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Articles de revues sur le sujet « Brain functional Network »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

Chan, John S. Y., Yifeng Wang, Jin H. Yan, and Huafu Chen. "Developmental implications of children’s brain networks and learning." Reviews in the Neurosciences 27, no. 7 (2016): 713–27. http://dx.doi.org/10.1515/revneuro-2016-0007.

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AbstractThe human brain works as a synergistic system where information exchanges between functional neuronal networks. Rudimentary networks are observed in the brain during infancy. In recent years, the question of how functional networks develop and mature in children has been a hotly discussed topic. In this review, we examined the developmental characteristics of functional networks and the impacts of skill training on children’s brains. We first focused on the general rules of brain network development and on the typical and atypical development of children’s brain networks. After that, w

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Wang, Zhongyang, Junchang Xin, Qi Chen, Zhiqiong Wang, and Xinlei Wang. "NDCN-Brain: An Extensible Dynamic Functional Brain Network Model." Diagnostics 12, no. 5 (2022): 1298. http://dx.doi.org/10.3390/diagnostics12051298.

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As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain’s connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes of the network after the signal ends. In this paper, an extensible dynamic brain function network model is proposed. The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex network

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Carnevale, Lorenzo, Angelo Maffei, Alessandro Landolfi, Giovanni Grillea, Daniela Carnevale, and Giuseppe Lembo. "Brain Functional Magnetic Resonance Imaging Highlights Altered Connections and Functional Networks in Patients With Hypertension." Hypertension 76, no. 5 (2020): 1480–90. http://dx.doi.org/10.1161/hypertensionaha.120.15296.

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Hypertension is one of the main risk factors for vascular dementia and Alzheimer disease. To predict the onset of these diseases, it is necessary to develop tools to detect the early effects of vascular risk factors on the brain. Resting-state functional magnetic resonance imaging can investigate how the brain modulates its resting activity and analyze how hypertension impacts cerebral function. Here, we used resting-state functional magnetic resonance imaging to explore brain functional-hemodynamic coupling across different regions and their connectivity in patients with hypertension, as comp

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Zheng, Weihao, Choong-Wan Woo, Zhijun Yao, et al. "Pain-Evoked Reorganization in Functional Brain Networks." Cerebral Cortex 30, no. 5 (2019): 2804–22. http://dx.doi.org/10.1093/cercor/bhz276.

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Abstract Recent studies indicate that a significant reorganization of cerebral networks may occur in patients with chronic pain, but how immediate pain experience influences the organization of large-scale functional networks is not yet well characterized. To investigate this question, we used functional magnetic resonance imaging in 106 participants experiencing both noxious and innocuous heat. Painful stimulation caused network-level reorganization of cerebral connectivity that differed substantially from organization during innocuous stimulation and standard resting-state networks. Noxious

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Li, Gang, Yanting Xu, Yonghua Jiang, Weidong Jiao, Wanxiu Xu, and Jianhua Zhang. "Mental Fatigue Has Great Impact on the Fractal Dimension of Brain Functional Network." Neural Plasticity 2020 (November 12, 2020): 1–11. http://dx.doi.org/10.1155/2020/8825547.

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Mental fatigue has serious negative impacts on the brain cognitive functions and has been widely explored by the means of brain functional networks with the neuroimaging technique of electroencephalogram (EEG). Recently, several researchers reported that brain functional network constructed from EEG signals has fractal feature, raising an important question: what are the effects of mental fatigue on the fractal dimension of brain functional network? In the present study, the EEG data of alpha1 rhythm (8-10 Hz) at task state obtained by a mental fatigue model were chosen to construct brain func

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Li, Han, Qizhong Zhang, Ziying Lin, and Farong Gao. "Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network." Brain Sciences 11, no. 8 (2021): 1066. http://dx.doi.org/10.3390/brainsci11081066.

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Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks

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Liu, Xiao, Shuaizong Si, Bo Hu, Hai Zhao, and Jian Zhu. "A Generative Network Model of the Human Brain Normal Aging Process." Symmetry 12, no. 1 (2020): 91. http://dx.doi.org/10.3390/sym12010091.

