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Habibey R, Striebel J, Meinert M, Latiftikhereshki R, Schmieder F, Nasiri R, Latifi S. Engineered modular neuronal networks-on-chip represent structure-function relationship. Biosens Bioelectron 2024; 261:116518. [PMID: 38924816 DOI: 10.1016/j.bios.2024.116518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 06/28/2024]
Abstract
Brain function is substantially linked to the highly organized modular structure of neuronal networks. However, the structure of in vitro assembled neuronal circuits often exhibits variability, complicating the consistent recording of network functional output and its correlation to network structure. Therefore, engineering neuronal structures with predefined geometry and reproducible functional features is essential to precisely model in vivo neuronal circuits. Here, we engineered microchannel devices to assemble 2D and 3D modular networks. The microchannel devices were coupled with a multi-electrode array (MEA) electrophysiology system to enable recordings from circuits. Each network consisted of 64 modules connected to their adjacent modules by micron-sized channels. Modular circuits within microchannel devices showed enhanced activity and functional connectivity traits. This includes metrics such as connection weights, clustering coefficient, global efficiency, and the number of hub neurons with higher betweenness centrality. In addition, modular networks demonstrated an increased functional modularity score compared to the randomly formed circuits. Neurons within individual modules displayed uniform network characteristics and predominantly participated in their respective functional communities within the same or neighboring physical modules. These observations highlight that the modular network structure promotes the development of segregated functional connectivity traits while simultaneously enhancing the efficiency of overall network connectivity. Our findings emphasize the significant impact of physical constraints on the activity patterns and functional organization within engineered modular networks. These circuits, characterized by stable modular architecture and intricate functional dynamics-key features of the brain networks-offer a robust in vitro model for advancing neuroscience research.
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Affiliation(s)
- Rouhollah Habibey
- Department of Ophthalmology, Medical Faculty, University of Bonn, Bonn, Germany; CRTD - Center for Regenerative Therapies TU Dresden, 01307, Dresden, Germany; Dept. Neuroscience, Italian Institute of Technology. Genova, Italy.
| | - Johannes Striebel
- Department of Ophthalmology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Melissa Meinert
- Department of Ophthalmology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Roshanak Latiftikhereshki
- Department of Computer Engineering, Faculty of Engineering, Kermanshah Branch, Azad University, Kermanshah, Iran
| | - Felix Schmieder
- Laboratory of Measurement and Sensor System Technique, Faculty of Electrical and Computer Engineering, TU Dresden, Helmholtzstraße 18, 01069, Dresden, Germany
| | - Rohollah Nasiri
- Division of Nanobiotechnology, Department of Protein Science, Science for Life Laboratory, KTH Royal Institute of Technology, Solna, Sweden; AIMES, Center for the Advancement of Integrated Medical and Engineering Sciences, Department of Neuroscience, Karolinska Institute, Solna, Sweden
| | - Shahrzad Latifi
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Department of Neuroscience, Rockefeller Neuroscience Institute West Virginia University, Morgantown, WV, 26506, USA
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2
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Barzon G, Artime O, Suweis S, Domenico MD. Unraveling the mesoscale organization induced by network-driven processes. Proc Natl Acad Sci U S A 2024; 121:e2317608121. [PMID: 38968099 DOI: 10.1073/pnas.2317608121] [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: 10/23/2023] [Accepted: 05/21/2024] [Indexed: 07/07/2024] Open
Abstract
Complex systems are characterized by emergent patterns created by the nontrivial interplay between dynamical processes and the networks of interactions on which these processes unfold. Topological or dynamical descriptors alone are not enough to fully embrace this interplay in all its complexity, and many times one has to resort to dynamics-specific approaches that limit a comprehension of general principles. To address this challenge, we employ a metric-that we name Jacobian distance-which captures the spatiotemporal spreading of perturbations, enabling us to uncover the latent geometry inherent in network-driven processes. We compute the Jacobian distance for a broad set of nonlinear dynamical models on synthetic and real-world networks of high interest for applications from biological to ecological and social contexts. We show, analytically and computationally, that the process-driven latent geometry of a complex network is sensitive to both the specific features of the dynamics and the topological properties of the network. This translates into potential mismatches between the functional and the topological mesoscale organization, which we explain by means of the spectrum of the Jacobian matrix. Finally, we demonstrate that the Jacobian distance offers a clear advantage with respect to traditional methods when studying human brain networks. In particular, we show that it outperforms classical network communication models in explaining functional communities from structural data, therefore highlighting its potential in linking structure and function in the brain.
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Affiliation(s)
- Giacomo Barzon
- Padova Neuroscience Center, University of Padua, Padova 35131, Italy
- Complex Human Behaviour Lab, Fondazione Bruno Kessler, Povo 38123, Italy
| | - Oriol Artime
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona 08028, Spain
- Institute of Complex Systems, Universitat de Barcelona, Barcelona 08028, Spain
- Universitat de les Illes Balears, Palma 07122, Spain
| | - Samir Suweis
- Padova Neuroscience Center, University of Padua, Padova 35131, Italy
- Department of Physics and Astronomy "G. Galilei", University of Padova, Padova 35131, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Padova, Padova 35131, Italy
| | - Manlio De Domenico
- Padova Neuroscience Center, University of Padua, Padova 35131, Italy
- Department of Physics and Astronomy "G. Galilei", University of Padova, Padova 35131, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Padova, Padova 35131, Italy
- Padua Center for Network Medicine, University of Padova, Padova 35131, Italy
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3
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Larivière S, Park BY, Royer J, DeKraker J, Ngo A, Sahlas E, Chen J, Rodríguez-Cruces R, Weng Y, Frauscher B, Liu R, Wang Z, Shafiei G, Mišić B, Bernasconi A, Bernasconi N, Fox MD, Zhang Z, Bernhardt BC. Connectome reorganization associated with temporal lobe pathology and its surgical resection. Brain 2024; 147:2483-2495. [PMID: 38701342 PMCID: PMC11224603 DOI: 10.1093/brain/awae141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 03/23/2024] [Accepted: 04/05/2024] [Indexed: 05/05/2024] Open
Abstract
Network neuroscience offers a unique framework to understand the organizational principles of the human brain. Despite recent progress, our understanding of how the brain is modulated by focal lesions remains incomplete. Resection of the temporal lobe is the most effective treatment to control seizures in pharmaco-resistant temporal lobe epilepsy (TLE), making this syndrome a powerful model to study lesional effects on network organization in young and middle-aged adults. Here, we assessed the downstream consequences of a focal lesion and its surgical resection on the brain's structural connectome, and explored how this reorganization relates to clinical variables at the individual patient level. We included adults with pharmaco-resistant TLE (n = 37) who underwent anterior temporal lobectomy between two imaging time points, as well as age- and sex-matched healthy controls who underwent comparable imaging (n = 31). Core to our analysis was the projection of high-dimensional structural connectome data-derived from diffusion MRI tractography from each subject-into lower-dimensional gradients. We then compared connectome gradients in patients relative to controls before surgery, tracked surgically-induced connectome reconfiguration from pre- to postoperative time points, and examined associations to patient-specific clinical and imaging phenotypes. Before surgery, individuals with TLE presented with marked connectome changes in bilateral temporo-parietal regions, reflecting an increased segregation of the ipsilateral anterior temporal lobe from the rest of the brain. Surgery-induced connectome reorganization was localized to this temporo-parietal subnetwork, but primarily involved postoperative integration of contralateral regions with the rest of the brain. Using a partial least-squares analysis, we uncovered a latent clinical imaging signature underlying this pre- to postoperative connectome reorganization, showing that patients who displayed postoperative integration in bilateral fronto-occipital cortices also had greater preoperative ipsilateral hippocampal atrophy, lower seizure frequency and secondarily generalized seizures. Our results bridge the effects of focal brain lesions and their surgical resections with large-scale network reorganization and interindividual clinical variability, thus offering new avenues to examine the fundamental malleability of the human brain.
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Affiliation(s)
- Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard University, Boston, MA 02115, USA
| | - Bo-yong Park
- Department of Data Science, Inha University, Incheon 22212, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 34126, Republic of Korea
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Ella Sahlas
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Judy Chen
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raúl Rodríguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Ruoting Liu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Zhengge Wang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Golia Shafiei
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bratislav Mišić
- Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard University, Boston, MA 02115, USA
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
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4
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Wang B, Yuan Y, Yang L, Huang Y, Zhang X, Zhang X, Yan W, Li Y, Li D, Xiang J, Yang J, Liu M. Multi-hierarchy Network Configuration Can Predict Brain States and Performance. J Cogn Neurosci 2024; 36:1695-1714. [PMID: 38579269 DOI: 10.1162/jocn_a_02153] [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] [Indexed: 04/07/2024]
Abstract
The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we propose an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.
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Affiliation(s)
- Bin Wang
- Taiyuan University of Technology
| | | | - Lan Yang
- Taiyuan University of Technology
| | | | - Xi Zhang
- Taiyuan University of Technology
| | | | | | - Ying Li
- Taiyuan University of Technology
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5
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Duong-Tran D, Nguyen N, Mu S, Chen J, Bao J, Xu F, Garai S, Cadena-Pico J, Kaplan AD, Chen T, Zhao Y, Shen L, Goñi J. A principled framework to assess information theoretical fitness of brain functional sub-circuits. ARXIV 2024:arXiv:2406.18531v1. [PMID: 38979488 PMCID: PMC11230349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. Our results pave the way for the proper use of predetermined FNs and thresholding methods and provide insights for future research in individualized parcellations.
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Affiliation(s)
- Duy Duong-Tran
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Nghi Nguyen
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Shizhuo Mu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Frederick Xu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jose Cadena-Pico
- Machine Learning Group, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Alan David Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Tianlong Chen
- Department of Computer Science, The University of North Carolina at Chapel Hill
| | - Yize Zhao
- School of Public Health, Yale University, New Heaven, CT, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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6
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Barjuan L, Soriano J, Serrano MÁ. Optimal navigability of weighted human brain connectomes in physical space. Neuroimage 2024; 297:120703. [PMID: 38936648 DOI: 10.1016/j.neuroimage.2024.120703] [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: 04/11/2024] [Revised: 06/17/2024] [Accepted: 06/21/2024] [Indexed: 06/29/2024] Open
Abstract
Communication protocols in the brain connectome describe how to transfer information from one region to another. Typically, these protocols hinge on either the spatial distances between brain regions or the intensity of their connections. Yet, none of them combine both factors to achieve optimal efficiency. Here, we introduce a continuous spectrum of decentralized routing strategies that integrates link weights and the spatial embedding of connectomes to route signal transmission. We implemented the protocols on connectomes from individuals in two cohorts and on group-representative connectomes designed to capture weighted connectivity properties. We identified an intermediate domain of routing strategies, a sweet spot, where navigation achieves maximum communication efficiency at low transmission cost. This phenomenon is robust and independent of the particular configuration of weights. Our findings suggest an interplay between the intensity of neural connections and their topology and geometry that amplifies communicability, where weights play the role of noise in a stochastic resonance phenomenon. Such enhancement may support more effective responses to external and internal stimuli, underscoring the intricate diversity of brain functions.
