1
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Hahn MA, Lendner JD, Anwander M, Slama KSJ, Knight RT, Lin JJ, Helfrich RF. A tradeoff between efficiency and robustness in the hippocampal-neocortical memory network during human and rodent sleep. Prog Neurobiol 2024:102672. [PMID: 39369838 DOI: 10.1016/j.pneurobio.2024.102672] [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: 05/13/2024] [Revised: 08/30/2024] [Accepted: 10/03/2024] [Indexed: 10/08/2024]
Abstract
Sleep constitutes a brain state of disengagement from the external world that supports memory consolidation and restores cognitive resources. The precise mechanisms how sleep and its varied stages support information processing remain largely unknown. Synaptic scaling models imply that daytime learning accumulates neural information, which is then consolidated and downregulated during sleep. Currently, there is a lack of in-vivo data from humans and rodents that elucidate if, and how, sleep renormalizes information processing capacities. From an information-theoretical perspective, a consolidation process should entail a reduction in neural pattern variability over the course of a night. Here, in a cross-species intracranial study, we identify a tradeoff in the neural population code during sleep where information coding efficiency is higher in the neocortex than in hippocampal archicortex in humans than in rodents as well as during wakefulness compared to sleep. Critically, non-REM sleep selectively reduces information coding efficiency through pattern repetition in the neocortex in both species, indicating a transition to a more robust information coding regime. Conversely, the coding regime in the hippocampus remained consistent from wakefulness to non-REM sleep. These findings suggest that new information could be imprinted to the long-term mnemonic storage in the neocortex through pattern repetition during sleep. Lastly, our results show that task engagement increased coding efficiency, while medically-induced unconsciousness disrupted the population code. In sum, these findings suggest that neural pattern variability could constitute a fundamental principle underlying cognitive engagement and memory formation, while pattern repetition reflects robust coding, possibly underlying the consolidation process.
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Affiliation(s)
- Michael A Hahn
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany.
| | - Janna D Lendner
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany; Department of Anesthesiology and Intensive Care Medicine, University Medical Center Tübingen; Hoppe-Seyler-Str 3, 72076 Tübingen, Germany
| | - Matthias Anwander
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany
| | - Katarina S J Slama
- Department of Psychology and the Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, 130 Barker Hall, CA 94720, USA
| | - Robert T Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, 130 Barker Hall, CA 94720, USA
| | - Jack J Lin
- Department of Neurology, UC Davis; 3160 Folsom Blvd, Sacramento, CA 95816, USA; Center for Mind and Brain, UC Davis; 267 Cousteau Pl, Davis, CA 95618, USA
| | - Randolph F Helfrich
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany.
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2
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Forlim CG, Brandt V, Jakubovski E, Ganos C, Kühn S, Müller-Vahl K. Symptom Network Analysis in a Large Sample of Children and Adults with a Chronic Tic Disorder. Mov Disord Clin Pract 2024. [PMID: 39054607 DOI: 10.1002/mdc3.14167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/06/2024] [Accepted: 06/28/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Chronic tic disorders (CTD) are multifaceted disorders characterized by multiple motor and/or vocal tics. They are often associated with complex tics including echophenomena, paliphenomena, and coprophenomena as well as psychiatric comorbidities such as attention deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). OBJECTIVES Our goal was to uncover the inter-relational structure of CTD and comorbid symptoms in children and adults and to understand changes in symptom structure across development. METHODS We used network and graph analyses to uncover the structure of association of symptoms in childhood/adolescence (n = 529) and adulthood (n = 503) and how this structure might change from childhood to adulthood, pinpointing core symptoms as a main target for interventions. RESULTS The analysis yielded core symptom networks in young and adult patients with CTD including complex tics and tic-related phenomena as well as touching people and objects. Core symptoms in childhood also included ADHD symptoms, whereas core symptoms in adults included symptoms of OCD instead. Interestingly, self-injurious behavior did not play a core role in the young CTD network, but became one of the central symptoms in adults with CDT. In addition, we found strong connections between complex motor and vocal tics as well as echolalia and echopraxia. CONCLUSIONS Next to other complex tics, echophenomena, paliphenomena, and coprophenomena can be regarded core symptoms of CTD. ADHD symptoms are closely related to CTD in childhood, whereas symptoms of OCD and self-injurious behavior are closely associated with CTD in adults. Our results suggest that a differentiation between motor and vocal tics is somewhat arbitrary.
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Affiliation(s)
- Caroline Garcia Forlim
- Neuronal Plasticity Working Group, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
| | - Valerie Brandt
- School of Psychology, Centre for Innovation in Mental Health, University of Southampton, Southampton, UK
- Clinic of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Ewgeni Jakubovski
- Clinic of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Christos Ganos
- Movement Disorder Clinic, Edmond J. Safra Program in Parkinson's Disease, Division of Neurology, University of Toronto, Toronto Western Hospital, Toronto, Ontario, Canada
| | - Simone Kühn
- Neuronal Plasticity Working Group, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
| | - Kirsten Müller-Vahl
- Clinic of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
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3
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Nair AS, Ghosh I, Fatoyinbo HO, Muni SS. On the higher-order smallest ring-star network of Chialvo neurons under diffusive couplings. CHAOS (WOODBURY, N.Y.) 2024; 34:073135. [PMID: 39038467 DOI: 10.1063/5.0217017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 07/03/2024] [Indexed: 07/24/2024]
Abstract
Network dynamical systems with higher-order interactions are a current trending topic, pervasive in many applied fields. However, our focus in this work is neurodynamics. We numerically study the dynamics of the smallest higher-order network of neurons arranged in a ring-star topology. The dynamics of each node in this network is governed by the Chialvo neuron map, and they interact via linear diffusive couplings. This model is perceived to imitate the nonlinear dynamical properties exhibited by a realistic nervous system where the neurons transfer information through multi-body interactions. We deploy the higher-order coupling strength as the primary bifurcation parameter. We start by analyzing our model using standard tools from dynamical systems theory: fixed point analysis, Jacobian matrix, and bifurcation patterns. We observe the coexistence of disparate chaotic attractors. We also observe an interesting route to chaos from a fixed point via period-doubling and the appearance of cyclic quasiperiodic closed invariant curves. Furthermore, we numerically observe the existence of codimension-1 bifurcation points: saddle-node, period-doubling, and Neimark-Sacker. We also qualitatively study the typical phase portraits of the system, and numerically quantify chaos and complexity using the 0-1 test and sample entropy measure, respectively. Finally, we study the synchronization behavior among the neurons using the cross correlation coefficient and the Kuramoto order parameter. We conjecture that unfolding these patterns and behaviors of the network model will help us identify different states of the nervous system, further aiding us in dealing with various neural diseases and nervous disorders.
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Affiliation(s)
- Anjana S Nair
- School of Digital Sciences, Digital University Kerala, Technopark Phase-IV campus, Mangalapuram 695317, Kerala, India
| | - Indranil Ghosh
- School of Mathematical and Computational Sciences, Massey University, Colombo Road, Palmerston North 4410, New Zealand
| | - Hammed O Fatoyinbo
- Department of Mathematical Sciences, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand
| | - Sishu S Muni
- School of Digital Sciences, Digital University Kerala, Technopark Phase-IV campus, Mangalapuram 695317, Kerala, India
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4
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Bardella G, Giuffrida V, Giarrocco F, Brunamonti E, Pani P, Ferraina S. Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network. Netw Neurosci 2024; 8:597-622. [PMID: 38952814 PMCID: PMC11168728 DOI: 10.1162/netn_a_00365] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 01/18/2024] [Indexed: 07/03/2024] Open
Abstract
Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.
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Affiliation(s)
- Giampiero Bardella
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Valentina Giuffrida
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Franco Giarrocco
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Emiliano Brunamonti
- 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
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5
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Tan H, Zeng X, Ni J, Liang K, Xu C, Zhang Y, Wang J, Li Z, Yang J, Han C, Gao Y, Yu X, Han S, Meng F, Ma Y. Intracranial EEG signals disentangle multi-areal neural dynamics of vicarious pain perception. Nat Commun 2024; 15:5203. [PMID: 38890380 PMCID: PMC11189531 DOI: 10.1038/s41467-024-49541-1] [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: 07/03/2023] [Accepted: 06/07/2024] [Indexed: 06/20/2024] Open
Abstract
Empathy enables understanding and sharing of others' feelings. Human neuroimaging studies have identified critical brain regions supporting empathy for pain, including the anterior insula (AI), anterior cingulate (ACC), amygdala, and inferior frontal gyrus (IFG). However, to date, the precise spatio-temporal profiles of empathic neural responses and inter-regional communications remain elusive. Here, using intracranial electroencephalography, we investigated electrophysiological signatures of vicarious pain perception. Others' pain perception induced early increases in high-gamma activity in IFG, beta power increases in ACC, but decreased beta power in AI and amygdala. Vicarious pain perception also altered the beta-band-coordinated coupling between ACC, AI, and amygdala, as well as increased modulation of IFG high-gamma amplitudes by beta phases of amygdala/AI/ACC. We identified a necessary combination of neural features for decoding vicarious pain perception. These spatio-temporally specific regional activities and inter-regional interactions within the empathy network suggest a neurodynamic model of human pain empathy.
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Affiliation(s)
- Huixin Tan
- State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Xiaoyu Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Jun Ni
- State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Kun Liang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Cuiping Xu
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yanyang Zhang
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
| | - Jiaxin Wang
- State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Zizhou Li
- State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Jiaxin Yang
- State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Chunlei Han
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuan Gao
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xinguang Yu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
| | - Shihui Han
- School of Psychological and Cognitive Sciences, PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Fangang Meng
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- Chinese Institute for Brain Research, Beijing, China.
| | - Yina Ma
- State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
- Chinese Institute for Brain Research, Beijing, China.
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6
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Kanemura I, Kitano K. Emergence of input selective recurrent dynamics via information transfer maximization. Sci Rep 2024; 14:13631. [PMID: 38871759 DOI: 10.1038/s41598-024-64417-6] [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: 03/11/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024] Open
Abstract
Network structures of the brain have wiring patterns specialized for specific functions. These patterns are partially determined genetically or evolutionarily based on the type of task or stimulus. These wiring patterns are important in information processing; however, their organizational principles are not fully understood. This study frames the maximization of information transmission alongside the reduction of maintenance costs as a multi-objective optimization challenge, utilizing information theory and evolutionary computing algorithms with an emphasis on the visual system. The goal is to understand the underlying principles of circuit formation by exploring the patterns of wiring and information processing. The study demonstrates that efficient information transmission necessitates sparse circuits with internal modular structures featuring distinct wiring patterns. Significant trade-offs underscore the necessity of balance in wiring pattern development. The dynamics of effective circuits exhibit moderate flexibility in response to stimuli, in line with observations from prior visual system studies. Maximizing information transfer may allow for the self-organization of information processing functions similar to actual biological circuits, without being limited by modality. This study offers insights into neuroscience and the potential to improve reservoir computing performance.
