Journal articles: 'Brain functional Network'

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 (October 1, 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 (May 23, 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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Zheng, Weihao, Choong-Wan Woo, Zhijun Yao, Pavel Goldstein, Lauren Y. Atlas, Mathieu Roy, Liane Schmidt, et al. "Pain-Evoked Reorganization in Functional Brain Networks." Cerebral Cortex 30, no. 5 (December 9, 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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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 (November 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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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 (December 28, 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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Hahn, Andreas, Georg S. Kranz, Ronald Sladky, Sebastian Ganger, Christian Windischberger, Siegfried Kasper, and Rupert Lanzenberger. "Individual Diversity of Functional Brain Network Economy." Brain Connectivity 5, no. 3 (April 2015): 156–65. http://dx.doi.org/10.1089/brain.2014.0306.

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7

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 (August 13, 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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8

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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Betzel, Richard F. "Organizing principles of whole-brain functional connectivity in zebrafish larvae." Network Neuroscience 4, no. 1 (January 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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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 (February 15, 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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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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Gomez Portillo, Ignacio J., and Pablo M. Gleiser. "An Adaptive Complex Network Model for Brain Functional Networks." PLoS ONE 4, no. 9 (September 7, 2009): e6863. http://dx.doi.org/10.1371/journal.pone.0006863.

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13

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 (January 3, 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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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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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 (November 2018): 549–57. http://dx.doi.org/10.1089/brain.2018.0634.

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Gordon, Evan M., Timothy O. Laumann, Scott Marek, Ryan V. Raut, Caterina Gratton, Dillan J. Newbold, Deanna J. Greene, et al. "Default-mode network streams for coupling to language and control systems." Proceedings of the National Academy of Sciences 117, no. 29 (July 6, 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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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 (October 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 (May 9, 2011): 1393–406. http://dx.doi.org/10.1002/hbm.21297.

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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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Hart, Michael G., Stephen J. Price, and John Suckling. "Functional connectivity networks for preoperative brain mapping in neurosurgery." Journal of Neurosurgery 126, no. 6 (August 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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Xu, Yuehua, Miao Cao, Xuhong Liao, Mingrui Xia, Xindi Wang, Tina Jeon, Minhui Ouyang, et al. "Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain." Cerebral Cortex 29, no. 10 (December 7, 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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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 (December 3, 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, Pedro Viana, Joel Winston, Zulqarnain Rashid, Yatharth Ranjan, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (October 21, 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, Pedro Viana, Joel Winston, Zulqarnain Rashid, Yatharth Ranjan, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (October 21, 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, Pedro Viana, Joel Winston, Zulqarnain Rashid, Yatharth Ranjan, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (October 21, 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, Pedro Viana, Joel Winston, Zulqarnain Rashid, Yatharth Ranjan, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (October 21, 2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

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Abstract:

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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27

Laiou, Petroula, Andrea Biondi, Elisa Bruno, Pedro Viana, Joel Winston, Zulqarnain Rashid, Yatharth Ranjan, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (October 21, 2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

Full text

Abstract:

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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28

Laiou, Petroula, Andrea Biondi, Elisa Bruno, Pedro Viana, Joel Winston, Zulqarnain Rashid, Yatharth Ranjan, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (October 21, 2022): 2662. http://dx.doi.org/10.3390/biomedicines10102662.

Full text

Abstract:

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, Pedro Viana, Joel Winston, Zulqarnain Rashid, Yatharth Ranjan, et al. "Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence." Biomedicines 10, no. 10 (October 21, 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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Petri, G., P. Expert, F. Turkheimer, R. Carhart-Harris, D. Nutt, P. J. Hellyer, and F. Vaccarino. "Homological scaffolds of brain functional networks." Journal of The Royal Society Interface 11, no. 101 (December 6, 2014): 20140873. http://dx.doi.org/10.1098/rsif.2014.0873.

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Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10 , 186–198. ( doi:10.1038/nrn2618 )). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects— homological cycles —associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds , a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle, we apply these tools to compare resting-state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin—the main psychoactive component of magic mushrooms. The results show that the homological structure of the brain's functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo.

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Seitzman, Benjamin A., Caterina Gratton, Timothy O. Laumann, Evan M. Gordon, Babatunde Adeyemo, Ally Dworetsky, Brian T. Kraus, et al. "Trait-like variants in human functional brain networks." Proceedings of the National Academy of Sciences 116, no. 45 (October 14, 2019): 22851–61. http://dx.doi.org/10.1073/pnas.1902932116.

