101
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Whi W, Ha S, Kang H, Lee DS. Hyperbolic disc embedding of functional human brain connectomes using resting-state fMRI. Netw Neurosci 2022; 6:745-764. [PMID: 36607197 PMCID: PMC9810369 DOI: 10.1162/netn_a_00243] [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: 07/23/2021] [Accepted: 03/03/2022] [Indexed: 01/10/2023] Open
Abstract
The brain presents a real complex network of modular, small-world, and hierarchical nature, which are features of non-Euclidean geometry. Using resting-state functional magnetic resonance imaging, we constructed a scale-free binary graph for each subject, using internodal time series correlation of regions of interest as a proximity measure. The resulting network could be embedded onto manifolds of various curvatures and dimensions. While maintaining the fidelity of embedding (low distortion, high mean average precision), functional brain networks were found to be best represented in the hyperbolic disc. Using the 𝕊1/ℍ2 model, we reduced the dimension of the network into two-dimensional hyperbolic space and were able to efficiently visualize the internodal connections of the brain, preserving proximity as distances and angles on the hyperbolic discs. Each individual disc revealed relevance with its anatomic counterpart and absence of center-spaced node. Using the hyperbolic distance on the 𝕊1/ℍ2 model, we could detect the anomaly of network in autism spectrum disorder subjects. This procedure of embedding grants us a reliable new framework for studying functional brain networks and the possibility of detecting anomalies of the network in the hyperbolic disc on an individual scale.
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Affiliation(s)
- Wonseok Whi
- Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, South Korea,Department of Nuclear Medicine, Seoul National University and Seoul National University Hospital, Seoul, South Korea
| | - Seunggyun Ha
- Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary's Hospital, Catholic University of Korea, Seoul, South Korea
| | - Hyejin Kang
- Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea,* Corresponding Authors: ;
| | - Dong Soo Lee
- Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, South Korea,Department of Nuclear Medicine, Seoul National University and Seoul National University Hospital, Seoul, South Korea,Medical Research Center, Seoul National University, Seoul, South Korea,* Corresponding Authors: ;
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102
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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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103
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Kristanto D, Liu X, Sommer W, Hildebrandt A, Zhou C. What do neuroanatomical networks reveal about the ontology of human cognitive abilities? iScience 2022; 25:104706. [PMID: 35865139 PMCID: PMC9293763 DOI: 10.1016/j.isci.2022.104706] [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: 12/20/2021] [Revised: 05/15/2022] [Accepted: 06/28/2022] [Indexed: 11/18/2022] Open
Abstract
Over the last decades, cognitive psychology has come to a fair consensus about the human intelligence ontological structure. However, it remains an open question whether anatomical properties of the brain support the same ontology. The present study explored the ontological structure derived from neuroanatomical networks associated with performance on 15 cognitive tasks indicating various abilities. Results suggest that the brain-derived (neurometric) ontology partly agrees with the cognitive performance-derived (psychometric) ontology complemented with interpretable differences. Moreover, the cortical areas associated with different inferred abilities are segregated, with little or no overlap. Nevertheless, these spatially segregated cortical areas are integrated via denser white matter structural connections as compared with the general brain connectome. The integration of ability-related cortical networks constitutes a neural counterpart to the psychometric construct of general intelligence, while the consistency and difference between psychometric and neurometric ontologies represent crucial pieces of knowledge for theory building, clinical diagnostics, and treatment. Psychometric and neurometric cognitive ontologies are partly equivalent Ability-related brain areas are ontologically segregated with little to no overlap However, ability-related brain areas are densely interconnected by fiber tracts
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104
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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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105
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Development of improved coyote optimization with deep neural network for intelligent skill knowledge transfer for human to robot interaction. INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2022. [DOI: 10.1007/s41315-022-00236-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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106
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Brede M, Romero-Moreno G. Sensing Enhancement on Social Networks: The Role of Network Topology. ENTROPY 2022; 24:e24050738. [PMID: 35626621 PMCID: PMC9140841 DOI: 10.3390/e24050738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/16/2022] [Accepted: 05/18/2022] [Indexed: 02/05/2023]
Abstract
Sensing and processing information from dynamically changing environments is essential for the survival of animal collectives and the functioning of human society. In this context, previous work has shown that communication between networked agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that, whilst bestowing advantages on a small group of agents, degree heterogeneity tends to impede overall sensing enhancement. In contrast, clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that ring graphs exhibit superior enhancement for large connectivity and that random graphs outperform for small connectivity. Further exploring the role of clustering and path lengths in small-world models, we find that sensing enhancement tends to be boosted in the small-world regime.
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Affiliation(s)
- Markus Brede
- School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
- Correspondence:
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107
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Seguin C, Mansour L S, Sporns O, Zalesky A, Calamante F. Network communication models narrow the gap between the modular organization of structural and functional brain networks. Neuroimage 2022; 257:119323. [PMID: 35605765 DOI: 10.1016/j.neuroimage.2022.119323] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/25/2022] [Accepted: 05/17/2022] [Indexed: 11/28/2022] Open
Abstract
Structural and functional brain networks are modular. Canonical functional systems, such as the default mode network, are well-known modules of the human brain and have been implicated in a large number of cognitive, behavioral and clinical processes. However, modules delineated in structural brain networks inferred from tractography generally do not recapitulate canonical functional systems. Neuroimaging evidence suggests that functional connectivity between regions in the same systems is not always underpinned by anatomical connections. As such, direct structural connectivity alone would be insufficient to characterize the functional modular organization of the brain. Here, we demonstrate that augmenting structural brain networks with models of indirect (polysynaptic) communication unveils a modular network architecture that more closely resembles the brain's established functional systems. We find that diffusion models of polysynaptic connectivity, particularly communicability, narrow the gap between the modular organization of structural and functional brain networks by 20-60%, whereas routing models based on single efficient paths do not improve mesoscopic structure-function correspondence. This suggests that functional modules emerge from the constraints imposed by local network structure that facilitates diffusive neural communication. Our work establishes the importance of modeling polysynaptic communication to understand the structural basis of functional systems.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States.
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States; Cognitive Science Program, Indiana University, Bloomington, IN, United States; Program in Neuroscience, Indiana University, Bloomington, IN, United States; Network Science Institute, Indiana University, Bloomington, IN, United States
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Fernando Calamante
- The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia; Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Sydney Imaging, The University of Sydney, Sydney, NSW, Australia
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108
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Scholtens LH, Pijnenburg R, de Lange SC, Huitinga I, van den Heuvel MP. Common Microscale and Macroscale Principles of Connectivity in the Human Brain. J Neurosci 2022; 42:4147-4163. [PMID: 35422441 PMCID: PMC9121834 DOI: 10.1523/jneurosci.1572-21.2022] [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: 07/30/2021] [Revised: 01/27/2022] [Accepted: 03/04/2022] [Indexed: 11/21/2022] Open
Abstract
The brain requires efficient information transfer between neurons and large-scale brain regions. Brain connectivity follows predictable organizational principles. At the cellular level, larger supragranular pyramidal neurons have larger, more branched dendritic trees, more synapses, and perform more complex computations; at the macroscale, region-to-region connections display a diverse architecture with highly connected hub areas facilitating complex information integration and computation. Here, we explore the hypothesis that the branching structure of large-scale region-to-region connectivity follows similar organizational principles as the neuronal scale. We examine microscale connectivity of basal dendritic trees of supragranular pyramidal neurons (300+) across 10 cortical areas in five human donor brains (1 male, 4 female). Dendritic complexity was quantified as the number of branch points, tree length, spine count, spine density, and overall branching complexity. High-resolution diffusion-weighted MRI was used to construct white matter trees of corticocortical wiring. Examining complexity of the resulting white matter trees using the same measures as for dendritic trees shows heteromodal association areas to have larger, more complex white matter trees than primary areas (p < 0.0001) and macroscale complexity to run in parallel with microscale measures, in terms of number of inputs (r = 0.677, p = 0.032), branch points (r = 0.797, p = 0.006), tree length (r = 0.664, p = 0.036), and branching complexity (r = 0.724, p = 0.018). Our findings support the integrative theory that brain connectivity follows similar principles of connectivity at neuronal and macroscale levels and provide a framework to study connectivity changes in brain conditions at multiple levels of organization.SIGNIFICANCE STATEMENT Within the human brain, cortical areas are involved in a wide range of processes, requiring different levels of information integration and local computation. At the cellular level, these regional differences reflect a predictable organizational principle with larger, more complexly branched supragranular pyramidal neurons in higher order regions. We hypothesized that the 3D branching structure of macroscale corticocortical connections follows the same organizational principles as the cellular scale. Comparing branching complexity of dendritic trees of supragranular pyramidal neurons and of MRI-based regional white matter trees of macroscale connectivity, we show that macroscale branching complexity is larger in higher order areas and that microscale and macroscale complexity go hand in hand. Our findings contribute to a multiscale integrative theory of brain connectivity.
