The DOI matches its Crossref record; 29 of the 30 references it cites were found in a registry or an index.
Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets
Abstract
A new approach in studying interregional functional connectivity using functional magnetic resonance imaging (fMRI) is presented. Functional connectivity may be detected by means of cross correlating time course data from functionally related brain regions. These data exhibit high temporal coherence of low frequency fluctuations due to synchronized blood flow changes. In the past, this fMRI technique for studying functional connectivity has been applied to subjects that performed no prescribed task ("resting" state). This paper presents the results of applying the same method to task-related activation datasets. Functional connectivity analysis is first performed in areas not involved with the task. Then a method is devised to remove the effects of activation from the data using independent component analysis (ICA) and functional connectivity analysis is repeated. Functional connectivity, which is demonstrated in the "resting brain," is not affected by tasks which activate unrelated brain regions. In addition, ICA effectively removes activation from the data and may allow us to study functional connectivity even in the activated regions.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Protective factors increase a person's resilience, i.e. the ability to maintain or regain mental health in the face of adversity or to bounce back from hardship and trauma. 287-289”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [288]
In the citing paper
“First, similar topography of BOLD correlations has been observed across very different behavioral states including different resting conditions (28), low-level task performance (39,40), and even light anesthesia (41).”
From Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems (2006) · cites this paper as [40]
In the citing paper
“Second, coherent spontaneous fluctuations have been observed within systems associated with specific behavior in the absence of that behavior such as in the motor system in the absence of movement (30,40,42).”
From Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems (2006) · cites this paper as [40]
In the citing paper
“Third, task-evoked activity due to a specific behavior seems to be distinct from and superimposed on top of underlying spontaneous activity (40,42).”
From Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems (2006) · cites this paper as [40]
Citations
Cites
Independent Component Analysis
Hyvärinen, A., Karhunen, J., & Oja, E. (2001) · Encyclopedia of Biometrics · doi:10.1007/978-0-387-73003-5_305 · Cited by 10,459
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
Self-citationAn Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,324
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S., & Turner, R. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.12.5675 · Cited by 4,212
Gray, C. M., König, P., Engel, A. K., & Singer, W. (1989) · Nature · doi:10.1038/338334a0 · Cited by 4,183
Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., & Ugurbil, K. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.13.5951 · Cited by 3,681
Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets
Friston, K. J., Frith, C., Liddle, P. F., & Frackowiak, R. S. J. (1993) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.1993.4 · Cited by 1,997
Processing strategies for time‐course data sets in functional mri of the human brain
Bandettini, P. A., Jesmanowicz, A., Wong, E. C., & Hyde, J. S. (1993) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910300204 · Cited by 1,762
Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations
Lowe, M. J., Mock, B. J., & Sorenson, J. A. (1998) · NeuroImage · doi:10.1006/nimg.1997.0315 · Cited by 1,394
Research works printed but not linked: 5
- Jezzard P, LeBihan D, Cuenod D, Pannier L, Prinster A, Turner R. An investigation of the contribution of physiologic noise in human functional MRI studies at 1.5 tesla and 4 tesla. In: Book of abstracts: twelfth annual meeting of the Society of Magnetic Resonance in Medicine, Vol. 3. New York, NY: SMRM, 1993. p. 1392.
- Dora, 1980, Metabolic and vascular volume oscillation in the cat brain cortex, Acts Physiologica Acad Sci Hungaricae, 57, 261
- Tomita, 1981, 4–6 cycle per minute fluctuation in cerebral blood volume of feline cortical tissue in situ, J Cereb Blood Flow Metab, 1, S443
- Arfanakis K, Cordes D, Sorenson JA, Haughton VM, Quigley MA, Meyerand ME. Effects of task related activation on physiological noise using functional connectivity analysis. In: Book of abstracts: seventh annual meeting of the International Society of Magnetic Resonance in Medicine, Vol. 3. Philadelphia, PA: ISMRM; 1999. p. 1713.
- Liu WC, Schulder M, Kalnin AJ, Holodny A, Gray A. Mixing task paradigm in fMRI for radiosurgery. In: Book of abstracts: seventh annual meeting of the International Society of Magnetic Resonance in Medicine, Vol. 3. Philadelphia, PA: ISMRM, 1999. p. 1692.
