1
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Kang H, Kanold PO. Sparse representation of neurons for encoding complex sounds in the auditory cortex. Prog Neurobiol 2024; 241:102661. [PMID: 39303758 DOI: 10.1016/j.pneurobio.2024.102661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 08/20/2024] [Accepted: 09/05/2024] [Indexed: 09/22/2024]
Abstract
Listening in complex sound environments requires rapid segregation of different sound sources, e.g., having a conversation with multiple speakers or other environmental sounds. Efficient processing requires fast encoding of inputs to adapt to target sounds and identify relevant information from past experiences. This adaptation process represents an early phase of implicit learning of the sound statistics to form auditory memory. The auditory cortex (ACtx) plays a crucial role in this implicit learning process, but the underlying circuits are unknown. In awake mice, we recorded neuronal responses in different ACtx subfields using in vivo 2-photon imaging of excitatory and inhibitory (parvalbumin; PV) neurons. We used a paradigm adapted from human studies that induced rapid implicit learning from passively presented complex sounds and imaged A1 Layer 4 (L4), A1 L2/3, and A2 L2/3. In this paradigm, a frozen spectro-temporally complex 'Target' sound randomly re-occurred within a stream of other random complex sounds. All ACtx subregions contained distinct groups of cells specifically responsive to complex acoustic sequences, indicating that even thalamocortical input layers (A1 L4) respond to complex sounds. Subgroups of excitatory and inhibitory cells in all subfields showed decreased responses for re-occurring Target sounds, indicating that ACtx is highly involved in the early implicit learning phase. At the population level, activity was more decorrelated to Target sounds independent of the duration of frozen token, subregions, and cell type. These findings suggest that ACtx and its input layers contribute to the early phase of auditory memory for complex sounds, suggesting a parallel strategy across ACtx areas and between excitatory and inhibitory neurons.
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Affiliation(s)
- HiJee Kang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Patrick O Kanold
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Kavli NDI, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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2
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard J, Carhart-Harris RL, Williams GB, Craig MM, Finoia P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. A synergistic workspace for human consciousness revealed by Integrated Information Decomposition. eLife 2024; 12:RP88173. [PMID: 39022924 PMCID: PMC11257694 DOI: 10.7554/elife.88173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Abstract
How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Pedro AM Mediano
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Center for Complexity Science, Imperial College LondonLondonUnited Kingdom
- Data Science Institute, Imperial College LondonLondonUnited Kingdom
| | - Judith Allanson
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - John Pickard
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Psychedelics Division - Neuroscape, Department of Neurology, University of CaliforniaSan FranciscoUnited States
| | - Guy B Williams
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Michael M Craig
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Paola Finoia
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
| | - Adrian M Owen
- Department of Psychology and Department of Physiology and Pharmacology, The Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity CollegeDublinIreland
| | - David K Menon
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Daniel Bor
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Emmanuel A Stamatakis
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
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3
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Luppi AI, Rosas FE, Mediano PAM, Demertzi A, Menon DK, Stamatakis EA. Unravelling consciousness and brain function through the lens of time, space, and information. Trends Neurosci 2024; 47:551-568. [PMID: 38824075 DOI: 10.1016/j.tins.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada; St John's College, University of Cambridge, Cambridge, UK; Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK.
| | - Fernando E Rosas
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK; Center for Psychedelic Research, Imperial College London, London, UK
| | | | - Athena Demertzi
- Physiology of Cognition Lab, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liège, Liège 4000, Belgium; Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège 4000, Belgium; National Fund for Scientific Research (FNRS), Brussels 1000, Belgium
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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4
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Brockett AT, Francis NA. Psilocybin decreases neural responsiveness and increases functional connectivity while preserving pure-tone frequency selectivity in mouse auditory cortex. J Neurophysiol 2024; 132:45-53. [PMID: 38810366 PMCID: PMC11383378 DOI: 10.1152/jn.00124.2024] [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: 03/26/2024] [Revised: 04/29/2024] [Accepted: 05/17/2024] [Indexed: 05/31/2024] Open
Abstract
Psilocybin is a serotonergic psychedelic believed to have therapeutic potential for neuropsychiatric conditions. Despite well-documented prevalence of perceptual alterations, hallucinations, and synesthesia associated with psychedelic experiences, little is known about how psilocybin affects sensory cortex or alters the activity of neurons in awake animals. To investigate, we conducted two-photon imaging experiments in auditory cortex of awake mice and collected video of free-roaming mouse behavior, both at baseline and during psilocybin treatment. In comparison with pre-dose neural activity, a 2 mg/kg ip dose of psilocybin initially increased the amplitude of neural responses to sound. Thirty minutes post-dose, behavioral activity and neural response amplitudes decreased, yet functional connectivity increased. In contrast, control mice given intraperitoneal saline injections showed no significant changes in either neural or behavioral activity across conditions. Notably, neuronal stimulus selectivity remained stable during psilocybin treatment, for both tonotopic cortical maps and single-cell pure-tone frequency tuning curves. Our results mirror similar findings regarding the effects of serotonergic psychedelics in visual cortex and suggest that psilocybin modulates the balance of intrinsic versus stimulus-driven influences on neural activity in auditory cortex.NEW & NOTEWORTHY Recent studies have shown promising therapeutic potential for psychedelics in treating neuropsychiatric conditions. Musical experience during psilocybin-assisted therapy is predictive of treatment outcome, yet little is known about how psilocybin affects auditory processing. Here, we conducted two-photon imaging experiments in auditory cortex of awake mice that received a dose of psilocybin. Our results suggest that psilocybin modulates the roles of intrinsic neural activity versus stimulus-driven influences on auditory perception.