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Affiliation(s)
- Itsuki Kanemura
- Graduate School of Information Science and Engineering, Ritsumeikan University, 2-150, Iwakuracho, Ibaraki, Osaka, 5670871, Japan.
| | - Katsunori Kitano
- Department of Information Science and Engineering, Ritsumeikan University, 2-150, Iwakuracho, Ibaraki, Osaka, 5670871, Japan
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7
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Song YM, Jeong J, de Los Reyes AA, Lim D, Cho CH, Yeom JW, Lee T, Lee JB, Lee HJ, Kim JK. Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data. EBioMedicine 2024; 103:105094. [PMID: 38579366 PMCID: PMC11002811 DOI: 10.1016/j.ebiom.2024.105094] [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: 10/02/2023] [Revised: 03/14/2024] [Accepted: 03/17/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown. METHODS We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method. FINDINGS The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms. INTERPRETATION Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively. FUNDING Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.
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Affiliation(s)
- Yun Min Song
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Jaegwon Jeong
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Aurelio A de Los Reyes
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Institute of Mathematics, University of the Philippines Diliman, Quezon City, 1101, Philippines
| | - Dongju Lim
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Chul-Hyun Cho
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Ji Won Yeom
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Taek Lee
- Division of Computer Science and Engineering, Sun Moon University, Asan, 31460, Republic of Korea
| | - Jung-Been Lee
- Division of Computer Science and Engineering, Sun Moon University, Asan, 31460, Republic of Korea
| | - Heon-Jeong Lee
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea.
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
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8
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Álvarez Chaves M, Gupta HV, Ehret U, Guthke A. On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data. ENTROPY (BASEL, SWITZERLAND) 2024; 26:387. [PMID: 38785636 PMCID: PMC11119730 DOI: 10.3390/e26050387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k-nearest neighbors (k-NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback-Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators' performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k-NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines.
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Affiliation(s)
- Manuel Álvarez Chaves
- Stuttgart Center for Simulation Science, Cluster of Excellence EXC 2075, University of Stuttgart, 70569 Stuttgart, Germany
| | - Hoshin V. Gupta
- Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ 85721, USA
| | - Uwe Ehret
- Institute of Water and River Basin Management, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Anneli Guthke
- Stuttgart Center for Simulation Science, Cluster of Excellence EXC 2075, University of Stuttgart, 70569 Stuttgart, Germany
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9
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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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10
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Farrell JS, Hwaun E, Dudok B, Soltesz I. Neural and behavioural state switching during hippocampal dentate spikes. Nature 2024; 628:590-595. [PMID: 38480889 PMCID: PMC11023929 DOI: 10.1038/s41586-024-07192-8] [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: 03/14/2023] [Accepted: 02/01/2024] [Indexed: 04/06/2024]
Abstract
Distinct brain and behavioural states are associated with organized neural population dynamics that are thought to serve specific cognitive functions1-3. Memory replay events, for example, occur during synchronous population events called sharp-wave ripples in the hippocampus while mice are in an 'offline' behavioural state, enabling cognitive mechanisms such as memory consolidation and planning4-11. But how does the brain re-engage with the external world during this behavioural state and permit access to current sensory information or promote new memory formation? Here we found that the hippocampal dentate spike, an understudied population event that frequently occurs between sharp-wave ripples12, may underlie such a mechanism. We show that dentate spikes are associated with distinctly elevated brain-wide firing rates, primarily observed in higher order networks, and couple to brief periods of arousal. Hippocampal place coding during dentate spikes aligns to the mouse's current spatial location, unlike the memory replay accompanying sharp-wave ripples. Furthermore, inhibiting neural activity during dentate spikes disrupts associative memory formation. Thus, dentate spikes represent a distinct brain state and support memory during non-locomotor behaviour, extending the repertoire of cognitive processes beyond the classical offline functions.
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Affiliation(s)
- Jordan S Farrell
- Department of Neurosurgery, Stanford University, Stanford, CA, USA.
- F.M. Kirby Neurobiology Center and Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Ernie Hwaun
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Barna Dudok
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Departments of Neurology and Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
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11
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Corsini A, Tomassini A, Pastore A, Delis I, Fadiga L, D'Ausilio A. Speech perception difficulty modulates theta-band encoding of articulatory synergies. J Neurophysiol 2024; 131:480-491. [PMID: 38323331 DOI: 10.1152/jn.00388.2023] [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: 10/23/2023] [Revised: 01/04/2024] [Accepted: 01/25/2024] [Indexed: 02/08/2024] Open
Abstract
The human brain tracks available speech acoustics and extrapolates missing information such as the speaker's articulatory patterns. However, the extent to which articulatory reconstruction supports speech perception remains unclear. This study explores the relationship between articulatory reconstruction and task difficulty. Participants listened to sentences and performed a speech-rhyming task. Real kinematic data of the speaker's vocal tract were recorded via electromagnetic articulography (EMA) and aligned to corresponding acoustic outputs. We extracted articulatory synergies from the EMA data with principal component analysis (PCA) and employed partial information decomposition (PID) to separate the electroencephalographic (EEG) encoding of acoustic and articulatory features into unique, redundant, and synergistic atoms of information. We median-split sentences into easy (ES) and hard (HS) based on participants' performance and found that greater task difficulty involved greater encoding of unique articulatory information in the theta band. We conclude that fine-grained articulatory reconstruction plays a complementary role in the encoding of speech acoustics, lending further support to the claim that motor processes support speech perception.NEW & NOTEWORTHY Top-down processes originating from the motor system contribute to speech perception through the reconstruction of the speaker's articulatory movement. This study investigates the role of such articulatory simulation under variable task difficulty. We show that more challenging listening tasks lead to increased encoding of articulatory kinematics in the theta band and suggest that, in such situations, fine-grained articulatory reconstruction complements acoustic encoding.
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Affiliation(s)
- Alessandro Corsini
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Università di Ferrara, Ferrara, Italy
| | - Alice Tomassini
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Università di Ferrara, Ferrara, Italy
| | - Aldo Pastore
- Laboratorio NEST, Scuola Normale Superiore, Pisa, Italy
| | - Ioannis Delis
- School of Biomedical Sciences, University of Leeds, Leeds, United Kingdom
| | - Luciano Fadiga
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Università di Ferrara, Ferrara, Italy
| | - Alessandro D'Ausilio
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Università di Ferrara, Ferrara, Italy
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12
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Machida I, Shishikura M, Yamane Y, Sakai K. Representation of Natural Contours by a Neural Population in Monkey V4. eNeuro 2024; 11:ENEURO.0445-23.2024. [PMID: 38423791 PMCID: PMC10946029 DOI: 10.1523/eneuro.0445-23.2024] [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: 10/25/2023] [Revised: 02/18/2024] [Accepted: 02/22/2024] [Indexed: 03/02/2024] Open
Abstract
The cortical visual area, V4, has been considered to code contours that contribute to the intermediate-level representation of objects. The neural responses to the complex contour features intrinsic to natural contours are expected to clarify the essence of the representation. To approach the cortical coding of natural contours, we investigated the simultaneous coding of multiple contour features in monkey (Macaca fuscata) V4 neurons and their population-level representation. A substantial number of neurons showed significant tuning for two or more features such as curvature and closure, indicating that a substantial number of V4 neurons simultaneously code multiple contour features. A large portion of the neurons responded vigorously to acutely curved contours that surrounded the center of classical receptive field, suggesting that V4 neurons tend to code prominent features of object contours. The analysis of mutual information (MI) between the neural responses and each contour feature showed that most neurons exhibited similar magnitudes for each type of MI, indicating that many neurons showing the responses depended on multiple contour features. We next examined the population-level representation by using multidimensional scaling analysis. The neural preferences to the multiple contour features and that to natural stimuli compared with silhouette stimuli increased along with the primary and secondary axes, respectively, indicating the contribution of the multiple contour features and surface textures in the population responses. Our analyses suggested that V4 neurons simultaneously code multiple contour features in natural images and represent contour and surface properties in population.
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Affiliation(s)
- Itsuki Machida
- Department of Computer Science, University of Tsukuba, Tsukuba 305-8573, Japan
| | - Motofumi Shishikura
- Department of Computer Science, University of Tsukuba, Tsukuba 305-8573, Japan
| | - Yukako Yamane
- Neural Computation Unit, Okinawa Institute of Science and Technology, Okinawa 904-0495, Japan
| | - Ko Sakai
- Department of Computer Science, University of Tsukuba, Tsukuba 305-8573, Japan
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13
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Bird AD, Cuntz H, Jedlicka P. Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus. PLoS Comput Biol 2024; 20:e1010706. [PMID: 38377108 PMCID: PMC10906873 DOI: 10.1371/journal.pcbi.1010706] [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: 11/03/2022] [Revised: 03/01/2024] [Accepted: 12/13/2023] [Indexed: 02/22/2024] Open
Abstract
Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles.
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Affiliation(s)
- Alexander D. Bird
- Computer-Based Modelling in the field of 3R Animal Protection, ICAR3R, Faculty of Medicine, Justus Liebig University, Giessen, Germany
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt-am-Main, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt-am-Main, Germany
- Translational Neuroscience Network Giessen, Germany
| | - Hermann Cuntz
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt-am-Main, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt-am-Main, Germany
- Translational Neuroscience Network Giessen, Germany
| | - Peter Jedlicka
- Computer-Based Modelling in the field of 3R Animal Protection, ICAR3R, Faculty of Medicine, Justus Liebig University, Giessen, Germany
- Translational Neuroscience Network Giessen, Germany
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14
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Pérez-González D, Lao-Rodríguez AB, Aedo-Sánchez C, Malmierca MS. Acetylcholine modulates the precision of prediction error in the auditory cortex. eLife 2024; 12:RP91475. [PMID: 38241174 PMCID: PMC10942646 DOI: 10.7554/elife.91475] [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: 01/21/2024] Open
Abstract
A fundamental property of sensory systems is their ability to detect novel stimuli in the ambient environment. The auditory brain contains neurons that decrease their response to repetitive sounds but increase their firing rate to novel or deviant stimuli; the difference between both responses is known as stimulus-specific adaptation or neuronal mismatch (nMM). Here, we tested the effect of microiontophoretic applications of ACh on the neuronal responses in the auditory cortex (AC) of anesthetized rats during an auditory oddball paradigm, including cascade controls. Results indicate that ACh modulates the nMM, affecting prediction error responses but not repetition suppression, and this effect is manifested predominantly in infragranular cortical layers. The differential effect of ACh on responses to standards, relative to deviants (in terms of averages and variances), was consistent with the representational sharpening that accompanies an increase in the precision of prediction errors. These findings suggest that ACh plays an important role in modulating prediction error signaling in the AC and gating the access of these signals to higher cognitive levels.