Cited by
Sorted by citations
Items 101–191 of 191
A split–merge‐based region‐growing method for fMRI activation detection
Lu, Y., Jiang, T., & Zang, Y. (2004) · Human Brain Mapping · doi:10.1002/hbm.20034 · Cited by 28
Default mode network deactivation during odor–visual association
Karunanayaka, P., Wilson, D. A., Tobia, M. J., Martinez, B., Meadowcroft, M. D., Eslinger, P. J., & Yang, Q. X. (2016) · Human Brain Mapping · doi:10.1002/hbm.23440 · Cited by 27
Combining task-evoked and spontaneous activity to improve pre-operative brain mapping with fMRI
Fox, M., Qian, T., Madsen, J. R., Wang, D., Li, M., Ge, M., Zuo, H., Groppe, D. M., Mehta, A. D., Hong, B., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.030 · Cited by 25
How to Avoid Spurious Cluster Validation? A Methodological Investigation on Simulated and fMRI Data
Möller, U., Ligges, M., Georgiewa, P., Ligges, C., Kaiser, W. A., Witte, H., & Blanz, B. (2002) · NeuroImage · doi:10.1006/nimg.2002.1166 · Cited by 25
Huang, H., Ding, Z., Mao, D., Yuan, J., Zhu, F., Chen, S., Yan, X., Lou, L., Feng, X., Qi, L., et al. (2016) · Neuroinformatics · doi:10.1007/s12021-016-9304-y · Cited by 24
Determination of Dynamic Brain Connectivity via Spectral Analysis
Robinson, P. A., Henderson, J. A., Gabay, N. C., Aquino, K. M., Babaie-Janvier, T., & Gao, X. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.655576 · Cited by 23
Winder, R. K., Cortes, C. R., Reggia, J. A., & Tagamets, M.-A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.10.008 · Cited by 23
The Effects of Computational Method, Data Modeling, and TR on Effective Connectivity Results
Witt, S. T., & Meyerand, M. E. (2009) · Brain Imaging and Behavior · doi:10.1007/s11682-009-9064-5 · Cited by 23
Self-citationKucukboyaci, N. E., Kemmotsu, N., Cheng, C. E., Girard, H. M., Tecoma, E. S., Iragui, V. J., & McDonald, C. R. (2013) · Brain Connectivity · doi:10.1089/brain.2013.0150 · Cited by 25
Wang, Y., & Xia, J. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2014863 · Cited by 21
Task-residual functional connectivity of language and attention networks
Tran, S. M., McGregor, K., James, G. A., Gopinath, K., Krishnamurthy, V., Krishnamurthy, L. C., & Crosson, B. (2018) · Brain and Cognition · doi:10.1016/j.bandc.2018.02.003 · Cited by 23
A comparison of signal instability in 2D and 3D EPI resting‐state fMRI
Goerke, U., Möller, H. E., Norris, D. G., & Schwarzbauer, C. (2005) · NMR in Biomedicine · doi:10.1002/nbm.987 · Cited by 21
Independent Component Analysis in the Study of Focal Seizures
Iriarte, J., Urrestarazu, E., Artieda, J., Valencia, M., LeVan, P., Viteri, C., & Alegre, M. (2006) · Journal of Clinical Neurophysiology · doi:10.1097/01.wnp.0000236579.08698.23 · Cited by 21
Within-individual precision mapping of brain networks exclusively using task data
Du, J., Tripathi, V., Elliott, M. L., Ladopoulou, J., Sun, W., Eldaief, M. C., & Buckner, R. L. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.029 · Cited by 24
Bazán, P. R., Biazoli, C. E., Sato, J. R., & Amaro, E. (2015) · Brain Connectivity · doi:10.1089/brain.2014.0332 · Cited by 20
Tivarus, M. E., Hillier, A. J., Schmalbrock, P., & Beversdorf, D. Q. (2007) · Brain and Language · doi:10.1016/j.bandl.2007.02.007 · Cited by 18
Wang, J. X., Bartolotti, J., Amaral, L. A. N., & Booth, J. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059204 · Cited by 18
Tian, L., Jiang, T., Liang, M., Li, X., Yong He, A., Wang, K., Cao, B., & Jiang, T. (2007) · Human Brain Mapping · doi:10.1002/hbm.20300 · Cited by 18
Goelman, G. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.050 · Cited by 18
A NOVEL METHOD FOR VISUALIZING FUNCTIONAL CONNECTIVITY USING PRINCIPAL COMPONENT ANALYSIS
Mikula, S., & Niebur, E. (2006) · International Journal of Neuroscience · doi:10.1080/00207450500505761 · Cited by 17
Is Rest Really Rest? Resting-State Functional Connectivity During Rest and Motor Task Paradigms
Jurkiewicz, M. T., Crawley, A. P., & Mikulis, D. J. (2018) · Brain Connectivity · doi:10.1089/brain.2017.0495 · Cited by 17
Mapping brain state-dependent sensory responses across the mouse cortex
Montagni, E., Resta, F., Tort‐Colet, N., Scaglione, A., Mazzamuto, G., Alain Destexhe, A., Pavone, F. S., & Mascaro, A. L. A. (2024) · iScience · doi:10.1016/j.isci.2024.109692 · Cited by 15
A comparison between neural and fuzzy cluster analysis techniques for functional MRI