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Affiliation(s)
- Laia Barjuan
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain; Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Martí i Franquès 1, E-08028, Barcelona, Spain
| | - Jordi Soriano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain; Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Martí i Franquès 1, E-08028, Barcelona, Spain
| | - M Ángeles Serrano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain; Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Martí i Franquès 1, E-08028, Barcelona, Spain; ICREA, Pg. Lluís Companys 23, E-08010 Barcelona, Spain.
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7
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Bergs J, Morr AS, Silva RV, Infante-Duarte C, Sack I. The Networking Brain: How Extracellular Matrix, Cellular Networks, and Vasculature Shape the In Vivo Mechanical Properties of the Brain. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2402338. [PMID: 38874205 DOI: 10.1002/advs.202402338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/22/2024] [Indexed: 06/15/2024]
Abstract
Mechanically, the brain is characterized by both solid and fluid properties. The resulting unique material behavior fosters proliferation, differentiation, and repair of cellular and vascular networks, and optimally protects them from damaging shear forces. Magnetic resonance elastography (MRE) is a noninvasive imaging technique that maps the mechanical properties of the brain in vivo. MRE studies have shown that abnormal processes such as neuronal degeneration, demyelination, inflammation, and vascular leakage lead to tissue softening. In contrast, neuronal proliferation, cellular network formation, and higher vascular pressure result in brain stiffening. In addition, brain viscosity has been reported to change with normal blood perfusion variability and brain maturation as well as disease conditions such as tumor invasion. In this article, the contributions of the neuronal, glial, extracellular, and vascular networks are discussed to the coarse-grained parameters determined by MRE. This reductionist multi-network model of brain mechanics helps to explain many MRE observations in terms of microanatomical changes and suggests that cerebral viscoelasticity is a suitable imaging marker for brain disease.
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Affiliation(s)
- Judith Bergs
- Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Anna S Morr
- Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Rafaela V Silva
- Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité - Universitätsmedizin Berlin, Lindenberger Weg 80, 13125, Berlin, Germany
- Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Straße 10, 13125, Berlin, Germany
| | - Carmen Infante-Duarte
- Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité - Universitätsmedizin Berlin, Lindenberger Weg 80, 13125, Berlin, Germany
- Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Straße 10, 13125, Berlin, Germany
| | - Ingolf Sack
- Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
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Powell NJ, Hein B, Kong D, Elpelt J, Mulholland HN, Kaschube M, Smith GB. Developmental maturation of millimeter-scale functional networks across brain areas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.595371. [PMID: 38853883 PMCID: PMC11160666 DOI: 10.1101/2024.05.28.595371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Interacting with the environment to process sensory information, generate perceptions, and shape behavior engages neural networks in brain areas with highly varied representations, ranging from unimodal sensory cortices to higher-order association areas. Recent work suggests a much greater degree of commonality across areas, with distributed and modular networks present in both sensory and non-sensory areas during early development. However, it is currently unknown whether this initially common modular structure undergoes an equally common developmental trajectory, or whether such a modular functional organization persists in some areas-such as primary visual cortex-but not others. Here we examine the development of network organization across diverse cortical regions in ferrets of both sexes using in vivo widefield calcium imaging of spontaneous activity. We find that all regions examined, including both primary sensory cortices (visual, auditory, and somatosensory-V1, A1, and S1, respectively) and higher order association areas (prefrontal and posterior parietal cortices) exhibit a largely similar pattern of changes over an approximately 3 week developmental period spanning eye opening and the transition to predominantly externally-driven sensory activity. We find that both a modular functional organization and millimeter-scale correlated networks remain present across all cortical areas examined. These networks weakened over development in most cortical areas, but strengthened in V1. Overall, the conserved maintenance of modular organization across different cortical areas suggests a common pathway of network refinement, and suggests that a modular organization-known to encode functional representations in visual areas-may be similarly engaged in highly diverse brain areas.
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Affiliation(s)
- Nathaniel J Powell
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Bettina Hein
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Deyue Kong
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany
- International Max Planck Research School for Neural Circuits, Frankfurt, Germany
| | - Jonas Elpelt
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany
| | - Haleigh N Mulholland
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Matthias Kaschube
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany
| | - Gordon B Smith
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
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9
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Russo A, Mazzone S, Landolina L, Colucci R, Baccari F, Fetta A, Boni A, Cordelli DM. Efficacy and Safety of Pulse Intravenous Methylprednisolone in Pediatric Epileptic Encephalopathies: Timing and Networks Consideration. J Clin Med 2024; 13:2497. [PMID: 38731025 PMCID: PMC11084200 DOI: 10.3390/jcm13092497] [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: 03/05/2024] [Revised: 04/10/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
Background: Epileptic encephalopathies (EE) are characterized by severe drug-resistant seizures, early onset, and unfavorable developmental outcomes. This article discusses the use of intravenous methylprednisolone (IVMP) pulse therapy in pediatric patients with EE to evaluate its efficacy and tolerability. Methods: This is a retrospective study from 2020 to 2023. Inclusion criteria were ≤18 years at the time of IVMP pulse therapy and at least 6 months of follow-up. Efficacy and outcome, defined as seizure reduction > 50% (responder rate), were evaluated at 6 and 9 months of therapy, and 6 months after therapy suspension; quality of life (QoL) was also assessed. Variables predicting positive post-IVMP outcomes were identified using statistical analysis. Results: The study included 21 patients, with a responder rate of 85.7% at 6 and 9 months of therapy, and 80.9% at 6 months after therapy suspension. Variables significantly predicting favorable outcome were etiology (p = 0.0475) and epilepsy type (p = 0.0475), with the best outcome achieved in patients with genetic epilepsy and those with encephalopathy related to electrical status epilepticus during slow-wave sleep (ESES). All patients evidenced improvements in QoL at the last follow-up, with no relevant adverse events reported. Conclusions: Our study confirmed the efficacy and high tolerability of IVMP pulse therapy in pediatric patients with EE. Genetic epilepsy and ESES were positive predictors of a favorable clinical outcome. QOL, EEG tracing, and postural-motor development showed an improving trend as well. IVMP pulse therapy should be considered earlier in patients with EE.
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Affiliation(s)
- Angelo Russo
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
| | - Serena Mazzone
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
| | - Laura Landolina
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| | - Roberta Colucci
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| | - Flavia Baccari
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOS Epidemiologia e Biostatistica, 40139 Bologna, Italy;
| | - Anna Fetta
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
| | - Antonella Boni
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
| | - Duccio Maria Cordelli
- IRCCS, Istituto delle Scienze Neurologiche di Bologna, UOC Neuropsichiatria Dell’età Pediatrica, 40139 Bologna, Italy; (S.M.); (L.L.); (R.C.); (A.F.); (A.B.); (D.M.C.)
- Dipartimento di Scienze Mediche e Chirurgiche (DIMEC), Università di Bologna, 40126 Bologna, Italy
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10
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Zárate-Rochín AM. Contemporary neurocognitive models of memory: A descriptive comparative analysis. Neuropsychologia 2024; 196:108846. [PMID: 38430963 DOI: 10.1016/j.neuropsychologia.2024.108846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.
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Affiliation(s)
- Alba Marcela Zárate-Rochín
- Instituto de Investigaciones Cerebrales, Universidad Veracruzana, Dr. Castelazo Ayala s/n, Industrial Animas, 91190, Xalapa-Enríquez, Veracruz, Mexico.
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11
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Shao Z, Tan Y, Zhan Y, He L. Modular organization of functional brain networks in patients with degenerative cervical myelopathy. Sci Rep 2024; 14:8593. [PMID: 38615051 PMCID: PMC11016091 DOI: 10.1038/s41598-024-58764-7] [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: 11/07/2023] [Accepted: 04/03/2024] [Indexed: 04/15/2024] Open
Abstract
Previous studies have indicated that brain functional plasticity and reorganization in patients with degenerative cervical myelopathy (DCM). However, the effects of cervical cord compression on the functional integration and separation between and/or within modules remain unclear. This study aimed to address these questions using graph theory. Functional MRI was conducted on 46 DCM patients and 35 healthy controls (HCs). The intra- and inter-modular connectivity properties of the whole-brain functional network and nodal topological properties were then calculated using theoretical graph analysis. The difference in categorical variables between groups was compared using a chi-squared test, while that between continuous variables was evaluated using a two-sample t-test. Correlation analysis was conducted between modular connectivity properties and clinical parameters. Modules interaction analyses showed that the DCM group had significantly greater inter-module connections than the HCs group (DMN-FPN: t = 2.38, p = 0.02); inversely, the DCM group had significantly lower intra-module connections than the HCs group (SMN: t = - 2.13, p = 0.036). Compared to HCs, DCM patients exhibited higher nodal topological properties in the default-mode network and frontal-parietal network. In contrast, DCM patients exhibited lower nodal topological properties in the sensorimotor network. The Japanese Orthopedic Association (JOA) score was positively correlated with inter-module connections (r = 0.330, FDR p = 0.029) but not correlated with intra-module connections. This study reported alterations in modular connections and nodal centralities in DCM patients. Decreased nodal topological properties and intra-modular connection in the sensory-motor regions may indicate sensory-motor dysfunction. Additionally, increased nodal topological properties and inter-modular connection in the default mode network and frontal-parietal network may serve as a compensatory mechanism for sensory-motor dysfunction in DCM patients. This could provide an implicative neural basis to better understand alterations in brain networks and the patterns of changes in brain plasticity in DCM patients.
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Affiliation(s)
- Ziwei Shao
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Clinical Research Center for Medical Imaging In Jiangxi Province, Nanchang, China
| | - Yongming Tan
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Clinical Research Center for Medical Imaging In Jiangxi Province, Nanchang, China
| | - Yaru Zhan
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Clinical Research Center for Medical Imaging In Jiangxi Province, Nanchang, China
| | - Laichang He
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
- Clinical Research Center for Medical Imaging In Jiangxi Province, Nanchang, China.