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Affiliation(s)
- David Pérez-González
- Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando GallegoSalamancaSpain
- Institute for Biomedical Research of Salamanca (IBSAL)SalamancaSpain
- Department of Basic Psychology, Psychobiology and Behavioural Science Methodology, Faculty of Psychology, Campus Ciudad Jardín, University of SalamancaSalamancaSpain
| | - Ana Belén Lao-Rodríguez
- Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando GallegoSalamancaSpain
- Institute for Biomedical Research of Salamanca (IBSAL)SalamancaSpain
| | - Cristian Aedo-Sánchez
- Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando GallegoSalamancaSpain
- Institute for Biomedical Research of Salamanca (IBSAL)SalamancaSpain
| | - Manuel S Malmierca
- Cognitive and Auditory Neuroscience Laboratory, Institute of Neuroscience of Castilla y León, Calle Pintor Fernando GallegoSalamancaSpain
- Institute for Biomedical Research of Salamanca (IBSAL)SalamancaSpain
- Department of Biology and Pathology, Faculty of Medicine, Campus Miguel de Unamuno, University of SalamancaSalamancaSpain
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15
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Kotamraju BP, Eggers TE, McCallum GA, Durand DM. Selective chronic recording in small nerve fascicles of sciatic nerve with carbon nanotube yarns in rats. J Neural Eng 2024; 20:066041. [PMID: 38100824 PMCID: PMC10765114 DOI: 10.1088/1741-2552/ad1611] [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: 08/02/2023] [Revised: 11/15/2023] [Accepted: 12/15/2023] [Indexed: 12/17/2023]
Abstract
Objective. The primary challenge faced in the field of neural rehabilitation engineering is the limited advancement in nerve interface technology, which currently fails to match the mechanical properties of small-diameter nerve fascicles. Novel developments are necessary to enable long-term, chronic recording from a multitude of small fascicles, allowing for the recovery of motor intent and sensory signals.Approach. In this study, we analyze the chronic recording capabilities of carbon nanotube yarn electrodes in the peripheral somatic nervous system. The electrodes were surgically implanted in the sciatic nerve's three individual fascicles in rats, enabling the recording of neural activity during gait. Signal-to-noise ratio (SNR) and information theory were employed to analyze the data, demonstrating the superior recording capabilities of the electrodes. Flat interface nerve electrode and thin-film longitudinal intrafascicular electrode electrodes were used as a references to assess the results from SNR and information theory analysis.Main results. The electrodes exhibited the ability to record chronic signals with SNRs reaching as high as 15 dB, providing 12 bits of information for the sciatic nerve, a significant improvement over previous methods. Furthermore, the study revealed that the SNR and information content of the neural signals remained consistent over a period of 12 weeks across three different fascicles, indicating the stability of the interface. The signals recorded from these electrodes were also analyzed for selectivity using information theory metrics, which showed an information sharing of approximately 1.4 bits across the fascicles.Significance. The ability to safely and reliably record from multiple fascicles of different nerves simultaneously over extended periods of time holds substantial implications for the field of neural and rehabilitation engineering. This advancement addresses the limitation of current nerve interface technologies and opens up new possibilities for enhancing neural rehabilitation and control.
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Affiliation(s)
- B P Kotamraju
- Case Western Reserve University, Neural Engineering Center, Biomedical Engineering, Cleveland, OH, United States of America
| | - Thomas E Eggers
- Department of Neurosurgery, Emory University, Atlanta, GA, United States of America
| | - Grant A McCallum
- Case Western Reserve University, Neural Engineering Center, Biomedical Engineering, Cleveland, OH, United States of America
| | - Dominique M Durand
- Case Western Reserve University, Neural Engineering Center, Biomedical Engineering, Cleveland, OH, United States of America
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16
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Griffa A, Mach M, Dedelley J, Gutierrez-Barragan D, Gozzi A, Allali G, Grandjean J, Van De Ville D, Amico E. Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice. Nat Commun 2023; 14:8216. [PMID: 38081838 PMCID: PMC10713651 DOI: 10.1038/s41467-023-43971-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brain's computational capacities that subtend almost all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapt across evolution to accomplish increasingly complex functions? By applying a graph- and information-theory approach to assess information-related pathways in male mouse, macaque and human brains, we show a brain communication gap between selective information transmission in non-human mammals, where brain regions share information through single polysynaptic pathways, and parallel information transmission in humans, where regions share information through multiple parallel pathways. In humans, parallel transmission acts as a major connector between unimodal and transmodal systems. The layout of information-related pathways is unique to individuals across different mammalian species, pointing at the individual-level specificity of information routing architecture. Our work provides evidence that different communication patterns are tied to the evolution of mammalian brain networks.
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Affiliation(s)
- Alessandra Griffa
- Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - Mathieu Mach
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Julien Dedelley
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Gilles Allali
- Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Joanes Grandjean
- Department of Medical Imaging, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 EN, Nijmegen, The Netherlands
| | - Dimitri Van De Ville
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Medical Image Processing Laboratory, Neuro-X Institute, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
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17
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Abbasi S, Wolff A, Çatal Y, Northoff G. Increased noise relates to abnormal excitation-inhibition balance in schizophrenia: a combined empirical and computational study. Cereb Cortex 2023; 33:10477-10491. [PMID: 37562844 PMCID: PMC10560578 DOI: 10.1093/cercor/bhad297] [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: 05/29/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/12/2023] Open
Abstract
Electroencephalography studies link sensory processing issues in schizophrenia to increased noise level-noise here is background spontaneous activity-as measured by the signal-to-noise ratio. The mechanism, however, of such increased noise is unknown. We investigate if this relates to changes in cortical excitation-inhibition balance, which has been observed to be atypical in schizophrenia, by combining electroencephalography and computational modeling. Our electroencephalography task results, for which the local field potentials can be used as a proxy, show lower signal-to-noise ratio due to higher noise in schizophrenia. Both electroencephalography rest and task states exhibit higher levels of excitation in the functional excitation-inhibition (as a proxy of excitation-inhibition balance). This suggests a relationship between increased noise and atypical excitation in schizophrenia, which was addressed by using computational modeling. A Leaky Integrate-and-Fire model was used to simulate the effects of varying degrees of noise on excitation-inhibition balance, local field potential, NMDA current, and . Results show a noise-related increase in the local field potential, excitation in excitation-inhibition balance, pyramidal NMDA current, and spike rate. Mutual information and mediation analysis were used to explore a cross-level relationship, showing that the cortical local field potential plays a key role in transferring the effect of noise to the cellular population level of NMDA.
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Affiliation(s)
- Samira Abbasi
- University of Ottawa, Institute of Mental Health Research, Ottawa ON K1Z 7K4, Canada
- Department of Biomedical Engineering, Hamedan University of Technology, Hamedan 65169-13733, Iran
| | - Annemarie Wolff
- University of Ottawa, Institute of Mental Health Research, Ottawa ON K1Z 7K4, Canada
| | - Yasir Çatal
- University of Ottawa, Institute of Mental Health Research, Ottawa ON K1Z 7K4, Canada
| | - Georg Northoff
- University of Ottawa, Institute of Mental Health Research, Ottawa ON K1Z 7K4, Canada
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18
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Glanz RM, Sokoloff G, Blumberg MS. Neural decoding reveals specialized kinematic tuning after an abrupt cortical transition. Cell Rep 2023; 42:113119. [PMID: 37690023 PMCID: PMC10591925 DOI: 10.1016/j.celrep.2023.113119] [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: 02/01/2023] [Revised: 06/08/2023] [Accepted: 08/24/2023] [Indexed: 09/12/2023] Open
Abstract
The primary motor cortex (M1) exhibits a protracted period of development, including the development of a sensory representation long before motor outflow emerges. In rats, this representation is present by postnatal day (P) 8, when M1 activity is "discontinuous." Here, we ask how the representation changes upon the transition to "continuous" activity at P12. We use neural decoding to predict forelimb movements from M1 activity and show that a linear decoder effectively predicts limb movements at P8 but not at P12; instead, a nonlinear decoder better predicts limb movements at P12. The altered decoder performance reflects increased complexity and uniqueness of kinematic information in M1. We next show that M1's representation at P12 is more susceptible to "lesioning" of inputs and "transplanting" of M1's encoding scheme from one pup to another. Thus, the emergence of continuous M1 activity signals the developmental onset of more complex, informationally sparse, and individualized sensory representations.
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Affiliation(s)
- Ryan M Glanz
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - Greta Sokoloff
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, USA
| | - Mark S Blumberg
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, USA.
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19
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Abstract
The QBIT theory is a recently introduced multi-disciplinary approach to the problem of consciousness. One of the main axioms of the theory is that when information-theoretic certainty of an observer about a stimulus goes beyond a certain threshold, the observer becomes conscious of that stimulus. This axiom could provide an explanation for how the brain generates consciousness.In short, the QBIT theory suggests that the brain generates consciousness by reducing the entropy of its internal representations below a critical threshold. This paper explains how the brain gradually minimizes the entropy of its internal representations and consequently generate minimum-entropy representations (also known as conscious representations or qualia). The paper also explores the consequences of this entropy-minimization process in the context of quantum information theory.
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Affiliation(s)
- Majid Beshkar
- Tehran University of Medical Sciences, Tehran, Iran.
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20
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Martínez Vásquez DA, Posada-Quintero HF, Rivera Pinzón DM. Mutual Information between EDA and EEG in Multiple Cognitive Tasks and Sleep Deprivation Conditions. Behav Sci (Basel) 2023; 13:707. [PMID: 37753985 PMCID: PMC10525564 DOI: 10.3390/bs13090707] [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: 06/30/2023] [Revised: 08/05/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Sleep deprivation, a widespread phenomenon that affects one-third of normal American adults, induces adverse changes in physical and cognitive performance, which in turn increases the occurrence of accidents. Sleep deprivation is known to increase resting blood pressure and decrease muscle sympathetic nerve activity. Monitoring changes in the interplay between the central and autonomic sympathetic nervous system can be a potential indicator of human's readiness to perform tasks that involve a certain level of cognitive load (e.g., driving). The electroencephalogram (EEG) is the standard to assess the brain's activity. The electrodermal activity (EDA) is a reflection of the general state of arousal regulated by the activation of the sympathetic nervous system through sweat gland stimulation. In this work, we calculated the mutual information between EDA and EEG recordings in order to consider linear and non-linear interactions and provide an insight of the relationship between brain activity and peripheral autonomic sympathetic activity. We analyzed EEG and EDA data from ten participants performing four cognitive tasks every two hours during 24 h (12 trials). We decomposed EEG data into delta, theta, alpha, beta, and gamma spectral components, and EDA into tonic and phasic components. The results demonstrate high values of mutual information between the EDA and delta component of EEG, mainly in working memory tasks. Additionally, we found an increase in the theta component of EEG in the presence of fatigue caused by sleep deprivation, the alpha component in tasks demanding inhibition and attention, and the delta component in working memory tasks. In terms of the location of brain activity, most of the tasks report high mutual information in frontal regions in the initial trials, with a trend to decrease and become uniform for all the nine analyzed EEG channels as a consequence of the sleep deprivation effect. Our results evidence the interplay between central and sympathetic nervous activity and can be used to mitigate the consequences of sleep deprivation.
