple spatial and temporal scales. Nat Commun 2024; 15:4503. [PMID: 38802334 PMCID: PMC11130179 DOI: 10.1038/s41467-024-48757-5] [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: 06/30/2023] [Accepted: 05/11/2024] [Indexed: 05/29/2024] Open
Abstract
The emergence of glioblastoma in cortical tissue initiates early and persistent neural hyperexcitability with signs ranging from mild cognitive impairment to convulsive seizures. The influence of peritumoral synaptic density, expansion dynamics, and spatial contours of excess glutamate upon higher order neuronal network modularity is unknown. We combined cellular and widefield imaging of calcium and glutamate fluorescent reporters in two glioblastoma mouse models with distinct synaptic microenvironments and infiltration profiles. Functional metrics of neural ensembles are dysregulated during tumor invasion depending on the stage of malignant progression and tumor cell proximity. Neural activity is differentially modulated during periods of accelerated and inhibited tumor expansion. Abnormal glutamate accumulation precedes and outpaces the spatial extent of baseline neuronal calcium signaling, indicating these processes are uncoupled in tumor cortex. Distinctive excitability homeostasis patterns and functional connectivity of local and remote neuronal populations support the promise of precision genetic diagnosis and management of this devastating brain disease.
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Affiliation(s)
- Jochen Meyer
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX, USA.
| | - Kwanha Yu
- Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | | | - Benjamin Deneen
- Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Jeffrey Noebels
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX, USA.
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6
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Yang W, Sun C, Huszár R, Hainmueller T, Kiselev K, Buzsáki G. Selection of experience for memory by hippocampal sharp wave ripples. Science 2024; 383:1478-1483. [PMID: 38547293 PMCID: PMC11068097 DOI: 10.1126/science.adk8261] [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: 09/12/2023] [Accepted: 02/23/2024] [Indexed: 04/02/2024]
Abstract
Experiences need to be tagged during learning for further consolidation. However, neurophysiological mechanisms that select experiences for lasting memory are not known. By combining large-scale neural recordings in mice with dimensionality reduction techniques, we observed that successive maze traversals were tracked by continuously drifting populations of neurons, providing neuronal signatures of both places visited and events encountered. When the brain state changed during reward consumption, sharp wave ripples (SPW-Rs) occurred on some trials, and their specific spike content decoded the trial blocks that surrounded them. During postexperience sleep, SPW-Rs continued to replay those trial blocks that were reactivated most frequently during waking SPW-Rs. Replay content of awake SPW-Rs may thus provide a neurophysiological tagging mechanism to select aspects of experience that are preserved and consolidated for future use.
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Affiliation(s)
- Wannan Yang
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York City, NY, USA
- Center for Neural Science, New York University, New York City, NY, USA
| | - Chen Sun
- Mila - Quebec AI Institute, Montréal, Quebec, Canada
| | - Roman Huszár
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York City, NY, USA
- Center for Neural Science, New York University, New York City, NY, USA
| | - Thomas Hainmueller
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York City, NY, USA
- Department of Psychiatry, New York University Langone Medical Center, New York City, NY, USA
| | - Kirill Kiselev
- Center for Neural Science, New York University, New York City, NY, USA
| | - György Buzsáki
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York City, NY, USA
- Center for Neural Science, New York University, New York City, NY, USA
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7
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MacDowell CJ, Briones BA, Lenzi MJ, Gustison ML, Buschman TJ. Differences in the expression of cortex-wide neural dynamics are related to behavioral phenotype. Curr Biol 2024; 34:1333-1340.e6. [PMID: 38417445 PMCID: PMC10965364 DOI: 10.1016/j.cub.2024.02.004] [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: 04/03/2023] [Revised: 01/12/2024] [Accepted: 02/05/2024] [Indexed: 03/01/2024]
Abstract
Behavior differs across individuals, ranging from typical to atypical phenotypes.1 Understanding how differences in behavior relate to differences in neural activity is critical for developing treatments of neuropsychiatric and neurodevelopmental disorders. One hypothesis is that differences in behavior reflect individual differences in the dynamics of how information flows through the brain. In support of this, the correlation of neural activity between brain areas, termed "functional connectivity," varies across individuals2 and is disrupted in autism,3 schizophrenia,4 and depression.5 However, the changes in neural activity that underlie altered behavior and functional connectivity remain unclear. Here, we show that individual differences in the expression of different patterns of cortical neural dynamics explain variability in both functional connectivity and behavior. Using mesoscale imaging, we recorded neural activity across the dorsal cortex of behaviorally "typical" and "atypical" mice. All mice shared the same recurring cortex-wide spatiotemporal motifs of neural activity, and these motifs explained the large majority of variance in cortical activity (>75%). However, individuals differed in how frequently different motifs were expressed. These differences in motif expression explained differences in functional connectivity and behavior across both typical and atypical mice. Our results suggest that differences in behavior and functional connectivity are due to changes in the processes that select which pattern of neural activity is expressed at each moment in time.
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Affiliation(s)
- Camden J MacDowell
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA; Rutgers Robert Wood Johnson Medical School, 125 Paterson Street, New Brunswick, NJ 08901, USA
| | - Brandy A Briones
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA; Department of Psychology, Princeton University, Washington Road, Princeton, NJ 08540, USA; Department of Anesthesiology and Pain Medicine at University of Washington, Seattle, WA 98105, USA
| | - Michael J Lenzi
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA
| | - Morgan L Gustison
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA; Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA; Department of Psychology, Western University, London, ON N6A 3K7, Canada
| | - Timothy J Buschman
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA; Department of Psychology, Princeton University, Washington Road, Princeton, NJ 08540, USA.
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8
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Tolooshams B, Matias S, Wu H, Temereanca S, Uchida N, Murthy VN, Masset P, Ba D. Interpretable deep learning for deconvolutional analysis of neural signals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574379. [PMID: 38260512 PMCID: PMC10802267 DOI: 10.1101/2024.01.05.574379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on "black-box" approaches that lack an interpretable link between neural activity and function. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition method, we uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons in an unbiased manner, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the responses of neurons in the piriform cortex. Our work leverages the advances in interpretable deep learning to gain a mechanistic understanding of neural dynamics.
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Affiliation(s)
- Bahareh Tolooshams
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge MA, 02138
- Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125
| | - Sara Matias
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
| | - Hao Wu
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
| | - Simona Temereanca
- Carney Institute for Brain Science, Brown University, Providence, RI, 02906
| | - Naoshige Uchida
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
| | - Venkatesh N. Murthy
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
| | - Paul Masset
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- Department of Molecular and Cellular Biology, Harvard University, Cambridge MA, 02138
- Department of Psychology, McGill University, Montréal QC, H3A 1G1
| | - Demba Ba
- Center for Brain Science, Harvard University, Cambridge MA, 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge MA, 02138
- Kempner Institute for the Study of Natural & Artificial Intelligence, Harvard University, Cambridge MA, 02138
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9
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Young ME, Spencer-Salmon C, Mosher C, Tamang S, Rajan K, Rudebeck PH. Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation. Neuron 2023; 111:3668-3682.e5. [PMID: 37586366 PMCID: PMC10840822 DOI: 10.1016/j.neuron.2023.07.012] [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: 02/04/2021] [Revised: 04/25/2023] [Accepted: 07/20/2023] [Indexed: 08/18/2023]
Abstract
Functional neuroimaging studies indicate that interconnected parts of the subcallosal anterior cingulate cortex (ACC), striatum, and amygdala play a fundamental role in affect in health and disease. Yet, although the patterns of neural activity engaged in the striatum and amygdala during affective processing are well established, especially during reward anticipation, less is known about subcallosal ACC. Here, we recorded neural activity in non-human primate subcallosal ACC and compared this with interconnected parts of the basolateral amygdala and rostromedial striatum while macaque monkeys performed reward-based tasks. Applying multiple analysis approaches, we found that neurons in subcallosal ACC and rostromedial striatum preferentially signal anticipated reward using short bursts of activity that form temporally specific patterns. By contrast, the basolateral amygdala uses a mixture of both temporally specific and more sustained patterns of activity to signal anticipated reward. Thus, dynamic patterns of neural activity across populations of neurons are engaged in affect, especially in subcallosal ACC.