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12
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Jiang A, Ma X, Li S, Wang L, Yang B, Wang S, Li M, Dong G. Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach. Psychol Med 2024:1-12. [PMID: 38563297 DOI: 10.1017/s0033291724000138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level. METHODS Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks. RESULTS We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms. CONCLUSIONS Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.
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Affiliation(s)
- Anhang Jiang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Xuefeng Ma
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Shuang Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang Province, China
| | - Bo Yang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
| | - Shizhen Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Mei Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Center for Mental Health Education and Counselling, Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Guangheng Dong
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
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13
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Molnár F, Horvát S, Ribeiro Gomes AR, Martinez Armas J, Molnár B, Ercsey-Ravasz M, Knoblauch K, Kennedy H, Toroczkai Z. Predictability of cortico-cortical connections in the mammalian brain. Netw Neurosci 2024; 8:138-157. [PMID: 38562298 PMCID: PMC10861169 DOI: 10.1162/netn_a_00345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/23/2023] [Indexed: 04/04/2024] Open
Abstract
Despite a five order of magnitude range in size, the brains of mammals share many anatomical and functional characteristics that translate into cortical network commonalities. Here we develop a machine learning framework to quantify the degree of predictability of the weighted interareal cortical matrix. Partial network connectivity data were obtained with retrograde tract-tracing experiments generated with a consistent methodology, supplemented by projection length measurements in a nonhuman primate (macaque) and a rodent (mouse). We show that there is a significant level of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an area under the ROC curve of at least 0.8 for the macaque. Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species. These observations reinforce earlier observations that the formation and evolution of the cortical network at the mesoscale is, to a large extent, rule based. Using the methodology presented here, we performed imputations on all area pairs, generating samples for the complete interareal network in both species. These are necessary for comparative studies of the connectome with minimal bias, both within and across species.
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Affiliation(s)
- Ferenc Molnár
- Department of Physics, University of Notre Dame, Notre Dame, IN, USA
| | - Szabolcs Horvát
- Center for Systems Biology Dresden, Dresden, Germany
- Max Planck Institute for Cell Biology and Genetics, Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
- Department of Computer Science, Reykjavik University, Reykjavík, Iceland
| | - Ana R. Ribeiro Gomes
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute, Bron, France
| | | | - Botond Molnár
- Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania
- Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, Romania
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Mária Ercsey-Ravasz
- Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, Romania
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Kenneth Knoblauch
- National Centre for Optics, Vision and Eye Care, Faculty of Health and Social Sciences, University of South-Eastern Norway, Kongsberg, Norway
| | - Henry Kennedy
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China
| | - Zoltan Toroczkai
- Department of Physics, University of Notre Dame, Notre Dame, IN, USA
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14
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Faryadras M, Burles F, Iaria G, Davidsen J. Functional brain networks in Developmental Topographical Disorientation. Cereb Cortex 2024; 34:bhae104. [PMID: 38566506 PMCID: PMC10987990 DOI: 10.1093/cercor/bhae104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Despite a decade-long study on Developmental Topographical Disorientation, the underlying mechanism behind this neurological condition remains unknown. This lifelong selective inability in orientation, which causes these individuals to get lost even in familiar surroundings, is present in the absence of any other neurological disorder or acquired brain damage. Herein, we report an analysis of the functional brain network of individuals with Developmental Topographical Disorientation ($n = 19$) compared against that of healthy controls ($n = 21$), all of whom underwent resting-state functional magnetic resonance imaging, to identify if and how their underlying functional brain network is altered. While the established resting-state networks (RSNs) are confirmed in both groups, there is, on average, a greater connectivity and connectivity strength, in addition to increased global and local efficiency in the overall functional network of the Developmental Topographical Disorientation group. In particular, there is an enhanced connectivity between some RSNs facilitated through indirect functional paths. We identify a handful of nodes that encode part of these differences. Overall, our findings provide strong evidence that the brain networks of individuals suffering from Developmental Topographical Disorientation are modified by compensatory mechanisms, which might open the door for new diagnostic tools.
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Affiliation(s)
- Mahsa Faryadras
- Department of Physics and Astronomy, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
| | - Ford Burles
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
| | - Giuseppe Iaria
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
- Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, T2N 4N1 AB, Canada
| | - Jörn Davidsen
- Department of Physics and Astronomy, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
- Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, T2N 4N1 AB, Canada
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15
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Guichet C, Banjac S, Achard S, Mermillod M, Baciu M. Modeling the neurocognitive dynamics of language across the lifespan. Hum Brain Mapp 2024; 45:e26650. [PMID: 38553863 PMCID: PMC10980845 DOI: 10.1002/hbm.26650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 02/08/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.
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Affiliation(s)
| | - Sonja Banjac
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
| | - Sophie Achard
- LJK, UMR CNRS 5224, Université Grenoble AlpesGrenobleFrance
| | | | - Monica Baciu
- Université Grenoble Alpes, CNRS LPNC UMR 5105GrenobleFrance
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16
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Broeders TAA, Linsen F, Louter TS, Nawijn L, Penninx BWJH, van Tol MJ, van der Wee NJA, Veltman DJ, van der Werf YD, Schoonheim MM, Vinkers CH. Dynamic reconfigurations of brain networks in depressive and anxiety disorders: The influence of antidepressants. Psychiatry Res 2024; 334:115774. [PMID: 38341928 DOI: 10.1016/j.psychres.2024.115774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 02/13/2024]
Abstract
Major Depressive Disorder (MDD) and anxiety disorders are highly comorbid recurrent psychiatric disorders. Reduced dynamic reconfiguration of brain regions across subnetworks may play a critical role underlying these deficits, with indications of normalization after treatment with antidepressants. This study investigated dynamic reconfigurations in controls and individuals with a current MDD and/or anxiety disorder including antidepressant users and non-users in a large sample (N = 207) of adults. We quantified the number of subnetworks a region switched to (promiscuity) as well as the total number of switches (flexibility). Average whole-brain (i.e., global) values and subnetwork-specific values were compared between diagnosis and antidepressant groups. No differences in reconfiguration dynamics were found between individuals with a current MDD (N = 49), anxiety disorder (N = 46), comorbid MDD and anxiety disorder (N = 55), or controls (N = 57). Global and sensorimotor network (SMN) promiscuity and flexibility were higher in antidepressant users (N = 49, regardless of diagnosis) compared to non-users (N = 101) and controls. Dynamic reconfigurations were considerably higher in antidepressant users relative to non-users and controls, but not significantly altered in individuals with a MDD and/or anxiety disorder. The increase in antidepressant users was apparent across the whole brain and in the SMN when investigating subnetworks. These findings help disentangle how antidepressants improve symptoms.
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Affiliation(s)
- T A A Broeders
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - F Linsen
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - T S Louter
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - L Nawijn
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - B W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M J van Tol
- Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
| | - N J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - D J Veltman
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Y D van der Werf
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - M M Schoonheim
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - C H Vinkers
- Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands; GGZ inGeest Mental Health Care, Amsterdam, The Netherlands
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17
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Powell NJ, Hein B, Kong D, Elpelt J, Mulholland HN, Kaschube M, Smith GB. Common modular architecture across diverse cortical areas in early development. Proc Natl Acad Sci U S A 2024; 121:e2313743121. [PMID: 38446851 PMCID: PMC10945769 DOI: 10.1073/pnas.2313743121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 01/16/2024] [Indexed: 03/08/2024] Open
Abstract
In order to deal with a complex environment, animals form a diverse range of neural representations that vary across cortical areas, ranging from largely unimodal sensory input to higher-order representations of goals, outcomes, and motivation. The developmental origin of this diversity is currently unclear, as representations could arise through processes that are already area-specific from the earliest developmental stages or alternatively, they could emerge from an initially common functional organization shared across areas. Here, we use spontaneous activity recorded with two-photon and widefield calcium imaging to reveal the functional organization across the early developing cortex in ferrets, a species with a well-characterized columnar organization and modular structure of spontaneous activity in the visual cortex. We find that in animals 7 to 14 d prior to eye-opening and ear canal opening, spontaneous activity in both sensory areas (auditory and somatosensory cortex, A1 and S1, respectively), and association areas (posterior parietal and prefrontal cortex, PPC and PFC, respectively) showed an organized and modular structure that is highly similar to the organization in V1. In all cortical areas, this modular activity was distributed across the cortical surface, forming functional networks that exhibit millimeter-scale correlations. Moreover, this modular structure was evident in highly coherent spontaneous activity at the cellular level, with strong correlations among local populations of neurons apparent in all cortical areas examined. Together, our results demonstrate a common distributed and modular organization across the cortex during early development, suggesting that diverse cortical representations develop initially according to similar design principles.
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Affiliation(s)
- Nathaniel J. Powell
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN55455
| | - Bettina Hein
- Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY10027
| | - Deyue Kong
- Frankfurt Institute for Advanced Studies, Frankfurt am Main60438, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt am Main60629, Germany
- International Max Planck Research School for Neural Circuits, Frankfurt am Main60438, Germany
| | - Jonas Elpelt
- Frankfurt Institute for Advanced Studies, Frankfurt am Main60438, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt am Main60629, Germany
| | - Haleigh N. Mulholland
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN55455
| | - Matthias Kaschube
- Frankfurt Institute for Advanced Studies, Frankfurt am Main60438, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt am Main60629, Germany
| | - Gordon B. Smith
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN55455
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18
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Orsher Y, Rom A, Perel R, Lahini Y, Blinder P, Shein-Idelson M. Sequentially activated discrete modules appear as traveling waves in neuronal measurements with limited spatiotemporal sampling. eLife 2024; 12:RP92254. [PMID: 38451063 PMCID: PMC10942589 DOI: 10.7554/elife.92254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024] Open
Abstract
Numerous studies have identified traveling waves in the cortex and suggested they play important roles in brain processing. These waves are most often measured using macroscopic methods that are unable to assess the local spiking activity underlying wave dynamics. Here, we investigated the possibility that waves may not be traveling at the single neuron scale. We first show that sequentially activating two discrete brain areas can appear as traveling waves in EEG simulations. We next reproduce these results using an analytical model of two sequentially activated regions. Using this model, we were able to generate wave-like activity with variable directions, velocities, and spatial patterns, and to map the discriminability limits between traveling waves and modular sequential activations. Finally, we investigated the link between field potentials and single neuron excitability using large-scale measurements from turtle cortex ex vivo. We found that while field potentials exhibit wave-like dynamics, the underlying spiking activity was better described by consecutively activated spatially adjacent groups of neurons. Taken together, this study suggests caution when interpreting phase delay measurements as continuously propagating wavefronts in two different spatial scales. A careful distinction between modular and wave excitability profiles across scales will be critical for understanding the nature of cortical computations.
