Relevant bibliographies by topics / Brain functional Network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Brain functional Network'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Brain functional Network"

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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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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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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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Dissertations / Theses on the topic "Brain functional Network"

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

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Thesis (Ph.D.)--Biomedical Engineering, Georgia Institute of Technology, 2007.
Committee Chair: Hu, Xiaoping; Committee Member: Brummer, Marijn; Committee Member: Butera, Robert; Committee Member: Oshinski, John; Committee Member: Sathian, Krish.
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SALA, SARA. "Statistical analysis of brain network." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/43723.

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Recent developments in the complex networks analysis, based largely on graph theory, have been used to study the brain network organization. The brain is a complex system that can be represented by a graph. A graph is a mathematical representation which can be useful to study the connectivity of the brain. Nodes in the brain can be identified dividing its volume in regions of interest and links can be identified calculating a measure of dependence between pairs of regions whose activation signal, measured by functional magnetic resonance imaging (fMRI) techniques, represents the strength of the connec-tion between regions. A graph can be synthesized by the so-called adjacency matrix, which, in its simplest form, is an undirected, binary, and symmetric matrix, whose en-tries are set to one if a link exists between a pair of brain areas and zero otherwise. The adjacency matrix is particularly useful because allows the calculation of several measures which summarize global and local character-istics of functional brain connectivity, such as centrality, e ciency, density and small worldness property. In this work, we consider the global measures, such as the clustering coe cient, the characteristic path length and the global e ciency, and the local measures, such as centrality measures and local e ciency, in order to represent global and local dynam-ics and changes between networks. This is achieved by studying with resting state (rs) fMRI data of healthy subjects and patients with neurodegenerative diseases. Furthermore we illustrate an original methodology to construct the adjacency matrix. Its entries, containing the information about the ex-istence of links, are identified by testing the correlation between the time series that characterized the dynamic behavior of the nodes. This involves the problem of multiple comparisons in order to control the error rates. The method based on the estimation of positive false discovery rate (pFDR) has been used. A similar measure involving false negatives (type II errors), called the positive false nondiscovery rate (pFNR) is then considered, proposing new point and interval estimators for pFNR and a method for balancing the two types of error. This approach is demonstrated using both simulations and fMRI data, and providing nite sample as well as large sample results for pFDR and pFNR estimators. Besides a ranking of the most central nodes in the networks is proposed using q-values, the pFDR analog of the p-values. The di erences on the inter-regional connectivity between cases and controls are studied. Finally network models are discussed. In order to gain deeper insights into the complex neurobiological interaction, exponential random graph models (ERGMs) are applied to assess several network properties simultaneously and to compare case/control brain networks.
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Jao, Tun. "Functional brain network organization in altered states of consciousness." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709230.

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

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Complex alterations in brain function and neurochemistry underlie pathology and treatment in multiple neuropsychiatric disorders, yet remain incompletely characterised. This thesis outlines possibilities for human neuroimaging techniques sensitive to spontaneous fluctuations in large-scale neurobiological signalling, or ‘resting-state network (RSN) functional connectivity’, to address such knowledge gaps. Novel RSN-sensitive analysis approaches to functional magnetic resonance imaging data are introduced. These techniques are then evaluated experimentally, in contexts relevant for maladaptive cognitive and motivational processing, for their utility to identify and characterise systems-level signatures of individual differences in neurochemistry and psychopharmacological responsiveness. Firstly, RSN functional connectivity measures are investigated in the context of pharmacological intervention with nicotine replacement therapy in habitual smokers. Results identify connectivity between executive control and ‘default mode’ RSNs as a neural signature of pharmacotherapeutic efficacy in treating cognitive symptoms of nicotine withdrawal. Secondly, RSN connectivity is investigated alongside specific neuroreceptor-sensitive measures to investigate the extent to which network connectivity patterns reflect fundamental neurobiology in healthy subjects. Individual differences in dopamine D3 receptor availability - a possible marker for reward-related behaviours -are associated with topographic connectivity signatures within RSNs implicated in cognitive and motivational control. Thirdly, the ability of RSN metrics to characterise distinct neurochemical manipulations is tested in healthy subjects. Dopamine agnostic and antagonistic neuromodulations display differential effects on signalling in cortico-cubcortical and cortico-cortical reward circuitry and interact selectively with subject impulsivity. Finally, RSN cortico-subcortical connectivity metrics are investigated for their sensitivity to clinical-pharmacological effects in Parkinson’s disease. Results reveal evidence for both compensatory large-scale network mechanisms and ‘non-normalising’ dopaminergic medication effects in patients. Overall, findings indicate novel systems-level neuroimaging methodology probing interactions within and between RSNs to provide sensitive, biologically plausible markers for behavioural and neuropharamacological characteristics of neuropsychiatric disorders. Continued developments of functional network analysis approaches may facilitate their direct application to clinical and drug development domains.
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García-García, Isabel, María Ángeles Jurado, Maite Garolera, Idoia Marqués-Iturria, Annette Horstmann, Bàrbara Segura, Roser Pueyo, et al. "Functional network centrality in obesity." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-205556.

