Cited by 110Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

The DOI matches its Crossref record; 111 of the 114 references it cites were found in a registry or an index.

Current methods and new directions in resting state fMRI

Open access

Yang, Jackie, Gohel, Suril, Vachha, Behroze·2020·Clinical Imaging

DOI 10.1016/j.clinimag.2020.04.004PMC PMC7365764

Open this paper in CoChat — free account

Abstract

Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.clinimag.2020.04.004

Cites

111 of 111 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Dissociable intrinsic connectivity networks for salience processing and executive control
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5587-06.2007 · Cited by 7,426
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
Exploring the brain network: A review on resting-state fMRI functional connectivity
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2010.03.008 · Cited by 3,326
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260
A method for making group inferences from functional MRI data using independent component analysis
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001) · Human Brain Mapping · doi:10.1002/hbm.1048 · Cited by 2,994
Tracking whole-brain connectivity dynamics in the resting state
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs352 · Cited by 2,992
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780
Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI
Yu-Feng, Z., Yong, H., Chao-Zhe, Z., Qing-Jiu, C., Man-Qiu, S., Meng, L., Li-Xia, T., Tian-Zi, J., & Yu-Feng, W. (2007) · Brain and Development · doi:10.1016/j.braindev.2006.07.002 · Cited by 2,660
Regional homogeneity approach to fMRI data analysis.
Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.030 · Cited by 2,614
Small-World Brain Networks
Bassett, D. S., & Bullmore, E. (2006) · The Neuroscientist · doi:10.1177/1073858406293182 · Cited by 2,600
An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF
Zou, Q., Zhu, C.-Z., Yang, Y., Zuo, X., Long, X., Cao, Q., Wang, Y., & Zang, Y. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.04.012 · Cited by 2,169
The connectomics of brain disorders
Fornito, A., Zalesky, A., & Breakspear, M. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3901 · Cited by 1,734
The oscillating brain: Complex and reliable
Zuo, X., Martino, A. D., Kelly, C. A. M., Shehzad, Z. E., Gee, D. G., Klein, D. F., Castellanos, F. X., Biswal, B. B., & Milham, M. P. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.09.037 · Cited by 1,466
Scale-Free Brain Functional Networks
Eguı́luz, V. M., Chialvo, D. R., Cecchi, G., Baliki, M. N., & Apkarian, A. V. (2005) · Physical Review Letters · doi:10.1103/physrevlett.94.018102 · Cited by 1,464
The dynamic functional connectome: State-of-the-art and perspectives
Preti, M. G., Bolton, T. A. W., & Ville, D. V. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.061 · Cited by 1,454
Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging
Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., Glasser, M. F., Miller, K. L., Ugurbil, K., & Yacoub, E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015710 · Cited by 1,296
Resting-State fMRI: A Review of Methods and Clinical Applications
Lee, M. H., Smyser, C. D., & Shimony, J. S. (2012) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3263 · Cited by 1,195
Advances and pitfalls in the analysis and interpretation of resting-state FMRI data
Cole, D. M. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00008 · Cited by 1,011
Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain
van den Heuvel, M. P., Mandl, R. C. W., Kahn, R. S., & Hulshoff Pol, H. E. (2009) · Human Brain Mapping · doi:10.1002/hbm.20737 · Cited by 987
Functional connectomics from resting-state fMRI
Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., et al. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.016 · Cited by 952
Small-World Brain Networks Revisited
Bassett, D. S., & Bullmore, E. T. (2016) · The Neuroscientist · doi:10.1177/1073858416667720 · Cited by 887
Fundamentals of Brain Network Analysis
Fornito, A., Zalesky, A., & Bullmore, E. T. (2016) · Elsevier eBooks · doi:10.1016/c2012-0-06036-x · Cited by 820
Time-resolved resting-state brain networks
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400181111 · Cited by 753
Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks
Smitha, K. A., Raja, K. A., Arun, K., Rajesh, P. G., Thomas, B., Kapilamoorthy, T., & Kesavadas, C. (2017) · The Neuroradiology Journal · doi:10.1177/1971400917697342 · Cited by 690
Default-Mode Activity during a Passive Sensory Task: Uncoupled from Deactivation but Impacting Activation
Greicius, M. D., & Menon, V. (2004) · Journal of Cognitive Neuroscience · doi:10.1162/0898929042568532 · Cited by 684
Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective
Zuo, X., & Xing, X.-X. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.05.009 · Cited by 678
Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters
Triantafyllou, C., Hoge, R. D., Krueger, G., Wiggins, C. J., Potthast, A., Wiggins, G. C., & Wald, L. L. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.01.007 · Cited by 648
Graph-based network analysis of resting-state functional MRI
Wang, J. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00016 · Cited by 614
Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI
Yang, H., Long, X., Yang, Y., Yan, H., Zhu, C.-Z., Zhou, X.-P., Zang, Y., & Gong, Q. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.01.054 · Cited by 613
Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know
Lv, H., Wang, Z., Tong, E., Williams, L. M., Zaharchuk, G., Zeineh, M., Goldstein‐Piekarski, A. N., Ball, T. M., Liao, C., & Wintermark, M. (2018) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5527 · Cited by 613
Functional brain connectivity using fMRI in aging and Alzheimer’s disease
Dennis, E. L., & Thompson, P. M. (2014) · Neuropsychology Review · doi:10.1007/s11065-014-9249-6 · Cited by 583
Regional coherence changes in the early stages of Alzheimer’s disease: A combined structural and resting-state functional MRI study
Yong He, A., Wang, L. Y., Zang, Y., Tian, L., Zhang, X., Li, K., & Jiang, T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.042 · Cited by 546
Resting-state functional connectivity in autism spectrum disorders: a review
Hull, J. V., Dokovna, L. B., Jacokes, Z. J., Torgerson, C. M., Irimia, A., & Van Horn, J. D. (2017) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00205 · Cited by 546
The application of graph theoretical analysis to complex networks in the brain
Reijneveld, J. C., Ponten, S. C., Berendse, H. W., & Stam, C. J. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.08.010 · Cited by 520
Non-Stationarity in the “Resting Brain’s” Modular Architecture
Jones, D. T., Vemuri, P., Murphy, M. C., Gunter, J. L., Senjem, M. L., Machulda, M. M., Przybelski, S. A., Gregg, B. E., Kantarci, K., Knopman, D. S., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039731 · Cited by 435
Making big data open: data sharing in neuroimaging
Poldrack, R. A., & Gorgolewski, K. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3818 · Cited by 429
Cognition and resting-state functional connectivity in schizophrenia
Sheffield, J. M., & Barch, D. M. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.12.007 · Cited by 384
A Hitchhiker's Guide to Functional Magnetic Resonance Imaging
Soares, J. M., Magalhães, R. J. D. S., Moreira, P. S., Sousa, A., Ganz, E., Sampaio, A., Alves, V., Marques, P., & Sousa, N. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00515 · Cited by 304
Resting brain dynamics at different timescales capture distinct aspects of human behavior
Liégeois, R., Li, J., Kong, R., Orbán, C., Ville, D. V. D., Ge, T., Sabuncu, M. R., & Yeo, B. T. T. (2019) · Nature Communications · doi:10.1038/s41467-019-10317-7 · Cited by 300
Independent Vector Analysis: An Extension of ICA to Multivariate Components
Kim, T., Eltoft, T., & Lee, T.-W. (2006) · Lecture notes in computer science · doi:10.1007/11679363_21 · Cited by 293
Resting functional connectivity of language networks: characterization and reproducibility
Tomasi, D. G., & Volkow, N. D. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.177 · Cited by 285
Making data sharing work: The FCP/INDI experience
Mennes, M., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.064 · Cited by 280
A systematic literature review of resting state network—functional MRI in bipolar disorder
Vargas, C., López‐Jaramillo, C., & Vieta, E. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2013.05.083 · Cited by 278
Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?
Hohenfeld, C., Werner, C. J., & Reetz, K. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.03.013 · Cited by 232
Review of thalamocortical resting-state fMRI studies in schizophrenia
Giraldo‐Chica, M., & Woodward, N. D. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.08.005 · Cited by 229
Joint Blind Source Separation With Multivariate Gaussian Model: Algorithms and Performance Analysis
Anderson, M., Adalı, T., & Li, X.-L. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2181836 · Cited by 225
Functional Integration Between Brain Regions at Rest Occurs in Multiple-Frequency Bands
Gohel, S., & Biswal, B. B. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0210 · Cited by 215
Self-citation
Clustering of Resting State Networks
Lee, M. H., Hacker, C. D., Snyder, A. Z., Corbetta, M., Zhang, D., Leuthardt, E. C., & Shimony, J. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040370 · Cited by 203