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The human brain is approximately a symmetric structure, although the functional brain does not exhibit symmetry. Functional brain aging process modelling is essential for the understanding of hypothesized generative mechanisms for human brain networks throughout one’s lifespan. We present a novel generative network model of the human functional brain network, which is the hybrid of the local naïve Bayes model and the anatomical similarity correction (LNBE). We use LNBE, as well as published generative network models to simulate the aging process of the functional brain network, to construct ar

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Lama, Ramesh Kumar, and Goo-Rak Kwon. "Resting-State Functional Connectivity Difference in Alzheimer’s Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement." Diagnostics 13, no. 19 (2023): 3074. http://dx.doi.org/10.3390/diagnostics13193074.

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The disruption of functional connectivity is one of the early events that occurs in the brains of Alzheimer’s disease (AD) patients. This paper reports a study on the clustering structure of functional connectivity in eight important brain networks in healthy, AD, and prodromal stage subjects. We used the threshold-free cluster enhancement (TFCE) method to explore the connectivity from resting-state functional MR images (rs-fMRIs). We conducted the study on a total of 32 AD, 32 HC, and 31 MCI subjects. We modeled the brain as a graph-based network to study these impairments, and pairwise Pears

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Betzel, Richard F. "Organizing principles of whole-brain functional connectivity in zebrafish larvae." Network Neuroscience 4, no. 1 (2020): 234–56. http://dx.doi.org/10.1162/netn_a_00121.

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Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and functionally flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchi

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Gleiser, Pablo M., and Victor I. Spoormaker. "Modelling hierarchical structure in functional brain networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1933 (2010): 5633–44. http://dx.doi.org/10.1098/rsta.2010.0279.

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In this work, we focus on a complex-network approach for the study of the brain. In particular, we consider functional brain networks, where the vertices represent different anatomical regions and the links their functional connectivity. First, we build these networks using data obtained with functional magnetic resonance imaging. Then, we analyse the main characteristics of these complex networks, including degree distribution, the presence of modules and hierarchical structure. Finally, we present a network model with dynamical nodes and adaptive links. We show that the model allows for the

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Mizuno, Megumi, Tomoyuki Hiroyasu, and Satoru Hiwa. "A Functional NIRS Study of Brain Functional Networks Induced by Social Time Coordination." Brain Sciences 9, no. 2 (2019): 43. http://dx.doi.org/10.3390/brainsci9020043.

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The ability to coordinate one’s behavior with the others’ behavior is essential to achieve a joint action in daily life. In this paper, the brain activity during synchronized tapping task was measured using functional near infrared spectroscopy (fNIRS) to investigate the relationship between time coordination and brain function. Furthermore, using brain functional network analysis based on graph theory, we examined important brain regions and network structures that serve as the hub when performing the synchronized tapping task. Using the data clustering method, two types of brain function net

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Wang, Mingliang, Jiashuang Huang, Mingxia Liu, and Daoqiang Zhang. "Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1198–205. http://dx.doi.org/10.1609/aaai.v33i01.33011198.

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Brain network analysis can help reveal the pathological basis of neurological disorders and facilitate automated diagnosis of brain diseases, by exploring connectivity patterns in the human brain. Effectively representing the brain network has always been the fundamental task of computeraided brain network analysis. Previous studies typically utilize human-engineered features to represent brain connectivity networks, but these features may not be well coordinated with subsequent classifiers. Besides, brain networks are often equipped with multiple hubs (i.e., nodes occupying a central position

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Zeng, Lingwei, Chunchen Wang, Kewei Sun, et al. "Upregulation of a Small-World Brain Network Improves Inhibitory Control: An fNIRS Neurofeedback Training Study." Brain Sciences 13, no. 11 (2023): 1516. http://dx.doi.org/10.3390/brainsci13111516.

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The aim of this study was to investigate the inner link between the small-world brain network and inhibitory control. Functional near-infrared spectroscopy (fNIRS) was used to construct a neurofeedback (NF) training system and regulate the frontal small-world brain network. The small-world network downregulation group (DOWN, n = 17) and the small-world network upregulation group (UP, n = 17) received five days of fNIRS-NF training and performed the color–word Stroop task before and after training. The behavioral and functional brain network topology results of both groups were analyzed by a re

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Xu, Yuehua, Miao Cao, Xuhong Liao, et al. "Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain." Cerebral Cortex 29, no. 10 (2018): 4208–22. http://dx.doi.org/10.1093/cercor/bhy302.

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Abstract Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31–42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variabilit

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Gordon, Evan M., Timothy O. Laumann, Scott Marek, et al. "Default-mode network streams for coupling to language and control systems." Proceedings of the National Academy of Sciences 117, no. 29 (2020): 17308–19. http://dx.doi.org/10.1073/pnas.2005238117.