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Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention.
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Gozdas, Elveda. "Quantitative Trends and Topology in Developing Functional Brain Networks." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535381148527108.

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

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Huntington’s disease (HD) is a neurodegenerative disorder caused by a CAG repeat expansion in the Huntingtin gene on chromosome 4. In recent years there have been significant advances in understanding both the cellular pathology and the macrostructural changes that occur in the striatum and cortical structures as the disease proceeds. However, it remains unclear how abnormalities at the cellular level lead to characteristic patterns of macrostructural change in the brains of HD patients. In this thesis I aim to link structural and functional brain network abnormalities with regional changes at the cellular level. Using diffusion tractography and resting state functional MRI in well characterised HD cohorts I examine the relationship between structural and functional brain network organisation. I link these changes in structure and function to the neuropsychiatric symptoms prevalent in HD, occurring years before the manifestation of motor symptoms. By characterising changes in white matter brain networks I reveal how the brain network breaks down as HD progresses and show how this network deterioration leads to the emergence of clinical deficits. Using characteristics of the healthy white matter brain network I demonstrate how it is possible to predict the atrophy of specific brain connections in HD over time. In doing so I highlight a hierarchy of white matter connection vulnerability showing cortico-striatal connections are the first to be affected. In order to link these macrostructural white matter changes to cellular level abnormalities I utilise data from the Allen Institute transcription atlas and show how differences in regional gene expression in the healthy brain can account for the selective vulnerability of specific white matter connections in HD. The work presented in this thesis demonstrates how linking systems and cellular pathobiology in HD can inform us about disease mechanisms that drive brain atrophy and ultimately lead to clinical deficits.
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Hart, Michael Gavin. "Network approaches to understanding the functional effects of focal brain lesions." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274018.

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Complex network models of functional connectivity have emerged as a paradigm shift in brain mapping over the past decade. Despite significant attention within the neuroimaging and cognitive neuroscience communities, these approaches have hitherto not been extensively explored in neurosurgery. The aim of this thesis is to investigate how the field of connectomics can contribute to understanding the effects of focal brain lesions and to functional brain mapping in neurosurgery. This datasets for this thesis include a clinical population with focal brain tumours and a cohort focused on healthy adolescent brain development. Multiple network analyses of increasing complexity are performed based upon resting state functional MRI. In patients with focal brain tumours, the full complement of resting state networks were apparent, while also suggesting putative patterns of network plasticity. Connectome analysis was able to identify potential signatures of node robustness and connections at risk that could be used to individually plan surgery. Focal lesions induced the formation of new hubs while down regulating previously established hubs. Overall these data are consistent with a dynamic rather than a static response to the presence of focal lesions. Adolescent brain development demonstrated discrete dynamics with distinct gender specific and age-gender interactions. Network architecture also became more robust, particularly to random removal of nodes and edges. Overall these data provide evidence for the early vulnerability rather than enhanced plasticity of brain networks. In summary, this thesis presents a combined analysis of pathological and healthy development datasets focused on understanding the functional effects of focal brain lesions at a network level. The coda serves as an introduction to a forthcoming study, known as Connectomics and Electrical Stimulation for Augmenting Resection (CAESAR), which is an evolution of the results and methods herein.
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Elkin-Frankston, Seth. "Anatomical and functional impact of critical brain areas to network activity and basic visual function." Thesis, Boston University, 2013. https://hdl.handle.net/2144/12752.

