1
|
Bigand F, Bianco R, Abalde SF, Nguyen T, Novembre G. EEG of the Dancing Brain: Decoding Sensory, Motor, and Social Processes during Dyadic Dance. J Neurosci 2025; 45:e2372242025. [PMID: 40228893 PMCID: PMC12096039 DOI: 10.1523/jneurosci.2372-24.2025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2024] [Revised: 03/05/2025] [Accepted: 03/11/2025] [Indexed: 04/16/2025] Open
Abstract
Real-world social cognition requires processing and adapting to multiple dynamic information streams. Interpreting neural activity in such ecological conditions remains a key challenge for neuroscience. This study leverages advancements in denoising techniques and multivariate modeling to extract interpretable EEG signals from pairs of (male and/or female) participants engaged in spontaneous dyadic dance. Using multivariate temporal response functions (mTRFs), we investigated how music acoustics, self-generated kinematics, other-generated kinematics, and social coordination uniquely contributed to EEG activity. Electromyogram recordings from ocular, face, and neck muscles were also modeled to control for artifacts. The mTRFs effectively disentangled neural signals associated with four processes: (I) auditory tracking of music, (II) control of self-generated movements, (III) visual monitoring of partner movements, and (IV) visual tracking of social coordination. We show that the first three neural signals are driven by event-related potentials: the P50-N100-P200 triggered by acoustic events, the central lateralized movement-related cortical potentials triggered by movement initiation, and the occipital N170 triggered by movement observation. Notably, the (previously unknown) neural marker of social coordination encodes the spatiotemporal alignment between dancers, surpassing the encoding of self- or partner-related kinematics taken alone. This marker emerges when partners can see each other, exhibits a topographical distribution over occipital areas, and is specifically driven by movement observation rather than initiation. Using data-driven kinematic decomposition, we further show that vertical bounce movements best drive observers' EEG activity. These findings highlight the potential of real-world neuroimaging, combined with multivariate modeling, to uncover the mechanisms underlying complex yet natural social behaviors.
Collapse
Affiliation(s)
- Félix Bigand
- Neuroscience of Perception & Action Lab, Italian Institute of Technology, Rome 00161, Italy
| | - Roberta Bianco
- Neuroscience of Perception & Action Lab, Italian Institute of Technology, Rome 00161, Italy
| | - Sara F Abalde
- Neuroscience of Perception & Action Lab, Italian Institute of Technology, Rome 00161, Italy
- The Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), Genova 16163, Italy
| | - Trinh Nguyen
- Neuroscience of Perception & Action Lab, Italian Institute of Technology, Rome 00161, Italy
| | - Giacomo Novembre
- Neuroscience of Perception & Action Lab, Italian Institute of Technology, Rome 00161, Italy
| |
Collapse
|
2
|
Bounds HA, Adesnik H. Network influence determines the impact of cortical ensembles on stimulus detection. Neuron 2025:S0896-6273(25)00306-X. [PMID: 40378835 DOI: 10.1016/j.neuron.2025.04.023] [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: 07/09/2024] [Revised: 01/23/2025] [Accepted: 04/24/2025] [Indexed: 05/19/2025]
Abstract
Causally connecting neural activity patterns to behavioral decisions is essential to understand the neural code but requires direct perturbation of the pattern of interest with high specificity. We combined two-photon imaging and cellular-resolution holographic optogenetic photostimulation to causally test how neural activity in the mouse visual cortex is read out to detect visual stimuli. Contrary to expectations, targeted activation of visually sensitive neural ensembles did not preferentially modify behavior compared with targeting randomly selected ensembles. Instead, an activated ensemble's effect on local network activity was the main predictor of its impact on perception. This suggests that downstream regions summate visual cortex activity without preferentially weighting more informative neurons, a notion confirmed by analyzing the impact of photostimulation on decoding models of neural activity. This work challenges conventional notions for how sensory representations mediate perception and demonstrates that perturbing activity is essential to determine which features of neural activity drive behavior.
Collapse
Affiliation(s)
- Hayley A Bounds
- Department of Neuroscience, University of California, Berkeley, Berkeley, CA, USA; The Helen Wills Neuroscience Institute, Berkeley, CA, USA
| | - Hillel Adesnik
- Department of Neuroscience, University of California, Berkeley, Berkeley, CA, USA; The Helen Wills Neuroscience Institute, Berkeley, CA, USA.
| |
Collapse
|
3
|
Dimakou A, Pezzulo G, Zangrossi A, Corbetta M. The predictive nature of spontaneous brain activity across scales and species. Neuron 2025; 113:1310-1332. [PMID: 40101720 DOI: 10.1016/j.neuron.2025.02.009] [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: 11/04/2024] [Revised: 01/30/2025] [Accepted: 02/12/2025] [Indexed: 03/20/2025]
Abstract
Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.
Collapse
Affiliation(s)
- Anastasia Dimakou
- Padova Neuroscience Center, Padova, Italy; Veneto Institute of Molecular Medicine, VIMM, Padova, Italy
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Andrea Zangrossi
- Padova Neuroscience Center, Padova, Italy; Department of General Psychology, University of Padova, Padova, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, Padova, Italy; Veneto Institute of Molecular Medicine, VIMM, Padova, Italy; Department of Neuroscience, University of Padova, Padova, Italy.
| |
Collapse
|
4
|
Kajiya R, Miyawaki H, Nakahara H, Mizuseki K. Firing Activities of REM- and NREM-Preferring Neurons Are Differently Modulated by Fast Network Oscillations and Behavior in the Hippocampus, Prelimbic Cortex, and Amygdala. eNeuro 2025; 12:ENEURO.0575-24.2025. [PMID: 40374559 PMCID: PMC12118951 DOI: 10.1523/eneuro.0575-24.2025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2024] [Revised: 04/20/2025] [Accepted: 05/08/2025] [Indexed: 05/17/2025] Open
Abstract
Sleep consists of two alternating states-rapid eye movement (REM) and non-REM (NREM) sleep. Neurons adjust their firing activity based on brain state, however, the extent to which this modulation varies across neurons and brain regions remains poorly understood. This study analyzed previously acquired 17-h continuous recordings of single-unit activity and local field potentials in the ventral hippocampal CA1 region, prelimbic cortex layer 5, and basolateral nucleus of the amygdala of fear-conditioned rats. The findings indicate that more than half of the neurons fired faster during REM sleep than during NREM sleep, although a notable subset of neurons exhibited the opposite preference, firing preferentially during NREM sleep. During sleep, the overall firing activity of both REM- and NREM-preferring neurons decreased. However, fast network oscillations, including hippocampal sharp-wave ripples (SWRs), amygdalar high-frequency oscillations, cortical ripples, and cortical spindles, differentially modulated REM- versus NREM-preferring neurons. During wakefulness, REM-preferring neurons fired more slowly but were more intensely activated by SWRs and shock presentations than NREM-preferring neurons. Moreover, during fast network oscillations in sleep, neurons with similar REM/NREM preferences exhibited stronger within- and cross-regional coactivation than those with differing preferences. Conversely, during awake SWRs in fear conditioning sessions, neurons with different REM/NREM preferences showed stronger interregional coactivation than those with similar preferences. These findings suggest that the distinct activity patterns of REM- and NREM-preferring neurons, potentially reflecting different roles in memory, affect local and global networks differently, thereby balancing experience-dependent network modifications with sleep-dependent homeostatic regulation of neuronal excitability.
Collapse
Affiliation(s)
- Risa Kajiya
- Department of Physiology, Osaka Metropolitan University Graduate School of Medicine, Osaka 545-8585, Japan
- Department of Oral and Maxillofacial Surgery, Osaka Metropolitan University Graduate School of Medicine, Osaka 545-8585, Japan
- Department of Physiology, Osaka City University Graduate School of Medicine, Osaka 545-8585, Japan
- Department of Oral and Maxillofacial Surgery, Osaka City University Graduate School of Medicine, Osaka 545-8585, Japan
| | - Hiroyuki Miyawaki
- Department of Physiology, Osaka Metropolitan University Graduate School of Medicine, Osaka 545-8585, Japan
- Department of Physiology, Osaka City University Graduate School of Medicine, Osaka 545-8585, Japan
| | - Hirokazu Nakahara
- Department of Oral and Maxillofacial Surgery, Osaka Metropolitan University Graduate School of Medicine, Osaka 545-8585, Japan
- Department of Oral and Maxillofacial Surgery, Osaka City University Graduate School of Medicine, Osaka 545-8585, Japan
| | - Kenji Mizuseki
- Department of Physiology, Osaka Metropolitan University Graduate School of Medicine, Osaka 545-8585, Japan
- Department of Physiology, Osaka City University Graduate School of Medicine, Osaka 545-8585, Japan
| |
Collapse
|
5
|
Inácio AR, Lam KC, Zhao Y, Pereira F, Gerfen CR, Lee S. Brain-wide presynaptic networks of functionally distinct cortical neurons. Nature 2025; 641:162-172. [PMID: 40011781 PMCID: PMC12043506 DOI: 10.1038/s41586-025-08631-w] [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: 05/02/2023] [Accepted: 01/10/2025] [Indexed: 02/28/2025]
Abstract
Revealing the connectivity of functionally identified individual neurons is necessary to understand how activity patterns emerge and support behaviour. Yet the brain-wide presynaptic wiring rules that lay the foundation for the functional selectivity of individual neurons remain largely unexplored. Cortical neurons, even in primary sensory cortex, are heterogeneous in their selectivity, not only to sensory stimuli but also to multiple aspects of behaviour. Here, to investigate presynaptic connectivity rules underlying the selectivity of pyramidal neurons to behavioural state1-10 in primary somatosensory cortex (S1), we used two-photon calcium imaging, neuropharmacology, single-cell-based monosynaptic input tracing and optogenetics. We show that behavioural state-dependent activity patterns are stable over time. These are minimally affected by direct neuromodulatory inputs and are driven primarily by glutamatergic inputs. Analysis of brain-wide presynaptic networks of individual neurons with distinct behavioural state-dependent activity profiles revealed that although behavioural state-related and behavioural state-unrelated neurons shared a similar pattern of local inputs within S1, their long-range glutamatergic inputs differed. Individual cortical neurons, irrespective of their functional properties, received converging inputs from the main S1-projecting areas. Yet neurons that tracked behavioural state received a smaller proportion of motor cortical inputs and a larger proportion of thalamic inputs. Optogenetic suppression of thalamic inputs reduced behavioural state-dependent activity in S1, but this activity was not externally driven. Our results reveal distinct long-range glutamatergic inputs as a substrate for preconfigured network dynamics associated with behavioural state.
Collapse
Affiliation(s)
- Ana R Inácio
- Unit on Functional Neural Circuits, Systems Neurodevelopment Laboratory, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Ka Chun Lam
- Machine Learning Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Yuan Zhao
- Machine Learning Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Francisco Pereira
- Machine Learning Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Charles R Gerfen
- Section on Neuroanatomy, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Soohyun Lee
- Unit on Functional Neural Circuits, Systems Neurodevelopment Laboratory, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
6
|
Chang S, Zheng B, Keniston L, Xu J, Yu L. Auditory cortex learns to discriminate audiovisual cues through selective multisensory enhancement. eLife 2025; 13:RP102926. [PMID: 40261274 PMCID: PMC12014134 DOI: 10.7554/elife.102926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/24/2025] Open
Abstract
Multisensory object discrimination is essential in everyday life, yet the neural mechanisms underlying this process remain unclear. In this study, we trained rats to perform a two-alternative forced-choice task using both auditory and visual cues. Our findings reveal that multisensory perceptual learning actively engages auditory cortex (AC) neurons in both visual and audiovisual processing. Importantly, many audiovisual neurons in the AC exhibited experience-dependent associations between their visual and auditory preferences, displaying a unique integration model. This model employed selective multisensory enhancement for the auditory-visual pairing guiding the contralateral choice, which correlated with improved multisensory discrimination. Furthermore, AC neurons effectively distinguished whether a preferred auditory stimulus was paired with its associated visual stimulus using this distinct integrative mechanism. Our results highlight the capability of sensory cortices to develop sophisticated integrative strategies, adapting to task demands to enhance multisensory discrimination abilities.
