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Yu D, Li T, Ding Q, Wu Y, Fu Z, Zhan X, Yang L, Jia Y. Maintenance of delay-period activity in working memory task is modulated by local network structure. PLoS Comput Biol 2024; 20:e1012415. [PMID: 39226309 DOI: 10.1371/journal.pcbi.1012415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 08/14/2024] [Indexed: 09/05/2024] Open
Abstract
Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM.
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Affiliation(s)
- Dong Yu
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Tianyu Li
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Qianming Ding
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Yong Wu
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Ziying Fu
- Institute of Biophysics, Central China Normal University, Wuhan, China
- School of Life Sciences, Central China Normal University, Wuhan, China
| | - Xuan Zhan
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Lijian Yang
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Ya Jia
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
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2
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Zhang D, Kabuka MR. MARML: Motif-Aware Deep Representation Learning in Multilayer Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:11649-11660. [PMID: 38113155 DOI: 10.1109/tnnls.2023.3341347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
The rapid increase in high-throughput, complex, and heterogeneous data has led to the adoption of network-structured models and analyses for interpretation. However, these data are inherently complex and challenging to understand, prompting researchers to turn to graph embedding methods to facilitate analysis. While general network embedding techniques have shown promise in improving downstream prediction and classification tasks, real-world data are complicated due to cross-domain interactions between different types of entities. Multilayered networks have been successful in integrating biological data to represent biological systems' hierarchy, but embedding nodes based on different types of interactions remains an unsolved problem. To address this challenge, we propose the Motif-aware deep representation learning in multilayer (MARML) networks for learning network representations. Our method considers recurring motif patterns, topological information, and attributive information from other sources as node features. We validated the MARML method using various multilayer network datasets. In addition, by incorporating motif information, MARML considers higher order connections across different hierarchies. The learned features exhibited excellent accuracy in tasks related to link prediction and link differentiation, enabling us to distinguish between existing and disconnected triplets. Through the integration of both intrinsic node attributes and topological network structures, we enhance our understanding of complex biological systems.
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3
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Tang D, Zylberberg J, Jia X, Choi H. Stimulus type shapes the topology of cellular functional networks in mouse visual cortex. Nat Commun 2024; 15:5753. [PMID: 38982078 PMCID: PMC11233648 DOI: 10.1038/s41467-024-49704-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
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Affiliation(s)
- Disheng Tang
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
| | - Joel Zylberberg
- Department of Physics and Astronomy, and Centre for Vision Research, York University, Toronto, ON M3J 1P3, ON, Canada.
- Learning in Machines and Brains Program, CIFAR, Toronto, ON M5G 1M1, ON, Canada.
| | - Xiaoxuan Jia
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, PR China.
| | - Hannah Choi
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- School of Mathematics, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
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4
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Mittal D, Narayanan R. Network motifs in cellular neurophysiology. Trends Neurosci 2024; 47:506-521. [PMID: 38806296 DOI: 10.1016/j.tins.2024.04.008] [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: 01/15/2024] [Revised: 04/08/2024] [Accepted: 04/29/2024] [Indexed: 05/30/2024]
Abstract
Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease.
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Affiliation(s)
- Divyansh Mittal
- Centre for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.
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5
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Wang H, Liu Y, Ding Y. Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm. Neuroinformatics 2024; 22:285-296. [PMID: 38771433 DOI: 10.1007/s12021-024-09662-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] [Accepted: 03/23/2024] [Indexed: 05/22/2024]
Abstract
In the field of neuroimaging, more studies of abnormalities in brain regions of the autism spectrum disorder (ASD) usually focused on two brain regions connected, and less on abnormalities of higher-order interactions of brain regions. To explore the complex relationships of brain regions, we used the partial entropy decomposition (PED) algorithm to capture higher-order interactions by computing the higher-order dependencies of all three brain regions (triads). We proposed a method for examining the effect of individual brain regions on triads based on the PED and surrogate tests. The key triads were discovered by analyzing the effects. Further, the hypergraph modularity maximization algorithm revealed the higher-order brain structures, of which the link between right thalamus and left thalamus in ASD was more loose compared with the typical control (TC). Redundant key triad (left cerebellum crus 1 and left precuneus and right inferior occipital gyrus) exhibited a discernible attenuation in interaction in ASD, while the synergistic key triad (right cerebellum crus 1 and left postcentral gyrus and left lingual gyrus) indicated a notable decline. The results of classification model further confirmed the potential of the key triads as diagnostic biomarkers.
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Affiliation(s)
- Hao Wang
- School of Science, Jiangnan University, Wuxi, Jiangsu, China
| | - Yanting Liu
- School of Science, Jiangnan University, Wuxi, Jiangsu, China
| | - Yanrui Ding
- School of Science, Jiangnan University, Wuxi, Jiangsu, China.
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6
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Pailthorpe BA. Simulated dynamical transitions in a heterogeneous marmoset pFC cluster. Front Comput Neurosci 2024; 18:1398898. [PMID: 38863681 PMCID: PMC11165126 DOI: 10.3389/fncom.2024.1398898] [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: 03/11/2024] [Accepted: 05/06/2024] [Indexed: 06/13/2024] Open
Abstract
Network analysis of the marmoset cortical connectivity data indicates a significant 3D cluster in and around the pre-frontal cortex. A multi-node, heterogeneous neural mass model of this six-node cluster was constructed. Its parameters were informed by available experimental and simulation data so that each neural mass oscillated in a characteristic frequency band. Nodes were connected with directed, weighted links derived from the marmoset structural connectivity data. Heterogeneity arose from the different link weights and model parameters for each node. Stimulation of the cluster with an incident pulse train modulated in the standard frequency bands induced a variety of dynamical state transitions that lasted in the range of 5-10 s, suggestive of timescales relevant to short-term memory. A short gamma burst rapidly reset the beta-induced transition. The theta-induced transition state showed a spontaneous, delayed reset to the resting state. An additional, continuous gamma wave stimulus induced a new beating oscillatory state. Longer or repeated gamma bursts were phase-aligned with the beta oscillation, delivering increasing energy input and causing shorter transition times. The relevance of these results to working memory is yet to be established, but they suggest interesting opportunities.
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Affiliation(s)
- Bernard A. Pailthorpe
- Brain Dynamics Group, School of Physics, University of Sydney, Sydney, NSW, Australia
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7
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Hao B, Kovács IA. Proper network randomization is key to assessing social balance. SCIENCE ADVANCES 2024; 10:eadj0104. [PMID: 38701217 PMCID: PMC11068007 DOI: 10.1126/sciadv.adj0104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 04/01/2024] [Indexed: 05/05/2024]
Abstract
Social ties, either positive or negative, lead to signed network patterns, the subject of balance theory. For example, strong balance introduces cycles with even numbers of negative edges. The statistical significance of such patterns is routinely assessed by comparisons to null models. Yet, results in signed networks remain controversial. Here, we show that even if a network exhibits strong balance by construction, current null models can fail to identify it. Our results indicate that matching the signed degree preferences of the nodes is a critical step and so is the preservation of network topology in the null model. As a solution, we propose the STP null model, which integrates both constraints within a maximum entropy framework. STP randomization leads to qualitatively different results, with most social networks consistently demonstrating strong balance in three- and four-node patterns. On the basis our results, we present a potential wiring mechanism behind the observed signed patterns and outline further applications of STP randomization.
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Affiliation(s)
- Bingjie Hao
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
| | - István A. Kovács
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA
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8
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Wang Y, Wang Y, Zhang X, Du J, Zhang T, Xu B. Brain topology improved spiking neural network for efficient reinforcement learning of continuous control. Front Neurosci 2024; 18:1325062. [PMID: 38694900 PMCID: PMC11062182 DOI: 10.3389/fnins.2024.1325062] [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: 10/20/2023] [Accepted: 03/27/2024] [Indexed: 05/04/2024] Open
Abstract
The brain topology highly reflects the complex cognitive functions of the biological brain after million-years of evolution. Learning from these biological topologies is a smarter and easier way to achieve brain-like intelligence with features of efficiency, robustness, and flexibility. Here we proposed a brain topology-improved spiking neural network (BT-SNN) for efficient reinforcement learning. First, hundreds of biological topologies are generated and selected as subsets of the Allen mouse brain topology with the help of the Tanimoto hierarchical clustering algorithm, which has been widely used in analyzing key features of the brain connectome. Second, a few biological constraints are used to filter out three key topology candidates, including but not limited to the proportion of node functions (e.g., sensation, memory, and motor types) and network sparsity. Third, the network topology is integrated with the hybrid numerical solver-improved leaky-integrated and fire neurons. Fourth, the algorithm is then tuned with an evolutionary algorithm named adaptive random search instead of backpropagation to guide synaptic modifications without affecting raw key features of the topology. Fifth, under the test of four animal-survival-like RL tasks (i.e., dynamic controlling in Mujoco), the BT-SNN can achieve higher scores than not only counterpart SNN using random topology but also some classical ANNs (i.e., long-short-term memory and multi-layer perception). This result indicates that the research effort of incorporating biological topology and evolutionary learning rules has much in store for the future.
