1
|
Khanjanianpak M, Azimi-Tafreshi N, Valizadeh A. Emergence of complex oscillatory dynamics in the neuronal networks with long activity time of inhibitory synapses. iScience 2024; 27:109401. [PMID: 38532887 PMCID: PMC10963234 DOI: 10.1016/j.isci.2024.109401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 12/30/2023] [Accepted: 02/28/2024] [Indexed: 03/28/2024] Open
Abstract
The brain displays complex dynamics, including collective oscillations, and extensive research has been conducted to understand their generation. However, our understanding of how biological constraints influence these oscillations is incomplete. This study investigates the essential properties of neuronal networks needed to generate oscillations resembling those in the brain. A simple discrete-time model of interconnected excitable elements is developed, capable of closely resembling the complex oscillations observed in biological neural networks. In the model, synaptic connections remain active for a duration exceeding individual neuron activity. We show that the inhibitory synapses must exhibit longer activity than excitatory synapses to produce a diverse range of the dynamical states, including biologically plausible oscillations. Upon meeting this condition, the transition between different dynamical states can be controlled by external stochastic input to the neurons. The study provides a comprehensive explanation for the emergence of distinct dynamical states in neural networks based on specific parameters.
Collapse
Affiliation(s)
- Mozhgan Khanjanianpak
- Physics Department, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran 1991633357, Iran
| | - Nahid Azimi-Tafreshi
- Physics Department, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
| | - Alireza Valizadeh
- Physics Department, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran 1991633357, Iran
| |
Collapse
|
2
|
Osaki T, Duenki T, Chow SYA, Ikegami Y, Beaubois R, Levi T, Nakagawa-Tamagawa N, Hirano Y, Ikeuchi Y. Complex activity and short-term plasticity of human cerebral organoids reciprocally connected with axons. Nat Commun 2024; 15:2945. [PMID: 38600094 PMCID: PMC11006899 DOI: 10.1038/s41467-024-46787-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 03/11/2024] [Indexed: 04/12/2024] Open
Abstract
An inter-regional cortical tract is one of the most fundamental architectural motifs that integrates neural circuits to orchestrate and generate complex functions of the human brain. To understand the mechanistic significance of inter-regional projections on development of neural circuits, we investigated an in vitro neural tissue model for inter-regional connections, in which two cerebral organoids are connected with a bundle of reciprocally extended axons. The connected organoids produced more complex and intense oscillatory activity than conventional or directly fused cerebral organoids, suggesting the inter-organoid axonal connections enhance and support the complex network activity. In addition, optogenetic stimulation of the inter-organoid axon bundles could entrain the activity of the organoids and induce robust short-term plasticity of the macroscopic circuit. These results demonstrated that the projection axons could serve as a structural hub that boosts functionality of the organoid-circuits. This model could contribute to further investigation on development and functions of macroscopic neuronal circuits in vitro.
Collapse
Affiliation(s)
- Tatsuya Osaki
- Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, 153-8505, Japan
- Institute for AI and Beyond, The University of Tokyo, Bunkyo, Tokyo, 113-8655, Japan
| | - Tomoya Duenki
- Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, 153-8505, Japan
- Institute for AI and Beyond, The University of Tokyo, Bunkyo, Tokyo, 113-8655, Japan
- Department of Chemistry and Biotechnology, The University of Tokyo, Bunkyo, Tokyo, 113-8655, Japan
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Siu Yu A Chow
- Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, 153-8505, Japan
- Institute for AI and Beyond, The University of Tokyo, Bunkyo, Tokyo, 113-8655, Japan
| | - Yasuhiro Ikegami
- Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, 153-8505, Japan
- Institute for AI and Beyond, The University of Tokyo, Bunkyo, Tokyo, 113-8655, Japan
| | - Romain Beaubois
- Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, 153-8505, Japan
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- IMS Laboratory, UMR5218, University of Bordeaux, Talence, France
| | - Timothée Levi
- Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, 153-8505, Japan
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
- IMS Laboratory, UMR5218, University of Bordeaux, Talence, France
| | - Nao Nakagawa-Tamagawa
- Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, 153-8505, Japan
- Department of Physiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Yoji Hirano
- Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, 153-8505, Japan
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Psychiatry, Division of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Yoshiho Ikeuchi
- Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, 153-8505, Japan.
- Institute for AI and Beyond, The University of Tokyo, Bunkyo, Tokyo, 113-8655, Japan.
- Department of Chemistry and Biotechnology, The University of Tokyo, Bunkyo, Tokyo, 113-8655, Japan.
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
| |
Collapse
|
3
|
Sampaio Filho CIN, de Arcangelis L, Herrmann HJ, Plenz D, Kells P, Ribeiro TL, Andrade JS. Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks. Sci Rep 2024; 14:7002. [PMID: 38523136 DOI: 10.1038/s41598-024-55922-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/28/2024] [Indexed: 03/26/2024] Open
Abstract
We analyze time-averaged experimental data from in vitro activities of neuronal networks. Through a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron as well as the statistical correlations between the activities of each pair of neurons in the system. The specific information about the type of neurons is mainly stored in the local fields, while a symmetric distribution of interaction constants seems generic. Our findings demonstrate that, despite not being directly incorporated into the inference approach, the experimentally observed correlations among groups of three neurons are accurately captured by the derived Ising-like model. Within the context of the thermodynamic analogy inherent to the Ising-like models developed in this study, our findings additionally indicate that these models demonstrate characteristics of second-order phase transitions between ferromagnetic and paramagnetic states at temperatures above, but close to, unity. Considering that the operating temperature utilized in the Maximum-Entropy method isT o = 1 , this observation further expands the thermodynamic conceptual parallelism postulated in this work for the manifestation of criticality in neuronal network behavior.
Collapse
Affiliation(s)
| | - Lucilla de Arcangelis
- Department of Mathematics and Physics, University of Campania "Luigi Vanvitelli", 81100, Caserta, Italy
| | - Hans J Herrmann
- Departamento de Física, Universidade Federal do Ceará, Fortaleza, 60451-970, Brazil
- PMMH, ESPCI, CNRS UMR 7636, 7 Quai St. Bernard, 75005, Paris, France
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, NIMH, Bethesda, MD, 20892, USA
| | - Patrick Kells
- Section on Critical Brain Dynamics, NIMH, Bethesda, MD, 20892, USA
| | | | - José S Andrade
- Departamento de Física, Universidade Federal do Ceará, Fortaleza, 60451-970, Brazil
| |
Collapse
|
4
|
Apablaza-Yevenes DE, Corsi-Cabrera M, Martinez-Guerrero A, Northoff G, Romaniello C, Farinelli M, Bertoletti E, Müller MF, Muñoz-Torres Z. Stationary stable cross-correlation pattern and task specific deviations in unresponsive wakefulness syndrome as well as clinically healthy subjects. PLoS One 2024; 19:e0300075. [PMID: 38489260 PMCID: PMC10942032 DOI: 10.1371/journal.pone.0300075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
Brain dynamics is highly non-stationary, permanently subject to ever-changing external conditions and continuously monitoring and adjusting internal control mechanisms. Finding stationary structures in this system, as has been done recently, is therefore of great importance for understanding fundamental dynamic trade relationships. Here we analyse electroencephalographic recordings (EEG) of 13 subjects with unresponsive wakefulness syndrome (UWS) during rest and while being influenced by different acoustic stimuli. We compare the results with a control group under the same experimental conditions and with clinically healthy subjects during overnight sleep. The main objective of this study is to investigate whether a stationary correlation pattern is also present in the UWS group, and if so, to what extent this structure resembles the one found in healthy subjects. Furthermore, we extract transient dynamical features via specific deviations from the stationary interrelation pattern. We find that (i) the UWS group is more heterogeneous than the two groups of healthy subjects, (ii) also the EEGs of the UWS group contain a stationary cross-correlation pattern, although it is less pronounced and shows less similarity to that found for healthy subjects and (iii) deviations from the stationary pattern are notably larger for the UWS than for the two groups of healthy subjects. The results suggest that the nervous system of subjects with UWS receive external stimuli but show an overreaching reaction to them, which may disturb opportune information processing.
Collapse
Affiliation(s)
- David E. Apablaza-Yevenes
- Instituto de Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Morelos, México
| | - María Corsi-Cabrera
- Unidad de Investigación en Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
| | | | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, People’s Republic of China
- Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China
| | | | | | | | - Markus F. Müller
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad de México, México
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Morelos, México
- Centro Internacional de Ciencias A.C., Morelos, México
| | - Zeidy Muñoz-Torres
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad de México, México
- Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, México
| |
Collapse
|
5
|
Papo D, Buldú JM. Does the brain behave like a (complex) network? I. Dynamics. Phys Life Rev 2024; 48:47-98. [PMID: 38145591 DOI: 10.1016/j.plrev.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/27/2023]
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.
Collapse
Affiliation(s)
- D Papo
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy; Center for Translational Neurophysiology, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy.
| | - J M Buldú
- Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, Madrid, Spain
| |
Collapse
|
6
|
Zhang X, Dou Z, Kim SH, Upadhyay G, Havert D, Kang S, Kazemi K, Huang K, Aydin O, Huang R, Rahman S, Ellis‐Mohr A, Noblet HA, Lim KH, Chung HJ, Gritton HJ, Saif MTA, Kong HJ, Beggs JM, Gazzola M. Mind In Vitro Platforms: Versatile, Scalable, Robust, and Open Solutions to Interfacing with Living Neurons. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306826. [PMID: 38161217 PMCID: PMC10953569 DOI: 10.1002/advs.202306826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/12/2023] [Indexed: 01/03/2024]
Abstract
Motivated by the unexplored potential of in vitro neural systems for computing and by the corresponding need of versatile, scalable interfaces for multimodal interaction, an accurate, modular, fully customizable, and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated is presented. This approach entails a reconfigurable platform that works across multiple industry standards and that enables a complete signal chain, from neural substrates sampled through micro-electrode arrays (MEAs) to data acquisition, downstream analysis, and cloud storage. Built-in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations, spatial topologies, and constitutive materials. Through a dedicated analysis and management software suite, the utility and robustness of this system are demonstrated across neural cultures and applications, including embryonic stem cell-derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging, and long-term recording. Overall, this technology, termed "mind in vitro" to underscore the computing inspiration, provides an end-to-end solution that can be widely deployed due to its affordable (>10× cost reduction) and open-source nature, catering to the expanding needs of both conventional and unconventional electrophysiology.