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Affiliation(s)
- Megan E Young
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Camille Spencer-Salmon
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Clayton Mosher
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Sarita Tamang
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Kanaka Rajan
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Peter H Rudebeck
- Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
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10
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Yang W, Sun C, Huszár R, Hainmueller T, Buzsáki G. Selection of experience for memory by hippocampal sharp wave ripples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.07.565935. [PMID: 37987008 PMCID: PMC10659301 DOI: 10.1101/2023.11.07.565935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
A general wisdom is that experiences need to be tagged during learning for further consolidation. However, brain mechanisms that select experiences for lasting memory are not known. Combining large-scale neural recordings with a novel application of dimensionality reduction techniques, we observed that successive traversals in the maze were tracked by continuously drifting populations of neurons, providing neuronal signatures of both places visited and events encountered (trial number). When the brain state changed during reward consumption, sharp wave ripples (SPW-Rs) occurred on some trials and their unique spike content most often decoded the trial in which they occurred. In turn, during post-experience sleep, SPW-Rs continued to replay those trials that were reactivated most frequently during awake SPW-Rs. These findings suggest that replay content of awake SPW-Rs provides a tagging mechanism to select aspects of experience that are preserved and consolidated for future use.
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Affiliation(s)
- Wannan Yang
- Center for Neural Science, New York University, NY, USA
- Neuroscience Institute, NYU Grossman School of Medicine, New York University, New York, NY, USA
| | - Chen Sun
- Mila - Quebec AI Institute, Montréal, Canada
| | - Roman Huszár
- Neuroscience Institute, NYU Grossman School of Medicine, New York University, New York, NY, USA
| | - Thomas Hainmueller
- Neuroscience Institute, NYU Grossman School of Medicine, New York University, New York, NY, USA
- Department of Psychiatry, New York University Langone Medical Center, New York, NY, USA
| | - György Buzsáki
- Center for Neural Science, New York University, NY, USA
- Neuroscience Institute, NYU Grossman School of Medicine, New York University, New York, NY, USA
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11
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Tanabe S, Lee H, Wang S, Hudetz AG. Spontaneous and Visual Stimulation Evoked Firing Sequences Are Distinct Under Desflurane Anesthesia. Neuroscience 2023; 528:54-63. [PMID: 37473851 DOI: 10.1016/j.neuroscience.2023.07.016] [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: 05/20/2023] [Revised: 07/09/2023] [Accepted: 07/12/2023] [Indexed: 07/22/2023]
Abstract
Recurring spike sequences are thought to underlie cortical computations and may be essential for information processing in the conscious state. How anesthesia at graded levels may influence spontaneous and stimulus-related spike sequences in visual cortex has not been fully elucidated. We recorded extracellular single-unit activity in the rat primary visual cortex in vivo during wakefulness and three levels of anesthesia produced by desflurane. The latencies of spike sequences within 0-200 ms from the onset of spontaneous UP states and visual flash-evoked responses were compared. During wakefulness, spike latency patterns linked to the local field potential theta cycle were similar to stimulus-evoked patterns. Under desflurane anesthesia, spontaneous UP state sequences differed from flash-evoked sequences due to the recruitment of low-firing excitatory neurons to the UP state. Flash-evoked spike sequences showed higher reliability and longer latency when stimuli were applied during DOWN states compared to UP states. At deeper levels, desflurane altered both UP state and flash-evoked spike sequences by selectively suppressing inhibitory neuron firing. The results reveal desflurane-induced complex changes in cortical firing sequences that may influence visual information processing.
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Affiliation(s)
- Sean Tanabe
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48105, USA
| | - Heonsoo Lee
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48105, USA
| | - Shiyong Wang
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48105, USA
| | - Anthony G Hudetz
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48105, USA.
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12
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Hassan SI, Bigler S, Siegelbaum SA. Social odor discrimination and its enhancement by associative learning in the hippocampal CA2 region. Neuron 2023; 111:2232-2246.e5. [PMID: 37192623 PMCID: PMC10524117 DOI: 10.1016/j.neuron.2023.04.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/25/2022] [Accepted: 04/21/2023] [Indexed: 05/18/2023]
Abstract
Although the hippocampus is crucial for social memory, how social sensory information is combined with contextual information to form episodic social memories remains unknown. Here, we investigated the mechanisms for social sensory information processing using two-photon calcium imaging from hippocampal CA2 pyramidal neurons (PNs)-which are crucial for social memory-in awake head-fixed mice exposed to social and non-social odors. We found that CA2 PNs represent social odors of individual conspecifics and that these representations are refined during associative social odor-reward learning to enhance the discrimination of rewarded compared with unrewarded odors. Moreover, the structure of the CA2 PN population activity enables CA2 to generalize along categories of rewarded versus unrewarded and social versus non-social odor stimuli. Finally, we found that CA2 is important for learning social but not non-social odor-reward associations. These properties of CA2 odor representations provide a likely substrate for the encoding of episodic social memory.
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Affiliation(s)
- Sami I Hassan
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, The Kavli Institute for Brain Science, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA.
| | - Shivani Bigler
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, The Kavli Institute for Brain Science, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA
| | - Steven A Siegelbaum
- Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, The Kavli Institute for Brain Science, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA.
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13
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Sotomayor-Gómez B, Battaglia FP, Vinck M. SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns. PLoS Comput Biol 2023; 19:e1011335. [PMID: 37523401 PMCID: PMC10414626 DOI: 10.1371/journal.pcbi.1011335] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 08/10/2023] [Accepted: 07/07/2023] [Indexed: 08/02/2023] Open
Abstract
Neural coding and memory formation depend on temporal spiking sequences that span high-dimensional neural ensembles. The unsupervised discovery and characterization of these spiking sequences requires a suitable dissimilarity measure to spiking patterns, which can then be used for clustering and decoding. Here, we present a new dissimilarity measure based on optimal transport theory called SpikeShip, which compares multi-neuron spiking patterns based on all the relative spike-timing relationships among neurons. SpikeShip computes the optimal transport cost to make all the relative spike-timing relationships (across neurons) identical between two spiking patterns. We show that this transport cost can be decomposed into a temporal rigid translation term, which captures global latency shifts, and a vector of neuron-specific transport flows, which reflect inter-neuronal spike timing differences. SpikeShip can be effectively computed for high-dimensional neuronal ensembles, has a low (linear) computational cost that has the same order as the spike count, and is sensitive to higher-order correlations. Furthermore, SpikeShip is binless, can handle any form of spike time distributions, is not affected by firing rate fluctuations, can detect patterns with a low signal-to-noise ratio, and can be effectively combined with a sliding window approach. We compare the advantages and differences between SpikeShip and other measures like SPIKE and Victor-Purpura distance. We applied SpikeShip to large-scale Neuropixel recordings during spontaneous activity and visual encoding. We show that high-dimensional spiking sequences detected via SpikeShip reliably distinguish between different natural images and different behavioral states. These spiking sequences carried complementary information to conventional firing rate codes. SpikeShip opens new avenues for studying neural coding and memory consolidation by rapid and unsupervised detection of temporal spiking patterns in high-dimensional neural ensembles.
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Affiliation(s)
- Boris Sotomayor-Gómez
- Donders Centre for Neuroscience, Department of Neurophysics, Radboud University Nijmegen, Nijmegen, Netherlands
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
| | - Francesco P. Battaglia
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Martin Vinck
- Donders Centre for Neuroscience, Department of Neurophysics, Radboud University Nijmegen, Nijmegen, Netherlands
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
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14
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Mackevicius EL, Gu S, Denisenko NI, Fee MS. Self-organization of songbird neural sequences during social isolation. eLife 2023; 12:e77262. [PMID: 37252761 PMCID: PMC10229124 DOI: 10.7554/elife.77262] [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: 01/21/2022] [Accepted: 04/19/2023] [Indexed: 05/31/2023] Open
Abstract
Behaviors emerge via a combination of experience and innate predispositions. As the brain matures, it undergoes major changes in cellular, network, and functional properties that can be due to sensory experience as well as developmental processes. In normal birdsong learning, neural sequences emerge to control song syllables learned from a tutor. Here, we disambiguate the role of tutor experience and development in neural sequence formation by delaying exposure to a tutor. Using functional calcium imaging, we observe neural sequences in the absence of tutoring, demonstrating that tutor experience is not necessary for the formation of sequences. However, after exposure to a tutor, pre-existing sequences can become tightly associated with new song syllables. Since we delayed tutoring, only half our birds learned new syllables following tutor exposure. The birds that failed to learn were the birds in which pre-tutoring neural sequences were most 'crystallized,' that is, already tightly associated with their (untutored) song.