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Affiliation(s)
- David Alejandro Martínez Vásquez
- Electronic Engineering Faculty, Universidad Santo Tomás, Bogotá 110231, Colombia
- Department of Technology, Universidad Pedagógica Nacional, Bogotá 110221, Colombia;
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21
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Merritt SH, Gaffuri K, Zak PJ. Accurately predicting hit songs using neurophysiology and machine learning. Front Artif Intell 2023; 6:1154663. [PMID: 37408542 PMCID: PMC10318137 DOI: 10.3389/frai.2023.1154663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 05/09/2023] [Indexed: 07/07/2023] Open
Abstract
Identifying hit songs is notoriously difficult. Traditionally, song elements have been measured from large databases to identify the lyrical aspects of hits. We took a different methodological approach, measuring neurophysiologic responses to a set of songs provided by a streaming music service that identified hits and flops. We compared several statistical approaches to examine the predictive accuracy of each technique. A linear statistical model using two neural measures identified hits with 69% accuracy. Then, we created a synthetic set data and applied ensemble machine learning to capture inherent non-linearities in neural data. This model classified hit songs with 97% accuracy. Applying machine learning to the neural response to 1st min of songs accurately classified hits 82% of the time showing that the brain rapidly identifies hit music. Our results demonstrate that applying machine learning to neural data can substantially increase classification accuracy for difficult to predict market outcomes.
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Affiliation(s)
- Sean H. Merritt
- Center for Neuroeconomics Studies, Claremont Graduate University, Claremont, CA, United States
| | - Kevin Gaffuri
- Center for Neuroeconomics Studies, Claremont Graduate University, Claremont, CA, United States
| | - Paul J. Zak
- Center for Neuroeconomics Studies, Claremont Graduate University, Claremont, CA, United States
- Immersion Neuroscience, Henderson, NV, United States
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22
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Fuhrer J, Glette K, Llorens A, Endestad T, Solbakk AK, Blenkmann AO. Quantifying evoked responses through information-theoretical measures. Front Neuroinform 2023; 17:1128866. [PMID: 37287586 PMCID: PMC10242156 DOI: 10.3389/fninf.2023.1128866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/04/2023] [Indexed: 06/09/2023] Open
Abstract
Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t-test, is rare. Here, such a comparison is carried out by evaluating the novel method of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t-test. We do so by applying each method to event-related potentials and event-related activity in different frequency bands originating from intracranial electroencephalography recordings of humans and marmoset monkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition effects are present.
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Affiliation(s)
- Julian Fuhrer
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Kyrre Glette
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Anaïs Llorens
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Tor Endestad
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Anne-Kristin Solbakk
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Alejandro Omar Blenkmann
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
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23
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Page WK, Sulon DW, Duffy CJ. Neural activity during monkey vehicular wayfinding. J Neurol Sci 2023; 446:120593. [PMID: 36827811 DOI: 10.1016/j.jns.2023.120593] [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: 07/23/2022] [Revised: 02/06/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
Navigation gets us from place to place, creating a path to arrive at a goal. We trained a monkey to steer a motorized cart in a large room, beginning at its trial-by-trial start location and ending at a trial-by-trial cued goal location. While the monkey steered its autonomously chosen path to its goal, we recorded neural activity simultaneously in both the hippocampus (HPC) and medial superior temporal (MST) cortex. Local field potentials (LFPs) in these sites show similar patterns of activity with the 15-30 Hz band highlighting specific room locations. In contrast, 30-100 Hz LFPs support a unified map of the behaviorally relevant start and goal locations. The single neuron responses (SNRs) do not substantially contribute to room or start-goal maps. Rather, the SNRs form a continuum from neurons that are most active when the monkey is moving on a path toward the goal, versus other neurons that are most active when the monkey deviates from paths toward the goal. Granger analyses suggest that HPC firing precedes MST firing during cueing at the trial start location, mainly mediated by off-path neurons. In contrast, MST precedes HPC firing during steering, mainly mediated by on-path neurons. Interactions between MST and HPC are mediated by the parallel activation of on-path and off-path neurons, selectively activated across stages of this wayfinding task.
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Affiliation(s)
- William K Page
- Dept. of Neurology, University of Rochester Medical Ctr., Rochester, NY 14642, USA
| | - David W Sulon
- Dept. of Neurology, Penn State Health Medical Ctr., Hershey, PA 17036, USA
| | - Charles J Duffy
- Dept. of Neurology, University of Rochester Medical Ctr., Rochester, NY 14642, USA; Dept. of Neurology, Penn State Health Medical Ctr., Hershey, PA 17036, USA; Dept. of Neurology, University Hospitals and Case Western Reserve University, Cleveland, OH 44122, USA.
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24
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Hintze A, Adami C. Detecting Information Relays in Deep Neural Networks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:401. [PMID: 36981289 PMCID: PMC10047156 DOI: 10.3390/e25030401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/20/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Deep learning of artificial neural networks (ANNs) is creating highly functional processes that are, unfortunately, nearly as hard to interpret as their biological counterparts. Identification of functional modules in natural brains plays an important role in cognitive and neuroscience alike, and can be carried out using a wide range of technologies such as fMRI, EEG/ERP, MEG, or calcium imaging. However, we do not have such robust methods at our disposal when it comes to understanding functional modules in artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here, we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information IR. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to identify computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.
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Affiliation(s)
- Arend Hintze
- Department of MicroData Analytics, Dalarna University, 791 31 Falun, Sweden
- BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA
| | - Christoph Adami
- BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
- Program in Evolution, Ecology, and Behavior, Michigan State University, East Lansing, MI 48824, USA
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25
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Niizato T, Murakami H, Musha T. Functional duality in group criticality via ambiguous interactions. PLoS Comput Biol 2023; 19:e1010869. [PMID: 36791061 PMCID: PMC9931117 DOI: 10.1371/journal.pcbi.1010869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 01/10/2023] [Indexed: 02/16/2023] Open
Abstract
Critical phenomena are wildly observed in living systems. If the system is at criticality, it can quickly transfer information and achieve optimal response to external stimuli. Especially, animal collective behavior has numerous critical properties, which are related to other research regions, such as the brain system. Although the critical phenomena influencing collective behavior have been extensively studied, two important aspects require clarification. First, these critical phenomena never occur on a single scale but are instead nested from the micro- to macro-levels (e.g., from a Lévy walk to scale-free correlation). Second, the functional role of group criticality is unclear. To elucidate these aspects, the ambiguous interaction model is constructed in this study; this model has a common framework and is a natural extension of previous representative models (such as the Boids and Vicsek models). We demonstrate that our model can explain the nested criticality of collective behavior across several scales (considering scale-free correlation, super diffusion, Lévy walks, and 1/f fluctuation for relative velocities). Our model can also explain the relationship between scale-free correlation and group turns. To examine this relation, we propose a new method, applying partial information decomposition (PID) to two scale-free induced subgroups. Using PID, we construct information flows between two scale-free induced subgroups and find that coupling of the group morphology (i.e., the velocity distributions) and its fluctuation power (i.e., the fluctuation distributions) likely enable rapid group turning. Thus, the flock morphology may help its internal fluctuation convert to dynamic behavior. Our result sheds new light on the role of group morphology, which is relatively unheeded, retaining the importance of fluctuation dynamics in group criticality.
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Affiliation(s)
- Takayuki Niizato
- Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Ibaraki, Japan
- * E-mail:
| | - Hisashi Murakami
- Faculty of Information and Human Science, Kyoto Institute of Technology, Sakyo-ku, Kyoto city, Kyoto, Japan
| | - Takuya Musha
- Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Ibaraki, Japan
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26
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Glanz R, Sokoloff G, Blumberg MS. Cortical Representation of Movement Across the Developmental Transition to Continuous Neural Activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.22.525085. [PMID: 36711887 PMCID: PMC9882351 DOI: 10.1101/2023.01.22.525085] [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: 06/18/2023]
Abstract
Primary motor cortex (M1) exhibits a protracted period of development that includes the establishment of a somatosensory map long before motor outflow emerges. In rats, the sensory representation is established by postnatal day (P) 8 when cortical activity is still "discontinuous." Here, we ask how the representation survives the sudden transition to noisy "continuous" activity at P12. Using neural decoding to predict forelimb movements based solely on M1 activity, we show that a linear decoder is sufficient to predict limb movements at P8, but not at P12; in contrast, a nonlinear decoder effectively predicts limb movements at P12. The change in decoder performance at P12 reflects an increase in both the complexity and uniqueness of kinematic information available in M1. We next show that the representation at P12 is more susceptible to the deleterious effects of "lesioning" inputs and to "transplanting" M1's encoding scheme from one pup to another. We conclude that the emergence of continuous cortical activity signals the developmental onset in M1 of more complex, informationally sparse, and individualized sensory representations.
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Affiliation(s)
- Ryan Glanz
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242 USA
| | - Greta Sokoloff
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242 USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242 USA
| | - Mark S. Blumberg
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242 USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242 USA
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27
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Diehl GW, Redish AD. Differential processing of decision information in subregions of rodent medial prefrontal cortex. eLife 2023; 12:e82833. [PMID: 36652289 PMCID: PMC9848391 DOI: 10.7554/elife.82833] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/05/2023] [Indexed: 01/19/2023] Open
Abstract
Decision-making involves multiple cognitive processes requiring different aspects of information about the situation at hand. The rodent medial prefrontal cortex (mPFC) has been hypothesized to be central to these abilities. Functional studies have sought to link specific processes to specific anatomical subregions, but past studies of mPFC have yielded controversial results, leaving the precise nature of mPFC function unclear. To settle this debate, we recorded from the full dorso-ventral extent of mPFC in each of 8 rats, as they performed a complex economic decision task. These data revealed four distinct functional domains within mPFC that closely mirrored anatomically identified subregions, including novel evidence to divide prelimbic cortex into dorsal and ventral components. We found that dorsal aspects of mPFC (ACC, dPL) were more involved in processing information about active decisions, while ventral aspects (vPL, IL) were more engaged in motivational factors.