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Thesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
A set of widely distributed brain areas, collectively known as the fronto-parietal network, serve to modulate aspects of visual perception. However, the unique influence exerted by these regions on low-level visual processing remains unclear. The goals of this thesis were (1) to examine how right frontal, parietal and occipital brain areas interact to process and modulate visual function and (2) to investigate the ability to improve foveal visual performance by means of noninvasive neurostimulation. In a first set of experiments, visual percepts known as 'phosphenes' were measured following low-frequency neurostimulation of the right occipital pole, Intraparietal Sulcus (IPS) or Frontal Eye Fields (FEF). Stimulation of the occipital pole and IPS were capable of evoking phosphenes with similar appearances. Furthermore, occipital or IPS stimulation decreased the excitability of the locally stimulated region but had no effect on the non-stimulated brain area. These results indicate a lack of sufficient inter-regional interactions capable of supporting long-range changes in brain activity. In a second set of experiments, contrast sensitivity and reaction times were assessed as the capacity to detect centrally located, high or low spatial frequency stimuli. Low-frequency rTMS to the FEF, but not the occipital pole or IPS improved contrast sensitivity for high spatial frequency stimuli. Stimulation of the occipital pole decreased reaction times for low spatial frequency stimuli and was shown to depend on transcollicular projections. Finally, stimulation of the IPS decreased reaction times for both types of stimuli. These effects however did not appear to depend on transcollicular pathways, indicating that performance was enhanced through cortico-cortical connections. In a final set of experiments, we investigated whether patterns of individual white matter connectivity linking stimulated brain regions could predict the effects of neurostimulation on visual processing and performance. None of the probability measures however correlated with changes in visual performance. Overall, these data suggest that occipital, parietal, frontal and tectal areas uniquely contribute to the modulation of visual perception. Moreover, results show that targeted stimulation to these brain regions serves to generate lasting improvements in visual performance, which could be used to enhance aspects of vision in healthy and clinical populations.
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Ghumman, Sukhmanjit. "Functional connectivity in patients with brain tumours." Mémoire, Université de Sherbrooke, 2018. http://hdl.handle.net/11143/12001.

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Abstract: The default mode network of the brain is a set of functionally connected regions associated with introspection and daydreaming. Recent fMRI studies have discovered that the default mode network is often perturbed in the diseased brain. For example, the default mode network is known to be modulated in dementia, ADHD, depression, and schizophrenia, among others. This has led many into believing that this network could have a role in the physiopathology of nervous system disease, or could be a useful marker of brain function. However, very few studies have yet been done which investigate how surgical lesions such as brain tumours affect the default mode network. Consequently, the goal of this project was to characterise the effect of brain tumours on the default mode network based on their location, histological type, and other parameters.
Le mode de fonctionement par défaut du cerveau est un réseau cérébral associé à la rêverie et à l’introspection. Des études récentes sur ce réseau ont découvert qu’il est perturbé dans plusieurs pathologies cérébrales. Par example, le mode de fonctionnement par défaut est modulé en démence, TDAH, dépression, schizophrénie et plusieurs autres maladies liés au cerveau. Ceci a mené à l’hypothèse que le mode de fonctionnement par défaut pourrait avoir un rôle dans la physiopathologie des maladies du système nerveux, ou pourrait être un marqueur utile du fonctionnement cérébral. Par contre, très peu d’études ont investigué l’effet de lésions chirurgicaux comme les tumeurs cérébrales sur le mode de fonctionnement par défaut. Par conséquent, le but de ce projet était de caractériser l’importance de l’histologie, de la localisation et de plusieurs autres paramètres de l’effet d’une tumeur cérébrale sur le mode de fonctionnement par défaut.
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Books on the topic "Brain functional Network"

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

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

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

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1933-, Cotterill Rodney, ed. Models of brain function. Cambridge: Cambridge University Press, 1989.

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

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Biological neural networks: Hierarchical concept of brain function. Boston: Birkhäuser, 1998.

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Modeling brain function: The world of attractor neural networks. Cambridge [England]: Cambridge University Press, 1989.

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The metaphorical brain 2: Neural networks and beyond. New York, N.Y: Wiley, 1989.

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

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

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Book chapters on the topic "Brain functional Network"

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Moorthigari, Vishnu, Emily Carlson, Petri Toiviainen, Elvira Brattico, and Vinoo Alluri. "Differential Effects of Trait Empathy on Functional Network Centrality." In Brain Informatics, 107–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59277-6_10.

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Fiddyment, Grant M., Stefania Sokolowski, and Mark Kramer. "Functional Network Observations of Diseased Brain States." In Encyclopedia of Computational Neuroscience, 1234–36. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_440.

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Ke, Ming, Hui Shen, Zongtan Zhou, Xiaolin Zhou, Dewen Hu, and Xuhui Chen. "Brain Functional Network for Chewing of Gum." In Studies in Computational Intelligence, 169–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21378-6_13.

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Fiddyment, Grant M., Stefania Sokolowski, and Mark Kramer. "Functional Network Observations of Diseased Brain States." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_440-2.

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Yu, Qingbao, and Vince D. Calhoun. "Resting-State Functional Network Disturbances in Schizophrenia." In Brain Network Dysfunction in Neuropsychiatric Illness, 187–215. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59797-9_10.

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Rey, Gwladys, Camille Piguet, and Patrik Vuilleumier. "Functional Resting-State Network Disturbances in Bipolar Disorder." In Brain Network Dysfunction in Neuropsychiatric Illness, 273–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59797-9_13.