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Affiliation(s)
- Lianne H Scholtens
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Rory Pijnenburg
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Siemon C de Lange
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Inge Huitinga
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, 1105 BA Amsterdam, The Netherlands
- Brain Plasticity Group, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Department of Child Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, 1081 HV Amsterdam, The Netherlands
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109
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Kelly AM. A consideration of brain networks modulating social behavior. Horm Behav 2022; 141:105138. [PMID: 35219166 DOI: 10.1016/j.yhbeh.2022.105138] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/30/2022] [Accepted: 02/13/2022] [Indexed: 11/04/2022]
Abstract
A primary goal of the field of behavioral neuroendocrinology is to understand how the brain modulates complex behavior. Over the last 20 years we have proposed various brain networks to explain behavioral regulation, however, the parameters by which these networks are identified are often ill-defined and reflect our personal scientific biases based on our area of expertise. In this perspective article, I question our characterization of brain networks underlying behavior and their utility. Using the Social Behavior Network as a primary example, I outline issues with brain networks commonly discussed in the field of behavioral neuroendocrinology, argue that we reconsider how we identify brain networks underlying behavior, and urge the future use of analytical tools developed by the field of Network Neuroscience. With modern statistical/mathematical tools and state of the art technology for brain imaging, we can strive to minimize our bias and generate brain networks that may more accurately reflect how the brain produces behavior.
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Affiliation(s)
- Aubrey M Kelly
- Department of Psychology, Emory University, 36 Eagle Row, Atlanta, GA 30322, United States of America.
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110
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Bruner E. A network approach to the topological organization of the Brodmann map. Anat Rec (Hoboken) 2022; 305:3504-3515. [PMID: 35485307 DOI: 10.1002/ar.24941] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/30/2022] [Accepted: 04/11/2022] [Indexed: 11/07/2022]
Abstract
Brain morphology is the result of functional factors associated with cortical areas, but it is also influenced by structural aspects due to physical and spatial constraints. Despite the noticeable advances in brain mapping, Brodmann's map is still used in many research fields that rely on macroscopic cortical features for practical or theoretical issues. Here, the topological relationships among the Brodmann areas were modelled according to the principles of network analysis, in order to provide a synthetic view of their spatial properties following a criterion of contiguity. The model evidences the importance of the parieto-temporal region in terms of biological burden and topological complexity. The retrosplenial region is particularly influenced by spatial constraints, and the cingulate cortex occupies a position that bridges the anterior and posterior topological blocks. Such spatial framework should be taken into account when dealing with brain morphology in both ontogeny and phylogeny. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Emiliano Bruner
- Centro Nacional de Investigación sobre la Evolución Humana, Burgos, Spain
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111
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Müller V. Neural Synchrony and Network Dynamics in Social Interaction: A Hyper-Brain Cell Assembly Hypothesis. Front Hum Neurosci 2022; 16:848026. [PMID: 35572007 PMCID: PMC9101304 DOI: 10.3389/fnhum.2022.848026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/25/2022] [Indexed: 11/13/2022] Open
Abstract
Mounting neurophysiological evidence suggests that interpersonal interaction relies on continual communication between cell assemblies within interacting brains and continual adjustments of these neuronal dynamic states between the brains. In this Hypothesis and Theory article, a Hyper-Brain Cell Assembly Hypothesis is suggested on the basis of a conceptual review of neural synchrony and network dynamics and their roles in emerging cell assemblies within the interacting brains. The proposed hypothesis states that such cell assemblies can emerge not only within, but also between the interacting brains. More precisely, the hyper-brain cell assembly encompasses and integrates oscillatory activity within and between brains, and represents a common hyper-brain unit, which has a certain relation to social behavior and interaction. Hyper-brain modules or communities, comprising nodes across two or several brains, are considered as one of the possible representations of the hypothesized hyper-brain cell assemblies, which can also have a multidimensional or multilayer structure. It is concluded that the neuronal dynamics during interpersonal interaction is brain-wide, i.e., it is based on common neuronal activity of several brains or, more generally, of the coupled physiological systems including brains.
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Affiliation(s)
- Viktor Müller
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
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112
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Li F, Li H, Li Y, Wu H, Fu B, Ji Y, Wang C, Shi G. Decoupling Representation Learning for Imbalanced Electroencephalography Classification in Rapid Serial Visual Presentation Task. J Neural Eng 2022; 19. [PMID: 35472762 DOI: 10.1088/1741-2552/ac6a7d] [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: 12/02/2021] [Accepted: 04/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The class imbalance problem considerably restricts the performance of electroencephalography (EEG) classification in the rapid serial visual presentation (RSVP) task. Existing solutions typically employ re-balancing strategies (e.g. re-weighting and re-sampling) to alleviate the impact of class imbalance, which enhances the classifier learning of deep networks but unexpectedly damages the representative ability of the learned deep features as original distributions become distorted. APPROACH In this study, a novel decoupling representation learning (DRL) model, has been proposed that separates the representation learning and classification processes to capture the discriminative feature of imbalanced RSVP EEG data while classifying it accurately. The representation learning process is responsible for learning universal patterns for the classification of all samples, while the classifier determines a better bounding for the target and non-target classes. Specifically, the representation learning process adopts a dual-branch architecture, which minimizes the contrastive loss to regularize the representation space. In addition, to learn more discriminative information from RSVP EEG data, a novel multi-granular information (MGI) based extractor is designed to extract spatial-temporal information. Considering the class re-balancing strategies can significantly promote classifier learning, the classifier was trained with rebalanced EEG data while freezing the parameters of the representation learning process. MAIN RESULTS To evaluate the proposed method, experiments were conducted on two public datasets and one self-conducted dataset. The results demonstrate that the proposed DRL can achieve state-of-the-art performance for EEG classification in the RSVP task. SIGNIFICANCE This is the first study to focus on the class imbalance problem and propose a generic solution in the RSVP task. Furthermore, multi-granular data was explored to extract more complementary spatial-temporal information. The code is open-source and available at https://github.com/Tammie-Li/DRL.
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Affiliation(s)
- Fu Li
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Hongxin Li
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Yang Li
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, 710071, CHINA
| | - Hao Wu
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Boxun Fu
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Youshuo Ji
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
| | - Chong Wang
- Xidian University, No. 2 South Taibai Road, Xi'an, Shaanxi, Xian, Shaanxi, 710071, CHINA
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113
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Brain Functional Connectivity Asymmetry: Left Hemisphere Is More Modular. Symmetry (Basel) 2022. [DOI: 10.3390/sym14040833] [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] Open
Abstract
Graph-theoretical approaches are increasingly used to study the brain and may enhance our understanding of its asymmetries. In this paper, we hypothesize that the structure of the left hemisphere is, on average, more modular. To this end, we analyzed resting-state functional magnetic resonance imaging data of 90 healthy subjects. We computed functional connectivity by Pearson’s correlation coefficient, turned the matrix into an unweighted graph by keeping a certain percentage of the strongest connections, and quantified modularity separately for the subgraph formed by each hemisphere. Our results show that the left hemisphere is more modular. The result is consistent across a range of binarization thresholds, regardless of whether the two hemispheres are thresholded together or separately. This illustrates that graph-theoretical analysis can provide a robust characterization of lateralization of brain functional connectivity.
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114
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Broeders TA, Douw L, Eijlers AJ, Dekker I, Uitdehaag BM, Barkhof F, Hulst HE, Vinkers CH, Geurts JJ, Schoonheim MM. A more unstable resting-state functional network in cognitively declining multiple sclerosis. Brain Commun 2022; 4:fcac095. [PMID: 35620116 PMCID: PMC9128379 DOI: 10.1093/braincomms/fcac095] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/14/2022] [Accepted: 04/11/2022] [Indexed: 11/24/2022] Open
Abstract
Cognitive impairment is common in people with multiple sclerosis and strongly
affects their daily functioning. Reports have linked disturbed cognitive
functioning in multiple sclerosis to changes in the organization of the
functional network. In a healthy brain, communication between brain regions and
which network a region belongs to is continuously and dynamically adapted to
enable adequate cognitive function. However, this dynamic network adaptation has
not been investigated in multiple sclerosis, and longitudinal network data
remain particularly rare. Therefore, the aim of this study was to longitudinally
identify patterns of dynamic network reconfigurations that are related to the
worsening of cognitive decline in multiple sclerosis. Resting-state functional
MRI and cognitive scores (expanded Brief Repeatable Battery of
Neuropsychological tests) were acquired in 230 patients with multiple sclerosis
and 59 matched healthy controls, at baseline (mean disease duration: 15 years)
and at 5-year follow-up. A sliding-window approach was used for functional MRI
analyses, where brain regions were dynamically assigned to one of seven
literature-based subnetworks. Dynamic reconfigurations of subnetworks were
characterized using measures of promiscuity (number of subnetworks switched to),
flexibility (number of switches), cohesion (mutual switches) and disjointedness
(independent switches). Cross-sectional differences between cognitive groups and
longitudinal changes were assessed, as well as relations with structural damage
and performance on specific cognitive domains. At baseline, 23% of
patients were cognitively impaired (≥2/7 domains
Z < −2) and 18% were mildly
impaired (≥2/7 domains
Z < −1.5). Longitudinally,
28% of patients declined over time (0.25 yearly change on ≥2/7
domains based on reliable change index). Cognitively impaired patients displayed
more dynamic network reconfigurations across the whole brain compared with
cognitively preserved patients and controls, i.e. showing higher promiscuity
(P = 0.047), flexibility
(P = 0.008) and cohesion
(P = 0.008). Over time, cognitively
declining patients showed a further increase in cohesion
(P = 0.004), which was not seen in stable
patients (P = 0.544). More cohesion was
related to more severe structural damage (average
r = 0.166,
P = 0.015) and worse verbal memory
(r = −0.156,
P = 0.022), information processing speed
(r = −0.202,
P = 0.003) and working memory
(r = −0.163,
P = 0.017). Cognitively impaired multiple
sclerosis patients exhibited a more unstable network reconfiguration compared to
preserved patients, i.e. brain regions switched between subnetworks more often,
which was related to structural damage. This shift to more unstable network
reconfigurations was also demonstrated longitudinally in patients that showed
cognitive decline only. These results indicate the potential relevance of a
progressive destabilization of network topology for understanding cognitive
decline in multiple sclerosis.
