Relevant bibliographies by topics / Brain Connectivity Networks

Contents

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

Academic literature on the topic 'Brain Connectivity Networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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Journal articles on the topic "Brain Connectivity Networks"

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Shi, Yuhu. "Dynamic Functional Connectivity Analysis of Seafarer’s Brain Functional Networks." International Journal of Pharma Medicine and Biological Sciences 9, no. 1 (2020): 33–37. http://dx.doi.org/10.18178/ijpmbs.9.1.33-37.

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Damicelli, Fabrizio, Claus C. Hilgetag, and Alexandros Goulas. "Brain connectivity meets reservoir computing." PLOS Computational Biology 18, no. 11 (2022): e1010639. http://dx.doi.org/10.1371/journal.pcbi.1010639.

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The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs architectures? Can we learn from ANNs about what network features support computation in the brain when solving a task? At a meso/macro-scale level of the connectivity, ANNs’ architectures are carefully engineered and such those design decisions have crucial importance in many recent performance improvements. On the other hand, BNNs exhibit complex emergent connectivity patterns at all scales. At the individual level, B
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Bonkhoff, Anna K., Flor A. Espinoza, Harshvardhan Gazula, et al. "Acute ischaemic stroke alters the brain’s preference for distinct dynamic connectivity states." Brain 143, no. 5 (2020): 1525–40. http://dx.doi.org/10.1093/brain/awaa101.

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Abstract Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute
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Briley, Paul M., Elizabeth B. Liddle, Madeleine J. Groom, et al. "Development of human electrophysiological brain networks." Journal of Neurophysiology 120, no. 6 (2018): 3122–30. http://dx.doi.org/10.1152/jn.00293.2018.

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Functional activity in the human brain is intrinsically organized into independently active, connected brain regions. These networks include sensorimotor systems, as well as higher-order cognitive networks such as the default mode network (DMN), which dominates activity when the brain is at rest, and the frontoparietal (FPN) and salience (SN) networks, which are often engaged during demanding tasks. Evidence from functional magnetic resonance imaging (fMRI) suggests that although sensory systems are mature by the end of childhood, the integrity of the FPN and SN develops throughout adolescence
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Geng, Haiyang, Pengfei Xu, Iris E. Sommer, Yue-Jia Luo, André Aleman, and Branislava Ćurčić-Blake. "Abnormal dynamic resting-state brain network organization in auditory verbal hallucination." Brain Structure and Function 225, no. 8 (2020): 2315–30. http://dx.doi.org/10.1007/s00429-020-02119-1.

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Abstract Auditory-verbal hallucinations (AVH) are a key symptom of schizophrenia. Recent neuroimaging studies examining dynamic functional connectivity suggest that disrupted dynamic interactions between brain networks characterize complex symptoms in mental illness including schizophrenia. Studying dynamic connectivity may be especially relevant for hallucinations, given their fluctuating phenomenology. Indeed, it remains unknown whether AVH in schizophrenia are directly related to altered dynamic connectivity within and between key brain networks involved in auditory perception and language,
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Oliver, Isaura, Jaroslav Hlinka, Jakub Kopal, and Jörn Davidsen. "Quantifying the Variability in Resting-State Networks." Entropy 21, no. 9 (2019): 882. http://dx.doi.org/10.3390/e21090882.

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Recent precision functional mapping of individual human brains has shown that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies. How this variability affects the connectivity within individual resting-state networks remains an open question. This is particularly important since certain resting-state networks such as the default mode network (DMN) and the fronto-parietal network (FPN) play an important role in the early detection of neurophysiological diseases like Alzheimer’s, Parkinson’s, and at
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Lama, Ramesh Kumar, and Goo-Rak Kwon. "Resting-State Functional Connectivity Difference in Alzheimer’s Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement." Diagnostics 13, no. 19 (2023): 3074. http://dx.doi.org/10.3390/diagnostics13193074.

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The disruption of functional connectivity is one of the early events that occurs in the brains of Alzheimer’s disease (AD) patients. This paper reports a study on the clustering structure of functional connectivity in eight important brain networks in healthy, AD, and prodromal stage subjects. We used the threshold-free cluster enhancement (TFCE) method to explore the connectivity from resting-state functional MR images (rs-fMRIs). We conducted the study on a total of 32 AD, 32 HC, and 31 MCI subjects. We modeled the brain as a graph-based network to study these impairments, and pairwise Pears
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Wang, Mingliang, Jiashuang Huang, Mingxia Liu, and Daoqiang Zhang. "Functional Connectivity Network Analysis with Discriminative Hub Detection for Brain Disease Identification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1198–205. http://dx.doi.org/10.1609/aaai.v33i01.33011198.