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Affiliation(s)
- Yongjian Wang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yansong Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xinhe Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jiulin Du
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Tielin Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
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9
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Donner C, Bartram J, Hornauer P, Kim T, Roqueiro D, Hierlemann A, Obozinski G, Schröter M. Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains. PLoS Comput Biol 2024; 20:e1011964. [PMID: 38683881 PMCID: PMC11081509 DOI: 10.1371/journal.pcbi.1011964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 05/09/2024] [Accepted: 03/02/2024] [Indexed: 05/02/2024] Open
Abstract
Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.
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Affiliation(s)
- Christian Donner
- Swiss Data Science Center, ETH Zürich & EPFL, Zürich & Lausanne, Switzerland
| | - Julian Bartram
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Philipp Hornauer
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Taehoon Kim
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Damian Roqueiro
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Guillaume Obozinski
- Swiss Data Science Center, ETH Zürich & EPFL, Zürich & Lausanne, Switzerland
| | - Manuel Schröter
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
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10
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Normand F, Gajwani M, Côté DC, Allard A. NBS-SNI, an extension of the network-based statistic: Abnormal functional connections between important structural actors. Netw Neurosci 2024; 8:44-80. [PMID: 38562286 PMCID: PMC10861162 DOI: 10.1162/netn_a_00344] [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: 02/27/2023] [Accepted: 10/11/2023] [Indexed: 04/04/2024] Open
Abstract
Elucidating the coupling between the structure and the function of the brain and its development across maturation has attracted a lot of interest in the field of network neuroscience in the last 15 years. Mounting evidence supports the hypothesis that the onset of certain brain disorders is linked with the interplay between the structural architecture of the brain and its functional processes, often accompanied with unusual connectivity features. This paper introduces a method called the network-based statistic-simultaneous node investigation (NBS-SNI) that integrates both representations into a single framework, and identifies connectivity abnormalities in case-control studies. With this method, significance is given to the properties of the nodes, as well as to their connections. This approach builds on the well-established network-based statistic (NBS) proposed in 2010. We uncover and identify the regimes in which NBS-SNI offers a gain in statistical resolution to identify a contrast of interest using synthetic data. We also apply our method on two real case-control studies, one consisting of individuals diagnosed with autism and the other consisting of individuals diagnosed with early psychosis. Using NBS-SNI and node properties such as the closeness centrality and local information dimension, we found hypo- and hyperconnected subnetworks and show that our method can offer a 9 percentage points gain in prediction power over the standard NBS.
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Affiliation(s)
- Francis Normand
- Centre de Recherche CERVO, Québec, Canada
- Centre Interdisciplinaire en Modélisation Mathématique, Université Laval, Québec, Canada
- The Turner Institute for Brain and Mental Health and Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Mehul Gajwani
- The Turner Institute for Brain and Mental Health and Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Daniel C. Côté
- Centre de Recherche CERVO, Québec, Canada
- Département de Physique, de Génie Physique et d’Optique, Université Laval, Québec, Canada
| | - Antoine Allard
- Centre Interdisciplinaire en Modélisation Mathématique, Université Laval, Québec, Canada
- Département de Physique, de Génie Physique et d’Optique, Université Laval, Québec, Canada
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11
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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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12
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Lin A, Yang R, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Bates AS, Eckstein N, Funke J, Jefferis GSXE, Murthy M. Network Statistics of the Whole-Brain Connectome of Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.29.551086. [PMID: 37547019 PMCID: PMC10402125 DOI: 10.1101/2023.07.29.551086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.
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Affiliation(s)
- Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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13
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Gosti G, Milanetti E, Folli V, de Pasquale F, Leonetti M, Corbetta M, Ruocco G, Della Penna S. A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG. Neural Netw 2024; 170:72-93. [PMID: 37977091 DOI: 10.1016/j.neunet.2023.11.027] [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: 02/17/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.
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Affiliation(s)
- Giorgio Gosti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; Istituto di Scienze del Patrimonio Culturale, Sede di Roma, Consiglio Nazionale delle Ricerche, CNR-ISPC, Via Salaria km, 34900 Rome, Italy.
| | - Edoardo Milanetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Viola Folli
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Francesco de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, 64100 Piano D'Accio, Teramo, Italy.
| | - Marco Leonetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Via Belzoni, 160, 35121, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Via Orus, 2/B, 35129, Padova, Italy; Veneto Institute of Molecular Medicine (VIMM), Via Orus, 2, 35129, Padova, Italy.
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Via Luigi Polacchi, 11, 66100 Chieti, Italy.
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14
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Fan D, Qi L, Hou S, Wang Q, Baier G. The seizure classification of focal epilepsy based on the network motif analysis. Brain Res Bull 2024; 207:110879. [PMID: 38237873 DOI: 10.1016/j.brainresbull.2024.110879] [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: 08/23/2023] [Revised: 12/10/2023] [Accepted: 01/13/2024] [Indexed: 01/23/2024]
Abstract
Due to the complexity of focal epilepsy and its risk for transiting to the generalized epilepsy, the development of reliable classification methods to accurately predict and classify focal and generalized seizures is critical for the clinical management of patients with epilepsy. In order to holistically understand the seizure propagation behavior of focal epilepsy, we propose a three-node motif reduced network by respectively simplifying the focal region, surrounding healthy region and their critical regions as the single node. Because three-node motif can richly characterize information evolutions, the motif analysis method could comprehensively investigate the seizure behavior of focal epilepsy. Firstly, we define a new seizure propagation marker value to capture the seizure onsets and intensity. Based on the three-node motif analysis, it is shown that the focal seizure and spreading can be categorized as inhibitory seizure, focal seizure, focal-critical seizure and generalized seizures, respectively. The four types of seizures correspond to specific modal types respectively, reflecting the strong correlation between seizure behavior and information flow evolution. In addition, it is found that the intensity difference of outflow and inflow information from the critical node (connection heterogeneity) and the excitability of the critical node significantly affected the distribution and transition of the four seizure types. In particular, the method of local linear stability analysis also verifies the effectiveness of four types of seizures classification. In sum, this paper computationally confirms the complex dynamic behavior of focal seizures, and the study of criticality is helpful to propose novel seizure control strategies.
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Affiliation(s)
- Denggui Fan
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
| | - Lixue Qi
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
| | - Songan Hou
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing 100191, China.
| | - Gerold Baier
- Cell and Developmental Biology, University College London, London WC1E 6BT, United Kingdom
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15
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Londei F, Arena G, Ferrucci L, Russo E, Ceccarelli F, Genovesio A. Connecting the dots in the zona incerta: A study of neural assemblies and motifs of inter-area coordination in mice. iScience 2024; 27:108761. [PMID: 38274403 PMCID: PMC10808920 DOI: 10.1016/j.isci.2023.108761] [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: 07/19/2023] [Revised: 10/23/2023] [Accepted: 11/11/2023] [Indexed: 01/27/2024] Open
Abstract
The zona incerta (ZI), a subthalamic area connected to numerous brain regions, has raised clinical interest because its stimulation alleviates the motor symptoms of Parkinson's disease. To explore its coordinative nature, we studied the assembly formation in a dataset of neural recordings in mice and quantified the degree of functional coordination of ZI with other 24 brain areas. We found that the ZI is a highly integrative area. The analysis in terms of "loop-like" motifs, directional assemblies composed of three neurons spanning two areas, has revealed reciprocal functional interactions with reentrant signals that, in most cases, start and end with the activation of ZI units. In support of its proposed integrative role, we found that almost one-third of the ZI's neurons formed assemblies with more than half of the other recorded areas and that loop-like assemblies may stand out as hyper-integrative motifs compared to other types of activation patterns.