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Affiliation(s)
- Emily L Mackevicius
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, MITCambridgeUnited States
| | - Shijie Gu
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, MITCambridgeUnited States
| | - Natalia I Denisenko
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, MITCambridgeUnited States
| | - Michale S Fee
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, MITCambridgeUnited States
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15
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Chen ZS, Wilson MA. How our understanding of memory replay evolves. J Neurophysiol 2023; 129:552-580. [PMID: 36752404 PMCID: PMC9988534 DOI: 10.1152/jn.00454.2022] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023] Open
Abstract
Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, United States
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, New York, United States
- Neuroscience Institute, New York University Grossman School of Medicine, New York, New York, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York, United States
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
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16
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MacDowell CJ, Libby A, Jahn CI, Tafazoli S, Buschman TJ. Multiplexed Subspaces Route Neural Activity Across Brain-wide Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.08.527772. [PMID: 36798411 PMCID: PMC9934668 DOI: 10.1101/2023.02.08.527772] [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: 02/13/2023]
Abstract
Cognition is flexible. Behaviors can change on a moment-by-moment basis. Such flexibility is thought to rely on the brain's ability to route information through different networks of brain regions in order to support different cognitive computations. However, the mechanisms that determine which network of brain regions is engaged are unknown. To address this, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in eight cortical and subcortical regions of mice. Different dimensions within the population activity of each brain region were functionally connected with different cortex-wide 'subspace networks' of regions. These subspace networks were multiplexed, allowing a brain region to simultaneously interact with multiple independent, yet overlapping, networks. Alignment of neural activity within a region to a specific subspace network dimension predicted how neural activity propagated between regions. Thus, changing the geometry of the neural representation within a brain region could be a mechanism to selectively engage different brain-wide networks to support cognitive flexibility.
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Affiliation(s)
- Camden J. MacDowell
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
- Rutgers Robert Wood Johnson Medical School, 125 Paterson St, New Brunswick, NJ
| | - Alexandra Libby
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
| | - Caroline I. Jahn
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
| | - Sina Tafazoli
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
| | - Timothy J. Buschman
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ
- Department of Psychology, Princeton University, Washington Rd, Princeton, NJ
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17
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Aery Jones EA, Giocomo LM. Neural ensembles in navigation: From single cells to population codes. Curr Opin Neurobiol 2023; 78:102665. [PMID: 36542882 PMCID: PMC9845194 DOI: 10.1016/j.conb.2022.102665] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/27/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022]
Abstract
The brain can represent behaviorally relevant information through the firing of individual neurons as well as the coordinated firing of ensembles of neurons. Neurons in the hippocampus and associated cortical regions participate in a variety of types of ensembles to support navigation. These ensemble types include single cell codes, population codes, time-compressed sequences, behavioral sequences, and engrams. We present the physiological basis and behavioral relevance of ensemble firing. We discuss how these traditional definitions of ensembles can constrain or expand potential analyses due to the underlying assumptions and abstractions made. We highlight how coding can change at the ensemble level while underlying single cell codes remain intact. Finally, we present how ensemble definitions could be broadened to better understand the full complexity of the brain.
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Affiliation(s)
- Emily A Aery Jones
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
| | - Lisa M Giocomo
- Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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18
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Ramandi D, Michelson NJ, Raymond LA, Murphy TH. Chronic multiscale resolution of mouse brain networks using combined mesoscale cortical imaging and subcortical fiber photometry. NEUROPHOTONICS 2023; 10:015001. [PMID: 36694618 PMCID: PMC9867602 DOI: 10.1117/1.nph.10.1.015001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
SIGNIFICANCE Genetically encoded optical probes to image calcium levels in neurons in vivo are used widely as a real-time measure of neuronal activity in the brain. Mesoscale calcium imaging through a cranial window provides a method of studying the interaction of circuit activity between cortical areas but lacks access to subcortical regions. AIM We have developed an optical and surgical preparation that preserves wide-field imaging of the cortical surface while also permitting access to specific subcortical networks. APPROACH This was achieved using an optical fiber implanted in the striatum, along with a bilateral widefield cranial window, enabling simultaneous mesoscale cortical imaging and subcortical fiber photometry recording of calcium signals in a transgenic animal expressing GCaMP. Subcortical signals were collected from the dorsal regions of the striatum. We combined this approach with multiple sensory-motor tasks, including specific auditory and visual stimulation, and video monitoring of animal movements and pupillometry during head-fixed behaviors. RESULTS We found high correlations between cortical and striatal activity in response to sensory stimulation or movement. Furthermore, spontaneous activity recordings revealed that specific motifs of cortical activity are correlated with presynaptic activity recorded in the striatum, enabling us to select for corticostriatal activity motifs. CONCLUSION We believe that this method can be utilized to reveal not only global patterns but also cell-specific connectivity that provides insight into corticobasal ganglia circuit organization.
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Affiliation(s)
- Daniel Ramandi
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, Vancouver, British Columbia, Canada
| | - Nicholas J. Michelson
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, Vancouver, British Columbia, Canada
| | - Lynn A. Raymond
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, Vancouver, British Columbia, Canada
| | - Timothy H. Murphy
- University of British Columbia, Djavad Mowafaghian Centre for Brain Health, Department of Psychiatry, Vancouver, British Columbia, Canada
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19
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Lovis C, Escobar M, Stukel TA, Austin PC, Jaakkimainen L. Comparison of Methods for Estimating Temporal Topic Models From Primary Care Clinical Text Data: Retrospective Closed Cohort Study. JMIR Med Inform 2022; 10:e40102. [PMID: 36534443 PMCID: PMC9808604 DOI: 10.2196/40102] [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: 06/06/2022] [Revised: 09/01/2022] [Accepted: 09/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Health care organizations are collecting increasing volumes of clinical text data. Topic models are a class of unsupervised machine learning algorithms for discovering latent thematic patterns in these large unstructured document collections. OBJECTIVE We aimed to comparatively evaluate several methods for estimating temporal topic models using clinical notes obtained from primary care electronic medical records from Ontario, Canada. METHODS We used a retrospective closed cohort design. The study spanned from January 01, 2011, through December 31, 2015, discretized into 20 quarterly periods. Patients were included in the study if they generated at least 1 primary care clinical note in each of the 20 quarterly periods. These patients represented a unique cohort of individuals engaging in high-frequency use of the primary care system. The following temporal topic modeling algorithms were fitted to the clinical note corpus: nonnegative matrix factorization, latent Dirichlet allocation, the structural topic model, and the BERTopic model. RESULTS Temporal topic models consistently identified latent topical patterns in the clinical note corpus. The learned topical bases identified meaningful activities conducted by the primary health care system. Latent topics displaying near-constant temporal dynamics were consistently estimated across models (eg, pain, hypertension, diabetes, sleep, mood, anxiety, and depression). Several topics displayed predictable seasonal patterns over the study period (eg, respiratory disease and influenza immunization programs). CONCLUSIONS Nonnegative matrix factorization, latent Dirichlet allocation, structural topic model, and BERTopic are based on different underlying statistical frameworks (eg, linear algebra and optimization, Bayesian graphical models, and neural embeddings), require tuning unique hyperparameters (optimizers, priors, etc), and have distinct computational requirements (data structures, computational hardware, etc). Despite the heterogeneity in statistical methodology, the learned latent topical summarizations and their temporal evolution over the study period were consistently estimated. Temporal topic models represent an interesting class of models for characterizing and monitoring the primary health care system.
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Affiliation(s)
| | - Michael Escobar
- Dalla Lana School of Public Health, Division of Biostatistics, University of Toronto, Toronto, ON, Canada
| | - Therese A Stukel
- ICES, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Peter C Austin
- ICES, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Liisa Jaakkimainen
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada.,ICES, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
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20
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Zhu F, Grier HA, Tandon R, Cai C, Agarwal A, Giovannucci A, Kaufman MT, Pandarinath C. A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution. Nat Neurosci 2022; 25:1724-1734. [PMID: 36424431 PMCID: PMC9825112 DOI: 10.1038/s41593-022-01189-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/23/2022] [Indexed: 11/26/2022]
Abstract
In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced.