Ultra-High-Field MR Neuroimaging
Balchandani, P., & Naidich, T. P. (2014) · American Journal of Neuroradiology · doi:10.3174/ajnr.a4180 · Cited by 196
Anatomical insights into disrupted small-world networks in schizophrenia
Wang, Q., Su, T., Zhou, Y., Chou, K., Chen, I.-Y., Jiang, T., & Lin, C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.035 · Cited by 195
A multi-site resting state fMRI study on the amplitude of low frequency fluctuations in schizophrenia
Turner, J. A. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00137 · Cited by 177
Traumatic brain injury impairs small-world topology
Pandit, A. S., Expert, P., Lambiotte, R., Bonnelle, V., Leech, R., Turkheimer, F. E., & Sharp, D. J. (2013) · Neurology · doi:10.1212/wnl.0b013e3182929f38 · Cited by 176
Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis
Ma, S., Calhoun, V. D., Phlypo, R., & Adalı, T. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.063 · Cited by 173
Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest
Boubela, R. N., Kalcher, K., Huf, W., Kronnerwetter, C., Peter Filzmoser, A., & Moser, E. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00168 · Cited by 162
Independent vector analysis (IVA): Multivariate approach for fMRI group study
Lee, J., Lee, T.-W., Jolesz, F. A., & Yoo, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.019 · Cited by 151
Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder
Zhi, D., Calhoun, V. D., Lv, L., Ma, X., Ke, Q., Fu, Z., Du, Y., Yang, Y., Yang, X., Pan, M., et al. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00339 · Cited by 150
Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients
Li, X., Zhu, D., Jiang, X., Jin, C., Zhang, X., Guo, L., Zhang, J., Hu, X. P., Li, L., & Liu, T. (2013) · Human Brain Mapping · doi:10.1002/hbm.22290 · Cited by 139
Clinical fMRI: Evidence for a 7T benefit over 3T
Beisteiner, R., Robinson, S., Wurnig, M. C., Hilbert, M., Merksa, K., Rath, J., Hoellinger, I., Klinger, N., Marosi, C., Trattnig, S., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.010 · Cited by 128
The rapid development of high speed, resolution and precision in fMRI
Feinberg, D. A., & Yacoub, E. S. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.049 · Cited by 126
Identifying dynamic functional connectivity biomarkers using GIG‐ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder
Du, Y., Pearlson, G. D., Lin, D., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Chen, J., Salman, M. S., Tamminga, C. A., Ivleva, E. I., Sweeney, J. A., Keshavan, M. S., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23553 · Cited by 117
P4‐158: Nonstationarity in the ‘resting brain's’ modular architecture
Jones, D. T., Vemuri, P., Murphy, M. C., Gunter, J. L., Senjem, M. L., Machulda, M. M., Przybelski, S. A., Gregg, B. E., Kantarci, K., Knopman, D. S., et al. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.05.1862 · Cited by 116
A comprehensive evaluation of increasing temporal resolution with multiband-accelerated protocols and effects on statistical outcome measures in fMRI
Demetriou, L., Kowalczyk, O. S., Tyson, G., Bello, T., Newbould, R. D., & Wall, M. B. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.011 · Cited by 115
Investigation of Long-Term Reproducibility of Intrinsic Connectivity Network Mapping: A Resting-State fMRI Study
Chou, Y., Panych, L. P., Dickey, C. C., Petrella, J. R., & Chen, N. (2012) · American Journal of Neuroradiology · doi:10.3174/ajnr.a2894 · Cited by 113
Stability of low-frequency fluctuation amplitudes in prolonged resting-state fMRI
Küblböck, M., Woletz, M., Höflich, A. S., Sladky, R., Kranz, G. S., Hoffmann, A., Lanzenberger, R. R., & Windischberger, C. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.038 · Cited by 100
Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions
Keilholz, S. D., Caballero‐Gaudes, C., Bandettini, P. A., Deco, G., & Calhoun, V. D. (2017) · Brain Connectivity · doi:10.1089/brain.2017.0543 · Cited by 92
Frequency Specificity of Regional Homogeneity in the Resting-State Human Brain
Song, X., Zhang, Y., & Liu, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086818 · Cited by 83
Abnormal resting-state activities and functional connectivities of the anterior and the posterior cortexes in medication-naïve patients with obsessive-compulsive disorder
Cheng, Y., Xu, J., Nie, B., Luo, C., Yang, T., Li, H., Lu, J., Xu, L., Shan, B., & Xu, X. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067478 · Cited by 82
Frequency Dependent Alterations in Regional Homogeneity of Baseline Brain Activity in Schizophrenia
Yu, R., Hsieh, M. H., Wang, H. S., Liu, C., Liu, C., Hwang, T., Chien, Y., Hwu, H., & Tseng, W. I. (2013) · PLoS ONE · doi:10.1371/journal.pone.0057516 · Cited by 81