Lange, O., Meyer‐Baese, A., Hurdal, M. K., & Foo, S. Y. (2006) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2006.11.002 · Cited by 16
Zhu, Y., Cheng, L., He, N., Yang, Y., Ling, H., Ayaz, H., Tong, S., Sun, J., & Fu, Y. (2017) · Computational and Mathematical Methods in Medicine · doi:10.1155/2017/4198430 · Cited by 16
Empirical Comparison of Sources of Variation for FMRI Connectivity Analysis
Rogers, B. P., & Gore, J. C. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003708 · Cited by 16
Posterior Exploration for Computationally Intensive Forward Models
Higdon, D., Reese, C. S., Moulton, D., Vrugt, J. A., & Fox, C. (2011) · Chapman & Hall/CRC eBooks · doi:10.1201/b10905-17 · Cited by 15
Netsiri, C., Bradley, D. P., Takeda, T., Smith, M. I., Papadakis, N. G., Hall, L. D., Parsons, A. A., James, M. F., & Huang, C. L.-H. (2003) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(03)00199-1 · Cited by 14
Sheng, J., Wu, S., Zhang, Q., Li, Z., & Huang, H. (2022) · IEEE Access · doi:10.1109/access.2022.3186888 · Cited by 18
Characteristics of canonical intrinsic connectivity networks across tasks and monozygotic twin pairs
Moodie, C. A., Wisner, K., & MacDonald, A. W. (2014) · Human Brain Mapping · doi:10.1002/hbm.22568 · Cited by 14
Sarpal, D. K., Tarcijonas, G., Calabro, F. J., Foran, W., Haas, G. L., Luna, B., & Murty, V. P. (2020) · Psychological Medicine · doi:10.1017/s0033291720004201 · Cited by 14
Wu, Y., An, H., Krim, H., & Lin, W. (2006) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/sj.jcbfm.9600374 · Cited by 14
ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data
Gruber, P., Meyer‐Baese, A., Foo, S. Y., & Theis, F. J. (2009) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2008.11.010 · Cited by 13
Effect of initial fMRI data modeling on the connectivity reported between brain areas
Caclin, A., & Fonlupt, P. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.07.019 · Cited by 13
Performance evaluation based on cluster validity indices in medical imaging
Lange, O., Meyer‐Baese, A., & Wismüller, A. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.660670 · Cited by 12
fMRI assessment of thalamocortical connectivity during attentional performance
Zhang, J., Chu, K.-W., Teague, E. B., Newmark, R. E., & Buchsbaum, M. S. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2013.01.008 · Cited by 11
Tree-dependent and topographic independent component analysis for fMRI analysis
Lange, O., Meyer‐Baese, A., Wismueller, A., Hurdal, M. K., Sumners, D. L., & Auer, D. P. (2004) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.541779 · Cited by 11
Ibinson, J. W., Vogt, K. M., Taylor, K. B., Dua, S., Becker, C. J., Loggia, M. L., & Wasan, A. D. (2015) · Brain Connectivity · doi:10.1089/brain.2015.0354 · Cited by 11
Detecting low-frequency functional connectivity in fMRI using unsupervised clustering algorithms
Lange, O., Meyer‐Baese, A., & Wismüller, A. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.660669 · Cited by 10
Kemik, K., Ada, E., Çavuşoğlu, B., Aykaç, C., Emek‐Savaş, D. D., & Yener, G. (2024) · Brain and Behavior · doi:10.1002/brb3.3518 · Cited by 10
Karakasis, P. A., Liavas, A. P., Sidiropoulos, N. D., Simos, P. G., & Papadaki, E. Z. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3159125 · Cited by 13
Current Trends in ERP Analysis Using EEG and EEG/fMRI Synergistic Methods
Michalopoulos, K., Zervakis, M. E., & Bourbakis, N. (2013) · Neuromethods · doi:10.1007/7657_2013_67 · Cited by 8
Zhao, J. L., Long, Z., Li, Y., Qin, Y., & Liu, Y. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00542-4 · Cited by 8
Elkhetali, A. S., Fleming, L. L., Vaden, R. J., Nenert, R. E., Mendle, J., & Visscher, K. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.040 · Cited by 8
Kemik, K., Ada, E., Çavuşoğlu, B., Aykaç, C., Emek‐Savaş, D. D., & Yener, G. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14371 · Cited by 7
Xiong, Z., Tian, C., Zeng, X., Huang, J., & Wang, R. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.592720 · Cited by 7
The impact of high grade glial neoplasms on human cortical electrophysiology
Bandt, S. K., Roland, J. L., Pahwa, M., Hacker, C. D., Bundy, D. T., Breshears, J. D., Sharma, M., Shimony, J. S., & Leuthardt, E. C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173448 · Cited by 7
Tree-Dependent and Topographic Independent Component Analysis for fMRI Analysis