Collapse
Affiliation(s)
- Xiaotian Zhang
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Zhi Dou
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Seung Hyun Kim
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Gaurav Upadhyay
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Daniel Havert
- Department of PhysicsIndiana University BloomingtonBloomingtonIN47405USA
| | - Sehong Kang
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Kimia Kazemi
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Kai‐Yu Huang
- Department of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Onur Aydin
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Raymond Huang
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Saeedur Rahman
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Austin Ellis‐Mohr
- Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Hayden A. Noblet
- Molecular and Integrative PhysiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Neuroscience ProgramUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Ki H. Lim
- Molecular and Integrative PhysiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Hee Jung Chung
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Molecular and Integrative PhysiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Neuroscience ProgramUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Howard J. Gritton
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Department of Comparative BiosciencesUniversity of Illinois at Urbana–ChampaignUrbanaIL61802USA
| | - M. Taher A. Saif
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - Hyun Joon Kong
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Department of Chemical and Biomolecular EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| | - John M. Beggs
- Department of PhysicsIndiana University BloomingtonBloomingtonIN47405USA
| | - Mattia Gazzola
- Carl R. Woese Institute for Genomic BiologyUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
- Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA
| |
Collapse
|
7
|
Ramon C, Doud A, Holmes MD. Decrease in phase slip rates and phase cone structures during seizure evolution and epileptogenic activities derived from microgrid ECoG data. CURRENT RESEARCH IN NEUROBIOLOGY 2024; 6:100126. [PMID: 38616959 PMCID: PMC11015059 DOI: 10.1016/j.crneur.2024.100126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 12/25/2023] [Accepted: 02/03/2024] [Indexed: 04/16/2024] Open
Abstract
Sudden phase changes are related to cortical phase transitions, which likely change in frequency and spatial distribution as epileptogenic activity evolves. A 100 s long section of micro-ECoG data obtained before and during a seizure was selected and analyzed. In addition, nine other short-duration epileptic events were also examined. The data was collected at 420 Hz, imported into MATLAB, downsampled to 200 Hz, and filtered in the 1-50 Hz band. The Hilbert transform was applied to compute the analytic phase, which was then unwrapped, and detrended to look for sudden phase changes. The phase slip rate (counts/s) and its acceleration (counts/s2) were computed with a stepping window of 1-s duration and with a step size of 5 ms. The analysis was performed for theta (3-7 Hz), alpha (7-12 Hz), and beta (12-30 Hz) bands. The phase slip rate on all electrodes in the theta band decreased while it increased for the alpha and beta bands during the seizure period. Similar patterns were observed for isolated epileptogenic events. Spatiotemporal contour plots of the phase slip rates were also constructed using a montage layout of 8 × 8 electrode positions. These plots exhibited dynamic and oscillatory formation of phase cone-like structures which were higher in the theta band and lower in the alpha and beta bands during the seizure period and epileptogenic events. These results indicate that the formation of phase cones might be an excellent biomarker to study the evolution of a seizure and also the cortical dynamics of isolated epileptogenic events.
Collapse
Affiliation(s)
- Ceon Ramon
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA
- Regional Epilepsy Center, Harborview Medical Center, Department of Neurology, University of Washington, Seattle, WA, 98195, USA
| | - Alexander Doud
- Providence Spokane Neuroscience Institute, 105 West 8th Avenue, Spokane, WA, 99204, USA
| | - Mark D. Holmes
- Regional Epilepsy Center, Harborview Medical Center, Department of Neurology, University of Washington, Seattle, WA, 98195, USA
| |
Collapse
|
8
|
Czoch A, Kaposzta Z, Mukli P, Stylianou O, Eke A, Racz FS. Resting-state fractal brain connectivity is associated with impaired cognitive performance in healthy aging. GeroScience 2024; 46:473-489. [PMID: 37458934 PMCID: PMC10828136 DOI: 10.1007/s11357-023-00836-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/20/2023] [Indexed: 01/31/2024] Open
Abstract
Aging affects cognitive functions even in the absence of ongoing pathologies. The neurophysiological basis of age-related cognitive decline (CD), however, is not completely understood. Alterations in both functional brain connectivity and in the fractal scaling of neuronal dynamics have been linked to aging and cognitive performance. Recently, fractal connectivity (FrC) has been proposed - combining the two concepts - for capturing long-term interactions among brain regions. FrC was shown to be influenced by increased mental workload; however, no prior studies investigated how resting-state FrC relates to cognitive performance and plausible CD in healthy aging. We recruited 19 healthy elderly (HE) and 24 young control (YC) participants, who underwent resting-state electroencephalography (EEG) measurements and comprehensive cognitive evaluation using 7 tests of the Cambridge Neurophysiological Test Automated Battery. FrC networks were reconstructed from EEG data using the recently introduced multiple-resampling cross-spectral analysis (MRCSA). Elderly individuals could be characterized with increased response latency and reduced performance in 4-4 tasks, respectively, with both reaction time and accuracy being affected in two tasks. Auto- and cross-spectral exponents - characterizing regional fractal dynamics and FrC, respectively, - were found reduced in HE when compared to YC over most of the cortex. Additionally, fractal scaling of frontoparietal connections expressed an inverse relationship with task performance in visual memory and sustained attention domains in elderly, but not in young individuals. Our results confirm that the fractal nature of brain connectivity - as captured by MRCSA - is affected in healthy aging. Furthermore, FrC appears as a sensitive neurophysiological marker of age-related CD.
Collapse
Affiliation(s)
- Akos Czoch
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- International Training Program in Geroscience, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
| | - Orestis Stylianou
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- Berlin Institute of Health at Charité, University Hospital Berlin, Berlin, Germany
- Department of Neurology With Experimental Neurology, Charité-University Hospital Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, Berlin, Germany
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Frigyes Samuel Racz
- Department of Physiology, Semmelweis University, Budapest, Hungary.
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
- Mulva Clinic for the Neurosciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
| |
Collapse
|
9
|
Lassers SB, Vakilna YS, Tang WC, Brewer GJ. The flow of axonal information among hippocampal sub-regions 2: patterned stimulation sharpens routing of information transmission. Front Neural Circuits 2023; 17:1272925. [PMID: 38144878 PMCID: PMC10739322 DOI: 10.3389/fncir.2023.1272925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/20/2023] [Indexed: 12/26/2023] Open
Abstract
The sub-regions of the hippocampal formation are essential for episodic learning and memory formation, yet the spike dynamics of each region contributing to this function are poorly understood, in part because of a lack of access to the inter-regional communicating axons. Here, we reconstructed hippocampal networks confined to four subcompartments in 2D cultures on a multi-electrode array that monitors individual communicating axons. In our novel device, somal, and axonal activity was measured simultaneously with the ability to ascertain the direction and speed of information transmission. Each sub-region and inter-regional axons had unique power-law spiking dynamics, indicating differences in computational functions, with abundant axonal feedback. After stimulation, spiking, and burst rates decreased in all sub-regions, spikes per burst generally decreased, intraburst spike rates increased, and burst duration decreased, which were specific for each sub-region. These changes in spiking dynamics post-stimulation were found to occupy a narrow range, consistent with the maintenance of the network at a critical state. Functional connections between the sub-region neurons and communicating axons in our device revealed homeostatic network routing strategies post-stimulation in which spontaneous feedback activity was selectively decreased and balanced by decreased feed-forward activity. Post-stimulation, the number of functional connections per array decreased, but the reliability of those connections increased. The networks maintained a balance in spiking and bursting dynamics in response to stimulation and sharpened network routing. These plastic characteristics of the network revealed the dynamic architecture of hippocampal computations in response to stimulation by selective routing on a spatiotemporal scale in single axons.
Collapse
Affiliation(s)
- Samuel Brandon Lassers
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Yash S. Vakilna
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center (UTHealth), Houston, TX, United States
| | - William C. Tang
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
| | - Gregory J. Brewer
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- Memory Impairments and Neurological Disorders (MIND) Institute, Center for Neuroscience of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| |
Collapse
|
10
|
Reisinger D, Adam R, Tschofenig F, Füllsack M, Jäger G. Modular tipping points: How local network structure impacts critical transitions in networked spin systems. PLoS One 2023; 18:e0292935. [PMID: 37963138 PMCID: PMC10645300 DOI: 10.1371/journal.pone.0292935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/29/2023] [Indexed: 11/16/2023] Open
Abstract
Critical transitions describe a phenomenon where a system abruptly shifts from one stable state to an alternative, often detrimental, stable state. Understanding and possibly preventing the occurrence of a critical transition is thus highly relevant to many ecological, sociological, and physical systems. In this context, it has been shown that the underlying network structure of a system heavily impacts the transition behavior of that system. In this paper, we study a crucial but often overlooked aspect in critical transitions: the modularity of the system's underlying network topology. In particular, we investigate how the transition behavior of a networked system changes as we alter the local network structure of the system through controlled changes of the degree assortativity. We observe that systems with high modularity undergo cascading transitions, while systems with low modularity undergo more unified transitions. We also observe that networked systems that consist of nodes with varying degrees of connectivity tend to transition earlier in response to changes in a control parameter than one would anticipate based solely on the average degree of that network. However, in rare cases, such as when there is both low modularity and high degree disassortativity, the transition behavior aligns with what we would expected given the network's average degree. Results are confirmed for a diverse set of degree distributions including stylized two-degree networks, uniform, Poisson, and power-law degree distributions. On the basis of these results, we argue that to understand critical transitions in networked systems, they must be understood in terms of individual system components and their roles within the network structure.
Collapse
Affiliation(s)
- Daniel Reisinger
- Institute of Environmental Systems Sciences, University of Graz, Graz, Styria, Austria
| | - Raven Adam
- Institute of Environmental Systems Sciences, University of Graz, Graz, Styria, Austria
| | - Fabian Tschofenig
- Institute of Environmental Systems Sciences, University of Graz, Graz, Styria, Austria
| | - Manfred Füllsack
- Institute of Environmental Systems Sciences, University of Graz, Graz, Styria, Austria
| | - Georg Jäger
- Institute of Environmental Systems Sciences, University of Graz, Graz, Styria, Austria
| |
Collapse
|
11
|
Wang SH, Siebenhühner F, Arnulfo G, Myrov V, Nobili L, Breakspear M, Palva S, Palva JM. Critical-like Brain Dynamics in a Continuum from Second- to First-Order Phase Transition. J Neurosci 2023; 43:7642-7656. [PMID: 37816599 PMCID: PMC10634584 DOI: 10.1523/jneurosci.1889-22.2023] [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: 10/05/2022] [Revised: 06/07/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Abstract
The classic brain criticality hypothesis postulates that the brain benefits from operating near a continuous second-order phase transition. Slow feedback regulation of neuronal activity could, however, lead to a discontinuous first-order transition and thereby bistable activity. Observations of bistability in awake brain activity have nonetheless remained scarce and its functional significance unclear. Moreover, there is no empirical evidence to support the hypothesis that the human brain could flexibly operate near either a first- or second-order phase transition despite such a continuum being common in models. Here, using computational modeling, we found bistable synchronization dynamics to emerge through elevated positive feedback and occur exclusively in a regimen of critical-like dynamics. We then assessed bistability in vivo with resting-state MEG in healthy adults (7 females, 11 males) and stereo-electroencephalography in epilepsy patients (28 females, 36 males). This analysis revealed that a large fraction of the neocortices exhibited varying degrees of bistability in neuronal oscillations from 3 to 200 Hz. In line with our modeling results, the neuronal bistability was positively correlated with classic assessment of brain criticality across narrow-band frequencies. Excessive bistability was predictive of epileptic pathophysiology in the patients, whereas moderate bistability was positively correlated with task performance in the healthy subjects. These empirical findings thus reveal the human brain as a one-of-a-kind complex system that exhibits critical-like dynamics in a continuum between continuous and discontinuous phase transitions.SIGNIFICANCE STATEMENT In the model, while synchrony per se was controlled by connectivity, increasing positive local feedback led to gradually emerging bistable synchrony with scale-free dynamics, suggesting a continuum between second- and first-order phase transitions in synchrony dynamics inside a critical-like regimen. In resting-state MEG and SEEG, bistability of ongoing neuronal oscillations was pervasive across brain areas and frequency bands and was observed only with concurring critical-like dynamics as the modeling predicted. As evidence for functional relevance, moderate bistability was positively correlated with executive functioning in the healthy subjects, and excessive bistability was associated with epileptic pathophysiology. These findings show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.