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Affiliation(s)
- Feng Zhu
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA
| | - Harrison A Grier
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, USA
| | - Raghav Tandon
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Changjia Cai
- Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
| | | | - Andrea Giovannucci
- Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA.
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Closed-Loop Engineering for Advanced Rehabilitation (CLEAR), North Carolina State University, Raleigh, NC, USA.
| | - Matthew T Kaufman
- Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL, USA.
- Neuroscience Institute, The University of Chicago, Chicago, IL, USA.
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
- Department of Neurosurgery, Emory University, Atlanta, GA, USA.
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA.
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21
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Cortical regulation of two-stage rapid eye movement sleep. Nat Neurosci 2022; 25:1675-1682. [PMID: 36396977 DOI: 10.1038/s41593-022-01195-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 10/05/2022] [Indexed: 11/18/2022]
Abstract
Rapid eye movement (REM) sleep is a sleep state characterized by skeletal muscle paralysis and cerebral cortical activation. Yet, global cortical dynamics and their role in regulating REM sleep remain unclear. Here we show that in mice, REM sleep is accompanied by highly patterned cortical activity waves, with the retrosplenial cortex (RSC) as a major initiation site. Two-photon imaging of layer 2/3 pyramidal neurons of the RSC revealed two distinct patterns of population activities during REM sleep. These activities encoded two sequential REM sleep substages, characterized by contrasting facial movement and autonomic activity and by distinguishable electroencephalogram theta oscillations. Closed-loop optogenetic inactivation of RSC during REM sleep altered cortical activity dynamics and shortened REM sleep duration via inhibition of the REM substage transition. These results highlight an important role for the RSC in dictating cortical dynamics and regulating REM sleep progression.
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22
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Alasfour A, Gabriel P, Jiang X, Shamie I, Melloni L, Thesen T, Dugan P, Friedman D, Doyle W, Devinsky O, Gonda D, Sattar S, Wang S, Halgren E, Gilja V. Spatiotemporal dynamics of human high gamma discriminate naturalistic behavioral states. PLoS Comput Biol 2022; 18:e1010401. [PMID: 35939509 PMCID: PMC9387937 DOI: 10.1371/journal.pcbi.1010401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/18/2022] [Accepted: 07/18/2022] [Indexed: 11/18/2022] Open
Abstract
In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as “engaging in dialogue” and “using electronics”. Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity’s covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.
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Affiliation(s)
- Abdulwahab Alasfour
- Department of Electrical Engineering, Kuwait University, Kuwait City, Kuwait
- Department of Electrical and Computer Engineering, UC San Diego, San Diego, California, United States of America
- * E-mail:
| | - Paolo Gabriel
- Department of Electrical and Computer Engineering, UC San Diego, San Diego, California, United States of America
| | - Xi Jiang
- Department of Neurosciences, UC San Diego, San Diego, California, United States of America
| | - Isaac Shamie
- Department of Neurosciences, UC San Diego, San Diego, California, United States of America
| | - Lucia Melloni
- Comprehensive Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York City, New York, United States of America
| | - Thomas Thesen
- Comprehensive Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York City, New York, United States of America
- Department of Biomedical Sciences, College of Medicine, University of Houston, Houston, Texas, United States of America
| | - Patricia Dugan
- Comprehensive Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York City, New York, United States of America
| | - Daniel Friedman
- Comprehensive Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York City, New York, United States of America
| | - Werner Doyle
- Comprehensive Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York City, New York, United States of America
| | - Orin Devinsky
- Comprehensive Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York City, New York, United States of America
| | - David Gonda
- Department of Neurosciences, UC San Diego, San Diego, California, United States of America
- Rady Children’s Hospital San Diego, San Diego, California, United States of America
| | - Shifteh Sattar
- Department of Neurosciences, UC San Diego, San Diego, California, United States of America
- Rady Children’s Hospital San Diego, San Diego, California, United States of America
| | - Sonya Wang
- Rady Children’s Hospital San Diego, San Diego, California, United States of America
- Department of Neurology, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America
| | - Eric Halgren
- Department of Neurosciences, UC San Diego, San Diego, California, United States of America
| | - Vikash Gilja
- Department of Electrical and Computer Engineering, UC San Diego, San Diego, California, United States of America
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23
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Ecological dynamics of the gut microbiome in response to dietary fiber. THE ISME JOURNAL 2022; 16:2040-2055. [PMID: 35597888 PMCID: PMC9296629 DOI: 10.1038/s41396-022-01253-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 05/05/2022] [Accepted: 05/11/2022] [Indexed: 12/19/2022]
Abstract
Dietary fibers are generally thought to benefit intestinal health. Their impacts on the composition and metabolic function of the gut microbiome, however, vary greatly across individuals. Previous research showed that each individual's response to fibers depends on their baseline gut microbiome, but the ecology driving microbiota remodeling during fiber intake remained unclear. Here, we studied the long-term dynamics of the gut microbiome and short-chain fatty acids (SCFAs) in isogenic mice with distinct microbiota baselines fed with the fermentable fiber inulin and resistant starch compared to the non-fermentable fiber cellulose. We found that inulin produced a generally rapid response followed by gradual stabilization to new equilibria, and those dynamics were baseline-dependent. We parameterized an ecology model from the time-series data, which revealed a group of bacteria whose growth significantly increased in response to inulin and whose baseline abundance and interspecies competition explained the baseline dependence of microbiome density and community composition dynamics. Fecal levels of SCFAs, such as propionate, were associated with the abundance of inulin responders, yet inter-individual variation of gut microbiome impeded the prediction of SCFAs by machine learning models. We showed that our methods and major findings were generalizable to dietary resistant starch. Finally, we analyzed time-series data of synthetic and natural human gut microbiome in response to dietary fiber and validated the inferred interspecies interactions in vitro. This study emphasizes the importance of ecological modeling to understand microbiome responses to dietary changes and the need for personalized interventions.
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24
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Asabuki T, Kokate P, Fukai T. Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data. PLoS Comput Biol 2022; 18:e1010214. [PMID: 35727828 PMCID: PMC9249189 DOI: 10.1371/journal.pcbi.1010214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 07/01/2022] [Accepted: 05/16/2022] [Indexed: 11/24/2022] Open
Abstract
The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can efficiently solve difficult segmentation tasks. In this model, multiplicative recurrent connections learn a context-dependent gating of dendro-somatic information transfers to minimize error in the prediction of somatic responses by the dendrites. Consequently, these connections filter the redundant input features represented by the dendrites but unnecessary in the given context. The model was tested on both synthetic and real neural data. In particular, the model was successful for segmenting multiple cell assemblies repeating in large-scale calcium imaging data containing thousands of cortical neurons. Our results suggest that recurrent gating of dendro-somatic signal transfers is crucial for cortical learning of context-dependent segmentation tasks. The brain learns about the environment from continuous streams of information to generate adequate behavior. This is not easy when sensory and motor sequences are hierarchically organized. Some cortical regions jointly represent multiple levels of sequence hierarchy, but how local cortical circuits learn hierarchical sequences remains largely unknown. Evidence shows that the dendrites of cortical neurons learn redundant representations of sensory information compared to the soma, suggesting a filtering process within a neuron. Our model proposes that recurrent synaptic inputs multiplicatively regulate this intracellular process by gating dendrite-to-soma information transfers depending on the context of sequence learning. Furthermore, our model provides a powerful tool to analyze the spatiotemporal patterns of neural activity in large-scale recording data.
