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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, we highlighted the essentials of neural plasticity and the effects of learning on brain network development. We also discussed two important theoretical and practical concerns in brain network training. Finally, we concluded by presenting the significance of network training in typically and atypically developed brains.
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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 networks (NDCN) and constructs a dynamic network model structure that can provide more than the original signal range. Experimental results show that every snapshot in the network obtained by the proposed method has a usable network structure and that it also has a good classification result in the diagnosis of cognitive impairment diseases.
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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 compared to subjects with normotension. In addition, we leveraged multimodal imaging to identify the signature of hypertension injury on the brain. Our study included 37 subjects (18 normotensives and 19 hypertensives), characterized by microstructural integrity by diffusion tensor imaging and cognitive profile, who were subjected to resting-state functional magnetic resonance imaging analysis. We mapped brain functional connectivity networks and evaluated the connectivity differences among regions, identifying the altered connections in patients with hypertension compared with subjects with normotension in the (1) dorsal attention network and sensorimotor network; (2) dorsal attention network and visual network; (3) dorsal attention network and frontoparietal network. Then we tested how diffusion tensor imaging fractional anisotropy of superior longitudinal fasciculus correlates with the connections between dorsal attention network and default mode network and Montreal Cognitive Assessment scores with a widespread network of functional connections. Finally, based on our correlation analysis, we applied a feature selection to highlight those most relevant to describing brain injury in patients with hypertension. Our multimodal imaging data showed that hypertensive brains present a network of functional connectivity alterations that correlate with cognitive dysfunction and microstructural integrity. Registration— URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02310217.
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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 stimuli increased somatosensory network connectivity with (a) frontoparietal networks involved in context representation, (b) “ventral attention network” regions involved in motivated action selection, and (c) basal ganglia and brainstem regions. This resulted in reduced “small-worldness,” modularity (fewer networks), and global network efficiency and in the emergence of an integrated “pain supersystem” (PS) whose activity predicted individual differences in pain sensitivity across 5 participant cohorts. Network hubs were reorganized (“hub disruption”) so that more hubs were localized in PS, and there was a shift from “connector” hubs linking disparate networks to “provincial” hubs connecting regions within PS. Our findings suggest that pain reorganizes the network structure of large-scale brain systems. These changes may prioritize responses to painful events and provide nociceptive systems privileged access to central control of cognition and action during pain.
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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 functional networks. A modified greedy colouring algorithm was proposed for fractal dimension calculation in both binary and weighted brain functional networks. The results indicate that brain functional networks still maintain fractal structures even when the brain is at fatigue state; fractal dimension presented an increasing trend along with the deepening of mental fatigue fractal dimension of the weighted network was more sensitive to mental fatigue than that of binary network. Our current results suggested that mental fatigue has great regular impacts on the fractal dimension in both binary and weighted brain functional networks.
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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 on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.
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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 artificial brain networks and to reveal the generative mechanisms and evolutionary patterns of human functional brain across human lifespans. It is suggested that the idea of classifying common neighbours while considering anatomical distances during network formation can provide a much more similar generative mechanism of the human fMRI brain aging process as well as a more practical generative network model of it. We hold that studies on brain normal aging process modelling have the potential to improve the way in which early warnings for latent injury or disease are practised today and advance healthcare.
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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 Pearson’s correlation-based functional connectivity was used to construct the brain network. The study found that connections in the sensory motor network (SMN), dorsal attention network (DAN), salience network (SAN), default mode network (DMN), and cerebral network were severely affected in AD and MCI. The disruption in these networks may serve as potential biomarkers for distinguishing AD and MCI from HC. The study suggests that alterations in functional connectivity in these networks may contribute to cognitive deficits observed in AD and MCI. Additionally, a negative correlation was observed between the global clinical dementia rating (CDR) score and the Z-score of functional connectivity within identified clusters in AD subjects. These findings provide compelling evidence suggesting that the neurodegenerative disruption of functional magnetic resonance imaging (fMRI) connectivity is extensively distributed across multiple networks in individuals diagnosed with AD.
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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 hierarchical modular structure and hubs that span module boundaries. We go on to show that spontaneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal basic organizing principles of whole-brain functional brain networks at the mesoscale. Our overarching methodological framework provides a blueprint for studying correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and mesoscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.
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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 emergence of complex networks with characteristics similar to those observed in functional brain networks.
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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 networks were extracted and associated with time coordination, suggesting that they were involved in expectation and imitation behaviors.