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Affiliation(s)
- Yuval Orsher
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
- School of Physics & Astronomy, Faculty of Exact Sciences, Tel Aviv UniversityTel AvivIsrael
| | - Ariel Rom
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
- Sagol School of Neuroscience, Tel Aviv University, IsraelTel AvivIsrael
| | - Rotem Perel
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | - Yoav Lahini
- School of Physics & Astronomy, Faculty of Exact Sciences, Tel Aviv UniversityTel AvivIsrael
- Sagol School of Neuroscience, Tel Aviv University, IsraelTel AvivIsrael
| | - Pablo Blinder
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
- Sagol School of Neuroscience, Tel Aviv University, IsraelTel AvivIsrael
| | - Mark Shein-Idelson
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
- Sagol School of Neuroscience, Tel Aviv University, IsraelTel AvivIsrael
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19
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Yang L, Wang Z, Wang G, Liang L, Liu M, Wang J. Brain-inspired modular echo state network for EEG-based emotion recognition. Front Neurosci 2024; 18:1305284. [PMID: 38495107 PMCID: PMC10940514 DOI: 10.3389/fnins.2024.1305284] [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: 10/01/2023] [Accepted: 01/10/2024] [Indexed: 03/19/2024] Open
Abstract
Previous studies have successfully applied a lightweight recurrent neural network (RNN) called Echo State Network (ESN) for EEG-based emotion recognition. These studies use intrinsic plasticity (IP) and synaptic plasticity (SP) to tune the hidden reservoir layer of ESN, yet they require extra training procedures and are often computationally complex. Recent neuroscientific research reveals that the brain is modular, consisting of internally dense and externally sparse subnetworks. Furthermore, it has been proved that this modular topology facilitates information processing efficiency in both biological and artificial neural networks (ANNs). Motivated by these findings, we propose Modular Echo State Network (M-ESN), where the hidden layer of ESN is directly initialized to a more efficient modular structure. In this paper, we first describe our novel implementation method, which enables us to find the optimal module numbers, local and global connectivity. Then, the M-ESN is benchmarked on the DEAP dataset. Lastly, we explain why network modularity improves model performance. We demonstrate that modular organization leads to a more diverse distribution of node degrees, which increases network heterogeneity and subsequently improves classification accuracy. On the emotion arousal, valence, and stress/calm classification tasks, our M-ESN outperforms regular ESN by 5.44, 5.90, and 5.42%, respectively, while this difference when comparing with adaptation rules tuned ESNs are 0.77, 5.49, and 0.95%. Notably, our results are obtained using M-ESN with a much smaller reservoir size and simpler training process.
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Affiliation(s)
- Liuyi Yang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Zhaoze Wang
- School of Engineering and Applied Science, University of Pennsylvania, Pennsylvania, PA, United States
| | - Guoyu Wang
- Department of Auromation, Tiangong University, Tianjin, China
| | - Lixin Liang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Meng Liu
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Junsong Wang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
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20
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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.
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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
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21
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Jimenez-Marin A, Diez I, Erramuzpe A, Stramaglia S, Bonifazi P, Cortes JM. Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain. Sci Data 2024; 11:256. [PMID: 38424112 PMCID: PMC10904384 DOI: 10.1038/s41597-024-03060-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.
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Affiliation(s)
- Antonio Jimenez-Marin
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Ibai Diez
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, United States of America
| | - Asier Erramuzpe
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Sebastiano Stramaglia
- Dipartamento Interateneo di Fisica, Universita Degli Studi di Bari Aldo Moro, INFN, Bari, Italy
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Jesus M Cortes
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain.
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain.
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain.
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22
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Sakaibara M, Yamamoto H, Murota H, Monma N, Sato S, Hirano-Iwata A. Enhanced responses to inflammatory cytokine interleukin-6 in micropatterned networks of cultured cortical neurons. Biochem Biophys Res Commun 2024; 695:149379. [PMID: 38159413 DOI: 10.1016/j.bbrc.2023.149379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 12/09/2023] [Indexed: 01/03/2024]
Abstract
Cortical neurons in dissociated cultures are an indispensable model system for pharmacological research that provides insights into chemical responses in well-defined environments. However, cortical neurons plated on homogeneous substrates develop an unstructured network that exhibits excessively synchronized activity, which occasionally masks the consequences induced by external substances. Here, we show that hyperactivity and excessive synchrony in cultured cortical networks can be effectively suppressed by growing neurons in microfluidic devices. These devices feature a hierarchically modular design that resembles the in vivo network. We focused on interleukin-6, a pro-inflammatory cytokine, and assessed its acute and chronic effects. Fluorescence calcium imaging of spontaneous neural activity for up to 20 days of culture showed detectable modulation of collective activity events and neural correlation in micropatterned neurons, which was not apparent in neurons cultured on homogeneous substrates. Our results indicate that engineered neuronal networks provide a unique platform for detecting and understanding the fundamental effects of biochemical compounds on neuronal networks.
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Affiliation(s)
- Mamoru Sakaibara
- Research Institute of Electrical Communication, Tohoku University, Sendai, Japan; Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Hideaki Yamamoto
- Research Institute of Electrical Communication, Tohoku University, Sendai, Japan; Graduate School of Engineering, Tohoku University, Sendai, Japan.
| | - Hakuba Murota
- Research Institute of Electrical Communication, Tohoku University, Sendai, Japan; Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Nobuaki Monma
- Research Institute of Electrical Communication, Tohoku University, Sendai, Japan; Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Shigeo Sato
- Research Institute of Electrical Communication, Tohoku University, Sendai, Japan; Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Ayumi Hirano-Iwata
- Research Institute of Electrical Communication, Tohoku University, Sendai, Japan; Graduate School of Engineering, Tohoku University, Sendai, Japan; Advanced Institute for Materials Research, Tohoku University, Sendai, Japan; Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan
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23
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Raykov PP, Knights E, Cam-Can, Henson RN. Does functional system segregation mediate the effects of lifestyle on cognition in older adults? Neurobiol Aging 2024; 134:126-134. [PMID: 38070445 PMCID: PMC10789480 DOI: 10.1016/j.neurobiolaging.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 11/22/2023] [Accepted: 11/22/2023] [Indexed: 01/02/2024]
Abstract
Healthy aging is typically accompanied by cognitive decline. Previous work has shown that engaging in multiple, non-work activities during midlife can have a protective effect on cognition several decades later, rendering it less dependent on brain structural health; the definition of "cognitive reserve". Other work has shown that increasing age is associated with reduced segregation of large-scale brain functional networks. Here we tested the hypothesis that functional segregation (SyS) mediates this effect of middle-aged lifestyle on late-life cognition. We used fMRI data from three tasks in the CamCAN dataset, together with cognitive data on fluid intelligence, episodic memory, and retrospective lifestyle data from the Lifetime of Experiences Questionnaire (LEQ). In all three tasks, we showed that SyS related to fluid intelligence even after adjusting for the (nonlinear) age effects. However, we found no evidence that SyS in late-life mediated the relationship between non-specific (non-occupation) midlife activities and either measure of cognition in late-life. Thus, the brain correlates of cognitive reserve arising from mid-life activities remain to be discovered.
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Affiliation(s)
- Petar P Raykov
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, UK.
| | - Ethan Knights
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - Cam-Can
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK
| | - Richard N Henson
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK
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24
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Bell AM. The evolution of decision-making mechanisms under competing demands. Trends Ecol Evol 2024; 39:141-151. [PMID: 37783626 PMCID: PMC10922085 DOI: 10.1016/j.tree.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 10/04/2023]
Abstract
Animals in nature are constantly managing multiple demands, and decisions about how to adjust behavior in response to ecologically relevant demands is critical for fitness. Evidence for behavioral correlations across functional contexts (behavioral syndromes) and growing appreciation for shared proximate substrates of behavior prompts novel questions about the existence of distinct neural, molecular, and genetic mechanisms involved in decision-making. Those proximate mechanisms are likely to be an important target of selection, but little is known about how they evolve, their evolutionary history, or where they harbor genetic variation. Herein I provide a conceptual framework for understanding the evolution of mechanisms for decision-making, highlighting insights on decision-making in humans and model organisms, and sketch an emerging synthesis.
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Affiliation(s)
- Alison M Bell
- Department of Evolution, Ecology and Behavior, 505 S. Goodwin Ave, Urbana, IL 61801, USA.
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25
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Crombie KM, Azar A, Botsford C, Heilicher M, Jaeb M, Gruichich TS, Schomaker CM, Williams R, Stowe ZN, Dunsmoor JE, Cisler JM. Decoding context memories for threat in large-scale neural networks. Cereb Cortex 2024; 34:bhae018. [PMID: 38300181 PMCID: PMC10839849 DOI: 10.1093/cercor/bhae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
Humans are often tasked with determining the degree to which a given situation poses threat. Salient cues present during prior events help bring online memories for context, which plays an informative role in this process. However, it is relatively unknown whether and how individuals use features of the environment to retrieve context memories for threat, enabling accurate inferences about the current level of danger/threat (i.e. retrieve appropriate memory) when there is a degree of ambiguity surrounding the present context. We leveraged computational neuroscience approaches (i.e. independent component analysis and multivariate pattern analyses) to decode large-scale neural network activity patterns engaged during learning and inferring threat context during a novel functional magnetic resonance imaging task. Here, we report that individuals accurately infer threat contexts under ambiguous conditions through neural reinstatement of large-scale network activity patterns (specifically striatum, salience, and frontoparietal networks) that track the signal value of environmental cues, which, in turn, allows reinstatement of a mental representation, primarily within a ventral visual network, of the previously learned threat context. These results provide novel insight into distinct, but overlapping, neural mechanisms by which individuals may utilize prior learning to effectively make decisions about ambiguous threat-related contexts as they navigate the environment.