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Affiliation(s)
- Toshitake Asabuki
- Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
- * E-mail:
| | - Prajakta Kokate
- Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
| | - Tomoki Fukai
- Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
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25
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Adaptive erasure of spurious sequences in sensory cortical circuits. Neuron 2022; 110:1857-1868.e5. [PMID: 35358415 PMCID: PMC9616807 DOI: 10.1016/j.neuron.2022.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 11/12/2021] [Accepted: 03/07/2022] [Indexed: 12/02/2022]
Abstract
Sequential activity reflecting previously experienced temporal sequences is considered a hallmark of learning across cortical areas. However, it is unknown how cortical circuits avoid the converse problem: producing spurious sequences that are not reflecting sequences in their inputs. We develop methods to quantify and study sequentiality in neural responses. We show that recurrent circuit responses generally include spurious sequences, which are specifically prevented in circuits that obey two widely known features of cortical microcircuit organization: Dale’s law and Hebbian connectivity. In particular, spike-timing-dependent plasticity in excitation-inhibition networks leads to an adaptive erasure of spurious sequences. We tested our theory in multielectrode recordings from the visual cortex of awake ferrets. Although responses to natural stimuli were largely non-sequential, responses to artificial stimuli initially included spurious sequences, which diminished over extended exposure. These results reveal an unexpected role for Hebbian experience-dependent plasticity and Dale’s law in sensory cortical circuits. Recurrent circuits generate spurious sequences without sequential inputs A principled measure of total sequentiality in population responses is developed Theory predicts that Hebbian plasticity should abolish spurious sequences Spurious sequences in the visual cortex diminish with experience
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Gorur-Shandilya S, Cronin EM, Schneider AC, Haddad SA, Rosenbaum P, Bucher D, Nadim F, Marder E. Mapping circuit dynamics during function and dysfunction. eLife 2022; 11:e76579. [PMID: 35302489 PMCID: PMC9000962 DOI: 10.7554/elife.76579] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/18/2022] [Indexed: 11/13/2022] Open
Abstract
Neural circuits can generate many spike patterns, but only some are functional. The study of how circuits generate and maintain functional dynamics is hindered by a poverty of description of circuit dynamics across functional and dysfunctional states. For example, although the regular oscillation of a central pattern generator is well characterized by its frequency and the phase relationships between its neurons, these metrics are ineffective descriptors of the irregular and aperiodic dynamics that circuits can generate under perturbation or in disease states. By recording the circuit dynamics of the well-studied pyloric circuit in Cancer borealis, we used statistical features of spike times from neurons in the circuit to visualize the spike patterns generated by this circuit under a variety of conditions. This approach captures both the variability of functional rhythms and the diversity of atypical dynamics in a single map. Clusters in the map identify qualitatively different spike patterns hinting at different dynamic states in the circuit. State probability and the statistics of the transitions between states varied with environmental perturbations, removal of descending neuromodulatory inputs, and the addition of exogenous neuromodulators. This analysis reveals strong mechanistically interpretable links between complex changes in the collective behavior of a neural circuit and specific experimental manipulations, and can constrain hypotheses of how circuits generate functional dynamics despite variability in circuit architecture and environmental perturbations.
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Affiliation(s)
| | - Elizabeth M Cronin
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers UniversityNewarkUnited States
| | - Anna C Schneider
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers UniversityNewarkUnited States
| | - Sara Ann Haddad
- Volen Center and Biology Department, Brandeis UniversityWalthamUnited States
| | - Philipp Rosenbaum
- Volen Center and Biology Department, Brandeis UniversityWalthamUnited States
| | - Dirk Bucher
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers UniversityNewarkUnited States
| | - Farzan Nadim
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers UniversityNewarkUnited States
| | - Eve Marder
- Volen Center and Biology Department, Brandeis UniversityWalthamUnited States
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Costa RM, Baxter DA, Byrne JH. Neuronal population activity dynamics reveal a low-dimensional signature of operant learning in Aplysia. Commun Biol 2022; 5:90. [PMID: 35075264 PMCID: PMC8786933 DOI: 10.1038/s42003-022-03044-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 01/07/2022] [Indexed: 11/24/2022] Open
Abstract
Learning engages a high-dimensional neuronal population space spanning multiple brain regions. However, it remains unknown whether it is possible to identify a low-dimensional signature associated with operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior. Using single-neuron resolution voltage imaging, here we identify two low-dimensional motor modules in the neuronal population underlying Aplysia feeding. Our findings point to a temporal shift in module recruitment as the primary signature of operant learning. Our findings can help guide characterization of learning signatures in systems in which only a smaller fraction of the relevant neuronal population can be monitored. Costa et al. use single-neuron resolution voltage imaging to identify two low-dimensional motor modules in the neuronal population underlying Aplysia feeding. Their findings point to a temporal shift in module recruitment as the primary signature of operant learning.
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Terada S, Geiller T, Liao Z, O'Hare J, Vancura B, Losonczy A. Adaptive stimulus selection for consolidation in the hippocampus. Nature 2022; 601:240-244. [PMID: 34880499 PMCID: PMC9380538 DOI: 10.1038/s41586-021-04118-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 10/08/2021] [Indexed: 01/03/2023]
Abstract
Associative memories guide behavioural adaptation by binding together outcome-predictive sensory stimuli1,2. However, in a feature-rich environment, only a subset of stimuli may predict a desired outcome3,4. How neural circuits enable behavioural adaptation by selectively and durably representing subsets of sensory stimuli that are pertinent to a specific outcome is not known. We investigated this feature selection process in the hippocampus during memory acquisition and subsequent consolidation. Two-photon calcium imaging of CA3 axonal projections to CA1 combined with simultaneous local field potential recordings revealed that CA3 projections that encode behaviourally informative sensory stimuli were selectively recruited during the memory replay events that underlie hippocampal memory consolidation5. These axonal projections formed sequential assemblies that conjunctively link sensory features to spatial location and thus reward proximity. By contrast, axons encoding uninformative, peripatetic sensory cues were notably suppressed during memory replay. Thus, while the hippocampus comprehensively encodes the real-time sensory environment, it implements a flexible filtering mechanism to maximize the utility of memories destined for long-term storage. We propose that utility-dependent recruitment of sensory experience during memory consolidation is a general coding principle for associative learning.
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Affiliation(s)
- Satoshi Terada
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Tristan Geiller
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Zhenrui Liao
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Justin O'Hare
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Bert Vancura
- Department of Neuroscience, Columbia University, New York, NY, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY, USA.
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
- The Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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29
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Jia S, Xing D, Yu Z, Liu JK. Dissecting cascade computational components in spiking neural networks. PLoS Comput Biol 2021; 17:e1009640. [PMID: 34843460 PMCID: PMC8659421 DOI: 10.1371/journal.pcbi.1009640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 12/09/2021] [Accepted: 11/14/2021] [Indexed: 01/15/2023] Open
Abstract
Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity. It is well known that the computation of neuronal circuits is carried out through the staged and cascade structure of different types of neurons. Nevertheless, the information, particularly sensory information, is processed in a network primarily with feedforward connections through different pathways. A peculiar example is the early visual system, where light is transcoded by the retinal cells, routed by the lateral geniculate nucleus, and reached the primary visual cortex. One meticulous interest in recent years is to map out these physical structures of neuronal pathways. However, most methods so far are limited to taking snapshots of a static view of connections between neurons. It remains unclear how to obtain a functional and dynamical neuronal circuit beyond the simple scenarios of a few randomly sampled neurons. Using simulated spiking neural networks of visual pathways with different scenarios of multiple stages, mixed cell types, and natural image stimuli, we demonstrate that a recent computational tool, named spike-triggered non-negative matrix factorization, can resolve these issues. It enables us to recover the entire structural components of neural networks underlying the computation, together with the functional components of each individual neuron. Utilizing it for complex cells of the primary visual cortex allows us to reveal every underpinning of the nonlinear computation. Our results, together with other recent experimental and computational efforts, show that it is possible to systematically dissect neural circuitry with detailed structural and functional components.
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Affiliation(s)
- Shanshan Jia
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
| | - Dajun Xing
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhaofei Yu
- Institute for Artificial Intelligence, Department of Computer Science and Technology, Peking University, Beijing, China
- * E-mail: (ZY); (JKL)
| | - Jian K. Liu
- School of Computing, University of Leeds, Leeds, United Kingdom
- * E-mail: (ZY); (JKL)
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30
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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.
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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.