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Brain network analysis can help reveal the pathological basis of neurological disorders and facilitate automated diagnosis of brain diseases, by exploring connectivity patterns in the human brain. Effectively representing the brain network has always been the fundamental task of computeraided brain network analysis. Previous studies typically utilize human-engineered features to represent brain connectivity networks, but these features may not be well coordinated with subsequent classifiers. Besides, brain networks are often equipped with multiple hubs (i.e., nodes occupying a central position
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Titone, Simon, Jessica Samogin, Philippe Peigneux, Stephan Swinnen, Dante Mantini, and Genevieve Albouy. "Connectivity in Large-Scale Resting-State Brain Networks Is Related to Motor Learning: A High-Density EEG Study." Brain Sciences 12, no. 5 (2022): 530. http://dx.doi.org/10.3390/brainsci12050530.

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Previous research has shown that resting-state functional connectivity (rsFC) between different brain regions (seeds) is related to motor learning and motor memory consolidation. Using high-density electroencephalography (hdEEG), we addressed this question from a brain network perspective. Specifically, we examined frequency-dependent functional connectivity in resting-state networks from twenty-nine young healthy participants before and after they were trained on a motor sequence learning task. Consolidation was assessed with an overnight retest on the motor task. Our results showed training-
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Hart, Michael G., Stephen J. Price, and John Suckling. "Functional connectivity networks for preoperative brain mapping in neurosurgery." Journal of Neurosurgery 126, no. 6 (2016): 1941–50. http://dx.doi.org/10.3171/2016.6.jns1662.

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

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Abou, Elseoud A. (Ahmed). "Exploring functional brain networks using independent component analysis:functional brain networks connectivity." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526201597.

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Abstract Functional communication between brain regions is likely to play a key role in complex cognitive processes that require continuous integration of information across different regions of the brain. This makes the studying of functional connectivity in the human brain of high importance. It also provides new insights into the hierarchical organization of the human brain regions. Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. A growing number of ICA studies have reported alter
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Esposito, Umberto. "Investigating connectivity in brain-like networks." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/12307/.

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Experimental research over the last two decades has shown that the anatomical connectivity among neurons is largely non-random across brain areas. This complex organisation shapes the flow of information, giving rise to specific pathways and motifs, which are ultimately responsible for processes like emotions, cognitive functions and behaviour, just to mention few. Due to the spectacular progress of technology, the study of the brain wiring diagram, known as connectomics, has received considerable attention in recent years, resulting in the proliferation of large data sets. From one side, this
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Sala, Llonch Roser. "Multimodal MRI Study of Human Brain Connectivity: Cognitive Networks." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/291682.

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Aquesta tesi inclou 6 estudis en els quals s’han utilitzat diversos mètodes d’imatge per ressonància magnètica (IRM) per caracteritzar la connectivitat cerebral i la seva relació amb l’estat cognitiu en joves sans, persones d’edat avançada i pacients amb malaltia d’Alzheimer. Les tècniques d’IRM permeten estudiar la connectivitat estructural a partir del Tensor de Difusió (DTI), i la connectivitat funcional mitjançant la IRM funcional (IRMf), mesurada durant l’execució d’una tasca o durant el repòs. S’ha descrit un conjunt de xarxes neuronals, que inclou la xarxa neuronal per defecte (o Defa
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Pelletier, Marc 1973. "Abnormal brain connectivity in schizophrenia : investigations into episodic memory networks." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98766.

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Abnormal connectivity between the prefrontal cortex (PFC) and other brain regions has been demonstrated in subjects with schizophrenia. We tested if abnormal connectivity, particularly between PFC and the medial temporal lobes (MTL), underlies the reduced brain activity and level of accuracy observed in schizophrenia subjects during episodic memory tests. We used fMRI to examine activation in fifteen chronic, medicated schizophrenia subjects and eighteen control subjects in two different recognition memory tests. The item recognition memory test required subjects to make old/new judgments, and
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Cattai, Tiziana. "Leveraging brain connectivity networks to detect mental states during motor imagery." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS081.

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Le cerveau est un réseau complexe et nous savons que les mécanismes de synchronisation et de désynchronisation sont essentiels pour effectuer des taches motrices et cognitives. De nos jours, les interactions fonctionnelles cérébrales sont étudiées dans des applications d'interface cerveau-ordinateur (BCI) avec de plus en plus d'intérêt. Cela pourrait avoir un fort impact sur les systèmes BCI, généralement bases sur des caractéristiques univariées qui caractérisent séparément les activités régionales du cerveau. En effet, les fonctionnalités de connectivité cérébrale peuvent être utilisées pour
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Su, M. T. "Functional and structural connectivity of reading networks in the adult brain." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1527606/.