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The human brain is organized into large-scale networks identifiable using resting-state functional connectivity (RSFC). These functional networks correspond with broad cognitive domains; for example, the Default-mode network (DMN) is engaged during internally oriented cognition. However, functional networks may contain hierarchical substructures corresponding with more specific cognitive functions. Here, we used individual-specific precision RSFC to test whether network substructures could be identified in 10 healthy human brains. Across all subjects and networks, individualized network subdiv

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Li, Xun, Yu-Feng Zang, and Han Zhang. "Exploring Dynamic Brain Functional Networks Using Continuous “State-Related” Functional MRI." BioMed Research International 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/824710.

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We applied a “temporal decomposition” method, which decomposed a single brain functional network into several “modes”; each of them dominated a short temporal period, on a continuous, “state-” related, “finger-force feedback” functional magnetic resonance imaging experiment. With the hypothesis that attention and internal/external information processing interaction could be manipulated by different (real and sham) feedback conditions, we investigated functional network dynamics of the “default mode,” “executive control,” and sensorimotor networks. They were decomposed into several modes. Durin

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Hahn, Andreas, Georg S. Kranz, Ronald Sladky, et al. "Individual Diversity of Functional Brain Network Economy." Brain Connectivity 5, no. 3 (2015): 156–65. http://dx.doi.org/10.1089/brain.2014.0306.

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Hsu, Howard Muchen, Zai-Fu Yao, Kai Hwang, and Shulan Hsieh. "Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition." PLOS ONE 15, no. 12 (2020): e0242985. http://dx.doi.org/10.1371/journal.pone.0242985.

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The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside

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Laiou, Petroula, Andrea Biondi, Elisa Bruno, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

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Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-

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Laiou, Petroula, Andrea Biondi, Elisa Bruno, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

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Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-

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Laiou, Petroula, Andrea Biondi, Elisa Bruno, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

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Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-

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Laiou, Petroula, Andrea Biondi, Elisa Bruno, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

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Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-

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Laiou, Petroula, Andrea Biondi, Elisa Bruno, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

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Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-

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Laiou, Petroula, Andrea Biondi, Elisa Bruno, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

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Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-

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Laiou, Petroula, Andrea Biondi, Elisa Bruno, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

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Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-

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Hart, Michael G., Stephen J. Price, and John Suckling. "Functional connectivity networks for preoperative brain mapping in neurosurgery." Journal of Neurosurgery 126, no. 6 (2016): 1941–50. http://dx.doi.org/10.3171/2016.6.jns1662.

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OBJECTIVEResection of focal brain lesions involves maximizing the resection while preserving brain function. Mapping brain function has entered a new era focusing on distributed connectivity networks at “rest,” that is, in the absence of a specific task or stimulus, requiring minimal participant engagement. Central to this frame shift has been the development of methods for the rapid assessment of whole-brain connectivity with functional MRI (fMRI) involving blood oxygenation level–dependent imaging. The authors appraised the feasibility of fMRI-based mapping of a repertoire of functional conn

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Gomez Portillo, Ignacio J., and Pablo M. Gleiser. "An Adaptive Complex Network Model for Brain Functional Networks." PLoS ONE 4, no. 9 (2009): e6863. http://dx.doi.org/10.1371/journal.pone.0006863.

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Zhang, Guifeng, Shanshan Qu, Yu Zheng, et al. "Key Regions of the Cerebral Network are Altered after Electroacupuncture at the Baihui (GV20) and Yintang Acupuncture Points in Healthy Volunteers: An Analysis Based on Resting fcMRI." Acupuncture in Medicine 31, no. 4 (2013): 383–88. http://dx.doi.org/10.1136/acupmed-2012-010301.

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Objective To identify the key cerebral functional region affected by acupuncture point needling by examining cerebral networks using functional connectivity MRI (fcMRI) and analysing changes in the key regions of these brain networks at different time points after needle removal. Methods Twelve healthy volunteers received 30 min of electroacupuncture (EA) at the Baihui (GV20) and Yintang acupuncture points and then underwent two fMRI scans, one each at 5 and 15 min after needle removal. Related brain networks were analysed centred at different ‘seeds’, centres which functionally connect the ot

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De Vico Fallani, Fabrizio, Jonas Richiardi, Mario Chavez, and Sophie Achard. "Graph analysis of functional brain networks: practical issues in translational neuroscience." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1653 (2014): 20130521. http://dx.doi.org/10.1098/rstb.2013.0521.