Collapse
Affiliation(s)
- Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Doctoral Programme Brain & Mind, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, 16136 Genoa, Italy
| | - Vladislav Myrov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Lino Nobili
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Children's Sciences, University of Genoa, 16136 Genoa, Italy
- Child Neuropsychiatry Unit, Istituto Di Ricovero e Cura a Carattere Scientifico Istituto Giannina Gaslini, 16147 Genoa, Italy
- Centre of Epilepsy Surgery "C. Munari," Department of Neuroscience, Niguarda Hospital, 20162 Milan, Italy
| | - Michael Breakspear
- College of Engineering, Science and Environment, College of Health and Medicine, University of Newcastle, Callaghan, 2308 Australia
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
| |
Collapse
|
12
|
Kardan O, Stier AJ, Layden EA, Choe KW, Lyu M, Zhang X, Beilock SL, Rosenberg MD, Berman MG. Improvements in task performance after practice are associated with scale-free dynamics of brain activity. Netw Neurosci 2023; 7:1129-1152. [PMID: 37781143 PMCID: PMC10473260 DOI: 10.1162/netn_a_00319] [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: 07/13/2022] [Accepted: 04/11/2023] [Indexed: 10/03/2023] Open
Abstract
Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.
Collapse
Affiliation(s)
- Omid Kardan
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Andrew J. Stier
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Elliot A. Layden
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Kyoung Whan Choe
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Muxuan Lyu
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Department of Management and Marketing, The Hong Kong Polytechnic University, Hong Kong
| | - Xihan Zhang
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Sian L. Beilock
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Barnard College, Columbia University, New York, NY, USA
| | | | - Marc G. Berman
- Department of Psychology, University of Chicago, Chicago, IL, USA
| |
Collapse
|
13
|
Moraes JT, Ferreira SC. Visibility graphs of critical and off-critical time series for absorbing state phase transitions. Phys Rev E 2023; 108:044309. [PMID: 37978633 DOI: 10.1103/physreve.108.044309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 09/11/2023] [Indexed: 11/19/2023]
Abstract
It is possible to investigate emergence in many real systems using time-ordered data. However, classical time series analysis is usually conditioned by data accuracy and quantity. A modern method is to map time series onto graphs and study these structures using the toolbox available in complex network analysis. An important practical problem to investigate the criticality in experimental systems is to determine whether an observed time series is associated with a critical regime or not. We contribute to this problem by investigating the mapping called visibility graph (VG) of a time series generated in dynamical processes with absorbing-state phase transitions. Analyzing degree correlation patterns of the VGs, we are able to distinguish between critical and off-critical regimes. One central hallmark is an asymptotic disassortative correlation on the degree for series near the critical regime in contrast with a pure assortative correlation observed for noncritical dynamics. We are also able to distinguish between continuous (critical) and discontinuous (noncritical) absorbing state phase transitions, the second of which is commonly involved in catastrophic phenomena. The determination of critical behavior converges very quickly in higher dimensions, where many complex system dynamics are relevant.
Collapse
Affiliation(s)
- Juliane T Moraes
- Departamento de Física, Universidade Federal de Viçosa, 36570-900 Viçosa, Minas Gerais, Brazil
| | - Silvio C Ferreira
- Departamento de Física, Universidade Federal de Viçosa, 36570-900 Viçosa, Minas Gerais, Brazil
- National Institute of Science and Technology for Complex Systems, 22290-180, Rio de Janeiro, Brazil
| |
Collapse
|
14
|
Habibollahi F, Kagan BJ, Burkitt AN, French C. Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks. Nat Commun 2023; 14:5287. [PMID: 37648737 PMCID: PMC10469171 DOI: 10.1038/s41467-023-41020-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 08/17/2023] [Indexed: 09/01/2023] Open
Abstract
Understanding how brains process information is an incredibly difficult task. Amongst the metrics characterising information processing in the brain, observations of dynamic near-critical states have generated significant interest. However, theoretical and experimental limitations associated with human and animal models have precluded a definite answer about when and why neural criticality arises with links from attention, to cognition, and even to consciousness. To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of 'Pong' to demonstrate Synthetic Biological Intelligence (SBI). We demonstrate that critical dynamics emerge when neural networks receive task-related structured sensory input, reorganizing the system to a near-critical state. Additionally, better task performance correlated with proximity to critical dynamics. However, criticality alone is insufficient for a neuronal network to demonstrate learning in the absence of additional information regarding the consequences of previous actions. These findings offer compelling support that neural criticality arises as a base feature of incoming structured information processing without the need for higher order cognition.
Collapse
Affiliation(s)
- Forough Habibollahi
- Cortical Labs Pty Ltd, Melbourne, 3056, VIC, Australia
- Biomedical Engineering Department, University of Melbourne, Parkville, 3010, VIC, Australia
- Neural Dynamics Laboratory, Department of Medicine, University of Melbourne, Parkville, 3010, VIC, Australia
| | - Brett J Kagan
- Cortical Labs Pty Ltd, Melbourne, 3056, VIC, Australia.
| | - Anthony N Burkitt
- Biomedical Engineering Department, University of Melbourne, Parkville, 3010, VIC, Australia
| | - Chris French
- Neural Dynamics Laboratory, Department of Medicine, University of Melbourne, Parkville, 3010, VIC, Australia
- Neurology Department, Royal Melbourne Hospital, Melbourne, Australia
| |
Collapse
|
15
|
Jaeger H, Noheda B, van der Wiel WG. Toward a formal theory for computing machines made out of whatever physics offers. Nat Commun 2023; 14:4911. [PMID: 37587135 PMCID: PMC10432384 DOI: 10.1038/s41467-023-40533-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 08/01/2023] [Indexed: 08/18/2023] Open
Abstract
Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to engineer unconventional computing systems in a systematic way, we need guidance from a formal theory that is different from the classical symbolic-algorithmic Turing machine theory. We propose a general strategy for developing such a theory, and within that general view, a specific approach that we call fluent computing. In contrast to Turing, who modeled computing processes from a top-down perspective as symbolic reasoning, we adopt the scientific paradigm of physics and model physical computing systems bottom-up by formalizing what can ultimately be measured in a physical computing system. This leads to an understanding of computing as the structuring of processes, while classical models of computing systems describe the processing of structures.
Collapse
Affiliation(s)
- Herbert Jaeger
- Bernoulli Institute, University of Groningen, 9700 AB, Groningen, The Netherlands.
- Groningen Cognitive Systems and Materials Center (CogniGron), University of Groningen, 9700 AB, Groningen, The Netherlands.
| | - Beatriz Noheda
- Groningen Cognitive Systems and Materials Center (CogniGron), University of Groningen, 9700 AB, Groningen, The Netherlands
- Zernike Institute for Advanced Materials, University of Groningen, 9700 AB, Groningen, The Netherlands
| | - Wilfred G van der Wiel
- BRAINS Center for Brain-Inspired Nano Systems, University of Twente, 7500 AE, Enschede, The Netherlands
- MESA+ Institute for Nanotechnology, University of Twente, 7500 AE, Enschede, The Netherlands
- Institute of Physics, Westfälische Wilhelms-Universität Münster, Münster, Germany
| |
Collapse
|
16
|
Castaldo F, Páscoa Dos Santos F, Timms RC, Cabral J, Vohryzek J, Deco G, Woolrich M, Friston K, Verschure P, Litvak V. Multi-modal and multi-model interrogation of large-scale functional brain networks. Neuroimage 2023; 277:120236. [PMID: 37355200 PMCID: PMC10958139 DOI: 10.1016/j.neuroimage.2023.120236] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/26/2023] Open
Abstract
Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.
Collapse
Affiliation(s)
- Francesca Castaldo
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom.
| | - Francisco Páscoa Dos Santos
- Eodyne Systems SL, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ryan C Timms
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - Portuguese Government Associate Laboratory, Braga/Guimarães, Portugal; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom
| | - Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom; Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gustavo Deco
- Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Paul Verschure
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| |
Collapse
|
17
|
Mackay M, Huo S, Kaiser M. Spatial organisation of the mesoscale connectome: A feature influencing synchrony and metastability of network dynamics. PLoS Comput Biol 2023; 19:e1011349. [PMID: 37552650 PMCID: PMC10437862 DOI: 10.1371/journal.pcbi.1011349] [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: 05/07/2022] [Revised: 08/18/2023] [Accepted: 07/12/2023] [Indexed: 08/10/2023] Open
Abstract
Significant research has investigated synchronisation in brain networks, but the bulk of this work has explored the contribution of brain networks at the macroscale. Here we explore the effects of changing network topology on functional dynamics in spatially constrained random networks representing mesoscale neocortex. We use the Kuramoto model to simulate network dynamics and explore synchronisation and critical dynamics of the system as a function of topology in randomly generated networks with a distance-related wiring probability and no preferential attachment term. We show networks which predominantly make short-distance connections smooth out the critical coupling point and show much greater metastability, resulting in a wider range of coupling strengths demonstrating critical dynamics and metastability. We show the emergence of cluster synchronisation in these geometrically-constrained networks with functional organisation occurring along structural connections that minimise the participation coefficient of the cluster. We show that these cohorts of internally synchronised nodes also behave en masse as weakly coupled nodes and show intra-cluster desynchronisation and resynchronisation events related to inter-cluster interaction. While cluster synchronisation appears crucial to healthy brain function, it may also be pathological if it leads to unbreakable local synchronisation which may happen at extreme topologies, with implications for epilepsy research, wider brain function and other domains such as social networks.
Collapse
Affiliation(s)
- Michael Mackay
- Newcastle University, School of Computing, Newcastle upon Tyne, United Kingdom
| | - Siyu Huo
- East China Normal University, School of Physics and Electronic Science, Shanghai, China
- University of Nottingham, NIHR Nottingham Biomedical Research Centre, School of Medicine, Nottingham, United Kingdom
| | - Marcus Kaiser
- University of Nottingham, NIHR Nottingham Biomedical Research Centre, School of Medicine, Nottingham, United Kingdom
- University of Nottingham, Sir Peter Mansfield Imaging Centre, School of Medicine, Nottingham, United Kingdom
- Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| |
Collapse
|
18
|
Galinsky VL, Frank LR. Neuronal avalanches: Sandpiles of self-organized criticality or critical dynamics of brain waves? FRONTIERS OF PHYSICS 2023; 18:45301. [PMID: 37008280 PMCID: PMC10062440 DOI: 10.1007/s11467-023-1273-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/23/2023] [Indexed: 06/19/2023]
Abstract
Analytical expressions for scaling of brain wave spectra derived from the general nonlinear wave Hamiltonian form show excellent agreement with experimental "neuronal avalanche" data. The theory of the weakly evanescent nonlinear brain wave dynamics [Phys. Rev. Research 2, 023061 (2020); J. Cognitive Neurosci. 32, 2178 (2020)] reveals the underlying collective processes hidden behind the phenomenological statistical description of the neuronal avalanches and connects together the whole range of brain activity states, from oscillatory wave-like modes, to neuronal avalanches, to incoherent spiking, showing that the neuronal avalanches are just the manifestation of the different nonlinear side of wave processes abundant in cortical tissue. In a more broad way these results show that a system of wave modes interacting through all possible combinations of the third order nonlinear terms described by a general wave Hamiltonian necessarily produces anharmonic wave modes with temporal and spatial scaling properties that follow scale free power laws. To the best of our knowledge this has never been reported in the physical literature and may be applicable to many physical systems that involve wave processes and not just to neuronal avalanches.