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Affiliation(s)
- Tommy A.A. Broeders
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anand J.C. Eijlers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Iris Dekker
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bernard M.J. Uitdehaag
- Departments of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK
| | - Hanneke E. Hulst
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Christiaan H. Vinkers
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Departments of Psychiatry, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J.G. Geurts
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M. Schoonheim
- Departments of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Duan S, Yu S, Principe JC. Modularizing Deep Learning via Pairwise Learning With Kernels. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:1441-1451. [PMID: 33400656 DOI: 10.1109/tnnls.2020.3042346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not require between-module backpropagation. This modular approach brings new insights into the label requirement of deep learning (DL). It leverages only implicit pairwise labels (weak supervision) when learning the hidden modules. When training the output module, on the other hand, it requires full supervision but achieves high label efficiency, needing as few as ten randomly selected labeled examples (one from each class) to achieve 94.88% accuracy on CIFAR-10 using a ResNet-18 backbone. Moreover, modular training enables fully modularized DL workflows, which then simplify the design and implementation of pipelines and improve the maintainability and reusability of models. To showcase the advantages of such a modularized workflow, we describe a simple yet reliable method for estimating reusability of pretrained modules as well as task transferability in a transfer learning setting. At practically no computation overhead, it precisely described the task space structure of 15 binary classification tasks from CIFAR-10.
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116
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Zhao L, Bo Q, Zhang Z, Chen Z, Wang Y, Zhang D, Li T, Yang N, Zhou Y, Wang C. Altered Dynamic Functional Connectivity in Early Psychosis Between the Salience Network and Visual Network. Neuroscience 2022; 491:166-175. [DOI: 10.1016/j.neuroscience.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 11/29/2022]
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Hu Y, Zeydabadinezhad M, Li L, Guo Y. A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development. J Am Stat Assoc 2022; 117:1134-1148. [PMID: 36204347 PMCID: PMC9531911 DOI: 10.1080/01621459.2022.2055559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging data sets from Philadelphia Neurodevelopmental Cohort (PNC) study to characterize developmental changes in both structural as well as functional brain connectomes. A multimodal multilevel model (MMM) is developed and implemented in PNC study to investigate brain maturation in both white matter structural connection and intrinsic functional connection. MMM addresses several major challenges in multimodal connectivity analysis. First, by using a first-level data generative model for observed measures and a second-level latent network modeling, MMM effectively infers underlying connection states from noisy imaging-based connectivity measurements. Secondly, MMM models the interplay between the structural and functional connections to capture the relationship between different brain connectomes. Thirdly, MMM incorporates covariate effects in the network modeling to investigate network heterogeneity across subpopoulations. Finally, by using a module-wise parameterization based on brain network topology, MMM is scalable to whole-brain connectomics. MMM analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing the majority of the white fiber connectivity growth are related to the cognitive networks where the most significant increase is found between the default mode and the executive control network with a 15% increase in the probability of structural connections. We also uncover functional connectome development mainly derived from global functional integration rather than direct anatomical connections. To the best of our knowledge, these findings have not been reported in the literature using multimodal connectomics. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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Affiliation(s)
- Yingtian Hu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322
| | | | - Longchuan Li
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322
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118
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The overlapping modular organization of human brain functional networks across the adult lifespan. Neuroimage 2022; 253:119125. [PMID: 35331872 DOI: 10.1016/j.neuroimage.2022.119125] [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: 12/02/2021] [Revised: 03/02/2022] [Accepted: 03/19/2022] [Indexed: 01/06/2023] Open
Abstract
Previous studies have demonstrated that the brain functional modular organization, which is a fundamental feature of the human brain, would change along the adult lifespan. However, these studies assumed that each brain region belonged to a single functional module, although there has been convergent evidence supporting the existence of overlap among functional modules in the human brain. To reveal how age affects the overlapping functional modular organization, this study applied an overlapping module detection algorithm that requires no prior knowledge to the resting-state fMRI data of a healthy cohort (N = 570) aged from 18 to 88 years old. A series of measures were derived to delineate the characteristics of the overlapping modular structure and the set of overlapping nodes (brain regions participating in two or more modules) identified from each participant. Age-related regression analyses on these measures found linearly decreasing trends in the overlapping modularity and the modular similarity. The number of overlapping nodes was found increasing with age, but the increment was not even over the brain. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently had positive correlations with both functional gradient and flexibility. Further, by correlation and mediation analyses, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the brain functional overlapping modular organization perspective, which could provide new insight into the adult lifespan changes in brain function and the influence of such changes on cognitive performance.
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119
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Yan S, Zhang G, Zhou Y, Tian T, Qin Y, Wu D, Lu J, Zhang S, Liu WV, Zhu W. Abnormalities of Cortical Morphology and Structural Covariance Network in Patients with Subacute Basal Ganglia Stroke. Acad Radiol 2022; 29 Suppl 3:S157-S165. [PMID: 34556428 DOI: 10.1016/j.acra.2021.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 07/29/2021] [Accepted: 08/05/2021] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES The direct damage caused by ischemic stroke is relatively localized, but structural reorganization of cortical regions could occur across the brain. Changes of large-scale, cortical structural brain networks after basal ganglia stroke are less well reported. We, therefore, aim to explore the abnormalities of cortical morphology and structural network topology in patients with unilateral basal ganglia stroke during the subacute period. MATERIALS AND METHODS Thirty patients with first-ever basal ganglia stroke and thirty age- and sex-matched healthy controls were recruited for our analysis. Patients underwent structural magnetic resonance imaging examinations and clinical assessment from seven days to three months post-stroke. Alterations in cortical morphology and topological properties of the cortical structural network were measured respectively using the surface-based morphology and graph-theoretical methods. RESULTS We observed focal cortical atrophy, specifically in areas of frontal and temporal cortices. Moreover, the cortical thickness in the contralesional transverse temporal gyrus and superior temporal gyrus was positively correlated with cognitive function scores. Network analysis revealed that patients with basal ganglia stroke showed increased clustering coefficient, increased mean local efficiency as well as a reorganization of degree-based hubs. In addition, these patients also showed reduced robustness under a random attack compared to healthy controls. CONCLUSION These findings indicated a unique pattern of cortical reorganization and the abnormal topological organization of cortical thickness-based structural covariance networks in patients with basal ganglia stroke, which is beneficial to understand the pathophysiological mechanisms of functional disorders at the cortical structural network level and find potential targets for induced neuromodulation.
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120
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Migliorelli C, Medina-Rivera I, Bachiller A, Tost A, Alonso JF, López-Sala A, Armstrong J, O'Callahan MDM, Pineda M, Mañanas MA, Romero S, García-Cazorla Á. Cognitive stimulation has potential for brain activation in individuals with Rett syndrome. JOURNAL OF INTELLECTUAL DISABILITY RESEARCH : JIDR 2022; 66:213-224. [PMID: 34796573 DOI: 10.1111/jir.12902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Knowledge regarding neuropsychological training in Rett syndrome (RS) is scarce. The aim of this study was to assess the outcome and the duration of the effect of cognitive stimulation on topographic electroencephalography (EEG) data in RS. METHODS Twenty female children diagnosed with RS were included in the analysis. Girls with RS conducted a cognitive task using an eye-tracker designed to evaluate access and choice skills. EEG data were acquired during the experimental procedure including two 10-min baseline stages before and after the task. Topographical changes of several EEG spectral markers including absolute and relative powers, Brain Symmetry Index and entropy were assessed. RESULTS Topographic significance probability maps suggested statistical decreases on delta activity and increases on beta rhythm associated with the cognitive task. Entropy increased during and after the task, likely related to more complex brain activity. A significant positive interaction was obtained between Brain Symmetry Index and age showing that the improvement of interhemispheric symmetry was higher in younger girls (5-10 years). CONCLUSIONS According to our findings, significant alterations of brain rhythms were observed during and after cognitive stimulation, suggesting that cognitive stimulation may have effects on brain activity beyond the stimulation period. Finally, our promising results also showed an increase brain symmetry that was especially relevant for the younger group. This could suggest an interaction of the eye-tracking cognitive task; however, further studies in this field are needed to assess the relation between brain asymmetries and age.