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The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essen

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Oliver, Isaura, Jaroslav Hlinka, Jakub Kopal, and Jörn Davidsen. "Quantifying the Variability in Resting-State Networks." Entropy 21, no. 9 (2019): 882. http://dx.doi.org/10.3390/e21090882.

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Recent precision functional mapping of individual human brains has shown that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies. How this variability affects the connectivity within individual resting-state networks remains an open question. This is particularly important since certain resting-state networks such as the default mode network (DMN) and the fronto-parietal network (FPN) play an important role in the early detection of neurophysiological diseases like Alzheimer’s, Parkinson’s, and at

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Shah, Disha, Ines Blockx, Georgios A. Keliris, et al. "Cholinergic and serotonergic modulations differentially affect large-scale functional networks in the mouse brain." Brain Structure and Function 221, no. 6 (2015): 3067–79. https://doi.org/10.1007/s00429-015-1087-7.

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Resting-state functional MRI (rsfMRI) is a widely implemented technique used to investigate large-scale topology in the human brain during health and disease. Studies in mice provide additional advantages, including the possibility to flexibly modulate the brain by pharmacological or genetic manipulations in combination with high-throughput functional connectivity (FC) investigations. Pharmacological modulations that target specific neurotransmitter systems, partly mimicking the effect of pathological events, could allow discriminating the effect of specific systems on functional network disru

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Navas, Adrián, David Papo, Stefano Boccaletti, et al. "Functional Hubs in Mild Cognitive Impairment." International Journal of Bifurcation and Chaos 25, no. 03 (2015): 1550034. http://dx.doi.org/10.1142/s0218127415500340.

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We investigate how hubs of functional brain networks are modified as a result of mild cognitive impairment (MCI), a condition causing a slight but noticeable decline in cognitive abilities, which sometimes precedes the onset of Alzheimer's disease. We used magnetoencephalography (MEG) to investigate the functional brain networks of a group of patients suffering from MCI and a control group of healthy subjects, during the execution of a short-term memory task. Couplings between brain sites were evaluated using synchronization likelihood, from which a network of functional interdependencies was

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Kahali, Sayan, Marcus E. Raichle, and Dmitriy A. Yablonskiy. "The Role of the Human Brain Neuron–Glia–Synapse Composition in Forming Resting-State Functional Connectivity Networks." Brain Sciences 11, no. 12 (2021): 1565. http://dx.doi.org/10.3390/brainsci11121565.

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While significant progress has been achieved in studying resting-state functional networks in a healthy human brain and in a wide range of clinical conditions, many questions related to their relationship to the brain’s cellular constituents remain. Here, we use quantitative Gradient-Recalled Echo (qGRE) MRI for mapping the human brain cellular composition and BOLD (blood–oxygen level-dependent) MRI to explore how the brain cellular constituents relate to resting-state functional networks. Results show that the BOLD signal-defined synchrony of connections between cellular circuits in network-d

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Barredo, Jennifer, Emily Aiken, Mascha van 't Wout-Frank, Benjamin D. Greenberg, Linda L. Carpenter, and Noah S. Philip. "Network Functional Architecture and Aberrant Functional Connectivity in Post-Traumatic Stress Disorder: A Clinical Application of Network Convergence." Brain Connectivity 8, no. 9 (2018): 549–57. http://dx.doi.org/10.1089/brain.2018.0634.

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Wylie, Korey P., Donald C. Rojas, Jody Tanabe, Laura F. Martin, and Jason R. Tregellas. "Nicotine increases brain functional network efficiency." NeuroImage 63, no. 1 (2012): 73–80. http://dx.doi.org/10.1016/j.neuroimage.2012.06.079.

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Langer, Nicolas, Andreas Pedroni, Lorena R. R. Gianotti, Jürgen Hänggi, Daria Knoch, and Lutz Jäncke. "Functional brain network efficiency predicts intelligence." Human Brain Mapping 33, no. 6 (2011): 1393–406. http://dx.doi.org/10.1002/hbm.21297.

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Cui, Dong, Han Li, Hongyuan Shao, Guanghua Gu, Xiaonan Guo, and Xiaoli Li. "Construction and Analysis of a New Resting-State Whole-Brain Network Model." Brain Sciences 14, no. 3 (2024): 240. http://dx.doi.org/10.3390/brainsci14030240.