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Affiliation(s)
- Kevin M Crombie
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
- Department of Kinesiology, The University of Alabama, 620 Judy Bonner Drive, Box 870312, Tuscaloosa, AL 35487, United States
| | - Ameera Azar
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
| | - Chloe Botsford
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Mickela Heilicher
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Michael Jaeb
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Tijana Sagorac Gruichich
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Chloe M Schomaker
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
| | - Rachel Williams
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Zachary N Stowe
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Joseph E Dunsmoor
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX 78712, United States
- Department of Neuroscience, The University of Texas at Austin, 1 University Station, Stop C7000, Austin, TX 78712, United States
| | - Josh M Cisler
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
- Institute for Early Life Adversity Research, The University of Texas at Austin Dell Medical School, 1601 Trinity Street, Building B, Austin, TX 78712, United States
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26
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Ruffini G, Lopez-Sola E, Vohryzek J, Sanchez-Todo R. Neural Geometrodynamics, Complexity, and Plasticity: A Psychedelics Perspective. ENTROPY (BASEL, SWITZERLAND) 2024; 26:90. [PMID: 38275498 PMCID: PMC11154528 DOI: 10.3390/e26010090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/14/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
We explore the intersection of neural dynamics and the effects of psychedelics in light of distinct timescales in a framework integrating concepts from dynamics, complexity, and plasticity. We call this framework neural geometrodynamics for its parallels with general relativity's description of the interplay of spacetime and matter. The geometry of trajectories within the dynamical landscape of "fast time" dynamics are shaped by the structure of a differential equation and its connectivity parameters, which themselves evolve over "slow time" driven by state-dependent and state-independent plasticity mechanisms. Finally, the adjustment of plasticity processes (metaplasticity) takes place in an "ultraslow" time scale. Psychedelics flatten the neural landscape, leading to heightened entropy and complexity of neural dynamics, as observed in neuroimaging and modeling studies linking increases in complexity with a disruption of functional integration. We highlight the relationship between criticality, the complexity of fast neural dynamics, and synaptic plasticity. Pathological, rigid, or "canalized" neural dynamics result in an ultrastable confined repertoire, allowing slower plastic changes to consolidate them further. However, under the influence of psychedelics, the destabilizing emergence of complex dynamics leads to a more fluid and adaptable neural state in a process that is amplified by the plasticity-enhancing effects of psychedelics. This shift manifests as an acute systemic increase of disorder and a possibly longer-lasting increase in complexity affecting both short-term dynamics and long-term plastic processes. Our framework offers a holistic perspective on the acute effects of these substances and their potential long-term impacts on neural structure and function.
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Affiliation(s)
- Giulio Ruffini
- Brain Modeling Department, Neuroelectrics, 08035 Barcelona, Spain; (E.L.-S.); (R.S.-T.)
| | - Edmundo Lopez-Sola
- Brain Modeling Department, Neuroelectrics, 08035 Barcelona, Spain; (E.L.-S.); (R.S.-T.)
- Computational Neuroscience Group, Universitat Pompeu Fabra, 08018 Barcelona, Spain;
| | - Jakub Vohryzek
- Computational Neuroscience Group, Universitat Pompeu Fabra, 08018 Barcelona, Spain;
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford OX3 9BX, UK
| | - Roser Sanchez-Todo
- Brain Modeling Department, Neuroelectrics, 08035 Barcelona, Spain; (E.L.-S.); (R.S.-T.)
- Computational Neuroscience Group, Universitat Pompeu Fabra, 08018 Barcelona, Spain;
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27
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Fauchon C, Bastuji H, Peyron R, Garcia-Larrea L. Fractal Similarity of Pain Brain Networks. ADVANCES IN NEUROBIOLOGY 2024; 36:639-657. [PMID: 38468056 DOI: 10.1007/978-3-031-47606-8_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The conscious perception of pain is the result of dynamic interactions of neural activities from local brain regions to distributed brain networks. Mapping out the networks of functional connections between brain regions that form and disperse when an experimental participant received nociceptive stimulations allow to characterize the pattern of network connections related to the pain experience.Although the pattern of intra- and inter-areal connections across the brain are incredibly complex, they appear also largely scale free, with "fractal" connectivity properties reproducing at short and long-time scales. Our results combining intracranial recordings and functional imaging in humans during pain indicate striking similarities in the activity and topological representation of networks at different orders of temporality, with reproduction of patterns of activation from the millisecond to the multisecond range. The connectivity analyzed using graph theory on fMRI data was organized in four sets of brain regions matching those identified through iEEG (i.e., sensorimotor, default mode, central executive, and amygdalo-hippocampal).Here, we discuss similarities in brain network organization at different scales or "orders," in participants as they feel pain. Description of this fractal-like organization may provide clues about how our brain regions work together to create the perception of pain and how pain becomes chronic when its organization is altered.
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Affiliation(s)
- Camille Fauchon
- Université Clermont Auvergne, CHU de Clermont-Ferrand, Inserm, Neuro-Dol, Clermont-Ferrand, France.
- Université Jean Monnet, Inserm, CRNL, NeuroPain, Saint-Etienne, France.
| | - Hélène Bastuji
- Université Claude Bernard Lyon 1, UJM, Inserm, CRNL, NeuroPain, Bron, France
| | - Roland Peyron
- Université Jean Monnet, Inserm, CRNL, NeuroPain, Saint-Etienne, France
- CHU, centre de la douleur, Saint-Etienne, France
| | - Luis Garcia-Larrea
- Université Claude Bernard Lyon 1, UJM, Inserm, CRNL, NeuroPain, Bron, France
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28
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Shao G, Xu G, Huo C, Nie Z, Zhang Y, Yi L, Wang D, Shao Z, Weng S, Sun J, Li Z. Effect of the VR-guided grasping task on the brain functional network. BIOMEDICAL OPTICS EXPRESS 2024; 15:77-94. [PMID: 38223191 PMCID: PMC10783918 DOI: 10.1364/boe.504669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/16/2024]
Abstract
Virtual reality (VR) technology has been demonstrated to be effective in rehabilitation training with the assistance of VR games, but its impact on brain functional networks remains unclear. In this study, we used functional near-infrared spectroscopy imaging to examine the brain hemodynamic signals from 18 healthy participants during rest and grasping tasks with and without VR game intervention. We calculated and compared the graph theory-based topological properties of the brain networks using phase locking values (PLV). The results revealed significant differences in the brain network properties when VR games were introduced compared to the resting state. Specifically, for the VR-guided grasping task, the modularity of the brain network was significantly higher than the resting state, and the average clustering coefficient of the motor cortex was significantly lower compared to that of the resting state and the simple grasping task. Correlation analyses showed that a higher clustering coefficient, local efficiency, and modularity were associated with better game performance during VR game participation. This study demonstrates that a VR game task intervention can better modulate the brain functional network compared to simple grasping movements and may be more beneficial for the recovery of grasping abilities in post-stroke patients with hand paralysis.
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Affiliation(s)
- Guangjian Shao
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Gongcheng Xu
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Congcong Huo
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Zichao Nie
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Yizheng Zhang
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Li Yi
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Dongyang Wang
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Zhiyong Shao
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Shanfan Weng
- School of Medicine, Foshan University, Foshan, China
| | - Jinyan Sun
- School of Medicine, Foshan University, Foshan, China
| | - Zengyong Li
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
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29
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Liu Z, Gan E, Tegmark M. Seeing Is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability. ENTROPY (BASEL, SWITZERLAND) 2023; 26:41. [PMID: 38248167 PMCID: PMC10814460 DOI: 10.3390/e26010041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/21/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
We introduce Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable. Inspired by brains, BIMT embeds neurons in a geometric space and augments the loss function with a cost proportional to the length of each neuron connection. This is inspired by the idea of minimum connection cost in evolutionary biology, but we are the first the combine this idea with training neural networks with gradient descent for interpretability. We demonstrate that BIMT discovers useful modular neural networks for many simple tasks, revealing compositional structures in symbolic formulas, interpretable decision boundaries and features for classification, and mathematical structure in algorithmic datasets. Qualitatively, BIMT-trained networks have modules readily identifiable by the naked eye, but regularly trained networks seem much more complicated. Quantitatively, we use Newman's method to compute the modularity of network graphs; BIMT achieves the highest modularity for all our test problems. A promising and ambitious future direction is to apply the proposed method to understand large models for vision, language, and science.
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Affiliation(s)
- Ziming Liu
- Institute for Artificial Intelligence and Fundamental Interactions, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; (E.G.); (M.T.)
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30
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Wang C, Zhang T, Chen X, He S, Li S, Wu S. BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming. eLife 2023; 12:e86365. [PMID: 38132087 PMCID: PMC10796146 DOI: 10.7554/elife.86365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 12/20/2023] [Indexed: 12/23/2023] Open
Abstract
Elucidating the intricate neural mechanisms underlying brain functions requires integrative brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose programming framework that allows users to freely define neural models across multiple scales, efficiently simulate, train, and analyze model dynamics, and conveniently incorporate new modeling approaches. In response to this need, we present BrainPy. BrainPy leverages the advanced just-in-time (JIT) compilation capabilities of JAX and XLA to provide a powerful infrastructure tailored for brain dynamics programming. It offers an integrated platform for building, simulating, training, and analyzing brain dynamics models. Models defined in BrainPy can be JIT compiled into binary instructions for various devices, including Central Processing Unit, Graphics Processing Unit, and Tensor Processing Unit, which ensures high-running performance comparable to native C or CUDA. Additionally, BrainPy features an extensible architecture that allows for easy expansion of new infrastructure, utilities, and machine-learning approaches. This flexibility enables researchers to incorporate cutting-edge techniques and adapt the framework to their specific needs.
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Affiliation(s)
- Chaoming Wang
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
- Guangdong Institute of Intelligence Science and TechnologyGuangdongChina
| | - Tianqiu Zhang
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| | - Xiaoyu Chen
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| | - Sichao He
- Beijing Jiaotong UniversityBeijingChina
| | - Shangyang Li
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
| | - Si Wu
- School of Psychological and Cognitive Sciences, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Center of Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Bejing Key Laboratory of Behavior and Mental Health, Peking UniversityBeijingChina
- Guangdong Institute of Intelligence Science and TechnologyGuangdongChina
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31
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Ruttorf M, Tal Z, Amaral L, Fang F, Bi Y, Almeida J. Neuroplastic changes in functional wiring in sensory cortices of the congenitally deaf: A network analysis. Hum Brain Mapp 2023; 44:6523-6536. [PMID: 37956260 PMCID: PMC10681644 DOI: 10.1002/hbm.26530] [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: 07/14/2023] [Revised: 10/10/2023] [Accepted: 10/22/2023] [Indexed: 11/15/2023] Open
Abstract
Congenital sensory deprivation induces significant changes in the structural and functional organisation of the brain. These are well-characterised by cross-modal plasticity, in which deprived cortical areas are recruited to process information from non-affected sensory modalities, as well as by other neuroplastic alterations within regions dedicated to the remaining senses. Here, we analysed visual and auditory networks of congenitally deaf and hearing individuals during different visual tasks to assess changes in network community structure and connectivity patterns due to congenital deafness. In the hearing group, the nodes are clearly divided into three communities (visual, auditory and subcortical), whereas in the deaf group a fourth community consisting mainly of bilateral superior temporal sulcus and temporo-insular regions is present. Perhaps more importantly, the right lateral geniculate body, as well as bilateral thalamus and pulvinar joined the auditory community of the deaf. Moreover, there is stronger connectivity between bilateral thalamic and pulvinar and auditory areas in the deaf group, when compared to the hearing group. No differences were found in the number of connections of these nodes to visual areas. Our findings reveal substantial neuroplastic changes occurring within the auditory and visual networks caused by deafness, emphasising the dynamic nature of the sensory systems in response to congenital deafness. Specifically, these results indicate that in the deaf but not the hearing group, subcortical thalamic nuclei are highly connected to auditory areas during processing of visual information, suggesting that these relay areas may be responsible for rerouting visual information to the auditory cortex under congenital deafness.