Collapse
Affiliation(s)
- Song Chang
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Life Sciences, East China Normal UniversityShanghaiChina
| | - Beilin Zheng
- College of Information Engineering, Hangzhou Vocational and Technical CollegeHangzhouChina
| | - Les Keniston
- Department of Biomedical Sciences, Kentucky College of Osteopathic Medicine, University of PikevillePikevilleUnited States
| | - Jinghong Xu
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Life Sciences, East China Normal UniversityShanghaiChina
| | - Liping Yu
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Life Sciences, East China Normal UniversityShanghaiChina
| |
Collapse
|
7
|
Haydaroğlu A, Chang T, Landau A, Krumin M, Dodgson S, Baruchin LJ, Cozan M, Guo J, Meyer D, Reddy CB, Zhong J, Ji N, Schröder S, Harris KD, Vaziri A, Carandini M. Suite3D: Volumetric cell detection for two-photon microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.26.645628. [PMID: 40236252 PMCID: PMC11996422 DOI: 10.1101/2025.03.26.645628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/17/2025]
Abstract
In two-photon imaging of neuronal activity it is common to acquire 3-dimensional volumes. However, these volumes are typically processed plane by plane, leading to duplicated cells across planes, reduced signal-to-noise ratio per cell, uncorrected axial movement, and missed cells. To overcome these limitations, we introduce Suite3D, a volumetric cell detection pipeline. Suite3D corrects for 3D brain motion, estimating axial motion and improving estimates of lateral motion. It detects neurons using 3D correlation, which improves the signal-to-background ratio and detectability of cells. Finally, it performs 3D segmentation, detecting cells across imaging planes. We validated Suite3D with data from conventional multi-plane microscopes and advanced volumetric microscopes, at various resolutions and in various brain regions. Suite3D successfully detected cells appearing on multiple imaging planes, improving cell detectability and signal quality, avoiding duplications, and running >20x faster than a prior volumetric pipeline. Suite3D offers a powerful solution for analyzing volumetric two-photon data.
Collapse
|
8
|
Russo S, Claar LD, Furregoni G, Marks LC, Krishnan G, Zauli FM, Hassan G, Solbiati M, d'Orio P, Mikulan E, Sarasso S, Rosanova M, Sartori I, Bazhenov M, Pigorini A, Massimini M, Koch C, Rembado I. Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans. Nat Commun 2025; 16:3627. [PMID: 40240330 PMCID: PMC12003640 DOI: 10.1038/s41467-025-58717-2] [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: 05/22/2024] [Accepted: 03/27/2025] [Indexed: 04/18/2025] Open
Abstract
Cortical stimulation with single pulses is a common technique in clinical practice and research. However, we still do not understand the extent to which it engages subcortical circuits that may contribute to the associated evoked potentials (EPs). Here we show that cortical stimulation generates remarkably similar EPs in humans and mice, with a late component similarly modulated by the state of the targeted cortico-thalamic network. We then optogenetically dissect the underlying circuit in mice, demonstrating that the EPs late component is caused by a thalamic hyperpolarization and rebound. The magnitude of this late component correlates with bursting frequency and synchronicity of thalamic neurons, modulated by the subject's behavioral state. A simulation of the thalamo-cortical circuit highlights that both intrinsic thalamic currents as well as cortical and thalamic GABAergic neurons contribute to this response profile. We conclude that single pulse cortical stimulation engages cortico-thalamo-cortical circuits largely preserved across different species and stimulation modalities.
Collapse
Affiliation(s)
- Simone Russo
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- Department of Philosophy 'Piero Martinetti', University of Milan, Milan, Italy
- Brain and Consciousness, Allen Institute, Seattle, USA
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | | | - Giulia Furregoni
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- School of Advanced Studies, Center of Neuroscience, University of Camerino, Camerino, Italy
| | - Lydia C Marks
- Brain and Consciousness, Allen Institute, Seattle, USA
| | - Giri Krishnan
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Flavia Maria Zauli
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- Department of Philosophy 'Piero Martinetti', University of Milan, Milan, Italy
- ASST Grande Ospedale Metropolitano Niguarda, "C. Munari" Epilepsy Surgery Centre, Milan, Italy
| | - Gabriel Hassan
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- Department of Philosophy 'Piero Martinetti', University of Milan, Milan, Italy
| | - Michela Solbiati
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- ASST Grande Ospedale Metropolitano Niguarda, "C. Munari" Epilepsy Surgery Centre, Milan, Italy
| | - Piergiorgio d'Orio
- ASST Grande Ospedale Metropolitano Niguarda, "C. Munari" Epilepsy Surgery Centre, Milan, Italy
- University of Parma, Parma, 43121, Italy
| | - Ezequiel Mikulan
- Department of Health Sciences, University of Milan, Milan, Italy
| | - Simone Sarasso
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
| | - Ivana Sartori
- ASST Grande Ospedale Metropolitano Niguarda, "C. Munari" Epilepsy Surgery Centre, Milan, Italy
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, 20122, Italy
- UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan, 20122, Italy
- Azrieli Program in Brain, Mind and Consciousness, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, M5G 1M1, Canada
| | - Christof Koch
- Brain and Consciousness, Allen Institute, Seattle, USA
| | - Irene Rembado
- Brain and Consciousness, Allen Institute, Seattle, USA.
| |
Collapse
|
9
|
Rosen MC, Freedman DJ. Multiplexing of cognitive encoding by oculomotor networks leads to incidental gaze shifts. Proc Natl Acad Sci U S A 2025; 122:e2422331122. [PMID: 40198709 PMCID: PMC12012544 DOI: 10.1073/pnas.2422331122] [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: 10/28/2024] [Accepted: 02/27/2025] [Indexed: 04/10/2025] Open
Abstract
Humans and other animals are adept at learning to perform cognitively demanding behavioral tasks. Neurophysiological recordings in nonhuman primates during such tasks find that the requisite cognitive variables are encoded strongly in core oculomotor brain regions. Here, we assembled a large dataset-11 monkeys performing an abstract visual categorization task, surveyed across more than 1,000 neural recording sessions-to reveal that this produces a robust but uninstructed behavioral "tell," observed in all subjects and experiments: small, cognitively modulated eye movements. We find that these eye movements are causally linked to activity in SC but not LIP, and that they occur following transient alignment of cognitive and saccadic population coding subspaces in SC. This behavioral signature of oculomotor engagement is absent during a similar task that does not require rule-based categorization, suggesting that abstract task behaviors recruit primate oculomotor networks more strongly than previously understood.
Collapse
Affiliation(s)
- Matthew C. Rosen
- Department of Neurobiology, The University of Chicago, Chicago, IL60637
| | - David J. Freedman
- Department of Neurobiology, The University of Chicago, Chicago, IL60637
- Neuroscience Institute, The University of Chicago, Chicago, IL60637
| |
Collapse
|
10
|
Zhang R, Wang J, Cai X, Tang R, Lu HD. Dynamic grouping of ongoing activity in V1 hypercolumns. Neuroimage 2025; 310:121157. [PMID: 40120782 DOI: 10.1016/j.neuroimage.2025.121157] [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: 09/09/2024] [Revised: 02/27/2025] [Accepted: 03/17/2025] [Indexed: 03/25/2025] Open
Abstract
Neurons' spontaneous activity provides rich information about the brain. A single neuron's activity has close relationships with the local network. In order to understand such relationships, we studied the spontaneous activity of thousands of neurons in macaque V1 and V2 with two-photon calcium imaging. In V1, the ongoing activity was dominated by global fluctuations in which the activity of majority of neurons were correlated. Neurons' activity also relied on their relative locations within the local functional architectures, including ocular dominance, orientation, and color maps. Neurons with similar preferences dynamically grouped into co-activating ensembles and exhibited spatial patterns resembling the local functional maps. Different ensembles had different strengths and frequencies. This observation was consistent across all hypercolumn-sized V1 locations we examined. In V2, different imaging sites had different orientation and color features. However, the spontaneous activity in the sampled regions also correlated with the underlying functional architectures. These results indicate that functional architectures play an essential role in influencing neurons' spontaneous activity.
Collapse
Affiliation(s)
- Rui Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jiayu Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xingya Cai
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rendong Tang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Haidong D Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Department of Neurology, Zhongshan Hospital, Institute for Translational Brain Research, Fudan University, Shanghai, China.
| |
Collapse
|
11
|
Henderson MM, Serences JT, Rungratsameetaweemana N. Dynamic categorization rules alter representations in human visual cortex. Nat Commun 2025; 16:3459. [PMID: 40216798 PMCID: PMC11992282 DOI: 10.1038/s41467-025-58707-4] [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: 01/14/2024] [Accepted: 03/26/2025] [Indexed: 04/14/2025] Open
Abstract
Everyday tasks often require stimuli to be categorized dynamically, such that an identical object can elicit different responses based on the current decision rule. Traditionally, sensory regions have been viewed as separate from such context-dependent processing, functioning primarily to process incoming inputs. However, an alternative view suggests sensory regions also integrate inputs with current task goals, facilitating more efficient information relay to higher-level areas. Here we test this by asking human participants to visually categorize novel shape stimuli based on different decision boundaries. Using fMRI and multivariate analyses of retinotopically-defined visual areas, we show that cortical shape representations become more distinct across relevant decision boundaries in a context-dependent manner, with the largest changes in discriminability observed for stimuli near the decision boundary. Importantly, these modulations are associated with improved task performance. These findings demonstrate that visual cortex representations are adaptively modulated to support dynamic behavior.
Collapse
Affiliation(s)
- Margaret M Henderson
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - John T Serences
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Psychology, University of California, San Diego, La Jolla, CA, USA
| | - Nuttida Rungratsameetaweemana
- The Salk Institute for Biological Studies, La Jolla, CA, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| |
Collapse
|
12
|
Vergara P, Wang Y, Srinivasan S, Dong Z, Feng Y, Koyanagi I, Kumar D, Chérasse Y, Naoi T, Sugaya Y, Sakurai T, Kano M, Shuman T, Cai D, Yanagisawa M, Sakaguchi M. A comprehensive suite for extracting neuron signals across multiple sessions in one-photon calcium imaging. Nat Commun 2025; 16:3443. [PMID: 40216771 PMCID: PMC11992088 DOI: 10.1038/s41467-025-58817-z] [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: 05/30/2023] [Accepted: 04/01/2025] [Indexed: 04/14/2025] Open
Abstract
We developed CaliAli, a comprehensive suite designed to extract neuronal signals from one-photon calcium imaging data collected across multiple sessions in free-moving conditions in mice. CaliAli incorporates information from blood vessels and neurons to correct inter-session misalignments, making it robust against non-rigid brain deformations even after substantial changes in the field of view across sessions. This also makes CaliAli robust against high neuron overlap and changes in active neuron population across sessions. CaliAli performs computationally efficient signal extraction from concatenated video sessions that enhances the detectability of weak calcium signals. Notably, CaliAli enhanced the spatial coding accuracy of extracted hippocampal CA1 neuron activity across sessions. An optogenetic tagging experiment showed that CaliAli enhanced neuronal trackability in the dentate gyrus across a time scale of weeks. Finally, dentate gyrus neurons tracked using CaliAli exhibited stable population activity for 99 days. Overall, CaliAli advances our capacity to understand the activity dynamics of neuronal ensembles over time, which is crucial for deciphering the complex neuronal substrates of natural animal behaviors.