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Affiliation(s)
- Geoffrey W Diehl
- Department of Neuroscience, University of MinnesotaMinneapolisUnited States
| | - A David Redish
- Department of Neuroscience, University of MinnesotaMinneapolisUnited States
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28
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Varley TF, Sporns O, Schaffelhofer S, Scherberger H, Dann B. Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior. Proc Natl Acad Sci U S A 2023; 120:e2207677120. [PMID: 36603032 PMCID: PMC9926243 DOI: 10.1073/pnas.2207677120] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 11/29/2022] [Indexed: 01/06/2023] Open
Abstract
One of the essential functions of biological neural networks is the processing of information. This includes everything from processing sensory information to perceive the environment, up to processing motor information to interact with the environment. Due to methodological limitations, it has been historically unclear how information processing changes during different cognitive or behavioral states and to what extent information is processed within or between the network of neurons in different brain areas. In this study, we leverage recent advances in the calculation of information dynamics to explore neural-level processing within and between the frontoparietal areas AIP, F5, and M1 during a delayed grasping task performed by three macaque monkeys. While information processing was high within all areas during all cognitive and behavioral states of the task, interareal processing varied widely: During visuomotor transformation, AIP and F5 formed a reciprocally connected processing unit, while no processing was present between areas during the memory period. Movement execution was processed globally across all areas with predominance of processing in the feedback direction. Furthermore, the fine-scale network structure reconfigured at the neuron level in response to different grasping conditions, despite no differences in the overall amount of information present. These results suggest that areas dynamically form higher-order processing units according to the cognitive or behavioral demand and that the information-processing network is hierarchically organized at the neuron level, with the coarse network structure determining the behavioral state and finer changes reflecting different conditions.
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Affiliation(s)
- Thomas F. Varley
- Department of Psychological & Brain Sciences, Indiana University47405-7007, Bloomington, IN
- School of Informatics, Computing, and Engineering, Indiana University47405-7007, Bloomington, IN
| | - Olaf Sporns
- Department of Psychological & Brain Sciences, Indiana University47405-7007, Bloomington, IN
| | - Stefan Schaffelhofer
- Neurobiology Laboratory, German Primate Center37077, Goettingen, Germany
- Faculty of Biology and Psychology, University of Goettingen37073, Goettingen, Germany
| | - Hansjörg Scherberger
- Neurobiology Laboratory, German Primate Center37077, Goettingen, Germany
- Faculty of Biology and Psychology, University of Goettingen37073, Goettingen, Germany
| | - Benjamin Dann
- Neurobiology Laboratory, German Primate Center37077, Goettingen, Germany
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29
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Yuan P, Zhang M, Tong L, Morse TM, McDougal RA, Ding H, Chan D, Cai Y, Grutzendler J. PLD3 affects axonal spheroids and network defects in Alzheimer's disease. Nature 2022; 612:328-337. [PMID: 36450991 PMCID: PMC9729106 DOI: 10.1038/s41586-022-05491-6] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 10/27/2022] [Indexed: 12/03/2022]
Abstract
The precise mechanisms that lead to cognitive decline in Alzheimer's disease are unknown. Here we identify amyloid-plaque-associated axonal spheroids as prominent contributors to neural network dysfunction. Using intravital calcium and voltage imaging, we show that a mouse model of Alzheimer's disease demonstrates severe disruption in long-range axonal connectivity. This disruption is caused by action-potential conduction blockades due to enlarging spheroids acting as electric current sinks in a size-dependent manner. Spheroid growth was associated with an age-dependent accumulation of large endolysosomal vesicles and was mechanistically linked with Pld3-a potential Alzheimer's-disease-associated risk gene1 that encodes a lysosomal protein2,3 that is highly enriched in axonal spheroids. Neuronal overexpression of Pld3 led to endolysosomal vesicle accumulation and spheroid enlargement, which worsened axonal conduction blockades. By contrast, Pld3 deletion reduced endolysosomal vesicle and spheroid size, leading to improved electrical conduction and neural network function. Thus, targeted modulation of endolysosomal biogenesis in neurons could potentially reverse axonal spheroid-induced neural circuit abnormalities in Alzheimer's disease, independent of amyloid removal.
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Affiliation(s)
- Peng Yuan
- Department of Neurology, Yale University, New Haven, CT, USA.,Department of Rehabilitation Medicine, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Institute for Translational Brain Research, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Mengyang Zhang
- Department of Neurology, Yale University, New Haven, CT, USA.,Department of Neuroscience, Yale University, New Haven, CT, USA.,Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.,Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA
| | - Lei Tong
- Department of Neurology, Yale University, New Haven, CT, USA
| | - Thomas M Morse
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Robert A McDougal
- Department of Neuroscience, Yale University, New Haven, CT, USA.,Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA
| | - Hui Ding
- Department of Neurology, Yale University, New Haven, CT, USA.,Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Diane Chan
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Yifei Cai
- Department of Neurology, Yale University, New Haven, CT, USA
| | - Jaime Grutzendler
- Department of Neurology, Yale University, New Haven, CT, USA. .,Department of Neuroscience, Yale University, New Haven, CT, USA. .,Wu Tsai Institute, Yale University, New Haven, CT, USA.
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30
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Carleton M, Oesch NW. Differences in the spatial fidelity of evoked and spontaneous signals in the degenerating retina. Front Cell Neurosci 2022; 16:1040090. [PMID: 36419935 PMCID: PMC9676928 DOI: 10.3389/fncel.2022.1040090] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 10/20/2022] [Indexed: 07/01/2024] Open
Abstract
Vision restoration strategies aim to reestablish vision by replacing the function of lost photoreceptors with optoelectronic hardware or through gene therapy. One complication to these approaches is that retinal circuitry undergoes remodeling after photoreceptor loss. Circuit remodeling following perturbation is ubiquitous in the nervous system and understanding these changes is crucial for treating neurodegeneration. Spontaneous oscillations that arise during retinal degeneration have been well-studied, however, other changes in the spatiotemporal processing of evoked and spontaneous activity have received less attention. Here we use subretinal electrical stimulation to measure the spatial and temporal spread of both spontaneous and evoked activity during retinal degeneration. We found that electrical stimulation synchronizes spontaneous oscillatory activity, over space and through time, thus leading to increased correlations in ganglion cell activity. Intriguingly, we found that spatial selectivity was maintained in rd10 retina for evoked responses, with spatial receptive fields comparable to wt retina. These findings indicate that different biophysical mechanisms are involved in mediating feed forward excitation, and the lateral spread of spontaneous activity in the rd10 retina, lending support toward the possibility of high-resolution vision restoration.
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Affiliation(s)
- Maya Carleton
- Department of Psychology, University of California, San Diego, La Jolla, CA, United States
| | - Nicholas W. Oesch
- Department of Psychology, University of California, San Diego, La Jolla, CA, United States
- Department of Ophthalmology, University of California, San Diego, La Jolla, CA, United States
- The Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States
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31
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Sukman LJ, Stark E. Cortical Pyramidal and Parvalbumin Cells Exhibit Distinct Spatiotemporal Extracellular Electric Potentials. eNeuro 2022; 9:ENEURO.0265-22.2022. [PMID: 36414411 PMCID: PMC9744183 DOI: 10.1523/eneuro.0265-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 10/18/2022] [Accepted: 11/07/2022] [Indexed: 11/23/2022] Open
Abstract
Brain circuits are composed of diverse cell types with distinct morphologies, connections, and distributions of ion channels. Modeling suggests that the spatial distribution of the extracellular voltage during a spike depends on cellular morphology, connectivity, and identity. However, experimental evidence from the intact brain is lacking. Here, we combined high-density recordings from hippocampal region CA1 and neocortex of freely moving mice with optogenetic tagging of parvalbumin-immunoreactive (PV) cells. We used ground truth tagging of the recorded pyramidal cells (PYR) and PV cells to construct binary classification models. Features derived from single-channel waveforms or from spike timing alone allowed near-perfect classification of PYR and PV cells. To determine whether there is unique information in the spatial distribution of the extracellular potentials, we removed all single-channel waveform information from the multichannel waveforms using an event-based delta-transformation. We found that spatiotemporal features derived from the transformed waveforms yield accurate classification. The extracellular analog of the spatial distribution of the initial depolarization phase provided the highest contribution to the spatially based prediction. Compared with PV cell spikes, PYR spikes exhibited higher spatial synchrony at the beginning of the extracellular spike and lower synchrony at the trough. The successful classification of PYR and PV cells based on purely spatial features provides direct experimental evidence that spikes of distinct cell types are associated with distinct spatial distributions of extracellular potentials.
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Affiliation(s)
- Lior J Sukman
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Stark
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
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32
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Meneghetti N, Cerri C, Vannini E, Tantillo E, Tottene A, Pietrobon D, Caleo M, Mazzoni A. Synaptic alterations in visual cortex reshape contrast-dependent gamma oscillations and inhibition-excitation ratio in a genetic mouse model of migraine. J Headache Pain 2022; 23:125. [PMID: 36175826 PMCID: PMC9523950 DOI: 10.1186/s10194-022-01495-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/09/2022] [Indexed: 11/21/2022] Open
Abstract
Background Migraine affects a significant fraction of the world population, yet its etiology is not completely understood. In vitro results highlighted thalamocortical and intra-cortical glutamatergic synaptic gain-of-function associated with a monogenic form of migraine (familial-hemiplegic-migraine-type-1: FHM1). However, how these alterations reverberate on cortical activity remains unclear. As altered responsivity to visual stimuli and abnormal processing of visual sensory information are common hallmarks of migraine, herein we investigated the effects of FHM1-driven synaptic alterations in the visual cortex of awake mice. Methods We recorded extracellular field potentials from the primary visual cortex (V1) of head-fixed awake FHM1 knock-in (n = 12) and wild type (n = 12) mice in response to square-wave gratings with different visual contrasts. Additionally, we reproduced in silico the obtained experimental results with a novel spiking neurons network model of mouse V1, by implementing in the model both the synaptic alterations characterizing the FHM1 genetic mouse model adopted. Results FHM1 mice displayed similar amplitude but slower temporal evolution of visual evoked potentials. Visual contrast stimuli induced a lower increase of multi-unit activity in FHM1 mice, while the amount of information content about contrast level remained, however, similar to WT. Spectral analysis of the local field potentials revealed an increase in the β/low γ range of WT mice following the abrupt reversal of contrast gratings. Such frequency range transitioned to the high γ range in FHM1 mice. Despite this change in the encoding channel, these oscillations preserved the amount of information conveyed about visual contrast. The computational model showed how these network effects may arise from a combination of changes in thalamocortical and intra-cortical synaptic transmission, with the former inducing a lower cortical activity and the latter inducing the higher frequencies ɣ oscillations. Conclusions Contrast-driven ɣ modulation in V1 activity occurs at a much higher frequency in FHM1. This is likely to play a role in the altered processing of visual information. Computational studies suggest that this shift is specifically due to enhanced cortical excitatory transmission. Our network model can help to shed light on the relationship between cellular and network levels of migraine neural alterations. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s10194-022-01495-9.
