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Journal articles on the topic 'Resting state in fMRI'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

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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2

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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3

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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4

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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5

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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7

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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8

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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9

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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10

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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11

Mitra, A., A. Z. Snyder, C. D. Hacker, and M. E. Raichle. "Lag structure in resting-state fMRI." Journal of Neurophysiology 111, no. 11 (June 1, 2014): 2374–91. http://dx.doi.org/10.1152/jn.00804.2013.

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The discovery that spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals contain information about the functional organization of the brain has caused a paradigm shift in neuroimaging. It is now well established that intrinsic brain activity is organized into spatially segregated resting-state networks (RSNs). Less is known regarding how spatially segregated networks are integrated by the propagation of intrinsic activity over time. To explore this question, we examined the latency structure of spontaneous fluctuations in the fMRI BOLD signal. Our data reveal that intrinsic activity propagates through and across networks on a timescale of ∼1 s. Variations in the latency structure of this activity resulting from sensory state manipulation (eyes open vs. closed), antecedent motor task (button press) performance, and time of day (morning vs. evening) suggest that BOLD signal lags reflect neuronal processes rather than hemodynamic delay. Our results emphasize the importance of the temporal structure of the brain's spontaneous activity.
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12

Acer, Niyazi. "Does the Brain Work While Resting? Resting State fMRI." Erciyes Tıp Dergisi/Erciyes Medical Journal 40, no. 4 (December 17, 2018): 175–76. http://dx.doi.org/10.5152/etd.2018.18133.

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13

Smitha, KA, K. Akhil Raja, KM Arun, PG Rajesh, Bejoy Thomas, TR Kapilamoorthy, and Chandrasekharan Kesavadas. "Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks." Neuroradiology Journal 30, no. 4 (March 29, 2017): 305–17. http://dx.doi.org/10.1177/1971400917697342.

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The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at ‘resting state’. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.
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14

Cieri, Filippo, and Roberto Esposito. "Neuroaging through the Lens of the Resting State Networks." BioMed Research International 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/5080981.

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Resting state functional magnetic resonance imaging (rs-fMRI) allows studying spontaneous brain activity in absence of task, recording changes of Blood Oxygenation Level Dependent (BOLD) signal. rs-fMRI enables identification of brain networks also called Resting State Networks (RSNs) including the most studied Default Mode Network (DMN). The simplicity and speed of execution make rs-fMRI applicable in a variety of normal and pathological conditions. Since it does not require any task, rs-fMRI is particularly useful for protocols on patients, children, and elders, increasing participant’s compliance and reducing intersubjective variability due to the task performance. rs-fMRI has shown high sensitivity in identification of RSNs modifications in several diseases also in absence of structural modifications. In this narrative review, we provide the state of the art of rs-fMRI studies about physiological and pathological aging processes. First, we introduce the background of resting state; then we review clinical findings provided by rs-fMRI in physiological aging, Mild Cognitive Impairment (MCI), Alzheimer Dementia (AD), and Late Life Depression (LLD). Finally, we suggest future directions in this field of research and its potential clinical applications.
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15

Smirnov, A. S., M. G. Sharaev, T. V. Melnikova-Pitskhelauri, V. Yu Zhukov, A. E. Bikanov, E. V. Sharova, E. L. Pogosbekyan, et al. "Resting state fMRI in pre-surgical brain mapping. Literature review." Medical Visualization, no. 5 (October 28, 2018): 6–13. http://dx.doi.org/10.24835/1607-0763-2018-5-6-13.

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Today, functional magnetic resonance imaging (fMRI) allows to plan surgery based on the topography of functionally important areas of the human brain cortex and tumor. This method can complement the surgical strategy with significant clinical information. The stimulus-dependent fMRI with motor and language paradigms is generally used for preoperative planning. The study outcome depends on the patient's ability to perform tasks paradigm, which is broken in brain tumors. In an attempt to overcome this problem, resting-state fMRI (rs-fMRI) is used for brain mapping. Rs-fMRI is based on the measurement of spontaneous fluctuations of the BOLD signal (blood oxygen level-dependent), representing the functional structure of the brain. In contrast to stimulus-dependent fMRI, rs-fMRI provides more complete information about functional architecture of the brain. rs-fMRI is used in conditions where the results of stimulusdependent fMRI may be falsely positive or in the absence of the possibility of its implementation. In aggregate, both methods significantly expand the efficiency and specificity of preoperative planning.
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16

JeongHoJin and Eunjoo Kang. "Resting-state fMRI analysis: techniques and implications." Korean Journal of Cognitive and Biological Psychology 28, no. 3 (July 2016): 445–78. http://dx.doi.org/10.22172/cogbio.2016.28.3.004.