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Affiliation(s)
- Michaela Ruttorf
- Computer Assisted Clinical MedicineHeidelberg UniversityMannheimGermany
- Mannheim Institute for Intelligent Systems in MedicineHeidelberg UniversityMannheimGermany
| | - Zohar Tal
- Proaction LaboratoryUniversity of CoimbraPortugal
- Faculty of Psychology and Educational SciencesUniversity of CoimbraPortugal
| | - Lénia Amaral
- Department of NeuroscienceGeorgetown University Medical CenterWashingtonDistrict of ColumbiaUSA
| | - Fang Fang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental HealthPeking UniversityBeijingChina
- IDG/McGovern Institute for Brain ResearchPeking UniversityBeijingChina
- Peking‐Tsinghua Center for Life SciencesPeking UniversityBeijingChina
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern, Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
| | - Jorge Almeida
- Proaction LaboratoryUniversity of CoimbraPortugal
- Faculty of Psychology and Educational SciencesUniversity of CoimbraPortugal
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Maltsev AV, Stern MD, Lakatta EG, Maltsev VA. A novel conceptual model of heart rate autonomic modulation based on a small-world modular structure of the sinoatrial node. Front Physiol 2023; 14:1276023. [PMID: 38148905 PMCID: PMC10750401 DOI: 10.3389/fphys.2023.1276023] [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: 08/11/2023] [Accepted: 11/27/2023] [Indexed: 12/28/2023] Open
Abstract
The present view on heartbeat initiation is that a primary pacemaker cell or a group of cells in the sinoatrial node (SAN) center paces the rest of the SAN and the atria. However, recent high-resolution imaging studies show a more complex paradigm of SAN function that emerges from heterogeneous signaling, mimicking brain cytoarchitecture and function. Here, we developed and tested a new conceptual numerical model of SAN organized similarly to brain networks featuring a modular structure with small-world topology. In our model, a lower rate module leads action potential (AP) firing in the basal state and during parasympathetic stimulation, whereas a higher rate module leads during β-adrenergic stimulation. Such a system reproduces the respective shift of the leading pacemaker site observed experimentally and a wide range of rate modulation and robust function while conserving energy. Since experimental studies found functional modules at different scales, from a few cells up to the highest scale of the superior and inferior SAN, the SAN appears to feature hierarchical modularity, i.e., within each module, there is a set of sub-modules, like in the brain, exhibiting greater robustness, adaptivity, and evolvability of network function. In this perspective, our model offers a new mainframe for interpreting new data on heterogeneous signaling in the SAN at different scales, providing new insights into cardiac pacemaker function and SAN-related cardiac arrhythmias in aging and disease.
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Affiliation(s)
| | | | | | - Victor A. Maltsev
- Intramural Research Program, National Institute on Aging, Baltimore, MD, United States
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Lord LD, Carletti T, Fernandes H, Turkheimer FE, Expert P. Altered dynamical integration/segregation balance during anesthesia-induced loss of consciousness. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1279646. [PMID: 38116461 PMCID: PMC10728865 DOI: 10.3389/fnetp.2023.1279646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023]
Abstract
In recent years, brain imaging studies have begun to shed light on the neural correlates of physiologically-reversible altered states of consciousness such as deep sleep, anesthesia, and psychedelic experiences. The emerging consensus is that normal waking consciousness requires the exploration of a dynamical repertoire enabling both global integration i.e., long-distance interactions between brain regions, and segregation, i.e., local processing in functionally specialized clusters. Altered states of consciousness have notably been characterized by a tipping of the integration/segregation balance away from this equilibrium. Historically, functional MRI (fMRI) has been the modality of choice for such investigations. However, fMRI does not enable characterization of the integration/segregation balance at sub-second temporal resolution. Here, we investigated global brain spatiotemporal patterns in electrocorticography (ECoG) data of a monkey (Macaca fuscata) under either ketamine or propofol general anesthesia. We first studied the effects of these anesthetics from the perspective of band-specific synchronization across the entire ECoG array, treating individual channels as oscillators. We further aimed to determine whether synchrony within spatially localized clusters of oscillators was differently affected by the drugs in comparison to synchronization over spatially distributed subsets of ECoG channels, thereby quantifying changes in integration/segregation balance on physiologically-relevant time scales. The findings reflect global brain dynamics characterized by a loss of long-range integration in multiple frequency bands under both ketamine and propofol anesthesia, most pronounced in the beta (13-30 Hz) and low-gamma bands (30-80 Hz), and with strongly preserved local synchrony in all bands.
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Affiliation(s)
- Louis-David Lord
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
| | - Timoteo Carletti
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
- Department of Mathematics and Namur Institute for Complex Systems (naXys), University of Namur, Namur, Belgium
| | - Henrique Fernandes
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
- Centre for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Paul Expert
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
- Global Business School for Health, University College London, London, United Kingdom
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Bufacchi RJ, Battaglia-Mayer A, Iannetti GD, Caminiti R. Cortico-spinal modularity in the parieto-frontal system: A new perspective on action control. Prog Neurobiol 2023; 231:102537. [PMID: 37832714 DOI: 10.1016/j.pneurobio.2023.102537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/22/2023] [Accepted: 10/04/2023] [Indexed: 10/15/2023]
Abstract
Classical neurophysiology suggests that the motor cortex (MI) has a unique role in action control. In contrast, this review presents evidence for multiple parieto-frontal spinal command modules that can bypass MI. Five observations support this modular perspective: (i) the statistics of cortical connectivity demonstrate functionally-related clusters of cortical areas, defining functional modules in the premotor, cingulate, and parietal cortices; (ii) different corticospinal pathways originate from the above areas, each with a distinct range of conduction velocities; (iii) the activation time of each module varies depending on task, and different modules can be activated simultaneously; (iv) a modular architecture with direct motor output is faster and less metabolically expensive than an architecture that relies on MI, given the slow connections between MI and other cortical areas; (v) lesions of the areas composing parieto-frontal modules have different effects from lesions of MI. Here we provide examples of six cortico-spinal modules and functions they subserve: module 1) arm reaching, tool use and object construction; module 2) spatial navigation and locomotion; module 3) grasping and observation of hand and mouth actions; module 4) action initiation, motor sequences, time encoding; module 5) conditional motor association and learning, action plan switching and action inhibition; module 6) planning defensive actions. These modules can serve as a library of tools to be recombined when faced with novel tasks, and MI might serve as a recombinatory hub. In conclusion, the availability of locally-stored information and multiple outflow paths supports the physiological plausibility of the proposed modular perspective.
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Affiliation(s)
- R J Bufacchi
- Neuroscience and Behaviour Laboratory, Istituto Italiano di Tecnologia, Rome, Italy; International Center for Primate Brain Research (ICPBR), Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS), Shanghai, China
| | - A Battaglia-Mayer
- Department of Physiology and Pharmacology, University of Rome, Sapienza, Italy
| | - G D Iannetti
- Neuroscience and Behaviour Laboratory, Istituto Italiano di Tecnologia, Rome, Italy; Department of Neuroscience, Physiology and Pharmacology, University College London (UCL), London, UK
| | - R Caminiti
- Neuroscience and Behaviour Laboratory, Istituto Italiano di Tecnologia, Rome, Italy.
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Li S, Chen Y, Ren P, Li Z, Zhang J, Liang X. Alterations in rat brain modular organization during unconsciousness are dependent on communication efficiency and metabolic cost. Brain Struct Funct 2023; 228:2115-2124. [PMID: 37733058 DOI: 10.1007/s00429-023-02708-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/01/2023] [Indexed: 09/22/2023]
Abstract
Spontaneous brain activity exhibits a highly structured modular organization that varies across individuals and reconfigures over time. Although it has been proposed that brain organization is shaped by an economic trade-off between minimizing costs and facilitating efficient information transfer, it remains untested whether modular variability and its changes during unconscious conditions might be constrained by the economy of brain organization. We acquired functional MRI and FDG-PET in rats under three different levels of consciousness induced by propofol administration. We examined alterations in brain modular variability during loss of consciousness from mild sedation to deep anesthesia. We also investigated the relationships between modular variability with glucose metabolism and functional connectivity strength as well as their alterations during unconsciousness. We observed that modular variability increased during loss of consciousness. Critically, across-individual modular variability is oppositely associated with functional connectivity strength and cerebral metabolism, and with deepening dosage of anesthesia, becoming increasingly dependent on basal metabolism over functional connectivity. These results suggested that, propofol-induced unconsciousness may lead to brain modular reorganization, which are putatively shaped by re-negotiations between energetic resources and communication efficiency.
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Affiliation(s)
- Siyang Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin, 150001, China
| | - Yali Chen
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032, China
| | - Peng Ren
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Zhipeng Li
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin, 150001, China
| | - Jun Zhang
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Xia Liang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin, 150001, China.