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Affiliation(s)
- C Migliorelli
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - I Medina-Rivera
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Neurology Department, Neurometabolic Unit and Synaptic Metabolism Lab, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, Barcelona, Spain
| | - A Bachiller
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - A Tost
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - J F Alonso
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - A López-Sala
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Neurology Department, Neurometabolic Unit and Synaptic Metabolism Lab, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, Barcelona, Spain
| | - J Armstrong
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Molecular Genetics Medicine Section, Hospital Sant Joan de Déu, Barcelona, Spain
| | - M D M O'Callahan
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Neurology Department, Neurometabolic Unit and Synaptic Metabolism Lab, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, Barcelona, Spain
| | - M Pineda
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - M A Mañanas
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - S Romero
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Á García-Cazorla
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Neurology Department, Neurometabolic Unit and Synaptic Metabolism Lab, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, Barcelona, Spain
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121
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Networks behind the morphology and structural design of living systems. Phys Life Rev 2022; 41:1-21. [DOI: 10.1016/j.plrev.2022.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/04/2022] [Indexed: 01/06/2023]
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122
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Pondelis NJ, Moulton EA. Supraspinal Mechanisms Underlying Ocular Pain. Front Med (Lausanne) 2022; 8:768649. [PMID: 35211480 PMCID: PMC8862711 DOI: 10.3389/fmed.2021.768649] [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: 09/01/2021] [Accepted: 12/27/2021] [Indexed: 12/04/2022] Open
Abstract
Supraspinal mechanisms of pain are increasingly understood to underlie neuropathic ocular conditions previously thought to be exclusively peripheral in nature. Isolating individual causes of centralized chronic conditions and differentiating them is critical to understanding the mechanisms underlying neuropathic eye pain and ultimately its treatment. Though few functional imaging studies have focused on the eye as an end-organ for the transduction of noxious stimuli, the brain networks related to pain processing have been extensively studied with functional neuroimaging over the past 20 years. This article will review the supraspinal mechanisms that underlie pain as they relate to the eye.
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Affiliation(s)
- Nicholas J Pondelis
- Brain and Eye Pain Imaging Lab, Pain and Affective Neuroscience Center, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - Eric A Moulton
- Brain and Eye Pain Imaging Lab, Pain and Affective Neuroscience Center, Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Ophthalmology, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
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123
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Zibetti C. Deciphering the Retinal Epigenome during Development, Disease and Reprogramming: Advancements, Challenges and Perspectives. Cells 2022; 11:cells11050806. [PMID: 35269428 PMCID: PMC8908986 DOI: 10.3390/cells11050806] [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: 01/14/2022] [Revised: 02/15/2022] [Accepted: 02/18/2022] [Indexed: 02/01/2023] Open
Abstract
Retinal neurogenesis is driven by concerted actions of transcription factors, some of which are expressed in a continuum and across several cell subtypes throughout development. While seemingly redundant, many factors diversify their regulatory outcome on gene expression, by coordinating variations in chromatin landscapes to drive divergent retinal specification programs. Recent studies have furthered the understanding of the epigenetic contribution to the progression of age-related macular degeneration, a leading cause of blindness in the elderly. The knowledge of the epigenomic mechanisms that control the acquisition and stabilization of retinal cell fates and are evoked upon damage, holds the potential for the treatment of retinal degeneration. Herein, this review presents the state-of-the-art approaches to investigate the retinal epigenome during development, disease, and reprogramming. A pipeline is then reviewed to functionally interrogate the epigenetic and transcriptional networks underlying cell fate specification, relying on a truly unbiased screening of open chromatin states. The related work proposes an inferential model to identify gene regulatory networks, features the first footprinting analysis and the first tentative, systematic query of candidate pioneer factors in the retina ever conducted in any model organism, leading to the identification of previously uncharacterized master regulators of retinal cell identity, such as the nuclear factor I, NFI. This pipeline is virtually applicable to the study of genetic programs and candidate pioneer factors in any developmental context. Finally, challenges and limitations intrinsic to the current next-generation sequencing techniques are discussed, as well as recent advances in super-resolution imaging, enabling spatio-temporal resolution of the genome.
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Affiliation(s)
- Cristina Zibetti
- Department of Ophthalmology, Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, Building 36, 0455 Oslo, Norway
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A hands-on tutorial on network and topological neuroscience. Brain Struct Funct 2022; 227:741-762. [PMID: 35142909 PMCID: PMC8930803 DOI: 10.1007/s00429-021-02435-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 11/23/2021] [Indexed: 02/08/2023]
Abstract
The brain is an extraordinarily complex system that facilitates the optimal integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pairwise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses the 1000 Functional Connectomes Project dataset. Moreover, we would like to highlight one part of our notebook dedicated to the realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pairwise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.
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125
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Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots. KNOWL ENG REV 2022. [DOI: 10.1017/s0269888921000151] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Abstract
Artificial neural networks (ANNs) can be employed as controllers for robotic agents. Their structure is often complex, with many neurons and connections, especially when the robots have many sensors and actuators distributed across their bodies and/or when high expressive power is desirable. Pruning (removing neurons or connections) reduces the complexity of the ANN, thus increasing its energy efficiency, and has been reported to improve the generalization capability, in some cases. In addition, it is well-known that pruning in biological neural networks plays a fundamental role in the development of brains and their ability to learn. In this study, we consider the evolutionary optimization of neural controllers for the case study of Voxel-based soft robots, a kind of modular, bio-inspired soft robots, applying pruning during fitness evaluation. For a locomotion task, and for centralized as well as distributed controllers, we experimentally characterize the effect of different forms of pruning on after-pruning effectiveness, life-long effectiveness, adaptability to new terrains, and behavior. We find that incorporating some forms of pruning in neuroevolution leads to almost equally effective controllers as those evolved without pruning, with the benefit of higher robustness to pruning. We also observe occasional improvements in generalization ability.
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126
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O'Reilly D, Delis I. A network information theoretic framework to characterise muscle synergies in space and time. J Neural Eng 2022; 19. [PMID: 35108699 DOI: 10.1088/1741-2552/ac5150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 02/02/2022] [Indexed: 11/12/2022]
Abstract
Objective Current approaches to muscle synergy extraction rely on linear dimensionality reduction algorithms that make specific assumptions on the underlying signals. However, to capture nonlinear time varying, large-scale but also muscle-specific interactions, a more generalised approach is required. Approach Here we developed a novel framework for muscle synergy extraction that relaxes model assumptions by using a combination of information- and network theory and dimensionality reduction. We first quantify informational dynamics between muscles, time-samples or muscle-time pairings using a novel mutual information formulation. We then model these pairwise interactions as multiplex networks and identify modules representing the network architecture. We employ this modularity criterion as the input parameter for dimensionality reduction, which verifiably extracts the identified modules, and also to characterise salient structures within each module. Main results This novel framework captures spatial, temporal and spatiotemporal interactions across two benchmark datasets of reaching movements, producing distinct spatial groupings and both tonic and phasic temporal patterns. Readily interpretable muscle synergies spanning multiple spatial and temporal scales were identified, demonstrating significant task dependence, ability to capture trial-to-trial fluctuations and concordance across participants. Furthermore, our framework identifies submodular structures that represent the distributed networks of co-occurring signal interactions across scales. Significance The capabilities of this framework are illustrated through the concomitant continuity with previous research and novelty of the insights gained. Several previous limitations are circumvented including the extraction of functionally meaningful and multiplexed pairwise muscle couplings under relaxed model assumptions. The extracted synergies provide a holistic view of the movement while important details of task performance are readily interpretable. The identified muscle groupings transcend biomechanical constraints and the temporal patterns reveal characteristics of fundamental motor control mechanisms. We conclude that this framework opens new opportunities for muscle synergy research and can constitute a bridge between existing models and recent network-theoretic endeavours.
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Affiliation(s)
- David O'Reilly
- University of Leeds, Faculty of Biological sciences, Leeds, LS2 9JT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Ioannis Delis
- University of Leeds, Faculty of Biological sciences, Leeds, Leeds, LS2 9JT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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127
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Mijalkov M, Volpe G, Pereira JB. Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease. Cereb Cortex 2022; 32:593-607. [PMID: 34331060 PMCID: PMC8805861 DOI: 10.1093/cercor/bhab237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 05/19/2021] [Accepted: 06/17/2021] [Indexed: 11/14/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.
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Affiliation(s)
- Mite Mijalkov
- Address correspondence to Mite Mijalkov and Joana B. Pereira, Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Neo 7th floor, Blickagången 16, 141 83 Huddinge, Sweden. (M.M.); (J.B.P.)
| | | | - Joana B Pereira
- Address correspondence to Mite Mijalkov and Joana B. Pereira, Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Neo 7th floor, Blickagången 16, 141 83 Huddinge, Sweden. (M.M.); (J.B.P.)