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31
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Stamatakis AM, Resendez SL, Chen KS, Favero M, Liang-Guallpa J, Nassi JJ, Neufeld SQ, Visscher K, Ghosh KK. Miniature microscopes for manipulating and recording in vivo brain activity. Microscopy (Oxf) 2021; 70:399-414. [PMID: 34283242 PMCID: PMC8491619 DOI: 10.1093/jmicro/dfab028] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/02/2021] [Accepted: 07/19/2021] [Indexed: 12/23/2022] Open
Abstract
Here we describe the development and application of miniature integrated microscopes (miniscopes) paired with microendoscopes that allow for the visualization and manipulation of neural circuits in superficial and subcortical brain regions in freely behaving animals. Over the past decade the miniscope platform has expanded to include simultaneous optogenetic capabilities, electrically-tunable lenses that enable multi-plane imaging, color-corrected optics, and an integrated data acquisition platform that streamlines multimodal experiments. Miniscopes have given researchers an unprecedented ability to monitor hundreds to thousands of genetically-defined neurons from weeks to months in both healthy and diseased animal brains. Sophisticated algorithms that take advantage of constrained matrix factorization allow for background estimation and reliable cell identification, greatly improving the reliability and scalability of source extraction for large imaging datasets. Data generated from miniscopes have empowered researchers to investigate the neural circuit underpinnings of a wide array of behaviors that cannot be studied under head-fixed conditions, such as sleep, reward seeking, learning and memory, social behaviors, and feeding. Importantly, the miniscope has broadened our understanding of how neural circuits can go awry in animal models of progressive neurological disorders, such as Parkinson's disease. Continued miniscope development, including the ability to record from multiple populations of cells simultaneously, along with continued multimodal integration of techniques such as electrophysiology, will allow for deeper understanding into the neural circuits that underlie complex and naturalistic behavior.
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Affiliation(s)
| | | | - Kai-Siang Chen
- Inscopix Inc., 2462 Embarcadero Way, Palo Alto, CA 94303, USA
| | - Morgana Favero
- Inscopix Inc., 2462 Embarcadero Way, Palo Alto, CA 94303, USA
| | | | | | - Shay Q Neufeld
- Inscopix Inc., 2462 Embarcadero Way, Palo Alto, CA 94303, USA
| | - Koen Visscher
- Inscopix Inc., 2462 Embarcadero Way, Palo Alto, CA 94303, USA
| | - Kunal K Ghosh
- Inscopix Inc., 2462 Embarcadero Way, Palo Alto, CA 94303, USA
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32
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Williams AH, Linderman SW. Statistical neuroscience in the single trial limit. Curr Opin Neurobiol 2021; 70:193-205. [PMID: 34861596 DOI: 10.1016/j.conb.2021.10.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 09/29/2021] [Accepted: 10/27/2021] [Indexed: 11/24/2022]
Abstract
Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic 'noise' and systematic changes in the animal's cognitive and behavioral state. Disentangling these sources of variability is of great scientific interest in its own right, but it is also increasingly inescapable as neuroscientists aspire to study more complex and naturalistic animal behaviors. In these settings, behavioral actions never repeat themselves exactly and may rarely do so even approximately. Thus, new statistical methods that extract reliable features of neural activity using few, if any, repeated trials are needed. Accurate statistical modeling in this severely trial-limited regime is challenging, but still possible if simplifying structure in neural data can be exploited. We review recent works that have identified different forms of simplifying structure - including shared gain modulations across neural subpopulations, temporal smoothness in neural firing rates, and correlations in responses across behavioral conditions - and exploited them to reveal novel insights into the trial-by-trial operation of neural circuits.
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Affiliation(s)
- Alex H Williams
- Department of Statistics and Wu Tsai Neurosciences Institute, Stanford University, USA
| | - Scott W Linderman
- Department of Statistics and Wu Tsai Neurosciences Institute, Stanford University, USA.
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33
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McCullough MH, Goodhill GJ. Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain. Curr Opin Neurobiol 2021; 70:89-100. [PMID: 34482006 DOI: 10.1016/j.conb.2021.07.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/20/2021] [Accepted: 07/21/2021] [Indexed: 01/02/2023]
Abstract
Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.
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Affiliation(s)
- Michael H McCullough
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Geoffrey J Goodhill
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia; School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, 4072, Australia.
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34
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Rupprecht P, Carta S, Hoffmann A, Echizen M, Blot A, Kwan AC, Dan Y, Hofer SB, Kitamura K, Helmchen F, Friedrich RW. A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging. Nat Neurosci 2021; 24:1324-1337. [PMID: 34341584 PMCID: PMC7611618 DOI: 10.1038/s41593-021-00895-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 06/23/2021] [Indexed: 02/06/2023]
Abstract
Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.
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Affiliation(s)
- Peter Rupprecht
- Brain Research Institute, University of Zürich, Zurich, Switzerland.
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
| | - Stefano Carta
- Brain Research Institute, University of Zürich, Zurich, Switzerland
| | - Adrian Hoffmann
- Brain Research Institute, University of Zürich, Zurich, Switzerland
| | - Mayumi Echizen
- Department of Neurophysiology, University of Tokyo, Tokyo, Japan
- Department of Anesthesiology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Antonin Blot
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, United Kingdom
- Biozentrum, University of Basel, Basel, Switzerland
| | - Alex C Kwan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Yang Dan
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley CA, USA
| | - Sonja B Hofer
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, United Kingdom
- Biozentrum, University of Basel, Basel, Switzerland
| | - Kazuo Kitamura
- Department of Neurophysiology, University of Tokyo, Tokyo, Japan
- Department of Neurophysiology, University of Yamanashi, Yamanashi, Japan
| | - Fritjof Helmchen
- Brain Research Institute, University of Zürich, Zurich, Switzerland.
| | - Rainer W Friedrich
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
- University of Basel, Basel, Switzerland.
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35
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Linden NJ, Tabuena DR, Steinmetz NA, Moody WJ, Brunton SL, Brunton BW. Go with the FLOW: visualizing spatiotemporal dynamics in optical widefield calcium imaging. J R Soc Interface 2021; 18:20210523. [PMID: 34428947 PMCID: PMC8385384 DOI: 10.1098/rsif.2021.0523] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/02/2021] [Indexed: 12/11/2022] Open
Abstract
Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunity to characterize spatiotemporally coherent structures that underlie neural activity across many regions of the brain. In this work, we leverage analytic techniques from fluid dynamics to develop a visualization framework that highlights features of flow across the cortex, mapping wavefronts that may be correlated with behavioural events. First, we transform the time series of widefield calcium images into time-varying vector fields using optic flow. Next, we extract concise diagrams summarizing the dynamics, which we refer to as FLOW (flow lines in optical widefield imaging) portraits. These FLOW portraits provide an intuitive map of dynamic calcium activity, including regions of initiation and termination, as well as the direction and extent of activity spread. To extract these structures, we use the finite-time Lyapunov exponent technique developed to analyse time-varying manifolds in unsteady fluids. Importantly, our approach captures coherent structures that are poorly represented by traditional modal decomposition techniques. We demonstrate the application of FLOW portraits on three simple synthetic datasets and two widefield calcium imaging datasets, including cortical waves in the developing mouse and spontaneous cortical activity in an adult mouse.
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Affiliation(s)
- Nathaniel J. Linden
- Department of Bioengineering, University of Washington, Seattle, WA 98195-0005, USA
- Department of Biology, University of Washington, Seattle, WA 98195-0005, USA
| | - Dennis R. Tabuena
- Department of Biology, University of Washington, Seattle, WA 98195-0005, USA
- Graduate Program in Neuroscience, University of Washington, Seattle, WA 98195-0005, USA
| | - Nicholas A. Steinmetz
- Department of Biological Structure, University of Washington, Seattle, WA 98195-0005, USA
| | - William J. Moody
- Department of Biology, University of Washington, Seattle, WA 98195-0005, USA
| | - Steven L. Brunton
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195-0005, USA
| | - Bingni W. Brunton
- Department of Biology, University of Washington, Seattle, WA 98195-0005, USA
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36
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Pérez-Ortega J, Alejandre-García T, Yuste R. Long-term stability of cortical ensembles. eLife 2021; 10:e64449. [PMID: 34328414 PMCID: PMC8376248 DOI: 10.7554/elife.64449] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 07/29/2021] [Indexed: 12/25/2022] Open
Abstract
Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it is still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses. Less than half of the neurons remained active across any two imaging sessions. These stable neurons formed ensembles that lasted weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved most of their neurons for up to 46 days, our longest imaged period, and these 'core' cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.