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Affiliation(s)
- Fabrizio Londei
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Giulia Arena
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Eleonora Russo
- The BioRobotics Institute, Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Francesco Ceccarelli
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
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16
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Lyu H, Kureh YH, Vendrow J, Porter MA. Learning low-rank latent mesoscale structures in networks. Nat Commun 2024; 15:224. [PMID: 38172092 PMCID: PMC10764844 DOI: 10.1038/s41467-023-42859-2] [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: 07/28/2021] [Accepted: 10/24/2023] [Indexed: 01/05/2024] Open
Abstract
Researchers in many fields use networks to represent interactions between entities in complex systems. To study the large-scale behavior of complex systems, it is useful to examine mesoscale structures in networks as building blocks that influence such behavior. In this paper, we present an approach to describe low-rank mesoscale structures in networks. We find that many real-world networks possess a small set of latent motifs that effectively approximate most subgraphs at a fixed mesoscale. Such low-rank mesoscale structures allow one to reconstruct networks by approximating subgraphs of a network using combinations of latent motifs. Employing subgraph sampling and nonnegative matrix factorization enables the discovery of these latent motifs. The ability to encode and reconstruct networks using a small set of latent motifs has many applications in network analysis, including network comparison, network denoising, and edge inference.
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Affiliation(s)
- Hanbaek Lyu
- Department of Mathematics, University of Wisconsin-Madison, Madison, WI, 53706, USA.
| | - Yacoub H Kureh
- Department of Mathematics, University of California, Los Angeles, CA, 90095, USA
| | - Joshua Vendrow
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Mason A Porter
- Department of Mathematics, University of California, Los Angeles, CA, 90095, USA
- Department of Sociology, University of California, Los Angeles, CA, 90095, USA
- Santa Fe Institute, Sante FE, NM, 87501, USA
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17
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Bazinet V, Hansen JY, Misic B. Towards a biologically annotated brain connectome. Nat Rev Neurosci 2023; 24:747-760. [PMID: 37848663 DOI: 10.1038/s41583-023-00752-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.
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Affiliation(s)
- Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
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18
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Dai W, Qiu E, Lin X, Zhang S, Zhang M, Han X, Jia Z, Su H, Bian X, Zang X, Li M, Zhang Q, Ran Y, Gong Z, Wang X, Wang R, Tian L, Dong Z, Yu S. Abnormal Thalamo-Cortical Interactions in Overlapping Communities of Migraine: An Edge Functional Connectivity Study. Ann Neurol 2023; 94:1168-1181. [PMID: 37635687 DOI: 10.1002/ana.26783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/24/2023] [Accepted: 08/24/2023] [Indexed: 08/29/2023]
Abstract
OBJECTIVE Migraine has been demonstrated to exhibit abnormal functional connectivity of large-scale brain networks, which is closely associated with its pathophysiology and has not yet been explored by edge functional connectivity. We used an edge-centric approach combined with motif analysis to evaluate higher-order communication patterns of brain networks in migraine. METHODS We investigated edge-centric metrics in 108 interictal migraine patients and 71 healthy controls. We parcellated the brain into networks using independent component analysis. We applied edge graph construction, k-means clustering, community overlap detection, graph-theory-based evaluations, and clinical correlation analysis. We conducted motif analysis to explore the interactions among regions, and a classification model to test the specificity of edge-centric results. RESULTS The normalized entropy of lateral thalamus was significantly increased in migraine, which was positively correlated with the baseline headache duration, and negatively correlated with headache duration reduction following preventive medications at 3-month follow-up. Network-wise entropy of the sensorimotor network was significantly elevated in migraine. The community similarity between lateral thalamus and postcentral gyrus was enhanced in migraine. Migraine patients showed overrepresented L-shape and diverse motifs, and underrepresented forked motifs with lateral thalamus serving as the reference node. Furthermore, migraine patients presented with overrepresented L-shape triads, where the postcentral gyrus shared different edges with the lateral thalamus. The classification model showed that entropy of the lateral thalamus had the highest discriminative power, with an area under the curve of 0.86. INTERPRETATION Our findings indicated an abnormal higher-order thalamo-cortical communication pattern in migraine patients. The thalamo-cortical-somatosensory disturbance of concerted working may potentially lead to aberrant information flow and deficit pain processing of migraine. ANN NEUROL 2023;94:1168-1181.
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Affiliation(s)
- Wei Dai
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Enchao Qiu
- Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Xiaoxue Lin
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shuhua Zhang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Mingjie Zhang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xun Han
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Zhihua Jia
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Hui Su
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiangbing Bian
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiao Zang
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Meng Li
- Department of Radiology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Qingkui Zhang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ye Ran
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Zihua Gong
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaolin Wang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Rongfei Wang
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Lixia Tian
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Zhao Dong
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shengyuan Yu
- Department of Neurology, First Medical Center of Chinese PLA General Hospital, Beijing, China
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19
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Miranda M, Estrada-Rodriguez G, Estrada E. What Is in a Simplicial Complex? A Metaplex-Based Approach to Its Structure and Dynamics. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1599. [PMID: 38136479 PMCID: PMC10742477 DOI: 10.3390/e25121599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/24/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
Geometric realization of simplicial complexes makes them a unique representation of complex systems. The existence of local continuous spaces at the simplices level with global discrete connectivity between simplices makes the analysis of dynamical systems on simplicial complexes a challenging problem. In this work, we provide some examples of complex systems in which this representation would be a more appropriate model of real-world phenomena. Here, we generalize the concept of metaplexes to embrace that of geometric simplicial complexes, which also includes the definition of dynamical systems on them. A metaplex is formed by regions of a continuous space of any dimension interconnected by sinks and sources that works controlled by discrete (graph) operators. The definition of simplicial metaplexes given here allows the description of the diffusion dynamics of this system in a way that solves the existing problems with previous models. We make a detailed analysis of the generalities and possible extensions of this model beyond simplicial complexes, e.g., from polytopal and cell complexes to manifold complexes, and apply it to a real-world simplicial complex representing the visual cortex of a macaque.
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Affiliation(s)
- Manuel Miranda
- Institute of Cross-Disciplinary Physics and Complex Systems, IFISC (UIB-CSIC), 07122 Palma de Mallorca, Spain;
| | | | - Ernesto Estrada
- Institute of Cross-Disciplinary Physics and Complex Systems, IFISC (UIB-CSIC), 07122 Palma de Mallorca, Spain;
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20
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Chacko TP, Toole JT, Morris MC, Page J, Forsten RD, Barrett JP, Reinhard MJ, Brewster RC, Costanzo ME, Broderick G. A regulatory pathway model of neuropsychological disruption in Havana syndrome. Front Psychiatry 2023; 14:1180929. [PMID: 37965360 PMCID: PMC10642174 DOI: 10.3389/fpsyt.2023.1180929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 09/29/2023] [Indexed: 11/16/2023] Open
Abstract
Introduction In 2016 diplomatic personnel serving in Havana, Cuba, began reporting audible sensory phenomena paired with onset of complex and persistent neurological symptoms consistent with brain injury. The etiology of these Anomalous Health Incidents (AHI) and subsequent symptoms remains unknown. This report investigates putative exposure-symptom pathology by assembling a network model of published bio-behavioral pathways and assessing how dysregulation of such pathways might explain loss of function in these subjects using data available in the published literature. Given similarities in presentation with mild traumatic brain injury (mTBI), we used the latter as a clinically relevant means of evaluating if the neuropsychological profiles observed in Havana Syndrome Havana Syndrome might be explained at least in part by a dysregulation of neurotransmission, neuro-inflammation, or both. Method Automated text-mining of >9,000 publications produced a network consisting of 273 documented regulatory interactions linking 29 neuro-chemical markers with 9 neuropsychological constructs from the Brief Mood Survey, PTSD Checklist, and the Frontal Systems Behavior Scale. Analysis of information flow through this network produced a set of regulatory rules reconciling to within a 6% departure known mechanistic pathways with neuropsychological profiles in N = 6 subjects. Results Predicted expression of neuro-chemical markers that jointly satisfy documented pathways and observed symptom profiles display characteristically elevated IL-1B, IL-10, NGF, and norepinephrine levels in the context of depressed BDNF, GDNF, IGF1, and glutamate expression (FDR < 5%). Elevations in CRH and IL-6 were also predicted unanimously across all subjects. Furthermore, simulations of neurological regulatory dynamics reveal subjects do not appear to be "locked in" persistent illness but rather appear to be engaged in a slow recovery trajectory. Discussion This computational analysis of measured neuropsychological symptoms in Havana-based diplomats proposes that these AHI symptoms may be supported in part by disruption of known neuroimmune and neurotransmission regulatory mechanisms also associated with mTBI.