Comparison of the small-world topology between anatomical and functional connectivity in the human brain
Park, C.-H., Kim, S. Y., Kim, Y., & Kim, K. (2008) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.06.048 · Cited by 77
Whole brain high-resolution functional imaging at ultra high magnetic fields: An application to the analysis of resting state networks
Martino, F. D., Esposito, F., de Moortele, P. V., Harel, N., Formisano, E., Goebel, R., Uǧurbil, K., & Yacoub, E. S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.008 · Cited by 75
Altered temporal variance and neural synchronization of spontaneous brain activity in anesthesia
Huang, Z., Wang, Z., Zhang, J., Dai, R., Wu, J., Li, Y., Liang, W., Mao, Y. Q., Yang, Z. H., Holland, G., et al. (2014) · Human Brain Mapping · doi:10.1002/hbm.22556 · Cited by 69
Handedness- and Hemisphere-Related Differences in Small-World Brain Networks: A Diffusion Tensor Imaging Tractography Study
Li, M., Chen, H., 汪俊萍, Liu, F., Long, Z., Wang, Y., Iturria‐Medina, Y., Zhang, J., Yu, C., & Chen, H. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0211 · Cited by 62
Dynamic Graph Theoretical Analysis of Functional Connectivity in Parkinson's Disease: The Importance of Fiedler Value
Cai, J., Liu, A., Mi, T., Garg, S., Trappe, W. K., McKeown, M. J., & Wang, Z. J. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2875456 · Cited by 60
A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures
Gorgolewski, K. J., Mendes, N., Wilfling, D., Wladimirow, E., Gauthier, C. J., Bonnen, T., Ruby, F. J. M., Trampel, R., Bazin, P., Cozatl, R., et al. (2015) · Scientific Data · doi:10.1038/sdata.2014.54 · Cited by 60
Comparing connectivity pattern and small-world organization between structural correlation and resting-state networks in healthy adults
Hosseini, S. M. H., & Kesler, S. R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.032 · Cited by 59
Neural and metabolic basis of dynamic resting state fMRI
Thompson, G. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.010 · Cited by 57
Functional Connectivity in Adult Brain Tumor Patients: A Systematic Review
Fox, M. E., & King, T. Z. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0623 · Cited by 57
Structural brain networks and neuropsychiatric disorders
Wen, W. X., Yong He, A., & Sachdev, P. S. (2011) · Current Opinion in Psychiatry · doi:10.1097/yco.0b013e32834591f8 · Cited by 56
Disturbed Small-World Networks and Neurocognitive Function in Frontal Lobe Low-Grade Glioma Patients
Huang, Q., Zhang, R., Hu, X., Ding, S., Qian, J., Lei, T., Cao, X., Tao, L., Qian, Z., & Liu, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094095 · Cited by 55
Model of brain activation predicts the neural collective influence map of the brain
Morone, F., Roth, K., Min, B., Stanley, E., & Makse, H. A. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1620808114 · Cited by 55
Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis
Phinyomark, A., Ibáñez‐Marcelo, E., & Petri, G. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2734883 · Cited by 55
Abnormal dynamic functional connectivity between speech and auditory areas in schizophrenia patients with auditory hallucinations
Zhang, W., Li, S., Wang, X., Gong, Y., Li, Y., Xiao, Y., Liu, J., Keedy, S., Gong, Q., Sweeney, J. A., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.06.018 · Cited by 55
Alterations of static functional connectivity and dynamic functional connectivity in motor execution regions after stroke
Chen, J., Sun, D., Shi, Y., Jin, W., Wang, Y., Xi, Q., & Ren, C. (2018) · Neuroscience Letters · doi:10.1016/j.neulet.2018.09.008 · Cited by 54
Comparison of functional connectivity in default mode and sensorimotor networks at 3 and 7T
Hale, J. R., Brookes, M. J., Hall, E. L., Zumer, J. M., Stevenson, C. M., Francis, S., & Morris, P. G. (2010) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-010-0220-0 · Cited by 51
Examining resting-state functional connectivity in first-episode schizophrenia with 7T fMRI and MEG
Lottman, K. K., Gawne, T. J., Kraguljac, N. V., Killen, J. F., Reid, M. A., & Lahti, A. C. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101959 · Cited by 48
Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis
Valsasina, P., de la Cruz, M. H., Filippi, M., & Rocca, M. A. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00618 · Cited by 42
Memory performance-related dynamic brain connectivity indicates pathological burden and genetic risk for Alzheimer’s disease
Quevenco, F. C., Preti, M. G., Bergen, J. M. G. V., Hua, J., Wyss, M., Li, X., Schreiner, S. J., Steininger, S. C., Meyer, R., Meier, I. B., et al. (2017) · Alzheimer s Research & Therapy · doi:10.1186/s13195-017-0249-7 · Cited by 41