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Affiliation(s)
- Jesús Pérez-Ortega
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
| | | | - Rafael Yuste
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
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37
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Hamid AA, Frank MJ, Moore CI. Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. Cell 2021; 184:2733-2749.e16. [PMID: 33861952 PMCID: PMC8122079 DOI: 10.1016/j.cell.2021.03.046] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 12/31/2020] [Accepted: 03/23/2021] [Indexed: 12/17/2022]
Abstract
Significant evidence supports the view that dopamine shapes learning by encoding reward prediction errors. However, it is unknown whether striatal targets receive tailored dopamine dynamics based on regional functional specialization. Here, we report wave-like spatiotemporal activity patterns in dopamine axons and release across the dorsal striatum. These waves switch between activational motifs and organize dopamine transients into localized clusters within functionally related striatal subregions. Notably, wave trajectories were tailored to task demands, propagating from dorsomedial to dorsolateral striatum when rewards are contingent on animal behavior and in the opponent direction when rewards are independent of behavioral responses. We propose a computational architecture in which striatal dopamine waves are sculpted by inference about agency and provide a mechanism to direct credit assignment to specialized striatal subregions. Supporting model predictions, dorsomedial dopamine activity during reward-pursuit signaled the extent of instrumental control and interacted with reward waves to predict future behavioral adjustments.
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Affiliation(s)
- Arif A Hamid
- Department of Neuroscience, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA.
| | - Michael J Frank
- Department of Cognitive Linguistics & Psychological Sciences, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA.
| | - Christopher I Moore
- Department of Neuroscience, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA.
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38
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Maes A, Barahona M, Clopath C. Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons. PLoS Comput Biol 2021; 17:e1008866. [PMID: 33764970 PMCID: PMC8023498 DOI: 10.1371/journal.pcbi.1008866] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 04/06/2021] [Accepted: 03/08/2021] [Indexed: 11/17/2022] Open
Abstract
Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.
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Affiliation(s)
- Amadeus Maes
- Bioengineering Department, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Mathematics Department, Imperial College London, London, United Kingdom
| | - Claudia Clopath
- Bioengineering Department, Imperial College London, London, United Kingdom
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39
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Williams AH, Degleris A, Wang Y, Linderman SW. Point process models for sequence detection in high-dimensional neural spike trains. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 2020; 33:14350-14361. [PMID: 35002191 PMCID: PMC8734964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Sparse sequences of neural spikes are posited to underlie aspects of working memory [1], motor production [2], and learning [3, 4]. Discovering these sequences in an unsupervised manner is a longstanding problem in statistical neuroscience [5-7]. Promising recent work [4, 8] utilized a convolutive nonnegative matrix factorization model [9] to tackle this challenge. However, this model requires spike times to be discretized, utilizes a sub-optimal least-squares criterion, and does not provide uncertainty estimates for model predictions or estimated parameters. We address each of these shortcomings by developing a point process model that characterizes fine-scale sequences at the level of individual spikes and represents sequence occurrences as a small number of marked events in continuous time. This ultra-sparse representation of sequence events opens new possibilities for spike train modeling. For example, we introduce learnable time warping parameters to model sequences of varying duration, which have been experimentally observed in neural circuits [10]. We demonstrate these advantages on experimental recordings from songbird higher vocal center and rodent hippocampus.
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Affiliation(s)
- Alex H Williams
- Department of Statistics, Stanford University, Stanford, CA 94305
| | - Anthony Degleris
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305
| | - Yixin Wang
- Department of Statistics, Columbia University, New York NY 10027
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40
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Pereira TD, Shaevitz JW, Murthy M. Quantifying behavior to understand the brain. Nat Neurosci 2020; 23:1537-1549. [PMID: 33169033 PMCID: PMC7780298 DOI: 10.1038/s41593-020-00734-z] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 10/02/2020] [Indexed: 02/07/2023]
Abstract
Over the past years, numerous methods have emerged to automate the quantification of animal behavior at a resolution not previously imaginable. This has opened up a new field of computational ethology and will, in the near future, make it possible to quantify in near completeness what an animal is doing as it navigates its environment. The importance of improving the techniques with which we characterize behavior is reflected in the emerging recognition that understanding behavior is an essential (or even prerequisite) step to pursuing neuroscience questions. The use of these methods, however, is not limited to studying behavior in the wild or in strictly ethological settings. Modern tools for behavioral quantification can be applied to the full gamut of approaches that have historically been used to link brain to behavior, from psychophysics to cognitive tasks, augmenting those measurements with rich descriptions of how animals navigate those tasks. Here we review recent technical advances in quantifying behavior, particularly in methods for tracking animal motion and characterizing the structure of those dynamics. We discuss open challenges that remain for behavioral quantification and highlight promising future directions, with a strong emphasis on emerging approaches in deep learning, the core technology that has enabled the markedly rapid pace of progress of this field. We then discuss how quantitative descriptions of behavior can be leveraged to connect brain activity with animal movements, with the ultimate goal of resolving the relationship between neural circuits, cognitive processes and behavior.
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Affiliation(s)
- Talmo D Pereira
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Joshua W Shaevitz
- Department of Physics, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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41
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Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making. Nat Neurosci 2020; 23:1410-1420. [PMID: 33020653 PMCID: PMC7610668 DOI: 10.1038/s41593-020-0696-5] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 07/21/2020] [Indexed: 01/27/2023]
Abstract
Recent work has suggested that prefrontal cortex (PFC) plays a key role in context-dependent perceptual decision-making. Here we address that role using a new method for identifying task-relevant dimensions of neural population activity. Specifically, we show that PFC has a multi-dimensional code for context, decisions, and both relevant and irrelevant sensory information. Moreover, these representations evolve in time, with an early linear accumulation phase followed by a phase with rotational dynamics. We identify the dimensions of neural activity associated with these phases, and show that they do not arise from distinct populations, but of a single population with broad tuning characteristics. Finally, we use model-based decoding to show that the transition from linear to rotational dynamics coincides with a plateau in decoding accuracy, revealing that rotational dynamics in PFC preserve sensory choice information for the duration of the stimulus integration period.
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42
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Veuthey TL, Derosier K, Kondapavulur S, Ganguly K. Single-trial cross-area neural population dynamics during long-term skill learning. Nat Commun 2020; 11:4057. [PMID: 32792523 PMCID: PMC7426952 DOI: 10.1038/s41467-020-17902-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 07/22/2020] [Indexed: 11/09/2022] Open
Abstract
Mammalian cortex has both local and cross-area connections, suggesting vital roles for both local and cross-area neural population dynamics in cortically-dependent tasks, like movement learning. Prior studies of movement learning have focused on how single-area population dynamics change during short-term adaptation. It is unclear how cross-area dynamics contribute to movement learning, particularly long-term learning and skill acquisition. Using simultaneous recordings of rodent motor (M1) and premotor (M2) cortex and computational methods, we show how cross-area activity patterns evolve during reach-to-grasp learning in rats. The emergence of reach-related modulation in cross-area activity correlates with skill acquisition, and single-trial modulation in cross-area activity predicts reaction time and reach duration. Local M2 neural activity precedes local M1 activity, supporting top-down hierarchy between the regions. M2 inactivation preferentially affects cross-area dynamics and behavior, with minimal disruption of local M1 dynamics. Together, these results indicate that cross-area population dynamics are necessary for learned motor skills.
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Affiliation(s)
- T L Veuthey
- Neuroscience Graduate Program, University of California San Francisco, San Francisco, CA, USA
- Medical Scientist Training Program, University of California San Francisco, San Francisco, CA, USA
- Neurology and Rehabilitation Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - K Derosier
- Neuroscience Graduate Program, University of California San Francisco, San Francisco, CA, USA
- Neurology and Rehabilitation Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - S Kondapavulur
- Medical Scientist Training Program, University of California San Francisco, San Francisco, CA, USA
- Neurology and Rehabilitation Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - K Ganguly
- Neurology and Rehabilitation Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
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43
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MacDowell CJ, Buschman TJ. Low-Dimensional Spatiotemporal Dynamics Underlie Cortex-wide Neural Activity. Curr Biol 2020; 30:2665-2680.e8. [PMID: 32470366 DOI: 10.1016/j.cub.2020.04.090] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/07/2020] [Accepted: 04/30/2020] [Indexed: 01/04/2023]
Abstract
Cognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity (∼75%) could be explained by a limited set of ∼14 "motifs" of neural activity. Each motif captured a unique spatiotemporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states, and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic but that these dynamics are restricted to a low-dimensional set of motifs, potentially allowing for efficient control of behavior.
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Affiliation(s)
- Camden J MacDowell
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Department of Molecular Biology, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Rutgers Robert Wood Johnson Medical School, 125 Paterson St., New Brunswick, NJ 08901, USA.
| | - Timothy J Buschman
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Washington Rd., Princeton, NJ 08540, USA.