Collapse
Affiliation(s)
- Vitaly L. Galinsky
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92037-0854, USA
| | - Lawrence R. Frank
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92037-0854, USA
- Center for Functional MRI, University of California at San Diego, La Jolla, CA 92037-0677, USA
| |
Collapse
|
19
|
Kloucek MB, Machon T, Kajimura S, Royall CP, Masuda N, Turci F. Biases in inverse Ising estimates of near-critical behavior. Phys Rev E 2023; 108:014109. [PMID: 37583208 DOI: 10.1103/physreve.108.014109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/27/2023] [Indexed: 08/17/2023]
Abstract
Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.
Collapse
Affiliation(s)
- Maximilian B Kloucek
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
- Bristol Centre for Functional Nanomaterials, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
| | - Thomas Machon
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
| | - Shogo Kajimura
- Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto 606-8585, Japan
| | - C Patrick Royall
- Gulliver UMR CNRS 7083, ESPCI Paris, Université PSL, 75005 Paris, France
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York 14260-2900, USA
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, New York 14260-5030, USA
| | - Francesco Turci
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
| |
Collapse
|
20
|
Liu S, Li F, Wan F. Distance to Criticality Undergoes Critical Transition Before Epileptic Seizure Attacks. Brain Res Bull 2023:110684. [PMID: 37353038 DOI: 10.1016/j.brainresbull.2023.110684] [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: 05/15/2023] [Revised: 06/03/2023] [Accepted: 06/10/2023] [Indexed: 06/25/2023]
Abstract
Epilepsy is a common neurological disorder characterized by recurring seizures, but its underlying mechanisms remain poorly understood. Despite extensive research, there are still gaps in our knowledge about the relationship between brain dynamics and seizures. In this study, our aim is to address these gaps by proposing a novel approach to assess the role of brain network dynamics in the onset of seizures. Specifically, we investigate the relationship between brain dynamics and seizures by tracking the distance to criticality. Our hypothesis is that this distance plays a crucial role in brain state changes and that seizures may be related to critical transitions of this distance. To test this hypothesis, we develop a method to measure the evolution of the brain network's distance to the critical dynamic systems (i.e., the distance to the tipping point, DTP) using dynamic network biomarker theory and random matrix theory. The results show that the DTP of the brain decreases significantly immediately after onset of an epileptic seizure, suggesting that the brain loses its well-defined quasi-critical state during seizures. We refer to this phenomenon as the "criticality of the criticality" (COC). Furthermore, we observe that DTP exhibits a shape transition before and after the onset of the seizures. This phenomenon suggests the possibility of early warning signal (EWS) identification in the dynamic sequence of DTP, which could be utilized for seizure prediction. Our results show that the Hurst exponent, skewness, kurtosis, autocorrelation, and variance of the DTP sequence are potential EWS features. This study advances our understanding of the relationship between brain dynamics and seizures and highlights the potential for using criticality-based measures to predict and prevent seizures.
Collapse
Affiliation(s)
- Shun Liu
- The Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau; The Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau; The Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, University of Macau, Macau
| | - Fali Li
- The Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuro-information, University of Electronic Science and Technology of China, Chengdu, China; School of Life Science and Technology, the Center for Information in Bio-Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Wan
- The Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau; The Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau; The Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, University of Macau, Macau.
| |
Collapse
|
21
|
Ma Z, Chen W, Cao X, Diao S, Liu Z, Ge J, Pan S. Criticality and Neuromorphic Sensing in a Single Memristor. NANO LETTERS 2023. [PMID: 37326403 DOI: 10.1021/acs.nanolett.3c00389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Resistive random access memory (RRAM) is an important technology for both data storage and neuromorphic computation, where the dynamics of nanoscale conductive filaments lies at the core of the technology. Here, we analyze the current noise of various silicon-based memristors that involves the creation of a percolation path at the intermediate phase of filament growth. Remarkably, we find that these atomic switching events follow scale-free avalanche dynamics with exponents satisfying the criteria for criticality. We further prove that the switching dynamics are universal and show little dependence on device sizes or material features. Utilizing criticality in memristors, we simulate the functionality of hair cells in auditory sensory systems by observing the frequency selectivity of input stimuli with tunable characteristic frequency. We further demonstrate a single-memristor-based sensing primitive for representation of input stimuli that exceeds the theoretical limits dictated by the Nyquist-Shannon theorem.
Collapse
Affiliation(s)
- Zelin Ma
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
| | - Wanjun Chen
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
| | - Xucheng Cao
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
| | - Shanqing Diao
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
| | - Zhiyu Liu
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
| | - Jun Ge
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
- Key Lab of Si-based Information Materials & Devices and Integrated Circuits Design, Department of Education of Guangdong Province, Guangzhou 510006, China
| | - Shusheng Pan
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
- Key Lab of Si-based Information Materials & Devices and Integrated Circuits Design, Department of Education of Guangdong Province, Guangzhou 510006, China
| |
Collapse
|
22
|
Ramon C, Graichen U, Gargiulo P, Zanow F, Knösche TR, Haueisen J. Spatiotemporal phase slip patterns for visual evoked potentials, covert object naming tasks, and insight moments extracted from 256 channel EEG recordings. Front Integr Neurosci 2023; 17:1087976. [PMID: 37384237 PMCID: PMC10293627 DOI: 10.3389/fnint.2023.1087976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 05/19/2023] [Indexed: 06/30/2023] Open
Abstract
Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4-7 Hz), alpha (7-12 Hz), beta (12-30 Hz), and low gamma (30-49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the 'Eureka' moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.
Collapse
Affiliation(s)
- Ceon Ramon
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
- Regional Epilepsy Center, Harborview Medical Center, University of Washington, Seattle, WA, United States
| | - Uwe Graichen
- Department of Biostatistics and Data Science, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Paolo Gargiulo
- Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland
- Department of Science, Landspitali University Hospital, Reykjavik, Iceland
| | | | - Thomas R. Knösche
- Max Planck Institute for Human Cognitive and Neurosciences, Leipzig, Germany
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| |
Collapse
|
23
|
Yurchenko SB. A systematic approach to brain dynamics: cognitive evolution theory of consciousness. Cogn Neurodyn 2023; 17:575-603. [PMID: 37265655 PMCID: PMC10229528 DOI: 10.1007/s11571-022-09863-6] [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: 03/29/2022] [Revised: 06/29/2022] [Accepted: 07/21/2022] [Indexed: 12/18/2022] Open
Abstract
The brain integrates volition, cognition, and consciousness seamlessly over three hierarchical (scale-dependent) levels of neural activity for their emergence: a causal or 'hard' level, a computational (unconscious) or 'soft' level, and a phenomenal (conscious) or 'psyche' level respectively. The cognitive evolution theory (CET) is based on three general prerequisites: physicalism, dynamism, and emergentism, which entail five consequences about the nature of consciousness: discreteness, passivity, uniqueness, integrity, and graduation. CET starts from the assumption that brains should have primarily evolved as volitional subsystems of organisms, not as prediction machines. This emphasizes the dynamical nature of consciousness in terms of critical dynamics to account for metastability, avalanches, and self-organized criticality of brain processes, then coupling it with volition and cognition in a framework unified over the levels. Consciousness emerges near critical points, and unfolds as a discrete stream of momentary states, each volitionally driven from oldest subcortical arousal systems. The stream is the brain's way of making a difference via predictive (Bayesian) processing. Its objective observables could be complexity measures reflecting levels of consciousness and its dynamical coherency to reveal how much knowledge (information gain) the brain acquires over the stream. CET also proposes a quantitative classification of both disorders of consciousness and mental disorders within that unified framework.
Collapse
|
24
|
Gervais C, Boucher LP, Villar GM, Lee U, Duclos C. A scoping review for building a criticality-based conceptual framework of altered states of consciousness. Front Syst Neurosci 2023; 17:1085902. [PMID: 37304151 PMCID: PMC10248073 DOI: 10.3389/fnsys.2023.1085902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 05/08/2023] [Indexed: 06/13/2023] Open
Abstract
The healthy conscious brain is thought to operate near a critical state, reflecting optimal information processing and high susceptibility to external stimuli. Conversely, deviations from the critical state are hypothesized to give rise to altered states of consciousness (ASC). Measures of criticality could therefore be an effective way of establishing the conscious state of an individual. Furthermore, characterizing the direction of a deviation from criticality may enable the development of treatment strategies for pathological ASC. The aim of this scoping review is to assess the current evidence supporting the criticality hypothesis, and the use of criticality as a conceptual framework for ASC. Using the PRISMA guidelines, Web of Science and PubMed were searched from inception to February 7th 2022 to find articles relating to measures of criticality across ASC. N = 427 independent papers were initially found on the subject. N = 378 were excluded because they were either: not related to criticality; not related to consciousness; not presenting results from a primary study; presenting model data. N = 49 independent papers were included in the present research, separated in 7 sub-categories of ASC: disorders of consciousness (DOC) (n = 5); sleep (n = 13); anesthesia (n = 18); epilepsy (n = 12); psychedelics and shamanic state of consciousness (n = 4); delirium (n = 1); meditative state (n = 2). Each category included articles suggesting a deviation of the critical state. While most studies were only able to identify a deviation from criticality without being certain of its direction, the preliminary consensus arising from the literature is that non-rapid eye movement (NREM) sleep reflects a subcritical state, epileptic seizures reflect a supercritical state, and psychedelics are closer to the critical state than normal consciousness. This scoping review suggests that, though the literature is limited and methodologically inhomogeneous, ASC are characterized by a deviation from criticality, though its direction is not clearly reported in a majority of studies. Criticality could become, with more extensive research, an effective and objective way to characterize ASC, and help identify therapeutic avenues to improve criticality in pathological brain states. Furthermore, we suggest how anesthesia and psychedelics could potentially be used as neuromodulation techniques to restore criticality in DOC.