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128
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Ma C, Jiang X, Ren Y, Gu G, Fu A, Wang C, Bai P, Zhou T, Qin S, Fu S. Fiber connectivity density mapping in end-stage renal disease patients: a preliminary study. Brain Imaging Behav 2022; 16:1314-1323. [PMID: 35000065 DOI: 10.1007/s11682-021-00604-7] [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] [Accepted: 11/10/2021] [Indexed: 11/25/2022]
Abstract
Abnormal brain structural connectivity of end-stage renal disease(ESRD) is associated with cognitive impairment. However, the characteristics of cortical structural connectivity have not been investigated in ESRD patients. Here, we study structural connectivity of the entire cerebral cortex using a fiber connectivity density(FiCD) mapping method derived from diffusion tensor imaging(DTI) data of 25 ESRD patients and 20 healthy controls, and between-group differences were compared in a vertexwise manner. We also investigated the associations between these abnormal cortical connectivities and the clinical variables using Pearson correlation analysis and multifactor linear regression analysis. Our results demonstrated that the mean global FiCD value was significantly decreased in ESRD patients. Notably, FiCD values were significantly changed(decreased or increased) in certain cortical regions, which mainly involved the bilateral dorsolateral prefrontal cortex(DLPFC), inferior parietal cortex, lateral temporal cortex and middle occipital cortex. In ESRD patients, we found a trend of negative correlation between the increased FiCD values of bilateral middle frontal gyrus and serum creatinine, urea, parathyroid hormone(PTH) levels and dialysis duration. Only the white matter hyperintensity(WMH) scores were significantly negatively correlated with the global FiCD value in multifactor regression analysis. Our results suggested that ESRD patients exhibited extensive impaired cortical structural connectivity, which was related to the severity of WMHs. A compensation mechanism of cortical structural recombination may play a role in how the brain adapts to maintain optimal network function. Additionally, the serum creatinine, urea and PTH levels may be risk factors for brain structural network decompensation in ESRD patients.
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Affiliation(s)
- Chi Ma
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Xinghai Jiang
- Center for Disease Control and Prevention, West Coast New District, Qingdao, Shandong, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
| | - Gaojie Gu
- College of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China
| | - Airong Fu
- Department of Cardiovascular Medicine, West Coast New District Central Hospital, Qingdao, Shangdong, China
| | - Chengjian Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Peirui Bai
- College of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China
| | - Tong Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shanshan Qin
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shengli Fu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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Ahmed MM, Tazyeen S, Haque S, Alsulimani A, Ali R, Sajad M, Alam A, Ali S, Bagabir HA, Bagabir RA, Ishrat R. Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease. Front Cardiovasc Med 2022; 8:755321. [PMID: 35071341 PMCID: PMC8767007 DOI: 10.3389/fcvm.2021.755321] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 11/17/2021] [Indexed: 01/28/2023] Open
Abstract
In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.
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Affiliation(s)
- Mohd Murshad Ahmed
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Safia Tazyeen
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Shafiul Haque
- Research and Scientific Unit, College of Nursing and Allied Health Science, Jazan University, Jazan, Saudi Arabia
| | - Ahmad Alsulimani
- Department of Medical Laboratory Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arbia
| | - Rafat Ali
- Department of Bioscience, Jamia Millia Islamia, New Delhi, India
| | - Mohd Sajad
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Aftab Alam
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Shahnawaz Ali
- Centre for Stem Cell & Regenerative Medicine, KING' College London, Guy's Hospital, London, United Kingdom
| | - Hala Abubaker Bagabir
- Department of Medical Physiology, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia
| | - Rania Abubaker Bagabir
- Department of Hematology and Immunology, College of Medicine, Umm-Al-Qura University, Mecca, Saudi Arabia
| | - Romana Ishrat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India,*Correspondence: Romana Ishrat ; orcid.org/0000-0001-9744-9047
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130
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Haqiqatkhah MM, van Leeuwen C. Adaptive rewiring in nonuniform coupled oscillators. Netw Neurosci 2022; 6:90-117. [PMID: 35356195 PMCID: PMC8959120 DOI: 10.1162/netn_a_00211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 10/02/2021] [Indexed: 11/04/2022] Open
Abstract
Abstract
Structural plasticity of the brain can be represented in a highly simplified form as adaptive rewiring, the relay of connections according to the spontaneous dynamic synchronization in network activity. Adaptive rewiring, over time, leads from initial random networks to brain-like complex networks, that is, networks with modular small-world structures and a rich-club effect. Adaptive rewiring has only been studied, however, in networks of identical oscillators with uniform or random coupling strengths. To implement information-processing functions (e.g., stimulus selection or memory storage), it is necessary to consider symmetry-breaking perturbations of oscillator amplitudes and coupling strengths. We studied whether nonuniformities in amplitude or connection strength could operate in tandem with adaptive rewiring. Throughout network evolution, either amplitude or connection strength of a subset of oscillators was kept different from the rest. In these extreme conditions, subsets might become isolated from the rest of the network or otherwise interfere with the development of network complexity. However, whereas these subsets form distinctive structural and functional communities, they generally maintain connectivity with the rest of the network and allow the development of network complexity. Pathological development was observed only in a small proportion of the models. These results suggest that adaptive rewiring can robustly operate alongside information processing in biological and artificial neural networks.
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Affiliation(s)
- MohamamdHossein Manuel Haqiqatkhah
- Brain and Cognition Research Unit, KU Leuven, Leuven, Belgium
- Department of Methodology and Statistics, Utrecht University, Utrecht, The Netherlands
| | - Cees van Leeuwen
- Brain and Cognition Research Unit, KU Leuven, Leuven, Belgium
- Center for Cognitive Science, TU Kaiserslautern, Kaiserslautern, Germany
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131
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Alvand A, Kuruvilla-Mathew A, Kirk IJ, Roberts RP, Pedersen M, Purdy SC. Altered brain network topology in children with auditory processing disorder: A resting-state multi-echo fMRI study. NEUROIMAGE: CLINICAL 2022; 35:103139. [PMID: 36002970 PMCID: PMC9421544 DOI: 10.1016/j.nicl.2022.103139] [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: 04/14/2022] [Revised: 07/19/2022] [Accepted: 07/27/2022] [Indexed: 11/29/2022] Open
Abstract
A graph-theoretical approach was used to assess the functional topology in APD. Brain networks in APD are similarly integrated and segregated compared to HCs. Children with APD have different hub organization. Significant group differences were found in the PC measure in the bilateral STG. Regional differences observed within the DMN indicate multimodal roles in APD.
Children with auditory processing disorder (APD) experience hearing difficulties, particularly in the presence of competing sounds, despite having normal audiograms. There is considerable debate on whether APD symptoms originate from bottom-up (e.g., auditory sensory processing) and/or top-down processing (e.g., cognitive, language, memory). A related issue is that little is known about whether functional brain network topology is altered in APD. Therefore, we used resting-state functional magnetic resonance imaging data to investigate the functional brain network organization of 57 children from 8 to 14 years old, diagnosed with APD (n = 28) and without hearing difficulties (healthy control, HC; n = 29). We applied complex network analysis using graph theory to assess the whole-brain integration and segregation of functional networks and brain hub architecture. Our results showed children with APD and HC have similar global network properties –i.e., an average of all brain regions– and modular organization. Still, the APD group showed different hub architecture in default mode-ventral attention, somatomotor and frontoparietal-dorsal attention modules. At the nodal level –i.e., single-brain regions–, we observed decreased participation coefficient (PC – a measure quantifying the diversity of between-network connectivity) in auditory cortical regions in APD, including bilateral superior temporal gyrus and left middle temporal gyrus. Beyond auditory regions, PC was also decreased in APD in bilateral posterior temporo-occipital cortices, left intraparietal sulcus, and right posterior insular cortex. Correlation analysis suggested a positive association between PC in the left parahippocampal gyrus and the listening-in-spatialized-noise -sentences task where APD children were engaged in auditory perception. In conclusion, our findings provide evidence of altered brain network organization in children with APD, specific to auditory networks, and shed new light on the neural systems underlying children's listening difficulties.