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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 in the overall organization of a network), providing essential clues to describe connectivity patterns. However, existing studies often fail to explore such hubs from brain connectivity networks. To address these two issues, we propose a Connectivity Network analysis method with discriminative Hub Detection (CNHD) for brain disease diagnosis using functional magnetic resonance imaging (fMRI) data. Specifically, we incorporate both feature extraction of brain networks and network-based classification into a unified model, while discriminative hubs can be automatically identified from data via ℓ1-norm and ℓ2,1-norm regularizers. The proposed CNHD method is evaluated on three real-world schizophrenia datasets with fMRI scans. Experimental results demonstrate that our method not only outperforms several state-of-the-art approaches in disease diagnosis, but also is effective in automatically identifying disease-related network hubs in the human brain.
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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 repeated-measures analysis of variance (ANOVA), which showed that the upregulation training helped to improve inhibitory control. The upregulated small-world brain network exhibits an increase in the brain network regularization, links widely dispersed brain resources, and reduces the lateralization of brain functional networks between hemispheres. This suggests an inherent correlation between small-world functional brain networks and inhibitory control; moreover, dynamic optimization under cost efficiency trade-offs provides a neural basis for inhibitory control. Inhibitory control is not a simple function of a single brain region or connectivity but rather an emergent property of a broader network.
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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 variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.
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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 subdivisions were more valid—more internally homogeneous and better matching spatial patterns of task activation—than canonical networks. These measures of validity were maximized at a hierarchical scale that contained ∼83 subnetworks across the brain. At this scale, nine DMN subnetworks exhibited topographical similarity across subjects, suggesting that this approach identifies homologous neurobiological circuits across individuals. Some DMN subnetworks matched known features of brain organization corresponding with cognitive functions. Other subnetworks represented separate streams by which DMN couples with other canonical large-scale networks, including language and control networks. Together, this work provides a detailed organizational framework for studying the DMN in individual humans.
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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. During real feedback, the occurrence of “default mode-executive control competition-related” mode was higher than that during sham feedback (P=0.0003); the “default mode-visual facilitation-related” mode more frequently appeared during sham than real feedback (P=0.0004). However, the dynamics of the sensorimotor network did not change significantly between two conditions (P>0.05). Our results indicated that the visual-guided motor feedback involves higher cognitive functional networks rather than primary motor network. The dynamics monitoring of inner and outside environment and multisensory integration could be the mechanisms. This study is an extension of our previous region-specific and static-styled study of our brain functional architecture.
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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 the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.
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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-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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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.
Full textAbstract:
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-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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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.
Full textAbstract:
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-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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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.
Full textAbstract:
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-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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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.
Full textAbstract:
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-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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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.
Full textAbstract:
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-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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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.
Full textAbstract:
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-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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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 connectivity networks in neurosurgical patients with focal lesions and the potential benefits of resting-state connectivity mapping for surgical planning.METHODSResting-state fMRI sequences with a 3-T scanner and multiecho echo-planar imaging coupled to independent component analysis were acquired preoperatively from 5 study participants who had a right temporoparietooccipital glioblastoma. Seed-based functional connectivity analysis was performed with InstaCorr. Network identification focused on 7 major functional connectivity networks described in the literature and a putative language network centered on Broca's area.RESULTSAll 8 functional connectivity networks were identified in each participant. Tumor-related topological changes to the default mode network were observed in all participants. In addition, each participant had at least 1 other abnormal network, and each network was abnormal in at least 1 participant. Individual patterns of network irregularities were identified with a qualitative approach and included local displacement due to mass effect, loss of a functional network component, and recruitment of new regions.CONCLUSIONSResting-state fMRI can reliably and rapidly detect common functional connectivity networks in patients with glioblastoma and also has sufficient sensitivity for identifying patterns of network alterations. Mapping of functional connectivity networks offers the possibility to expand investigations to less commonly explored neuropsychological processes, such as executive control, attention, and salience. Changes in these networks may allow insights into mechanisms underlying the functional consequences of tumor growth, surgical intervention, and patient rehabilitation.