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Resting-state functional magnetic resonance imaging (fMRI) has provided converging descriptions of group-level functional brain organization. Recent work has revealed that functional networks identified in individuals contain local features that differ from the group-level description. We define these features as network variants. Building on these studies, we ask whether distributions of network variants reflect stable, trait-like differences in brain organization. Across several datasets of highly-sampled individuals we show that 1) variants are highly stable within individuals, 2) variants are found in characteristic locations and associate with characteristic functional networks across large groups, 3) task-evoked signals in variants demonstrate a link to functional variation, and 4) individuals cluster into subgroups on the basis of variant characteristics that are related to differences in behavior. These results suggest that distributions of network variants may reflect stable, trait-like, functionally relevant individual differences in functional brain organization.

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Aso, Toshihiko, and Hidenao Fukuyama. "Functional Heterogeneity in the Default Mode Network Edges." Brain Connectivity 5, no. 4 (May 2015): 203–13. http://dx.doi.org/10.1089/brain.2014.0256.

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Li, Wenjing, Jianhong Li, Jieqiong Wang, Peng Zhou, Zhenchang Wang, Junfang Xian, and Huiguang He. "Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents." Neural Plasticity 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9849087.

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Previous neuroimaging studies suggested structural or functional brain reorganizations occurred in prelingually deaf subjects. However, little is known about the reorganizations of brain network architectures in prelingually deaf adolescents. The present study aims to investigate alterations of whole-brain functional network using resting-state fMRI and graph theory analysis. We recruited 16 prelingually deaf adolescents (10~18 years) and 16 normal controls matched in age and gender. Brain networks were constructed from mean time courses of 90 regions. Widely distributed network was observed in deaf subjects, with increased connectivity between the limbic system and regions involved in visual and language processing, suggesting reinforcement of the processing for the visual and verbal information in deaf adolescents. Decreased connectivity was detected between the visual regions and language regions possibly due to inferior reading or speaking skills in deaf subjects. Using graph theory analysis, we demonstrated small-worldness property did not change in prelingually deaf adolescents relative to normal controls. However, compared with healthy adolescents, eight regions involved in visual, language, and auditory processing were identified as hubs only present in prelingually deaf adolescents. These findings revealed reorganization of brain functional networks occurred in prelingually deaf adolescents to adapt to deficient auditory input.

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Navas, Adrián, David Papo, Stefano Boccaletti, F. Del-Pozo, Ricardo Bajo, Fernando Maestú, J. H. Martínez, Pablo Gil, Irene Sendiña-Nadal, and Javier M. Buldú. "Functional Hubs in Mild Cognitive Impairment." International Journal of Bifurcation and Chaos 25, no. 03 (March 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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Markošová, Mária, Boris Rudolf, Peter Náther, and Ľubica Beňušková. "Network models for changing degree distributions of functional brain networks." Neural Network World 30, no. 5 (2020): 309–32. http://dx.doi.org/10.14311/nnw.2020.30.021.

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Joudaki, Amir, Niloufar Salehi, Mahdi Jalili, and Maria G. Knyazeva. "EEG-Based Functional Brain Networks: Does the Network Size Matter?" PLoS ONE 7, no. 4 (April 25, 2012): e35673. http://dx.doi.org/10.1371/journal.pone.0035673.

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Ryyppö, Elisa, Enrico Glerean, Elvira Brattico, Jari Saramäki, and Onerva Korhonen. "Regions of Interest as nodes of dynamic functional brain networks." Network Neuroscience 2, no. 4 (October 2018): 513–35. http://dx.doi.org/10.1162/netn_a_00047.

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The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), predetermined groupings of fMRI measurement voxels. Earlier, we demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIs in commonly used brain atlases. Here, we ask how ROIs behave as nodes of dynamic brain networks. To this end, we use two measures: spatiotemporal consistency measures changes in spatial consistency across time and network turnover quantifies the changes in the local network structure around an ROI. We find that spatial consistency varies non-uniformly in space and time, which is reflected in the variation of spatiotemporal consistency across ROIs. Furthermore, we see time-dependent changes in the network neighborhoods of the ROIs, reflected in high network turnover. Network turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover. Finally, we reveal that there is rich voxel-level correlation structure inside ROIs. Because the internal structure and the connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate. Therefore, network neuroscience would greatly benefit from node definition strategies tailored for dynamical networks.