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17

Orringer, Daniel A. "Editorial. Resting-state fMRI for the masses." Journal of Neurosurgery 131, no. 3 (September 2019): 757–58. http://dx.doi.org/10.3171/2018.5.jns181058.

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18

Khosla, Meenakshi, Keith Jamison, Gia H. Ngo, Amy Kuceyeski, and Mert R. Sabuncu. "Machine learning in resting-state fMRI analysis." Magnetic Resonance Imaging 64 (December 2019): 101–21. http://dx.doi.org/10.1016/j.mri.2019.05.031.

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19

Park, Hae-Jeong, Karl J. Friston, Chongwon Pae, Bumhee Park, and Adeel Razi. "Dynamic effective connectivity in resting state fMRI." NeuroImage 180 (October 2018): 594–608. http://dx.doi.org/10.1016/j.neuroimage.2017.11.033.

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20

Yuste, Rafael, and Adrienne L. Fairhall. "Temporal dynamics in fMRI resting-state activity." Proceedings of the National Academy of Sciences 112, no. 17 (April 20, 2015): 5263–64. http://dx.doi.org/10.1073/pnas.1505898112.

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21

Xing, Wu, Wei Shi, Yueshuang Leng, Xianting Sun, Tingting Guan, Weihua Liao, and Xiaoyi Wang. "Resting-state fMRI in primary Sjögren syndrome." Acta Radiologica 59, no. 9 (January 8, 2018): 1091–96. http://dx.doi.org/10.1177/0284185117749993.

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Background The involvement of the central nervous system in primary Sjögren syndrome (pSS) remains controversial. Functional magnetic resonance imaging (fMRI) is a relatively new method that can be applied to investigate the heterogeneity of central nervous system (CNS) involvement in pSS patients through regional homogeneity (ReHo) analysis. Purpose To collect data from pSS patients and healthy controls, and use ReHo analysis to elucidate the neurobiological mechanism of CNS involvement in pSS. Material and Methods Fourteen clinically diagnosed pSS patients and 14 age- and gender-matched healthy controls underwent resting-state fMRI. The data were processed by ReHo analysis. The double sample t-test was used to compare ReHo data between groups. Results Compared to controls, pSS patients had significantly increased ReHo values in the right cerebrum, left limbic lobe, right middle temporal gyrus, and the inferior parietal lobe. However, ReHo values significantly decreased in the right lingual gyrus, left cuneiform lobe, left superior occipital gyrus, bilateral middle occipital gyrus, and the fronto-parietal junction area ( P < 0.01, clusters ≥ 50 voxels). Conclusion This study demonstrates the abnormal brain activity in the visual cortex and fronto-parietal junction area in pSS patients, suggesting pathological neuronal dysfunction in these regions.
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22

Razi, Adeel, Mohamed L. Seghier, Yuan Zhou, Peter McColgan, Peter Zeidman, Hae-Jeong Park, Olaf Sporns, Geraint Rees, and Karl J. Friston. "Large-scale DCMs for resting-state fMRI." Network Neuroscience 1, no. 3 (October 2017): 222–41. http://dx.doi.org/10.1162/netn_a_00015.

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This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.
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23

Polania, R. "IS 25. tDCS & resting state fMRI." Clinical Neurophysiology 124, no. 10 (October 2013): e47. http://dx.doi.org/10.1016/j.clinph.2013.04.044.