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Affiliation(s)
- Nicolò Meneghetti
- The Biorobotics Institute, Scuola Superiore Sant'Anna, 56025, Pisa, Italy.,Department of Excellence for Robotics and AI, Scuola Superiore Sant'Anna, 56025, Pisa, Italy
| | - Chiara Cerri
- Neuroscience Institute, National Research Council (CNR), 56124, Pisa, Italy.,Fondazione Umberto Veronesi, 20122, Milan, Italy.,Department of Pharmacy, University of Pisa, 56126, Pisa, Italy
| | - Eleonora Vannini
- Neuroscience Institute, National Research Council (CNR), 56124, Pisa, Italy.,Fondazione Umberto Veronesi, 20122, Milan, Italy
| | - Elena Tantillo
- Neuroscience Institute, National Research Council (CNR), 56124, Pisa, Italy.,Fondazione Pisana per la Scienza Onlus (FPS), 56017, Pisa, Italy.,Scuola Normale Superiore, 56100, Pisa, Italy
| | - Angelita Tottene
- Department of Biomedical Sciences, University of Padova, 35131, Padova, Italy
| | - Daniela Pietrobon
- Department of Biomedical Sciences, University of Padova, 35131, Padova, Italy.,Padova Neuroscience Center, University of Padova, 35131, Padova, Italy.,CNR Institute of Neuroscience, 35131, Padova, Italy
| | - Matteo Caleo
- Neuroscience Institute, National Research Council (CNR), 56124, Pisa, Italy.,Department of Biomedical Sciences, University of Padova, 35131, Padova, Italy.,Padova Neuroscience Center, University of Padova, 35131, Padova, Italy
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant'Anna, 56025, Pisa, Italy. .,Department of Excellence for Robotics and AI, Scuola Superiore Sant'Anna, 56025, Pisa, Italy.
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33
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Weineck K, Wen OX, Henry MJ. Neural synchronization is strongest to the spectral flux of slow music and depends on familiarity and beat salience. eLife 2022; 11:e75515. [PMID: 36094165 PMCID: PMC9467512 DOI: 10.7554/elife.75515] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 07/25/2022] [Indexed: 11/29/2022] Open
Abstract
Neural activity in the auditory system synchronizes to sound rhythms, and brain-environment synchronization is thought to be fundamental to successful auditory perception. Sound rhythms are often operationalized in terms of the sound's amplitude envelope. We hypothesized that - especially for music - the envelope might not best capture the complex spectro-temporal fluctuations that give rise to beat perception and synchronized neural activity. This study investigated (1) neural synchronization to different musical features, (2) tempo-dependence of neural synchronization, and (3) dependence of synchronization on familiarity, enjoyment, and ease of beat perception. In this electroencephalography study, 37 human participants listened to tempo-modulated music (1-4 Hz). Independent of whether the analysis approach was based on temporal response functions (TRFs) or reliable components analysis (RCA), the spectral flux of music - as opposed to the amplitude envelope - evoked strongest neural synchronization. Moreover, music with slower beat rates, high familiarity, and easy-to-perceive beats elicited the strongest neural response. Our results demonstrate the importance of spectro-temporal fluctuations in music for driving neural synchronization, and highlight its sensitivity to musical tempo, familiarity, and beat salience.
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Affiliation(s)
- Kristin Weineck
- Research Group “Neural and Environmental Rhythms”, Max Planck Institute for Empirical AestheticsFrankfurt am MainGermany
- Goethe University Frankfurt, Institute for Cell Biology and NeuroscienceFrankfurt am MainGermany
| | - Olivia Xin Wen
- Research Group “Neural and Environmental Rhythms”, Max Planck Institute for Empirical AestheticsFrankfurt am MainGermany
| | - Molly J Henry
- Research Group “Neural and Environmental Rhythms”, Max Planck Institute for Empirical AestheticsFrankfurt am MainGermany
- Department of Psychology, Toronto Metropolitan UniversityTorontoCanada
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34
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Kim SH, Woo J, Choi K, Choi M, Han K. Neural Information Processing and Computations of Two-Input Synapses. Neural Comput 2022; 34:2102-2131. [PMID: 36027799 DOI: 10.1162/neco_a_01534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 06/02/2022] [Indexed: 11/04/2022]
Abstract
Information processing in artificial neural networks is largely dependent on the nature of neuron models. While commonly used models are designed for linear integration of synaptic inputs, accumulating experimental evidence suggests that biological neurons are capable of nonlinear computations for many converging synaptic inputs via homo- and heterosynaptic mechanisms. This nonlinear neuronal computation may play an important role in complex information processing at the neural circuit level. Here we characterize the dynamics and coding properties of neuron models on synaptic transmissions delivered from two hidden states. The neuronal information processing is influenced by the cooperative and competitive interactions among synapses and the coherence of the hidden states. Furthermore, we demonstrate that neuronal information processing under two-input synaptic transmission can be mapped to linearly nonseparable XOR as well as basic AND/OR operations. In particular, the mixtures of linear and nonlinear neuron models outperform the fashion-MNIST test compared to the neural networks consisting of only one type. This study provides a computational framework for assessing information processing of neuron and synapse models that may be beneficial for the design of brain-inspired artificial intelligence algorithms and neuromorphic systems.
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Affiliation(s)
- Soon Ho Kim
- Laboratory of Computational Neurophysics, Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Junhyuk Woo
- Laboratory of Computational Neurophysics, Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Kiri Choi
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, South Korea
| | - MooYoung Choi
- Department of Physics and Astronomy and Center for Theoretical Physics, Seoul National University, Seoul 08826, South Korea
| | - Kyungreem Han
- Laboratory of Computational Neurophysics, Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, South Korea
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35
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Kay JW, Schulz JM, Phillips WA. A Comparison of Partial Information Decompositions Using Data from Real and Simulated Layer 5b Pyramidal Cells. ENTROPY 2022; 24:e24081021. [PMID: 35893001 PMCID: PMC9394329 DOI: 10.3390/e24081021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 02/04/2023]
Abstract
Partial information decomposition allows the joint mutual information between an output and a set of inputs to be divided into components that are synergistic or shared or unique to each input. We consider five different decompositions and compare their results using data from layer 5b pyramidal cells in two different studies. The first study was on the amplification of somatic action potential output by apical dendritic input and its regulation by dendritic inhibition. We find that two of the decompositions produce much larger estimates of synergy and shared information than the others, as well as large levels of unique misinformation. When within-neuron differences in the components are examined, the five methods produce more similar results for all but the shared information component, for which two methods produce a different statistical conclusion from the others. There are some differences in the expression of unique information asymmetry among the methods. It is significantly larger, on average, under dendritic inhibition. Three of the methods support a previous conclusion that apical amplification is reduced by dendritic inhibition. The second study used a detailed compartmental model to produce action potentials for many combinations of the numbers of basal and apical synaptic inputs. Decompositions of the entire data set produce similar differences to those in the first study. Two analyses of decompositions are conducted on subsets of the data. In the first, the decompositions reveal a bifurcation in unique information asymmetry. For three of the methods, this suggests that apical drive switches to basal drive as the strength of the basal input increases, while the other two show changing mixtures of information and misinformation. Decompositions produced using the second set of subsets show that all five decompositions provide support for properties of cooperative context-sensitivity—to varying extents.
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Affiliation(s)
- Jim W. Kay
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
- Correspondence:
| | - Jan M. Schulz
- Department of Biomedicine, University of Basel, 4001 Basel, Switzerland;
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36
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Kramer BA, Sarabia del Castillo J, Pelkmans L. Multimodal perception links cellular state to decision making in single cells. Science 2022; 377:642-648. [DOI: 10.1126/science.abf4062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Individual cells take decisions that are adapted to their internal state and surroundings, but how cells can reliably do this remains unclear. Using multiplexed quantification of signaling responses and markers of the cellular state, we find that signaling nodes in a network display adaptive information processing, which leads to heterogeneous growth factor responses and enables nodes to capture partially non-redundant information about the cellular state. Collectively, as a multimodal percept, this gives individual cells a large information processing capacity to accurately place growth factor concentration within the context of their cellular state and make cellular state-dependent decisions. We propose that heterogeneity and complexity in signaling networks have co-evolved to enable specific and context-aware cellular decision making in a multicellular setting.
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Affiliation(s)
- Bernhard A. Kramer
- Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
- Molecular Life Sciences PhD program, Life Science Zurich Graduate School, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Jacobo Sarabia del Castillo
- Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Lucas Pelkmans
- Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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37
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Mediano PAM, Rosas FE, Luppi AI, Jensen HJ, Seth AK, Barrett AB, Carhart-Harris RL, Bor D. Greater than the parts: a review of the information decomposition approach to causal emergence. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210246. [PMID: 35599558 PMCID: PMC9125226 DOI: 10.1098/rsta.2021.0246] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 02/07/2022] [Indexed: 05/28/2023]
Abstract
Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons. Despite the broad interest that exists on this concept, the study of emergence has suffered from a lack of formalisms that could be used to guide discussions and advance theories. Here, we summarize, elaborate on, and extend a recent formal theory of causal emergence based on information decomposition, which is quantifiable and amenable to empirical testing. This theory relates emergence with information about a system's temporal evolution that cannot be obtained from the parts of the system separately. This article provides an accessible but rigorous introduction to the framework, discussing the merits of the approach in various scenarios of interest. We also discuss several interpretation issues and potential misunderstandings, while highlighting the distinctive benefits of this formalism. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
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Affiliation(s)
- Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
| | - Fernando E Rosas
- Centre for Psychedelic Research, Imperial College London, London, UK
- Data Science Institute, Imperial College London, London, UK
- Centre for Complexity Science, Imperial College London, London, UK
| | - Andrea I Luppi
- University Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Henrik J Jensen
- Centre for Complexity Science, Imperial College London, London, UK
- Department of Mathematics, Imperial College London, London, UK
- Institute of Innovative Research, Tokyo Institute of Technology Tokyo, Japan
| | - Anil K Seth
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK
- CIFAR Program on Brain, Mind, and Consciousness, Toronto, Canada
| | - Adam B Barrett
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK
- The Data Intensive Science Centre, Department of Informatics, University of Sussex, Brighton, UK
| | - Robin L Carhart-Harris
- Centre for Psychedelic Research, Imperial College London, London, UK
- Psychedelics Division, Neuroscape, Department of Neurology, University of California, San Francisco, CA, USA
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychology, Queen Mary University of London, London, UK
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38
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Timme NM, Ma B, Linsenbardt D, Cornwell E, Galbari T, Lapish CC. Compulsive alcohol drinking in rodents is associated with altered representations of behavioral control and seeking in dorsal medial prefrontal cortex. Nat Commun 2022; 13:3990. [PMID: 35810193 PMCID: PMC9271071 DOI: 10.1038/s41467-022-31731-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/27/2022] [Indexed: 12/17/2022] Open
Abstract
A key feature of compulsive alcohol drinking is continuing to drink despite negative consequences. To examine the changes in neural activity that underlie this behavior, compulsive alcohol drinking was assessed in a validated rodent model of heritable risk for excessive drinking (alcohol preferring (P) rats). Neural activity was measured in dorsal medial prefrontal cortex (dmPFC-a brain region involved in maladaptive decision-making) and assessed via change point analyses and novel principal component analyses. Neural population representations of specific decision-making variables were measured to determine how they were altered in animals that drink alcohol compulsively. Compulsive animals showed weakened representations of behavioral control signals, but strengthened representations of alcohol seeking-related signals. Finally, chemogenetic-based excitation of dmPFC prevented escalation of compulsive alcohol drinking. Collectively, these data indicate that compulsive alcohol drinking in rats is associated with alterations in dmPFC neural activity that underlie diminished behavioral control and enhanced seeking.