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Magielse N, Heuer K, Toro R, Schutter DJLG, Valk SL. A Comparative Perspective on the Cerebello-Cerebral System and Its Link to Cognition. CEREBELLUM (LONDON, ENGLAND) 2023; 22:1293-1307. [PMID: 36417091 PMCID: PMC10657313 DOI: 10.1007/s12311-022-01495-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/11/2022] [Indexed: 11/24/2022]
Abstract
The longstanding idea that the cerebral cortex is the main neural correlate of human cognition can be elaborated by comparative analyses along the vertebrate phylogenetic tree that support the view that the cerebello-cerebral system is suited to support non-motor functions more generally. In humans, diverse accounts have illustrated cerebellar involvement in cognitive functions. Although the neocortex, and its transmodal association cortices such as the prefrontal cortex, have become disproportionately large over primate evolution specifically, human neocortical volume does not appear to be exceptional relative to the variability within primates. Rather, several lines of evidence indicate that the exceptional volumetric increase of the lateral cerebellum in conjunction with its connectivity with the cerebral cortical system may be linked to non-motor functions and mental operation in primates. This idea is supported by diverging cerebello-cerebral adaptations that potentially coevolve with cognitive abilities across other vertebrates such as dolphins, parrots, and elephants. Modular adaptations upon the vertebrate cerebello-cerebral system may thus help better understand the neuroevolutionary trajectory of the primate brain and its relation to cognition in humans. Lateral cerebellar lobules crura I-II and their reciprocal connections to the cerebral cortical association areas appear to have substantially expanded in great apes, and humans. This, along with the notable increase in the ventral portions of the dentate nucleus and a shift to increased relative prefrontal-cerebellar connectivity, suggests that modular cerebellar adaptations support cognitive functions in humans. In sum, we show how comparative neuroscience provides new avenues to broaden our understanding of cerebellar and cerebello-cerebral functions in the context of cognition.
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Affiliation(s)
- Neville Magielse
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Center Jülich, Jülich, Germany
- Otto Hahn Cognitive Neurogenetics Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Systems Neuroscience, Heinrich Heine University, Düsseldorf, Germany
| | - Katja Heuer
- Institute Pasteur, Unité de Neuroanatomie Appliquée et Théorique, Université Paris Cité, Paris, France
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roberto Toro
- Institute Pasteur, Unité de Neuroanatomie Appliquée et Théorique, Université Paris Cité, Paris, France
| | - Dennis J L G Schutter
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Center Jülich, Jülich, Germany.
- Otto Hahn Cognitive Neurogenetics Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University, Düsseldorf, Germany.
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Rubinov M. Circular and unified analysis in network neuroscience. eLife 2023; 12:e79559. [PMID: 38014843 PMCID: PMC10684154 DOI: 10.7554/elife.79559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.
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Affiliation(s)
- Mika Rubinov
- Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt UniversityNashvilleUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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Sun Y, Shi Q, Ye M, Miao A. Topological properties and connectivity patterns in brain networks of patients with refractory epilepsy combined with intracranial electrical stimulation. Front Neurosci 2023; 17:1282232. [PMID: 38075280 PMCID: PMC10701286 DOI: 10.3389/fnins.2023.1282232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/07/2023] [Indexed: 02/12/2024] Open
Abstract
Objective Although intracranial electrical stimulation has emerged as a treatment option for various diseases, its impact on the properties of brain networks remains challenging due to its invasive nature. The combination of intracranial electrical stimulation and whole-brain functional magnetic resonance imaging (fMRI) in patients with refractory epilepsy (RE) makes it possible to study the network properties associated with electrical stimulation. Thus, our study aimed to investigate the brain network characteristics of RE patients with concurrent electrical stimulation and obtain possible clinical biomarkers. Methods Our study used the GRETNA toolbox, a graph theoretical network analysis toolbox for imaging connectomics, to calculate and analyze the network topological attributes including global measures (small-world parameters and network efficiency) and nodal characteristics. The resting-state fMRI (rs-fMRI) and the fMRI concurrent electrical stimulation (es-fMRI) of RE patients were utilized to make group comparisons with healthy controls to identify the differences in network topology properties. Network properties comparisons before and after electrode implantation in the same patient were used to further analyze stimulus-related changes in network properties. Modular analysis was used to examine connectivity and distribution characteristics in the brain networks of all participants in study. Results Compared to healthy controls, the rs-fMRI and the es-fMRI of RE patients exhibited impaired small-world property and reduced network efficiency. Nodal properties, such as nodal clustering coefficient (NCp), betweenness centrality (Bc), and degree centrality (Dc), exhibited differences between RE patients (including rs-fMRI and es-fMRI) and healthy controls. The network connectivity of RE patients (including rs-fMRI and es-fMRI) showed reduced intra-modular connections in subcortical areas and the occipital lobe, as well as decreased inter-modular connections between frontal and subcortical regions, and parieto-occipital regions compared to healthy controls. The brain networks of es-fMRI showed a relatively weaker small-world structure compared to rs-fMRI. Conclusion The brain networks of RE patients exhibited a reduced small-world property, with a tendency toward random networks. The network connectivity patterns in RE patients exhibited reduced connections between cortical and subcortical regions and enhanced connections among parieto-occipital regions. Electrical stimulation can modulate brain network activity, leading to changes in network connectivity patterns and properties.
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Affiliation(s)
- Yulei Sun
- Department of Neurology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qi Shi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, Jiangsu, China
| | - Min Ye
- Department of Neurology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ailiang Miao
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Lee H, Yi GS, Nam Y. Connectivity and network burst properties of in-vitro neuronal networks induced by a clustered structure with alginate hydrogel patterning. Biomed Eng Lett 2023; 13:659-670. [PMID: 37872997 PMCID: PMC10590365 DOI: 10.1007/s13534-023-00289-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 10/25/2023] Open
Abstract
Modularity is one of the important structural properties that affect information processing and other functionalities of neuronal networks. Researchers have developed in-vitro clustered network models for reproducing the modularity, but it is still challenging to control the segregation and integration of several sub-populations of them. We cultured clustered networks with alginate patterning and collected the electrophysiological signals to investigate the changes in functional properties during the development. We built inter-connected neuronal clusters using alginate micro-patterning with a circular shape on the surface of the micro-electrode array. The neuronal clusters were enabled to be connected at 3 or 10 days-in-vitro (DIV) by removing the barrier. The neuronal signals from different types of networks were collected from 16 to 34 DIV, and functional characteristics were examined. Connectivity and burst motif analysis were carried out to find out the relation between the structure and function of the networks. Neuronal networks with clustered structure showed different activity properties from the random networks along the development. The clustered networks had more short-range connections compared to the random networks. In the network burst motif analysis, the clustered networks showed more various patterns and a slower propagation of the activation patterns. In this study, we successfully cultured neuronal networks with clustered structure, and the structure affected the functional properties. The network model suggested in this study will be a good solution for observing the effect of structure on function during their development. Supplementary Information The online version contains supplementary material available at 10.1007/s13534-023-00289-5.
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Affiliation(s)
- Hyungsub Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Gwan-Su Yi
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Yoonkey Nam
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
- KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
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Zeng L, Wang C, Sun K, Pu Y, Gao Y, Wang H, Liu X, Wen Z. Upregulation of a Small-World Brain Network Improves Inhibitory Control: An fNIRS Neurofeedback Training Study. Brain Sci 2023; 13:1516. [PMID: 38002477 PMCID: PMC10670110 DOI: 10.3390/brainsci13111516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/22/2023] [Accepted: 10/24/2023] [Indexed: 11/26/2023] Open
Abstract
The aim of this study was to investigate the inner link between the small-world brain network and inhibitory control. Functional near-infrared spectroscopy (fNIRS) was used to construct a neurofeedback (NF) training system and regulate the frontal small-world brain network. The small-world network downregulation group (DOWN, n = 17) and the small-world network upregulation group (UP, n = 17) received five days of fNIRS-NF training and performed the color-word Stroop task before and after training. The behavioral and functional brain network topology results of both groups were analyzed by a repeated-measures analysis of variance (ANOVA), which showed that the upregulation training helped to improve inhibitory control. The upregulated small-world brain network exhibits an increase in the brain network regularization, links widely dispersed brain resources, and reduces the lateralization of brain functional networks between hemispheres. This suggests an inherent correlation between small-world functional brain networks and inhibitory control; moreover, dynamic optimization under cost efficiency trade-offs provides a neural basis for inhibitory control. Inhibitory control is not a simple function of a single brain region or connectivity but rather an emergent property of a broader network.
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Affiliation(s)
- Lingwei Zeng
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Chunchen Wang
- Department of Aerospace Medicine, Fourth Military Medical University, Xi’an 710032, China;
| | - Kewei Sun
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Yue Pu
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Yuntao Gao
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Hui Wang
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Xufeng Liu
- Department of Medical Psychology, Fourth Military Medical University, Xi’an 710032, China; (L.Z.); (K.S.); (Y.P.); (Y.G.); (H.W.)
| | - Zhihong Wen
- Department of Aerospace Medicine, Fourth Military Medical University, Xi’an 710032, China;
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Karpychev V, Malyutina S, Zhuravleva A, Bronov O, Kuzin V, Marinets A, Dragoy O. Disruptions in modular structure and network integration of language-related network predict language performance in temporal lobe epilepsy: Evidence from graph-based analysis. Epilepsy Behav 2023; 147:109407. [PMID: 37688840 DOI: 10.1016/j.yebeh.2023.109407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/03/2023] [Accepted: 08/19/2023] [Indexed: 09/11/2023]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is a network disorder that alters the total organization of the language-related network. Task-based functional magnetic resonance imaging (fMRI) aimed at functional connectivity is a direct method to investigate how the network is reorganized. However, such studies are scarce and represented mostly by the resting-state analysis of the individual connections between regions. To fill this gap, we used a graph-based analysis, which allows us to cover the total language-related network changes, such as disruptions in an integration/segregation balance, during a language task in TLE. METHODS We collected task-based fMRI data with sentence completion from 19 healthy controls and 28 people with left TLE. Using graph-based analysis, we estimated how the language-related network segregated into modules and tested whether they differed between groups. We evaluated the total network integration and the integration within modules. To assess intermodular integration, we considered the number and location of connector hubs-regions with high connectivity. RESULTS The language-related network was differently segregated during language processing in the groups. While healthy controls showed a module consisting of left perisylvian regions, people with TLE exhibited a bilateral module formed by the anterior language-related areas and a module in the left temporal lobe, reflecting hyperconnectivity within the epileptic focus. As a consequence of this reorganization, there was a statistical tendency that the dominance of the intramodular integration over the total network integration was greater in TLE, which predicted language performance. The increase in the number of connector hubs in the right hemisphere, in turn, was compensatory in TLE. SIGNIFICANCE Our study provides insights into the reorganization of the language-related network in TLE, revealing specific network changes in segregation and integration. It confirms reduced global connectivity and compensation across the healthy hemisphere, commonly observed in epilepsy. These findings advance the understanding of the network-based reorganizational processes underlying language processing in TLE.