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Affiliation(s)
- Thomas P. Chacko
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - J. Tory Toole
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - Matthew C. Morris
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - Jeffrey Page
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - Robert D. Forsten
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
| | - John P. Barrett
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
- Department of Preventive Medicine and Biostatistics, Uniformed Services University, Bethesda, MD, United States
| | - Matthew J. Reinhard
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
- Complex Exposures Threats Center, Department of Veterans Affairs, Washington, DC, United States
| | - Ryan C. Brewster
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
| | - Michelle E. Costanzo
- War Related Illness and Injury Study Center (WRIISC), Department of Veterans Affairs, Washington, DC, United States
- Complex Exposures Threats Center, Department of Veterans Affairs, Washington, DC, United States
- Department of Medicine, Uniformed Services University, Bethesda, MD, United States
| | - Gordon Broderick
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
- Complex Exposures Threats Center, Department of Veterans Affairs, Washington, DC, United States
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Troidl J, Warchol S, Choi J, Matelsky J, Dhanyasi N, Wang X, Wester B, Wei D, Lichtman JW, Pfister H, Beyer J. Vimo - Visual Analysis of Neuronal Connectivity Motifs. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; PP:10.1109/TVCG.2023.3327388. [PMID: 37883279 PMCID: PMC11045665 DOI: 10.1109/tvcg.2023.3327388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Recent advances in high-resolution connectomics provide researchers with access to accurate petascale reconstructions of neuronal circuits and brain networks for the first time. Neuroscientists are analyzing these networks to better understand information processing in the brain. In particular, scientists are interested in identifying specific small network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. Although such motifs are typically small (e.g., 2 - 6 neurons), the vast data sizes and intricate data complexity present significant challenges to the search and analysis process. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings. To simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by visual abstractions. This allows users to transition from a highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly (e.g., 2 - 4 times) to form a larger network structure. We evaluate Vimo in a user study and an in-depth case study with seven domain experts on motifs in a large connectome of the fruit fly, including more than 21,000 neurons and 20 million synapses. We find that Vimo enables hypothesis generation and confirmation through fast analysis iterations and connectivity highlighting.
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22
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Wu X, Guo Y, Xue J, Dong Y, Sun Y, Wang B, Xiang J, Liu Y. Abnormal and Changing Information Interaction in Adults with Attention-Deficit/Hyperactivity Disorder Based on Network Motifs. Brain Sci 2023; 13:1331. [PMID: 37759932 PMCID: PMC10526475 DOI: 10.3390/brainsci13091331] [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: 07/31/2023] [Revised: 08/27/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Network motif analysis approaches provide insights into the complexity of the brain's functional network. In recent years, attention-deficit/hyperactivity disorder (ADHD) has been reported to result in abnormal information interactions in macro- and micro-scale functional networks. However, most existing studies remain limited due to potentially ignoring meso-scale topology information. To address this gap, we aimed to investigate functional motif patterns in ADHD to unravel the underlying information flow and analyze motif-based node roles to characterize the different information interaction methods for identifying the abnormal and changing lesion sites of ADHD. The results showed that the interaction functions of the right hippocampus and the right amygdala were significantly increased, which could lead patients to develop mood disorders. The information interaction of the bilateral thalamus changed, influencing and modifying behavioral results. Notably, the capability of receiving information in the left inferior temporal and the right lingual gyrus decreased, which may cause difficulties for patients in processing visual information in a timely manner, resulting in inattention. This study revealed abnormal and changing information interactions based on network motifs, providing important evidence for understanding information interactions at the meso-scale level in ADHD patients.
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Affiliation(s)
- Xubin Wu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yuxiang Guo
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China;
| | - Jiayue Xue
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yanqing Dong
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yumeng Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.W.); (J.X.); (Y.D.); (Y.S.); (B.W.)
| | - Yi Liu
- Department of Anesthesiology, Shanxi Province Cancer Hospital, Taiyuan 030013, China
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23
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Zhao H, Shao C, Shi Z, He S, Gong Z. The Intrinsic Similarity of Topological Structure in Biological Neural Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3292-3305. [PMID: 37224366 DOI: 10.1109/tcbb.2023.3279443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Most previous studies mainly have focused on the analysis of structural properties of individual neuronal networks from C. elegans. In recent years, an increasing number of synapse-level neural maps, also known as biological neural networks, have been reconstructed. However, it is not clear whether there are intrinsic similarities of structural properties of biological neural networks from different brain compartments or species. To explore this issue, we collected nine connectomes at synaptic resolution including C. elegans, and analyzed their structural properties. We found that these biological neural networks possess small-world properties and modules. Excluding the Drosophila larval visual system, these networks have rich clubs. The distributions of synaptic connection strength for these networks can be fitted by the truncated pow-law distributions. Additionally, compared with the power-law model, a log-normal distribution is a better model to fit the complementary cumulative distribution function (CCDF) of degree for these neuronal networks. Moreover, we also observed that these neural networks belong to the same superfamily based on the significance profile (SP) of small subgraphs in the network. Taken together, these findings suggest that biological neural networks share intrinsic similarities in their topological structure, revealing some principles underlying the formation of biological neural networks within and across species.
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24
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Hadjiabadi D, Soltesz I. From single-neuron dynamics to higher-order circuit motifs in control and pathological brain networks. J Physiol 2023; 601:3011-3024. [PMID: 35815823 PMCID: PMC10655857 DOI: 10.1113/jp282749] [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: 04/15/2022] [Accepted: 06/27/2022] [Indexed: 11/08/2022] Open
Abstract
The convergence of advanced single-cell in vivo functional imaging techniques, computational modelling tools and graph-based network analytics has heralded new opportunities to study single-cell dynamics across large-scale networks, providing novel insights into principles of brain communication and pointing towards potential new strategies for treating neurological disorders. A major recent finding has been the identification of unusually richly connected hub cells that have capacity to synchronize networks and may also be critical in network dysfunction. While hub neurons are traditionally defined by measures that consider solely the number and strength of connections, novel higher-order graph analytics now enables the mining of massive networks for repeating subgraph patterns called motifs. As an illustration of the power offered by higher-order analysis of neuronal networks, we highlight how recent methodological advances uncovered a new functional cell type, the superhub, that is predicted to play a major role in regulating network dynamics. Finally, we discuss open questions that will be critical for assessing the importance of higher-order cellular-scale network analytics in understanding brain function in health and disease.
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Affiliation(s)
- Darian Hadjiabadi
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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Varley TF, Pope M, Puxeddu MG, Faskowitz J, Sporns O. Partial entropy decomposition reveals higher-order information structures in human brain activity. Proc Natl Acad Sci U S A 2023; 120:e2300888120. [PMID: 37467265 PMCID: PMC10372615 DOI: 10.1073/pnas.2300888120] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/06/2023] [Indexed: 07/21/2023] Open
Abstract
The standard approach to modeling the human brain as a complex system is with a network, where the basic unit of interaction is a pairwise link between two brain regions. While powerful, this approach is limited by the inability to assess higher-order interactions involving three or more elements directly. In this work, we explore a method for capturing higher-order dependencies in multivariate data: the partial entropy decomposition (PED). Our approach decomposes the joint entropy of the whole system into a set of nonnegative atoms that describe the redundant, unique, and synergistic interactions that compose the system's structure. PED gives insight into the mathematics of functional connectivity and its limitation. When applied to resting-state fMRI data, we find robust evidence of higher-order synergies that are largely invisible to standard functional connectivity analyses. Our approach can also be localized in time, allowing a frame-by-frame analysis of how the distributions of redundancies and synergies change over the course of a recording. We find that different ensembles of regions can transiently change from being redundancy-dominated to synergy-dominated and that the temporal pattern is structured in time. These results provide strong evidence that there exists a large space of unexplored structures in human brain data that have been largely missed by a focus on bivariate network connectivity models. This synergistic structure is dynamic in time and likely will illuminate interesting links between brain and behavior. Beyond brain-specific application, the PED provides a very general approach for understanding higher-order structures in a variety of complex systems.
