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Relevant bibliographies by topics / Brain connectivity measure

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Academic literature on the topic 'Brain connectivity measure'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Journal articles on the topic "Brain connectivity measure"

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White, Tonya, and Vince D. Calhoun. "Dissecting Static and Dynamic Functional Connectivity: Example From the Autism Spectrum." Journal of Experimental Neuroscience 13 (January 2019): 117906951985180. http://dx.doi.org/10.1177/1179069519851809.

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The ability to measure the intrinsic functional architecture of the brain has grown exponentially over the last 2 decades. Measures of intrinsic connectivity within the brain, typically measured using resting-state functional magnetic resonance imaging (MRI), have evolved from primarily “static” approaches, to include dynamic measures of functional connectivity. Measures of dynamic functional connectivity expand the assumptions to allow brain regions to have temporally different patterns of communication between different regions. That is, connections within the brain can differentially fire b

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Thornton, Kirtley E. "Neurotherapy and Connectivity." Biofeedback 44, no. 4 (2016): 218–24. http://dx.doi.org/10.5298/1081-5937-44.4.03.

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Much of the research involving the quantitative EEG (QEEG), in terms of scientific research and clinical interventions, has focused on the four frequency ranges (Delta, Theta, Alpha, Beta) and their respective amplitudes (microvolt levels). The Beta frequency has typically been separated into segments defined by frequency. These measures can be conceptualized as involving different measures of the brain's arousal level. The other conceptual measure is focused on the communication patterns within the brain and involves coherence and phase measures. These communication measures have revealed sci

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Crofts, Jonathan J., and Desmond J. Higham. "A weighted communicability measure applied to complex brain networks." Journal of The Royal Society Interface 6, no. 33 (2009): 411–14. http://dx.doi.org/10.1098/rsif.2008.0484.

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Recent advances in experimental neuroscience allow non-invasive studies of the white matter tracts in the human central nervous system, thus making available cutting-edge brain anatomical data describing these global connectivity patterns. Through magnetic resonance imaging, this non-invasive technique is able to infer a snapshot of the cortical network within the living human brain. Here, we report on the initial success of a new weighted network communicability measure in distinguishing local and global differences between diseased patients and controls. This approach builds on recent advanc

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Pickler, Rita, Stephanie Sealschott, Margo Moore, et al. "Using Functional Connectivity Magnetic Resonance Imaging to Measure Brain Connectivity in Preterm Infants." Nursing Research 66, no. 6 (2017): 490–95. http://dx.doi.org/10.1097/nnr.0000000000000241.

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Sokolov, Arseny A., Peter Zeidman, Adeel Razi, et al. "Asymmetric high-order anatomical brain connectivity sculpts effective connectivity." Network Neuroscience 4, no. 3 (2020): 871–90. http://dx.doi.org/10.1162/netn_a_00150.

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Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics

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Marshall, William J., Christine L. Lackner, Paul Marriott, Diane L. Santesso, and Sidney J. Segalowitz. "Using Phase Shift Granger Causality to Measure Directed Connectivity in EEG Recordings." Brain Connectivity 4, no. 10 (2014): 826–41. http://dx.doi.org/10.1089/brain.2014.0241.

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Tousseyn, Simon, Balu Krishnan, Zhong I. Wang, et al. "Connectivity in ictal single photon emission computed tomography perfusion: a cortico-cortical evoked potential study." Brain 140, no. 7 (2017): 1872–84. http://dx.doi.org/10.1093/brain/awx123.

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Ictal SPECT is used in presurgical evaluations of refractory epilepsy. Tousseyn et al. examine whether ictal perfusion changes correspond to electrically connected networks. Using evoked responses following direct electrical stimulation during stereo-electroencephalography as a connectivity measure, they show that ictal perfusion is not random but is supported by underlying connectivity.

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Jamadar, Sharna D., Phillip G. D. Ward, Emma X. Liang, Edwina R. Orchard, Zhaolin Chen, and Gary F. Egan. "Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study." Cerebral Cortex 31, no. 6 (2021): 2855–67. http://dx.doi.org/10.1093/cercor/bhaa393.

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Abstract Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain—glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of “connectivity,” only across-subject “covariance.” H

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Glerean, Enrico, Juha Salmi, Juha M. Lahnakoski, Iiro P. Jääskeläinen, and Mikko Sams. "Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity." Brain Connectivity 2, no. 2 (2012): 91–101. http://dx.doi.org/10.1089/brain.2011.0068.