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Affiliation(s)
- Ashkan Alvand
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand.
| | - Abin Kuruvilla-Mathew
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand.
| | - Ian J Kirk
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand; Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
| | - Reece P Roberts
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
| | - Mangor Pedersen
- School of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand.
| | - Suzanne C Purdy
- School of Psychology, Faculty of Science, The University of Auckland, Auckland, New Zealand; Eisdell Moore Centre, Auckland, New Zealand; Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
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132
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Haber SN, Liu H, Seidlitz J, Bullmore E. Prefrontal connectomics: from anatomy to human imaging. Neuropsychopharmacology 2022; 47:20-40. [PMID: 34584210 PMCID: PMC8617085 DOI: 10.1038/s41386-021-01156-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/23/2021] [Accepted: 08/02/2021] [Indexed: 12/22/2022]
Abstract
The fundamental importance of prefrontal cortical connectivity to information processing and, therefore, disorders of cognition, emotion, and behavior has been recognized for decades. Anatomic tracing studies in animals have formed the basis for delineating the direct monosynaptic connectivity, from cells of origin, through axon trajectories, to synaptic terminals. Advances in neuroimaging combined with network science have taken the lead in developing complex wiring diagrams or connectomes of the human brain. A key question is how well these magnetic resonance imaging (MRI)-derived networks and hubs reflect the anatomic "hard wiring" first proposed to underlie the distribution of information for large-scale network interactions. In this review, we address this challenge by focusing on what is known about monosynaptic prefrontal cortical connections in non-human primates and how this compares to MRI-derived measurements of network organization in humans. First, we outline the anatomic cortical connections and pathways for each prefrontal cortex (PFC) region. We then review the available MRI-based techniques for indirectly measuring structural and functional connectivity, and introduce graph theoretical methods for analysis of hubs, modules, and topologically integrative features of the connectome. Finally, we bring these two approaches together, using specific examples, to demonstrate how monosynaptic connections, demonstrated by tract-tracing studies, can directly inform understanding of the composition of PFC nodes and hubs, and the edges or pathways that connect PFC to cortical and subcortical areas.
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Affiliation(s)
- Suzanne N. Haber
- grid.412750.50000 0004 1936 9166Department of Pharmacology and Physiology, University of Rochester School of Medicine & Dentistry, Rochester, NY 14642 USA ,grid.38142.3c000000041936754XDepartment of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA 02478 USA
| | - Hesheng Liu
- grid.259828.c0000 0001 2189 3475Department of Neuroscience, Medical University of South Carolina, Charleston, SC USA ,grid.38142.3c000000041936754XDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Jakob Seidlitz
- grid.25879.310000 0004 1936 8972Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ed Bullmore
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain and Mind Sciences, Cambridge Biomedical Campus, Cambridge, CB2 0SZ UK
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133
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Zhou JF, Jiang EH, Xu BL, Xu K, Zhou C, Yuan WJ. Synaptic changes modulate spontaneous transitions between tonic and bursting neural activities in coupled Hindmarsh-Rose neurons. Phys Rev E 2021; 104:054407. [PMID: 34942771 DOI: 10.1103/physreve.104.054407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 10/26/2021] [Indexed: 11/07/2022]
Abstract
Experimentally, certain cells in the brain exhibit a spike-burst activity with burst synchronization at transition to and during sleep (or drowsiness), while they demonstrate a desynchronized tonic activity in the waking state. We herein investigated the neural activities and their transitions by using a model of coupled Hindmarsh-Rose neurons in an Erdős-Rényi random network. By tuning synaptic strength, spontaneous transitions between tonic and bursting neural activities can be realized. With excitatory chemical synapses or electrical synapses, slow-wave activity (SWA) similar to that observed during sleep can appear, as a result of synchronized bursting activities. SWA cannot appear in a network that is dominated by inhibitory chemical synapses, because neurons exhibit desynchronized bursting activities. Moreover, we found that the critical synaptic strength related to the transitions of neural activities depends only on the network average degree (i.e., the average number of signals that all the neurons receive). We demonstrated, both numerically and analytically, that the critical synaptic strength and the network average degree obey a power-law relation with an exponent of -1. Our study provides a possible dynamical network mechanism of the transitions between tonic and bursting neural activities for the wakefulness-sleep cycle, and of the SWA during sleep. Further interesting and challenging investigations are briefly discussed as well.
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Affiliation(s)
- Jian-Fang Zhou
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China
| | - En-Hua Jiang
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China
| | - Bang-Lin Xu
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China
| | - Kesheng Xu
- College of Science, Jiangsu University, Zhenjiang 212000, China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong 999077, Hong Kong, China
| | - Wu-Jie Yuan
- College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China
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134
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Pang J, Guo H, Tang X, Fu Y, Yang Z, Li Y, An N, Luo J, Yao Z, Hu B. Uncovering the global task-modulated brain network in chunk decomposition with Chinese characters. Neuroimage 2021; 247:118826. [PMID: 34923135 DOI: 10.1016/j.neuroimage.2021.118826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 12/14/2021] [Accepted: 12/15/2021] [Indexed: 11/17/2022] Open
Abstract
Chunk decomposition, which requires the mental representation transformation in accordance with behavioral goals, is of vital importance to problem solving and creative thinking. Previous studies have identified that the frontal, parietal, and occipital cortex in the cognitive control network selectively activated in response to chunk tightness, however, functional localization strategy may overlook the interaction brain regions. Based on the notion of a global brain network, we proposed that multiple specialized regions have to be interconnected to maintain goal representation during the course of chunk decomposition. Therefore, the present study applied a beta-series correlation method to investigate interregional functional connectivity in the event-related design of chunk decomposition tasks using Chinese characters, which would highlight critical nodes irrespective to chunk tightness. The results reveal a network of functional hubs with highly within or between module connections, including the orbitofrontal cortex, superior/inferior parietal lobule, hippocampus, and thalamus. We speculate that the thalamus integrates information across modular as an integrative hub while the orbitofrontal cortex tracks the mental states of chunk decomposition on a moment-to-moment basis. The superior and inferior parietal lobule collaborate to manipulate the mental representation of chunk decomposition and the hippocampus associates the relationship between elements in the question and solution phase. Furthermore, the tightness of chunks is not only associated with different processors in visual systems but also leads to increased intermodular connections in right superior frontal gyrus and left precentral gyrus. To summary up, the present study first reveals the task-modulated brain network of chunk decomposition in addition to the tightness-related nodes in the frontal and occipital cortex.
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Affiliation(s)
- Jiaoyan Pang
- School of Government, Shanghai University of Political Science and Law, Shanghai, China
| | - Hanning Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China.
| | - Xiaochen Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Yu Fu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China.
| | - Zhengwu Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China
| | - Na An
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China
| | - Jing Luo
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Gansu 730000, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, China; Ministry of Education, Open Source Software and Real-Time System Lanzhou University, Lanzhou, China.
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135
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Markett S, Nothdurfter D, Focsa A, Reuter M, Jawinski P. Attention networks and the intrinsic network structure of the human brain. Hum Brain Mapp 2021; 43:1431-1448. [PMID: 34882908 PMCID: PMC8837576 DOI: 10.1002/hbm.25734] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 11/15/2021] [Accepted: 11/24/2021] [Indexed: 11/09/2022] Open
Abstract
Attention network theory distinguishes three independent systems, each supported by its own distributed network: an alerting network to deploy attentional resources in anticipation, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. The brain is intrinsically organized into several large‐scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks at the level of cifti‐grayordinates. Resulting group maps were compared to the group‐level topology of 23 intrinsic networks, which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto‐parietal and midcingulo‐insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.
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136
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Wainstein G, Rojas-Líbano D, Medel V, Alnæs D, Kolskår KK, Endestad T, Laeng B, Ossandon T, Crossley N, Matar E, Shine JM. The ascending arousal system promotes optimal performance through mesoscale network integration in a visuospatial attentional task. Netw Neurosci 2021; 5:890-910. [PMID: 35024535 PMCID: PMC8746119 DOI: 10.1162/netn_a_00205] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 08/02/2021] [Indexed: 01/23/2023] Open
Abstract
Previous research has shown that the autonomic nervous system provides essential constraints over ongoing cognitive function. However, there is currently a relative lack of direct empirical evidence for how this interaction manifests in the brain at the macroscale level. Here, we examine the role of ascending arousal and attentional load on large-scale network dynamics by combining pupillometry, functional MRI, and graph theoretical analysis to analyze data from a visual motion-tracking task with a parametric load manipulation. We found that attentional load effects were observable in measures of pupil diameter and in a set of brain regions that parametrically modulated their BOLD activity and mesoscale network-level integration. In addition, the regional patterns of network reconfiguration were correlated with the spatial distribution of the α2a adrenergic receptor. Our results further solidify the relationship between ascending noradrenergic activity, large-scale network integration, and cognitive task performance.
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Affiliation(s)
| | - Daniel Rojas-Líbano
- Centro de Neurociencia Humana y Neuropsicología, Facultad de Psicología, Universidad Diego Portales, Santiago, Chile
| | - Vicente Medel
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, University of Oslo, and Oslo University Hospital, Oslo, Norway
- Bjørnnes College, Oslo, Norway
| | - Knut K. Kolskår
- NORMENT, Division of Mental Health and Addiction, University of Oslo, and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Tor Endestad
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Oslo, Norway
- Helgelandssykehuset Mosjøen, Helse Nord, Norway
| | - Bruno Laeng
- Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Oslo, Norway
| | - Tomas Ossandon
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Nicolás Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Elie Matar
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - James M. Shine
- Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
- Centre for Complexity, University of Sydney, Sydney, NSW, Australia
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137
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The diversity and multiplexity of edge communities within and between brain systems. Cell Rep 2021; 37:110032. [PMID: 34788617 DOI: 10.1016/j.celrep.2021.110032] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/08/2021] [Accepted: 10/28/2021] [Indexed: 11/24/2022] Open
Abstract
The human brain is composed of functionally specialized systems that support cognition. Recently, we proposed an edge-centric model for detecting overlapping communities. It remains unclear how these communities and brain systems are related. Here, we address this question using data from the Midnight Scan Club and show that all brain systems are linked via at least two edge communities. We then examine the diversity of edge communities within each system, finding that heteromodal systems are more diverse than sensory systems. Next, we cluster the entire cortex to reveal it according to the regions' edge-community profiles. We find that regions in heteromodal systems are more likely to form their own clusters. Finally, we show that edge communities are personalized. Our work reveals the pervasive overlap of edge communities across the cortex and their relationship with brain systems. Our work provides pathways for future research using edge-centric brain networks.
