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Fontenele AJ, Sooter JS, Norman VK, Gautam SH, Shew WL. Low-dimensional criticality embedded in high-dimensional awake brain dynamics. SCIENCE ADVANCES 2024; 10:eadj9303. [PMID: 38669340 PMCID: PMC11051676 DOI: 10.1126/sciadv.adj9303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 03/26/2024] [Indexed: 04/28/2024]
Abstract
Whether cortical neurons operate in a strongly or weakly correlated dynamical regime determines fundamental information processing capabilities and has fueled decades of debate. We offer a resolution of this debate; we show that two important dynamical regimes, typically considered incompatible, can coexist in the same local cortical circuit by separating them into two different subspaces. In awake mouse motor cortex, we find a low-dimensional subspace with large fluctuations consistent with criticality-a dynamical regime with moderate correlations and multi-scale information capacity and transmission. Orthogonal to this critical subspace, we find a high-dimensional subspace containing a desynchronized dynamical regime, which may optimize input discrimination. The critical subspace is apparent only at long timescales, which explains discrepancies among some previous studies. Using a computational model, we show that the emergence of a low-dimensional critical subspace at large timescales agrees with established theory of critical dynamics. Our results suggest that the cortex leverages its high dimensionality to multiplex dynamical regimes across different subspaces.
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Liang J, Yang Z, Zhou C. Excitation-Inhibition Balance, Neural Criticality, and Activities in Neuronal Circuits. Neuroscientist 2024:10738584231221766. [PMID: 38291889 DOI: 10.1177/10738584231221766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Neural activities in local circuits exhibit complex and multilevel dynamic features. Individual neurons spike irregularly, which is believed to originate from receiving balanced amounts of excitatory and inhibitory inputs, known as the excitation-inhibition balance. The spatial-temporal cascades of clustered neuronal spikes occur in variable sizes and durations, manifested as neural avalanches with scale-free features. These may be explained by the neural criticality hypothesis, which posits that neural systems operate around the transition between distinct dynamic states. Here, we summarize the experimental evidence for and the underlying theory of excitation-inhibition balance and neural criticality. Furthermore, we review recent studies of excitatory-inhibitory networks with synaptic kinetics as a simple solution to reconcile these two apparently distinct theories in a single circuit model. This provides a more unified understanding of multilevel neural activities in local circuits, from spontaneous to stimulus-response dynamics.
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Affiliation(s)
- Junhao Liang
- Eberhard Karls University of Tübingen and Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Zhuda Yang
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Life Science Imaging Centre, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Research Centre, Hong Kong Baptist University Institute of Research and Continuing Education, Shenzhen, China
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Fontenele AJ, Sooter JS, Norman VK, Gautam SH, Shew WL. Low dimensional criticality embedded in high dimensional awake brain dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522896. [PMID: 37546833 PMCID: PMC10401950 DOI: 10.1101/2023.01.05.522896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Whether cortical neurons operate in a strongly or weakly correlated dynamical regime determines fundamental information processing capabilities and has fueled decades of debate. Here we offer a resolution of this debate; we show that two important dynamical regimes, typically considered incompatible, can coexist in the same local cortical circuit by separating them into two different subspaces. In awake mouse motor cortex, we find a low-dimensional subspace with large fluctuations consistent with criticality - a dynamical regime with moderate correlations and multi-scale information capacity and transmission. Orthogonal to this critical subspace, we find a high-dimensional subspace containing a desynchronized dynamical regime, which may optimize input discrimination. The critical subspace is apparent only at long timescales, which explains discrepancies among some previous studies. Using a computational model, we show that the emergence of a low-dimensional critical subspace at large timescale agrees with established theory of critical dynamics. Our results suggest that cortex leverages its high dimensionality to multiplex dynamical regimes across different subspaces.