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Discovering dynamic task-modulated functional networks with specific spectral modes using MEG. Neuroimage 2020; 218:116924. [PMID: 32445878 DOI: 10.1016/j.neuroimage.2020.116924] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 03/31/2020] [Accepted: 05/04/2020] [Indexed: 11/20/2022] Open
Abstract
Efficient neuronal communication between brain regions through oscillatory synchronization at certain frequencies is necessary for cognition. Such synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to support ongoing cognitive operations. However, few studies characterizing dynamic electrophysiological brain networks have simultaneously accounted for temporal non-stationarity, spectral structure, and spatial properties. Here, we propose an analysis framework for characterizing the large-scale phase-coupling network dynamics during task performance using magnetoencephalography (MEG). We exploit the high spatiotemporal resolution of MEG to measure time-frequency dynamics of connectivity between parcellated brain regions, yielding data in tensor format. We then use a tensor component analysis (TCA)-based procedure to identify the spatio-temporal-spectral modes of covariation among separate regions in the human brain. We validate our pipeline using MEG data recorded during a hand movement task, extracting a transient motor network with beta-dominant spectral mode, which is significantly modulated by the movement task. Next, we apply the proposed pipeline to explore brain networks that support cognitive operations during a working memory task. The derived results demonstrate the temporal formation and dissolution of multiple phase-coupled networks with specific spectral modes, which are associated with face recognition, vision, and movement. The proposed pipeline can characterize the spectro-temporal dynamics of functional connectivity in the brain on the subsecond timescale, commensurate with that of cognitive performance.
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45
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Etter G, Manseau F, Williams S. A Probabilistic Framework for Decoding Behavior From in vivo Calcium Imaging Data. Front Neural Circuits 2020; 14:19. [PMID: 32499681 PMCID: PMC7243991 DOI: 10.3389/fncir.2020.00019] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 04/06/2020] [Indexed: 11/13/2022] Open
Abstract
Understanding the role of neuronal activity in cognition and behavior is a key question in neuroscience. Previously, in vivo studies have typically inferred behavior from electrophysiological data using probabilistic approaches including Bayesian decoding. While providing useful information on the role of neuronal subcircuits, electrophysiological approaches are often limited in the maximum number of recorded neurons as well as their ability to reliably identify neurons over time. This can be particularly problematic when trying to decode behaviors that rely on large neuronal assemblies or rely on temporal mechanisms, such as a learning task over the course of several days. Calcium imaging of genetically encoded calcium indicators has overcome these two issues. Unfortunately, because calcium transients only indirectly reflect spiking activity and calcium imaging is often performed at lower sampling frequencies, this approach suffers from uncertainty in exact spike timing and thus activity frequency, making rate-based decoding approaches used in electrophysiological recordings difficult to apply to calcium imaging data. Here we describe a probabilistic framework that can be used to robustly infer behavior from calcium imaging recordings and relies on a simplified implementation of a naive Baysian classifier. Our method discriminates between periods of activity and periods of inactivity to compute probability density functions (likelihood and posterior), significance and confidence interval, as well as mutual information. We next devise a simple method to decode behavior using these probability density functions and propose metrics to quantify decoding accuracy. Finally, we show that neuronal activity can be predicted from behavior, and that the accuracy of such reconstructions can guide the understanding of relationships that may exist between behavioral states and neuronal activity.
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Affiliation(s)
- Guillaume Etter
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | | | - Sylvain Williams
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
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46
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Tingley D, Peyrache A. On the methods for reactivation and replay analysis. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190231. [PMID: 32248787 DOI: 10.1098/rstb.2019.0231] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
A major task in the history of neurophysiology has been to relate patterns of neural activity to ongoing external stimuli. More recently, this approach has branched out to relating current neural activity patterns to external stimuli or experiences that occurred in the past or future. Here, we aim to review the large body of methodological approaches used towards this goal, and to assess the assumptions each makes with reference to the statistics of neural data that are commonly observed. These methods primarily fall into two categories, those that quantify zero-lag relationships without examining temporal evolution, termed reactivation, and those that quantify the temporal structure of changing activity patterns, termed replay. However, no two studies use the exact same approach, which prevents an unbiased comparison between findings. These observations should instead be validated by multiple and, if possible, previously established tests. This will help the community to speak a common language and will eventually provide tools to study, more generally, the organization of neuronal patterns in the brain. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.
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Affiliation(s)
- David Tingley
- Neuroscience Institute, New York University, New York, NY, USA
| | - Adrien Peyrache
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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47
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van der Meer MAA, Kemere C, Diba K. Progress and issues in second-order analysis of hippocampal replay. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190238. [PMID: 32248780 DOI: 10.1098/rstb.2019.0238] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Patterns of neural activity that occur spontaneously during sharp-wave ripple (SWR) events in the hippocampus are thought to play an important role in memory formation, consolidation and retrieval. Typical studies examining the content of SWRs seek to determine whether the identity and/or temporal order of cell firing is different from chance. Such 'first-order' analyses are focused on a single time point and template (map), and have been used to show, for instance, the existence of preplay. The major methodological challenge in first-order analyses is the construction and interpretation of different chance distributions. By contrast, 'second-order' analyses involve a comparison of SWR content between different time points, and/or between different templates. Typical second-order questions include tests of experience-dependence (replay) that compare SWR content before and after experience, and comparisons or replay between different arms of a maze. Such questions entail additional methodological challenges that can lead to biases in results and associated interpretations. We provide an inventory of analysis challenges for second-order questions about SWR content, and suggest ways of preventing, identifying and addressing possible analysis biases. Given evolving interest in understanding SWR content in more complex experimental scenarios and across different time scales, we expect these issues to become increasingly pervasive. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.
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Affiliation(s)
| | - Caleb Kemere
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Kamran Diba
- Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA.,Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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48
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Zhu Y, Liu J, Mathiak K, Ristaniemi T, Cong F. Deriving Electrophysiological Brain Network Connectivity via Tensor Component Analysis During Freely Listening to Music. IEEE Trans Neural Syst Rehabil Eng 2020; 28:409-418. [DOI: 10.1109/tnsre.2019.2953971] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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49
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Maes A, Barahona M, Clopath C. Learning spatiotemporal signals using a recurrent spiking network that discretizes time. PLoS Comput Biol 2020; 16:e1007606. [PMID: 31961853 PMCID: PMC7028299 DOI: 10.1371/journal.pcbi.1007606] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 02/18/2020] [Accepted: 12/13/2019] [Indexed: 12/15/2022] Open
Abstract
Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.
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Affiliation(s)
- Amadeus Maes
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, United Kingdom
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50
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Tingley D, Buzsáki G. Routing of Hippocampal Ripples to Subcortical Structures via the Lateral Septum. Neuron 2020; 105:138-149.e5. [PMID: 31784288 PMCID: PMC6952543 DOI: 10.1016/j.neuron.2019.10.012] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 08/06/2019] [Accepted: 10/03/2019] [Indexed: 01/10/2023]
Abstract
The mnemonic functions of hippocampal sharp wave ripples (SPW-Rs) have been studied extensively. Because hippocampal outputs affect not only cortical but also subcortical targets, we examined the impact of SPW-Rs on the firing patterns of lateral septal (LS) neurons in behaving rats. A large fraction of SPW-Rs were temporally locked to high-frequency oscillations (HFOs) (120-180 Hz) in LS, with strongest coupling during non-rapid eye movement (NREM) sleep, followed by waking immobility. However, coherence and spike-local field potential (LFP) coupling between the two structures were low, suggesting that HFOs are generated locally within the LS GABAergic population. This hypothesis was supported by optogenetic induction of HFOs in LS. Spiking of LS neurons was largely independent of the sequential order of spiking in SPW-Rs but instead correlated with the magnitude of excitatory synchrony of the hippocampal output. Thus, LS is strongly activated by SPW-Rs and may convey hippocampal population events to its hypothalamic and brainstem targets.
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Affiliation(s)
- David Tingley
- Neuroscience Institute, New York University, New York, NY 10016, USA
| | - György Buzsáki
- Neuroscience Institute, New York University, New York, NY 10016, USA; Department of Neurology, Langone Medical Center, New York University, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA.
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