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Liu, Ying, and Selin Aviyente. "Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information." Computational and Mathematical Methods in Medicine 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/635103.

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Effective connectivity refers to the influence one neural system exerts on another and corresponds to the parameter of a model that tries to explain the observed dependencies. In this sense, effective connectivity corresponds to the intuitive notion of coupling or directed causal influence. Traditional measures to quantify the effective connectivity include model-based methods, such as dynamic causal modeling (DCM), Granger causality (GC), and information-theoretic methods. Directed information (DI) has been a recently proposed information-theoretic measure that captures the causality between

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Dissertations / Theses on the topic "Brain connectivity measure"

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Doležalová, Radka. "Hodnocení míry mentální zátěže za použití mozkové konektivity." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220723.

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Tato práce se zabývá využitím EEG dat pro výpočet mozkové konektivity a vytvořením klasifikátoru mentální zátěže. Nejdříve je popsán teoretický základ EEG, následně jsou rozebrány některé metody pro určení mozkové konektivity. Pro výpočet klasifikačních příznaků byla použita data nasnímaná během experimentu, který manipuloval s mentální zátěží ve dvou stupních. V práci je popsán průběh experimentu, zpracování a redukce nasnímaných dat, stejně jako extrakce příznaků z nasnímaných EEG dat pomocí několika metod měření konektivity (korelační funkce, kovariance, koherence a míra fázové soudržnosti)

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Zhu, Jie. "Entropic measures of connectivity with an application to intracerebral epileptic signals." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S006/document.

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Les travaux présentés dans cette thèse s'inscrivent dans la problématique de la connectivité cérébrale, connectivité tripartite puisqu'elle sous-tend les notions de connectivité structurelle, fonctionnelle et effective. Ces trois types de connectivité que l'on peut considérer à différentes échelles d'espace et de temps sont bien évidemment liés et leur analyse conjointe permet de mieux comprendre comment structures et fonctions cérébrales se contraignent mutuellement. Notre recherche relève plus particulièrement de la connectivité effective qui permet de définir des graphes de connectivité qui

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Tayaranian, Hosseini Pegah. "Brain connectivity measured from the EEG during auditory stimulation in normal hearing subjects and cochlear implant users." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/388101/.

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The human brain is regarded as an ensemble of dynamic systems in which communication between neural centres is very important. In order to perceive sounds many different cortical and subcortical brain areas have to coordinate their activity. After hearing loss, the connections and information pathways between these areas may rearrange and this may be one of the reasons for unsatisfactory speech perception after cochlear implantation (CI). It remains unclear how the brain connectivity and its re-organisation contribute to this, and this provides the motivation for the current study. The brain o

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Narula, Vaibhav, Antonio Giuliano Zippo, Alessandro Muscoloni, Gabriele Eliseo M. Biella, and Carlo Vittorio Cannistraci. "Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?" Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-230803.

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The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wi

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Books on the topic "Brain connectivity measure"

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Paus, Tomáš. Combining brain imaging with brain stimulation: causality and connectivity. Edited by Charles M. Epstein, Eric M. Wassermann, and Ulf Ziemann. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780198568926.013.0034.

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This article establishes the concept of a methodological approach to combine brain imaging with brain stimulation. Transcranial magnetic stimulation (TMS) is a tool that allows perturbing neural activity, in time and space, in a noninvasive manner. This approach allows the study of the brain-behaviour relationship. Under certain circumstances, the influence of one region on other, called the effective connectivity, can be measured. Functional connectivity is the extent of correlation in brain activity measured across a number of spatially distinct brain regions. This tool of connectivity can b

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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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Hanajima, Ritsuko, and Yoshikazu Ugawa. Paired-pulse measures. Edited by Charles M. Epstein, Eric M. Wassermann, and Ulf Ziemann. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780198568926.013.0011.

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This article reviews the physiology and application of the currently available paired-pulse protocols. Paired-pulse transcranial magnetic stimulation (TMS) techniques study the modulation of human motor cortical excitability. Paired-pulse experiments are designed to give insight into the nature of the cortical circuitry activated by TMS. Changes in motor cortical excitability produced by the conditioning pulse are estimated by changes in the size of the conditioned motor-evoked potential (MEP). It is possible to identify specific abnormalities in the balance between inhibitory and facilitatory

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Bandettini, Peter A., and Hanzhang Lu. Magnetic Resonance Methodologies. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0008.