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Affiliation(s)
- Thomas F. Varley
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN47405
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN47405
| | - Maria Pope
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN47405
- Program in Neuroscience, Indiana University, Bloomington, IN47405
| | - Maria Grazia Puxeddu
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN47405
| | - Joshua Faskowitz
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN47405
- Program in Neuroscience, Indiana University, Bloomington, IN47405
| | - Olaf Sporns
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN47405
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN47405
- Program in Neuroscience, Indiana University, Bloomington, IN47405
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26
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Tang D, Zylberberg J, Jia X, Choi H. Stimulus-dependent functional network topology in mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.03.547364. [PMID: 37461471 PMCID: PMC10349950 DOI: 10.1101/2023.07.03.547364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
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Affiliation(s)
- Disheng Tang
- School of Life Sciences, Tsinghua University
- Quantitative Biosciences Program, Georgia Institute of Technology
- IDG/McGovern Institute for Brain Research, Tsinghua University
| | - Joel Zylberberg
- Department of Physics and Astronomy, and Centre for Vision Research, York University
- Learning in Machines and Brains Program, CIFAR
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
| | - Xiaoxuan Jia
- School of Life Sciences, Tsinghua University
- IDG/McGovern Institute for Brain Research, Tsinghua University
- Tsinghua–Peking Center for Life Sciences
- Allen Institute for Brain Science
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
| | - Hannah Choi
- Quantitative Biosciences Program, Georgia Institute of Technology
- School of Mathematics, Georgia Institute of Technology
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
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27
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Kopsick JD, Tecuatl C, Moradi K, Attili SM, Kashyap HJ, Xing J, Chen K, Krichmar JL, Ascoli GA. Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus. Cognit Comput 2023; 15:1190-1210. [PMID: 37663748 PMCID: PMC10473858 DOI: 10.1007/s12559-021-09954-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 10/10/2021] [Indexed: 12/19/2022]
Abstract
Hippocampal area CA3 performs the critical auto-associative function underlying pattern completion in episodic memory. Without external inputs, the electrical activity of this neural circuit reflects the spontaneous spiking interplay among glutamatergic pyramidal neurons and GABAergic interneurons. However, the network mechanisms underlying these resting-state firing patterns are poorly understood. Leveraging the Hippocampome.org knowledge base, we developed a data-driven, large-scale spiking neural network (SNN) model of mouse CA3 with 8 neuron types, 90,000 neurons, 51 neuron-type specific connections, and 250,000,000 synapses. We instantiated the SNN in the CARLsim4 multi-GPU simulation environment using the Izhikevich and Tsodyks-Markram formalisms for neuronal and synaptic dynamics, respectively. We analyzed the resultant population activity upon transient activation. The SNN settled into stable oscillations with a biologically plausible grand-average firing frequency, which was robust relative to a wide range of transient activation. The diverse firing patterns of individual neuron types were consistent with existing knowledge of cell type-specific activity in vivo. Altered network structures that lacked neuron- or connection-type specificity were neither stable nor robust, highlighting the importance of neuron type circuitry. Additionally, external inputs reflecting dentate mossy fibers shifted the observed rhythms to the gamma band. We freely released the CARLsim4-Hippocampome framework on GitHub to test hippocampal hypotheses. Our SNN may be useful to investigate the circuit mechanisms underlying the computational functions of CA3. Moreover, our approach can be scaled to the whole hippocampal formation, which may contribute to elucidating how the unique neuronal architecture of this system subserves its crucial cognitive roles.
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Affiliation(s)
- Jeffrey D. Kopsick
- Interdepartmental Program in Neuroscience, George Mason University, Fairfax, VA, USA
| | - Carolina Tecuatl
- Bioengineering Department, Volgenau School of Engineering, George Mason University, Fairfax, VA, USA
| | - Keivan Moradi
- Interdepartmental Program in Neuroscience, George Mason University, Fairfax, VA, USA
| | - Sarojini M. Attili
- Interdepartmental Program in Neuroscience, George Mason University, Fairfax, VA, USA
| | - Hirak J. Kashyap
- Department of Computer Science, University of California, Irvine, Irvine, CA, USA
| | - Jinwei Xing
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA
| | - Kexin Chen
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA
| | - Jeffrey L. Krichmar
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA
- Department of Computer Science, University of California, Irvine, Irvine, CA, USA
| | - Giorgio A. Ascoli
- Interdepartmental Program in Neuroscience, George Mason University, Fairfax, VA, USA
- Bioengineering Department, Volgenau School of Engineering, George Mason University, Fairfax, VA, USA
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28
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Sawicki J, Berner R, Loos SAM, Anvari M, Bader R, Barfuss W, Botta N, Brede N, Franović I, Gauthier DJ, Goldt S, Hajizadeh A, Hövel P, Karin O, Lorenz-Spreen P, Miehl C, Mölter J, Olmi S, Schöll E, Seif A, Tass PA, Volpe G, Yanchuk S, Kurths J. Perspectives on adaptive dynamical systems. CHAOS (WOODBURY, N.Y.) 2023; 33:071501. [PMID: 37486668 DOI: 10.1063/5.0147231] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/24/2023] [Indexed: 07/25/2023]
Abstract
Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.
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Affiliation(s)
- Jakub Sawicki
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Rico Berner
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
| | - Sarah A M Loos
- DAMTP, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Mehrnaz Anvari
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53757 Sankt-Augustin, Germany
| | - Rolf Bader
- Institute of Systematic Musicology, University of Hamburg, Hamburg, Germany
| | - Wolfram Barfuss
- Transdisciplinary Research Area: Sustainable Futures, University of Bonn, 53113 Bonn, Germany
- Center for Development Research (ZEF), University of Bonn, 53113 Bonn, Germany
| | - Nicola Botta
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Computer Science and Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Nuria Brede
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Computer Science, University of Potsdam, An der Bahn 2, 14476 Potsdam, Germany
| | - Igor Franović
- Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia
| | - Daniel J Gauthier
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
| | - Sebastian Goldt
- Department of Physics, International School of Advanced Studies (SISSA), Trieste, Italy
| | - Aida Hajizadeh
- Research Group Comparative Neuroscience, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Philipp Hövel
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
| | - Omer Karin
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Philipp Lorenz-Spreen
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Christoph Miehl
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Jan Mölter
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748 Garching bei München, Germany
| | - Simona Olmi
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Eckehard Schöll
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Alireza Seif
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, USA
| | - Peter A Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California 94304, USA
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
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29
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Kaiser M. Connectomes: from a sparsity of networks to large-scale databases. Front Neuroinform 2023; 17:1170337. [PMID: 37377946 PMCID: PMC10291062 DOI: 10.3389/fninf.2023.1170337] [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: 02/20/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
The analysis of whole brain networks started in the 1980s when only a handful of connectomes were available. In these early days, information about the human connectome was absent and one could only dream about having information about connectivity in a single human subject. Thanks to non-invasive methods such as diffusion imaging, we now know about connectivity in many species and, for some species, in many individuals. To illustrate the rapid change in availability of connectome data, the UK Biobank is on track to record structural and functional connectivity in 100,000 human subjects. Moreover, connectome data from a range of species is now available: from Caenorhabditis elegans and the fruit fly to pigeons, rodents, cats, non-human primates, and humans. This review will give a brief overview of what structural connectivity data is now available, how connectomes are organized, and how their organization shows common features across species. Finally, I will outline some of the current challenges and potential future work in making use of connectome information.
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Affiliation(s)
- Marcus Kaiser
- NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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30
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Varley TF, Pope M, Faskowitz J, Sporns O. Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex. Commun Biol 2023; 6:451. [PMID: 37095282 PMCID: PMC10125999 DOI: 10.1038/s42003-023-04843-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 04/14/2023] [Indexed: 04/26/2023] Open
Abstract
One of the most well-established tools for modeling the brain is the functional connectivity network, which is constructed from pairs of interacting brain regions. While powerful, the network model is limited by the restriction that only pairwise dependencies are considered and potentially higher-order structures are missed. Here, we explore how multivariate information theory reveals higher-order dependencies in the human brain. We begin with a mathematical analysis of the O-information, showing analytically and numerically how it is related to previously established information theoretic measures of complexity. We then apply the O-information to brain data, showing that synergistic subsystems are widespread in the human brain. Highly synergistic subsystems typically sit between canonical functional networks, and may serve an integrative role. We then use simulated annealing to find maximally synergistic subsystems, finding that such systems typically comprise ≈10 brain regions, recruited from multiple canonical brain systems. Though ubiquitous, highly synergistic subsystems are invisible when considering pairwise functional connectivity, suggesting that higher-order dependencies form a kind of shadow structure that has been unrecognized by established network-based analyses. We assert that higher-order interactions in the brain represent an under-explored space that, accessible with tools of multivariate information theory, may offer novel scientific insights.
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Affiliation(s)
- Thomas F Varley
- School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN, 47405, USA.