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24

Hacker, Carl D., Jarod L. Roland, Albert H. Kim, Joshua S. Shimony, and Eric C. Leuthardt. "Resting-state network mapping in neurosurgical practice: a review." Neurosurgical Focus 47, no. 6 (December 2019): E15. http://dx.doi.org/10.3171/2019.9.focus19656.

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Resting-state functional MRI (rs-fMRI) is a well-established method for studying intrinsic connectivity and mapping the topography of functional networks in the human brain. In the clinical setting, rs-fMRI has been used to define functional topography, typically language and motor systems, in the context of preoperative planning for neurosurgery. Intraoperative mapping of critical speech and motor areas with electrocortical stimulation (ECS) remains standard practice, but preoperative noninvasive mapping has the potential to reduce operative time and provide functional localization when awake mapping is not feasible. Task-based fMRI has historically been used for this purpose, but it can be limited by the young age of the patient, cognitive impairment, poor cooperation, and need for sedation. Resting-state fMRI allows reliable analysis of all functional networks with a single study and is inherently independent of factors affecting task performance. In this review, the authors provide a summary of the theory and methods for resting-state network mapping. They provide case examples illustrating clinical implementation and discuss limitations of rs-fMRI and review available data regarding performance in comparison to ECS. Finally, they discuss novel opportunities for future clinical applications and prospects for rs-fMRI beyond mapping of regions to avoid during surgery but, instead, as a tool to guide novel network-based therapies.
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Desai, Virendra R., Aditya Vedantam, Sandi K. Lam, Lucia Mirea, Stephen T. Foldes, Daniel J. Curry, P. David Adelson, Angus A. Wilfong, and Varina L. Boerwinkle. "Language lateralization with resting-state and task-based functional MRI in pediatric epilepsy." Journal of Neurosurgery: Pediatrics 23, no. 2 (February 2019): 171–77. http://dx.doi.org/10.3171/2018.7.peds18162.

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OBJECTIVEDetermining language laterality in patients with intractable epilepsy is important in operative planning. Wada testing is the gold standard, but it has a risk of stroke. Both Wada and task-based functional MRI (tb-fMRI) require patient cooperation. Recently, resting-state fMRI (rs-fMRI) has been explored for language lateralization. In the present study, the correlation between rs-fMRI and tb-fMRI in language lateralization is estimated in a pediatric population with intractable epilepsy.METHODSrs-fMRI and tb-fMRI language lateralization testing performed as part of epilepsy surgery evaluation was retrospectively reviewed.RESULTSTwenty-nine patients underwent rs-fMRI and tb-fMRI; a total of 38 rs-fMRI studies and 30 tb-fMRI studies were obtained. tb-fMRI suggested left dominance in 25 of 30 cases (83%), right in 3 (10%), and in 2 (7%) the studies were nondiagnostic. In rs-fMRI, 26 of 38 studies (68%) suggested left dominance, 3 (8%) right dominance, 6 (16%) bilateral, and 3 (8%) were nondiagnostic. When tb-fMRI lateralized to the left hemisphere (25 cases), rs-fMRI was lateralized to the left in 23 patients (92%) and it was bilateral/equal in 2 (8%). When tb-fMRI lateralized to the right (3 cases), rs-fMRI lateralized to the right in all cases (100%). The overall concordance rate was 0.93 (95% CI 0.76–0.99) when considering cases with tb-fMRI and rs-fMRI performed within 6 months of each other, and tb-fMRI results were not nondiagnostic.CONCLUSIONSrs-fMRI significantly correlated with tb-fMRI in lateralizing language and suggests the potential role for identifying hemispheric dominance via rs-fMRI. Further investigation and validation studies are warranted.
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Zhang, Dongyang, James M. Johnston, Michael D. Fox, Eric C. Leuthardt, Robert L. Grubb, Michael R. Chicoine, Matthew D. Smyth, Abraham Z. Snyder, Marcus E. Raichle, and Joshua S. Shimony. "Preoperative Sensorimotor Mapping in Brain Tumor Patients Using Spontaneous Fluctuations in Neuronal Activity Imaged With Functional Magnetic Resonance Imaging: Initial Experience." Operative Neurosurgery 65, suppl_6 (December 1, 2009): ons226—ons236. http://dx.doi.org/10.1227/01.neu.0000350868.95634.ca.