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Affiliation(s)
- Adam T Brockett
- Department of Psychology, University of Maryland, College Park, Maryland, United States
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States
- Department of Biological Sciences, University of New Hampshire, Durham, New Hampshire, United States
| | - Nikolas A Francis
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, United States
- Department of Biology, University of Maryland, College Park, Maryland, United States
- Center for Comparative and Evolutionary Biology of Hearing, University of Maryland, College Park, Maryland, United States
- Brain and Behavior Institute, University of Maryland, College Park, Maryland, United States
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5
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Panniello M, Gillon CJ, Maffulli R, Celotto M, Richards BA, Panzeri S, Kohl MM. Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning. Cell Rep 2024; 43:114244. [PMID: 38796851 DOI: 10.1016/j.celrep.2024.114244] [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: 03/07/2023] [Revised: 01/16/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
Neurons in the primary cortex carry sensory- and behavior-related information, but it remains an open question how this information emerges and intersects together during learning. Current evidence points to two possible learning-related changes: sensory information increases in the primary cortex or sensory information remains stable, but its readout efficiency in association cortices increases. We investigated this question by imaging neuronal activity in mouse primary somatosensory cortex before, during, and after learning of an object localization task. We quantified sensory- and behavior-related information and estimated how much sensory information was used to instruct perceptual choices as learning progressed. We find that sensory information increases from the start of training, while choice information is mostly present in the later stages of learning. Additionally, the readout of sensory information becomes more efficient with learning as early as in the primary sensory cortex. Together, our results highlight the importance of primary cortical neurons in perceptual learning.
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Affiliation(s)
- Mariangela Panniello
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK; School of Psychology and Neuroscience, University of Glasgow, Glasgow G12 8QQ, UK; Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Colleen J Gillon
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada; Department of Cell & Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada; Mila, Montréal, QC H2S 3H1, Canada
| | - Roberto Maffulli
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Marco Celotto
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy; Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany; Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy
| | - Blake A Richards
- Mila, Montréal, QC H2S 3H1, Canada; School of Computer Science, McGill University, Montréal, QC H3A 2A7, Canada; Department of Neurology & Neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada; Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, ON M5G 1M1, Canada; Montreal Neurological Institute, Montréal, QC H3A 2B4, Canada
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy; Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
| | - Michael M Kohl
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK; School of Psychology and Neuroscience, University of Glasgow, Glasgow G12 8QQ, UK.
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6
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Kang H, Babola TA, Kanold PO. Rapid rebalancing of co-tuned ensemble activity in the auditory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.17.599418. [PMID: 38948779 PMCID: PMC11212947 DOI: 10.1101/2024.06.17.599418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Sensory information is represented by small neuronal ensembles in sensory cortices. Neuronal activity shows high trial-by-trial variability in that repeated presentation of the same stimulus, e. g., multiple presentations of the same sound activate differing ensembles in the auditory cortex (AC). How the differing ensembles interact to selectively activate to process incoming sound inputs with reduced energy is unknown. Efficient processing of complex acoustic signals requires that these sparsely distributed neuronal ensembles actively interact in order to provide a constant percept. Here, we probe interactions within and across ensembles by combining in vivo 2-photon Ca2+ imaging and holographic optogenetic stimulation to study how increased activity of single cells level affects the cortical network. We stimulated a small number of neurons sharing the same frequency preference alongside the presentation of a target pure tone, further increasing their tone-evoked activity. We found that other non-stimulated co-tuned neurons decreased their tone-evoked activity while non co-tuned neurons were unaffected. This shows that co-tuned ensembles communicated and balanced their total activity across the network. The rebalanced activity due to external stimulation remained constant. These effects suggest that co-tuned ensembles in AC interact and rapidly rebalance their activity to maintain encoding homeostasis, and that the rebalanced network is persistent.
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Affiliation(s)
- HiJee Kang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 20215
| | - Travis A. Babola
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 20215
| | - Patrick O. Kanold
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 20215
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 20215
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7
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Koçillari L, Lorenz GM, Engel NM, Celotto M, Curreli S, Malerba SB, Engel AK, Fellin T, Panzeri S. Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597303. [PMID: 38895197 PMCID: PMC11185652 DOI: 10.1101/2024.06.04.597303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.