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Affiliation(s)
- Victor Karpychev
- Center for Language and Brain, HSE University, Moscow, Russian Federation.
| | - Svetlana Malyutina
- Center for Language and Brain, HSE University, Moscow, Russian Federation
| | - Anna Zhuravleva
- Center for Language and Brain, HSE University, Moscow, Russian Federation
| | - Oleg Bronov
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation
| | - Vasiliy Kuzin
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation
| | - Aleksei Marinets
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russian Federation
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russian Federation; Institute of Linguistics, Russian Academy of Sciences, Moscow, Russian Federation
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Tang Q, Lu Y, Cai B, Wang Y. Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification. J Digit Imaging 2023; 36:2113-2124. [PMID: 37369942 PMCID: PMC10501984 DOI: 10.1007/s10278-023-00872-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/12/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
This work presents a novel approach to estimate brain functional connectivity networks via generative learning. Due to the complexity and variability of rs-fMRI signal, we consider it as a random variable, and utilize variational autoencoder networks to encode it as a confidence distribution in the latent space rather than as a fixed vector, so as to establish the relationship between them. First, the mean time series of each brain region of interest is mapped into a multivariate Gaussian distribution. The correlation between two brain regions is measured by the Jensen-Shannon divergence that describes the statistical similarity between two probability distributions, and then the adjacency matrix is created to indicate the functional connectivity strength of pairwise brain regions. Meanwhile, our findings show that the adjacency matrices obtained at VAE latent spaces of different dimensionalities have good complementarity for MCI identification in precision and recall, and the classification performance can be further boosted by an efficient cascade of classifiers. This proposal constructs brain functional networks from a statistical modeling standpoint, improving the statistical ability of population data and the generalization ability of observation data variability. We evaluate the proposed framework over the task of identifying subjects with MCI from normal controls, and the experimental results on the public dataset show that our method significantly outperforms both the baseline and current state-of-the-art methods.
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Affiliation(s)
- Qiling Tang
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, China.
| | - Yuhong Lu
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, China
| | - Bilian Cai
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, China
| | - Yan Wang
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, China
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Idesis S, Allegra M, Vohryzek J, Sanz Perl Y, Faskowitz J, Sporns O, Corbetta M, Deco G. A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke. Sci Rep 2023; 13:15698. [PMID: 37735201 PMCID: PMC10514061 DOI: 10.1038/s41598-023-42533-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023] Open
Abstract
Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients' diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction.
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Affiliation(s)
- Sebastian Idesis
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain.
| | - Michele Allegra
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padua, Italy
- Department of Physics and Astronomy "G. Galilei", University of Padova, via Marzolo 8, 35131, Padua, Italy
| | - Jakub Vohryzek
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Yonatan Sanz Perl
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
- Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Institut du Cerveau et de la Moelle Épinière, ICM, Paris, France
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padua, Italy
- Department of Neuroscience, University of Padova, via Giustiniani 5, 35128, Padua, Italy
- Veneto Institute of Molecular Medicine (VIMM), via Orus 2/B, 35129, Padua, Italy
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
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44
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Seguin C, Sporns O, Zalesky A. Brain network communication: concepts, models and applications. Nat Rev Neurosci 2023; 24:557-574. [PMID: 37438433 DOI: 10.1038/s41583-023-00718-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2023] [Indexed: 07/14/2023]
Abstract
Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Program in Cognitive Science, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia
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45
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Chen L, Yu C, Zhai J. How network structure affects the dynamics of a network of stochastic spiking neurons. CHAOS (WOODBURY, N.Y.) 2023; 33:093101. [PMID: 37656915 DOI: 10.1063/5.0164207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/14/2023] [Indexed: 09/03/2023]
Abstract
Up to now, it still remains an open question about the relation between the structure of brain networks and their functions. The effects of structure on the dynamics of neural networks are usually investigated via extensive numerical simulations, while analytical analysis is always very difficult and thus rare. In this work, we explored the effects of a random regular graph on the dynamics of a neural network of stochastic spiking neurons, which has a bistable region when fully connected. We showed by numerical simulations that as the number of each neuron's neighbors decreases, the bistable region shrinks and eventually seems to disappear, and a critical-like transition appears. In the meantime, we made analytical analysis that explains numerical results. We hope this would give some insights into how structure affects the dynamics of neural networks from a theoretical perspective, rather than merely by numerical simulations.
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Affiliation(s)
- Lei Chen
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaojun Yu
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jian Zhai
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
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46
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Yamamoto H, Spitzner FP, Takemuro T, Buendía V, Murota H, Morante C, Konno T, Sato S, Hirano-Iwata A, Levina A, Priesemann V, Muñoz MA, Zierenberg J, Soriano J. Modular architecture facilitates noise-driven control of synchrony in neuronal networks. SCIENCE ADVANCES 2023; 9:eade1755. [PMID: 37624893 PMCID: PMC10456864 DOI: 10.1126/sciadv.ade1755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 07/21/2023] [Indexed: 08/27/2023]
Abstract
High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.
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Affiliation(s)
- Hideaki Yamamoto
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan
- Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - F. Paul Spitzner
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Taiki Takemuro
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan
- Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan
| | - Victor Buendía
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Departamento de Electromagnetismo y Física de la Materia, Universidad de Granada, Granada, Spain
| | - Hakuba Murota
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan
- Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Carla Morante
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Barcelona, Spain
| | - Tomohiro Konno
- Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
| | - Shigeo Sato
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan
- Graduate School of Engineering, Tohoku University, Sendai, Japan
| | - Ayumi Hirano-Iwata
- Research Institute of Electrical Communication (RIEC), Tohoku University, Sendai, Japan
- Graduate School of Engineering, Tohoku University, Sendai, Japan
- Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan
- Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, Japan
| | - Anna Levina
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Miguel A. Muñoz
- Departamento de Electromagnetismo y Física de la Materia, Universidad de Granada, Granada, Spain
- Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada, Granada, Spain
| | | | - Jordi Soriano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Barcelona, Spain
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López-León CF, Soriano J, Planet R. Rheological Characterization of Three-Dimensional Neuronal Cultures Embedded in PEGylated Fibrin Hydrogels. Gels 2023; 9:642. [PMID: 37623097 PMCID: PMC10454106 DOI: 10.3390/gels9080642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Three-dimensional (3D) neuronal cultures are valuable models for studying brain complexity in vitro, and the choice of the bulk material in which the neurons grow is a crucial factor in establishing successful cultures. Indeed, neuronal development and network functionality are influenced by the mechanical properties of the selected material; in turn, these properties may change due to neuron-matrix interactions that alter the microstructure of the material. To advance our understanding of the interplay between neurons and their environment, here we utilized a PEGylated fibrin hydrogel as a scaffold for mouse primary neuronal cultures and carried out a rheological characterization of the scaffold over a three-week period, both with and without cells. We observed that the hydrogels exhibited an elastic response that could be described in terms of the Young's modulus E. The hydrogels without neurons procured a stable E≃420 Pa, while the neuron-laden hydrogels showed a higher E≃590 Pa during the early stages of development that decreased to E≃340 Pa at maturer stages. Our results suggest that neurons and their processes dynamically modify the hydrogel structure during development, potentially compromising both the stability of the material and the functional traits of the developing neuronal network.
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Affiliation(s)
- Clara F. López-León
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain; (C.F.L.-L.); (J.S.)
- Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
| | - Jordi Soriano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain; (C.F.L.-L.); (J.S.)
- Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
| | - Ramon Planet
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain; (C.F.L.-L.); (J.S.)
- Universitat de Barcelona Institute of Complex Systems (UBICS), E-08028 Barcelona, Spain
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48
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Astle DE, Johnson MH, Akarca D. Toward computational neuroconstructivism: a framework for developmental systems neuroscience. Trends Cogn Sci 2023; 27:726-744. [PMID: 37263856 DOI: 10.1016/j.tics.2023.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 01/05/2023] [Accepted: 04/19/2023] [Indexed: 06/03/2023]
Abstract
Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that neural functions are shaped by these interactions) is core to some developmental theories. However, due to their complexity, understanding underlying developmental mechanisms is challenging. Elsewhere in neurobiology, a computational revolution has shown that mathematical models of hidden biological mechanisms can bridge observations with theory building. Can we build a similar computational framework yielding mechanistic insights for brain development? Here, we outline the conceptual and technical challenges of addressing this theory gap, and demonstrate that there is great potential in specifying brain development as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, as the field explores computational explanations of system-wide development.
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Affiliation(s)
- Duncan E Astle
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 2QQ, UK; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK.
| | - Mark H Johnson
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, WC1E 7JL, UK
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK
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49
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Janarek J, Drogosz Z, Grela J, Ochab JK, Oświęcimka P. Investigating structural and functional aspects of the brain's criticality in stroke. Sci Rep 2023; 13:12341. [PMID: 37524891 PMCID: PMC10390586 DOI: 10.1038/s41598-023-39467-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
This paper addresses the question of the brain's critical dynamics after an injury such as a stroke. It is hypothesized that the healthy brain operates near a phase transition (critical point), which provides optimal conditions for information transmission and responses to inputs. If structural damage could cause the critical point to disappear and thus make self-organized criticality unachievable, it would offer the theoretical explanation for the post-stroke impairment of brain function. In our contribution, however, we demonstrate using network models of the brain, that the dynamics remain critical even after a stroke. In cases where the average size of the second-largest cluster of active nodes, which is one of the commonly used indicators of criticality, shows an anomalous behavior, it results from the loss of integrity of the network, quantifiable within graph theory, and not from genuine non-critical dynamics. We propose a new simple model of an artificial stroke that explains this anomaly. The proposed interpretation of the results is confirmed by an analysis of real connectomes acquired from post-stroke patients and a control group. The results presented refer to neurobiological data; however, the conclusions reached apply to a broad class of complex systems that admit a critical state.
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Affiliation(s)
- Jakub Janarek
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
| | - Zbigniew Drogosz
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
| | - Jacek Grela
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
- Mark Kac Center for Complex Systems Research, Jagiellonian University, 30-348, Kraków, Poland
| | - Jeremi K Ochab
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland.
- Mark Kac Center for Complex Systems Research, Jagiellonian University, 30-348, Kraków, Poland.
| | - Paweł Oświęcimka
- Institute of Theoretical Physics, Jagiellonian University, 30-348, Kraków, Poland
- Mark Kac Center for Complex Systems Research, Jagiellonian University, 30-348, Kraków, Poland
- Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, 31-342, Kraków, Poland
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50
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Jeon I, Kim T. Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network. Front Comput Neurosci 2023; 17:1092185. [PMID: 37449083 PMCID: PMC10336230 DOI: 10.3389/fncom.2023.1092185] [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: 11/07/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.
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Affiliation(s)
| | - Taegon Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
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