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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 other cerebral regions in an organised network, such as the anterior frontal lobe, anterior cingulate gyrus, parahippocampal gyrus, amygdala, hypothalamus, head of the caudate nucleus and anterior lobe of the cerebellum. Networks were analysed based on the resting cerebral functional connection, and the differences in the activities of the brain networks between the two time points were compared. Results At 5 min after needle removal, 12 brain functional regions were involved in organising the network centred at the caudate nucleus ‘seed.’ This number was greater than the number of related brain networks centred at the other ‘seeds’. At 15 min after needle removal, 15 and 14 brain functional regions were involved in organised networks centred at the parahippocampal and hypothalamus ‘seeds’, respectively; these numbers were greater than the numbers of other related brain networks centred at the other ‘seeds’. Conclusions A brain network composed of a large number of cerebral functional regions was found after EA at GV20 and Yintang in healthy volunteers. The key brain ‘seed’ supporting the largest brain network changed between 5 and 15 min after needle removal.
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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 essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.
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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 attention deficit hyperactivity disorder. Using different types of similarity measures including conditional mutual information, we show here that the backbone of the functional connectivity and the direct connectivity within both the DMN and the FPN does not vary significantly between healthy individuals for the AAL brain atlas. Weaker connections do vary however, having a particularly pronounced effect on the cross-connections between DMN and FPN. Our findings suggest that the link topology of single resting-state networks is quite robust if a fixed brain atlas is used and the recordings are sufficiently long—even if the whole brain network topology between different individuals is variable.
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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 disruptions. The current study investigated the effect of cholinergic and serotonergic antagonists on large-scale brain networks in mice. The cholinergic system is involved in cognitive functions and is impaired in, e.g., Alzheimer's disease, while the serotonergic system is involved in emotional and introspective functions and is impaired in, e.g., Alzheimer's disease, depression and autism. Specific interest goes to the default-mode-network (DMN), which is studied extensively in humans and is affected in many neurological disorders. The results show that both cholinergic and serotonergic antagonists impaired the mouse DMN-like network similarly, except that cholinergic modulation additionally affected the retrosplenial cortex. This suggests that both neurotransmitter systems are involved in maintaining integrity of FC within the DMN-like network in mice. Cholinergic and serotonergic modulations also affected other functional networks, however, serotonergic modulation impaired the frontal and thalamus networks more extensively. In conclusion, this study demonstrates the utility of pharmacological rsfMRI in animal models to provide insights into the role of specific neurotransmitter systems on functional networks in neurological disorders.
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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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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 constructed and the centrality, i.e. importance, of their nodes was quantified. The results showed that, with respect to healthy controls, MCI patients were associated with decreases and increases in hub centrality respectively in occipital and central scalp regions, supporting the hypothesis that MCI modifies functional brain network topology, leading to more random structures.
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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-defined individual functional units is mainly associated with the regional neuronal density, while the between-functional units’ connectivity strength is also influenced by the glia and synaptic components of brain tissue cellular constituents. These mechanisms lead to a rather broad distribution of resting-state functional network properties. Visual networks with the highest neuronal density (but lowest density of glial cells and synapses) exhibit the strongest coherence of the BOLD signal as well as the strongest intra-network connectivity. The Default Mode Network (DMN) is positioned near the opposite part of the spectrum with relatively low coherence of the BOLD signal but with a remarkably balanced cellular contents, enabling DMN to have a prominent role in the overall organization of the brain and hierarchy of functional networks.
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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 empirical functional connectivity matrix, an optimal global coupling coefficient was obtained. Then, the waveforms and spectra of simulated EEG signals and four commonly used measures from graph theory and small-world network properties of simulated brain networks under different thresholds were analyzed. (3) Results: The results showed that the correlation coefficient of the functional connectivity matrix of the simulated WBNM and empirical brain networks could reach a maximum value of 0.676 when the global coupling coefficient was set to 20.3. The simulated EEG signals showed rich waveform and frequency-band characteristics. The commonly used graph-theoretical measures and small-world properties of the constructed WBNM were similar to those of empirical brain networks. When the threshold was set to 0.22, the maximum correlation between the simulated WBNM and empirical brain networks was 0.709. (4) Conclusions: The constructed resting-state WBNM is similar to a real brain network to a certain extent and can be used to study the neurophysiological mechanisms of complex brain networks.