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Language processing draws upon many distributed regions in the brain. Reading in particular is a skill that emerges from the interaction between brain regions involved in phonological and orthographical processing. This project examined the reading network in adults (18-35 years old) with and without developmental dyslexia. Each participant was assessed on a comprehensive battery of standardised neuropsychological tests, which assessed IQ, reading accuracy and comprehension, spelling, phonological processing, working memory, grammatical understanding, motor coordination, and expressive and rec
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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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Obando, Forero Catalina. "Statistical graph models of temporal brain networks." Electronic Thesis or Diss., Sorbonne université, 2018. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2018SORUS454.pdf.

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La discipline encore naissante des réseaux complexes est vecteur d’un changement de paradigme dans la neuroscience. Les connectomes estimés à partir de mesures de neuroimagerie comme l’électroencéphalographie, la magnétoencéphalographie ou encore l’imagerie par résonance magnétique fonctionnelle fournissent une représentation abstraite du cerveau sous la forme d’un graphe, ce qui a permis des percées décisives dans la compréhension compacte et objective des propriétés topologiques et physiologiques des cerveaux sains. Cependant, les approches de pointe ignorent souvent l'incertitude et la natu
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Sargolzaei, Saman. "Intracranial Volume Estimation and Graph Theoretical Analysis of Brain Functional Connectivity Networks." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/1915.

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Understanding pathways of neurological disorders requires extensive research on both functional and structural characteristics of the brain. This dissertation introduced two interrelated research endeavors, describing (1) a novel integrated approach for constructing functional connectivity networks (FCNs) of brain using non-invasive scalp EEG recordings; and (2) a decision aid for estimating intracranial volume (ICV). The approach in (1) was developed to study the alterations of networks in patients with pediatric epilepsy. Results demonstrated the existence of statistically significant (p
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Krienen, Fenna Marie. "Large-Scale Networks in the Human Brain revealed by Functional Connectivity MRI." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11081.

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The human brain is composed of distributed networks that connect a disproportionately large neocortex to the brainstem, cerebellum and other subcortical structures. New methods for analyzing non-invasive imaging data have begun to reveal new insights into human brain organization. These methods permit characterization of functional interactions within and across brain networks, and allow us to appreciate points of departure between the human brain and non-human primates.<br>Psychology
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Books on the topic "Brain Connectivity Networks"

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Soriano-Mas, Carles, and Ben J. Harrison. Brain Functional Connectivity in OCD. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0024.

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This chapter provides an overview of studies assessing alterations in brain functional connectivity in obsessive-compulsive disorder (OCD) as assessed by functional magnetic resonance imaging (fMRI). Although most of the reviewed studies relate to the analysis of resting-state fMRI data, the chapter also reviews studies that have combined resting-state with structural or task-based approaches, as well as task-based studies in which the analysis of functional connectivity was reported. The main conclusions to be drawn from this review are that patients with OCD consistently demonstrate altered
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Konrad, Kerstin, Adriana Di Martino, and Yuta Aoki. Brain volumes and intrinsic brain connectivity in ADHD. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0006.

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Neuroimaging studies have increased our understanding of the neurobiological underpinnings of ADHD. Structural brain imaging studies demonstrate widespread changes in brain volumes, in particular in frontal-striatal-cerebellar networks. Based on the widespread nature of structural and functional brain abnormalities, approaches able to capture the organizing principles of large-scale neural systems have been used in ADHD. These include diffusion magnetic resonance imaging (MRI) and resting state functional MRI (R-fMRI). Complementary to findings of volumetric studies, diffusion investigations h
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Beaty, Roger E., and Rex E. Jung. Interacting Brain Networks Underlying Creative Cognition and Artistic Performance. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.10.

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Cognitive neuroscience research has begun to address the potential interaction of brain networks supporting creativity by employing new methods in brain network science. Network methods offer a significant advance compared to individual region of interest studies due to their ability to account for the complex and dynamic interactions among discrete brain regions. As this chapter demonstrates, several recent studies have reported a remarkably similar pattern of brain network connectivity across a range of creative tasks and domains. In general, such work suggests that creative thought may invo
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Posner, Michael I., Mary K. Rothbart, and M. Rosario Rueda. Developing Attention and Self-Regulation in Childhood. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.023.