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Magnetic resonance imaging is a noninvasive tool for assessing brain anatomy, perfusion, metabolism, and function with precision. In this chapter, the basics and the most cutting edge examples of MRI-based measures are described. The first is measurement of cerebral perfusion, including the latest techniques involving spin-labelling as well as the tracking of exogenous contrast agents. Functional MRI is then discussed, along with some of the cutting edge methodology that has yet to make it into routine clinical practice. Next, resting state fMRI is described, a powerful technique whereby the e

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Daskalakis, Zafiris J., and Robert Chen. Evaluating the interaction between cortical inhibitory and excitatory circuits measured by TMS. Edited by Charles M. Epstein, Eric M. Wassermann, and Ulf Ziemann. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780198568926.013.0012.

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Transcranial magnetic stimulation was first introduced in the late 1980s. Numerous studies have used TMS as an investigational tool to elucidate cortical physiology and to probe cognitive processes. This article introduces TMS paradigms and presents information gathered on cortical neuronal connectivity. TMS paradigms that demonstrate intracortical inhibition include short-interval cortical inhibition (SICI), cortical silence period (cSP) and long interval cortical inhibition (LICI). There are two types of cortical inhibitions from the stimulation of other brain areas, interhemispheric inhibit

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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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Roberts, Timothy P. L., and Luke Bloy. Neuroimaging in Pediatric Psychiatric Disorders. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0060.

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Noninvasive imaging and electrophysiological techniques have been developed to probe specific aspects of brain function and dysfunction, providing exquisite spatial maps of functional centers and temporal characteristics. The evolution of these techniques has advanced from single-modality methods identifying functional localization, specialization and segregation, through real-time measures of neuronal activity, toward multimodality integration of structural, functional, and spectro-temporal approaches. While these have an immediate impact in conditions where physical brain lesions are evident

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Book chapters on the topic "Brain connectivity measure"

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Salazar, Addisson, Gonzalo Safont, and Luis Vergara. "A New Graph Based Brain Connectivity Measure." In Advances in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20518-8_38.

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Nie, Lei, Xian Yang, Paul M. Matthews, Zhiwei Xu, and Yike Guo. "Minimum Partial Correlation: An Accurate and Parameter-Free Measure of Functional Connectivity in fMRI." In Brain Informatics and Health. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23344-4_13.

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Osmanlıoğlu, Yusuf, Jacob A. Alappatt, Drew Parker, Junghoon Kim, and Ragini Verma. "A Graph Based Similarity Measure for Assessing Altered Connectivity in Traumatic Brain Injury." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11723-8_19.

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Kumar, V. Santhosh, and T. V. K. Hanumantha Rao. "Functional Brain Connectivity analysis using Coherent Measures." In EMBEC & NBC 2017. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5122-7_195.

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Braitenberg, Valentino, and Almut Schüz. "Comparative Aspects: Statistical Measures in Larger Brains." In Cortex: Statistics and Geometry of Neuronal Connectivity. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03733-1_34.

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Labate, Domenico, Giuseppina Inuso, Gianluigi Occhiuto, Fabio La Foresta, and Francesco C. Morabito. "Measures of Brain Connectivity through Permutation Entropy in Epileptic Disorders." In Neural Nets and Surroundings. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35467-0_7.

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Lin, Fa-Hsuan, Thomas Witzel, Matti S. Hämäläinen, and Aapo Nummenmaa. "Combining Noninvasive Electromagnetic and Hemodynamic Measures of Human Brain Activity." In Brain and Human Body Modeling 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45623-8_10.

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AbstractMagnetoencephalography (MEG) is directly sensitive to postsynaptic neuronal activity with the millisecond temporal resolution. MEG is ideally to complement functional MRI (fMRI), which measures hemodynamic responses secondary to neuronal activity with the millimeter spatial resolution, for noninvasive imaging of human brain function. Here, using the Minimum-Norm Estimate as an example, we review how fMRI can be integrated with MEG (and electroencephalography, EEG) source modeling and summarize potential advantages and pitfalls of this data fusion technique. Neurovascular coupling as the physiological basis for MEG/EEG/fMRI integration is also discussed. Ultimately, we expect to develop multimodal MEG/EEG/fMRI neuroimaging methodology for characterizing spatiotemporal functional connectivity in large-scale neural networks of the human brain with high sensitivity and accuracy.