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(1) Background: Mathematical modeling and computer simulation are important methods for understanding complex neural systems. The whole-brain network model can help people understand the neurophysiological mechanisms of brain cognition and functional diseases of the brain. (2) Methods: In this study, we constructed a resting-state whole-brain network model (WBNM) by using the Wendling neural mass model as the node and a real structural connectivity matrix as the edge of the network. By analyzing the correlation between the simulated functional connectivity matrix in the resting state and the e

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Lee, Junghan, Deokjong Lee, Kee Namkoong, and Young-Chul Jung. "Aberrant posterior superior temporal sulcus functional connectivity and executive dysfunction in adolescents with internet gaming disorder." Journal of Behavioral Addictions 9, no. 3 (2020): 589–97. http://dx.doi.org/10.1556/2006.2020.00060.

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AbstractBackground and aimsThe clinical significance of Internet gaming disorder (IGD) is spreading worldwide, but its underlying neural mechanism still remains unclear. Moreover, the prevalence of IGD seems to be the highest in adolescents whose brains are in development. This study investigated the functional connectivity between large-scale intrinsic networks including default mode network, executive control network, and salience network. We hypothesized that adolescents with IGD would demonstrate different functional connectivity patterns among large-scale intrinsic networks, implying neur

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Lehnertz, Klaus, and Timo Bröhl. "Functional Importance Backbones of the Brain at Rest, Wakefulness, and Sleep." Brain Sciences 15, no. 7 (2025): 772. https://doi.org/10.3390/brainsci15070772.

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Background: The brain is never truly at rest. Even in the absence of external tasks, it remains active, continuously organizing itself into large-scale resting-state networks involved in shaping our internal thoughts and experiences. Understanding the networks’ structure and dynamics is key to uncovering how the brain functions as a whole. While previous studies have mapped resting-state networks and explored the roles of individual brain regions (network vertices), the relevance of the time-dependent functional interactions (network edges) between them remains largely unexplored. Methods: Her

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Han, Xiao, He Jin, Kuangshi Li, et al. "Acupuncture Modulates Disrupted Whole-Brain Network after Ischemic Stroke: Evidence Based on Graph Theory Analysis." Neural Plasticity 2020 (August 19, 2020): 1–10. http://dx.doi.org/10.1155/2020/8838498.

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Background. Stroke can lead to disruption of the whole-brain network in patients. Acupuncture can modulate the functional network on a large-scale level in healthy individuals. However, whether and how acupuncture can make a potential impact on the disrupted whole-brain network after ischemic stroke remains elusive. Methods. 26 stroke patients with a right hemispheric subcortical infarct were recruited. We gathered the functional magnetic resonance imaging (fMRI) from patients with stroke and healthy controls in the resting state and after acupuncture intervention, to investigate the instant a

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Zhu, Xiaofeng, Hongming Li, Heng Tao Shen, Zheng Zhang, Yanli Ji, and Yong Fan. "Fusing functional connectivity with network nodal information for sparse network pattern learning of functional brain networks." Information Fusion 75 (November 2021): 131–39. http://dx.doi.org/10.1016/j.inffus.2021.03.006.

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Iraji, Armin, Hanbo Chen, Natalie Wiseman, et al. "Compensation through Functional Hyperconnectivity: A Longitudinal Connectome Assessment of Mild Traumatic Brain Injury." Neural Plasticity 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4072402.

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Mild traumatic brain injury (mTBI) is a major public health concern. Functional MRI has reported alterations in several brain networks following mTBI. However, the connectome-scale brain network changes are still unknown. In this study, sixteen mTBI patients were prospectively recruited from an emergency department and followed up at 4–6 weeks after injury. Twenty-four healthy controls were also scanned twice with the same time interval. Three hundred fifty-eight brain landmarks that preserve structural and functional correspondence of brain networks across individuals were used to investigate

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Peng, Ciyuan, Huafei Huang, Tianqi Guo, et al. "Joint Structural-Functional Brain Graph Transformer." ACM Transactions on Intelligent Systems and Technology, April 12, 2025. https://doi.org/10.1145/3729243.

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Multimodal brain graph transformers have become one of the foundational architectures of graph foundation models for brain science, relying on multimodal brain network fusion. However, most current multimodal brain network fusion methods primarily focus on modality-specific information fusion. The interplays within structural-functional brain networks are often ignored. Therefore, they fail to acquire essential coupling information, which is crucial for obtaining robust joint brain network representations. This oversight inevitably limits the effectiveness and generalization of these represent

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Soriano. "Spontaneous functional recovery after focal damage in neuronal cultures." March 30, 2020. https://doi.org/10.1523/ENEURO.0254-19.2019.