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138
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Furlong S, Cohen JR, Hopfinger J, Snyder J, Robertson MM, Sheridan MA. Resting-state EEG Connectivity in Young Children with ADHD. JOURNAL OF CLINICAL CHILD AND ADOLESCENT PSYCHOLOGY : THE OFFICIAL JOURNAL FOR THE SOCIETY OF CLINICAL CHILD AND ADOLESCENT PSYCHOLOGY, AMERICAN PSYCHOLOGICAL ASSOCIATION, DIVISION 53 2021; 50:746-762. [PMID: 32809852 PMCID: PMC7889746 DOI: 10.1080/15374416.2020.1796680] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Objective: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and impairing neurodevelopmental disorder. While early childhood is a crucial time for early intervention, it is characterized by instability of ADHD diagnosis. Neural correlates of ADHD have potential to improve diagnostic accuracy; however, minimal research has focused on early childhood. Research indicates that disrupted neural connectivity is associated with ADHD in older children. Here, we explore network connectivity as a potential neural correlate of ADHD diagnosis in early childhood.Method: We collected EEG data in 52 medication-naïve children with ADHD and in 77 typically developing controls (3-7 years). Data was collected with the EGI 128 HydroCel Sensor Net System, but to optimize the ICA, the data was down sampled to the 10-10 system. Connectivity was measured as the synchronization of the time series of each pair of electrodes. Subsequent analyses utilized graph theoretical methods to further characterize network connectivity.Results: Increased global efficiency, which measures the efficiency of information transfer across the entire brain, was associated with increased inattentive symptom severity. Further, this association was robust to controls for age, IQ, SES, and internalizing psychopathology.Conclusions: Overall, our findings indicate that increased global efficiency, which suggests a hyper-connected neural network, is associated with elevated ADHD symptom severity. These findings extend previous work reporting disruption of neural network connectivity in older children with ADHD into early childhood.
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Affiliation(s)
- Sarah Furlong
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joseph Hopfinger
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jenna Snyder
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, USA
| | - Madeline M. Robertson
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Margaret A. Sheridan
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, USA
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139
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Batta I, Yao Q, Sabrin KM, Dovrolis C. A weighted network analysis framework for the hourglass effect-And its application in the C. elegans connectome. PLoS One 2021; 16:e0249846. [PMID: 34705821 PMCID: PMC8550382 DOI: 10.1371/journal.pone.0249846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/09/2021] [Indexed: 11/18/2022] Open
Abstract
Understanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of the C. elegans and identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.
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Affiliation(s)
- Ishaan Batta
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Qihang Yao
- School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Kaeser M. Sabrin
- School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Constantine Dovrolis
- School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America
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140
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Hwang K, Shine JM, Bruss J, Tranel D, Boes A. Neuropsychological evidence of multi-domain network hubs in the human thalamus. eLife 2021; 10:69480. [PMID: 34622776 PMCID: PMC8526062 DOI: 10.7554/elife.69480] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/30/2021] [Indexed: 12/23/2022] Open
Abstract
Hubs in the human brain support behaviors that arise from brain network interactions. Previous studies have identified hub regions in the human thalamus that are connected with multiple functional networks. However, the behavioral significance of thalamic hubs has yet to be established. Our framework predicts that thalamic subregions with strong hub properties are broadly involved in functions across multiple cognitive domains. To test this prediction, we studied human patients with focal thalamic lesions in conjunction with network analyses of the human thalamocortical functional connectome. In support of our prediction, lesions to thalamic subregions with stronger hub properties were associated with widespread deficits in executive, language, and memory functions, whereas lesions to thalamic subregions with weaker hub properties were associated with more limited deficits. These results highlight how a large-scale network model can broaden our understanding of thalamic function for human cognition.
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Affiliation(s)
- Kai Hwang
- Department of Psychological and Brain Sciences, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Cognitive Control Collaborative, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Psychiatry, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia
| | - Joel Bruss
- Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Neurology, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Pediatrics, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
| | - Daniel Tranel
- Department of Psychological and Brain Sciences, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Neurology, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
| | - Aaron Boes
- Iowa Neuroscience Institute, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Psychiatry, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Neurology, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States.,Department of Pediatrics, The University of Iowa & The University of Iowa College of Medicine, Iowa City, United States
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141
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Krupnik R, Yovel Y, Assaf Y. Inner Hemispheric and Interhemispheric Connectivity Balance in the Human Brain. J Neurosci 2021; 41:8351-8361. [PMID: 34465598 PMCID: PMC8496194 DOI: 10.1523/jneurosci.1074-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/25/2021] [Accepted: 08/08/2021] [Indexed: 11/21/2022] Open
Abstract
The connectome of the brain has a great impact on the function of the brain as the structure of the connectome affects the speed and efficiency of information transfer. As a highly energy-consuming organ, an efficient network structure is essential. A previous study has shown consistent overall brain connectivity across a large variety of species. This connectivity conservation was explained by a balance between interhemispheric and intrahemispheric connections; that is, spices with highly connected hemispheres appear to have weaker interhemisphere connections. This study examines this connectivity trade-off in the human brain using diffusion-based tractography and network analysis in the Human Connectome Project (970 subjects, 527 female). We explore the biological origins of this phenomenon, heritability, and the effect on cognitive measures.The proportion of commissural fibers in the brain had a negative correlation to hemispheric efficiency, pointing to a trade-off between inner hemispheric and interhemispheric connectivity. Network hubs including anterior and middle cingulate cortex, superior frontal areas, medial occipital areas, the parahippocampal gyrus, post- and precentral gyri, and the precuneus had the strongest contribution to this phenomenon. Other results show a high heritability as well as a strong connection to crystalized intelligence. This work presents cohort-based network analysis research, spanning a large variety of samples and exploring the overall architecture of the human connectome. Our results show a connectivity conservation phenomenon at the base of the overall brain network architecture. This network structure may explain much of the functional, behavioral, and cognitive variability among different brains.SIGNIFICANCE STATEMENT The network structure of the brain is at the basis of every brain function as it dictates the characteristics of information transfer. Understanding the patterns and mechanisms that guide the connectome structure is crucial to understanding the brain itself. Here we unravel the mechanism at the base of the connectivity conservation phenomenon by exploring the interaction between hemispheric and commissural connectivity in a large-scale cohort-based connectivity study. We describe the trade-off between the two components and examine the origins of the trade-off and observe the effect on cognitive abilities and behavior.
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Affiliation(s)
- Ronnie Krupnik
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yossi Yovel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Zoology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
- Steinhardt Museum of Natural History, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yaniv Assaf
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Neurobiology, Biochemistry and Biophysics, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
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142
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Gao S, Mishne G, Scheinost D. Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics. Hum Brain Mapp 2021; 42:4510-4524. [PMID: 34184812 PMCID: PMC8410525 DOI: 10.1002/hbm.25561] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 05/26/2021] [Accepted: 05/30/2021] [Indexed: 02/02/2023] Open
Abstract
Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portion of this space. Here, we establish that a shared, robust, and interpretable low-dimensional space of brain dynamics can be recovered from a rich repertoire of task-based functional magnetic resonance imaging (fMRI) data. This occurs when relying on nonlinear approaches as opposed to traditional linear methods. The embedding maintains proper temporal progression of the tasks, revealing brain states and the dynamics of network integration. We demonstrate that resting-state data embeds fully onto the same task embedding, indicating similar brain states are present in both task and resting-state data. Our findings suggest analysis of fMRI data from multiple cognitive tasks in a low-dimensional space is possible and desirable.
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Affiliation(s)
- Siyuan Gao
- Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
| | - Gal Mishne
- Halıcıoğlu Data Science Institute, University of California San DiegoLa JollaCaliforniaUSA
- Neurosciences Graduate Program, University of California San DiegoLa JollaCaliforniaUSA
| | - Dustin Scheinost
- Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
- Department of Statistics and Data ScienceYale UniversityNew HavenConnecticutUSA
- Child Study Center, Yale School of MedicineNew HavenConnecticutUSA
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143
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Primavera BA, Shainline JM. Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware. Front Neurosci 2021; 15:732368. [PMID: 34552465 PMCID: PMC8450355 DOI: 10.3389/fnins.2021.732368] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/09/2021] [Indexed: 11/24/2022] Open
Abstract
Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths toward achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure.