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Affiliation(s)
- Antonio J. Fontenele
- UA Integrative Systems Neuroscience Group, Department of Physics, University of Arkansas, Fayetteville, AR, USA, 72701
| | - J. Samuel Sooter
- UA Integrative Systems Neuroscience Group, Department of Physics, University of Arkansas, Fayetteville, AR, USA, 72701
| | - V. Kindler Norman
- UA Integrative Systems Neuroscience Group, Department of Physics, University of Arkansas, Fayetteville, AR, USA, 72701
| | - Shree Hari Gautam
- UA Integrative Systems Neuroscience Group, Department of Physics, University of Arkansas, Fayetteville, AR, USA, 72701
| | - Woodrow L. Shew
- UA Integrative Systems Neuroscience Group, Department of Physics, University of Arkansas, Fayetteville, AR, USA, 72701
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Cabej NR. On the origin and nature of nongenetic information in eumetazoans. Ann N Y Acad Sci 2023. [PMID: 37154677 DOI: 10.1111/nyas.15001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Nongenetic information implies all the forms of biological information not related to genes and DNA in general. Despite the deep scientific relevance of the concept, we currently lack reliable knowledge about its carriers and origins; hence, we still do not understand its true nature. Given that genes are the targets of nongenetic information, it appears that a parsimonious approach to find the ultimate source of that information is to trace back the sequential steps of the causal chain upstream of the target genes up to the ultimate link as the source of the nongenetic information. From this perspective, I examine seven nongenetically determined phenomena: placement of locus-specific epigenetic marks on DNA and histones, changes in snRNA expression patterns, neural induction of gene expression, site-specific alternative gene splicing, predator-induced morphological changes, and cultural inheritance. Based on the available evidence, I propose a general model of the common neural origin of all these forms of nongenetic information in eumetazoans.
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Affiliation(s)
- Nelson R Cabej
- Department of Biology, University of Tirana, Tirana, Albania
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Fogarty MJ. Inhibitory Synaptic Influences on Developmental Motor Disorders. Int J Mol Sci 2023; 24:ijms24086962. [PMID: 37108127 PMCID: PMC10138861 DOI: 10.3390/ijms24086962] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
During development, GABA and glycine play major trophic and synaptic roles in the establishment of the neuromotor system. In this review, we summarise the formation, function and maturation of GABAergic and glycinergic synapses within neuromotor circuits during development. We take special care to discuss the differences in limb and respiratory neuromotor control. We then investigate the influences that GABAergic and glycinergic neurotransmission has on two major developmental neuromotor disorders: Rett syndrome and spastic cerebral palsy. We present these two syndromes in order to contrast the approaches to disease mechanism and therapy. While both conditions have motor dysfunctions at their core, one condition Rett syndrome, despite having myriad symptoms, has scientists focused on the breathing abnormalities and their alleviation-to great clinical advances. By contrast, cerebral palsy remains a scientific quagmire or poor definitions, no widely adopted model and a lack of therapeutic focus. We conclude that the sheer abundance of diversity of inhibitory neurotransmitter targets should provide hope for intractable conditions, particularly those that exhibit broad spectra of dysfunction-such as spastic cerebral palsy and Rett syndrome.
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Affiliation(s)
- Matthew J Fogarty
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN 55902, USA
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Xu M, Qi S, Calhoun V, Dai J, Yu B, Zhang K, Pei M, Li C, Wei Y, Jiang R, Zhi D, Huang Z, Qiu Z, Liang Z, Sui J. Aberrant brain functional and structural developments in MECP2 duplication rats. Neurobiol Dis 2022; 173:105838. [PMID: 35985556 PMCID: PMC9631682 DOI: 10.1016/j.nbd.2022.105838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/22/2022] [Accepted: 08/11/2022] [Indexed: 12/02/2022] Open
Abstract
Transgenic animal models with homologous etiology provide a promising way to pursue the neurobiological substrates of the behavioral deficits in autism spectrum disorder (ASD). Gain-of-function mutations of MECP2 cause MECP2 duplication syndrome, a severe neurological disorder with core symptoms of ASD. However, abnormal brain developments underlying the autistic-like behavioral deficits of MECP2 duplication syndrome are rarely investigated. To this end, a human MECP2 duplication (MECP2-DP) rat model was created by the bacterial artificial chromosome transgenic method. Functional and structural magnetic resonance imaging (MRI) with high-field were performed on 16 male MECP2-DP rats and 15 male wildtype rats at postnatal 28 days, 42 days, and 56 days old. Multimodal fusion analyses guided by locomotor-relevant metrics and social novelty time separately were applied to identify abnormal brain networks associated with diverse behavioral deficits induced by MECP2 duplication. Aberrant functional developments of a core network primarily composed of the dorsal medial prefrontal cortex (dmPFC) and retrosplenial cortex (RSP) were detected to associate with diverse behavioral phenotypes in MECP2-DP rats. Altered developments of gray matter volume were detected in the hippocampus and thalamus. We conclude that gain-of-function mutations of MECP2 induce aberrant functional activities in the default-mode-like network and aberrant volumetric changes in the brain, resulting in autistic-like behavioral deficits. Our results gain critical insights into the biomarker of MECP2 duplication syndrome and the neurobiological underpinnings of the behavioral deficits in ASD.