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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 neurodevelopmental alterations, which might be associated with executive dysfunction.MethodsThis study included 17 male adolescents with Internet gaming disorder, and 18 age-matched male adolescents as healthy controls. Functional connectivity was examined using seed-to-voxel analysis and seed-to-seed analysis, with the nodes of large-scale intrinsic networks used as region of interests. Group independent component analysis was performed to investigate spatially independent network.ResultsWe identified aberrant functional connectivity of salience network and default mode network with the left posterior superior temporal sulcus (pSTS) in adolescents with IGD. Furthermore, functional connectivity between salience network and pSTS correlated with proneness to Internet addiction and self-reported cognitive problems. Independent component analysis revealed that pSTS was involved in social brain network.Discussion and conclusionsThe results imply that aberrant functional connectivity of social brain network with default mode network and salience network was identified in IGD that may be associated with executive dysfunction. Our results suggest that inordinate social stimuli during excessive online gaming leads to altered connections among large-scale networks during neurodevelopment of adolescents.
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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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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 alterations of the large-scale functional networks. The graph theory analysis was applied using the GRETNA and SPM12 software to construct the whole-brain network and yield the small-world parameters and network efficiency. Results. Compared with the healthy subjects, the stroke patients had a decreased normalized small-worldness (σ), global efficiency (Eg), and the mean local efficiency (Eloc) of the whole-brain network in the resting state. There was a correlation between the duration after stroke onset and Eloc. Acupuncture improved the patients’ clustering coefficient (Cp) and Eloc but did not make a significant impact on the σ and Eg. The postacupuncture variables of the whole-brain network had no association with the time of onset. Conclusion. The poststroke whole-brain network tended to a random network with reduced network efficiency. Acupuncture was able to modulate the disrupted patterns of the whole-brain network following the subcortical ischemic stroke. Our findings shed light on the potential mechanisms of the functional reorganization on poststroke brain networks involving acupuncture intervention from a large-scale perspective.
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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 longitudinal brain connectivity. Network-based statistic (NBS) analysis did not find significant difference in the group-by-time interaction and time effects. However, 258 functional pairs show group differences in which mTBI patients have higher functional connectivity. Meta-analysis showed that “Action” and “Cognition” are the most affected functional domains. Categorization of connectomic signatures using multiview group-wise cluster analysis identified two patterns of functional hyperconnectivity among mTBI patients: (I) between the posterior cingulate cortex and the association areas of the brain and (II) between the occipital and the frontal lobes of the brain. Our results demonstrate that brain concussion renders connectome-scale brain network connectivity changes, and the brain tends to be hyperactivated to compensate the pathophysiological disturbances.
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Chen, Wenyu, Ling Zhan, and Tao Jia. "Sex Differences in Hierarchical and Modular Organization of Functional Brain Networks: Insights from Hierarchical Entropy and Modularity Analysis." Entropy 26, no. 10 (2024): 864. http://dx.doi.org/10.3390/e26100864.
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Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functional brain network is a critical feature for assessing these neural mechanisms. But the sex differences in hierarchical organization have not been fully investigated. Here, we explore whether the hierarchical structure of the brain network differs between females and males using resting-state fMRI data. We measure the hierarchical entropy and the maximum modularity of each individual, and identify a significant negative correlation between the complexity of hierarchy and modularity in brain networks. At the mean level, females show higher modularity, whereas males exhibit a more complex hierarchy. At the consensus level, we use a co-classification matrix to perform a detailed investigation of the differences in the hierarchical organization between sexes and observe that the female group and the male group exhibit different interaction patterns of brain regions in the dorsal attention network (DAN) and visual network (VIN). Our findings suggest that the brains of females and males employ different network topologies to carry out brain functions. In addition, the negative correlation between hierarchy and modularity implies a need to balance the complexity in the hierarchical organization of the brain network, which sheds light on future studies of brain functions.
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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 representations in various downstream tasks. To this end, we propose a novel joint structural-functional brain graph transformer model (namely sfBGT). Technically, we design a cross-network assortativity quantification mechanism to enable structural-functional brain network coupling, thus capturing the interplays of brain structure and function. We then employ a multimodal graph transformer to effectively learn joint representations of structural-functional brain networks along with their coupling relation representations. Experimental results on three real-world datasets demonstrate the superiority of sfBGT over state-of-the-art baselines.
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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’s communicability in about 15 min. Functional7 restoration was led by the immediate neighbors around trauma but was orchestrated by the entire network.8 Our in vitro setup exposes the ability of neuronal circuits to articulate fast responses to acute damage, and9 may serve as a proxy to devise recovery strategies in actual brain circuits. Moreover, this biological setup10 can become a benchmark to empirically test network theories about the spontaneous recovery in dynamical11 networks.