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.
| | - Maria Pope
- School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
| | - Joshua Faskowitz
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
| | - Olaf Sporns
- School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN, 47405, USA
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
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31
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Liu H, Wu JJ, Li R, Wang PZ, Huang JH, Xu Y, Zhao JL, Wu PP, Li SJ, Wu ZX. Disexcitation in the ASH/RIM/ADL negative feedback circuit fine-tunes hyperosmotic sensation and avoidance in Caenorhabditis elegans. Front Mol Neurosci 2023; 16:1101628. [PMID: 37008778 PMCID: PMC10050701 DOI: 10.3389/fnmol.2023.1101628] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
Sensations, especially nociception, are tightly controlled and regulated by the central and peripheral nervous systems. Osmotic sensation and related physiological and behavioral reactions are essential for animal well-being and survival. In this study, we find that interaction between secondary nociceptive ADL and primary nociceptive ASH neurons upregulates Caenorhabditis elegans avoidance of the mild and medium hyperosmolality of 0.41 and 0.88 Osm but does not affect avoidance of high osmolality of 1.37 and 2.29 Osm. The interaction between ASH and ADL is actualized through a negative feedback circuit consisting of ASH, ADL, and RIM interneurons. In this circuit, hyperosmolality-sensitive ADL augments the ASH hyperosmotic response and animal hyperosmotic avoidance; RIM inhibits ADL and is excited by ASH; thus, ASH exciting RIM reduces ADL augmenting ASH. The neuronal signal integration modality in the circuit is disexcitation. In addition, ASH promotes hyperosmotic avoidance through ASH/RIC/AIY feedforward circuit. Finally, we find that in addition to ASH and ADL, multiple sensory neurons are involved in hyperosmotic sensation and avoidance behavior.
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32
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Zou B, Wang Y, Kwoh CK, Cen Y. Directed collaboration patterns in funded teams: A perspective of knowledge flow. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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33
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Madadi Asl M, Valizadeh A, Tass PA. Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity. PLoS Comput Biol 2023; 19:e1010853. [PMID: 36724144 PMCID: PMC9891531 DOI: 10.1371/journal.pcbi.1010853] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 01/05/2023] [Indexed: 02/02/2023] Open
Abstract
The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks.
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Affiliation(s)
- Mojtaba Madadi Asl
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
| | - Alireza Valizadeh
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Peter A. Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States of America
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34
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Wu Z, Chen H, Zhang J, Pei Y, Huang Z. Temporal motif-based attentional graph convolutional network for dynamic link prediction. INTELL DATA ANAL 2023. [DOI: 10.3233/ida-216169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Dynamic link prediction is an important component of the dynamic network analysis with many real-world applications. Currently, most advancements focus on analyzing link-defined neighborhoods with graph convolutional networks (GCN), while ignoring the influence of higher-order structural and temporal interacting features on link formation. Therefore, based on recent progress in modeling temporal graphs, we propose a novel temporal motif-based attentional graph convolutional network model (TMAGCN) for dynamic link prediction. As dynamic graphs usually contain periodical patterns, we first propose a temporal motif matrix construction method to capture higher-order structural and temporal features, then introduce a spatial convolution operation following a temporal motif-attention mechanism to encode these features into node embeddings. Furthermore, we design two methods to combine multiple temporal motif-based attentions, a dynamic attention-based method and a reinforcement learning-based method, to allow each individual node to make the most of the relevant motif-based neighborhood to propagate and aggregate information in the graph convolutional layers. Experimental results on various real-world datasets demonstrate that the proposed model is superior to state-of-the-art baselines on the dynamic link prediction task. It also reveals that temporal motif can manifest the essential dynamic mechanism of the network.
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Affiliation(s)
- Zheng Wu
- Information Engineering University, Zhengzhou, Henan, China
| | - Hongchang Chen
- Information Engineering University, Zhengzhou, Henan, China
| | - Jianpeng Zhang
- Information Engineering University, Zhengzhou, Henan, China
| | - Yulong Pei
- Eindhoven University of Technology, Eindhoven, The Netherlands
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35
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Deshpande SS, Smith GA, van Drongelen W. Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity. Sci Rep 2023; 13:238. [PMID: 36604489 PMCID: PMC9816122 DOI: 10.1038/s41598-022-27188-6] [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: 10/24/2022] [Accepted: 12/28/2022] [Indexed: 01/07/2023] Open
Abstract
Neuroscientific analyses balance between capturing the brain's complexity and expressing that complexity in meaningful and understandable ways. Here we present a novel approach that fully characterizes neural network activity and does so by uniquely transforming raw signals into easily interpretable and biologically relevant metrics of network behavior. We first prove that third-order (triple) correlation describes network activity in its entirety using the triple correlation uniqueness theorem. Triple correlation quantifies the relationships among three events separated by spatial and temporal lags, which are triplet motifs. Classifying these motifs by their event sequencing leads to fourteen qualitatively distinct motif classes that embody well-studied network behaviors including synchrony, feedback, feedforward, convergence, and divergence. Within these motif classes, the summed triple correlations provide novel metrics of network behavior, as well as being inclusive of commonly used analyses. We demonstrate the power of this approach on a range of networks with increasingly obscured signals, from ideal noiseless simulations to noisy experimental data. This approach can be easily applied to any recording modality, so existing neural datasets are ripe for reanalysis. Triple correlation is an accessible signal processing tool with a solid theoretical foundation capable of revealing previously elusive information within recordings of neural networks.
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Affiliation(s)
- Sarita S Deshpande
- Medical Scientist Training Program, The University of Chicago, Chicago, IL, 60637, USA
- Committee on Neurobiology, The University of Chicago, Chicago, IL, 60637, USA
- Section of Pediatric Neurology, The University of Chicago, Chicago, IL, 60637, USA
| | - Graham A Smith
- Section of Pediatric Neurology, The University of Chicago, Chicago, IL, 60637, USA
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, 60637, USA
| | - Wim van Drongelen
- Committee on Neurobiology, The University of Chicago, Chicago, IL, 60637, USA.
- Section of Pediatric Neurology, The University of Chicago, Chicago, IL, 60637, USA.
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, 60637, USA.
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36
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Manzo M, Giordano M, Maddalena L, Guarracino MR, Granata I. Novel Data Science Methodologies for Essential Genes Identification Based on Network Analysis. STUDIES IN COMPUTATIONAL INTELLIGENCE 2023:117-145. [DOI: 10.1007/978-3-031-24453-7_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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37
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Meram ED, Baajour S, Chowdury A, Kopchick J, Thomas P, Rajan U, Khatib D, Zajac-Benitez C, Haddad L, Amirsadri A, Stanley JA, Diwadkar VA. The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia. Netw Neurosci 2023; 7:184-212. [PMID: 37333998 PMCID: PMC10270714 DOI: 10.1162/netn_a_00278] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/05/2022] [Indexed: 07/21/2023] Open
Abstract
There is a paucity of graph theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node's integrative value was used to summarize network topology in each condition. Patients showed (a) differences in BC across multiple nodes and conditions; (b) decreased BC in more integrative nodes, but increased BC in less integrative nodes; (c) discordant node ranks in each of the conditions; and (d) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection.
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Affiliation(s)
- Emmanuel D. Meram
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Shahira Baajour
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Asadur Chowdury
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - John Kopchick
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Patricia Thomas
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Usha Rajan
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Dalal Khatib
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Caroline Zajac-Benitez
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Luay Haddad
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Alireza Amirsadri
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Jeffrey A. Stanley
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
| | - Vaibhav A. Diwadkar
- Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA
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38
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Menardi A, Momi D, Vallesi A, Barabási AL, Towlson EK, Santarnecchi E. Maximizing brain networks engagement via individualized connectome-wide target search. Brain Stimul 2022; 15:1418-1431. [PMID: 36252908 DOI: 10.1016/j.brs.2022.09.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/29/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND In recent years, the possibility to noninvasively interact with the human brain has led to unprecedented diagnostic and therapeutic opportunities. However, the vast majority of approved interventions and approaches still rely on anatomical landmarks and rarely on the individual structure of networks in the brain, drastically reducing the potential efficacy of neuromodulation. OBJECTIVE Here we implemented a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. By means of computational simulations, we aimed to identify the optimal stimulation target(s)- at the individual brain level- capable of reaching maximal engagement of the stimulated networks' nodes. RESULTS At the model level, in silico predictions suggest that stimulation of NCT-derived cerebral sites might induce significantly higher network engagement, compared to traditionally employed neuromodulation sites, demonstrating NCT to be a useful tool in guiding brain stimulation. Indeed, NCT allows us to computationally model different stimulation scenarios tailored on the individual structural connectivity profiles and initial brain states. CONCLUSIONS The use of NCT to computationally predict TMS pulse propagation suggests that individualized targeting is crucial for more successful network engagement. Future studies will be needed to verify such prediction in real stimulation scenarios.