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This chapter views attention as an organ system consisting of three brain networks with their own unique anatomy, connectivity, neuromodulators, and functions. These networks underlie the functions of attention including: obtaining and maintaining the alert state, orienting to sensory events, and voluntary control of responses. It traces the development of these attentional networks from infancy to adulthood. All three networks are present in infancy, but their functions and connectivity change in development. The change of control from the orienting to the executive network that takes place b
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Ribary, Urs, Alex L. MacKay, Alexander Rauscher, et al. Emerging neuroimaging technologies: Toward future personalized diagnostics, prognosis, targeted intervention, and ethical challenges. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198786832.003.0002.

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The human brain is a fine-tuned and balanced structural, functional, and dynamic electrochemical system. Any alterations, from slight slowing of partial brain networks to severe disruptions in structural, functional, and dynamic connectivity across local and large-scale brain networks will result in slight to severe changes in cognitive ability, awareness, and consciousness. Using future noninvasive technologies, the common goal is to relate typical or atypical resting-state, sensory-motor functions, cognition, and consciousness to underlying typical or altered quantified brain structure, bioc
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Raymont, Vanessa, and Robert D. Stevens. Cognitive Reserve. Oxford University Press, 2014. http://dx.doi.org/10.1093/med/9780199653461.003.0029.

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The cognitive reserve hypothesis suggests that the structure and function of an individual’s brain can modulate the clinical expression of brain damage and illness. This chapter describes passive and active models of reserve, their impact on neurological illness, and how these effects can be assessed. Passive models focus on the protective potential of anatomical features, such as brain size, neural density, and synaptic connectivity, while active models emphasize the connectivity and efficiency of neural networks and active compensation by alternative networks. It is likely that both models r
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Stamatakis, Emmanuel A., Eleni Orfanidou, and Andrew C. Papanicolaou. Functional Magnetic Resonance Imaging. Edited by Andrew C. Papanicolaou. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.7.

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Functional magnetic resonance imaging (fMRI) is the most frequently used functional neuroimaging method and the one that accounts for most of the neuroimaging literature. It measures the blood oxygen level-dependent (BOLD) signal in different parts of the brain during rest and during task-induced activation of functional networks mediating basic and higher functions. A basic understanding of the various instruments and techniques of recording the hemodynamic responses of different brain regions and the manner in which we establish activation and connectivity patterns out of these responses is
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Brennan, Brian P., and Scott L. Rauch. Functional Neuroimaging Studies in Obsessive-Compulsive Disorder: Overview and Synthesis. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0021.

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Studies using functional neuroimaging have played a critical role in the current understanding of the neurobiology of obsessive-compulsive disorder (OCD). Early studies using positron emission tomography (PET) identified a core cortico-striatal-thalamo-cortical circuit that is dysfunctional in OCD. Subsequent studies using behavioral paradigms in conjunction with functional magnetic resonance imaging (fMRI) have provided additional information about the neural substrates underlying specific psychological processes relevant to OCD. More recently, studies utilizing resting state fMRI have identi
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Ramani, Ramachandran, ed. Functional MRI. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.001.0001.

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Functional MRI with BOLD (Blood Oxygen Level Dependent) imaging is one of the commonly used modalities for studying brain function in neuroscience. The underlying source of the BOLD fMRI signal is the variation in oxyhemoglobin to deoxyhemoglobin ratio at the site of neuronal activity in the brain. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI was developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform any goal-directed task; i
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Ramirez, Dennis. Default Mode Network Dmn: Structural Connectivity, Impairments and Role in Daily Activities (Neuroscience Research Progress). Nova Science Publishers, Inc., 2015.

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Book chapters on the topic "Brain Connectivity Networks"

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Magalhães, Ricardo, Paulo Marques, Telma Veloso, José Miguel Soares, Nuno Sousa, and Victor Alves. "Construction of Functional Brain Connectivity Networks." In Distributed Computing and Artificial Intelligence, 12th International Conference. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19638-1_35.

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Liu, Dazhong, Ning Zhong, and Yulin Qin. "Exploring Functional Connectivity Networks in fMRI Data Using Clustering Analysis." In Brain Informatics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23605-1_17.

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Faber, Jean, Priscila C. Antoneli, Noemi S. Araújo, Daniel J. L. L. Pinheiro, and Esper Cavalheiro. "Critical Elements for Connectivity Analysis of Brain Networks." In Brain Informatics and Health. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6883-1_4.

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Kusztos, Razvan, Giovanna Maria Dimitri, and Pietro Lió. "Neural Models for Brain Networks Connectivity Analysis." In Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34585-3_19.