Collapse
Affiliation(s)
- Charles Gervais
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
- Centre for Advanced Research in Sleep Medicine & Integrated Trauma Centre, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal, Montréal, QC, Canada
| | - Louis-Philippe Boucher
- Centre for Advanced Research in Sleep Medicine & Integrated Trauma Centre, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal, Montréal, QC, Canada
- Department of Neuroscience, Université de Montréal, Montréal, QC, Canada
| | - Guillermo Martinez Villar
- Department of Psychology, Université de Montréal, Montréal, QC, Canada
- Centre for Advanced Research in Sleep Medicine & Integrated Trauma Centre, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal, Montréal, QC, Canada
- Department of Biomedical Sciences, Université de Montréal, Montréal, QC, Canada
| | - UnCheol Lee
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Catherine Duclos
- Centre for Advanced Research in Sleep Medicine & Integrated Trauma Centre, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal, Montréal, QC, Canada
- Department of Neuroscience, Université de Montréal, Montréal, QC, Canada
- Department of Anesthesiology and Pain Medicine, Université de Montréal, Montréal, QC, Canada
- CIFAR Azrieli Global Scholars Program, Toronto, ON, Canada
| |
Collapse
|
25
|
Yurchenko SB. Is information the other face of causation in biological systems? Biosystems 2023; 229:104925. [PMID: 37182834 DOI: 10.1016/j.biosystems.2023.104925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 05/08/2023] [Accepted: 05/08/2023] [Indexed: 05/16/2023]
Abstract
Is information the other face of causation? This issue cannot be clarified without discussing how these both are related to physical laws, logic, computation, networks, bio-signaling, and the mind-body problem. The relation between information and causation is also intrinsically linked to many other concepts in complex systems theory such as emergence, self-organization, synergy, criticality, and hierarchy, which in turn involve various notions such as observer-dependence, dimensionality reduction, and especially downward causation. A canonical example proposed for downward causation is the collective behavior of the whole system at a macroscale that may affect the behavior of each its member at a microscale. In neuroscience, downward causation is suggested as a strong candidate to account for mental causation (free will). However, this would be possible only on the condition that information might have causal power. After introducing the Causal Equivalence Principle expanding the relativity principle for coarse-grained and fine-grained linear causal chains, and a set-theoretical definition of multiscale nested hierarchy composed of modular ⊂-chains, it is shown that downward causation can be spurious. It emerges only in the eyes of an observer, though, due to information that could not be obtained by "looking" exclusively at the behavior of a system at a microscale. On the other hand, since biological systems are hierarchically organized, this information gain is indicative of how information can be a function of scale in these systems and a prerequisite for scale-dependent emergence of cognition and consciousness in neural networks.
Collapse
Affiliation(s)
- Sergey B Yurchenko
- Brain and Consciousness Independent Research Center, Andijan, Uzbekistan.
| |
Collapse
|
26
|
Ji P, Wang Y, Peron T, Li C, Nagler J, Du J. Structure and function in artificial, zebrafish and human neural networks. Phys Life Rev 2023; 45:74-111. [PMID: 37182376 DOI: 10.1016/j.plrev.2023.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/20/2023] [Indexed: 05/16/2023]
Abstract
Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.
Collapse
Affiliation(s)
- Peng Ji
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Yufan Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China
| | - Thomas Peron
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos 13566-590, São Paulo, Brazil.
| | - Chunhe Li
- Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, Shanghai 200433, China; Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.
| | - Jan Nagler
- Deep Dynamics, Frankfurt School of Finance & Management, Frankfurt, Germany; Centre for Human and Machine Intelligence, Frankfurt School of Finance & Management, Frankfurt, Germany
| | - Jiulin Du
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China.
| |
Collapse
|
27
|
Nanda A, Johnson GW, Mu Y, Ahrens MB, Chang C, Englot DJ, Breakspear M, Rubinov M. Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena. Cell Rep 2023; 42:112254. [PMID: 36966391 PMCID: PMC10518034 DOI: 10.1016/j.celrep.2023.112254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 12/22/2022] [Accepted: 02/28/2023] [Indexed: 03/27/2023] Open
Abstract
Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for sampling time series constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for diverse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity and provide stringent tests on future theories that seek to transcend these explanations.
Collapse
Affiliation(s)
- Aditya Nanda
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Yu Mu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Misha B Ahrens
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Michael Breakspear
- School of Psychology, University of Newcastle, Callaghan, NSW 2308, Australia; School of Medicine and Public Health, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Mikail Rubinov
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
| |
Collapse
|
28
|
Okujeni S, Egert U. Structural Modularity Tunes Mesoscale Criticality in Biological Neuronal Networks. J Neurosci 2023; 43:2515-2526. [PMID: 36868860 PMCID: PMC10082461 DOI: 10.1523/jneurosci.1420-22.2023] [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: 07/22/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/05/2023] Open
Abstract
Numerous studies suggest that biological neuronal networks self-organize toward a critical state with stable recruitment dynamics. Individual neurons would then statistically activate exactly one further neuron during activity cascades termed neuronal avalanches. Yet, it is unclear if and how this can be reconciled with the explosive recruitment dynamics within neocortical minicolumns in vivo and within neuronal clusters in vitro, which indicates that neurons form supercritical local circuits. Theoretical studies propose that modular networks with a mix of regionally subcritical and supercritical dynamics would create apparently critical dynamics, resolving this inconsistency. Here, we provide experimental support by manipulating the structural self-organization process of networks of cultured rat cortical neurons (either sex). Consistent with the prediction, we show that increasing clustering in neuronal networks developing in vitro strongly correlates with avalanche size distributions transitioning from supercritical to subcritical activity dynamics. Avalanche size distributions approximated a power law in moderately clustered networks, indicating overall critical recruitment. We propose that activity-dependent self-organization can tune inherently supercritical networks toward mesoscale criticality by creating a modular structure in neuronal networks.SIGNIFICANCE STATEMENT Critical recruitment dynamics in neuronal networks are considered optimal for information processing in the brain. However, it remains heavily debated how neuronal networks would self-organize criticality by detailed fine-tuning of connectivity, inhibition, and excitability. We provide experimental support for theoretical considerations that modularity tunes critical recruitment dynamics at the mesoscale level of interacting neuron clusters. This reconciles reports of supercritical recruitment dynamics in local neuron clusters with findings on criticality sampled at mesoscopic network scales. Intriguingly, altered mesoscale organization is a prominent aspect of various neuropathological diseases currently investigated in the framework of criticality. We therefore believe that our findings would also be of interest for clinical scientists searching to link the functional and anatomic signatures of such brain disorders.
Collapse
Affiliation(s)
- Samora Okujeni
- Laboratory for Biomicrotechnology, Department of Microsystems Engineering-Institut für Mikrosystemtechnik, Faculty of Engineering, University of Freiburg, 79110 Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
| | - Ulrich Egert
- Laboratory for Biomicrotechnology, Department of Microsystems Engineering-Institut für Mikrosystemtechnik, Faculty of Engineering, University of Freiburg, 79110 Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
| |
Collapse
|
29
|
Varley TF. Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions. PLoS One 2023; 18:e0282950. [PMID: 36952508 PMCID: PMC10035902 DOI: 10.1371/journal.pone.0282950] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 02/27/2023] [Indexed: 03/25/2023] Open
Abstract
A core feature of complex systems is that the interactions between elements in the present causally constrain their own futures, and the futures of other elements as the system evolves through time. To fully model all of these interactions (between elements, as well as ensembles of elements), it is possible to decompose the total information flowing from past to future into a set of non-overlapping temporal interactions that describe all the different modes by which information can be stored, transferred, or modified. To achieve this, I propose a novel information-theoretic measure of temporal dependency (Iτsx) based on the logic of local probability mass exclusions. This integrated information decomposition can reveal emergent and higher-order interactions within the dynamics of a system, as well as refining existing measures. To demonstrate the utility of this framework, I apply the decomposition to spontaneous spiking activity recorded from dissociated neural cultures of rat cerebral cortex to show how different modes of information processing are distributed over the system. Furthermore, being a localizable analysis, Iτsx can provide insight into the computational structure of single moments. I explore the time-resolved computational structure of neuronal avalanches and find that different types of information atoms have distinct profiles over the course of an avalanche, with the majority of non-trivial information dynamics happening before the first half of the cascade is completed. These analyses allow us to move beyond the historical focus on single measures of dependency such as information transfer or information integration, and explore a panoply of different relationships between elements (and groups of elements) in complex systems.
Collapse
Affiliation(s)
- Thomas F. Varley
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States of America
- School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States of America
- * E-mail:
| |
Collapse
|
30
|
Morales GB, di Santo S, Muñoz MA. Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics. Proc Natl Acad Sci U S A 2023; 120:e2208998120. [PMID: 36827262 PMCID: PMC9992863 DOI: 10.1073/pnas.2208998120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 12/31/2022] [Indexed: 02/25/2023] Open
Abstract
The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime with features, including the emergence of scale invariance, resembling those seen typically near phase transitions. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we construct a unified theoretical framework that synergistically combines a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population, while designing complementary tools. This strategy allows us to uncover strong signatures of scale invariance that are "quasiuniversal" across brain regions and experiments, revealing that all the analyzed areas operate, to a greater or lesser extent, near the edge of instability.
Collapse
Affiliation(s)
- Guillermo B. Morales
- Departamento de Electromagnetismo y Física de la Materia, Instituto Carlos I de Física Teórica y Computacional Universidad de Granada, GranadaE-18071, Spain
| | - Serena di Santo
- Morton B. Zuckerman Mind Brain Behavior Institute Columbia University, New York, NY10027
| | - Miguel A. Muñoz
- Departamento de Electromagnetismo y Física de la Materia, Instituto Carlos I de Física Teórica y Computacional Universidad de Granada, GranadaE-18071, Spain
| |
Collapse
|
31
|
Abstract
Analytical expressions for scaling of brain wave spectra derived from the general non-linear wave Hamiltonian form show excellent agreement with experimental "neuronal avalanche" data. The theory of the weakly evanescent non-linear brain wave dynamics reveals the underlying collective processes hidden behind the phenomenological statistical description of the neuronal avalanches and connects together the whole range of brain activity states, from oscillatory wave-like modes, to neuronal avalanches, to incoherent spiking, showing that the neuronal avalanches are just the manifestation of the different non-linear side of wave processes abundant in cortical tissue. In a more broad way these results show that a system of wave modes interacting through all possible combinations of the third order non-linear terms described by a general wave Hamiltonian necessarily produces anharmonic wave modes with temporal and spatial scaling properties that follow scale free power laws. To the best of our knowledge this has never been reported in the physical literature and may be applicable to many physical systems that involve wave processes and not just to neuronal avalanches.
Collapse
Affiliation(s)
- Vitaly L. Galinsky
- Center for Scientific Computation in Imaging, University of California, San Diego, San Diego, CA, United States
| | - Lawrence R. Frank
- Center for Scientific Computation in Imaging, University of California, San Diego, San Diego, CA, United States
- Center for Functional MRI, University of California, San Diego, San Diego, CA, United States
| |
Collapse
|
32
|
Pei L, Zhou X, Leung FKS, Ouyang G. Differential associations between scale-free neural dynamics and different levels of cognitive ability. Psychophysiology 2023; 60:e14259. [PMID: 36700291 DOI: 10.1111/psyp.14259] [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: 03/08/2022] [Revised: 12/14/2022] [Accepted: 01/08/2023] [Indexed: 01/27/2023]
Abstract
As indicators of cognitive function, scale-free neural dynamics are gaining increasing attention in cognitive neuroscience. Although the functional relevance of scale-free dynamics has been extensively reported, one fundamental question about its association with cognitive ability remains unanswered: is the association universal across a wide spectrum of cognitive abilities or confined to specific domains? Based on dual-process theory, we designed two categories of tasks to analyze two types of cognitive processes-automatic and controlled-and examined their associations with scale-free neural dynamics characterized from resting-state electroencephalography (EEG) recordings obtained from a large sample of human adults (N = 102). Our results showed that resting-state scale-free neural dynamics did not predict individuals' behavioral performance in tasks that primarily engaged the automatic process but did so in tasks that primarily engaged the controlled process. In addition, by fitting the scale-free parameters separately in different frequency bands, we found that the cognitive association of scale-free dynamics was more strongly manifested in higher-band EEG spectrum. Our findings indicate that resting-state scale-free dynamics are not universal neural indicators for all cognitive abilities but are mainly associated with high-level cognition that entails controlled processes. This finding is compatible with the widely claimed role of scale-free dynamics in reflecting properties of complex dynamic systems.