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Affiliation(s)
- Bryce A. Primavera
- National Institute of Standards and Technology, Boulder, CO, United States
- Department of Physics, University of Colorado Boulder, Boulder, CO, United States
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144
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Liégeois R, Yeo BTT, Van De Ville D. Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis. Neuroimage 2021; 243:118518. [PMID: 34469853 DOI: 10.1016/j.neuroimage.2021.118518] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/19/2021] [Accepted: 08/25/2021] [Indexed: 11/27/2022] Open
Abstract
Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fMRI) time series. Interpretation of null-model testing in this context may not be straightforward because (i) null hypotheses associated to different null models are sometimes unclear and (ii) fMRI metrics might be 'trivial', i.e. preserved under the null hypothesis, and still be useful in neuroimaging applications. In this commentary, we review several commonly used null models of fMRI time series and discuss the interpretation of the corresponding tests. We argue that, while null-model testing allows for a better characterization of the statistical properties of fMRI time series and associated metrics, it should not be considered as a mandatory validation step to assess their relevance in representing brain functional dynamics.
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Affiliation(s)
- Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland.
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland
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145
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146
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Chandra PK, Cikic S, Baddoo MC, Rutkai I, Guidry JJ, Flemington EK, Katakam PV, Busija DW. Transcriptome analysis reveals sexual disparities in gene expression in rat brain microvessels. J Cereb Blood Flow Metab 2021; 41:2311-2328. [PMID: 33715494 PMCID: PMC8392780 DOI: 10.1177/0271678x21999553] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Sex is an important determinant of brain microvessels (MVs) function and susceptibility to cerebrovascular and neurological diseases, but underlying mechanisms are unclear. Using high throughput RNA sequencing analysis, we examined differentially expressed (DE) genes in brain MVs from young, male, and female rats. Bioinformatics analysis of the 23,786 identified genes indicates that 298 (1.2%) genes were DE using False Discovery Rate criteria (FDR; p < 0.05), of which 119 (40%) and 179 (60%) genes were abundantly expressed in male and female MVs, respectively. Nucleic acid binding, enzyme modulator, and transcription factor were the top three DE genes, which were more highly expressed in male than female MVs. Synthesis of glycosylphosphatidylinositol (GPI), biosynthesis of GPI-anchored proteins, steroid and cholesterol synthesis, were the top three significantly enriched canonical pathways in male MVs. In contrast, respiratory chain, ribosome, and 3 ́-UTR-mediated translational regulation were the top three enriched canonical pathways in female MVs. Different gene functions of MVs were validated by proteomic analysis and western blotting. Our novel findings reveal major sex disparities in gene expression and canonical pathways of MVs and these differences provide a foundation to study the underlying mechanisms and consequences of sex-dependent differences in cerebrovascular and other neurological diseases.
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Affiliation(s)
- Partha K Chandra
- Department of Pharmacology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Sinisa Cikic
- Department of Pharmacology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Melody C Baddoo
- Tulane Cancer Center, Tulane University School of Medicine, New Orleans, LA, USA.,Department of Pathology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Ibolya Rutkai
- Department of Pharmacology, Tulane University School of Medicine, New Orleans, LA, USA.,Department of Pathology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Jessie J Guidry
- Tulane Brain Institute, Tulane University, New Orleans, LA, USA
| | - Erik K Flemington
- Tulane Cancer Center, Tulane University School of Medicine, New Orleans, LA, USA.,Department of Pathology, Tulane University School of Medicine, New Orleans, LA, USA
| | - Prasad Vg Katakam
- Department of Pharmacology, Tulane University School of Medicine, New Orleans, LA, USA.,Department of Pathology, Tulane University School of Medicine, New Orleans, LA, USA
| | - David W Busija
- Department of Pharmacology, Tulane University School of Medicine, New Orleans, LA, USA.,Department of Pathology, Tulane University School of Medicine, New Orleans, LA, USA
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147
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Yu M, Sporns O, Saykin AJ. The human connectome in Alzheimer disease - relationship to biomarkers and genetics. Nat Rev Neurol 2021; 17:545-563. [PMID: 34285392 PMCID: PMC8403643 DOI: 10.1038/s41582-021-00529-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/10/2021] [Indexed: 02/06/2023]
Abstract
The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.
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Affiliation(s)
- Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
| | - Olaf Sporns
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana University Network Science Institute, Bloomington, IN, USA.
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148
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Fan Y, Fan Q, Zhou L, Wang R, Lin P, Wu Y. Cohesive communities in dynamic brain functional networks. Phys Rev E 2021; 104:014302. [PMID: 34412232 DOI: 10.1103/physreve.104.014302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/21/2021] [Indexed: 11/07/2022]
Abstract
In large-scale brain network dynamics, brain nodes switching between modules has been found to correlate with cognition. However, how the brain nodes engage in this kind of reorganization of modules is unclear. Based on a functional magnetic resonance imaging dataset, we construct dynamic brain functional networks and investigate nodal module temporal dynamic behavior by applying the multilayer network analysis approach. We reveal three cohesive communities that are groups of brain nodes linked in the same community during brain module dynamic reorganization. We show that the cohesive communities have higher clustering coefficients and lower characteristic path lengths than the controlled community, indicating cohesive communities are the parts of brain networks with high information processing efficiency. The smaller sample entropy of functional connectivity in cohesive communities also proves their property of being more "static" compared with the controlled community in brain dynamics. Specifically, compared with the controlled community, the functional connectivity of cohesive communities is restricted strictly by structure connectivity and shows more similarity to structure connectivity. More importantly, we find that the cohesive communities are stable not only in the resting state but also when processing cognitive tasks. Our results not only show that cohesive communities may be the fundamental community organization to support brain network dynamics but also provide insights into the intrinsic structural relationship between the resting state and task states of the brain.
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Affiliation(s)
- Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Qiang Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Lv Zhou
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.,National Demonstration Center for Experimental Mechanics Education, Xi'an Jiaotong University, Xi'an 710049, China
| | - Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an 710049, China
| | - Pan Lin
- Department of Psychology and Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha 410081, China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.,National Demonstration Center for Experimental Mechanics Education, Xi'an Jiaotong University, Xi'an 710049, China
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149
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Lei T, Liao X, Chen X, Zhao T, Xu Y, Xia M, Zhang J, Xia Y, Sun X, Wei Y, Men W, Wang Y, Hu M, Zhao G, Du B, Peng S, Chen M, Wu Q, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y. Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence. Cereb Cortex 2021; 32:1024-1039. [PMID: 34378030 DOI: 10.1093/cercor/bhab263] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 11/14/2022] Open
Abstract
Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.
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Affiliation(s)
- Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaochen Sun
- Department of Linguistics, Beijing Language and Culture University, Beijing 100083, China
| | - Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Mingming Hu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Bin Du
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Siya Peng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Menglu Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qian Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
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150
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Argyropoulou MI, Xydis VG, Drougia A, Giantsouli AS, Giapros V, Astrakas LG. Structural and functional brain connectivity in moderate-late preterm infants with low-grade intraventricular hemorrhage. Neuroradiology 2021; 64:197-204. [PMID: 34342681 DOI: 10.1007/s00234-021-02770-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/11/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE Brain functional connectivity (FC) changes and microstructural abnormalities are reported in infants born moderate and late preterm (MLPT). We evaluated the effect of low-grade (grades I, II) intraventricular hemorrhage (IVH) in MLPT babies on brain structural connectivity (SC) and FC. METHODS Babies born MLPT between January 2014 and May 2017 underwent brain ultrasound (US) at 72 h and 7 days after birth, and MRI at around term equivalent. The MRI protocol comprised T1- and T2-weighted sequences, diffusion tensor imaging (DTI), and resting-state functional MRI (fMRI). SC and FC were assessed using graph analysis. RESULTS Of 350 MLPT neonates, 15 showed low-grade IVH on US at 72 h, for which brain MRI was available in 10. These 10 infants, with mean gestational age (GA) 34.0 ± 0.8 weeks, comprised the study group, and 10 MLPT infants of mean GA 33.9 ± 1.1 weeks, with no abnormalities on brain US and MRI, were control subjects. All study subjects presented modularity, small world topology, and rich club organization for both SC and FC. The patients with low-grade IVH had lower FC rich club coefficient and lower SC betweenness centrality in the left frontoparietal operculum, and lower SC rich club coefficient in the right superior orbitofrontal cortex than the control subjects. CONCLUSIONS Topological and functional properties of mature brain connectivity are present in MLPT infants. IVH in these infants was associated with structural and functional abnormalities in the left frontoparietal operculum and right orbitofrontal cortex, regions related to language and cognition.
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Affiliation(s)
- Maria I Argyropoulou
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, 45110, Ioannina, Greece.
| | - Vasileios G Xydis
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, 45110, Ioannina, Greece
| | - Aikaterini Drougia
- Neonatal Intensive Care Unit, Child Health Department, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | - Anastasia S Giantsouli
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, 45110, Ioannina, Greece
| | - Vasileios Giapros
- Neonatal Intensive Care Unit, Child Health Department, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Faculty of Medicine, University of Ioannina, Ioannina, Greece
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