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Affiliation(s)
- Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA 30303, USA
| | - Jiankun Dai
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bin Yu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kaiwei Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengchao Pei
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chenjian Li
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University School of Life Sciences, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Yusheng Wei
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University School of Life Sciences, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Zhimin Huang
- Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University School of Life Sciences, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Zilong Qiu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhifeng Liang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Jing Sui
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
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Miller CT, Gire D, Hoke K, Huk AC, Kelley D, Leopold DA, Smear MC, Theunissen F, Yartsev M, Niell CM. Natural behavior is the language of the brain. Curr Biol 2022; 32:R482-R493. [PMID: 35609550 PMCID: PMC10082559 DOI: 10.1016/j.cub.2022.03.031] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The breadth and complexity of natural behaviors inspires awe. Understanding how our perceptions, actions, and internal thoughts arise from evolved circuits in the brain has motivated neuroscientists for generations. Researchers have traditionally approached this question by focusing on stereotyped behaviors, either natural or trained, in a limited number of model species. This approach has allowed for the isolation and systematic study of specific brain operations, which has greatly advanced our understanding of the circuits involved. At the same time, the emphasis on experimental reductionism has left most aspects of the natural behaviors that have shaped the evolution of the brain largely unexplored. However, emerging technologies and analytical tools make it possible to comprehensively link natural behaviors to neural activity across a broad range of ethological contexts and timescales, heralding new modes of neuroscience focused on natural behaviors. Here we describe a three-part roadmap that aims to leverage the wealth of behaviors in their naturally occurring distributions, linking their variance with that of underlying neural processes to understand how the brain is able to successfully navigate the everyday challenges of animals' social and ecological landscapes. To achieve this aim, experimenters must harness one challenge faced by all neurobiological systems, namely variability, in order to gain new insights into the language of the brain.
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Affiliation(s)
- Cory T Miller
- Cortical Systems and Behavior Laboratory, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92039, USA.
| | - David Gire
- Department of Psychology, University of Washington, Guthrie Hall, Seattle, WA 98105, USA
| | - Kim Hoke
- Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA
| | - Alexander C Huk
- Center for Perceptual Systems, Departments of Neuroscience and Psychology, University of Texas at Austin, 116 Inner Campus Drive, Austin, TX 78712, USA
| | - Darcy Kelley
- Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY 10027, USA
| | - David A Leopold
- Section of Cognitive Neurophysiology and Imaging, National Institute of Mental Health, 49 Convent Drive, Bethesda, MD 20892, USA
| | - Matthew C Smear
- Department of Psychology and Institute of Neuroscience, University of Oregon, 1227 University Street, Eugene, OR 97403, USA
| | - Frederic Theunissen
- Department of Psychology, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA 94720, USA
| | - Michael Yartsev
- Department of Bioengineering, University of California Berkeley, 306 Stanley Hall, Berkeley, CA 94720, USA
| | - Cristopher M Niell
- Department of Biology and Institute of Neuroscience, University of Oregon, 222 Huestis Hall, Eugene, OR 97403, USA.
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Li W. Excitation and Inhibition Imbalance in Rett Syndrome. Front Neurosci 2022; 16:825063. [PMID: 35250460 PMCID: PMC8894599 DOI: 10.3389/fnins.2022.825063] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/31/2022] [Indexed: 12/12/2022] Open
Abstract
A loss of the excitation/inhibition (E/I) balance in the neural circuit has emerged as a common neuropathological feature in many neurodevelopmental disorders. Rett syndrome (RTT), a prevalent neurodevelopmental disorder that affects 1:10,000-15,000 women globally, is caused by loss-of-function mutations in the Methyl-CpG-binding Protein-2 (Mecp2) gene. E/I imbalance is recognized as the leading cellular and synaptic hallmark that is fundamental to diverse RTT neurological symptoms, including stereotypic hand movements, impaired motor coordination, breathing irregularities, seizures, and learning/memory dysfunctions. E/I balance in RTT is not homogeneously altered but demonstrates brain region and cell type specificity instead. In this review, I elaborate on the current understanding of the loss of E/I balance in a range of brain areas at molecular and cellular levels. I further describe how the underlying cellular mechanisms contribute to the disturbance of the proper E/I ratio. Last, I discuss current pharmacologic innervations for RTT and their role in modifying the E/I balance.
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Affiliation(s)
- Wei Li
- Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States
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