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Abstract Objective: To describe initial experience with resting-state correlation mapping as a potential aid for presurgical planning of brain tumor resection. Methods: Resting-state blood oxygenation-dependent functional magnetic resonance imaging (fMRI) scans were acquired in 17 healthy young adults and 4 patients with brain tumors invading sensorimotor cortex. Conventional fMRI motor mapping (finger-tapping protocol) was also performed in the patients. Intraoperatively, motor hand area was mapped using cortical stimulation. Results: Robust and consistent delineation of sensorimotor cortex was obtained using the resting-state blood oxygenation-dependent data. Resting-state functional mapping localized sensorimotor areas consistent with cortical stimulation mapping and in all patients performed as well as or better than task-based fMRI. Conclusion: Resting-state correlation mapping is a promising tool for reliable functional localization of eloquent cortex. This method compares well with “gold standard” cortical stimulation mapping and offers several advantages compared with conventional motor mapping fMRI.
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Shim, Miseon, Han-Jeong Hwang, Ulrike Kuhl, and Hyeon-Ae Jeon. "Resting-State Functional Connectivity in Mathematical Expertise." Brain Sciences 11, no. 4 (March 28, 2021): 430. http://dx.doi.org/10.3390/brainsci11040430.

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To what extent are different levels of expertise reflected in the functional connectivity of the brain? We addressed this question by using resting-state functional magnetic resonance imaging (fMRI) in mathematicians versus non-mathematicians. To this end, we investigated how the two groups of participants differ in the correlation of their spontaneous blood oxygen level-dependent fluctuations across the whole brain regions during resting state. Moreover, by using the classification algorithm in machine learning, we investigated whether the resting-state fMRI networks between mathematicians and non-mathematicians were distinguished depending on features of functional connectivity. We showed diverging involvement of the frontal–thalamic–temporal connections for mathematicians and the medial–frontal areas to precuneus and the lateral orbital gyrus to thalamus connections for non-mathematicians. Moreover, mathematicians who had higher scores in mathematical knowledge showed a weaker connection strength between the left and right caudate nucleus, demonstrating the connections’ characteristics related to mathematical expertise. Separate functional networks between the two groups were validated with a maximum classification accuracy of 91.19% using the distinct resting-state fMRI-based functional connectivity features. We suggest the advantageous role of preconfigured resting-state functional connectivity, as well as the neural efficiency for experts’ successful performance.
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Zheng, Hongyi, Lingmei Kong, Lanmei Chen, Haidu Zhang, and Wenbin Zheng. "Acute Effects of Alcohol on the Human Brain: A Resting-State fMRI Study." BioMed Research International 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/947529.

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The aim of this study is to assess the value of resting-state fMRI in detecting the acute effects of alcohol on healthy human brains. Thirty-two healthy volunteers were studied by conventional MR imaging and resting-state fMRI prior to and 0.5 hours after initiation of acute alcohol administration. The fMRI data, acquired during the resting state, were correlated with different breath alcohol concentrations (BrAC). We use the posterior cingulate cortex/precuneus as a seed for the default mode network (DMN) analysis. ALFF and ReHo were also used to investigate spontaneous neural activity in the resting state. Conventional MR imaging showed no abnormalities on all subjects. Compared with the prior alcohol administration, the ALFF and ReHo also indicated some specific brain regions which are affected by alcohol, including the superior frontal gyrus, cerebellum, hippocampal gyrus, left basal ganglia, and right internal capsule. Functional connectivity of the DMN was affected by alcohol. This resting-state fMRI indicates that brain regions implicated are affected by alcohol and might provide a neural basis for alcohol’s effects on behavioral performance.
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29

Beckmann, Christian F., Marilena DeLuca, Joseph T. Devlin, and Stephen M. Smith. "Investigations into resting-state connectivity using independent component analysis." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1457 (May 29, 2005): 1001–13. http://dx.doi.org/10.1098/rstb.2005.1634.