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Affiliation(s)
- Loren Koçillari
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Gabriel Matías Lorenz
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Nicola Marie Engel
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Marco Celotto
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
| | | | - Simone Blanco Malerba
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Andreas K. Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | | | - Stefano Panzeri
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
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8
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Wang M, Jendrichovsky P, Kanold PO. Auditory discrimination learning differentially modulates neural representation in auditory cortex subregions and inter-areal connectivity. Cell Rep 2024; 43:114172. [PMID: 38703366 PMCID: PMC11450637 DOI: 10.1016/j.celrep.2024.114172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/06/2024] [Accepted: 04/16/2024] [Indexed: 05/06/2024] Open
Abstract
Changes in sound-evoked responses in the auditory cortex (ACtx) occur during learning, but how learning alters neural responses in different ACtx subregions and changes their interactions is unclear. To address these questions, we developed an automated training and widefield imaging system to longitudinally track the neural activity of all mouse ACtx subregions during a tone discrimination task. We find that responses in primary ACtx are highly informative of learned stimuli and behavioral outcomes throughout training. In contrast, representations of behavioral outcomes in the dorsal posterior auditory field, learned stimuli in the dorsal anterior auditory field, and inter-regional correlations between primary and higher-order areas are enhanced with training. Moreover, ACtx response changes vary between stimuli, and such differences display lag synchronization with the learning rate. These results indicate that learning alters functional connections between ACtx subregions, inducing region-specific modulations by propagating behavioral information from primary to higher-order areas.
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Affiliation(s)
- Mingxuan Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Peter Jendrichovsky
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Patrick O Kanold
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21205, USA.
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9
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Lemke SM, Celotto M, Maffulli R, Ganguly K, Panzeri S. Information flow between motor cortex and striatum reverses during skill learning. Curr Biol 2024; 34:1831-1843.e7. [PMID: 38604168 PMCID: PMC11078609 DOI: 10.1016/j.cub.2024.03.023] [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: 12/13/2023] [Revised: 02/22/2024] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
The coordination of neural activity across brain areas during a specific behavior is often interpreted as neural communication involved in controlling the behavior. However, whether information relevant to the behavior is actually transferred between areas is often untested. Here, we used information-theoretic tools to quantify how motor cortex and striatum encode and exchange behaviorally relevant information about specific reach-to-grasp movement features during skill learning in rats. We found a temporal shift in the encoding of behaviorally relevant information during skill learning, as well as a reversal in the primary direction of behaviorally relevant information flow, from cortex-to-striatum during naive movements to striatum-to-cortex during skilled movements. Standard analytical methods that quantify the evolution of overall neural activity during learning-such as changes in neural signal amplitude or the overall exchange of information between areas-failed to capture these behaviorally relevant information dynamics. Using these standard methods, we instead found a consistent coactivation of overall neural signals during movement production and a bidirectional increase in overall information propagation between areas during learning. Our results show that skill learning is achieved through a transformation in how behaviorally relevant information is routed across cortical and subcortical brain areas and that isolating the components of neural activity relevant to and informative about behavior is critical to uncover directional interactions within a coactive and coordinated network.
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Affiliation(s)
- Stefan M Lemke
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Corso Bettini 31, 38068 Rovereto, Italy; Neurology Service, San Francisco Veterans Affairs Medical Center, 4150 Clement Street, San Francisco, CA 94121, USA; Department of Neurology, University of California, San Francisco, 1700 Owens Street, San Francisco, CA 94158, USA; Neuroscience Center, University of North Carolina, Chapel Hill, 116 Manning Drive, Chapel Hill, NC 27599, USA.
| | - Marco Celotto
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Corso Bettini 31, 38068 Rovereto, Italy; Department of Pharmacy and Biotechnology, University of Bologna, Via Irnerio 48, 40126 Bologna, Italy; Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251 Hamburg, Germany
| | - Roberto Maffulli
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Corso Bettini 31, 38068 Rovereto, Italy
| | - Karunesh Ganguly
- Neurology Service, San Francisco Veterans Affairs Medical Center, 4150 Clement Street, San Francisco, CA 94121, USA; Department of Neurology, University of California, San Francisco, 1700 Owens Street, San Francisco, CA 94158, USA
| | - Stefano Panzeri
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251 Hamburg, Germany.