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Affiliation(s)
- Nicholas M Timme
- Psychology Department, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46237, USA.
| | - Baofeng Ma
- Psychology Department, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46237, USA
| | - David Linsenbardt
- Department of Neurosciences, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Ethan Cornwell
- Psychology Department, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46237, USA
| | - Taylor Galbari
- Psychology Department, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46237, USA
| | - Christopher C Lapish
- Psychology Department, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46237, USA
- Stark Neurosciences Research Institute, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46237, USA
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Newman EL, Varley TF, Parakkattu VK, Sherrill SP, Beggs JM. Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition. ENTROPY (BASEL, SWITZERLAND) 2022; 24:930. [PMID: 35885153 PMCID: PMC9319160 DOI: 10.3390/e24070930] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 11/16/2022]
Abstract
The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions in neuroscience center on understanding the mechanisms of neural information processing. Classical information theory has long been understood to be a natural framework within which information processing can be understood, and recent advances in the field of multivariate information theory offer new insights into the structure of computation in complex systems. In this review, we provide an introduction to the conceptual and practical issues associated with using multivariate information theory to analyze information processing in neural circuits, as well as discussing recent empirical work in this vein. Specifically, we provide an accessible introduction to the partial information decomposition (PID) framework. PID reveals redundant, unique, and synergistic modes by which neurons integrate information from multiple sources. We focus particularly on the synergistic mode, which quantifies the "higher-order" information carried in the patterns of multiple inputs and is not reducible to input from any single source. Recent work in a variety of model systems has revealed that synergistic dynamics are ubiquitous in neural circuitry and show reliable structure-function relationships, emerging disproportionately in neuronal rich clubs, downstream of recurrent connectivity, and in the convergence of correlated activity. We draw on the existing literature on higher-order information dynamics in neuronal networks to illustrate the insights that have been gained by taking an information decomposition perspective on neural activity. Finally, we briefly discuss future promising directions for information decomposition approaches to neuroscience, such as work on behaving animals, multi-target generalizations of PID, and time-resolved local analyses.
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Affiliation(s)
- Ehren L. Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA;
| | - Thomas F. Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA;
| | - Vibin K. Parakkattu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA;
| | | | - John M. Beggs
- Department of Physics, Indiana University, Bloomington, IN 47405, USA;
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40
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Weninger L, Srivastava P, Zhou D, Kim JZ, Cornblath EJ, Bertolero MA, Habel U, Merhof D, Bassett DS. Information content of brain states is explained by structural constraints on state energetics. Phys Rev E 2022; 106:014401. [PMID: 35974521 DOI: 10.1103/physreve.106.014401] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/27/2022] [Indexed: 06/15/2023]
Abstract
Signal propagation along the structural connectome of the brain induces changes in the patterns of activity. These activity patterns define global brain states and contain information in accordance with their expected probability of occurrence. Being the physical substrate upon which information propagates, the structural connectome, in conjunction with the dynamics, determines the set of possible brain states and constrains the transition between accessible states. Yet, precisely how these structural constraints on state transitions relate to their information content remains unexplored. To address this gap in knowledge, we defined the information content as a function of the activation distribution, where statistically rare values of activation correspond to high information content. With this numerical definition in hand, we studied the spatiotemporal distribution of information content in functional magnetic resonance imaging (fMRI) data from the Human Connectome Project during different tasks, and report four key findings. First, information content strongly depends on cognitive context; its absolute level and spatial distribution depend on the cognitive task. Second, while information content shows similarities to other measures of brain activity, it is distinct from both Neurosynth maps and task contrast maps generated by a general linear model applied to the fMRI data. Third, the brain's structural wiring constrains the cost to control its state, where the cost to transition into high information content states is larger than that to transition into low information content states. Finally, all state transitions-especially those to high information content states-are less costly than expected from random network null models, thereby indicating the brains marked efficiency. Taken together, our findings establish an explanatory link between the information contained in a brain state and the energetic cost of attaining that state, thereby laying important groundwork for our understanding of large-scale cognitive computations.
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Affiliation(s)
- Leon Weninger
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Institute of Imaging & Computer Vision, RWTH Aachen University, 52072 Aachen, Germany
| | - Pragya Srivastava
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Dale Zhou
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Jason Z Kim
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Eli J Cornblath
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Maxwell A Bertolero
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany
- Institute of Neuroscience and Medicine 10, Research Centre Jülich, 52428 Jülich, Germany
| | - Dorit Merhof
- Institute of Imaging & Computer Vision, RWTH Aachen University, 52072 Aachen, Germany
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Santa Fe Institute, Santa Fe, New Mexico 87501, USA
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41
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Fuhrer J, Blenkmann A, Endestad T, Solbakk AK, Glette K. Complexity-based Encoded Information Quantification in Neurophysiological Recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2319-2323. [PMID: 36086266 DOI: 10.1109/embc48229.2022.9871501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Brain activity differs vastly between sleep, cognitive tasks, and action. Information theory is an appropriate concept to analytically quantify these brain states. Based on neurophysiological recordings, this concept can handle complex data sets, is free of any requirements about the data structure, and can infer the present underlying brain mechanisms. Specifically, by utilizing algorithmic information theory, it is possible to estimate the absolute information contained in brain responses. While current approaches that apply this theory to neurophysiological recordings can discriminate between different brain states, they are limited in directly quantifying the degree of similarity or encoded information between brain responses. Here, we propose a method grounded in algorithmic information theory that affords direct statements about responses' similarity by estimating the encoded information through a compression-based scheme. We validated this method by applying it to both synthetic and real neurophysiological data and compared its efficiency to the mutual information measure. This proposed procedure is especially suited for task paradigms contrasting different event types because it can precisely quantify the similarity of neuronal responses.
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42
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Wang MZ, Hayden BY, Heilbronner SR. A structural and functional subdivision in central orbitofrontal cortex. Nat Commun 2022; 13:3623. [PMID: 35750659 PMCID: PMC9232485 DOI: 10.1038/s41467-022-31273-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 06/07/2022] [Indexed: 11/09/2022] Open
Abstract
Economic choice requires many cognitive subprocesses, including stimulus detection, valuation, motor output, and outcome monitoring; many of these subprocesses are associated with the central orbitofrontal cortex (cOFC). Prior work has largely assumed that the cOFC is a single region with a single function. Here, we challenge that unified view with convergent anatomical and physiological results from rhesus macaques. Anatomically, we show that the cOFC can be subdivided according to its much stronger (medial) or weaker (lateral) bidirectional anatomical connectivity with the posterior cingulate cortex (PCC). We call these subregions cOFCm and cOFCl, respectively. These two subregions have notable functional differences. Specifically, cOFCm shows enhanced functional connectivity with PCC, as indicated by both spike-field coherence and mutual information. The cOFCm-PCC circuit, but not the cOFCl-PCC circuit, shows signatures of relaying choice signals from a non-spatial comparison framework to a spatially framed organization and shows a putative bidirectional mutually excitatory pattern.
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Affiliation(s)
- Maya Zhe Wang
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA.
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - Benjamin Y Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
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An Analysis of Algebraic Codes over Lattice Valued Intuitionistic Fuzzy Type-3 -Submodules. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8148284. [PMID: 35785082 PMCID: PMC9246640 DOI: 10.1155/2022/8148284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/19/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022]
Abstract
In the last few decades, the algebraic coding theory found widespread applications in various disciplines due to its rich fascinating mathematical structure. Linear codes, the basic codes in coding theory, are significant in data transmission. In this article, the authors' aim is to enlighten the reader about the role of linear codes in a fuzzy environment. Thus, the reader will be aware of linear codes over lattice valued intuitionistic fuzzy type-3 (LIF-3) R-submodule and α-intuitionistic fuzzy (α-IF) submodule. The proof that the level set of LIF-3 is contained in the level set of α-IF is given, and it is exclusively employed to define linear codes over α-IF submodule. Further, α-IF cyclic codes are presented along with their fundamental properties. Finally, an application based on genetic code is presented, and it is found that the technique of defining codes over α-IF submodule is entirely applicable in this scenario. More specifically, a mapping from the ℤ64 module to a lattice L (comprising 64 codons) is considered, and α-IF codes are defined along with the respective degrees.
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44
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Kim S, Roh H, Im M. Artificial Visual Information Produced by Retinal Prostheses. Front Cell Neurosci 2022; 16:911754. [PMID: 35734216 PMCID: PMC9208577 DOI: 10.3389/fncel.2022.911754] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/18/2022] [Indexed: 11/18/2022] Open
Abstract
Numerous retinal prosthetic systems have demonstrated somewhat useful vision can be restored to individuals who had lost their sight due to outer retinal degenerative diseases. Earlier prosthetic studies have mostly focused on the confinement of electrical stimulation for improved spatial resolution and/or the biased stimulation of specific retinal ganglion cell (RGC) types for selective activation of retinal ON/OFF pathway for enhanced visual percepts. To better replicate normal vision, it would be also crucial to consider information transmission by spiking activities arising in the RGC population since an incredible amount of visual information is transferred from the eye to the brain. In previous studies, however, it has not been well explored how much artificial visual information is created in response to electrical stimuli delivered by microelectrodes. In the present work, we discuss the importance of the neural information for high-quality artificial vision. First, we summarize the previous literatures which have computed information transmission rates from spiking activities of RGCs in response to visual stimuli. Second, we exemplify a couple of studies which computed the neural information from electrically evoked responses. Third, we briefly introduce how information rates can be computed in the representative two ways - direct method and reconstruction method. Fourth, we introduce in silico approaches modeling artificial retinal neural networks to explore the relationship between amount of information and the spiking patterns. Lastly, we conclude our review with clinical implications to emphasize the necessity of considering visual information transmission for further improvement of retinal prosthetics.
