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Littérature scientifique sur le sujet « Brain functional Network »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Brain functional Network"

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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Thèses sur le sujet "Brain functional Network"

Deshpande, Gopikrishna. "Nonlinear and network characterization of brain function using functional MRI." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/24760.

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Thesis (Ph.D.)--Biomedical Engineering, Georgia Institute of Technology, 2007.<br>Committee Chair: Hu, Xiaoping; Committee Member: Brummer, Marijn; Committee Member: Butera, Robert; Committee Member: Oshinski, John; Committee Member: Sathian, Krish.

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SALA, SARA. "Statistical analysis of brain network." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/43723.

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Recent developments in the complex networks analysis, based largely on graph theory, have been used to study the brain network organization. The brain is a complex system that can be represented by a graph. A graph is a mathematical representation which can be useful to study the connectivity of the brain. Nodes in the brain can be identified dividing its volume in regions of interest and links can be identified calculating a measure of dependence between pairs of regions whose activation signal, measured by functional magnetic resonance imaging (fMRI) techniques, represents the strength

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Jao, Tun. "Functional brain network organization in altered states of consciousness." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709230.

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Cole, David Michael. "Functional network analysis of human brain systems under pharmacological modulation." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/10933.

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Complex alterations in brain function and neurochemistry underlie pathology and treatment in multiple neuropsychiatric disorders, yet remain incompletely characterised. This thesis outlines possibilities for human neuroimaging techniques sensitive to spontaneous fluctuations in large-scale neurobiological signalling, or ‘resting-state network (RSN) functional connectivity’, to address such knowledge gaps. Novel RSN-sensitive analysis approaches to functional magnetic resonance imaging data are introduced. These techniques are then evaluated experimentally, in contexts relevant for maladaptive

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García-García, Isabel, María Ángeles Jurado, Maite Garolera, et al. "Functional network centrality in obesity." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-205556.

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Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Re

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Gozdas, Elveda. "Quantitative Trends and Topology in Developing Functional Brain Networks." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535381148527108.

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McColgan, Peter. "Structural brain network degeneration and functional up-regulation in Huntington's disease." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10041942/.

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Huntington’s disease (HD) is a neurodegenerative disorder caused by a CAG repeat expansion in the Huntingtin gene on chromosome 4. In recent years there have been significant advances in understanding both the cellular pathology and the macrostructural changes that occur in the striatum and cortical structures as the disease proceeds. However, it remains unclear how abnormalities at the cellular level lead to characteristic patterns of macrostructural change in the brains of HD patients. In this thesis I aim to link structural and functional brain network abnormalities with regional changes at

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Hart, Michael Gavin. "Network approaches to understanding the functional effects of focal brain lesions." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274018.

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Complex network models of functional connectivity have emerged as a paradigm shift in brain mapping over the past decade. Despite significant attention within the neuroimaging and cognitive neuroscience communities, these approaches have hitherto not been extensively explored in neurosurgery. The aim of this thesis is to investigate how the field of connectomics can contribute to understanding the effects of focal brain lesions and to functional brain mapping in neurosurgery. This datasets for this thesis include a clinical population with focal brain tumours and a cohort focused on healthy ad

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Elkin-Frankston, Seth. "Anatomical and functional impact of critical brain areas to network activity and basic visual function." Thesis, Boston University, 2013. https://hdl.handle.net/2144/12752.

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Thesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>A set of widely distributed brain areas, collectively known as the fronto-parietal network, serve to modulate aspects of visual perception. However, the unique influence exerted by these regions on low-level vi

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Ghumman, Sukhmanjit. "Functional connectivity in patients with brain tumours." Mémoire, Université de Sherbrooke, 2018. http://hdl.handle.net/11143/12001.

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Abstract: The default mode network of the brain is a set of functionally connected regions associated with introspection and daydreaming. Recent fMRI studies have discovered that the default mode network is often perturbed in the diseased brain. For example, the default mode network is known to be modulated in dementia, ADHD, depression, and schizophrenia, among others. This has led many into believing that this network could have a role in the physiopathology of nervous system disease, or could be a useful marker of brain function. However, very few studies have yet been done which investigat

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Livres sur le sujet "Brain functional Network"

Alessandro, Treves, ed. Neural networks and brain function. Oxford University Press, 1998.

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W, Thatcher Robert, ed. Functional neuroimaging: Technical foundations. Academic Press, 1994.

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De Vico Fallani, Fabrizio, and Fabio Babiloni. The Graph Theoretical Approach in Brain Functional Networks. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01644-8.

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Baev, Konstantin V. Biological Neural Networks: Hierarchical Concept of Brain Function. Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-4100-3.

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Paul, Cisek, Drew Trevor, and Kalaska John F, eds. Computational neuroscience: Theoretical insights into brain function. Elsevier, 2007.

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José, Hanson Stephen, and Olson Carl R, eds. Connectionist modeling and brain function: The developing interface. MIT Press, 1990.

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1947-, Kitamura Tadashi, ed. What should be computed to understand and model brain function?: From robotics, soft computing, biology and neuroscience to cognitive philosophy. ill., 2001.

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Elizabeth, Hillis Argye, ed. New techniques for identifying the neural substrates of language. Psychology Press, 2002.

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E, Raichle Marcus, ed. Images of mind. Scientific American Library, 1994.

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van, Pelt J., ed. Development, dynamics, and pathology of neuronal networks: From molecules to functional circuits : proceedings of the 23rd International Summer School of Brain Research, held at the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands, from 25-29 August 2003. Elsevier, 2005.