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Zhang, Guifeng, Shanshan Qu, Yu Zheng, Junqi Chen, Guizhu Deng, Canhong Yang, and Yong Huang. "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 (December 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 (October 5, 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 (September 11, 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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D’Souza, Maria M., Mukesh Kumar, Ajay Choudhary, Prabhjot Kaur, Pawan Kumar, Poonam Rana, Richa Trivedi, Tarun Sekhri, and Ajay K. Singh. "Alterations of connectivity patterns in functional brain networks in patients with mild traumatic brain injury: A longitudinal resting-state functional magnetic resonance imaging study." Neuroradiology Journal 33, no. 2 (January 28, 2020): 186–97. http://dx.doi.org/10.1177/1971400920901706.

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Aim In the present study, we aimed to characterise changes in functional brain networks in individuals who had sustained uncomplicated mild traumatic brain injury (mTBI). We assessed the progression of these changes into the chronic phase. We also attempted to explore how these changes influenced the severity of post-concussion symptoms as well as the cognitive profile of the patients. Methods A total of 65 patients were prospectively recruited for an advanced magnetic resonance imaging (MRI) scan within 7 days of sustaining mTBI. Of these, 25 were reassessed at 6 months post injury. Differences in functional brain networks were analysed between cases and age- and sex-matched healthy controls using independent component analysis of resting-state functional MRI. Results Our study revealed reduced functional connectivity in multiple networks, including the anterior default mode network, central executive network, somato-motor and auditory network in patients who had sustained mTBI. A negative correlation between network connectivity and severity of post-concussive symptoms was observed. Follow-up studies performed 6 months after injury revealed an increase in network connectivity, along with an improvement in the severity of post-concussion symptoms. Neurocognitive tests performed at this time point revealed a positive correlation between the functional connectivity and the test scores, along with a persistence of negative correlation between network connectivity and post-concussive symptom severity. Conclusion Our results suggest that uncomplicated mTBI is associated with specific abnormalities in functional brain networks that evolve over time and may contribute to the severity of post-concussive symptoms and cognitive deficits.

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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 (October 12, 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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Cheng, Chen, Wen Zhao Liu, and Jun Jie Chen. "Research on Functional Brain Network Metrics for Depression Patients Automatic Identification." Applied Mechanics and Materials 427-429 (September 2013): 1440–46. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1440.

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Nowadays, Brain network as a means of emerging brain disease research has been fully recognized which is applied to the neurological diseases, such as major depressive disorder (MDD). It also can detect the exception of the whole brain network topological. But there is no evidence to prove that abnormal brain network topology metrics can be an effective feature in the classification model to distinguish the healthy control and MDD. So, we hypothesize the abnormal brain network topology metrics can be used as an valid classification feature. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. According to the theory-based approaches, the global and local metrics were calculated. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. The current study demonstrate that MDD is associated with abnormal function brain network topological metrics and statistically significance network metrics can be successfully used for feature selection in classification algorithms.

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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 (November 27, 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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Ewers, Michael, Ying Luan, Lukas Frontzkowski, Julia Neitzel, Anna Rubinski, Martin Dichgans, Jason Hassenstab, et al. "Segregation of functional networks is associated with cognitive resilience in Alzheimer’s disease." Brain 144, no. 7 (March 16, 2021): 2176–85. http://dx.doi.org/10.1093/brain/awab112.

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Abstract Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer’s disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal ageing, we tested the hypothesis that higher segregation of the brain’s connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer’s disease. Using resting-state functional MRI, we assessed both resting-state functional MRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: (i) 108 individuals with autosomal dominantly inherited Alzheimer’s disease and 71 non-carrier controls; and (ii) 156 amyloid-PET-positive subjects across the spectrum of sporadic Alzheimer’s disease and 184 amyloid-negative controls. In the autosomal dominant Alzheimer’s disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer’s sample, disease stage was assessed by temporal lobe tau-PET (i.e. composite across Braak stage I and III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer’s disease, we found higher functional MRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (P = 0.007). Similarly, for patients with sporadic Alzheimer’s disease, higher functional MRI-assessed system segregation was associated with less decrement in global cognition (P = 0.001) and episodic memory (P = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer’s disease.

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Kim, Jinhee, Marion Criaud, Sang Soo Cho, María Díez-Cirarda, Alexander Mihaescu, Sarah Coakeley, Christine Ghadery, et al. "Abnormal intrinsic brain functional network dynamics in Parkinson’s disease." Brain 140, no. 11 (October 5, 2017): 2955–67. http://dx.doi.org/10.1093/brain/awx233.