Frequency-specific alteration of functional connectivity density in antipsychotic-naive adolescents with early-onset schizophrenia
Wang, X., Zhang, Y., Long, Z., Zheng, J., Zhang, Y., Han, S., Wang, Y., Duan, X., Yang, M., Zhao, J., et al. (2017) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2017.07.014 · Cited by 38
Demonstration of Brain Tumor-Induced Neurovascular Uncoupling in Resting-State fMRI at Ultrahigh Field
Agarwal, S., Sair, H. I., Airan, R. D., Hua, J., Jones, C. K., Heo, H., Olivi, A., Lindquist, M. A., Pekar, J. J., & Pillai, J. J. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0402 · Cited by 37
Nuisance Regression of High-Frequency Functional Magnetic Resonance Imaging Data: Denoising Can Be Noisy
Chen, J. E., Jahanian, H., & Glover, G. H. (2016) · Brain Connectivity · doi:10.1089/brain.2016.0441 · Cited by 35
A joint time-frequency analysis of resting-state functional connectivity reveals novel patterns of connectivity shared between or unique to schizophrenia patients and healthy controls
Yaesoubi, M., Miller, R. L., Bustillo, J., Lim, K. O., Vaidya, J. G., & Calhoun, V. D. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.023 · Cited by 34
Value of Frequency Domain Resting-State Functional Magnetic Resonance Imaging Metrics Amplitude of Low-Frequency Fluctuation and Fractional Amplitude of Low-Frequency Fluctuation in the Assessment of Brain Tumor-Induced Neurovascular Uncoupling
Agarwal, S., Lu, H., & Pillai, J. J. (2017) · Brain Connectivity · doi:10.1089/brain.2016.0480 · Cited by 33
Frequency-Specific Alterations of Local Synchronization in Idiopathic Generalized Epilepsy
Wang, J., Zhang, Z., Ji, G., Xu, Q., Huang, Y.-B., Wang, Z., Jiao, Q., Yang, F., Zang, Y., Liao, W., et al. (2015) · Medicine · doi:10.1097/md.0000000000001374 · Cited by 32
Compromised small-world efficiency of structural brain networks in schizophrenic patients and their unaffected parents
Yan, H., Tian, L., Wang, Q., Zhao, Q., Yue, W., Yan, J., Liu, B., & Zhang, D. (2015) · Neuroscience Bulletin · doi:10.1007/s12264-014-1518-0 · Cited by 29
Frequency-specific abnormalities in regional homogeneity among children with attention deficit hyperactivity disorder: a resting-state fMRI study
Yu, X., Yuan, B., Cao, Q., An, L., Wang, P., Vance, A. L. A., Silk, T. J., Zang, Y., Wang, Y., & Sun, L. (2015) · 中国科学通报：英文版 · doi:10.1007/s11434-015-0823-y · Cited by 28
Attenuated resting-state functional connectivity in patients with childhood- and adult-onset schizophrenia
Watsky, R. E., Gotts, S. J., Berman, R. A., McAdams, H. M., Zhou, X., Greenstein, D. K., Lalonde, F. M., Gochman, P., CLASEN, L. S., Shora, L., et al. (2018) · Schizophrenia Research · doi:10.1016/j.schres.2018.01.003 · Cited by 26
A Distributed Computing Platform for fMRI Big Data Analytics
Makkie, M., Li, X., Quinn, S. P., Lin, B., Ye, J., Mon, G., & Liu, T. (2018) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2811508 · Cited by 20
Frequency specific resting state functional abnormalities in psychosis
Gohel, S., Gallego, J. A., Robinson, D. G., DeRosse, P., Biswal, B. B., & Szeszko, P. R. (2018) · Human Brain Mapping · doi:10.1002/hbm.24302 · Cited by 19
Self-citation
White matter lesion loads associated with dynamic functional connectivity within attention network in patients with relapsing-remitting multiple sclerosis
Huang, M., Zhou, F., Wu, L., Wang, B., Guo, L., Zhao, Y., Wan, H., Li, F., Zeng, X., & Gong, H. (2019) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2019.03.034 · Cited by 19
Action observation training promotes motor improvement and modulates functional network dynamic connectivity in multiple sclerosis
Cordani, C., Valsasina, P., Preziosa, P., Meani, A., Filippi, M., & Rocca, M. A. (2019) · Multiple Sclerosis Journal · doi:10.1177/1352458519887332 · Cited by 18
Two-Axis Acceleration of Functional Connectivity Magnetic Resonance Imaging by Parallel Excitation of Phase-Tagged Slices and Half k-Space Acceleration
Jesmanowicz, A., Nencka, A. S., Li, S., & Hyde, J. S. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0004 · Cited by 17
Influences of Head Motion Regression on High-Frequency Oscillation Amplitudes of Resting-State fMRI Signals
Yuan, B., Zang, Y., & Liu, D. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00243 · Cited by 15
A comparison of distributional considerations with statistical analysis of resting state fMRI at 3T and 7T
Yang, X., Holmes, M. J., Newton, A. T., Morgan, V. L., & Landman, B. A. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.911307 · Cited by 14
Frequency-Specific Local Synchronization Changes in Paroxysmal Kinesigenic Dyskinesia
Liu, Z., Miao, H., Yu, Y., Ding, M., & Liao, W. (2016) · Medicine · doi:10.1097/md.0000000000003293 · Cited by 11