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Damage in biological neuronal networks triggers a complex functional reorganization whose mechanisms2 are still poorly understood. To delineate this reorganization process, here we investigate the functional3 alterations of in vitro rat cortical circuits following localized laser ablation. The analysis of the functional4 network configuration before and after ablation allowed us to quantify the extent of functional alterations5 and the characteristic spatial and temporal scales along recovery. We observed that damage precipitated6 a fast rerouting of information flow that restored network&rsqu

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Huang, Yali, Charles M. Glasier, Xiaoxu Na, and Xiawei Ou. "White matter functional networks in the developing brain." Frontiers in Neuroscience 18 (October 23, 2024). http://dx.doi.org/10.3389/fnins.2024.1467446.

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BackgroundFunctional magnetic resonance imaging (fMRI) is widely used to depict neural activity and understand human brain function. Studies show that functional networks in gray matter undergo complex transformations from neonatal age to childhood, supporting rapid cognitive development. However, white matter functional networks, given the much weaker fMRI signal, have not been characterized until recently, and changes in white matter functional networks in the developing brain remain unclear.PurposeAims to examine and compare white matter functional networks in neonates and 8-year-old childr

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Zhang, Xue, Yingying Xie, Jie Tang, et al. "Dissect Relationships Between Gene Co-expression and Functional Connectivity in Human Brain." Frontiers in Neuroscience 15 (December 9, 2021). http://dx.doi.org/10.3389/fnins.2021.797849.

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Although recent evidence indicates an association between gene co-expression and functional connectivity in human brain, specific association patterns remain largely unknown. Here, using neuroimaging-based functional connectivity data of living brains and brain-wide gene expression data of postmortem brains, we performed comprehensive analyses to dissect relationships between gene co-expression and functional connectivity. We identified 125 connectivity-related genes (20 novel genes) enriched for dendrite extension, signaling pathway and schizophrenia, and 179 gene-related functional connectio

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Guo, Yi, Zhonghua Lin, Zhen Fan, and Xin Tian. "Epileptic brain network mechanisms and neuroimaging techniques for the brain network." Neural Regeneration Research, December 21, 2023. http://dx.doi.org/10.4103/1673-5374.391307.

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Abstract Epilepsy can be defined as a dysfunction of the brain network, and each type of epilepsy involves different brain-network changes that are implicated differently in the control and propagation of interictal or ictal discharges. Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice. An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tractog

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Zanin, Massimiliano, Bahar Güntekin, Tuba Aktürk, et al. "Telling functional networks apart using ranked network features stability." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-06497-w.

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AbstractOver the past few years, it has become standard to describe brain anatomical and functional organisation in terms of complex networks, wherein single brain regions or modules and their connections are respectively identified with network nodes and the links connecting them. Often, the goal of a given study is not that of modelling brain activity but, more basically, to discriminate between experimental conditions or populations, thus to find a way to compute differences between them. This in turn involves two important aspects: defining discriminative features and quantifying differenc

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Saberi, Majid, Jenny R. Rieck, Shamim Golafshan, et al. "The brain selectively allocates energy to functional brain networks under cognitive control." Scientific Reports 14, no. 1 (2024). https://doi.org/10.1038/s41598-024-83696-7.

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AbstractNetwork energy has been conceptualized based on structural balance theory in the physics of complex networks. We utilized this framework to assess the energy of functional brain networks under cognitive control and to understand how energy is allocated across canonical functional networks during various cognitive control tasks. We extracted network energy from functional connectivity patterns of subjects who underwent fMRI scans during cognitive tasks involving working memory, inhibitory control, and cognitive flexibility, in addition to task-free scans. We found that the energy of the

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Cao, Bolin, Yu Guo, Fengguang Xia, et al. "Dynamic reconfiguration of brain functional networks in world class gymnasts: a resting-state functional MRI study." Brain Communications, February 19, 2025. https://doi.org/10.1093/braincomms/fcaf083.

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Abstract Long-term intensive training has enabled world class gymnasts to attain exceptional skill levels, inducing notable neuroplastic changes in their brains. Previous studies have identified optimized brain modularity related to long-term intensive training based on resting-state functional MRI, which is associated with higher efficiency in motor and cognitive functions. However, most studies assumed that functional topological networks remain static during the scans, neglecting the inherent dynamic changes over time. This study applied a multilayer network model to identify the effect of

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