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Koziol, Leonard F., Deborah Ely Budding, and Dana Chidekel. "Large-Scale Brain Networks and Functional Connectivity." In ADHD as a Model of Brain-Behavior Relationships. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8382-3_10.

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Yousuf, Sayeda Mayesha, and Amrina Rahman. "Understanding Brain Connectivity: From Synapses to Networks." In Brain Networks in Neuroscience: Personalization Unveiled Via Artificial Intelligence. River Publishers, 2025. https://doi.org/10.1201/9788770047371-3.

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Dimitrakopoulos, Georgios N., Ioannis Kakkos, Aristidis G. Vrahatis, et al. "Driving Mental Fatigue Classification Based on Brain Functional Connectivity." In Engineering Applications of Neural Networks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65172-9_39.

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Ji, Chengtao, Natasha M. Maurits, and Jos B. T. M. Roerdink. "Comparison of Brain Connectivity Networks Using Local Structure Analysis." In Studies in Computational Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05414-4_51.

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Amoroso, Nicola, Roberto Bellotti, Domenico Diacono, Marianna La Rocca, and Sabina Tangaro. "Salient Networks: A Novel Application to Study Brain Connectivity." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56148-6_39.

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Vangimalla, Reddy Rani, and Jaya Sreevalsan-Nair. "Comparing Community Detection Methods in Brain Functional Connectivity Networks." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9700-8_1.

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Conference papers on the topic "Brain Connectivity Networks"

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Pisarchik, Alexander N., Natalia Peña Serrano, and Rider Jaimes-Reátegui. "Brain Connectivity Hypergraphs." In 2024 8th Scientific School Dynamics of Complex Networks and their Applications (DCNA). IEEE, 2024. http://dx.doi.org/10.1109/dcna63495.2024.10718518.

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Isezaki, Takashi, Michiaki Suzuki, Kengo Okitsu, Yukio Koike, and Yukio Nishimura. "Muscle Synergy Analysis under the Constraint of Connectivity between Brain and Muscle Activity." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651288.

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Khorev, Vladimir, Semen Kurkin, Rositsa Paunova, Tatiana Bukina, and Drozdstoy Stoyanov. "Differences in optimal community structure in brain connectivity organization in major depressive disorder." In 2024 8th Scientific School Dynamics of Complex Networks and their Applications (DCNA). IEEE, 2024. http://dx.doi.org/10.1109/dcna63495.2024.10718407.

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Tan, Yee-Fan, Chee-Ming Ting, Fuad Noman, Raphaël C. W. Phan, and Hernando Ombao. "fMRI Functional Connectivity Augmentation Using Convolutional Generative Adversarial Networks for Brain Disorder Classification." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635290.

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Sargolzaei, Saman, and Christopher Giza. "Brain Functional Connectivity Network Adjustments After Lateral Fluid Percussion Injury in Immature Rats, a Longitudinal Study." In 2024 IEEE 20th International Conference on Body Sensor Networks (BSN). IEEE, 2024. https://doi.org/10.1109/bsn63547.2024.10780527.

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Carvalho, M., and R. Frayne. "Pilot Study of Distinct Graphs Models in Analysis of Brain Aging in Resting-State Functional Connectivity Networks." In 12th International Conference on Bioimaging. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013261200003911.

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Puxeddu, Maria Grazia, Greta Rinaldini, and Laura Astolfi. "Multi-dimensional networks as a tool to model, analyze, and interpret multi-subject brain connectivity in hyperscanning settings*." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821937.

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Montero-Hernandez, Samuel, Yuanyuan Gao, Yiwen Zhang, et al. "Repeatability of Default Mode and Dorsal Attention Networks Measured with Whole Head fNIRS." In Optics and the Brain. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/brain.2024.bw1c.1.

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Toppi, J., N. Sciaraffa, Y. Antonacci, et al. "Measuring the agreement between brain connectivity networks." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7590642.

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Chatelain, F., S. Achard, O. Michel, and C. Gouy-Pailler. "Multivariate approach for brain decomposable connectivity networks." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967830.

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Reports on the topic "Brain Connectivity Networks"

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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Is brain circuitry linked with early symptoms of autism spectrum disorder? ACAMH, 2020. http://dx.doi.org/10.13056/acamh.13231.

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Researchers in San Diego, USA, have studied the relationship between brain network connectivity and emerging autism spectrum disorder (ASD) symptoms in toddlers aged 17-45 months with (n=24) or without (n=23) ASD.
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