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10
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Luppi AI, Rosas FE, Mediano PAM, Menon DK, Stamatakis EA. Information decomposition and the informational architecture of the brain. Trends Cogn Sci 2024; 28:352-368. [PMID: 38199949 DOI: 10.1016/j.tics.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/09/2023] [Accepted: 11/17/2023] [Indexed: 01/12/2024]
Abstract
To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, information is not a monolithic entity. Information decomposition techniques provide a way to split information into its constituent elements: unique, redundant, and synergistic information. We review how disentangling synergistic and redundant interactions is redefining our understanding of integrative brain function and its neural organisation. To explain how the brain navigates the trade-offs between redundancy and synergy, we review converging evidence integrating the structural, molecular, and functional underpinnings of synergy and redundancy; their roles in cognition and computation; and how they might arise over evolution and development. Overall, disentangling synergistic and redundant information provides a guiding principle for understanding the informational architecture of the brain and cognition.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - David K Menon
- Department of Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
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11
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Oryshchuk A, Sourmpis C, Weverbergh J, Asri R, Esmaeili V, Modirshanechi A, Gerstner W, Petersen CCH, Crochet S. Distributed and specific encoding of sensory, motor, and decision information in the mouse neocortex during goal-directed behavior. Cell Rep 2024; 43:113618. [PMID: 38150365 DOI: 10.1016/j.celrep.2023.113618] [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: 09/13/2023] [Revised: 10/27/2023] [Accepted: 12/08/2023] [Indexed: 12/29/2023] Open
Abstract
Goal-directed behaviors involve coordinated activity in many cortical areas, but whether the encoding of task variables is distributed across areas or is more specifically represented in distinct areas remains unclear. Here, we compared representations of sensory, motor, and decision information in the whisker primary somatosensory cortex, medial prefrontal cortex, and tongue-jaw primary motor cortex in mice trained to lick in response to a whisker stimulus with mice that were not taught this association. Irrespective of learning, properties of the sensory stimulus were best encoded in the sensory cortex, whereas fine movement kinematics were best represented in the motor cortex. However, movement initiation and the decision to lick in response to the whisker stimulus were represented in all three areas, with decision neurons in the medial prefrontal cortex being more selective, showing minimal sensory responses in miss trials and motor responses during spontaneous licks. Our results reconcile previous studies indicating highly specific vs. highly distributed sensorimotor processing.
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Affiliation(s)
- Anastasiia Oryshchuk
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Christos Sourmpis
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; School of Life Sciences and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Julie Weverbergh
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Reza Asri
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Vahid Esmaeili
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Alireza Modirshanechi
- School of Life Sciences and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Wulfram Gerstner
- School of Life Sciences and School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Carl C H Petersen
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
| | - Sylvain Crochet
- Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Institut National de la Santé et de la Recherche Médicale (INSERM), 6900 Lyon, France.
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12
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O'Neill KM, Anderson ED, Mukherjee S, Gandu S, McEwan SA, Omelchenko A, Rodriguez AR, Losert W, Meaney DF, Babadi B, Firestein BL. Time-dependent homeostatic mechanisms underlie brain-derived neurotrophic factor action on neural circuitry. Commun Biol 2023; 6:1278. [PMID: 38110605 PMCID: PMC10728104 DOI: 10.1038/s42003-023-05638-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 11/27/2023] [Indexed: 12/20/2023] Open
Abstract
Plasticity and homeostatic mechanisms allow neural networks to maintain proper function while responding to physiological challenges. Despite previous work investigating morphological and synaptic effects of brain-derived neurotrophic factor (BDNF), the most prevalent growth factor in the central nervous system, how exposure to BDNF manifests at the network level remains unknown. Here we report that BDNF treatment affects rodent hippocampal network dynamics during development and recovery from glutamate-induced excitotoxicity in culture. Importantly, these effects are not obvious when traditional activity metrics are used, so we delve more deeply into network organization, functional analyses, and in silico simulations. We demonstrate that BDNF partially restores homeostasis by promoting recovery of weak and medium connections after injury. Imaging and computational analyses suggest these effects are caused by changes to inhibitory neurons and connections. From our in silico simulations, we find that BDNF remodels the network by indirectly strengthening weak excitatory synapses after injury. Ultimately, our findings may explain the difficulties encountered in preclinical and clinical trials with BDNF and also offer information for future trials to consider.
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Affiliation(s)
- Kate M O'Neill
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Biomedical Engineering Graduate Program, Rutgers University, Piscataway, NJ, USA
- Institute for Physical Science & Technology, University of Maryland, College Park, MD, USA
| | - Erin D Anderson
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Shoutik Mukherjee
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
| | - Srinivasa Gandu
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Cell and Developmental Biology Graduate Program, Rutgers University, Piscataway, NJ, USA
| | - Sara A McEwan
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Neuroscience Graduate Program, Rutgers University, Piscataway, NJ, USA
| | - Anton Omelchenko
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Neuroscience Graduate Program, Rutgers University, Piscataway, NJ, USA
| | - Ana R Rodriguez
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA
- Biomedical Engineering Graduate Program, Rutgers University, Piscataway, NJ, USA
| | - Wolfgang Losert
- Department of Physics, University of Maryland, College Park, MD, USA
- Institute for Physical Science & Technology, University of Maryland, College Park, MD, USA
| | - David F Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Behtash Babadi
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
| | - Bonnie L Firestein
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, USA.
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13
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Koçillari L, Celotto M, Francis NA, Mukherjee S, Babadi B, Kanold PO, Panzeri S. Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex. Brain Inform 2023; 10:34. [PMID: 38052917 PMCID: PMC10697912 DOI: 10.1186/s40708-023-00212-9] [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: 10/06/2023] [Accepted: 11/02/2023] [Indexed: 12/07/2023] Open
Abstract
Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity-that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.