Collapse
Affiliation(s)
- Leisi Pei
- Faculty of Education, The University of Hong Kong, Hong Kong, China
| | - Xinlin Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | | | - Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, China
| |
Collapse
|
33
|
Perquin MN, van Vugt MK, Hedge C, Bompas A. Temporal Structure in Sensorimotor Variability: A Stable Trait, But What For? COMPUTATIONAL BRAIN & BEHAVIOR 2023; 6:1-38. [PMID: 36618326 PMCID: PMC9810256 DOI: 10.1007/s42113-022-00162-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/22/2022] [Indexed: 01/05/2023]
Abstract
Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation come with several controversies. Furthermore, their potential benefit for studying individual differences in healthy and clinical populations remains unclear. Here, we gather new and archival datasets featuring 11 sensorimotor and cognitive tasks across 526 participants, to examine individual differences in temporal structures. We first investigate intra-individual repeatability of the most common measures of temporal structures - to test their potential for capturing stable individual differences. Secondly, we examine inter-individual differences in these measures using: (1) task performance assessed from the same data, (2) meta-cognitive ratings of on-taskness from thought probes occasionally presented throughout the task, and (3) self-assessed attention-deficit related traits. Across all datasets, autocorrelation at lag 1 and Power Spectra Density slope showed high intra-individual repeatability across sessions and correlated with task performance. The Detrended Fluctuation Analysis slope showed the same pattern, but less reliably. The long-term component (d) of the ARFIMA(1,d,1) model showed poor repeatability and no correlation to performance. Overall, these measures failed to show external validity when correlated with either mean subjective attentional state or self-assessed traits between participants. Thus, some measures of serial dependencies may be stable individual traits, but their usefulness in capturing individual differences in other constructs typically associated with variability in performance seems limited. We conclude with comprehensive recommendations for researchers. Supplementary Information The online version contains supplementary material available at 10.1007/s42113-022-00162-1.
Collapse
Affiliation(s)
- Marlou Nadine Perquin
- Biopsychology & Cognitive Neuroscience, Faculty of Psychology and Sports Science, Bielefeld University, Bielefeld, Germany
- Cognitive Neuroscience, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- CUBRIC, School of Psychology, Cardiff University, Cardiff, UK
| | - Marieke K. van Vugt
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - Craig Hedge
- School of Psychology, College of Health & Life Sciences, Aston University, Aston, UK
| | - Aline Bompas
- CUBRIC, School of Psychology, Cardiff University, Cardiff, UK
| |
Collapse
|
34
|
Shpurov I, Froese T. Evidence of Critical Dynamics in Movements of Bees inside a Hive. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1840. [PMID: 36554245 PMCID: PMC9777906 DOI: 10.3390/e24121840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Social insects such as honey bees exhibit complex behavioral patterns, and their distributed behavioral coordination enables decision-making at the colony level. It has, therefore, been proposed that a high-level description of their collective behavior might share commonalities with the dynamics of neural processes in brains. Here, we investigated this proposal by focusing on the possibility that brains are poised at the edge of a critical phase transition and that such a state is enabling increased computational power and adaptability. We applied mathematical tools developed in computational neuroscience to a dataset of bee movement trajectories that were recorded within the hive during the course of many days. We found that certain characteristics of the activity of the bee hive system are consistent with the Ising model when it operates at a critical temperature, and that the system's behavioral dynamics share features with the human brain in the resting state.
Collapse
|
35
|
Caetano I, Ferreira S, Coelho A, Amorim L, Castanho TC, Portugal-Nunes C, Soares JM, Gonçalves N, Sousa R, Reis J, Lima C, Marques P, Moreira PS, Rodrigues AJ, Santos NC, Morgado P, Magalhães R, Picó-Pérez M, Cabral J, Sousa N. Perceived stress modulates the activity between the amygdala and the cortex. Mol Psychiatry 2022; 27:4939-4947. [PMID: 36117211 DOI: 10.1038/s41380-022-01780-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 01/14/2023]
Abstract
The significant link between stress and psychiatric disorders has prompted research on stress's impact on the brain. Interestingly, previous studies on healthy subjects have demonstrated an association between perceived stress and amygdala volume, although the mechanisms by which perceived stress can affect brain function remain unknown. To better understand what this association entails at a functional level, herein, we explore the association of perceived stress, measured by the PSS10 questionnaire, with disseminated functional connectivity between brain areas. Using resting-state fMRI from 252 healthy subjects spanning a broad age range, we performed both a seed-based amygdala connectivity analysis (static connectivity, with spatial resolution but no temporal definition) and a whole-brain data-driven approach to detect altered patterns of phase interactions between brain areas (dynamic connectivity with spatiotemporal information). Results show that increased perceived stress is directly associated with increased amygdala connectivity with frontal cortical regions, which is driven by a reduced occurrence of an activity pattern where the signals in the amygdala and the hippocampus evolve in opposite directions with respect to the rest of the brain. Overall, these results not only reinforce the pathological effect of in-phase synchronicity between subcortical and cortical brain areas but also demonstrate the protective effect of counterbalanced (i.e., phase-shifted) activity between brain subsystems, which are otherwise missed with correlation-based functional connectivity analysis.
Collapse
Affiliation(s)
- Inês Caetano
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Sónia Ferreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Liliana Amorim
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal.,Association P5 Digital Medical Center (ACMP5), 4710-057, Braga, Portugal
| | - Teresa Costa Castanho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal.,Association P5 Digital Medical Center (ACMP5), 4710-057, Braga, Portugal
| | - Carlos Portugal-Nunes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal.,CECAV-Veterinary and Animal Science Research Centre, Quinta de Prados, 5000-801, Vila Real, Portugal
| | - José Miguel Soares
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Nuno Gonçalves
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Rui Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal.,Departamento de Psiquiatria e Saúde Mental, Centro Hospitalar Tondela-Viseu, 3500-228, Viseu, Portugal
| | - Joana Reis
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Catarina Lima
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Pedro Silva Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Ana João Rodrigues
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Nadine Correia Santos
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Maria Picó-Pérez
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal.,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal.,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057, Braga, Portugal. .,ICVS/3B's, PT Government Associate Laboratory, 4710-057, Braga/Guimarães, Portugal. .,Clinical Academic Center-Braga (2CA), 4710-243, Braga, Portugal. .,Association P5 Digital Medical Center (ACMP5), 4710-057, Braga, Portugal.
| |
Collapse
|
36
|
From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans. Transl Psychiatry 2022; 12:467. [PMID: 36344497 PMCID: PMC9640647 DOI: 10.1038/s41398-022-02218-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 08/22/2022] [Accepted: 10/06/2022] [Indexed: 11/09/2022] Open
Abstract
Brain function is a product of the balance between excitatory and inhibitory (E/I) brain activity. Variation in the regulation of this activity is thought to give rise to normal variation in human traits, and disruptions are thought to potentially underlie a spectrum of neuropsychiatric conditions (e.g., Autism, Schizophrenia, Downs' Syndrome, intellectual disability). Hypotheses related to E/I dysfunction have the potential to provide cross-diagnostic explanations and to combine genetic and neurological evidence that exists within and between psychiatric conditions. However, the hypothesis has been difficult to test because: (1) it lacks specificity-an E/I dysfunction could pertain to any level in the neural system- neurotransmitters, single neurons/receptors, local networks of neurons, or global brain balance - most researchers do not define the level at which they are examining E/I function; (2) We lack validated methods for assessing E/I function at any of these neural levels in humans. As a result, it has not been possible to reliably or robustly test the E/I hypothesis of psychiatric disorders in a large cohort or longitudinal patient studies. Currently available, in vivo markers of E/I in humans either carry significant risks (e.g., deep brain electrode recordings or using Positron Emission Tomography (PET) with radioactive tracers) and/or are highly restrictive (e.g., limited spatial extent for Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Spectroscopy (MRS). More recently, a range of novel Electroencephalography (EEG) features has been described, which could serve as proxy markers for E/I at a given level of inference. Thus, in this perspective review, we survey the theories and experimental evidence underlying 6 novel EEG markers and their biological underpinnings at a specific neural level. These cheap-to-record and scalable proxy markers may offer clinical utility for identifying subgroups within and between diagnostic categories, thus directing more tailored sub-grouping and, therefore, treatment strategies. However, we argue that studies in clinical populations are premature. To maximize the potential of prospective EEG markers, we first need to understand the link between underlying E/I mechanisms and measurement techniques.
Collapse
|
37
|
Yurchenko SB. From the origins to the stream of consciousness and its neural correlates. Front Integr Neurosci 2022; 16:928978. [PMID: 36407293 PMCID: PMC9672924 DOI: 10.3389/fnint.2022.928978] [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: 04/26/2022] [Accepted: 10/12/2022] [Indexed: 09/22/2023] Open
Abstract
There are now dozens of very different theories of consciousness, each somehow contributing to our understanding of its nature. The science of consciousness needs therefore not new theories but a general framework integrating insights from those, yet not making it a still-born "Frankenstein" theory. First, the framework must operate explicitly on the stream of consciousness, not on its static description. Second, this dynamical account must also be put on the evolutionary timeline to explain the origins of consciousness. The Cognitive Evolution Theory (CET), outlined here, proposes such a framework. This starts with the assumption that brains have primarily evolved as volitional subsystems of organisms, inherited from primitive (fast and random) reflexes of simplest neural networks, only then resembling error-minimizing prediction machines. CET adopts the tools of critical dynamics to account for metastability, scale-free avalanches, and self-organization which are all intrinsic to brain dynamics. This formalizes the stream of consciousness as a discrete (transitive, irreflexive) chain of momentary states derived from critical brain dynamics at points of phase transitions and mapped then onto a state space as neural correlates of a particular conscious state. The continuous/discrete dichotomy appears naturally between the brain dynamics at the causal level and conscious states at the phenomenal level, each volitionally triggered from arousal centers of the brainstem and cognitively modulated by thalamocortical systems. Their objective observables can be entropy-based complexity measures, reflecting the transient level or quantity of consciousness at that moment.
Collapse
|
38
|
Crofts JJ, Forrester M, Coombes S, O'Dea RD. Structure-function clustering in weighted brain networks. Sci Rep 2022; 12:16793. [PMID: 36202837 PMCID: PMC9537289 DOI: 10.1038/s41598-022-19994-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/07/2022] [Indexed: 11/09/2022] Open
Abstract
Functional networks, which typically describe patterns of activity taking place across the cerebral cortex, are widely studied in neuroscience. The dynamical features of these networks, and in particular their deviation from the relatively static structural network, are thought to be key to higher brain function. The interactions between such structural networks and emergent function, and the multimodal neuroimaging approaches and common analysis according to frequency band motivate a multilayer network approach. However, many such investigations rely on arbitrary threshold choices that convert dense, weighted networks to sparse, binary structures. Here, we generalise a measure of multiplex clustering to describe weighted multiplexes with arbitrarily-many layers. Moreover, we extend a recently-developed measure of structure-function clustering (that describes the disparity between anatomical connectivity and functional networks) to the weighted case. To demonstrate its utility we combine human connectome data with simulated neural activity and bifurcation analysis. Our results indicate that this new measure can extract neurologically relevant features not readily apparent in analogous single-layer analyses. In particular, we are able to deduce dynamical regimes under which multistable patterns of neural activity emerge. Importantly, these findings suggest a role for brain operation just beyond criticality to promote cognitive flexibility.