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Affiliation(s)
- Arianna Menardi
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Davide Momi
- Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio", Chieti, Italy; Krembil Centre for Neuroinformatics, Centre for Addiction & Mental Health, Toronto, Canada
| | - Antonino Vallesi
- Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Albert-László Barabási
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Emma K Towlson
- Department of Computer Science, University of Calgary, Calgary, AB, Canada; Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Emiliano Santarnecchi
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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39
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Nakuci J, McGuire M, Schweser F, Poulsen D, Muldoon SF. Differential Patterns of Change in Brain Connectivity Resulting from Severe Traumatic Brain Injury. Brain Connect 2022; 12:799-811. [PMID: 35302399 PMCID: PMC9805864 DOI: 10.1089/brain.2021.0168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Background: Traumatic brain injury (TBI) damages white matter tracts, disrupting brain network structure and communication. There exists a wide heterogeneity in the pattern of structural damage associated with injury, as well as a large heterogeneity in behavioral outcomes. However, little is known about the relationship between changes in network connectivity and clinical outcomes. Materials and Methods: We utilize the rat lateral fluid-percussion injury model of severe TBI to study differences in brain connectivity in 8 animals that received the insult and 11 animals that received only a craniectomy. Diffusion tensor imaging is performed 5 weeks after the injury and network theory is used to investigate changes in white matter connectivity. Results: We find that (1) global network measures are not able to distinguish between healthy and injured animals; (2) injury induced alterations predominantly exist in a subset of connections (subnetworks) distributed throughout the brain; and (3) injured animals can be divided into subgroups based on changes in network motifs-measures of local structural connectivity. In addition, alterations in predicted functional connectivity indicate that the subgroups have different propensities to synchronize brain activity, which could relate to the heterogeneity of clinical outcomes. Discussion: These results suggest that network measures can be used to quantify progressive changes in brain connectivity due to injury and differentiate among subpopulations with similar injuries, but different pathological trajectories.
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Affiliation(s)
- Johan Nakuci
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Matthew McGuire
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
- Department of Neurosurgery, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, SUNY, Buffalo, New York, USA
| | - David Poulsen
- Department of Neurosurgery, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Sarah F. Muldoon
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
- Department of Mathematics and CDSE Program, University at Buffalo, SUNY, Buffalo, New York, USA
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40
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Gansel KS. Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding. Front Integr Neurosci 2022; 16:900715. [PMID: 36262373 PMCID: PMC9574343 DOI: 10.3389/fnint.2022.900715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 09/13/2022] [Indexed: 11/13/2022] Open
Abstract
Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.
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41
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Bertacchini F, Scuro C, Pantano P, Bilotta E. Modelling brain dynamics by Boolean networks. Sci Rep 2022; 12:16543. [PMID: 36192582 PMCID: PMC9529940 DOI: 10.1038/s41598-022-20979-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/21/2022] [Indexed: 11/16/2022] Open
Abstract
Understanding the relationship between brain architecture and brain function is a central issue in neuroscience. We modeled realistic spatio-temporal patterns of brain activity on a human connectome with a Boolean networks model with the aim of computationally replicating certain cognitive functions as they emerge from the standardization of many fMRI studies, identified as patterns of human brain activity. Results from the analysis of simulation data, carried out for different parameters and initial conditions identified many possible paths in the space of parameters of these network models, with normal (ordered asymptotically constant patterns), chaotic (oscillating or disordered) but also highly organized configurations, with countless spatial–temporal patterns. We interpreted these results as routes to chaos, permanence of the systems in regimes of complexity, and ordered stationary behavior, associating these dynamics to cognitive processes. The most important result of this work is the study of emergent neural circuits, i.e., configurations of areas that synchronize over time, both locally and globally, determining the emergence of computational analogues of cognitive processes, which may or may not be similar to the functioning of biological brain. Furthermore, results put in evidence the creation of how the brain creates structures of remote communication. These structures have hierarchical organization, where each level allows for the emergence of brain organizations which behave at the next superior level. Taken together these results allow the interplay of dynamical and topological roots of the multifaceted brain dynamics to be understood.
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Affiliation(s)
- Francesca Bertacchini
- Department of Mechanics, Energy and Management Engineering, University of Calabria, Rende, Italy.,Laboratory of Cognitive Science and Mathematical Modelling, Department of Physics, University of Calabria, Rende, Italy
| | - Carmelo Scuro
- Laboratory of Cognitive Science and Mathematical Modelling, Department of Physics, University of Calabria, Rende, Italy.,Department of Physics, University of Calabria, Rende, Italy
| | - Pietro Pantano
- Laboratory of Cognitive Science and Mathematical Modelling, Department of Physics, University of Calabria, Rende, Italy.,Department of Physics, University of Calabria, Rende, Italy
| | - Eleonora Bilotta
- Laboratory of Cognitive Science and Mathematical Modelling, Department of Physics, University of Calabria, Rende, Italy. .,Department of Physics, University of Calabria, Rende, Italy.
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42
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Wilkerson G, Moschoyiannis S, Jensen HJ. Spontaneous emergence of computation in network cascades. Sci Rep 2022; 12:14925. [PMID: 36056137 PMCID: PMC9440044 DOI: 10.1038/s41598-022-19218-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/25/2022] [Indexed: 11/21/2022] Open
Abstract
Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.
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Affiliation(s)
- Galen Wilkerson
- School of Computer Science and Electronic Engineering, University of Surrey, Guildford, GU2 7XH, UK.
- Department of Mathematics, Centre for Complexity Science, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
| | - Sotiris Moschoyiannis
- School of Computer Science and Electronic Engineering, University of Surrey, Guildford, GU2 7XH, UK
| | - Henrik Jeldtoft Jensen
- Department of Mathematics, Centre for Complexity Science, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
- Institute of Innovative Research, Tokyo Institute of Technology, 4259, Nagatsuta-cho, Yokohama, 226-8502, Japan
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43
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Rifkin JA, Wu T, Rayfield AC, Anderson ED, Panzer MB, Meaney DF. Brain architecture-based vulnerability to traumatic injury. Front Bioeng Biotechnol 2022; 10:936082. [PMID: 36091446 PMCID: PMC9448929 DOI: 10.3389/fbioe.2022.936082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/01/2022] [Indexed: 02/03/2023] Open
Abstract
The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups’ dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.
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Affiliation(s)
- Jared A. Rifkin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
| | - Taotao Wu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Adam C. Rayfield
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Erin D. Anderson
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Matthew B. Panzer
- Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, United States
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
| | - David F. Meaney
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: David F. Meaney,
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44
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Chacko TP, Toole JT, Richman S, Spink GL, Reinhard MJ, Brewster RC, Costanzo ME, Broderick G. Mapping the network biology of metabolic response to stress in posttraumatic stress disorder and obesity. Front Psychol 2022; 13:941019. [PMID: 35959009 PMCID: PMC9362840 DOI: 10.3389/fpsyg.2022.941019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
The co-occurrence of stress-induced posttraumatic stress disorder (PTSD) and obesity is common, particularly among military personnel but the link between these conditions is unclear. Individuals with comorbid PTSD and obesity manifest other physical and psychological problems, which significantly diminish their quality of life. Current understanding of the pathways connecting stress to PTSD and obesity is focused largely on behavioral mediators alone with little consideration of the biological regulatory mechanisms that underlie their co-occurrence. In this work, we leverage prior knowledge to systematically highlight such bio-behavioral mechanisms and inform on the design of confirmatory pilot studies. We use natural language processing (NLP) to extract documented regulatory interactions involved in the metabolic response to stress and its impact on obesity and PTSD from over 8 million peer-reviewed papers. The resulting network describes the propagation of stress to PTSD and obesity through 34 metabolic mediators using 302 documented regulatory interactions supported by over 10,000 citations. Stress jointly affected both conditions through 21 distinct pathways involving only two intermediate metabolic mediators out of a total of 76 available paths through this network. Moreover, oxytocin (OXT), Neuropeptide-Y (NPY), and cortisol supported an almost direct propagation of stress to PTSD and obesity with different net effects. Although stress upregulated both NPY and cortisol, the downstream effects of both markers are reported to relieve PTSD severity but exacerbate obesity. The stress-mediated release of oxytocin, however, was found to concurrently downregulate the severity of both conditions. These findings highlight how a network-informed approach that leverages prior knowledge might be used effectively in identifying key mediators like OXT though experimental verification of signal transmission dynamics through each path will be needed to determine the actual likelihood and extent of each marker’s participation.