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Müller, Ralph-Axel, and Annika Linke. "Functional Connectivity in Autism Spectrum Disorders: Challenges and Perspectives." In Brain Network Dysfunction in Neuropsychiatric Illness, 239–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59797-9_12.

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Zhang, Xiaofei, Yang Yang, Ruohao Liu, and Ning Zhong. "Route Adjustment of Functional Brain Network in Mental Arithmetic Using Task-Evoked FMRI." In Brain Informatics, 51–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37078-7_6.

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Zhang, Xiaofei, Yang Yang, Ming-Hui Zhang, and Ning Zhong. "Network Analysis of Brain Functional Connectivity in Mental Arithmetic Using Task-Evoked fMRI." In Brain Informatics, 141–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05587-5_14.

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Lai, Chien-Han. "Task MRI-Based Functional Brain Network of Anxiety." In Advances in Experimental Medicine and Biology, 3–20. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-32-9705-0_1.

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Conference papers on the topic "Brain functional Network"

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Li, Hongming, and Yong Fan. "Functional brain atlas construction for brain network analysis." In SPIE Medical Imaging, edited by Sebastien Ourselin and David R. Haynor. SPIE, 2013. http://dx.doi.org/10.1117/12.2007394.

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Salsabilian, Shiva, Elena Bibineyshvili, David J. Margolis, and Laleh Najafizadeh. "Study of Functional Network Topology Alterations after Injury via Embedding Methods." In Optics and the Brain. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/brain.2020.bw4c.3.

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Ren, Dehua, Yu Zhao, Hanbo Chen, Qinglin Dong, Jinglei Lv, and Tianming Liu. "3-D functional brain network classification using Convolutional Neural Networks." In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 2017. http://dx.doi.org/10.1109/isbi.2017.7950736.

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Gonuguntla, V., K. C. Veluvolu, and Jae-Hun Kim. "Recognition of Event-associated Brain Functional Networks in EEG for Brain Network Based Applications." In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020. http://dx.doi.org/10.1109/isbi45749.2020.9098708.

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Wang, Zhongmin, Yue Tong, and Xia Heng. "Emotional Analysis Based on Dynamic Functional Brain Network." In 2019 International Conference on Networking and Network Applications (NaNA). IEEE, 2019. http://dx.doi.org/10.1109/nana.2019.00044.

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Pitsik, Elena N., and Nikita Frolov. "Artificial neural network predicts inter-areal functional connectivity." In Computations and Data Analysis: from Molecular Processes to Brain Functions, edited by Dmitry E. Postnov. SPIE, 2021. http://dx.doi.org/10.1117/12.2591376.

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Guo, Miaomiao, Guizhi Xu, Lei Wang, and Lingdi Fu. "Functional brain network analysis during auditory oddball task." In 2016 Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC). IEEE, 2016. http://dx.doi.org/10.1109/apemc.2016.7522955.

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Sun, Xiaofang, Bin Hu, Xiangwei Zheng, Yongqiang Yin, and Cun Ji. "Emotion Classification Based on Brain Functional Connectivity Network." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313522.

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Chen, Zikuan, and Vince Calhoun. "Brain functional mapping and network connectivity of reconstructed magnetic susceptibility data." In Biomedical Applications in Molecular, Structural, and Functional Imaging, edited by Barjor Gimi and Andrzej Krol. SPIE, 2018. http://dx.doi.org/10.1117/12.2292988.

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Zhou, Zhiguo, Rongfang Wang, Jing Yang, Rongbin Xu, and Jinkun Guo. "Multimodal weighted network for 3D brain tumor segmentation in MRI images." In Biomedical Applications in Molecular, Structural, and Functional Imaging, edited by Barjor S. Gimi and Andrzej Krol. SPIE, 2021. http://dx.doi.org/10.1117/12.2580879.

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Reports on the topic "Brain functional Network"

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

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Abstract:
Review question / Objective: Research question - What is the effectiveness of Early Childhood Intervention (ECI) in the first 3 years of life? Population (P) Infants and very young children with diagnosed visual impairment. Intervention (I) ECI programmes that includes vision and developmental stimulation, play, learning and responsive parenting Comparison (C) Standard care or control Outcomes (O) Primary: Vision function or and/or neurodevelopment and/or parent-child interaction outcomes Secondary: Parental context factors eg parental wellbeing and mental health, parental satisfaction with service provision. Condition being studied: Childhood congenital or very early visual impairment arising from congenital disorders of the peripheral or anterior visual system or cerebral-based vision disorders. This includes all vision disorders of the globe, retina and anterior optic nerve and all vision disorders that are considered cerebral based along visual pathways that are retro-chiasmatic and include central brain regions and networks involved in vision processing.
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