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Chapitres de livres sur le sujet "Brain functional Network"

Moorthigari, Vishnu, Emily Carlson, Petri Toiviainen, Elvira Brattico, and Vinoo Alluri. "Differential Effects of Trait Empathy on Functional Network Centrality." In Brain Informatics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59277-6_10.

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Fiddyment, Grant M., Stefania Sokolowski, and Mark Kramer. "Functional Network Observations of Diseased Brain States." In Encyclopedia of Computational Neuroscience. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_440.

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Ke, Ming, Hui Shen, Zongtan Zhou, Xiaolin Zhou, Dewen Hu, and Xuhui Chen. "Brain Functional Network for Chewing of Gum." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21378-6_13.

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Fiddyment, Grant M., Stefania Sokolowski, and Mark Kramer. "Functional Network Observations of Diseased Brain States." In Encyclopedia of Computational Neuroscience. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_440-2.

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Yu, Qingbao, and Vince D. Calhoun. "Resting-State Functional Network Disturbances in Schizophrenia." In Brain Network Dysfunction in Neuropsychiatric Illness. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59797-9_10.

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Rey, Gwladys, Camille Piguet, and Patrik Vuilleumier. "Functional Resting-State Network Disturbances in Bipolar Disorder." In Brain Network Dysfunction in Neuropsychiatric Illness. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59797-9_13.

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Zhang, Xiaofei, Yang Yang, Ruohao Liu, and Ning Zhong. "Route Adjustment of Functional Brain Network in Mental Arithmetic Using Task-Evoked FMRI." In Brain Informatics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37078-7_6.

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Zhang, Xiaofei, Yang Yang, Ming-Hui Zhang, and Ning Zhong. "Network Analysis of Brain Functional Connectivity in Mental Arithmetic Using Task-Evoked fMRI." In Brain Informatics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05587-5_14.

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Müller, Ralph-Axel, and Annika Linke. "Functional Connectivity in Autism Spectrum Disorders: Challenges and Perspectives." In Brain Network Dysfunction in Neuropsychiatric Illness. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59797-9_12.

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Lai, Chien-Han. "Task MRI-Based Functional Brain Network of Anxiety." In Advances in Experimental Medicine and Biology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9705-0_1.

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Actes de conférences sur le sujet "Brain functional Network"

Eldaly, Abdelrahman B. M., Guanrong Chen, and Leanne Lai-Hang Chan. "Functional Brain Network Alterations Against Scaling." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782009.

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Tao, Ye, Anand D. Sarwate, Sandeep Panta, Sergey Plis, and Vince D. Calhoun. "Privacy-Preserving Visualization of Brain Functional Network Connectivity." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635222.

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Yang, Defu, Jiaqi Zhao, Minghan Chen, et al. "Learning Functional Dynamics from a Multilayer Brain Network." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635441.

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Yang, Dengsong, Ruofan Wang, and Changzhi Song. "Classification of Alzheimer's Disease Using fMRI-Based Brain Functional Network Data." In 2024 6th Asia Symposium on Image Processing (ASIP). IEEE, 2024. http://dx.doi.org/10.1109/asip63198.2024.00025.

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Ke, Hepeng, Chang Cai, and Srikantan Nagarajan. "Joint Bayesian estimation of brain regional activity and network functional connectivity." In 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2024. https://doi.org/10.1109/biocas61083.2024.10798242.

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Guo, Yuanyuan, Xin Wen, Yi Lei, Xiaobo Liu, Zhenqi Liu, and Rui Cao. "Global-Local Brain Network based on Functional Connectivity for Individualized Prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822786.

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Sipes, Benjamin S., and Ashish Raj. "Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981308.

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Aeschlimann, Y., A. Calissano, T. Papadopoulo, and S. Deslauriers-Gauthier. "Brain Network Alignment Using Structural and Functional Connectivity With Anatomical Constraints." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981283.

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Guan, Hongzheng, Tao Jin, Li Xiao, Gang Qu, and Yu-Ping Wang. "Spatio-Temporal Mapping Generative Adversarial Network for Functional Connectivity Network Reconstruction across Brain Atlases." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890440.

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Piljugin, Oleg. "Functional Brain Network Analysis in Children Performing Working Memory Tasks: EEG Study." In 2024 8th Scientific School Dynamics of Complex Networks and their Applications (DCNA). IEEE, 2024. http://dx.doi.org/10.1109/dcna63495.2024.10718499.

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Rapports d'organisations sur le sujet "Brain functional Network"

Gong, Zhijia, Jiao He, and Li Zhang. Application of Brain-Computer Interface-Based Functional Electrical Stimulation, Transcranial Direct Current Stimulation and Motor Rehabilitation in Upper Limb Rehabilitation of Stroke Survivors: A Systematic Review and Network Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2025. https://doi.org/10.37766/inplasy2025.5.0066.

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa

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Yin, Ziqian, Youjia Qiu, Menghan Wang, et al. Motor function improvement and acceptability of non-invasive brain stimulation in patients with Parkinson's disease: A Bayesian network analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.4.0087.

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Dale, Naomi, Aneesa Khan, and Sophie Dale. Early intervention for vision and neurodevelopment in infants and very young children with visual impairment: a systematicreview. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.8.0080.

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Review question / Objective: Research question - What is the effectiveness of Early Childhood Intervention (ECI) in the first 3 years of life? Population (P) Infants and very young children with diagnosed visual impairment. Intervention (I) ECI programmes that includes vision and developmental stimulation, play, learning and responsive parenting Comparison (C) Standard care or control Outcomes (O) Primary: Vision function or and/or neurodevelopment and/or parent-child interaction outcomes Secondary: Parental context factors eg parental wellbeing and mental health, parental satisfaction with se

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