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Jao, Tun, Chia-Wei Li, Petra E. Vértes, Changwei Wesley Wu, Sophie Achard, Chao-Hsien Hsieh, Chien-Hui Liou, Jyh-Horng Chen, and Edward T. Bullmore. "Large-Scale Functional Brain Network Reorganization During Taoist Meditation." Brain Connectivity 6, no. 1 (February 2016): 9–24. http://dx.doi.org/10.1089/brain.2014.0318.

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Hou, Wenshuai, Chandler Sours Rhodes, Li Jiang, Steven Roys, Jiachen Zhuo, Joseph JaJa, and Rao P. Gullapalli. "Dynamic Functional Network Analysis in Mild Traumatic Brain Injury." Brain Connectivity 9, no. 6 (July 2019): 475–87. http://dx.doi.org/10.1089/brain.2018.0629.

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De Marco, Matteo, Riccardo Manca, Micaela Mitolo, and Annalena Venneri. "White Matter Hyperintensity Load Modulates Brain Morphometry and Brain Connectivity in Healthy Adults: A Neuroplastic Mechanism?" Neural Plasticity 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/4050536.

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White matter hyperintensities (WMHs) are acquired lesions that accumulate and disrupt neuron-to-neuron connectivity. We tested the associations between WMH load and (1) regional grey matter volumes and (2) functional connectivity of resting-state networks, in a sample of 51 healthy adults. Specifically, we focused on the positive associations (more damage, more volume/connectivity) to investigate a potential route of adaptive plasticity. WMHs were quantified with an automated procedure. Voxel-based morphometry was carried out to model grey matter. An independent component analysis was run to extract the anterior and posterior default-mode network, the salience network, the left and right frontoparietal networks, and the visual network. Each model was corrected for age, global levels of atrophy, and indices of brain and cognitive reserve. Positive associations were found with morphometry and functional connectivity of the anterior default-mode network and salience network. Within the anterior default-mode network, an association was found in the left mediotemporal-limbic complex. Within the salience network, an association was found in the right parietal cortex. The findings support the suggestion that, even in the absence of overt disease, the brain actuates a compensatory (neuroplastic) response to the accumulation of WMH, leading to increases in regional grey matter and modified functional connectivity.

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Flanagan, Ryan, Lucas Lacasa, Emma K. Towlson, Sang Hoon Lee, and Mason A. Porter. "Effect of antipsychotics on community structure in functional brain networks." Journal of Complex Networks 7, no. 6 (April 15, 2019): 932–60. http://dx.doi.org/10.1093/comnet/cnz013.

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AbstractSchizophrenia, a mental disorder that is characterized by abnormal social behaviour and failure to distinguish one’s own thoughts and ideas from reality, has been associated with structural abnormalities in the architecture of functional brain networks. In this article, we (1) investigate whether mesoscale network properties give relevant information to distinguish groups of patients from controls in different scenarios and (2) use this lens to examine network effects of different antipsychotic treatments. Using various methods of network analysis, we examine the effect of two classical therapeutic antipsychotics—Aripiprazole and Sulpiride—on the architecture of functional brain networks of both controls (i.e., a set of people who were deemed to be healthy) and patients (who were diagnosed with schizophrenia). We compare community structures of functional brain networks of different individuals using mesoscopic response functions, which measure how community structure changes across different scales of a network. Our approach does a reasonably good job of distinguishing patients from controls, and the distinction is sharper for patients and controls who have been treated with Aripiprazole. Unexpectedly, we find that this increased separation between patients and controls is associated with a change in the control group, as the functional brain networks of the patient group appear to be predominantly unaffected by this drug. This suggests that Aripiprazole has a significant and measurable effect on community structure in healthy individuals but not in individuals who are diagnosed with schizophrenia, something that conflicts with the naive assumption that the drug alters the mesoscale network properties of the patients (rather than the controls). By contrast, we are less successful at separating the networks of patients from those of controls when the subjects have been given the drug Sulpiride. Taken together, in our results, we observe differences in the effects of the drugs (and a placebo) on community structure in patients and controls and also that this effect differs across groups. From a network-science perspective, we thereby demonstrate that different types of antipsychotic drugs selectively affect mesoscale properties of brain networks, providing support that structures such as communities are meaningful functional units in the brain.

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Journal articles on the topic 'Brain functional Network'

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