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Affiliation(s)
- Sein Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, South Korea
| | - Hyeonhee Roh
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, South Korea
- School of Electrical Engineering, College of Engineering, Korea University, Seoul, South Korea
| | - Maesoon Im
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, South Korea
- Division of Bio-Medical Science & Technology, KIST School, University of Science and Technology, Seoul, South Korea
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45
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Combrisson E, Allegra M, Basanisi R, Ince RAA, Giordano B, Bastin J, Brovelli A. Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data. Neuroimage 2022; 258:119347. [PMID: 35660460 DOI: 10.1016/j.neuroimage.2022.119347] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 05/24/2022] [Accepted: 05/30/2022] [Indexed: 12/30/2022] Open
Abstract
The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites1 that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.
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Affiliation(s)
- Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France.
| | - Michele Allegra
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France; Dipartimento di Fisica e Astronomia "Galileo Galilei", Università di Padova, via Marzolo 8, 35131 Padova, Italy; Padua Neuroscience Center, Università di Padova, via Orus 2, 35131 Padova, Italy
| | - Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France
| | - Robin A A Ince
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
| | - Bruno Giordano
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France.
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Nitzan N, Swanson R, Schmitz D, Buzsáki G. Brain-wide interactions during hippocampal sharp wave ripples. Proc Natl Acad Sci U S A 2022; 119:e2200931119. [PMID: 35561219 PMCID: PMC9171920 DOI: 10.1073/pnas.2200931119] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/05/2022] [Indexed: 12/16/2022] Open
Abstract
During periods of disengagement from the environment, transient population bursts, known as sharp wave ripples (SPW-Rs), occur sporadically. While numerous experiments have characterized the bidirectional relationship between SPW-Rs and activity in chosen brain areas, the topographic relationship between different segments of the hippocampus and brain-wide target areas has not been studied at high temporal and spatial resolution. Yet, such knowledge is necessary to infer the direction of communication. We analyzed two publicly available datasets with simultaneous high-density silicon probe recordings from across the mouse forebrain. We found that SPW-Rs coincide with a transient brain-wide increase in functional connectivity. In addition, we show that the diversity in SPW-R features, such as their incidence, magnitude, and intrahippocampal topography in the septotemporal axis, are correlated with slower excitability fluctuations in cortical and subcortical areas. Further, variations in SPW-R features correlated with the timing, sign, and magnitude of downstream responses with large-amplitude SPW-Rs followed by transient silence in extrahippocampal structures. Our findings expand on previous results and demonstrate that the activity patterns in extrahippocampal structures depend both on the intrahippocampal topographic origin and magnitude of hippocampal SPW-Rs.
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Affiliation(s)
- Noam Nitzan
- Neuroscience Institute, Langone Medical Center, New York University, New York, NY 10016
| | - Rachel Swanson
- Neuroscience Institute, Langone Medical Center, New York University, New York, NY 10016
| | - Dietmar Schmitz
- Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - György Buzsáki
- Neuroscience Institute, Langone Medical Center, New York University, New York, NY 10016
- Department of Neurology, Langone Medical Center, New York University, New York, NY 10016
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47
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Corrigan BW, Gulli RA, Doucet G, Roussy M, Luna R, Pradeepan KS, Sachs AJ, Martinez-Trujillo JC. Distinct neural codes in primate hippocampus and lateral prefrontal cortex during associative learning in virtual environments. Neuron 2022; 110:2155-2169.e4. [PMID: 35561675 DOI: 10.1016/j.neuron.2022.04.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 02/24/2022] [Accepted: 04/14/2022] [Indexed: 11/26/2022]
Abstract
The hippocampus (HPC) and the lateral prefrontal cortex (LPFC) are two cortical areas of the primate brain deemed essential to cognition. Here, we hypothesized that the codes mediating neuronal communication in the HPC and LPFC microcircuits have distinctively evolved to serve plasticity and memory function at different spatiotemporal scales. We used a virtual reality task in which animals selected one of the two targets in the arms of the maze, according to a learned context-color rule. Our results show that during associative learning, HPC principal cells concentrate spikes in bursts, enabling temporal summation and fast synaptic plasticity in small populations of neurons and ultimately facilitating rapid encoding of associative memories. On the other hand, layer II/III LPFC pyramidal cells fire spikes more sparsely distributed over time. The latter would facilitate broadcasting of signals loaded in short-term memory across neuronal populations without necessarily triggering fast synaptic plasticity.
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Affiliation(s)
- Benjamin W Corrigan
- Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada; Robarts Research Institute, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Roberto A Gulli
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | | | - Megan Roussy
- Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada; Robarts Research Institute, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Rogelio Luna
- Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada; Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Kartik S Pradeepan
- Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada; Robarts Research Institute, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada
| | - Adam J Sachs
- The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada
| | - Julio C Martinez-Trujillo
- Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada; Robarts Research Institute, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada.
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48
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Świetlik D, Kusiak A, Ossowska A. Computational Modeling of Therapy with the NMDA Antagonist in Neurodegenerative Disease: Information Theory in the Mechanism of Action of Memantine. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:4727. [PMID: 35457595 PMCID: PMC9027074 DOI: 10.3390/ijerph19084727] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/11/2022] [Accepted: 04/13/2022] [Indexed: 02/04/2023]
Abstract
(1) Background: in patients with neurodegenerative diseases, noncompetitive N-methyl-D-aspartate (NMDA) receptor antagonists provide neuroprotective advantages. We performed memantine therapy and proved mathematical and computer modeling of neurodegenerative disease in this study. (2) Methods: a computer simulation environment of the N-methyl-D-aspartate receptor incorporating biological mechanisms of channel activation by high extracellular glutamic acid concentration. In comparison to controls, pathological models were essentially treated with doses of memantine 3−30 µM. (3) Results: the mean values and 95% CI for Shannon entropy in Alzheimer’s disease (AD) and memantine treatment models were 1.760 (95% CI, 1.704−1.818) vs. 2.385 (95% CI, 2.280−2.490). The Shannon entropy was significantly higher in the memantine treatment model relative to AD model (p = 0.0162). The mean values and 95% CI for the positive Lyapunov exponent in AD and memantine treatment models were 0.125 (95% CI, NE−NE) vs. 0.058 (95% CI, 0.044−0.073). The positive Lyapunov exponent was significantly higher in the AD model relative to the memantine treatment model (p = 0.0091). The mean values and 95% CI for transfer entropy in AD and memantine treatment models were 0.081 (95% CI, 0.048−0.114) vs. 0.040 (95% CI, 0.019−0.062). The transfer entropy was significantly higher in the AD model relative to the memantine treatment model (p = 0.0146). A correlation analysis showed positive and statistically significant correlations of the memantine concentrations and the positive Lyapunov exponent (correlation coefficient R = 0.87, p = 0.0023) and transfer entropy (TE) (correlation coefficient R = 0.99, p < 0.000001). (4) Conclusions: information theory results of simulation studies show that the NMDA antagonist, memantine, causes neuroprotective benefits in patients with AD. Our simulation study opens up remarkable new scenarios in which a medical product, drug, or device, can be developed and tested for efficacy based on parameters of information theory.
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Affiliation(s)
- Dariusz Świetlik
- Division of Biostatistics and Neural Networks, Medical University of Gdańsk, Dębinki 1, 80-211 Gdańsk, Poland
| | - Aida Kusiak
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdańsk, 80-204 Gdańsk, Poland; (A.K.); (A.O.)
| | - Agata Ossowska
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdańsk, 80-204 Gdańsk, Poland; (A.K.); (A.O.)
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49
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Zeng L, Guo M, Wu R, Luo Y, Wei P. The Effects of Electroencephalogram Feature-Based Transcranial Alternating Current Stimulation on Working Memory and Electrophysiology. Front Aging Neurosci 2022; 14:828377. [PMID: 35360204 PMCID: PMC8961031 DOI: 10.3389/fnagi.2022.828377] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/07/2022] [Indexed: 11/30/2022] Open
Abstract
Transcranial alternating current stimulation (tACS) can influence cognitive functions by modulating brain oscillations. However, results regarding the effectiveness of tACS in regulating cognitive performance have been inconsistent. In the present study, we aimed to find electroencephalogram (EEG) characteristics associated with the improvements in working memory performance, to select tACS stimulus targets and frequency based on this feature, and to explore effects of selected stimulus on verbal working memory. To achieve this goal, we first investigated the EEG characteristics associated with improvements in working memory performance with the aid of EEG analyses and machine learning techniques. These analyses suggested that 8 Hz activity in the prefrontal region was related to accuracy in the verbal working memory task. The tACS stimulus target and pattern were then selected based on the EEG feature. Finally, the selected tACS frequency (8 Hz tACS in the prefrontal region) was applied to modulate working memory. Such modulation resulted significantly greater improvements, compared with 40 Hz and sham modulations (especially for participants with weak verbal working memory). In conclusion, using EEG features related to positive behavioral changes to select brain regions and stimulation patterns for tACS is an effective intervention for improving working memory. Our results contribute to the groundwork for future tACS closed-loop interventions for cognitive deterioration.
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Affiliation(s)
- Lanting Zeng
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Mingrou Guo
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Ruoling Wu
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, China
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Yu Luo
- Shenzhen Zhongke Huayi Technology Co., Ltd., Shenzhen, China
| | - Pengfei Wei
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Pengfei Wei,
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Morin O, Müller TF, Morisseau T, Winters J. Cultural Evolution of Precise and Agreed-Upon Semantic Conventions in a Multiplayer Gaming App. Cogn Sci 2022; 46:e13113. [PMID: 35174902 DOI: 10.1111/cogs.13113] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 11/30/2021] [Accepted: 01/21/2022] [Indexed: 11/26/2022]
Abstract
The amount of information conveyed by linguistic conventions depends on their precision, yet the codes that humans and other animals use to communicate are quite ambiguous: they may map several vague meanings to the same symbol. How does semantic precision evolve, and what are the constraints that limit it? We address this question using a multiplayer gaming app, where individuals communicate with one another in a scaled-up referential game. Here, the goal is for a sender to use black and white symbols to communicate colors. We expected that the players' mappings between symbols and colors would grow more specific over time, through a selection process whereby precise mappings are preferentially copied. We found that players become increasingly more precise in their use of symbols over the course of their interactions. This trend did not, however, result from selective copying of precise mappings. We explore the implications of this result for the study of lexical ambiguity, Zipf's Law of Meaning, and disagreements over semantic conventions.
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Affiliation(s)
- Olivier Morin
- Minds and Traditions Research Group, Max Planck Institute for the Science of Human History.,Institut Jean Nicod, Département d'études cognitives, ENS, EHESS, CNRS, PSL University
| | - Thomas F Müller
- Center for Humans and Machines, Max Planck Institute for Human Development
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