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Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.
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Zhu, Yuanqiang, Zhiyan Feng, Junling Xu, Chang Fu, Jinbo Sun, Xuejuan Yang, Dapeng Shi, and Wei Qin. "Increased interhemispheric resting-state functional connectivity after sleep deprivation: a resting-state fMRI study." Brain Imaging and Behavior 10, no. 3 (December 3, 2015): 911–19. http://dx.doi.org/10.1007/s11682-015-9490-5.

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31

Lang, ST, B. Goodyear, J. Kelly, and P. Federico. "Neurophysiology (fMRI)." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 42, S1 (May 2015): S38. http://dx.doi.org/10.1017/cjn.2015.173.

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Background: Resting state functional MRI (rs-fMRI) provides many advantages to task-based fMRI in neurosurgical populations, foremost of which is the lack of the need to perform a task. Many networks can be identified by rs-fMRI in a single period of scanning. Despite the advantages, there is a paucity of literature on rs-fMRI in neurosurgical populations. Methods: Eight patients with tumours near areas traditionally considered as eloquent cortex participated in a five minute rs-fMRI scan. Resting-state fMRI data underwent Independent Component Analysis (ICA) using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) toolbox in FSL. Resting state networks (RSNs) were identified on a visual basis. Results: Several RSNs, including language (N=7), sensorimotor (N=7), visual (N=7), default mode network (N=8) and frontoparietal attentional control (n=7) networks were readily identifiable using ICA of rs-fMRI data. Conclusion: These pilot data suggest that ICA applied to rs-fMRI data can be used to identify motor and language networks in patients with brain tumours. We have also shown that RSNs associated with cognitive functioning, including the default mode network and the frontoparietal attentional control network can be identified in individual subjects with brain tumours. While preliminary, this suggests that rs-fMRI may be used pre-operatively to localize areas of cortex important for higher order cognitive functioning.
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Mitra, Anish, Abraham Z. Snyder, Tyler Blazey, and Marcus E. Raichle. "Lag threads organize the brain’s intrinsic activity." Proceedings of the National Academy of Sciences 112, no. 17 (March 30, 2015): E2235—E2244. http://dx.doi.org/10.1073/pnas.1503960112.

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It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term “lag threads,” are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals.
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33

He, Lianghua, Die Hu, Meng Wan, and Ying Wen. "Measuring Temporal Dynamics of Resting-state fMRI Data." Bio-Medical Materials and Engineering 24, no. 1 (2014): 939–45. http://dx.doi.org/10.3233/bme-130888.

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Liu, Yadong, Liangming Huang, Ming Li, Zongtan Zhou, and Dewen Hu. "Anticorrelated networks in resting-state fMRI-BOLD data." Bio-Medical Materials and Engineering 26, s1 (August 17, 2015): S1201—S1211. http://dx.doi.org/10.3233/bme-151417.

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35

Buyukgok, Deniz, Zubeyir Bayraktaroglu, H. Seda Buker, M. Isin Baral Kulaksizoglu, and I. Hakan Gurvit. "Resting-state fMRI analysis in apathetic Alzheimer's disease." Diagnostic and Interventional Radiology 26, no. 4 (July 2, 2020): 363–69. http://dx.doi.org/10.5152/dir.2019.19445.

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36

Sasai, Shuntaro, Fumitaka Homae, Hama Watanabe, Akihiro T. Sasaki, Hiroki C. Tanabe, Norihiro Sadato, and Gentaro Taga. "A NIRS–fMRI study of resting state network." NeuroImage 63, no. 1 (October 2012): 179–93. http://dx.doi.org/10.1016/j.neuroimage.2012.06.011.

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37

Davey, Catherine E., David B. Grayden, Gary F. Egan, and Leigh A. Johnston. "Filtering induces correlation in fMRI resting state data." NeuroImage 64 (January 2013): 728–40. http://dx.doi.org/10.1016/j.neuroimage.2012.08.022.

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38

Smith, Stephen M., Christian F. Beckmann, Jesper Andersson, Edward J. Auerbach, Janine Bijsterbosch, Gwenaëlle Douaud, Eugene Duff, et al. "Resting-state fMRI in the Human Connectome Project." NeuroImage 80 (October 2013): 144–68. http://dx.doi.org/10.1016/j.neuroimage.2013.05.039.