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Affiliation(s)
- Loren Koçillari
- Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy.
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany.
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), 20246, Hamburg, Germany.
| | - Marco Celotto
- Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany
- Department of Pharmacy and Biotechnology, University of Bologna, 40126, Bologna, Italy
| | - Nikolas A Francis
- Department of Biology and Brain and Behavior Institute, University of Maryland, College Park, MD, 20742, USA
| | - Shoutik Mukherjee
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Behtash Babadi
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Patrick O Kanold
- Department of Biomedical Engineering and Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany.
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14
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Mittelstadt JK, Kanold PO. Orbitofrontal cortex conveys stimulus and task information to the auditory cortex. Curr Biol 2023; 33:4160-4173.e4. [PMID: 37716349 PMCID: PMC10602585 DOI: 10.1016/j.cub.2023.08.059] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/29/2023] [Accepted: 08/21/2023] [Indexed: 09/18/2023]
Abstract
Auditory cortical neurons modify their response profiles in response to numerous external factors. During task performance, changes in primary auditory cortex (A1) responses are thought to be driven by top-down inputs from the orbitofrontal cortex (OFC), which may lead to response modification on a trial-by-trial basis. While OFC neurons respond to auditory stimuli and project to A1, the function of OFC projections to A1 during auditory tasks is unknown. Here, we observed the activity of putative OFC terminals in A1 in mice by using in vivo two-photon calcium imaging of OFC terminals under passive conditions and during a tone detection task. We found that behavioral activity modulates but is not necessary to evoke OFC terminal responses in A1. OFC terminals in A1 form distinct populations that exclusively respond to either the tone, reward, or error. Using tones against a background of white noise, we found that OFC terminal activity was modulated by the signal-to-noise ratio (SNR) in both the passive and active conditions and that OFC terminal activity varied with SNR, and thus task difficulty in the active condition. Therefore, OFC projections in A1 are heterogeneous in their modulation of auditory encoding and likely contribute to auditory processing under various auditory conditions.
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Affiliation(s)
- Jonah K Mittelstadt
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Patrick O Kanold
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21205, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21205, USA.
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15
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Ying R, Hamlette L, Nikoobakht L, Balaji R, Miko N, Caras ML. Organization of orbitofrontal-auditory pathways in the Mongolian gerbil. J Comp Neurol 2023; 531:1459-1481. [PMID: 37477903 PMCID: PMC10529810 DOI: 10.1002/cne.25525] [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: 04/25/2023] [Revised: 06/11/2023] [Accepted: 06/26/2023] [Indexed: 07/22/2023]
Abstract
Sound perception is highly malleable, rapidly adjusting to the acoustic environment and behavioral demands. This flexibility is the result of ongoing changes in auditory cortical activity driven by fluctuations in attention, arousal, or prior expectations. Recent work suggests that the orbitofrontal cortex (OFC) may mediate some of these rapid changes, but the anatomical connections between the OFC and the auditory system are not well characterized. Here, we used virally mediated fluorescent tracers to map the projection from OFC to the auditory midbrain, thalamus, and cortex in a classic animal model for auditory research, the Mongolian gerbil (Meriones unguiculatus). We observed no connectivity between the OFC and the auditory midbrain, and an extremely sparse connection between the dorsolateral OFC and higher order auditory thalamic regions. In contrast, we observed a robust connection between the ventral and medial subdivisions of the OFC and the auditory cortex, with a clear bias for secondary auditory cortical regions. OFC axon terminals were found in all auditory cortical lamina but were significantly more concentrated in the infragranular layers. Tissue-clearing and lightsheet microscopy further revealed that auditory cortical-projecting OFC neurons send extensive axon collaterals throughout the brain, targeting both sensory and non-sensory regions involved in learning, decision-making, and memory. These findings provide a more detailed map of orbitofrontal-auditory connections and shed light on the possible role of the OFC in supporting auditory cognition.
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Affiliation(s)
- Rose Ying
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, 20742
- Department of Biology, University of Maryland, College Park, Maryland, 20742
- Center for Comparative and Evolutionary Biology of Hearing, University of Maryland, College Park, Maryland, 20742
| | - Lashaka Hamlette
- Department of Biology, University of Maryland, College Park, Maryland, 20742
| | - Laudan Nikoobakht
- Department of Biology, University of Maryland, College Park, Maryland, 20742
| | - Rakshita Balaji
- Department of Biology, University of Maryland, College Park, Maryland, 20742
| | - Nicole Miko
- Department of Biology, University of Maryland, College Park, Maryland, 20742
| | - Melissa L. Caras
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, 20742
- Department of Biology, University of Maryland, College Park, Maryland, 20742
- Center for Comparative and Evolutionary Biology of Hearing, University of Maryland, College Park, Maryland, 20742
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16
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Celotto M, Bím J, Tlaie A, De Feo V, Lemke S, Chicharro D, Nili H, Bieler M, Hanganu-Opatz IL, Donner TH, Brovelli A, Panzeri S. An information-theoretic quantification of the content of communication between brain regions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.14.544903. [PMID: 37398375 PMCID: PMC10312682 DOI: 10.1101/2023.06.14.544903] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information flowing between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional anaytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously hidden feature-specific information flow.