Collapse
Grants
- JP21zf0127005, JP23wm0525003 Japan Agency for Medical Research and Development (AMED)
- JP21zf0127005 Japan Agency for Medical Research and Development (AMED)
- 24H00894, 23H02784, 22H00469, 16H06280, 20H03552, 21H05674, 21F21080 MEXT | Japan Society for the Promotion of Science (JSPS)
- JPMJSP2124 MEXT | Japan Science and Technology Agency (JST)
- 24H00894, 21J11746, 23K19393, 24K18212 Japan Society for the Promotion of Science London (JSPS London)
- 16H06280 Japan Society for the Promotion of Science London (JSPS London)
- Takeda Science Foundation
- Uehara Memorial Foundation
- G-7 Scholarship Foundation Uehara Memorial Foundation The Mitsubishi Foundation
Collapse
Affiliation(s)
- Pablo Vergara
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan.
| | - Yuteng Wang
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
- Doctoral Program in Neuroscience, Degree Programs in Comprehensive Human Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Sakthivel Srinivasan
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Zhe Dong
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Yu Feng
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Iyo Koyanagi
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Deependra Kumar
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yoan Chérasse
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Toshie Naoi
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yuki Sugaya
- Department of Neurophysiology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, 113-0033, Japan
| | - Takeshi Sakurai
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Masanobu Kano
- Department of Neurophysiology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, 113-0033, Japan
| | - Tristan Shuman
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Denise Cai
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Masashi Yanagisawa
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
- Doctoral Program in Neuroscience, Degree Programs in Comprehensive Human Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Masanori Sakaguchi
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan.
- Doctoral Program in Neuroscience, Degree Programs in Comprehensive Human Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan.
- Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.
| |
Collapse
|
13
|
MacDowell CJ, Libby A, Jahn CI, Tafazoli S, Ardalan A, Buschman TJ. Multiplexed subspaces route neural activity across brain-wide networks. Nat Commun 2025; 16:3359. [PMID: 40204762 PMCID: PMC11982558 DOI: 10.1038/s41467-025-58698-2] [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: 05/06/2024] [Accepted: 03/28/2025] [Indexed: 04/11/2025] Open
Abstract
Cognition is flexible, allowing behavior to change on a moment-by-moment basis. Such flexibility relies on the brain's ability to route information through different networks of brain regions to perform different cognitive computations. However, the mechanisms that determine which network of regions is active are not well understood. Here, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in eight cortical and subcortical regions of mice to understand the interactions between regions. We found different dimensions within the population activity of each region were functionally connected with different cortex-wide 'subspace networks' of regions. These subspace networks were multiplexed; each region was functionally connected with multiple independent, yet overlapping, subspace networks. The subspace network that was active changed from moment-to-moment. These changes were associated with changes in the geometric relationship between the neural response within a region and the subspace dimensions: when neural responses were aligned with (i.e., projected along) a subspace dimension, neural activity was increased in the associated regions. Together, our results suggest that changing the geometry of neural representations within a brain region may allow the brain to flexibly engage different brain-wide networks, thereby supporting cognitive flexibility.
Collapse
Affiliation(s)
- Camden J MacDowell
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ, USA
| | - Alexandra Libby
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ, USA
| | - Caroline I Jahn
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ, USA
| | - Sina Tafazoli
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ, USA
| | - Adel Ardalan
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ, USA
| | - Timothy J Buschman
- Princeton Neuroscience Institute, Princeton University, Washington Rd, Princeton, NJ, USA.
- Department of Psychology, Princeton University, Washington Rd, Princeton, NJ, USA.
| |
Collapse
|
14
|
Wu S, Huang H, Wang S, Chen G, Zhou C, Yang D. Neural heterogeneity enhances reliable neural information processing: Local sensitivity and globally input-slaved transient dynamics. SCIENCE ADVANCES 2025; 11:eadr3903. [PMID: 40173217 PMCID: PMC11963962 DOI: 10.1126/sciadv.adr3903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Accepted: 02/26/2025] [Indexed: 04/04/2025]
Abstract
Cortical neuronal activity varies over time and across repeated trials, yet consistently represents stimulus features. The dynamical mechanism underlying this reliable representation and computation remains elusive. This study uncovers a mechanism for reliable neural information processing, leveraging a biologically plausible network model incorporating neural heterogeneity. First, we investigate neuronal timescale diversity, revealing that it disrupts intrinsic coherent spatiotemporal patterns, induces firing rate heterogeneity, enhances local responsive sensitivity, and aligns network activity closely with input. The system exhibits globally input-slaved transient dynamics, essential for reliable neural information processing. Other neural heterogeneities, such as nonuniform input connections, spike threshold heterogeneity, and network in-degree heterogeneity, play similar roles, highlighting the importance of neural heterogeneity in shaping consistent stimulus representation. This mechanism offers a potentially general framework for understanding neural heterogeneity in reliable computation and informs the design of reservoir computing models endowed with liquid wave reservoirs for neuromorphic computing.
Collapse
Affiliation(s)
- Shengdun Wu
- Research Centre for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou 311100, China
| | - Haiping Huang
- PMI Lab, School of Physics, Sun Yat-sen University, Guangzhou 510275, China
| | - Shengjun Wang
- Department of Physics, Shaanxi Normal University, Xi’an 710119, China
| | - Guozhang Chen
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China
| | - Changsong Zhou
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
| | - Dongping Yang
- Research Centre for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou 311100, China
| |
Collapse
|
15
|
Sun W, Winnubst J, Natrajan M, Lai C, Kajikawa K, Bast A, Michaelos M, Gattoni R, Stringer C, Flickinger D, Fitzgerald JE, Spruston N. Learning produces an orthogonalized state machine in the hippocampus. Nature 2025; 640:165-175. [PMID: 39939774 PMCID: PMC11964937 DOI: 10.1038/s41586-024-08548-w] [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: 09/21/2023] [Accepted: 12/18/2024] [Indexed: 02/14/2025]
Abstract
Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, 'state cells'). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.
Collapse
Affiliation(s)
- Weinan Sun
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, USA.
| | - Johan Winnubst
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Maanasa Natrajan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Chongxi Lai
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Koichiro Kajikawa
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Arco Bast
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Michalis Michaelos
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Rachel Gattoni
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Carsen Stringer
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Daniel Flickinger
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - James E Fitzgerald
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Nelson Spruston
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
| |
Collapse
|
16
|
Yang Y, Leopold DA, Duyn JH, Sipe GO, Liu X. Sensory Encoding Alternates With Hippocampal Ripples across Cycles of Forebrain Spiking Cascades. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025; 12:e2406224. [PMID: 40017060 PMCID: PMC12021030 DOI: 10.1002/advs.202406224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 11/08/2024] [Indexed: 03/01/2025]
Abstract
The brain's response to external events depends on its internal arousal states, which are dynamically governed by neuromodulatory systems and have recently been linked to coordinated spike timing cascades in widespread brain networks. At rest, both arousal fluctuations and spiking cascades are evident throughout the forebrain and play out over multisecond time scales. Here, by analyzing large-scale neural recording data collected by the Allen Institute, it is demonstrated that these intrinsic processes persist across the mouse brain even during periods of continuous visual stimulation. In the stationary animal, each quasi-periodic cascade cycle systematically influenced 1) the efficacy of encoding in visually responsive brain areas and 2) the incidence of memory-related hippocampal ripples. During this cycle, the phase of high arousal is marked by high efficiency in visual encoding whereas the phase of low arousal is marked by the occurrence of hippocampal ripples. However, during bouts of active locomotion, this cycle is abolished and brain maintained a state of elevated visual coding efficiency, with ripples being nearly absent. It is hypothesized that the infra-slow cascade dynamics reflect an adaptive cycle of alternating exteroceptive sensory sampling and internal mnemonic function that persistently pervades the forebrain, only stopping during active exploration of the environment.
Collapse
Affiliation(s)
- Yifan Yang
- Department of Biomedical EngineeringThe Pennsylvania State UniversityUniversity ParkPA16802USA
| | - David A. Leopold
- Neurophysiology Imaging FacilityNational Institute of Mental HealthNational Institute of Neurological. Disorders and Strokeand National Eye InstituteNational Institutes of HealthBethesdaMD20892USA
- Section on Cognitive Neurophysiology and ImagingSystems Neurodevelopment LaboratoryNational Institute of Mental HealthNational Institutes of HealthBethesdaMD20892USA
| | - Jeff H. Duyn
- Advanced MRI SectionLaboratory of Functional and Molecular ImagingNational Institute of Neurological Disorders and StrokeNational Institutes of HealthBethesdaMD20892USA
| | - Grayson O. Sipe
- Department of BiologyThe Pennsylvania State UniversityUniversity ParkPA16802USA
| | - Xiao Liu
- Department of Biomedical EngineeringThe Pennsylvania State UniversityUniversity ParkPA16802USA
- Institute for Computational and Data SciencesThe Pennsylvania State UniversityUniversity ParkPA16802USA
| |
Collapse
|
17
|
Ding Z, Fahey PG, Papadopoulos S, Wang EY, Celii B, Papadopoulos C, Chang A, Kunin AB, Tran D, Fu J, Ding Z, Patel S, Ntanavara L, Froebe R, Ponder K, Muhammad T, Bae JA, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yin W, Yu SC, Yatsenko D, Froudarakis E, Sinz F, Josić K, Rosenbaum R, Seung HS, Collman F, da Costa NM, Reid RC, Walker EY, Pitkow X, Reimer J, Tolias AS. Functional connectomics reveals general wiring rule in mouse visual cortex. Nature 2025; 640:459-469. [PMID: 40205211 PMCID: PMC11981947 DOI: 10.1038/s41586-025-08840-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 02/24/2025] [Indexed: 04/11/2025]
Abstract
Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected1-8; however, broader connectivity rules remain unknown. Here we leverage the millimetre-scale MICrONS dataset to analyse synaptic connectivity and functional properties of neurons across cortical layers and areas. Our results reveal that neurons with similar response properties are preferentially connected within and across layers and areas-including feedback connections-supporting the universality of 'like-to-like' connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections beyond what could be explained by the proximity of axons and dendrites. We also discovered a higher-order rule whereby postsynaptic neuron cohorts downstream of presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Recurrent neural networks trained on a simple classification task develop connectivity patterns that mirror both pairwise and higher-order rules, with magnitudes similar to those in MICrONS data. Ablation studies in these recurrent neural networks reveal that disrupting like-to-like connections impairs performance more than disrupting random connections. These findings suggest that these connectivity principles may have a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.
Collapse
Affiliation(s)
- Zhuokun Ding
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology and Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Bio-X, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Paul G Fahey
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology and Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Bio-X, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Stelios Papadopoulos
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology and Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Bio-X, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Eric Y Wang
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Brendan Celii
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Christos Papadopoulos
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Andersen Chang
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Alexander B Kunin
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Mathematics, Creighton University, Omaha, NE, USA
| | - Dat Tran
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Jiakun Fu
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Salk Institute for Biological Studies, La Jolla, CA, United States
| | - Zhiwei Ding
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Saumil Patel
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology and Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Bio-X, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Lydia Ntanavara
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology and Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Bio-X, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Rachel Froebe
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology and Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Bio-X, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Kayla Ponder
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Taliah Muhammad
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, NJ, USA
| | | | | | | | | | - Manuel A Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Erick Cobos
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Dan Kapner
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Sam Kinn
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kai Li
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, NJ, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Dimitri Yatsenko
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- DataJoint, Houston, TX, USA
| | - Emmanouil Froudarakis
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Basic Sciences, Faculty of Medicine, University of Crete, Heraklion, Greece
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Fabian Sinz
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Institute for Bioinformatics and Medical Informatics, University Tübingen, Tübingen, Germany
- Institute for Computer Science and Campus Institute Data Science, University Göttingen, Göttingen, Germany
| | - Krešimir Josić
- Departments of Mathematics, Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Robert Rosenbaum
- Departments of Applied and Computational Mathematics and Statistics and Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Edgar Y Walker
- Department of Neurobiology and Biophysics, University of Washington, Seattle, WA, USA
- Computational Neuroscience Center, University of Washington, Seattle, WA, USA
| | - Xaq Pitkow
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jacob Reimer
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
| | - Andreas S Tolias
- Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Department of Ophthalmology and Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Bio-X, Stanford University, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| |
Collapse
|
18
|
Veillette JP, Chao AF, Nith R, Lopes P, Nusbaum HC. Overlapping Cortical Substrate of Biomechanical Control and Subjective Agency. J Neurosci 2025; 45:e1673242025. [PMID: 40127938 PMCID: PMC12044032 DOI: 10.1523/jneurosci.1673-24.2025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Revised: 02/13/2025] [Accepted: 03/06/2025] [Indexed: 03/26/2025] Open
Abstract
Every movement requires the nervous system to solve a complex biomechanical control problem, but this process is mostly veiled from one's conscious awareness. Simultaneously, we also have conscious experience of controlling our movements-our sense of agency (SoA). Whether SoA corresponds to those neural representations that implement actual neuromuscular control is an open question with ethical, medical, and legal implications. If SoA is the conscious experience of control, this predicts that SoA can be decoded from the same brain structures that implement the so-called "inverse dynamics" computations for planning movement. We correlated human (male and female) fMRI measurements during hand movements with the internal representations of a deep neural network (DNN) performing the same hand control task in a biomechanical simulation-revealing detailed cortical encodings of sensorimotor states, idiosyncratic to each subject. We then manipulated SoA by usurping control of participants' muscles via electrical stimulation, and found that the same voxels which were best explained by modeled inverse dynamics representations-which, strikingly, were located in canonically visual areas-also predicted SoA. Importantly, model-brain correspondences and robust SoA decoding could both be achieved within single subjects, enabling relationships between motor representations and awareness to be studied at the level of the individual.Significance Statement The inherent complexity of biomechanical control problems is belied by the seeming simplicity of directing movements in our subjective experience. This aspect of our experience suggests we have limited conscious access to the neural and mental representations involved in controlling the body - but of which of the many possible representations are we, in fact, aware? Understanding which motor control representations percolate into awareness has taken on increasing importance as emerging neural interface technologies push the boundaries of human autonomy. In our study, we leverage machine learning models that have learned to control simulated bodies to localize biomechanical control representations in the brain. Then, we show that these brain regions predict perceived agency over the musculature during functional electrical stimulation.
Collapse
Affiliation(s)
- John P Veillette
- Department of Psychology, University of Chicago, Chicago, IL 60637
| | - Alfred F Chao
- Department of Psychology, University of Chicago, Chicago, IL 60637
| | - Romain Nith
- Department of Computer Science, University of Chicago, Chicago, IL 60637
| | - Pedro Lopes
- Department of Computer Science, University of Chicago, Chicago, IL 60637
| | - Howard C Nusbaum
- Department of Psychology, University of Chicago, Chicago, IL 60637
| |
Collapse
|
19
|
Peelman K, Haider B. Environmental context influences visual processing in thalamus. Curr Biol 2025; 35:1422-1430.e5. [PMID: 40049173 PMCID: PMC11952198 DOI: 10.1016/j.cub.2025.02.009] [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: 07/26/2024] [Revised: 12/23/2024] [Accepted: 02/05/2025] [Indexed: 03/12/2025]
Abstract
Behavioral state modulates neural activity throughout the visual system.1,2,3 This is largely due to changes in arousal that alter internal brain states.4,5,6,7,8,9,10 Much is known about how these internal factors influence visual processing,7,8,9,10,11 but comparatively less is known about the role of external environmental contexts.12 Environmental contexts can promote or prevent certain actions,13 and it remains unclear if and how this affects visual processing. Here, we addressed this question in the thalamus of awake, head-fixed mice while they viewed stimuli but remained stationary in two different environmental contexts: either a cylindrical tube or a circular running wheel that enabled locomotion. We made silicon probe recordings in the dorsal lateral geniculate nucleus (dLGN) while simultaneously measuring multiple metrics of arousal changes so that we could control for these across contexts. We found surprising differences in spatial and temporal processing in dLGN across contexts. The wheel context (versus tube) showed elevated baseline activity and faster but less spatially selective visual responses; however, these visual processing differences disappeared if the wheel no longer enabled locomotion. Our results reveal an unexpected influence of the physical environmental context on fundamental aspects of early visual processing, even in otherwise identical states of alertness and stillness.
Collapse
Affiliation(s)
- Kayla Peelman
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Bilal Haider
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
| |
Collapse
|
20
|
Efron B, Ntelezos A, Katz Y, Lampl I. Detection and neural encoding of whisker-generated sounds in mice. Curr Biol 2025; 35:1211-1226.e8. [PMID: 39978346 DOI: 10.1016/j.cub.2025.01.061] [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: 10/09/2024] [Revised: 01/07/2025] [Accepted: 01/28/2025] [Indexed: 02/22/2025]
Abstract
The vibrissa system of mice and other rodents enables active sensing via whisker movements and is traditionally considered a purely tactile system. Here, we ask whether whisking against objects produces audible sounds and whether mice are capable of perceiving these sounds. We found that whisking by head-fixed mice against objects produces audible sounds well within their hearing range. We recorded neural activity in the auditory cortex of mice in which we had abolished vibrissae tactile sensation and found that the firing rate of auditory neurons was strongly modulated by whisking against objects. Furthermore, the object's identity could be reliably decoded from the population's neuronal activity and closely matched the decoding patterns derived from sounds that were recorded simultaneously, suggesting that neuronal activity reflects acoustic information. Lastly, trained mice, in which vibrissae tactile sensation was abolished, were able to accurately identify objects solely based on the sounds produced during whisking. Our results suggest that, beyond its traditional role as a tactile sensory system, the vibrissa system of rodents engages both tactile and auditory modalities in a multimodal manner during active exploration.
Collapse
Affiliation(s)
- Ben Efron
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Athanasios Ntelezos
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Yonatan Katz
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ilan Lampl
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
| |
Collapse
|
21
|
Huang JY, Hess M, Bajpai A, Li X, Hobson LN, Xu AJ, Barton SJ, Lu HC. From initial formation to developmental refinement: GABAergic inputs shape neuronal subnetworks in the primary somatosensory cortex. iScience 2025; 28:112104. [PMID: 40129704 PMCID: PMC11930745 DOI: 10.1016/j.isci.2025.112104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2024] [Revised: 01/07/2025] [Accepted: 02/21/2025] [Indexed: 03/26/2025] Open
Abstract
Neuronal subnetworks, also known as ensembles, are functional units formed by interconnected neurons for information processing and encoding in the adult brain. Our study investigates the establishment of neuronal subnetworks in the mouse primary somatosensory (S1) cortex from postnatal days (P)11 to P21 using in vivo two-photon calcium imaging. We found that at P11, neuronal activity was highly synchronized but became sparser by P21. Clustering analyses revealed that while the number of subnetworks remained constant, their activity patterns became more distinct, with increased coherence, independent of cortical layer or sex. Furthermore, the coherence of neuronal activity within individual subnetworks significantly increased when synchrony frequencies were reduced by augmenting gamma-aminobutyric acid (GABA)ergic activity at P15/16, a period when the neuronal subnetworks were still maturing. Together, these findings indicate the early formation of subnetworks and underscore the pivotal roles of GABAergic inputs in modulating S1 neuronal subnetworks.
Collapse
Affiliation(s)
- Jui-Yen Huang
- The Gill Institute for Neuroscience, Indiana University, Bloomington, IN 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA
| | - Michael Hess
- The Gill Institute for Neuroscience, Indiana University, Bloomington, IN 47405, USA
| | - Abhinav Bajpai
- Research Technologies, Indiana University, Bloomington, IN 47408, USA
| | - Xuan Li
- The Gill Institute for Neuroscience, Indiana University, Bloomington, IN 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Liam N. Hobson
- The Gill Institute for Neuroscience, Indiana University, Bloomington, IN 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Ashley J. Xu
- The Gill Institute for Neuroscience, Indiana University, Bloomington, IN 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA
| | - Scott J. Barton
- The Gill Institute for Neuroscience, Indiana University, Bloomington, IN 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Hui-Chen Lu
- The Gill Institute for Neuroscience, Indiana University, Bloomington, IN 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA
| |
Collapse
|
22
|
Cerquetella C, Gontier C, Forro T, Pfister JP, Ciocchi S. Scaling of Ventral Hippocampal Activity during Anxiety. J Neurosci 2025; 45:e1128242025. [PMID: 39870526 PMCID: PMC11924894 DOI: 10.1523/jneurosci.1128-24.2025] [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: 06/14/2024] [Revised: 01/07/2025] [Accepted: 01/19/2025] [Indexed: 01/29/2025] Open
Abstract
The hippocampus supports a multiplicity of functions, with the dorsal region contributing to spatial representations and memory and the ventral hippocampus (vH) being primarily involved in emotional processing. While spatial encoding has been extensively investigated, how the vH activity is tuned to emotional states, e.g., to different anxiety levels, is not well understood. We developed an adjustable linear track maze for male mice with which we could induce a scaling of behavioral anxiety levels within the same spatial environment. Using in vivo single-unit recordings, optogenetic manipulations, and population-level analysis, we examined the changes and causal effects of vH activity at different anxiety levels. We found that anxiogenic experiences activated the vH and that this activity scaled with increasing anxiety levels. We identified two processes that contributed to this scaling of anxiety-related activity: increased tuning and successive remapping of neurons to the anxiogenic compartment. Moreover, optogenetic inhibition of the vH reduced anxiety across different levels, while anxiety-related activity scaling could be decoded using a linear classifier. Collectively, our findings position the vH as a critical limbic region that functions as an "anxiometer" by scaling its activity based on perceived anxiety levels. Our discoveries go beyond the traditional theory of cognitive maps in the hippocampus underlying spatial navigation and memory, by identifying hippocampal mechanisms selectively regulating anxiety.
Collapse
Affiliation(s)
- Carlo Cerquetella
- Laboratory of Systems Neuroscience, Department of Physiology, University of Bern, Bern 3012, Switzerland
| | - Camille Gontier
- Theoretical Neuroscience Group, Department of Physiology, University of Bern, Bern 3012, Switzerland
- Rehab Neural Engineering Labs, Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania 15219
| | - Thomas Forro
- Laboratory of Systems Neuroscience, Department of Physiology, University of Bern, Bern 3012, Switzerland
| | - Jean-Pascal Pfister
- Theoretical Neuroscience Group, Department of Physiology, University of Bern, Bern 3012, Switzerland
| | - Stéphane Ciocchi
- Laboratory of Systems Neuroscience, Department of Physiology, University of Bern, Bern 3012, Switzerland
| |
Collapse
|
23
|
de Alteriis G, Sherwood O, Ciaramella A, Leech R, Cabral J, Turkheimer FE, Expert P. DySCo: A general framework for dynamic functional connectivity. PLoS Comput Biol 2025; 21:e1012795. [PMID: 40053563 PMCID: PMC11902199 DOI: 10.1371/journal.pcbi.1012795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 03/12/2025] [Accepted: 01/14/2025] [Indexed: 03/09/2025] Open
Abstract
A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional brain recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across brain areas change over time. However, the main dFC approaches have been developed and applied mostly empirically, lacking a common theoretical framework and a clear view on the interpretation of the results derived from the dFC matrices. Moreover, the dFC community has not been using the most efficient algorithms to compute and process the matrices efficiently, which has prevented dFC from showing its full potential with high-dimensional datasets and/or real-time applications. In this paper, we introduce the Dynamic Symmetric Connectivity Matrix analysis framework (DySCo), with its associated repository. DySCo is a framework that presents the most commonly used dFC measures in a common language and implements them in a computationally efficient way. This allows the study of brain activity at different spatio-temporal scales, down to the voxel level. DySCo provides a single framework that allows to: (1) Use dFC as a tool to capture the spatio-temporal interaction patterns of data in a form that is easily translatable across different imaging modalities. (2) Provide a comprehensive set of measures to quantify the properties and evolution of dFC over time: the amount of connectivity, the similarity between matrices, and their informational complexity. By using and combining the DySCo measures it is possible to perform a full dFC analysis. (3) Leverage the Temporal Covariance EVD algorithm (TCEVD) to compute and store the eigenvectors and values of the dFC matrices, and then also compute the DySCo measures from the EVD. Developing the framework in the eigenvector space is orders of magnitude faster and more memory efficient than naïve algorithms in the matrix space, without loss of information. The methodology developed here is validated on both a synthetic dataset and a rest/N-back task experimental paradigm from the fMRI Human Connectome Project dataset. We show that all the proposed measures are sensitive to changes in brain configurations and consistent across time and subjects. To illustrate the computational efficiency of the DySCo toolbox, we performed the analysis at the voxel level, a task which is computationally demanding but easily afforded by the TCEVD.
Collapse
Affiliation(s)
- Giuseppe de Alteriis
- Institute of Psychiatry, Psychology and Neuroscience (IoPPN) King’s College London, London, United Kingdom
| | - Oliver Sherwood
- Institute of Psychiatry, Psychology and Neuroscience (IoPPN) King’s College London, London, United Kingdom
| | | | - Robert Leech
- Institute of Psychiatry, Psychology and Neuroscience (IoPPN) King’s College London, London, United Kingdom
| | - Joana Cabral
- Life and Health Sciences Research Institute, University of Minho, Braga, Portugal
| | - Federico E Turkheimer
- Institute of Psychiatry, Psychology and Neuroscience (IoPPN) King’s College London, London, United Kingdom
| | - Paul Expert
- Global Business School for Health, UCL, London, United Kingdom
| |
Collapse
|
24
|
Giesbrecht B, Bullock T, Garrett J. Physically activated modes of attentional control. Trends Cogn Sci 2025; 29:295-307. [PMID: 39690081 DOI: 10.1016/j.tics.2024.11.006] [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: 08/01/2024] [Revised: 11/14/2024] [Accepted: 11/18/2024] [Indexed: 12/19/2024]
Abstract
As we navigate through the day, our attentional control processes are constantly challenged by changing sensory information, goals, expectations, and motivations. At the same time, our bodies and brains are impacted by changes in global physiological state that can influence attentional processes. Based on converging lines of evidence from brain recordings in physically active humans and nonhumans, we propose a new framework incorporating at least two physically activated modes of attentional control in humans: altered gain control and differential neuromodulation of control networks. We discuss the implications of this framework for understanding a broader range of states and cognitive functions studied both in the laboratory and in the wild.
Collapse
Affiliation(s)
- Barry Giesbrecht
- Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA 93106, USA.
| | - Tom Bullock
- Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA 93106, USA
| | - Jordan Garrett
- Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106, USA; Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA 93106, USA
| |
Collapse
|
25
|
Chen R, Nie P, Ma L, Wang G. Organizational Principles of the Primate Cerebral Cortex at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025; 12:e2411041. [PMID: 39846374 PMCID: PMC11923899 DOI: 10.1002/advs.202411041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Revised: 12/27/2024] [Indexed: 01/24/2025]
Abstract
The primate cerebral cortex, the major organ for cognition, consists of an immense number of neurons. However, the organizational principles governing these neurons remain unclear. By accessing the single-cell spatial transcriptome of over 25 million neuron cells across the entire macaque cortex, it is discovered that the distribution of neurons within cortical layers is highly non-random. Strikingly, over three-quarters of these neurons are located in distinct neuronal clusters. Within these clusters, different cell types tend to collaborate rather than function independently. Typically, excitatory neuron clusters mainly consist of excitatory-excitatory combinations, while inhibitory clusters primarily contain excitatory-inhibitory combinations. Both cluster types have roughly equal numbers of neurons in each layer. Importantly, most excitatory and inhibitory neuron clusters form spatial partnerships, indicating a balanced local neuronal network and correlating with specific functional regions. These organizational principles are conserved across mouse cortical regions. These findings suggest that different brain regions of the cortex may exhibit similar mechanisms at the neuronal population level.
Collapse
Affiliation(s)
- Renrui Chen
- CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai200031China
| | - Pengxing Nie
- CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai200031China
| | - Liangxiao Ma
- CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai200031China
| | - Guang‐Zhong Wang
- CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai200031China
| |
Collapse
|
26
|
Lowet AS, Zheng Q, Meng M, Matias S, Drugowitsch J, Uchida N. An opponent striatal circuit for distributional reinforcement learning. Nature 2025; 639:717-726. [PMID: 39972123 PMCID: PMC12007193 DOI: 10.1038/s41586-024-08488-5] [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: 01/02/2024] [Accepted: 12/04/2024] [Indexed: 02/21/2025]
Abstract
Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards-an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2, but little is known about whether, where and how neurons in this circuit encode information about higher-order moments of reward distributions3. Here, to fill this gap, we used high-density probes (Neuropixels) to record striatal activity from mice performing a classical conditioning task in which reward mean, reward variance and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons-D1 and D2-contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 medium spiny neurons4-9 to reap the computational benefits of distributional RL.
Collapse
Affiliation(s)
- Adam S Lowet
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Program in Neuroscience, Harvard University, Boston, MA, USA
| | - Qiao Zheng
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Melissa Meng
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Sara Matias
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Jan Drugowitsch
- Center for Brain Science, Harvard University, Cambridge, MA, USA.
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
| | - Naoshige Uchida
- Center for Brain Science, Harvard University, Cambridge, MA, USA.
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
| |
Collapse
|
27
|
Hasnain MA, Birnbaum JE, Ugarte Nunez JL, Hartman EK, Chandrasekaran C, Economo MN. Separating cognitive and motor processes in the behaving mouse. Nat Neurosci 2025; 28:640-653. [PMID: 39905210 DOI: 10.1038/s41593-024-01859-1] [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: 08/22/2023] [Accepted: 11/21/2024] [Indexed: 02/06/2025]
Abstract
The cognitive processes supporting complex animal behavior are closely associated with movements responsible for critical processes, such as facial expressions or the active sampling of our environments. These movements are strongly related to neural activity across much of the brain and are often highly correlated with ongoing cognitive processes. A fundamental issue for understanding the neural signatures of cognition and movements is whether cognitive processes are separable from related movements or if they are driven by common neural mechanisms. Here we demonstrate how the separability of cognitive and motor processes can be assessed and, when separable, how the neural dynamics associated with each component can be isolated. We designed a behavioral task in mice that involves multiple cognitive processes, and we show that dynamics commonly taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using a novel approach for subspace decomposition, we find that they exhibit distinct dynamical trajectories and are encoded by largely separate populations of cells. Accurately isolating dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function.
Collapse
Affiliation(s)
- Munib A Hasnain
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Center for Neurophotonics, Boston University, Boston, MA, USA
| | - Jaclyn E Birnbaum
- Center for Neurophotonics, Boston University, Boston, MA, USA
- Graduate Program for Neuroscience, Boston University, Boston, MA, USA
| | | | - Emma K Hartman
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Chandramouli Chandrasekaran
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Department of Neurobiology & Anatomy, Boston University, Boston, MA, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, USA
| | - Michael N Economo
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Center for Neurophotonics, Boston University, Boston, MA, USA.
- Center for Systems Neuroscience, Boston University, Boston, MA, USA.
| |
Collapse
|
28
|
Song H, Park J, Rosenberg MD. Understanding cognitive processes across spatial scales of the brain. Trends Cogn Sci 2025; 29:282-294. [PMID: 39500686 DOI: 10.1016/j.tics.2024.09.009] [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: 04/22/2024] [Revised: 09/23/2024] [Accepted: 09/24/2024] [Indexed: 03/08/2025]
Abstract
Cognition arises from neural operations at multiple spatial scales, from individual neurons to large-scale networks. Despite extensive research on coding principles and emergent cognitive processes across brain areas, investigation across scales has been limited. Here, we propose ways to test the idea that different cognitive processes emerge from distinct information coding principles at various scales, which collectively give rise to complex behavior. This approach involves comparing brain-behavior associations and the underlying neural geometry across scales, alongside an investigation of global and local scale interactions. Bridging findings across species and techniques through open science and collaborations is essential to comprehensively understand the multiscale brain and its functions.
Collapse
Affiliation(s)
- Hayoung Song
- Department of Psychology, University of Chicago, Chicago, IL, USA; Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA.
| | - JeongJun Park
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA.
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL, USA; Neuroscience Institute, University of Chicago, Chicago, IL, USA; Institute for Mind and Biology, University of Chicago, Chicago, IL, USA.
| |
Collapse
|
29
|
Waters J. A large field of view 2- and 3-photon microscope. LIGHT, SCIENCE & APPLICATIONS 2025; 14:106. [PMID: 40016184 PMCID: PMC11868528 DOI: 10.1038/s41377-025-01780-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2025]
Abstract
A new multiphoton fluorescence microscope has been developed, offering cellular resolution across a large field of view deep within biological tissues. This opens new possibilities across a range of biological sciences, particularly within neuroscience where optical approaches can reveal signaling in real time throughout an extended network of cells distributed through the brain of an awake, behaving mouse.
Collapse
Affiliation(s)
- Jack Waters
- Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA, 98109, USA.
| |
Collapse
|
30
|
Park J, Holmes CD, Snyder LH. Compositional architecture: Orthogonal neural codes for task context and spatial memory in prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.02.25.640211. [PMID: 40060470 PMCID: PMC11888474 DOI: 10.1101/2025.02.25.640211] [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: 03/15/2025]
Abstract
The prefrontal cortex (PFC) is crucial for maintaining working memory across diverse cognitive tasks, yet how it adapts to varying task demands remains unclear. Compositional theories propose that cognitive processes in neural network rely on shared components that can be reused to support different behaviors. However, previous studies have suggested that working memory components are task specific, challenging this framework. Here, we revisit this question using a population-based approach. We recorded neural activity in macaque monkeys performing two spatial working memory tasks with opposing goals: one requiring movement toward previously presented spatial locations (look task) and the other requiring avoidance of those locations (no-look task). Despite differences in task demands, we found that spatial memory representations were largely conserved at the population level, with a common low-dimensional neural subspace encoding memory across both tasks. In parallel, task identity was encoded in an orthogonal subspace, providing a stable and independent representation of contextual information. These results provide neural evidence for a compositional model of working memory, where representational geometry enables the efficient and flexible reuse of mnemonic codes across behavioral contexts while maintaining an independent representation of context.
Collapse
Affiliation(s)
- JeongJun Park
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
| | - Charles D Holmes
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
- Department of Cognitive Science, University of California San Diego, San Diego, CA, United States
| | - Lawrence H Snyder
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
| |
Collapse
|
31
|
Neske GT, Cardin JA. Higher-order thalamic input to cortex selectively conveys state information. Cell Rep 2025; 44:115292. [PMID: 39937647 PMCID: PMC11920878 DOI: 10.1016/j.celrep.2025.115292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 10/09/2024] [Accepted: 01/17/2025] [Indexed: 02/14/2025] Open
Abstract
Communication among neocortical areas is largely thought to be mediated by long-range synaptic interactions between cortical neurons, with the thalamus providing only an initial relay of information from the sensory periphery. Higher-order thalamic nuclei receive strong synaptic inputs from the cortex and send robust projections back to other cortical areas, providing a distinct and potentially critical route for corticocortical communication. However, the relative contributions of corticocortical and thalamocortical inputs to higher-order cortical function remain unclear. Using imaging of neurons and axon terminals in combination with optogenetic manipulations, we find that the higher-order visual thalamus of mice has a unique impact on the posterior medial visual cortex (PM). Whereas corticocortical projections from lower cortical areas convey robust visual information to PM, higher-order thalamocortical projections convey information about global arousal state. Together, these findings suggest a key role for the higher-order thalamus in providing contextual signals that may flexibly modulate cortical sensory processing.
Collapse
Affiliation(s)
- Garrett T Neske
- Department of Neuroscience, Kavli Institute for Neuroscience, Wu Tsai Neuroscience Institute, Yale University, New Haven, CT, USA
| | - Jessica A Cardin
- Department of Neuroscience, Kavli Institute for Neuroscience, Wu Tsai Neuroscience Institute, Yale University, New Haven, CT, USA.
| |
Collapse
|
32
|
Ye J, Xu Y, Huang K, Wang X, Wang L, Wang F. Hierarchical behavioral analysis framework as a platform for standardized quantitative identification of behaviors. Cell Rep 2025; 44:115239. [PMID: 40010299 DOI: 10.1016/j.celrep.2025.115239] [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: 08/21/2024] [Revised: 11/19/2024] [Accepted: 01/07/2025] [Indexed: 02/28/2025] Open
Abstract
Behavior is composed of modules that operate based on inherent logic. Understanding behavior and its neural mechanisms is facilitated by clear structural behavioral analysis. Here, we developed a hierarchical behavioral analysis framework (HBAF) that efficiently reveals the organizational logic of these modules by analyzing high-dimensional behavioral data. By creating a spontaneous behavior atlas for male and female mice, we discovered that spontaneous behavior patterns are hardwired, with sniffing serving as the hub node for movement transitions. The sniffing-to-grooming ratio accurately distinguished the spontaneous behavioral states in a high-throughput manner. These states are influenced by emotional status, circadian rhythms, and lighting conditions, leading to unique behavioral characteristics, spatiotemporal features, and dynamic patterns. By implementing the straightforward and achievable spontaneous behavior paradigm, HBAF enables swift and accurate assessment of animal behavioral states and bridges the gap between a theoretical understanding of the behavioral structure and practical analysis using comprehensive multidimensional behavioral information.
Collapse
Affiliation(s)
- Jialin Ye
- Shenzhen Key Laboratory of Neuropsychiatric Modulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yang Xu
- Shenzhen Key Laboratory of Neuropsychiatric Modulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Kang Huang
- Shenzhen Key Laboratory of Neuropsychiatric Modulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xinyu Wang
- Shenzhen Key Laboratory of Neuropsychiatric Modulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Liping Wang
- Shenzhen Key Laboratory of Neuropsychiatric Modulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 101408, China.
| | - Feng Wang
- Shenzhen Key Laboratory of Neuropsychiatric Modulation, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, the Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 101408, China.
| |
Collapse
|
33
|
Meier AM, D'Souza RD, Ji W, Han EB, Burkhalter A. Interdigitating Modules for Visual Processing During Locomotion and Rest in Mouse V1. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.02.21.639505. [PMID: 40060542 PMCID: PMC11888233 DOI: 10.1101/2025.02.21.639505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/20/2025]
Abstract
Layer 1 of V1 has been shown to receive locomotion-related signals from the dorsal lateral geniculate (dLGN) and lateral posterior (LP) thalamic nuclei (Roth et al., 2016). Inputs from the dLGN terminate in M2+ patches while inputs from LP target M2- interpatches (D'Souza et al., 2019) suggesting that motion related signals are processed in distinct networks. Here, we investigated by calcium imaging in head-fixed awake mice whether L2/3 neurons underneath L1 M2+ and M2- modules are differentially activated by locomotion, and whether distinct networks of feedback connections from higher cortical areas to L1 may contribute to these differences. We found that strongly locomotion-modulated cell clusters during visual stimulation were aligned with M2- interpatches, while weakly modulated cells clustered under M2+ patches. Unlike M2+ patch cells, pairs of M2- interpatch cells showed increased correlated variability of calcium transients when the sites in the visuotopic map were far apart, suggesting that activity is integrated across large parts of the visual field. Pathway tracing further suggests that strong locomotion modulation in L2/3 M2- interpatch cells of V1 relies on looped, like-to-like networks between apical dendrites of MOs-, PM- and RSP-projecting neurons and feedback input from these areas to L1. M2- interpatches receive strong inputs from SST neurons, suggesting that during locomotion these interneurons influence the firing of specific subnetworks by controlling the excitability of apical dendrites in M2- interpatches.
Collapse
Affiliation(s)
- A M Meier
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110; USA
| | - R D D'Souza
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110; USA
| | - W Ji
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110; USA
| | - E B Han
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110; USA
| | - A Burkhalter
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110; USA
| |
Collapse
|
34
|
Failor SW, Carandini M, Harris KD. Visual experience orthogonalizes visual cortical stimulus responses via population code transformation. Cell Rep 2025; 44:115235. [PMID: 39888718 DOI: 10.1016/j.celrep.2025.115235] [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: 07/15/2024] [Revised: 09/26/2024] [Accepted: 01/06/2025] [Indexed: 02/02/2025] Open
Abstract
Sensory and behavioral experience can alter visual cortical stimulus coding, but the precise form of this plasticity is unclear. We measured orientation tuning in 4,000-neuron populations of mouse V1 before and after training on a visuomotor task. Changes to single-cell tuning curves appeared complex, including development of asymmetries and of multiple peaks. Nevertheless, these complex tuning curve transformations can be explained by a simple equation: a convex transformation suppressing responses to task stimuli specifically in cells responding at intermediate levels. The strength of the transformation varies across trials, suggesting a dynamic circuit mechanism rather than static synaptic plasticity. The transformation results in sparsening and orthogonalization of population codes for task stimuli. It cannot improve the performance of an optimal stimulus decoder, which is already perfect even for naive codes, but it improves the performance of a suboptimal decoder model with inductive bias as might be found in downstream readout circuits.
Collapse
Affiliation(s)
- Samuel W Failor
- UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.
| |
Collapse
|
35
|
Mao Y, Zhang M, Peng X, Liu Y, Liu Y, Xia Q, Luo B, Chen L, Zhang Z, Wang Y, Wang H. Cross-modal cortical circuit for sound sensitivity in neuropathic pain. Curr Biol 2025; 35:831-842.e5. [PMID: 39889698 DOI: 10.1016/j.cub.2024.12.044] [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: 09/05/2024] [Revised: 11/26/2024] [Accepted: 12/18/2024] [Indexed: 02/03/2025]
Abstract
Hyperacusis, exaggerated sensitivity to sound, frequently accompanies chronic pain in humans, suggesting interactions between different sensory systems in the brain. However, the neural mechanisms underlying this comorbidity remain largely unexplored. In this study, behavioral tests measuring sound-evoked pupil dilation and reaction times to lick water following auditory stimuli showed hyperacusis-like behaviors in neuropathic pain model mice. Through viral tracing, fiber photometry, and multi-electrode recordings, we identified glutamatergic projections from primary somatosensory cortex (S1HLGlu) to the auditory cortex (ACx) that participate in amplifying sound-evoked neuronal activity following spared nerve injury in the hindlimb. Chemo- or optogenetic manipulation and electrophysiology recordings confirmed that the S1HLGlu → ACx pathway is essential for this heightened response to sound. Specifically, activating this pathway intensified glutamatergic neuronal activity in the ACx and induced hyperacusis-like behaviors, while chemogenetic suppression reversed these effects in neuropathic pain model mice. These findings illustrate the mechanism by which central gain increases in the ACx of neuropathic pain mice, improving our understanding of cross-modal sensory system interactions and suggesting circuit pathway targets for developing interventions to treat pain-associated hyperacusis in clinic.
Collapse
Affiliation(s)
- Yunfeng Mao
- Department of Anesthesiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Mingjun Zhang
- Department of Anesthesiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Xiaoqi Peng
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei 230022, China; School of Basic Medical Sciences, Anhui Medical University, Hefei 230022, China
| | - Yi Liu
- China High Magnetic Field Laboratory, CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, HFIPS, Hefei 230031, China
| | - Yehao Liu
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230022, China
| | - Qianhui Xia
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230022, China
| | - Bin Luo
- Auditory Research Laboratory, Department of Neurobiology and Biophysics, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Lin Chen
- Auditory Research Laboratory, Department of Neurobiology and Biophysics, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Zhi Zhang
- Department of Anesthesiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; School of Basic Medical Sciences, Anhui Medical University, Hefei 230022, China; Center for Advance Interdisciplinary Science and Biomedicine of IHM, Hefei 230026, China.
| | - Yuanyin Wang
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei 230022, China.
| | - Haitao Wang
- College & Hospital of Stomatology, Anhui Medical University, Key Laboratory of Oral Diseases Research of Anhui Province, Hefei 230022, China.
| |
Collapse
|
36
|
Zang B, Sun T, Lu Y, Zhang Y, Wang G, Wan S. Tensor-powered insights into neural dynamics. Commun Biol 2025; 8:298. [PMID: 39994447 PMCID: PMC11850929 DOI: 10.1038/s42003-025-07711-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 02/10/2025] [Indexed: 02/26/2025] Open
Abstract
The complex spatiotemporal dynamics of neurons encompass a wealth of information relevant to perception and decision-making, making the decoding of neural activity a central focus in neuroscience research. Traditional machine learning or deep learning-based neural information modeling approaches have achieved significant results in decoding. Nevertheless, such methodologies require the vectorization of data, a process that disrupts the intrinsic relationships inherent in high-dimensional spaces, consequently impeding their capability to effectively process information in high-order tensor domains. In this paper, we introduce a novel decoding approach, namely the Least Squares Sport Tensor Machine (LS-STM), which is based on tensor space and represents a tensorized improvement over traditional vector learning frameworks. In extensive evaluations using human and mouse data, our results demonstrate that LS-STM exhibits superior performance in neural signal decoding tasks compared to traditional vectorization-based decoding methods. Furthermore, LS-STM demonstrates better performance in decoding neural signals with limited samples and the tensor weights of the LS-STM decoder enable the retrospective identification of key neurons during the neural encoding process. This study introduces a novel tensor computing approach and perspective for decoding high-dimensional neural information in the field.
Collapse
Affiliation(s)
- Boyang Zang
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- School of Clinical Medicine, Tsinghua University, Beijing, China
- Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Tao Sun
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- School of Control Science and Engineering, Dalian University of Technology, Dalian, China
| | - Yang Lu
- School of Clinical Medicine, Tsinghua University, Beijing, China
- Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Yuhang Zhang
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
| | - Guihuai Wang
- School of Clinical Medicine, Tsinghua University, Beijing, China.
- Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Sen Wan
- Department of Automation, Tsinghua University, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
| |
Collapse
|
37
|
Akella S, Ledochowitsch P, Siegle JH, Belski H, Denman DD, Buice MA, Durand S, Koch C, Olsen SR, Jia X. Deciphering neuronal variability across states reveals dynamic sensory encoding. Nat Commun 2025; 16:1768. [PMID: 39971911 PMCID: PMC11839951 DOI: 10.1038/s41467-025-56733-w] [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: 04/04/2024] [Accepted: 01/29/2025] [Indexed: 02/21/2025] Open
Abstract
Influenced by non-stationary factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. However, it remains unclear how their relative impact on neuronal variability evolves over time. To address this question, we designed an encoding model conditioned on latent states to partition variability in the mouse visual cortex across internal brain dynamics, behavior, and external visual stimulus. Applying a hidden Markov model to local field potentials, we consistently identified three distinct oscillation states, each with a unique variability profile. Regression models within each state revealed a dynamic composition of factors influencing spiking variability, with the dominant factor switching within seconds. The state-conditioned regression model uncovered extensive diversity in source contributions across units, varying in accordance with anatomical hierarchy and internal state. This heterogeneity in encoding underscores the importance of partitioning variability over time, particularly when considering the influence of non-stationary factors on sensory processing.
Collapse
Affiliation(s)
| | | | | | | | - Daniel D Denman
- Allen Institute, Seattle, WA, USA
- Anschutz Medical Campus School of Medicine, University of Colorado, Aurora, CO, USA
| | | | | | | | | | - Xiaoxuan Jia
- School of Life Science, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
| |
Collapse
|
38
|
Park J, Polidoro P, Fortunato C, Arnold J, Mensh B, Gallego JA, Dudman JT. Conjoint specification of action by neocortex and striatum. Neuron 2025; 113:620-636.e6. [PMID: 39837325 DOI: 10.1016/j.neuron.2024.12.024] [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: 09/27/2023] [Revised: 09/09/2024] [Accepted: 12/19/2024] [Indexed: 01/23/2025]
Abstract
The interplay between two major forebrain structures-cortex and subcortical striatum-is critical for flexible, goal-directed action. Traditionally, it has been proposed that striatum is critical for selecting what type of action is initiated, while the primary motor cortex is involved in specifying the continuous parameters of an upcoming/ongoing movement. Recent data indicate that striatum may also be involved in specification. These alternatives have been difficult to reconcile because comparing very distinct actions, as is often done, makes essentially indistinguishable predictions. Here, we develop quantitative models to reveal a somewhat paradoxical insight: only comparing neural activity across similar actions makes strongly distinguishing predictions. We thus developed a novel reach-to-pull task in which mice reliably selected between two similar but distinct reach targets and pull forces. Simultaneous cortical and subcortical recordings were uniquely consistent with a model in which cortex and striatum jointly specify continuous parameters governing movement execution.
Collapse
Affiliation(s)
- Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
| | - Peter Polidoro
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Catia Fortunato
- Department of Bioengineering, Imperial College London, London W12 0BZ, UK
| | - Jon Arnold
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Brett Mensh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Juan A Gallego
- Department of Bioengineering, Imperial College London, London W12 0BZ, UK
| | - Joshua T Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
| |
Collapse
|
39
|
Bimbard C, Takács F, Catarino JA, Fabre JMJ, Gupta S, Lenzi SC, Melin MD, O'Neill N, Orsolic I, Robacha M, Street JS, Gomes Teixeira JM, Townsend S, van Beest EH, Zhang AM, Churchland AK, Duan CA, Harris KD, Kullmann DM, Lignani G, Mainen ZF, Margrie TW, Rochefort NL, Wikenheiser A, Carandini M, Coen P. An adaptable, reusable, and light implant for chronic Neuropixels probes. eLife 2025; 13:RP98522. [PMID: 39964835 PMCID: PMC11835385 DOI: 10.7554/elife.98522] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2025] Open
Abstract
Electrophysiology has proven invaluable to record neural activity, and the development of Neuropixels probes dramatically increased the number of recorded neurons. These probes are often implanted acutely, but acute recordings cannot be performed in freely moving animals and the recorded neurons cannot be tracked across days. To study key behaviors such as navigation, learning, and memory formation, the probes must be implanted chronically. An ideal chronic implant should (1) allow stable recordings of neurons for weeks; (2) allow reuse of the probes after explantation; (3) be light enough for use in mice. Here, we present the 'Apollo Implant', an open-source and editable device that meets these criteria and accommodates up to two Neuropixels 1.0 or 2.0 probes. The implant comprises a 'payload' module which is attached to the probe and is recoverable, and a 'docking' module which is cemented to the skull. The design is adjustable, making it easy to change the distance between probes, the angle of insertion, and the depth of insertion. We tested the implant across eight labs in head-fixed mice, freely moving mice, and freely moving rats. The number of neurons recorded across days was stable, even after repeated implantations of the same probe. The Apollo implant provides an inexpensive, lightweight, and flexible solution for reusable chronic Neuropixels recordings.
Collapse
Affiliation(s)
- Célian Bimbard
- UCL Institute of Ophthalmology, University College LondonLondonUnited Kingdom
| | - Flóra Takács
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College LondonLondonUnited Kingdom
| | - Joana A Catarino
- Champalimaud Research, Champalimaud Centre for the UnknownLisbonPortugal
| | - Julie MJ Fabre
- UCL Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Sukriti Gupta
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Stephen C Lenzi
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College LondonLondonUnited Kingdom
| | - Maxwell D Melin
- Department of Neurobiology, University of California, Los AngelesLos AngelesUnited States
| | - Nathanael O'Neill
- UCL Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Ivana Orsolic
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College LondonLondonUnited Kingdom
| | - Magdalena Robacha
- UCL Institute of Ophthalmology, University College LondonLondonUnited Kingdom
| | - James S Street
- UCL Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | | | - Simon Townsend
- The FabLab, Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College LondonLondonUnited Kingdom
| | - Enny H van Beest
- UCL Institute of Ophthalmology, University College LondonLondonUnited Kingdom
| | - Arthur M Zhang
- Centre for Discovery Brain Sciences, School of Biomedical Sciences, University of EdinburghEdinburghUnited Kingdom
| | - Anne K Churchland
- Department of Neurobiology, University of California, Los AngelesLos AngelesUnited States
| | - Chunyu A Duan
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College LondonLondonUnited Kingdom
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | | | - Gabriele Lignani
- UCL Queen Square Institute of Neurology, University College LondonLondonUnited Kingdom
| | - Zachary F Mainen
- Champalimaud Research, Champalimaud Centre for the UnknownLisbonPortugal
| | - Troy W Margrie
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College LondonLondonUnited Kingdom
| | - Nathalie L Rochefort
- Centre for Discovery Brain Sciences, School of Biomedical Sciences, University of EdinburghEdinburghUnited Kingdom
- Simons Initiative for the Developing Brain, University of EdinburghEdinburghUnited Kingdom
| | - Andrew Wikenheiser
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Matteo Carandini
- UCL Institute of Ophthalmology, University College LondonLondonUnited Kingdom
| | - Philip Coen
- UCL Institute of Ophthalmology, University College LondonLondonUnited Kingdom
- Department of Cell and Developmental Biology, University College LondonLondonUnited Kingdom
| |
Collapse
|
40
|
Raut RV, Rosenthal ZP, Wang X, Miao H, Zhang Z, Lee JM, Raichle ME, Bauer AQ, Brunton SL, Brunton BW, Kutz JN. Arousal as a universal embedding for spatiotemporal brain dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2023.11.06.565918. [PMID: 38187528 PMCID: PMC10769245 DOI: 10.1101/2023.11.06.565918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, multidimensional arousal-related process that regulates brain-wide physiology on the timescale of seconds. By framing this interpretation within dynamical systems theory, we arrive at a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multidimensional, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and brain blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology-widely interpreted to reflect regionally-specific neural communication-are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.
Collapse
Affiliation(s)
- Ryan V. Raut
- Allen Institute, Seattle, WA, USA
- Department of Physiology & Biophysics, University of Washington, Seattle, WA, USA
| | - Zachary P. Rosenthal
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiaodan Wang
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Hanyang Miao
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Zhanqi Zhang
- Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA, USA
| | - Jin-Moo Lee
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Marcus E. Raichle
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Adam Q. Bauer
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Steven L. Brunton
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | | | - J. Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| |
Collapse
|
41
|
Turner KL, Brockway DF, Hossain MS, Griffith KR, Greenawalt DI, Zhang Q, Gheres KW, Crowley NA, Drew PJ. Type-I nNOS neurons orchestrate cortical neural activity and vasomotion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.21.634042. [PMID: 39896560 PMCID: PMC11785022 DOI: 10.1101/2025.01.21.634042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2025]
Abstract
It is unknown how the brain orchestrates coordination of global neural and vascular dynamics. We sought to uncover the role of a sparse but unusual population of genetically-distinct interneurons known as type-I nNOS neurons, using a novel pharmacological strategic to unilaterally ablate these neurons from the somatosensory cortex of mice. Region-specific ablation produced changes in both neural activity and vascular dynamics, decreased power in the delta-band of the local field potential, reduced sustained vascular responses to prolonged sensory stimulation, and abolished the post-stimulus undershoot in cerebral blood volume. Coherence between the left and right somatosensory cortex gamma-band power envelope and blood volume at ultra-low frequencies was decreased, suggesting type-1 nNOS neurons integrate long-range coordination of brain signals. Lastly, we observed decreases in the amplitude of resting-state blood volume oscillations and decreased vasomotion following the ablation of type-I nNOS neurons. This demonstrates that a small population of nNOS-positive neurons are indispensable for regulating both neural and vascular dynamics in the whole brain and implicates disruption of these neurons in diseases ranging from neurodegeneration to sleep disturbances.
Collapse
Affiliation(s)
- Kevin L. Turner
- Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802
- Penn State Neuroscience Institute, The Pennsylvania State University, University Park, PA 16802
| | - Dakota F. Brockway
- Department of Biology, The Pennsylvania State University, University Park, PA 16802
- Penn State Neuroscience Institute, The Pennsylvania State University, University Park, PA 16802
| | - Md Shakhawat Hossain
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802
- Penn State Neuroscience Institute, The Pennsylvania State University, University Park, PA 16802
| | - Keith R. Griffith
- Department of Biology, The Pennsylvania State University, University Park, PA 16802
- Penn State Neuroscience Institute, The Pennsylvania State University, University Park, PA 16802
| | - Denver I. Greenawalt
- Graduate Program in Molecular Cellular and Integrative Biosciences, The Pennsylvania State University, University Park, PA 16802
- Penn State Neuroscience Institute, The Pennsylvania State University, University Park, PA 16802
| | - Qingguang Zhang
- Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802
- Penn State Neuroscience Institute, The Pennsylvania State University, University Park, PA 16802
- Department of Physiology, Michigan State University, East Lansing, MI 48824
| | - Kyle W. Gheres
- Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802
- Penn State Neuroscience Institute, The Pennsylvania State University, University Park, PA 16802
| | - Nicole A. Crowley
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802
- Department of Biology, The Pennsylvania State University, University Park, PA 16802
- Penn State Neuroscience Institute, The Pennsylvania State University, University Park, PA 16802
| | - Patrick J. Drew
- Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA 16802
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA 16802
- Department of Neurosurgery, The Pennsylvania State University, University Park, PA 16802
- Penn State Neuroscience Institute, The Pennsylvania State University, University Park, PA 16802
| |
Collapse
|
42
|
Candelori B, Bardella G, Spinelli I, Ramawat S, Pani P, Ferraina S, Scardapane S. Spatio-temporal transformers for decoding neural movement control. J Neural Eng 2025; 22:016023. [PMID: 39870043 DOI: 10.1088/1741-2552/adaef0] [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: 04/16/2024] [Accepted: 01/27/2025] [Indexed: 01/29/2025]
Abstract
Objective. Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activityin vivoremains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results.Approach. To address this challenge, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity. The model is tested on multi-electrode recordings from the dorsal premotor cortex of non-human primates performing a motor inhibition task.Main results. The proposed architecture provides an early prediction of the correct movement direction, achieving accurate results no later than 230 ms after the Go signal presentation across animals. Additionally, the model can forecast whether the movement will be generated or withheld before a stop signal, unattended, is actually presented. To further understand the internal dynamics of the model, we compute the predicted correlations between time steps and between neurons at successive layers of the architecture, with the evolution of these correlations mirrors findings from previous theoretical analyses.Significance. Overall, our framework provides a comprehensive use case for the practical implementation of deep learning tools in motor control research, highlighting both the predictive capabilities and interpretability of the proposed architecture.
Collapse
Affiliation(s)
- Benedetta Candelori
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy
| | - Giampiero Bardella
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Indro Spinelli
- Department of Computer Science, Sapienza University of Rome, Rome, Italy
| | - Surabhi Ramawat
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Pierpaolo Pani
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Stefano Ferraina
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Simone Scardapane
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy
| |
Collapse
|
43
|
Lakhera S, Herbert E, Gjorgjieva J. Modeling the Emergence of Circuit Organization and Function during Development. Cold Spring Harb Perspect Biol 2025; 17:a041511. [PMID: 38858072 PMCID: PMC11864115 DOI: 10.1101/cshperspect.a041511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
Developing neural circuits show unique patterns of spontaneous activity and structured network connectivity shaped by diverse activity-dependent plasticity mechanisms. Based on extensive experimental work characterizing patterns of spontaneous activity in different brain regions over development, theoretical and computational models have played an important role in delineating the generation and function of individual features of spontaneous activity and their role in the plasticity-driven formation of circuit connectivity. Here, we review recent modeling efforts that explore how the developing cortex and hippocampus generate spontaneous activity, focusing on specific connectivity profiles and the gradual strengthening of inhibition as the key drivers behind the observed developmental changes in spontaneous activity. We then discuss computational models that mechanistically explore how different plasticity mechanisms use this spontaneous activity to instruct the formation and refinement of circuit connectivity, from the formation of single neuron receptive fields to sensory feature maps and recurrent architectures. We end by highlighting several open challenges regarding the functional implications of the discussed circuit changes, wherein models could provide the missing step linking immature developmental and mature adult information processing capabilities.
Collapse
Affiliation(s)
- Shreya Lakhera
- School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Elizabeth Herbert
- School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Julijana Gjorgjieva
- School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| |
Collapse
|
44
|
Trägenap S, Whitney DE, Fitzpatrick D, Kaschube M. The developmental emergence of reliable cortical representations. Nat Neurosci 2025; 28:394-405. [PMID: 39905211 DOI: 10.1038/s41593-024-01857-3] [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: 04/05/2023] [Accepted: 11/20/2024] [Indexed: 02/06/2025]
Abstract
The fundamental structure of cortical networks arises early in development before the onset of sensory experience. However, how endogenously generated networks respond to the onset of sensory experience and how they form mature sensory representations with experience remain unclear. In this study, we examined this 'nature-nurture transform' at the single-trial level using chronic in vivo calcium imaging in ferret visual cortex. At eye opening, visual stimulation evokes robust patterns of modular cortical network activity that are highly variable within and across trials, severely limiting stimulus discriminability. These initial stimulus-evoked modular patterns are distinct from spontaneous network activity patterns present before and at the time of eye opening. Within a week of normal visual experience, cortical networks develop low-dimensional, highly reliable stimulus representations that correspond with reorganized patterns of spontaneous activity. Using a computational model, we propose that reliable visual representations derive from the alignment of feedforward and recurrent cortical networks shaped by novel patterns of visually driven activity.
Collapse
Affiliation(s)
- Sigrid Trägenap
- Frankfurt Institute for Advanced Studies (FIAS), Frankfurt, Germany
- International Max Planck Research School for Neural Circuits, Frankfurt, Germany
- Department of Physics, Goethe University Frankfurt, Frankfurt, Germany
| | - David E Whitney
- Department of Functional Architecture and Development of Cerebral Cortex, Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA
| | - David Fitzpatrick
- Department of Functional Architecture and Development of Cerebral Cortex, Max Planck Florida Institute for Neuroscience, Jupiter, FL, USA.
| | - Matthias Kaschube
- Frankfurt Institute for Advanced Studies (FIAS), Frankfurt, Germany.
- Department of Computer Science and Mathematics, Goethe University Frankfurt, Frankfurt, Germany.
| |
Collapse
|
45
|
Tu JC, Kim JH, Luckett P, Adeyemo B, Shimony JS, Elison JT, Eggebrecht AT, Wheelock MD. Deep-learning based Embedding of Functional Connectivity Profiles for Precision Functional Mapping. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.29.635570. [PMID: 39975052 PMCID: PMC11838398 DOI: 10.1101/2025.01.29.635570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
Spatial correlation of functional connectivity profiles across matching anatomical locations in individuals is often calculated to delineate individual differences in functional networks. Likewise, spatial correlation is assessed across average functional connectivity profiles of groups to evaluate the maturity of functional networks during development. Despite its widespread use, spatial correlation is limited to comparing two samples at a time. In this study, we employed a variational autoencoder to embed functional connectivity profiles from various anatomical locations, individuals, and group averages for simultaneous comparison. We demonstrate that our variational autoencoder, with pre-trained weights, can project new functional connectivity profiles from the vertex space to a latent space with as few as two dimensions, yet still retain meaningful global and local structures in the data. Functional connectivity profiles from various functional networks occupy distinct compartments of the latent space. Moreover, the variability of functional connectivity profiles from the same anatomical location is readily captured in the latent space. We believe that this approach could be useful for visualization and exploratory analyses in precision functional mapping.
Collapse
Affiliation(s)
- Jiaxin Cindy Tu
- Mallinckrodt Institute of Radiology, Washington University in St. Louis
| | - Jung-Hoon Kim
- Developing Brain Institute, Children's National Hospital
| | | | - Babatunde Adeyemo
- Mallinckrodt Institute of Radiology, Washington University in St. Louis
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University in St. Louis
| | - Jed T Elison
- Institute of Child Development, University of Minnesota
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Adam T Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University in St. Louis
| | - Muriah D Wheelock
- Mallinckrodt Institute of Radiology, Washington University in St. Louis
| |
Collapse
|
46
|
Dipoppa M, Nogueira R, Bugeon S, Friedman Y, Reddy CB, Harris KD, Ringach DL, Miller KD, Carandini M, Fusi S. Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.12.11.628035. [PMID: 39896460 PMCID: PMC11785004 DOI: 10.1101/2024.12.11.628035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/04/2025]
Abstract
Sensory adaptation dynamically changes neural responses as a function of previous stimuli, profoundly impacting perception. The response changes induced by adaptation have been characterized in detail in individual neurons and at the population level after averaging across trials. However, it is not clear how adaptation modifies the aspects of the representations that relate more directly to the ability to perceive stimuli, such as their geometry and the noise structure in individual trials. To address this question, we recorded from a population of neurons in the mouse visual cortex and presented one stimulus (an oriented grating) more frequently than the others. We then analyzed these data in terms of representational geometry and studied the ability of a linear decoder to discriminate between similar visual stimuli based on the single-trial population responses. Surprisingly, the discriminability of stimuli near the adaptor increased, even though the responses of individual neurons to these stimuli decreased. Similar changes were observed in artificial neural networks trained to reconstruct the visual stimulus under metabolic constraints. We conclude that the paradoxical effects of adaptation are consistent with the efficient coding framework, allowing the brain to improve the representation of frequent stimuli while limiting the associated metabolic cost.
Collapse
Affiliation(s)
- Mario Dipoppa
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Center for Theoretical Neuroscience, Zuckerman Institute for Brain Mind and Behavior, Columbia University, NY, USA
- Institute of Neurology, University College London, UK
| | - Ramon Nogueira
- Center for Theoretical Neuroscience, Zuckerman Institute for Brain Mind and Behavior, Columbia University, NY, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
| | | | - Yoni Friedman
- Center for Theoretical Neuroscience, Zuckerman Institute for Brain Mind and Behavior, Columbia University, NY, USA
- Massachusetts Institute of Technology, MA, USA
| | | | | | - Dario L. Ringach
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Kenneth D. Miller
- Center for Theoretical Neuroscience, Zuckerman Institute for Brain Mind and Behavior, Columbia University, NY, USA
- Kavli Institute for Brain Science, Columbia University, NY, USA
| | | | - Stefano Fusi
- Center for Theoretical Neuroscience, Zuckerman Institute for Brain Mind and Behavior, Columbia University, NY, USA
- Kavli Institute for Brain Science, Columbia University, NY, USA
| |
Collapse
|
47
|
Zerlaut Y, Tzilivaki A. Interneuronal modulations as a functional switch for cortical computations: mechanisms and implication for disease. Front Cell Neurosci 2025; 18:1479579. [PMID: 39916937 PMCID: PMC11799556 DOI: 10.3389/fncel.2024.1479579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Accepted: 12/27/2024] [Indexed: 02/09/2025] Open
Abstract
Understanding cortical inhibition and its diverse roles remains a key challenge in neurophysiological research. Traditionally, inhibition has been recognized for controlling the stability and rhythmicity of network dynamics, or refining the spatiotemporal properties of cortical representations. In this perspective, we propose that specific types of interneurons may play a complementary role, by modulating the computational properties of neural networks. We review experimental and theoretical evidence, mainly from rodent sensory cortices, that supports this view. Additionally, we explore how dysfunctions in these interneurons may disrupt the network's ability to switch between computational modes, impacting the flexibility of cortical processing and potentially contributing to various neurodevelopmental and psychiatric disorders.
Collapse
Affiliation(s)
- Yann Zerlaut
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Alexandra Tzilivaki
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Berlin, and Berlin Institute of Health, Neuroscience Research Center, Berlin, Germany
- Einstein Center for Neurosciences, Chariteplatz, Berlin, Germany
- NeuroCure Cluster of Excellence, Chariteplatz, Berlin, Germany
| |
Collapse
|
48
|
Hernandez DE, Ciuparu A, Garcia da Silva P, Velasquez CM, Rebouillat B, Gross MD, Davis MB, Chae H, Muresan RC, Albeanu DF. Fast updating feedback from piriform cortex to the olfactory bulb relays multimodal identity and reward contingency signals during rule-reversal. Nat Commun 2025; 16:937. [PMID: 39843439 PMCID: PMC11754465 DOI: 10.1038/s41467-025-56023-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 01/02/2025] [Indexed: 01/24/2025] Open
Abstract
While animals readily adjust their behavior to adapt to relevant changes in the environment, the neural pathways enabling these changes remain largely unknown. Here, using multiphoton imaging, we investigate whether feedback from the piriform cortex to the olfactory bulb supports such behavioral flexibility. To this end, we engage head-fixed male mice in a multimodal rule-reversal task guided by olfactory and auditory cues. Both odor and, surprisingly, the sound cues trigger responses in the cortical bulbar feedback axons which precede the behavioral report. Responses to the same sensory cue are strongly modulated upon changes in stimulus-reward contingency (rule-reversals). The re-shaping of individual bouton responses occurs within seconds of the rule-reversal events and is correlated with changes in behavior. Optogenetic perturbation of cortical feedback within the bulb disrupts the behavioral performance. Our results indicate that the piriform-to-olfactory bulb feedback axons carry stimulus identity and reward contingency signals which are rapidly re-formatted according to changes in the behavioral context.
Collapse
Affiliation(s)
| | - Andrei Ciuparu
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Pedro Garcia da Silva
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Champalimaud Neuroscience Program, Lisbon, Portugal
| | - Cristina M Velasquez
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- University of Oxford, Oxford, UK
| | - Benjamin Rebouillat
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- École Normale Supérieure, Paris, France
| | | | - Martin B Davis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Honggoo Chae
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Raul C Muresan
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania.
- STAR-UBB Institute, Babeş-Bolyai University, Cluj-Napoca, Romania.
| | - Dinu F Albeanu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- School for Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| |
Collapse
|
49
|
Zheng J, Meister M. The unbearable slowness of being: Why do we live at 10 bits/s? Neuron 2025; 113:192-204. [PMID: 39694032 PMCID: PMC11758279 DOI: 10.1016/j.neuron.2024.11.008] [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: 10/02/2024] [Revised: 10/31/2024] [Accepted: 11/12/2024] [Indexed: 12/20/2024]
Abstract
This article is about the neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at ∼109 bits/s. The stark contrast between these numbers remains unexplained and touches on fundamental aspects of brain function: what neural substrate sets this speed limit on the pace of our existence? Why does the brain need billions of neurons to process 10 bits/s? Why can we only think about one thing at a time? The brain seems to operate in two distinct modes: the "outer" brain handles fast high-dimensional sensory and motor signals, whereas the "inner" brain processes the reduced few bits needed to control behavior. Plausible explanations exist for the large neuron numbers in the outer brain, but not for the inner brain, and we propose new research directions to remedy this.
Collapse
Affiliation(s)
- Jieyu Zheng
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Markus Meister
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| |
Collapse
|
50
|
Manley J, Vaziri A. Whole-brain neural substrates of behavioral variability in the larval zebrafish. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.03.03.583208. [PMID: 38496592 PMCID: PMC10942351 DOI: 10.1101/2024.03.03.583208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Animals engaged in naturalistic behavior can exhibit a large degree of behavioral variability even under sensory invariant conditions. Such behavioral variability can include not only variations of the same behavior, but also variability across qualitatively different behaviors driven by divergent cognitive states, such as fight-or-flight decisions. However, the neural circuit mechanisms that generate such divergent behaviors across trials are not well understood. To investigate this question, here we studied the visual-evoked responses of larval zebrafish to moving objects of various sizes, which we found exhibited highly variable and divergent responses across repetitions of the same stimulus. Given that the neuronal circuits underlying such behaviors span sensory, motor, and other brain areas, we built a novel Fourier light field microscope which enables high-resolution, whole-brain imaging of larval zebrafish during behavior. This enabled us to screen for neural loci which exhibited activity patterns correlated with behavioral variability. We found that despite the highly variable activity of single neurons, visual stimuli were robustly encoded at the population level, and the visual-encoding dimensions of neural activity did not explain behavioral variability. This robustness despite apparent single neuron variability was due to the multi-dimensional geometry of the neuronal population dynamics: almost all neural dimensions that were variable across individual trials, i.e. the "noise" modes, were nearly orthogonal to those encoding for sensory information. Investigating this neuronal variability further, we identified two sparsely-distributed, brain-wide neuronal populations whose pre-motor activity predicted whether the larva would respond to a stimulus and, if so, which direction it would turn on a single-trial level. These populations predicted single-trial behavior seconds before stimulus onset, indicating they encoded time-varying internal modulating behavior, perhaps organizing behavior over longer timescales or enabling flexible behavior routines dependent on the animal's internal state. Our results provide the first whole-brain confirmation that sensory, motor, and internal variables are encoded in a highly mixed fashion throughout the brain and demonstrate that de-mixing each of these components at the neuronal population level is critical to understanding the mechanisms underlying the brain's remarkable flexibility and robustness.
Collapse
Affiliation(s)
- Jason Manley
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
| | - Alipasha Vaziri
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA
| |
Collapse
|