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Alexander, Andrew L., and Nancy J. Lobaugh. "Insights into Brain Connectivity Using Quantitative MRI Measures of White Matter." In Understanding Complex Systems. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71512-2_8.

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Dennis, Emily L., Neda Jahanshad, Arthur W. Toga, et al. "Test-Retest Reliability of Graph Theory Measures of Structural Brain Connectivity." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33454-2_38.

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Portegies, Jorg, Stephan Meesters, Pauly Ossenblok, Andrea Fuster, Luc Florack, and Remco Duits. "Brain Connectivity Measures via Direct Sub-Finslerian Front Propagation on the 5D Sphere Bundle of Positions and Directions." In Computational Diffusion MRI. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05831-9_24.

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Conference papers on the topic "Brain connectivity measure"

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Djordjevic, Z., A. Jovanovic, and A. Perovic. "Brain connectivity measure — the direct transfer function — advantages and weak points." In 2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2012. http://dx.doi.org/10.1109/sisy.2012.6339493.

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Jamal, Wasifa, Saptarshi Das, Koushik Maharatna, et al. "Using brain connectivity measure of EEG synchrostates for discriminating typical and Autism Spectrum Disorder." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6696205.

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Khadem, Ali, and Gholam-Ali Hossein-Zadeh. "The role of instantaneous relations in the estimation of brain effective connectivity and a modified measure." In 2011 18th Iranian Conference of Biomedical Engineering (ICBME). IEEE, 2011. http://dx.doi.org/10.1109/icbme.2011.6168564.

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Mehta, Ketan, and Jorg Kliewer. "A new EEG-based causal information measure for identifying brain connectivity in response to perceived audio quality." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7996908.

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Duncan, Elizabeth C., Wilburn E. Reddick, John O. Glass, et al. "Application of probabilistic fiber-tracking method of MR imaging to measure impact of cranial irradiation on structural brain connectivity in children treated for medulloblastoma." In SPIE Medical Imaging, edited by Barjor Gimi and Andrzej Krol. SPIE, 2016. http://dx.doi.org/10.1117/12.2217185.

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Pal, Chandrajit, Dwaipayan Biswas, Koushik Maharatna, and Amlan Chakrabarti. "Architecture for complex network measures of brain connectivity." In 2017 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2017. http://dx.doi.org/10.1109/iscas.2017.8050239.

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Beauchene, Christine, Alexander Leonessa, Subhradeep Roy, James Simon, and Nicole Abaid. "Closed-Loop Control of the Frequency Response of the Virtual Brain Model." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5117.

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The brain is a highly complex network and analyzing brain connectivity is a nontrivial task. Consequently, the neuroscience community created a large-scale, customizable, mathematical model which simulates brain activity called The Virtual Brain (TVB). Using TVB, we seek to control electroencephalography (EEG) measured brain states using auditory inputs, through TVB. A safe non-invasive brain stimulation method is binaural beats (BB) which arise from the brain’s interpretation of two pure tones, with a small frequency mismatch, delivered independently to each ear. A third phantom BB, whose fre

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Murta, Teresa, Patricia Figueiredo, and Alberto Leal. "EEG-fMRI measures of functional brain connectivity in epilepsy." In 2011 1st Portuguese Meeting in Bioengineering ¿ The Challenge of the XXI Century (ENBENG). IEEE, 2011. http://dx.doi.org/10.1109/enbeng.2011.6026094.

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Prasad, Gautam, Shantanu H. Joshi, Talia M. Nir, Arthur W. Toga, and Paul M. Thompson. "Flow-based network measures of brain connectivity in Alzheimer'S disease." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556461.

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Zhan, L., N. Jahanshad, Y. Jin, et al. "Understanding scanner upgrade effects on brain integrity & connectivity measures." In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014). IEEE, 2014. http://dx.doi.org/10.1109/isbi.2014.6867852.

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Reports on the topic "Brain connectivity measure"

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McGlaughlin, Alec S. Analyzing and Assessing Brain Structure with Graph Connectivity Measures. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada604781.

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