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Affiliation(s)
- Marco Celotto
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto (TN), Italy
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Jan Bím
- Datamole, s. r. o, Vitezne namesti 577/2 Dejvice, 160 00 Praha 6, The Czech Republic
| | - Alejandro Tlaie
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto (TN), Italy
| | - Vito De Feo
- Artificial Intelligence Team, Future Health Technology, and Brain-Computer Interfaces laboratories, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
| | - Stefan Lemke
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, United States
| | - Daniel Chicharro
- Department of Computer Science, City, University of London, London, UK
| | - Hamed Nili
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Malte Bieler
- Mobile Technology Lab, School of Economics, Innovation and Technology, University College Kristiania, Oslo, Norway
| | - Ileana L. Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center, Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias H. Donner
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Université, CNRS, Marseille, France
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto (TN), Italy
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17
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Gill NK, Francis NA. Repetition plasticity in primary auditory cortex occurs across long timescales for spectrotemporally randomized pure-tones. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.26.538446. [PMID: 37162964 PMCID: PMC10168329 DOI: 10.1101/2023.04.26.538446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Repetition plasticity is a ubiquitous property of sensory systems in which repetitive sensation causes either a decrease ("repetition suppression", i.e. "adaptation") or increase ("repetition enhancement", i.e. "facilitation") in the amplitude of neural responses. Timescales of repetition plasticity for sensory neurons typically span milliseconds to tens of seconds, with longer durations for cortical vs subcortical regions. Here, we used 2-photon (2P) imaging to study repetition plasticity in mouse primary auditory cortex (A1) layer 2/3 (L2/3) during the presentation of spectrotemporally randomized pure-tone frequencies. Our study revealed subpopulations of neurons with repetition plasticity for equiprobable frequencies spaced minutes apart over a 20-minute period. We found both repetition suppression and enhancement in individual neurons and on average across populations. Each neuron tended to show repetition plasticity for 1-2 pure-tone frequencies near the neuron's best frequency. Moreover, we found correlated changes in neural response amplitude and latency across stimulus repetitions. Together, our results highlight cortical specialization for pattern recognition over long timescales in complex acoustic sequences.
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Affiliation(s)
- Nasiru K Gill
- Department of Biology, University of Maryland, College Park, MD, 20742
| | - Nikolas A Francis
- Department of Biology, University of Maryland, College Park, MD, 20742
- Brain and Behavior Institute, University of Maryland, College Park, MD, 20742
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18
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Bowen Z, Magnusson G, Diep M, Ayyangar U, Smirnov A, Kanold PO, Losert W. NeuroWRAP: integrating, validating, and sharing neurodata analysis workflows. Front Neuroinform 2023; 17:1082111. [PMID: 37181735 PMCID: PMC10166805 DOI: 10.3389/fninf.2023.1082111] [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: 10/27/2022] [Accepted: 04/07/2023] [Indexed: 05/16/2023] Open
Abstract
Multiphoton calcium imaging is one of the most powerful tools in modern neuroscience. However, multiphoton data require significant pre-processing of images and post-processing of extracted signals. As a result, many algorithms and pipelines have been developed for the analysis of multiphoton data, particularly two-photon imaging data. Most current studies use one of several algorithms and pipelines that are published and publicly available, and add customized upstream and downstream analysis elements to fit the needs of individual researchers. The vast differences in algorithm choices, parameter settings, pipeline composition, and data sources combine to make collaboration difficult, and raise questions about the reproducibility and robustness of experimental results. We present our solution, called NeuroWRAP (www.neurowrap.org), which is a tool that wraps multiple published algorithms together, and enables integration of custom algorithms. It enables development of collaborative, shareable custom workflows and reproducible data analysis for multiphoton calcium imaging data enabling easy collaboration between researchers. NeuroWRAP implements an approach to evaluate the sensitivity and robustness of the configured pipelines. When this sensitivity analysis is applied to a crucial step of image analysis, cell segmentation, we find a substantial difference between two popular workflows, CaImAn and Suite2p. NeuroWRAP harnesses this difference by introducing consensus analysis, utilizing two workflows in conjunction to significantly increase the trustworthiness and robustness of cell segmentation results.