Sorted by citations

Neuroimaging in functional neurological disorder: State of the field and research agenda
Perez, D. L., Nicholson, T. R., Asadi-Pooya, A. A., Bègue, I., Butler, M., Carson, A. J., David, A. S., Deeley, Q., Diez, I., Edwards, M. J., et al. (2021) · NeuroImage: Clinical · doi:10.1016/j.nicl.2021.102623 · Cited by 216
Advances in resting state fMRI acquisitions for functional connectomics
Raimondo, L., de Oliveira, I. A. F., Heij, J., Priovoulos, N., Kundu, P., Leoni, R. F., & van der Zwaag, W. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118503 · Cited by 188
Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics
Bolton, T. A. W., Morgenroth, E., Preti, M. G., & Ville, D. V. D. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.06.005 · Cited by 94
Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping
Miranda, L., Paul, R., Pütz, B., Koutsouleris, N., & Müller‐Myhsok, B. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.665536 · Cited by 55
Resting state functional connectivity patterns as biomarkers of treatment response to escitalopram in patients with major depressive disorder
Martens, M., Filippini, N., Harmer, C. J., & Godlewska, B. R. (2021) · Psychopharmacology · doi:10.1007/s00213-021-05915-7 · Cited by 46

See all 110

Authors: Yang, J., Gohel, S., & Vachha, B.
Affiliations: New York University (US)
Rutgers, The State University of New Jersey (US)
Memorial Sloan Kettering Cancer Center (US)
Corresponding author: Vachha, Behroze
ORCID: Yang, Jackie
Gohel, Suril
Vachha, Behroze
Venue: Clinical Imaging
Publisher: Elsevier BV
Published: 2020-09
Volume: 65
Pages: 47-53
ISSN: 0899-7071 (ISSN-L)
DOI: 10.1016/j.clinimag.2020.04.004
PMC: PMC7365764
OpenAlex: W3015866295
Cited by: 110
References: 114
Citations by year: 2026: 15 · 2025: 26 · 2024: 15 · 2023: 19 · 2022: 16 · 2021: 15 · 2020: 4
FWCI: 1.82
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Clinical Imaging (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords: resting-state fMRI, functional connectivity, dynamic functional connectivity, independent vector analysis, network of networks, brain network analysis
MeSH terms: Adult, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Rest

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Current methods and new directions in resting state fMRI

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks