Relevant bibliographies by topics / Resting state in fMRI

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

Academic literature on the topic 'Resting state in fMRI'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Resting state in fMRI"

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Guerra-Carrillo, Belén, Allyson P. Mackey, and Silvia A. Bunge. "Resting-State fMRI." Neuroscientist 20, no. 5 (February 21, 2014): 522–33. http://dx.doi.org/10.1177/1073858414524442.

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Pan, Wen-Ju, Jacob Billings, Maysam Nezafati, Anzar Abbas, and Shella Keilholz. "Resting State fMRI in Rodents." Current Protocols in Neuroscience 83, no. 1 (April 2018): e45. http://dx.doi.org/10.1002/cpns.45.

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Craddock, RC, PE Holtzheimer, XP Hu, and HS Mayberg. "Disease State Prediction from Resting State FMRI." NeuroImage 47 (July 2009): S80. http://dx.doi.org/10.1016/s1053-8119(09)70559-2.

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Li, Xiaoling, Lina Cai, Xiaoxu Jiang, Xiaohui Liu, Jingxian Wang, Tiansong Yang, and Feng Wang. "Resting-State fMRI in Studies of Acupuncture." Evidence-Based Complementary and Alternative Medicine 2021 (March 23, 2021): 1–7. http://dx.doi.org/10.1155/2021/6616060.

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Research exploring the mechanism of acupuncture has been a hot topic in medicine. Resting-state functional magnetic resonance imaging (rs-fMRI) research is a noninvasive and extensive method, which is aimed at the research of the mechanism of acupuncture. Researchers use fMRI technologies to inspect the acupuncture process. The authors reviewed the application of rs-fMRI in acupuncture research in recent 10 years from the aspects of studying acupoints, subjects, acupuncture methods, and intensities. The results found that the application of rs-fMRI in acupuncture research mainly includes research on the onset mechanism of acupuncture treatment; visual evidence of diagnosis and treatment of dominant diseases; efficacy assessments; physiological mechanism of acupoint stimulation; and specific visualization of acupoints.
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Murphy, Kevin, Rasmus M. Birn, and Peter A. Bandettini. "Resting-state fMRI confounds and cleanup." NeuroImage 80 (October 2013): 349–59. http://dx.doi.org/10.1016/j.neuroimage.2013.04.001.

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Friston, Karl J., Joshua Kahan, Bharat Biswal, and Adeel Razi. "A DCM for resting state fMRI." NeuroImage 94 (July 2014): 396–407. http://dx.doi.org/10.1016/j.neuroimage.2013.12.009.

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Biswal, Bharat B. "Resting state fMRI: A personal history." NeuroImage 62, no. 2 (August 2012): 938–44. http://dx.doi.org/10.1016/j.neuroimage.2012.01.090.

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Smith, Stephen M., Diego Vidaurre, Christian F. Beckmann, Matthew F. Glasser, Mark Jenkinson, Karla L. Miller, Thomas E. Nichols, et al. "Functional connectomics from resting-state fMRI." Trends in Cognitive Sciences 17, no. 12 (December 2013): 666–82. http://dx.doi.org/10.1016/j.tics.2013.09.016.

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Haak, Koen V., Andre F. Marquand, and Christian F. Beckmann. "Connectopic mapping with resting-state fMRI." NeuroImage 170 (April 2018): 83–94. http://dx.doi.org/10.1016/j.neuroimage.2017.06.075.

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Wurina, Yu-Feng Zang, and Shi-Gang Zhao. "Resting-state fMRI studies in epilepsy." Neuroscience Bulletin 28, no. 4 (August 2012): 449–55. http://dx.doi.org/10.1007/s12264-012-1255-1.

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Dissertations / Theses on the topic "Resting state in fMRI"

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Lv, Yating. "Application of resting-state fMRI methods to acute ischemic stroke." Doctoral thesis, Universitätsbibliothek Leipzig, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-126910.

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Diffusion weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) perfusion-weighted imaging (PWI) are commonly employed in clinical practice and in research to give pathophysiological information for patients with acute ischemic stroke. DWI is thought to roughly reflect the severely damaged infarct core, while DSC-PWI reflects the area of hypoperfusion. The volumetric difference between DWI and DSC-PWI is termed the PWI/DWI-mismatch, and has been suggested as an MRI surrogate of the ischemic penumbra. However, due to the application of a contrast agent, which has potentially severe side-effects (e.g., nephrogenic systemic fibrosis), the DSC-PWI precludes repetitive examinations for monitoring purposes. New approaches are being sought to overcome this shortcoming. BOLD (blood oxygen-level dependent) signal can reflect the metabolism of blood oxygen in the brain and hemodynamics can be assessed with resting-state fMRI. The aim of this thesis was to use resting-state fMRI as a new approach to give similar information as DSC-PWI. This thesis comprises two studies: In the first study (see Chapter 2), two resting-state fMRI methods, local methods which compare low frequency amplitudes between two hemispheres and a k-means clustering approach, were applied to investigate the functional damage of patients with acute ischemic stroke both in the time domain and frequency domain. We found that the lesion areas had lower amplitudes than contralateral homotopic healthy tissues. We also differentiated the lesion areas from healthy tissues using a k-means clustering approach. In the second study (see Chapter 3), time-shift analysis (TSA), which assesses time delays of the spontaneous low frequency fluctuations of the resting-state BOLD signal, was applied to give similar pathophysiological information as DSC-PWI in the acute phase of stroke. We found that areas which showed a pronounced time delay to the respective mean time course were very similar to the hypoperfusion area. In summary, we suggest that the resting-state fMRI methods, especially the time-shift analysis (TSA), may provide comparable information to DSC-PWI and thus serve as a useful diagnostic tool for stroke MRI without the need for the application of a contrast agent.
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Corte, Coi Claudio. "Network approaches for the analysis of resting state fMRI data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10820/.

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Negli ultimi anni la teoria dei network è stata applicata agli ambiti più diversi, mostrando proprietà caratterizzanti tutti i network reali. In questo lavoro abbiamo applicato gli strumenti della teoria dei network a dati cerebrali ottenuti tramite MRI funzionale “resting”, provenienti da due esperimenti. I dati di fMRI sono particolarmente adatti ad essere studiati tramite reti complesse, poiché in un esperimento si ottengono tipicamente più di centomila serie temporali per ogni individuo, da più di 100 valori ciascuna. I dati cerebrali negli umani sono molto variabili e ogni operazione di acquisizione dati, così come ogni passo della costruzione del network, richiede particolare attenzione. Per ottenere un network dai dati grezzi, ogni passo nel preprocessamento è stato effettuato tramite software appositi, e anche con nuovi metodi da noi implementati. Il primo set di dati analizzati è stato usato come riferimento per la caratterizzazione delle proprietà del network, in particolare delle misure di centralità, dal momento che pochi studi a riguardo sono stati condotti finora. Alcune delle misure usate indicano valori di centralità significativi, quando confrontati con un modello nullo. Questo comportamento `e stato investigato anche a istanti di tempo diversi, usando un approccio sliding window, applicando un test statistico basato su un modello nullo pi`u complesso. Il secondo set di dati analizzato riguarda individui in quattro diversi stati di riposo, da un livello di completa coscienza a uno di profonda incoscienza. E' stato quindi investigato il potere che queste misure di centralità hanno nel discriminare tra diversi stati, risultando essere dei potenziali bio-marcatori di stati di coscienza. E’ stato riscontrato inoltre che non tutte le misure hanno lo stesso potere discriminante. Secondo i lavori a noi noti, questo `e il primo studio che caratterizza differenze tra stati di coscienza nel cervello di individui sani per mezzo della teoria dei network.
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Craddock, Richard Cameron. "Support vector classification analysis of resting state functional connectivity fMRI." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31774.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Chou, Ying-hui, Mark Sundman, Heather E. Whitson, Pooja Gaur, Mei-Lan Chu, Carol P. Weingarten, David J. Madden, et al. "Maintenance and Representation of Mind Wandering during Resting-State fMRI." NATURE PUBLISHING GROUP, 2017. http://hdl.handle.net/10150/622631.

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Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future.
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Zhang, Yiming. "Connectivity-based parcellation of putamen region using resting state fMRI." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/53970.

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Functional magnetic resonance imaging (fMRI) has shown great potential in studying the underlying neural systems. Functional connectivity measured by fMRI provides an efficient approach to study the interactions and relationships between different brain regions. However, functional connectivity studies require accurate definition of brain regions, which is often difficult and may not be achieved through anatomical landmarks. In this thesis, we present a novel framework for parcellation of a brain region into functional subunits based on their connectivity patterns with other reference brain regions. The proposed method takes the prior neurological information into consideration and aims at finding spatially continuous and functionally consistent sub-regions in a given brain region. The proposed framework relies on a sparse spatially regularized fused lasso regression model for feature extraction. The usual lasso model is a linear regression model commonly applied in high dimensional data such as fMRI signals. Compared with lasso, the proposed model further considers the spatial order of each voxel and thus encourages spatially and functionally adjacent voxels to share similar regression coefficients despite of the possible spatial noise. In order to achieve the accurate parcellation results, we propose a process by iteratively merging voxels (groups) and tuning the parameters adaptively. In addition, a Graph-Cut optimization algorithm is adopted for assigning the overlapped voxels into separate sub-regions. With spatial information incorporated, spatially continuous and functionally consistent subunits can be obtained which are desired for subsequent brain connectivity analysis. The simulation results demonstrate that the proposed method could reliably yield spatially continuous and functionally consistent subunits. When applied to real resting state fMRI datasets, two consistent functional subunits could be obtained in the putamen region for all normal subjects. Comparisons between the results of the Parkinson’s disease group and the normal group suggest that the obtained results are in accordance with our medical assumption. The extracted functional subunits themselves are of great interest in studying the influence of aging and a certain disease, and they may provide us deeper insights and serve as a biomarker in our future Parkinson’s disease study.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Long, Xiangyu. "Parcellation of the human sensorimotor cortex: a resting-state fMRI study." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-165728.

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The sensorimotor cortex is a brain region comprising the primary motor cortex (MI) and the primary somatosensory (SI) cortex. In humans, investigation into these regions suggests that MI and SI are involved in the modulation and control of motor and somatosensory processing, and are somatotopically organized according to a body plan (Penfield & Boldrey, 1937). Additional investigations into somatotopic mapping in relation to the limbs in the peripheral nervous system and SI in central nervous system have further born out the importance of this body-based organization (Wall & Dubner, 1972). Understanding the nature of the sensorimotor cortex‟s structure and function has broad implications not only for human development, but also motor learning (Taubert et al., 2011) and clinical applications in structural plasticity in Parkinson‟s disease (Sehm et al., 2014), among others. The aim of the present thesis is to identify functionally meaningful subregions within the sensorimotor cortex via parcellation analysis. Previously, cerebral subregions were identified in postmortem brains by invasive procedures based on histological features (Brodmann, 1909; Vogt. & Vogt., 1919; Economo, 1926; Sanides, 1970). One widely used atlas is based on Brodmann areas (BA). Brodmann divided human brains into several areas based on the visually inspected cytoarchitecture of the cortex as seen under a microscope (Brodmann, 1909). In this atlas, BA 4, BA 3, BA 1 and BA 2 together constitute the sensorimotor cortex (Vogt. & Vogt., 1919; Geyer et al., 1999; Geyer et al., 2000). However, BAs are incapable of delineating the somatotopic detail reflected in other research (Blankenburg et al., 2003). And, although invasive approaches have proven reliable in the discovery of functional parcellation in the past, such approaches are marked by their irreversibility which, according to ethical standards, makes them unsuitable for scientific inquiry. Therefore, it is necessary to develop non-invasive approaches to parcellate functional brain regions. In the present study, a non-invasive and task-free approach to parcellate the sensorimotor cortex with resting-state fMRI was developed. This approach used functional connectivity patterns of brain areas in order to delineate functional subregions as connectivity-based parcellations (Wig et al., 2014). We selected two adjacent BAs (BA 3 and BA 4) from a standard template to cover the area along the central sulcus (Eickhoff et al., 2005). Then subregions within this area were generated using resting-state fMRI data. These subregions were organized somatotopically from medial-dorsal to ventral-lateral (corresponding roughly to the face, hand and foot regions, respectively) by comparing them with the activity maps obtained by using independent motor tasks. Interestingly, resting-state parcellation map demonstrated higher correspondence to the task-based divisions after individuals had performed motor tasks. We also observed higher functional correlations between the hand area and the foot and tongue area, respectively, than between the foot and tongue regions. The functional relevance of those subregions indicates the feasibility of a wide range of potential applications to brain mapping (Nebel et al., 2014). In sum, the present thesis provides an investigation of functional network, functional structure, and properties of the sensorimotor cortex by state-of-art neuroimaging technology. The methodology and the results of the thesis hope to carry on the future research of the sensorimotor system.
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Williams, Kathleen Anne. "Resting State Connectivity in the Rat Brain." Thesis, Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14059.

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Functional MRI is a method of imaging changes in blood oxygenation that accompany neural activity in the brain. A specific area within fMRI studies investigates what the brain is doing when it is not being stimulated. It is postulated that there are distinctly separate regions of the brain that are connected based upon functional relations and that these connected regions synchronously communicate even during rest. Resting state connectivity has become a tool to investigate neurological disorders in humans without specific knowledge of the mechanisms that correlate neural activity with brain metabolism and blood flow. This work attempts to characterize resting state connectivity in the rat brain to establish a model that will help elucidate the relationship between functional connectivity, as measured with fMRI, and brain function. Four analysis techniques, power spectrum estimation, cross correlation analysis, principle component analysis, and independent component analysis, are employed to examine data acquired during a non-stimulation, single-slice, gradient echo EPI sequence in search of functionally connected, spatially distant regions of the rat brain.
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Starck, T. (Tuomo). "Dimensionality, noise separation and full frequency band perspectives of ICA in resting state fMRI:investigations into ICA in resting state fMRI." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205182.

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Abstract The concept of resting state functional magnetic resonance imaging (fMRI) is built onto an original finding in 1995 that brain hemispheres present synchronous signal fluctuations with distinct patterns. fMRI measurements rely on blood oxygenation changes that indirectly mirror neural activity. Therefore, the origin of functional connectivity patterns, resting state networks (RSNs), has been a widely debated research question and numerous contributing factors have been identified. According to current understanding the fluctuations reflect maintenance of the system integrity in addition to spontaneous thought and action processes in the resting state. A popular method to study the functional connectivity in resting state fMRI is spatial independent component analysis (ICA) that decomposes signal sources into statistically independent components. The dichotomy of functional specialization versus functional integration has a correspondence in fMRI studies where RSNs play the integrative viewpoint of brain function. Although canonical large-scale RSNs are broadly distributed they also express modularity that can be accomplished by ICA with a high number of estimated components. The characteristics of high ICA dimensionality are broadly investigated in the thesis. An enduring issue in resting state research has been the confounding noise sources like motion and cardiorespiratory processes which may hamper the analysis. In this thesis the ability of ICA to separate these noise sources from the default mode network, a major RSN, is studied. Additionally, the suitability of ICA for full frequency spectrum analysis, a relatively rare setting in biosignal analysis, is investigated. The results of the thesis support the viewpoint of ICA as a robust analysis method for functional connectivity analysis. Cardiorespiratory and motion induced noise did not confound the functional connectivity analyses with ICA. High dimensional ICA provided better signal source separation, revealed the modular structure of the RSNs and pinpointed the specific aberrations in the autism spectrum disorder population. ICA was also found applicable for fully explorative analysis in both the spatial and temporal domains and indicated functional connectivity changes induced by transcranial bright light stimulation
Tiivistelmä Konsepti lepotilan tutkimisesta toiminnallisella magneettikuvauksella (engl. functional magnetic resonance imaging, fMRI) on rakentunut vuonna 1995 tehdylle löydökselle aivopuoliskojen välillä synkronisesta signaalivaihtelusta. Mittaukset perustuvat veren hapetuksen muutoksiin, jotka epäsuorasti heijastelevat hermostollista toimintaa. Tämän takia toiminnallisen kytkennällisyyden muodot, lepotilaverkostot, ovat olleet laajasti väitelty tutkimusaihe ja monia verkostoihin vaikuttavia tekijöitä onkin tunnistettu. Nykykäsityksen mukaan signaalivaihtelut lepotilassa heijastelevat järjestelmän yhtenäisyyden ylläpitoa spontaanin ajattelun ja toiminnan lisäksi. Suosittu menetelmä toiminnallisen kytkennällisyyden tutkimiseen lepotilan fMRI:ssä on spatiaalinen itsenäisten komponenttien analyysi (engl. independent component analysis, ICA), joka hajottaa signaalilähteet tilastollisesti itsenäisiin komponentteihin. Aivotoiminnan mallintamisessa kahtiajaolla toiminnalliseen erikoistumiseen ja toiminnalliseen integraatioon on vastaavuus fMRI-tutkimukseen, jossa lepotilaverkostot vastaavat toiminnallisen integraation näkökulmasta. Vaikka kanoniset lepotilaverkostot ovat laaja-alaisia, ne ovat toisaalta modulaarisia, jota voidaan tutkia tutkimalla korkean komponenttimäärän ICA-hajotelmaa. Korkea- dimensioisen ICA-hajotelman ominaisuuksia tutkitaan laajasti tässä väitöskirjassa. Kestoaihe lepotilatutkimuksessa on ollut analyysiä hankaloittavien kohinalähteiden kuten liikkeen ja kardiorespiratoristen prosessien vaikutus. Väitöskirjassa tutkitaan ICA:n kykyä erotella kohinalähteitä ’default mode’ -verkostosta, joka on merkittävin lepotilaverkosto. Lisäksi tutkitaan ICA:n soveltuvuutta täyden taajuuskaistan analysointiin, joka on verrattain harvinaista biosignaalien analyysissä. Väitöskirjan tulokset tukevat näkemystä ICA:n suorituskyvystä toiminnallisen kytkennällisyyden analyysissä. Kardiorespiratorinen ja liikkeestä lähtöisin oleva kohina ei häirinnyt merkittävästi ICA-tuloksia. Korkeadimensioinen ICA tarjosi paremman erottelun signaalilähteille, paljasti lepotilaverkostojen modulaarisen rakenteen ja määritti erityisen poikkeaman autismin kirjon oireyhtymän populaatiossa. ICA:n havaittiin olevan soveltuva täyseksploratiiviselle analyysille ajassa ja avaruudessa; tulos viittaa toiminnallisen kytkennällisyyden muutoksiin kallon läpäisevän kirkasvalostimulaation aikaansaamana
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Glomb, Katharina. "Spatio-temporal dynamics of human fMRI resting rate." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/402438.

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Spontaneous brain activity, measured under the absence of any overt task, has been investigated under the label of “resting state” for about 20 years with rising interest. While it was known since the beginnings of modern electrophysiology that the brain exhibits spontaneous fluctuations also during rest, the discovery, in 1995, that these fluctuations possess a robust spatio-temporal structure had a profound impact on how we understand and investigate brain activity. In this dissertation, we characterize the spatio-temporal dynamics of resting state on a macroscopic level using fMRI recordings from humans and combining novel data analysis tools with theoretical models on the level of the whole brain. We demonstrate the presence of common patterns of functional connectivity, known as resting state networks (RSNs), that evolve in time in both empirical and model data. We show that spontaneous fluctuations and their statistics are determined by the structure of the brain network and its dynamics.
La actividad cerebral espontánea, o actividad de reposo, es aquella que uno puede registrar cuando el cerebro no está involucrado en ninguna tarea impuesta del exterior (tal como sería la presentación de un estímulo). El estudio de la actividad de reposo ha conocido un interés creciente durante los últimos 20 años. Si bien las fluctuaciones en la actividad de reposo eran conocidas desde los inicios de la electrofisiología moderna, el descubrimiento, en 1995, de que estas fluctuaciones muestran patrones espaciotemporales robustos ha tenido un impacto profundo en la manera de entender e investigar la actividad del cerebro. En esta disertación caracterizamos la dinámica espaciotemporal de la actividad de reposo a nivel macroscópico usando registros de fMRI en humanos y combinando nuevas herramientas de análisis y modelos teóricos del cerebro a gran escala. Observamos patrones comunes de conectividad funcional evolviendo en el tiempo tanto en los datos empíricos como en las simulaciones. Demostramos que las fluctuaciones de reposo y su estadística son determinadas por la estructura de la red cerebral y su dinámica.
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Jann, Kay. "Restless rest the brain's resting state explored by combined EEG and fMRI /." [S.l.] : [s.n.], 2009. http://www.zb.unibe.ch/download/eldiss/09jann_k.pdf.

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Books on the topic "Resting state in fMRI"

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Someren, Eus J. W. van, ed. Slow brain oscillations of sleep, resting state and vigilance: Proceedings of the 26th International Summer School of Brain Research, held at the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands, 29 June-2 July, 2010. Amsterdam: Elsevier, 2011.

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M, Smith Stephen, Janine Bijsterbosch, and Christian F. Beckmann. Introduction to Resting State fMRI Functional Connectivity. Oxford University Press, 2017.

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Chen, Jean, Garth John Thompson, Shella Keilholz, and Peter Herman, eds. Origins of the Resting-State fMRI Signal. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88966-285-2.

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Hu, Xiaoping Philip, and Nanyin Zhang, eds. Temporal Features in Resting State fMRI Data. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-408-5.

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Papanicolaou, Andrew C. The Default Mode and Other Resting State Networks. Edited by Andrew C. Papanicolaou. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.003.

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Brain activity during rest, as measured and imaged mainly by fMRI, appears to be due to a number of simultaneously active neuronal networks. The network identified first is the default mode network, which has been used as a marker of conscious awareness in patients with compromised consciousness. In this chapter, the methods of deriving this and other resting networks are outlined, the reliability of each network is assessed, and the question of the functional significance of the default mode network including its relevance to the theory of mind and morality is addressed through a critical appraisal of the relevant literature.
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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; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. The signals originate from spatially distinct functionally related brain regions but exhibit coherent time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. These networks represent the strength of the functional connectivity between distinct functionally related brain regions and have been used as imaging markers of various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. This book provides a review of the basic principles of fMRI (signal sources, acquisition methods, and data analysis) and its potential clinical applications.
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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 patterns of brain functional connectivity in large-scale “frontostriatal” and “default mode” networks, and that the heterogeneity of OCD symptoms is likely to partly arise via distinct modulatory influences on these networks by broader disturbances of affective, motivational, and regulatory systems. The variable nature of some findings across studies as well as the influence of medications on functional connectivity measures is also discussed.
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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 identified abnormal functional connectivity within intrinsic brain networks including the default mode and frontoparietal networks in OCD patients. Although these studies, as a whole, clearly substantiate the model of cortico-striatal-thalamo-cortical circuit dysfunction in OCD and support the continued investigation of neuromodulatory treatments targeting these brain regions, there is also growing evidence that brain regions outside this core circuit, particularly frontoparietal regions involved in cognitive control processes, may also play a significant role in the pathophysiology of OCD.
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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 have reported structural connectivity abnormalities in frontal-striatal-cerebellar networks. In parallel, R-fMRI studies point towards abnormalities in the interaction of multiple networks, extending the functional territory of explorations beyond cognitive and motor control. In the future, a deep phenotypic characterization beyond diagnostic categories combined with longitudinal study designs and novel analytical approaches will accelerate the pace towards clinical translations of neuroimaging to improve the detection and prediction of neural trajectories and treatment response in ADHD.
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Uchida, Mai, and Joseph Biederman. Young Adult Outcome of Attention Deficit Hyperactivity Disorder. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190213589.003.0006.

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The Massachusetts General Hospital (MGH) Longitudinal Studies of Attention Deficit Hyperactivity Disorder (ADHD) evaluated and followed a large sample of both boys and girls with ADHD and controls without ADHD, along with their families, ascertained from psychiatric and pediatric sources. These studies documented that ADHD in both sexes is associated with high levels of persistence onto adulthood; high levels of familiality with ADHD and other psychiatric disorders; a wide range of comorbid psychiatric and cognitive disorders including mood, anxiety, and substance use disorders; learning disabilities with reading and math; executive function deficits; emotional dysregulation and autistic traits; as well as educational, social, and occupational dysfunctions. The MGH studies also suggested that stimulant treatment significantly decreased the risk of developing comorbid psychiatric disorders, substance use disorders, and impaired functional outcomes. The studies also documented the neural basis of the persistence of ADHD using resting-state functional magnetic resonance imaging (fMRI).
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Book chapters on the topic "Resting state in fMRI"

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Power, Jonathan D. "Resting-State fMRI: Preclinical Foundations." In fMRI, 47–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41874-8_5.

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Rocca, Maria A., Ermelinda De Meo, and Massimo Filippi. "Resting-State fMRI in Multiple Sclerosis." In fMRI, 335–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41874-8_23.

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Shimony, Joshua S., Eric C. Leuthardt, Donna Dierker, Ki-Yun Park, Carl D. Hacker, and Abraham Z. Snyder. "Resting State Functional MRI for Presurgical Planning." In fMRI, 287–301. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41874-8_19.

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Allen, Monica G., Abraham Z. Snyder, Carl D. Hacker, Timothy J. Mitchell, Eric C. Leuthardt, and Joshua S. Shimony. "Presurgical Resting-State fMRI." In Clinical Functional MRI, 143–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45123-6_5.

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Niazy, Rami K., David M. Cole, Christian F. Beckmann, and Stephen M. Smith. "Resting-State Networks." In fMRI: From Nuclear Spins to Brain Functions, 387–425. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7591-1_14.

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Ge, Bao, Lei Guo, Jinglei Lv, Xintao Hu, Junwei Han, Tuo Zhang, and Tianming Liu. "Resting State fMRI-Guided Fiber Clustering." In Lecture Notes in Computer Science, 149–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23629-7_19.

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Grodd, Wolfgang, and Christian F. Beckmann. "Resting-State-fMRT." In Funktionelle MRT in Psychiatrie und Neurologie, 229–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29800-4_15.

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Ge, Ruiyang, Li Yao, Hang Zhang, Xia Wu, and Zhiying Long. "Over-Complete Analysis for Resting-State fMRI Data." In Advances in Cognitive Neurodynamics (V), 317–23. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0207-6_44.

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Chyzhyk, Darya, Ann K. Shinn, and Manuel Graña. "Exploration of LICA Detections in Resting State fMRI." In New Challenges on Bioinspired Applications, 104–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21326-7_12.

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Caponera, Alessia, Francesco Denti, Tommaso Rigon, Andrea Sottosanti, and Alan Gelfand. "Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data." In Studies in Neural Data Science, 111–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00039-4_7.

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Conference papers on the topic "Resting state in fMRI"

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Keilholz, Shella D., Jacob C. W. Billings, Kai Wang, Anzar Abbas, Claudia Hafeneger, Wen-Ju Pan, Sadia Shakil, and Maysam Nezafati. "Multiscale network activity in resting state fMRI." 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.2016.7590640.

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Lianghua He, Hongfei Ji, Meng Wan, and Shuang Liu. "Low frequency analysis of resting-state fMRI." In 2012 International Conference on Computer Science and Information Processing (CSIP). IEEE, 2012. http://dx.doi.org/10.1109/csip.2012.6308859.

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Fereidouni, Marzie, Hamid Soltanian-Zadeh, Quan Jiang, and Kost V. Elisevich. "Impaired resting state networks in temporal lobe epilepsy: A resting state fMRI study." In 2012 19th Iranian Conference of Biomedical Engineering (ICBME). IEEE, 2012. http://dx.doi.org/10.1109/icbme.2012.6519686.

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Vakarov, Kristina, Tatjana Loncar-Turukalo, Katarina Koprivsek, Milos Lucic, and Olivera Sveljo. "fMRI resting state analysis using empirical mode decomposition." In 2016 IEEE 14th International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2016. http://dx.doi.org/10.1109/sisy.2016.7601487.

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Eavani, Harini, Roman Filipovych, Christos Davatzikos, Theodore D. Satterthwaite, Raquel E. Gur, and Ruben C. Gur. "Sparse Dictionary Learning of Resting State fMRI Networks." In 2012 2nd International Workshop on Pattern Recognition in NeuroImaging (PRNI). IEEE, 2012. http://dx.doi.org/10.1109/prni.2012.25.

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Lee, Kangjoo, Paul Kyu Han, and Jong Chul Ye. "Sparse dictionary learning for resting-state fMRI analysis." In SPIE Optical Engineering + Applications, edited by Manos Papadakis, Dimitri Van De Ville, and Vivek K. Goyal. SPIE, 2011. http://dx.doi.org/10.1117/12.894241.

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Song, Xiaomu, and Nan-kuei Chen. "Resting state fMRI data analysis using support vector machines." In 2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2013. http://dx.doi.org/10.1109/spmb.2013.6736773.

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Sen, Bhaskar, Gail A. Bernstein, Tingting Xu, Bryon A. Mueller, Mindy W. Schreiner, Kathryn R. Cullen, and Keshab K. Parhi. "Classification of obsessive-compulsive disorder from resting-state fMRI." 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.2016.7591508.

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Sen, Bhaskar, Bryon Mueller, Bonnie Klimes-Dougan, Kathryn Cullen, and Keshab K. Parhi. "Classification of Major Depressive Disorder from Resting-State fMRI." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856453.

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Russell, M. D., F. A. Howe, T. R. Barrick, and N. Sofat. "SAT0570 Resting-state fmri brain connectivity in hand osteoarthritis." In Annual European Congress of Rheumatology, EULAR 2018, Amsterdam, 13–16 June 2018. BMJ Publishing Group Ltd and European League Against Rheumatism, 2018. http://dx.doi.org/10.1136/annrheumdis-2018-eular.4107.

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Reports on the topic "Resting state in fMRI"

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Ye, Qing, Quan Wen, Yi-ming Sun, Xin-ru Liu, Yu-xuan Peng, Xueping Yang, and Yu Dai. Resting-state fMRI in temporal lobe epilepsy patients with cognitive impairment: protocol for a meta-analysis and systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2021. http://dx.doi.org/10.37766/inplasy2021.3.0092.

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Wang, Chun, Qi Han, dongliang Zhu, Zhenmei Li, and Jia Li. Abnormalities of intrinsic brain activity in chronic primary insomnia: a protocol for systematic review and meta-analysis of resting-state functional imaging. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2021. http://dx.doi.org/10.37766/inplasy2021.5.0103.

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Li, Jia, Chun Wang, Zhenmei Li, Bo Fu, Qi Han, and Mao Ye. Abnormalities of intrinsic brain activity in irritable bowel syndrome (IBS): a protocol for systematic review and meta-analysis of resting-state functional imaging. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2021. http://dx.doi.org/10.37766/inplasy2021.3.0108.

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‘Longitudinal association between externalising behaviour and frontoamygdalar resting-state functional connectivity’ In conversation Dr. Sandra Thijssen. ACAMH, July 2021. http://dx.doi.org/10.13056/acamh.16224.

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In this podcast we talk to Assistant Professor Dr Sandra Thijjsen about her JCPP paper 'the longitudinal association between externalising behaviour and frontoamygdalar resting-state functional connectivity in late adolescence and young adulthood'.
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