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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 children.MethodsWe acquired resting-state fMRI data on 69 full-term healthy neonates and 38 healthy 8-year-old children using a same imaging protocol and studied their brain white matter functional networks using a similar pipeline. First, we utilized the ICA method to extract white matter functional networks. Next, we analyzed the characteristics of the white matter functional networks from both time-domain and frequency-domain perspectives, specifically, intra-network functional connectivity (intra-network FC), inter-network functional connectivity (inter-network FC), and fractional amplitude of low-frequency fluctuation (fALFF). Finally, the differences in the above functional networks’ characteristics between the two groups were evaluated. As a supplemental measure and to confirm with literature findings on gray matter functional network changes in the developing brain, we also studied and reported functional networks in gray matter.ResultsWhite matter functional networks in the developing brain can be depicted for both the neonates and the 8-year-old children. White matter intra-network FC within the optic radiations, corticospinal tract, and anterior corona radiata was lower in 8-year-old children compared to neonates (p < 0.05). Inter-network FC between cerebral peduncle (CP) and anterior corona radiation (ACR) was higher in 8-year-olds (p < 0.05). Additionally, 8-year-olds showed a greater distribution of brain activity energy in the high-frequency range of 0.01–0.15 Hz. Significant developmental differences in brain white matter functional networks exist between the two group, characterized by increased inter-network FC, decreased intra-network FC, and higher high-frequency energy distribution. Similar findings were also observed in gray matter functional networks.ConclusionWhite matter functional networks can be reliably measured in the developing brain, and the differences in these networks reflect functional differentiation and integration in brain development.
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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 connections mainly connecting intra-network regions, especially homologous cortical regions. In addition, 51 genes were associated with connectivity in all brain functional networks and enriched for action potential and schizophrenia; in contrast, 51 genes showed network-specific modulatory effects and enriched for ion transportation. These results indicate that functional connectivity is unequally affected by gene expression, and connectivity-related genes with different biological functions are involved in connectivity modulation of different networks.
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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 tractography, diffusion kurtosis imaging-based fiber tractography, fiber ball imaging-based tractography, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, molecular imaging, and functional ultrasound imaging have been extensively used to delineate epileptic networks. In this review, we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy, and extensively analyze the imaging mechanisms, advantages, limitations, and clinical application ranges of each technique. A greater focus on emerging advanced technologies, new data analysis software, a combination of multiple techniques, and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.
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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 differences between them. Here we show that the ranked dynamical stability of network features, from links or nodes to higher-level network properties, discriminates well between healthy brain activity and various pathological conditions. These easily computable properties, which constitute local but topographically aspecific aspects of brain activity, greatly simplify inter-network comparisons and spare the need for network pruning. Our results are discussed in terms of microstate stability. Some implications for functional brain activity are discussed.
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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 whole-brain network increases when exposed to cognitive control tasks compared to the task-free resting state, which serves as a reference point. The brain selectively allocates this elevated energy to canonical functional networks; sensory networks receive more energy to support flexibility for processing sensory stimuli, while cognitive networks relevant to the task, functioning efficiently, require less energy. Furthermore, employing network energy, as a global network measure, improves the performance of predictive modeling, particularly in classifying cognitive control tasks and predicting chronological age. Our results highlight the robustness of this framework and the utility of network energy in understanding brain and cognitive mechanisms, including its promising potential as a biomarker for mental conditions and neurological disorders.
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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 long-term intensive training on dynamic functional network properties in gymnasts. The imaging data were collected from 13 gymnasts and 14 age- and gender-matched non-athlete controls. We first construct dynamic functional connectivity matrices for each subject to capture the temporal information underlying these brain signals. Then, we applied a multilayer community detection approach to analyze how brain regions form modules and how this modularity changes over time. Graph theoretical parameters, including flexibility, promiscuity, cohesion, and disjointedness, were estimated to characterize the dynamic properties of functional networks across global, network, and nodal levels in the gymnasts. The gymnasts showed significantly lower flexibility, cohesion, and disjointedness at the global level than the controls. Then, we observed lower flexibility and cohesion in the auditory, dorsal attention, sensorimotor, subcortical, cingulo-opercular, and default mode networks in the gymnasts than in the controls. Furthermore, these gymnasts showed decreased flexibility and cohesion in several regions associated with motor function. Together, we found brain functional neuroplasticity related to long-term intensive training, primarily characterized by decreased flexibility of brain dynamics in the gymnasts, which provided new insights into brain reorganization in motor skill learning.