Collapse
Affiliation(s)
- Jonathan J Crofts
- Department of Physics and Mathematics, Nottingham Trent University, Nottingham, NG11 8NS, UK
| | - Michael Forrester
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.
| | - Stephen Coombes
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Reuben D O'Dea
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| |
Collapse
|
39
|
Tian Y, Tan Z, Hou H, Li G, Cheng A, Qiu Y, Weng K, Chen C, Sun P. Theoretical foundations of studying criticality in the brain. Netw Neurosci 2022; 6:1148-1185. [PMID: 38800464 PMCID: PMC11117095 DOI: 10.1162/netn_a_00269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 07/12/2022] [Indexed: 05/29/2024] Open
Abstract
Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information-processing capacities in the brain. While considerable evidence generally supports this hypothesis, nonnegligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the nontriviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, that is, ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistical techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions.
Collapse
Affiliation(s)
- Yang Tian
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
- Laboratory of Advanced Computing and Storage, Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd., Beijing, China
| | - Zeren Tan
- Institute for Interdisciplinary Information Science, Tsinghua University, Beijing, China
| | - Hedong Hou
- UFR de Mathématiques, Université de Paris, Paris, France
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Science, Beijing, China
- University of Chinese Academy of Science, Beijing, China
| | - Aohua Cheng
- Tsien Excellence in Engineering Program, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Yike Qiu
- Tsien Excellence in Engineering Program, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Kangyu Weng
- Tsien Excellence in Engineering Program, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Chun Chen
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
| | - Pei Sun
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
| |
Collapse
|
40
|
Talidou A, Frankland PW, Mabbott D, Lefebvre J. Homeostatic coordination and up-regulation of neural activity by activity-dependent myelination. NATURE COMPUTATIONAL SCIENCE 2022; 2:665-676. [PMID: 38177260 DOI: 10.1038/s43588-022-00315-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 08/10/2022] [Indexed: 01/06/2024]
Abstract
Activity-dependent myelination (ADM) is a fundamental dimension of brain plasticity through which myelin changes as a function of neural activity. Mediated by structural changes in glia, ADM notably regulates axonal conduction velocity. Yet, it remains unclear how neural activity impacts myelination to orchestrate the timing of neural signalling, and how ADM shapes neural activity. We developed a model of spiking neurons enhanced with neuron-oligodendrocyte feedback and examined the relationship between ADM and neural activity. We found that ADM implements a homeostatic gain control mechanism that enhances neural firing rates and correlations through the temporal coordination of action potentials as axon lengths increase. Stimuli engage ADM plasticity to trigger bidirectional and reversible changes in conduction delays, as may occur during learning. Furthermore, ADM was found to enhance information transmission under various types of time-varying stimuli. These results highlight the role of ADM in shaping neural activity and communication.
Collapse
Affiliation(s)
- Afroditi Talidou
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.
| | - Paul W Frankland
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Donald Mabbott
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Jérémie Lefebvre
- Department of Biology, University of Ottawa, Ottawa, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- Department of Physics, University of Ottawa, Ottawa, Ontario, Canada
- Department of Mathematics, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
41
|
Braccini M, Roli A, Barbieri E, Kauffman SA. On the Criticality of Adaptive Boolean Network Robots. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1368. [PMID: 37420388 DOI: 10.3390/e24101368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 07/09/2023]
Abstract
Systems poised at a dynamical critical regime, between order and disorder, have been shown capable of exhibiting complex dynamics that balance robustness to external perturbations and rich repertoires of responses to inputs. This property has been exploited in artificial network classifiers, and preliminary results have also been attained in the context of robots controlled by Boolean networks. In this work, we investigate the role of dynamical criticality in robots undergoing online adaptation, i.e., robots that adapt some of their internal parameters to improve a performance metric over time during their activity. We study the behavior of robots controlled by random Boolean networks, which are either adapted in their coupling with robot sensors and actuators or in their structure or both. We observe that robots controlled by critical random Boolean networks have higher average and maximum performance than that of robots controlled by ordered and disordered nets. Notably, in general, adaptation by change of couplings produces robots with slightly higher performance than those adapted by changing their structure. Moreover, we observe that when adapted in their structure, ordered networks tend to move to the critical dynamical regime. These results provide further support to the conjecture that critical regimes favor adaptation and indicate the advantage of calibrating robot control systems at dynamical critical states.
Collapse
Affiliation(s)
- Michele Braccini
- Department of Computer Science and Engineering, Università di Bologna, Campus of Cesena, I-47521 Cesena, Italy
| | - Andrea Roli
- Department of Computer Science and Engineering, Università di Bologna, Campus of Cesena, I-47521 Cesena, Italy
- European Centre for Living Technology, I-30123 Venezia, Italy
| | - Edoardo Barbieri
- Department of Computer Science and Engineering, Università di Bologna, Campus of Cesena, I-47521 Cesena, Italy
| | | |
Collapse
|
42
|
Beggs JM. Addressing skepticism of the critical brain hypothesis. Front Comput Neurosci 2022; 16:703865. [PMID: 36185712 PMCID: PMC9520604 DOI: 10.3389/fncom.2022.703865] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
The hypothesis that living neural networks operate near a critical phase transition point has received substantial discussion. This “criticality hypothesis” is potentially important because experiments and theory show that optimal information processing and health are associated with operating near the critical point. Despite the promise of this idea, there have been several objections to it. While earlier objections have been addressed already, the more recent critiques of Touboul and Destexhe have not yet been fully met. The purpose of this paper is to describe their objections and offer responses. Their first objection is that the well-known Brunel model for cortical networks does not display a peak in mutual information near its phase transition, in apparent contradiction to the criticality hypothesis. In response I show that it does have such a peak near the phase transition point, provided it is not strongly driven by random inputs. Their second objection is that even simple models like a coin flip can satisfy multiple criteria of criticality. This suggests that the emergent criticality claimed to exist in cortical networks is just the consequence of a random walk put through a threshold. In response I show that while such processes can produce many signatures criticality, these signatures (1) do not emerge from collective interactions, (2) do not support information processing, and (3) do not have long-range temporal correlations. Because experiments show these three features are consistently present in living neural networks, such random walk models are inadequate. Nevertheless, I conclude that these objections have been valuable for refining research questions and should always be welcomed as a part of the scientific process.
Collapse
Affiliation(s)
- John M. Beggs
- Department of Physics, Indiana University Bloomington, Bloomington, IN, United States
- Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, United States
- *Correspondence: John M. Beggs,
| |
Collapse
|
43
|
Feketa P, Meurer T, Kohlstedt H. Structural plasticity driven by task performance leads to criticality signatures in neuromorphic oscillator networks. Sci Rep 2022; 12:15321. [PMID: 36096910 PMCID: PMC9468161 DOI: 10.1038/s41598-022-19386-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 08/29/2022] [Indexed: 12/04/2022] Open
Abstract
Oscillator networks rapidly become one of the promising vehicles for energy-efficient computing due to their intrinsic parallelism of execution. The criticality property of the oscillator-based networks is regarded to be essential for performing complex tasks. There are numerous bio-inspired synaptic and structural plasticity mechanisms available, especially for spiking neural networks, which can drive the network towards the criticality. However, there is no solid connection between these self-adaption mechanisms and the task performance, and it is not clear how and why particular self-adaptation mechanisms contribute to the solution of the task, although their relation to criticality is understood. Here we propose an evolutionary approach for the structural plasticity that relies solely on the task performance and does not contain any task-independent adaptation mechanisms, which usually contribute towards the criticality of the network. As a driver for the structural plasticity, we use a direct binary search guided by the performance of the classification task that can be interpreted as an interaction of the network with the environment. Remarkably, such interaction with the environment brings the network to criticality, although this property was not a part of the objectives of the employed structural plasticity mechanism. This observation confirms a duality of criticality and task performance, and legitimizes internal activity-dependent plasticity mechanisms from the viewpoint of evolution as mechanisms contributing to the task performance, but following the dual route. Finally, we analyze the trained network against task-independent information-theoretic measures and identify the interconnection graph’s entropy to be an essential ingredient for the classification task performance and network’s criticality.
Collapse
Affiliation(s)
- Petro Feketa
- Chair of Automation and Control, Kiel University, Kaiserstraße 2, 24143, Kiel, Germany. .,Kiel Nano, Surface and Interface Science KiNSIS, Kiel University, Christian-Albrechts-Platz 4, 24118, Kiel, Germany.
| | - Thomas Meurer
- Chair of Automation and Control, Kiel University, Kaiserstraße 2, 24143, Kiel, Germany.,Kiel Nano, Surface and Interface Science KiNSIS, Kiel University, Christian-Albrechts-Platz 4, 24118, Kiel, Germany
| | - Hermann Kohlstedt
- Chair of Nanoelectronics, Kiel University, Kaiserstraße 2, 24143, Kiel, Germany.,Kiel Nano, Surface and Interface Science KiNSIS, Kiel University, Christian-Albrechts-Platz 4, 24118, Kiel, Germany
| |
Collapse
|
44
|
O'Byrne J, Jerbi K. How critical is brain criticality? Trends Neurosci 2022; 45:820-837. [PMID: 36096888 DOI: 10.1016/j.tins.2022.08.007] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/27/2022] [Accepted: 08/10/2022] [Indexed: 10/31/2022]
Abstract
Criticality is the singular state of complex systems poised at the brink of a phase transition between order and randomness. Such systems display remarkable information-processing capabilities, evoking the compelling hypothesis that the brain may itself be critical. This foundational idea is now drawing renewed interest thanks to high-density data and converging cross-disciplinary knowledge. Together, these lines of inquiry have shed light on the intimate link between criticality, computation, and cognition. Here, we review these emerging trends in criticality neuroscience, highlighting new data pertaining to the edge of chaos and near-criticality, and making a case for the distance to criticality as a useful metric for probing cognitive states and mental illness. This unfolding progress in the field contributes to establishing criticality theory as a powerful mechanistic framework for studying emergent function and its efficiency in both biological and artificial neural networks.