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Affiliation(s)
- Zac Bowen
- Fraunhofer USA Center Mid-Atlantic, Riverdale, MD, United States
| | - Gudjon Magnusson
- Fraunhofer USA Center Mid-Atlantic, Riverdale, MD, United States
| | - Madeline Diep
- Fraunhofer USA Center Mid-Atlantic, Riverdale, MD, United States
| | - Ujjwal Ayyangar
- Fraunhofer USA Center Mid-Atlantic, Riverdale, MD, United States
| | - Aleksandr Smirnov
- Institute for Physical Science and Technology, University of Maryland, College Park, College Park, MD, United States
| | - Patrick O. Kanold
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Wolfgang Losert
- Institute for Physical Science and Technology, University of Maryland, College Park, College Park, MD, United States
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19
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Chen X, Ginoux F, Carbo-Tano M, Mora T, Walczak AM, Wyart C. Granger causality analysis for calcium transients in neuronal networks, challenges and improvements. eLife 2023; 12:e81279. [PMID: 36749019 PMCID: PMC10017105 DOI: 10.7554/elife.81279] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023] Open
Abstract
One challenge in neuroscience is to understand how information flows between neurons in vivo to trigger specific behaviors. Granger causality (GC) has been proposed as a simple and effective measure for identifying dynamical interactions. At single-cell resolution however, GC analysis is rarely used compared to directionless correlation analysis. Here, we study the applicability of GC analysis for calcium imaging data in diverse contexts. We first show that despite underlying linearity assumptions, GC analysis successfully retrieves non-linear interactions in a synthetic network simulating intracellular calcium fluctuations of spiking neurons. We highlight the potential pitfalls of applying GC analysis on real in vivo calcium signals, and offer solutions regarding the choice of GC analysis parameters. We took advantage of calcium imaging datasets from motoneurons in embryonic zebrafish to show how the improved GC can retrieve true underlying information flow. Applied to the network of brainstem neurons of larval zebrafish, our pipeline reveals strong driver neurons in the locus of the mesencephalic locomotor region (MLR), driving target neurons matching expectations from anatomical and physiological studies. Altogether, this practical toolbox can be applied on in vivo population calcium signals to increase the selectivity of GC to infer flow of information across neurons.
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Affiliation(s)
- Xiaowen Chen
- Laboratoire de physique de l'École normale supérieure, CNRS, PSL UniversityParisFrance
| | - Faustine Ginoux
- Spinal Sensory Signaling team, Sorbonne Université, Paris Brain Institute (Institut du Cerveau, ICM)ParisFrance
| | - Martin Carbo-Tano
- Spinal Sensory Signaling team, Sorbonne Université, Paris Brain Institute (Institut du Cerveau, ICM)ParisFrance
| | - Thierry Mora
- Laboratoire de physique de l'École normale supérieure, CNRS, PSL UniversityParisFrance
| | - Aleksandra M Walczak
- Laboratoire de physique de l'École normale supérieure, CNRS, PSL UniversityParisFrance
| | - Claire Wyart
- Spinal Sensory Signaling team, Sorbonne Université, Paris Brain Institute (Institut du Cerveau, ICM)ParisFrance
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20
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Kang H, Kanold PO. Auditory memory of complex sounds in sparsely distributed, highly correlated neurons in the auditory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.02.526903. [PMID: 36778416 PMCID: PMC9915716 DOI: 10.1101/2023.02.02.526903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Listening in complex sound environments requires rapid segregation of different sound sources e.g., speakers from each other, speakers from other sounds, or different instruments in an orchestra, and also adjust auditory processing on the prevailing sound conditions. Thus, fast encoding of inputs and identifying and adapting to reoccurring sounds are necessary for efficient and agile sound perception. This adaptation process represents an early phase of developing implicit learning of sound statistics and thus represents a form of auditory memory. The auditory cortex (ACtx) is known to play a key role in this encoding process but the underlying circuits and if hierarchical processing exists are not known. To identify ACtx regions and cells involved in this process, we simultaneously imaged population of neurons in different ACtx subfields using in vivo 2-photon imaging in awake mice. We used an experimental stimulus paradigm adapted from human studies that triggers rapid and robust implicit learning to passively present complex sounds and imaged A1 Layer 4 (L4), A1 L2/3, and A2 L2/3. In this paradigm, a frozen spectro-temporally complex 'Target' sound would be randomly re-occurring within a stream of random other complex sounds. We find distinct groups of cells that are specifically responsive to complex acoustic sequences across all subregions indicating that even the initial thalamocortical input layers (A1 L4) respond to complex sounds. Cells in all imaged regions showed decreased response amplitude for reoccurring Target sounds indicating that a memory signature is present even in the thalamocortical input layers. On the population level we find increased synchronized activity across cells to the Target sound and that this synchronized activity was more consistent across cells regardless of the duration of frozen token within Target sounds in A2, compared to A1. These findings suggest that ACtx and its input layers play a role in auditory memory for complex sounds and suggest a hierarchical structure of processes for auditory memory.