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Affiliation(s)
- Thomas P. Chacko
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
- Institute of Health Sciences and Technology, Rochester Institute of Technology, Rochester, NY, United States
| | - J. Tory Toole
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
- Institute of Health Sciences and Technology, Rochester Institute of Technology, Rochester, NY, United States
| | - Spencer Richman
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
| | - Garry L. Spink
- Rochester Regional Behavioral Health, Rochester, NY, United States
| | - Matthew J. Reinhard
- War Related Illness and Injury Study Center, United States Department of Veterans Affairs, Washington, DC, United States
| | - Ryan C. Brewster
- War Related Illness and Injury Study Center, United States Department of Veterans Affairs, Washington, DC, United States
| | - Michelle E. Costanzo
- War Related Illness and Injury Study Center, United States Department of Veterans Affairs, Washington, DC, United States
| | - Gordon Broderick
- Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, United States
- *Correspondence: Gordon Broderick,
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45
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Kitazono J, Aoki Y, Oizumi M. Bidirectionally connected cores in a mouse connectome: towards extracting the brain subnetworks essential for consciousness. Cereb Cortex 2022; 33:1383-1402. [PMID: 35860874 PMCID: PMC9930638 DOI: 10.1093/cercor/bhac143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/28/2022] [Accepted: 03/02/2022] [Indexed: 11/15/2022] Open
Abstract
Where in the brain consciousness resides remains unclear. It has been suggested that the subnetworks supporting consciousness should be bidirectionally (recurrently) connected because both feed-forward and feedback processing are necessary for conscious experience. Accordingly, evaluating which subnetworks are bidirectionally connected and the strength of these connections would likely aid the identification of regions essential to consciousness. Here, we propose a method for hierarchically decomposing a network into cores with different strengths of bidirectional connection, as a means of revealing the structure of the complex brain network. We applied the method to a whole-brain mouse connectome. We found that cores with strong bidirectional connections consisted of regions presumably essential to consciousness (e.g. the isocortical and thalamic regions, and claustrum) and did not include regions presumably irrelevant to consciousness (e.g. cerebellum). Contrarily, we could not find such correspondence between cores and consciousness when we applied other simple methods that ignored bidirectionality. These findings suggest that our method provides a novel insight into the relation between bidirectional brain network structures and consciousness.
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Affiliation(s)
- Jun Kitazono
- Corresponding authors: Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan. ,
| | - Yuma Aoki
- Graduate School of Information Science and Technology, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Masafumi Oizumi
- Corresponding authors: Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Tokyo, Japan. ,
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46
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A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans. Curr Biol 2022; 32:3443-3459.e8. [PMID: 35809568 DOI: 10.1016/j.cub.2022.06.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/17/2022] [Accepted: 06/13/2022] [Indexed: 11/20/2022]
Abstract
The wiring architecture of neuronal networks is assumed to be a strong determinant of their dynamical computations. An ongoing effort in neuroscience is therefore to generate comprehensive synapse-resolution connectomes alongside brain-wide activity maps. However, the structure-function relationship, i.e., how the anatomical connectome and neuronal dynamics relate to each other on a global scale, remains unsolved. Systematically, comparing graph features in the C. elegans connectome with correlations in nervous system-wide neuronal dynamics, we found that few local connectivity motifs and mostly other non-local features such as triplet motifs and input similarities can predict functional relationships between neurons. Surprisingly, quantities such as connection strength and amount of common inputs do not improve these predictions, suggesting that the network's topology is sufficient. We demonstrate that hub neurons in the connectome are key to these relevant graph features. Consistently, inhibition of multiple hub neurons specifically disrupts brain-wide correlations. Thus, we propose that a set of hub neurons and non-local connectivity features provide an anatomical substrate for global brain dynamics.
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47
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Beck C, Kunze A, Zosso D. Archetypal Analysis for neuronal clique detection in low-rate calcium fluorescence imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:162-166. [PMID: 36086305 DOI: 10.1109/embc48229.2022.9871404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Archetypal analysis (AA) is a versatile data analysis method to cluster distinct features within a data set. Here, we demonstrate a framework showing the power of AA to spatio-temporally resolve events in calcium imaging, an imaging modality commonly used in neurobiology and neuroscience to capture neuronal communication patterns. After validation of our AA-based approach on synthetic data sets, we were able to characterize neuronal communication patterns in recorded calcium waves. Clinical relevance- Transient calcium events play an essential role in brain cell communication, growth, and network formation, as well as in neurodegeneration. To reliably interpret calcium events from personalized medicine data, where patterns may differ from patient to patient, appropriate image processing and signal analysis methods need to be developed for optimal network characterization.
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48
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Hu Y, Sompolinsky H. The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics. PLoS Comput Biol 2022; 18:e1010327. [PMID: 35862445 PMCID: PMC9345493 DOI: 10.1371/journal.pcbi.1010327] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 08/02/2022] [Accepted: 06/24/2022] [Indexed: 11/18/2022] Open
Abstract
A key question in theoretical neuroscience is the relation between the connectivity structure and the collective dynamics of a network of neurons. Here we study the connectivity-dynamics relation as reflected in the distribution of eigenvalues of the covariance matrix of the dynamic fluctuations of the neuronal activities, which is closely related to the network dynamics’ Principal Component Analysis (PCA) and the associated effective dimensionality. We consider the spontaneous fluctuations around a steady state in a randomly connected recurrent network of stochastic neurons. An exact analytical expression for the covariance eigenvalue distribution in the large-network limit can be obtained using results from random matrices. The distribution has a finitely supported smooth bulk spectrum and exhibits an approximate power-law tail for coupling matrices near the critical edge. We generalize the results to include second-order connectivity motifs and discuss extensions to excitatory-inhibitory networks. The theoretical results are compared with those from finite-size networks and the effects of temporal and spatial sampling are studied. Preliminary application to whole-brain imaging data is presented. Using simple connectivity models, our work provides theoretical predictions for the covariance spectrum, a fundamental property of recurrent neuronal dynamics, that can be compared with experimental data. Here we study the distribution of eigenvalues, or spectrum, of the neuron-to-neuron covariance matrix in recurrently connected neuronal networks. The covariance spectrum is an important global feature of neuron population dynamics that requires simultaneous recordings of neurons. The spectrum is essential to the widely used Principal Component Analysis (PCA) and generalizes the dimensionality measure of population dynamics. We use a simple model to emulate the complex connections between neurons, where all pairs of neurons interact linearly at a strength specified randomly and independently. We derive a closed-form expression of the covariance spectrum, revealing an interesting long tail of large eigenvalues following a power law as the connection strength increases. To incorporate connectivity features important to biological neural circuits, we generalize the result to networks with an additional low-rank connectivity component that could come from learning and networks consisting of sparsely connected excitatory and inhibitory neurons. To facilitate comparing the theoretical results to experimental data, we derive the precise modifications needed to account for the effect of limited time samples and having unobserved neurons. Preliminary applications to large-scale calcium imaging data suggest our model can well capture the high dimensional population activity of neurons.
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Affiliation(s)
- Yu Hu
- Department of Mathematics and Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- * E-mail: (YH); (HS)
| | - Haim Sompolinsky
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America
- * E-mail: (YH); (HS)
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49
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Antonello PC, Varley TF, Beggs J, Porcionatto M, Sporns O, Faber J. Self-organization of in vitro neuronal assemblies drives to complex network topology. eLife 2022; 11:74921. [PMID: 35708741 PMCID: PMC9203058 DOI: 10.7554/elife.74921] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 06/01/2022] [Indexed: 12/17/2022] Open
Abstract
Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of modules. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.
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Affiliation(s)
- Priscila C Antonello
- Department of Biochemistry - Escola Paulista de Medicina - Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Thomas F Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States.,Department of Informatics, Computing, and Engineering, Indiana University, Bloomington, United States
| | - John Beggs
- Department of Physics, Indiana University, Bloomington, United States
| | - Marimélia Porcionatto
- Department of Biochemistry - Escola Paulista de Medicina - Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, United States
| | - Jean Faber
- Department of Neurology and Neurosurgery - Escola Paulista de Medicina - Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
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50
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Abstract
Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.
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