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Friston, Karl J., Joshua Kahan, Adeel Razi, Klaas Enno Stephan, and Olaf Sporns. "On nodes and modes in resting state fMRI." NeuroImage 99 (October 2014): 533–47. http://dx.doi.org/10.1016/j.neuroimage.2014.05.056.

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Mezer, Aviv, Yossi Yovel, Ofer Pasternak, Tali Gorfine, and Yaniv Assaf. "Cluster analysis of resting-state fMRI time series." NeuroImage 45, no. 4 (May 2009): 1117–25. http://dx.doi.org/10.1016/j.neuroimage.2008.12.015.

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van Montfort, Simone J. T., Edwin van Dellen, Aletta M. R. van den Bosch, Willem M. Otte, Maya J. L. Schutte, Soo-Hee Choi, Tae-Sub Chung, Sunghyon Kyeong, Arjen J. C. Slooter, and Jae-Jin Kim. "Resting-state fMRI reveals network disintegration during delirium." NeuroImage: Clinical 20 (2018): 35–41. http://dx.doi.org/10.1016/j.nicl.2018.06.024.

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Vergani, Alberto Arturo, Samuele Martinelli, and Elisabetta Binaghi. "Resting state fMRI analysis using unsupervised learning algorithms." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 8, no. 3 (July 2, 2019): 252–65. http://dx.doi.org/10.1080/21681163.2019.1636413.

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Jo, H. J., R. W. Cox, J. H. Kim, K. Im, and J. M. Lee. "Structure-Function Spatial Covariance in Resting-State FMRI." NeuroImage 47 (July 2009): S146. http://dx.doi.org/10.1016/s1053-8119(09)71477-6.

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Smith, SM, AR Laird, D. Glahn, PM Fox, CE Mackay, N. Filippini, R. Toro, PT Fox, and CF Beckmann. "FMRI resting state networks match BrainMap activation networks." NeuroImage 47 (July 2009): S147. http://dx.doi.org/10.1016/s1053-8119(09)71492-2.

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Uh, J., F. Xu, U. Yezhuvath, Y. Cheng, H. Gu, Y. Yang, and H. Lu. "The Effect of Hypercapnia on Resting State fMRI." NeuroImage 47 (July 2009): S185. http://dx.doi.org/10.1016/s1053-8119(09)72046-4.

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Liu, Xiao, Nanyin Zhang, Catie Chang, and Jeff H. Duyn. "Co-activation patterns in resting-state fMRI signals." NeuroImage 180 (October 2018): 485–94. http://dx.doi.org/10.1016/j.neuroimage.2018.01.041.

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Scholvinck, M. L., A. Maier, F. Q. Ye, J. H. Duyn, and D. A. Leopold. "Neural basis of global resting-state fMRI activity." Proceedings of the National Academy of Sciences 107, no. 22 (May 3, 2010): 10238–43. http://dx.doi.org/10.1073/pnas.0913110107.

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Jann, Kay, Mara Kottlow, Thomas Dierks, Chris Boesch, and Thomas Koenig. "Topographic Electrophysiological Signatures of fMRI Resting State Networks." PLoS ONE 5, no. 9 (September 22, 2010): e12945. http://dx.doi.org/10.1371/journal.pone.0012945.

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Ji, Bing, Zhihao Li, Kaiming Li, Longchuan Li, Jason Langley, Hui Shen, Shengdong Nie, Renjie Zhang, and Xiaoping Hu. "Dynamic thalamus parcellation from resting-state fMRI data." Human Brain Mapping 37, no. 3 (December 26, 2015): 954–67. http://dx.doi.org/10.1002/hbm.23079.

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Lee, Megan H., Michelle M. Miller-Thomas, Tammie L. Benzinger, Daniel S. Marcus, Carl D. Hacker, Eric C. Leuthardt, and Joshua S. Shimony. "Clinical Resting-state fMRI in the Preoperative Setting." Topics in Magnetic Resonance Imaging 25, no. 1 (February 2016): 11–18. http://dx.doi.org/10.1097/rmr.0000000000000075.

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