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Affiliation(s)
- HiJee Kang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 20215
| | - Patrick O Kanold
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 20215
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21
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Koren V, Bondanelli G, Panzeri S. Computational methods to study information processing in neural circuits. Comput Struct Biotechnol J 2023; 21:910-922. [PMID: 36698970 PMCID: PMC9851868 DOI: 10.1016/j.csbj.2023.01.009] [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: 10/29/2022] [Revised: 01/09/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
The brain is an information processing machine and thus naturally lends itself to be studied using computational tools based on the principles of information theory. For this reason, computational methods based on or inspired by information theory have been a cornerstone of practical and conceptual progress in neuroscience. In this Review, we address how concepts and computational tools related to information theory are spurring the development of principled theories of information processing in neural circuits and the development of influential mathematical methods for the analyses of neural population recordings. We review how these computational approaches reveal mechanisms of essential functions performed by neural circuits. These functions include efficiently encoding sensory information and facilitating the transmission of information to downstream brain areas to inform and guide behavior. Finally, we discuss how further progress and insights can be achieved, in particular by studying how competing requirements of neural encoding and readout may be optimally traded off to optimize neural information processing.
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Affiliation(s)
- Veronika Koren
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, Hamburg 20251, Germany
| | | | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, Hamburg 20251, Germany
- Istituto Italiano di Tecnologia, Via Melen 83, Genova 16152, Italy
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22
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Celotto M, Lemke S, Panzeri S. Inferring the temporal evolution of synaptic weights from dynamic functional connectivity. Brain Inform 2022; 9:28. [PMID: 36480076 PMCID: PMC9732068 DOI: 10.1186/s40708-022-00178-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/14/2022] [Indexed: 12/13/2022] Open
Abstract
How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issue. We first simulated recurrent neural network models of spiking neurons with spike timing-dependent plasticity mechanisms that generate time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that infer fixed and time-varying properties of synaptic connectivity from directed functional connectivity measures, such as cross-covariance and transfer entropy. We found that, while both cross-covariance and transfer entropy provide robust estimates of which synapses are present in the network and their communication delays, dynamic functional connectivity measured via cross-covariance better captures the evolution of synaptic weights over time. We also established how measures of information transmission delays from static functional connectivity computed over long recording periods (i.e., several hours) can improve shorter time-scale estimates of the temporal evolution of synaptic weights from dynamic functional connectivity. These results provide useful information about how to accurately estimate the temporal variation of synaptic strength from spiking activity measures.
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Affiliation(s)
- Marco Celotto
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy.
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
| | - Stefan Lemke
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, USA
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy.
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Estimating the Temporal Evolution of Synaptic Weights from Dynamic Functional Connectivity. Brain Inform 2022. [DOI: 10.1007/978-3-031-15037-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
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Shilling-Scrivo K, Mittelstadt J, Kanold PO. Altered Response Dynamics and Increased Population Correlation to Tonal Stimuli Embedded in Noise in Aging Auditory Cortex. J Neurosci 2021; 41:9650-9668. [PMID: 34611028 PMCID: PMC8612470 DOI: 10.1523/jneurosci.0839-21.2021] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 09/25/2021] [Accepted: 09/29/2021] [Indexed: 11/21/2022] Open
Abstract
Age-related hearing loss (presbycusis) is a chronic health condition that affects one-third of the world population. One hallmark of presbycusis is a difficulty hearing in noisy environments. Presbycusis can be separated into two components: alterations of peripheral mechanotransduction of sound in the cochlea and central alterations of auditory processing areas of the brain. Although the effects of the aging cochlea in hearing loss have been well studied, the role of the aging brain in hearing loss is less well understood. Therefore, to examine how age-related central processing changes affect hearing in noisy environments, we used a mouse model (Thy1-GCaMP6s X CBA) that has excellent peripheral hearing in old age. We used in vivo two-photon Ca2+ imaging to measure the responses of neuronal populations in auditory cortex (ACtx) of adult (2-6 months, nine male, six female, 4180 neurons) and aging mice (15-17 months, six male, three female, 1055 neurons) while listening to tones in noisy backgrounds. We found that ACtx neurons in aging mice showed larger responses to tones and have less suppressed responses consistent with reduced inhibition. Aging neurons also showed less sensitivity to temporal changes. Population analysis showed that neurons in aging mice showed higher pairwise activity correlations and showed a reduced diversity in responses to sound stimuli. Using neural decoding techniques, we show a loss of information in neuronal populations in the aging brain. Thus, aging not only affects the responses of single neurons but also affects how these neurons jointly represent stimuli.SIGNIFICANCE STATEMENT Aging results in hearing deficits particularly under challenging listening conditions. We show that auditory cortex contains distinct subpopulations of excitatory neurons that preferentially encode different stimulus features and that aging selectively reduces certain subpopulations. We also show that aging increases correlated activity between neurons and thereby reduces the response diversity in auditory cortex. The loss of population response diversity leads to a decrease of stimulus information and deficits in sound encoding, especially in noisy backgrounds. Future work determining the identities of circuits affected by aging could provide new targets for therapeutic strategies.
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Affiliation(s)
- Kelson Shilling-Scrivo
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland 21230
| | - Jonah Mittelstadt
- Department of Biology, University of Maryland, College Park, Maryland 20742
| | - Patrick O Kanold
- Department of Biology, University of Maryland, College Park, Maryland 20742
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 20215
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21205
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