Collapse
Affiliation(s)
- Jordan O'Byrne
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada; MILA (Quebec Artificial Intelligence Institute), Montreal, Quebec, Canada; UNIQUE Center (Quebec Neuro-AI Research Center), Montreal, Quebec, Canada.
| |
Collapse
|
45
|
Suryadi, Cheng RK, Birkett E, Jesuthasan S, Chew LY. Dynamics and potential significance of spontaneous activity in the habenula. eNeuro 2022; 9:ENEURO.0287-21.2022. [PMID: 35981869 PMCID: PMC9450562 DOI: 10.1523/eneuro.0287-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 05/31/2022] [Accepted: 06/27/2022] [Indexed: 11/21/2022] Open
Abstract
The habenula is an evolutionarily conserved structure of the vertebrate brain that is essential for behavioural flexibility and mood control. It is spontaneously active and is able to access diverse states when the animal is exposed to sensory stimuli. Here we investigate the dynamics of habenula spontaneous activity, to gain insight into how sensitivity is optimized. Two-photon calcium imaging was performed in resting zebrafish larvae at single cell resolution. An analysis of avalanches of inferred spikes suggests that the habenula is subcritical. Activity had low covariance and a small mean, arguing against dynamic criticality. A multiple regression estimator of autocorrelation time suggests that the habenula is neither fully asynchronous nor perfectly critical, but is reverberating. This pattern of dynamics may enable integration of information and high flexibility in the tuning of network properties, thus providing a potential mechanism for the optimal responses to a changing environment.Significance StatementSpontaneous activity in neurons shapes the response to stimuli. One structure with a high level of spontaneous neuronal activity is the habenula, a regulator of broadly acting neuromodulators involved in mood and learning. How does this activity influence habenula function? We show here that the habenula of a resting animal is near criticality, in a state termed reverberation. This pattern of dynamics is consistent with high sensitivity and flexibility, and may enable the habenula to respond optimally to a wide range of stimuli.
Collapse
Affiliation(s)
- Suryadi
- School of Physical & Mathematical Sciences, Nanyang Technological University, Singapore 637371
| | - Ruey-Kuang Cheng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921
| | - Elliot Birkett
- Institute of Molecular and Cell Biology, Singapore 138673
- School of Biosciences, University of Sheffield, Sheffield, United Kingdom
| | - Suresh Jesuthasan
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921
- Institute of Molecular and Cell Biology, Singapore 138673
| | - Lock Yue Chew
- School of Physical & Mathematical Sciences, Nanyang Technological University, Singapore 637371
- Complexity Institute, Nanyang Technological University, Singapore 637335
| |
Collapse
|
46
|
Kelty-Stephen DG, Mangalam M. Turing's cascade instability supports the coordination of the mind, brain, and behavior. Neurosci Biobehav Rev 2022; 141:104810. [PMID: 35932950 DOI: 10.1016/j.neubiorev.2022.104810] [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: 04/15/2022] [Revised: 06/09/2022] [Accepted: 08/01/2022] [Indexed: 10/16/2022]
Abstract
Turing inspired a computer metaphor of the mind and brain that has been handy and has spawned decades of empirical investigation, but he did much more and offered behavioral and cognitive sciences another metaphor-that of the cascade. The time has come to confront Turing's cascading instability, which suggests a geometrical framework driven by power laws and can be studied using multifractal formalism and multiscale probability density function analysis. Here, we review a rapidly growing body of scientific investigations revealing signatures of cascade instability and their consequences for a perceiving, acting, and thinking organism. We review work related to executive functioning (planning to act), postural control (bodily poise for turning plans into action), and effortful perception (action to gather information in a single modality and action to blend multimodal information). We also review findings on neuronal avalanches in the brain, specifically about neural participation in body-wide cascades. Turing's cascade instability blends the mind, brain, and behavior across space and time scales and provides an alternative to the dominant computer metaphor.
Collapse
Affiliation(s)
- Damian G Kelty-Stephen
- Department of Psychology, State University of New York at New Paltz, New Paltz, NY, USA.
| | - Madhur Mangalam
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, USA.
| |
Collapse
|
47
|
Walter N, Hinterberger T. Self-organized criticality as a framework for consciousness: A review study. Front Psychol 2022; 13:911620. [PMID: 35911009 PMCID: PMC9336647 DOI: 10.3389/fpsyg.2022.911620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 06/29/2022] [Indexed: 01/04/2023] Open
Abstract
Objective No current model of consciousness is univocally accepted on either theoretical or empirical grounds, and the need for a solid unifying framework is evident. Special attention has been given to the premise that self-organized criticality (SOC) is a fundamental property of neural system. SOC provides a competitive model to describe the physical mechanisms underlying spontaneous brain activity, and thus, critical dynamics were proposed as general gauges of information processing representing a strong candidate for a surrogate measure of consciousness. As SOC could be a neurodynamical framework, which may be able to bring together existing theories and experimental evidence, the purpose of this work was to provide a comprehensive overview of progress of research on SOC in association with consciousness. Methods A comprehensive search of publications on consciousness and SOC published between 1998 and 2021 was conducted. The Web of Science database was searched, and annual number of publications and citations, type of articles, and applied methods were determined. Results A total of 71 publications were identified. The annual number of citations steadily increased over the years. Original articles comprised 50.7% and reviews/theoretical articles 43.6%. Sixteen studies reported on human data and in seven studies data were recorded in animals. Computational models were utilized in n = 12 studies. EcoG data were assessed in n = 4 articles, fMRI in n = 4 studies, and EEG/MEG in n = 10 studies. Notably, different analytical tools were applied in the EEG/MEG studies to assess a surrogate measure of criticality such as the detrended fluctuation analysis, the pair correlation function, parameters from the neuronal avalanche analysis and the spectral exponent. Conclusion Recent studies pointed out agreements of critical dynamics with the current most influencing theories in the field of consciousness research, the global workspace theory and the integrated information theory. Thus, the framework of SOC as a neurodynamical parameter for consciousness seems promising. However, identified experimental work was small in numbers, and a heterogeneity of applied analytical tools as a surrogate measure of criticality was observable, which limits the generalizability of findings.
Collapse
|
48
|
Tian Y, Sun P. Percolation may explain efficiency, robustness, and economy of the brain. Netw Neurosci 2022; 6:765-790. [PMID: 36605416 PMCID: PMC9810365 DOI: 10.1162/netn_a_00246] [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: 10/03/2021] [Accepted: 03/11/2022] [Indexed: 01/09/2023] Open
Abstract
The brain consists of billions of neurons connected by ultra-dense synapses, showing remarkable efficiency, robust flexibility, and economy in information processing. It is generally believed that these advantageous properties are rooted in brain connectivity; however, direct evidence remains absent owing to technical limitations or theoretical vacancy. This research explores the origins of these properties in the largest yet brain connectome of the fruit fly. We reveal that functional connectivity formation in the brain can be explained by a percolation process controlled by synaptic excitation-inhibition (E/I) balance. By increasing the E/I balance gradually, we discover the emergence of these properties as byproducts of percolation transition when the E/I balance arrives at 3:7. As the E/I balance keeps increase, an optimal E/I balance 1:1 is unveiled to ensure these three properties simultaneously, consistent with previous in vitro experimental predictions. Once the E/I balance reaches over 3:2, an intrinsic limitation of these properties determined by static (anatomical) brain connectivity can be observed. Our work demonstrates that percolation, a universal characterization of critical phenomena and phase transitions, may serve as a window toward understanding the emergence of various brain properties.
Collapse
Affiliation(s)
- Yang Tian
- Department of Psychology and Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China,Laboratory of Advanced Computing and Storage, Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd., Beijing, China,* Corresponding Author: ;
| | - Pei Sun
- Department of Psychology and Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China,* Corresponding Author: ;
| |
Collapse
|
49
|
Poel W, Daniels BC, Sosna MMG, Twomey CR, Leblanc SP, Couzin ID, Romanczuk P. Subcritical escape waves in schooling fish. SCIENCE ADVANCES 2022; 8:eabm6385. [PMID: 35731883 PMCID: PMC9217090 DOI: 10.1126/sciadv.abm6385] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
Theoretical physics predicts optimal information processing in living systems near transitions (or pseudo-critical points) in their collective dynamics. However, focusing on potential benefits of proximity to a critical point, such as maximal sensitivity to perturbations and fast dissemination of information, commonly disregards possible costs of criticality in the noisy, dynamic environmental contexts of biological systems. Here, we find that startle cascades in fish schools are subcritical (not maximally responsive to environmental cues) and that distance to criticality decreases when perceived risk increases. Considering individuals' costs related to two detection error types, associated to both true and false alarms, we argue that being subcritical, and modulating distance to criticality, can be understood as managing a trade-off between sensitivity and robustness according to the riskiness and noisiness of the environment. Our work emphasizes the need for an individual-based and context-dependent perspective on criticality and collective information processing and motivates future questions about the evolutionary forces that brought about a particular trade-off.
Collapse
Affiliation(s)
- Winnie Poel
- Institute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin, D-10099 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, D-10115 Berlin, Germany
| | - Bryan C. Daniels
- School of Complex Adaptive Systems, Arizona State University, Tempe, AZ 85287, USA
| | - Matthew M. G. Sosna
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - Colin R. Twomey
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Simon P. Leblanc
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
- Blend Labs, San Francisco, CA 94108, USA
| | - Iain D. Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, D-78547 Konstanz, Germany
- Department of Biology, University of Konstanz, D-78547 Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, D-78547 Konstanz, Germany
| | - Pawel Romanczuk
- Institute for Theoretical Biology, Department of Biology, Humboldt Universität zu Berlin, D-10099 Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, D-10115 Berlin, Germany
- Science of Intelligence, Research Cluster of Excellence, Marchstr. 23, D-10587 Berlin, Germany
| |
Collapse
|
50
|
Arata Y, Shiga I, Ikeda Y, Jurica P, Kimura H, Kiyono K, Sako Y. Insulin signaling shapes fractal scaling of C. elegans behavior. Sci Rep 2022; 12:10481. [PMID: 35729173 PMCID: PMC9213454 DOI: 10.1038/s41598-022-13022-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/03/2022] [Indexed: 11/08/2022] Open
Abstract
Fractal scaling in animal behavioral activity, where similar temporal patterns appear repeatedly over a series of magnifications among time scales, governs the complex behavior of various animal species and, in humans, can be altered by neurodegenerative diseases and aging. However, the mechanism underlying fractal scaling remains unknown. Here, we cultured C. elegans in a microfluidic device for 3 days and analyzed temporal patterns of C. elegans activity by fractal analyses. The residence-time distribution of C. elegans behaviors shared a common feature with those of human and mice. Specifically, the residence-time power-law distribution of the active state changed to an exponential-like decline at a longer time scale, whereas the inactive state followed a power-law distribution. An exponential-like decline appeared with nutrient supply in wild-type animals, whereas this decline disappeared in insulin-signaling-defective daf-2 and daf-16 mutants. The absolute value of the power-law exponent of the inactive state distribution increased with nutrient supply in wild-type animals, whereas the value decreased in daf-2 and daf-16 mutants. We conclude that insulin signaling differentially affects mechanisms that determine the residence time in active and inactive states in C. elegans behavior. In humans, diabetes mellitus, which is caused by defects in insulin signaling, is associated with mood disorders that affect daily behavioral activities. We hypothesize that comorbid behavioral defects in patients with diabetes may be attributed to altered fractal scaling of human behavior.
Collapse
Affiliation(s)
- Yukinobu Arata
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
| | - Itsuki Shiga
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan
| | - Yusaku Ikeda
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
- Department of Mechanical Engineering, School of Engineering, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan
| | - Peter Jurica
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Hiroshi Kimura
- Department of Mechanical Engineering, School of Engineering, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan
| | - Ken Kiyono
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan
| | - Yasushi Sako
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| |
Collapse
|