Meyer‐Baese, A., Theis, F. J., Lange, O., & Puntonet, C. G. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30110-3_99 · Cited by 6
Loitfelder, M., Pinter, D., Langkammer, C., Jehna, M., Ropele, S., Fazekas, F., Schmidt, R., & Enzinger, C. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0220 · Cited by 5
Correspondence Between Resting-State and Episodic Memory-Task Related Networks in Elderly Subjects
Simon‐Vermot, L., Taylor, A. N. W., Caballero, M. Á. A., Franzmeier, N., Büerger, K., Catak, C., Janowitz, D., Kambeitz‐Ilankovic, L., Ertl-Wagner, B. B., Duering, M., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00362 · Cited by 5
Observation of two distinct spatial–temporal BOLD clusters during sensory stimulation in rats
Goelman, G., Pelled, G., Dodd, S., & Koretsky, A. P. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.052 · Cited by 4
Functional connectivity among brain networks in continuous feedback of finger force
Zhang, H., Zhang, H., & Zang, Y. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2014.12.075 · Cited by 4
Model-free functional MRI analysis using transformation-based methods
Otto, T. D., Meyer‐Baese, A., Hurdal, M. K., Sunmers, D., Wismüller, A., & Auer, D. P. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.487368 · Cited by 4
The Chronoarchitecture of the Human Brain
(2004) · Elsevier eBooks · doi:10.1016/b978-012264841-0/50014-7 · Cited by 4
Análisis matemáticos en el estudio de señales electroencefalográficas
Bolumburu, E. U., & Franco, J. I. (2005) · Revista de Neurología · doi:10.33588/rn.4107.2004577 · Cited by 4
Selection of spatially independent components to explain functional connectivity in fMRI
Perlbarg, V., Bellec, P., Marrelec, G., Jbadi, S., & Benali, H. (2005) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2004.1398672 · Cited by 4
Identifying distributed and overlapping clusters of hemodynamic synchrony in fMRI data sets
Ghebreab, S., & Smeulders, A. W. M. (2010) · Pattern Analysis and Applications · doi:10.1007/s10044-010-0186-6 · Cited by 3
What light have resting state fMRI studies shed on cognition and mood in Parkinson’s disease?
YorkWilliams, S. L., & Poston, K. L. (2014) · Journal of Clinical Movement Disorders · doi:10.1186/2054-7072-1-4 · Cited by 3
Commentary Unrest at rest: Default activity and spontaneous network correlations
Buckner, R. L., & Vincent, J. L. (2007) · Cited by 3
Rumpel, H., Chan, L. L., Tan, J. S., Ng, I., & Lim, W. E. H. (2009) · Annals of the Academy of Medicine Singapore · doi:10.47102/annals-acadmedsg.v38n9p782 · Cited by 3
Methodological Issues in fMRI Functional Connectivity and Network Analysis
Finn, E. S., Scheinost, D., Shen, X., Papademetris, X., & Constable, R. T. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00352-3 · Cited by 3
Two for the Price of One? Analyzing Task-based fMRI Data with Resting-State fMRI Methods
Field, A. S., & Birn, R. M. (2021) · Radiology · doi:10.1148/radiol.2021211239 · Cited by 3
An MCMC-Based Analysis of a Multilevel Model for Functional MRI Data
Caffo, B. S., Bowman, D., Eberly, L. E., & Bassett, S. S. (2011) · Chapman & Hall/CRC eBooks · doi:10.1201/b10905-15 · Cited by 2
Multi-subject Task-related fMRI Data Analysis via Generalized Canonical Correlation Analysis
Karakasis, P. A., Liavas, A. P., Sidiropoulos, N. D., Simos, P. G., & Papadaki, E. Z. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175371 · Cited by 3
Combining temporal and spatial constraints in task-based fMRIPreprint
Roes, M., Takane, Y., & Woodward, T. S. (2022) · doi:10.31234/osf.io/g43bn · Cited by 2
Time-resolved large-scale neural coactivations in macaque monkey
Ivey, V., Yuan, H., & Ding, L. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121408 · Cited by 1
Clustering of Dependent Components: A New Paradigm for fMRI Signal Detection
Meyer‐Baese, A., Hurdal, M. K., Lange, O., & Ritter, H. J. (2005) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/asp.2005.3089 · Cited by 1
Calhoun, V. D. (2008) · IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) · doi:10.1109/ssiai.2008.4512320 · Cited by 1
Targeted Modulation of Neural Circuits: A New Treatment Strategy for Neuropsychiatric Disease
Mayberg, H. S., & Holtzheimer, P. E. (2010) · Neuromethods · doi:10.1007/978-1-60327-202-5_11 · Cited by 1
Bibliography on Independent Component Analysis in Functional Neuroimaging
Nielsen, F. Å. (2010) · Cited by 1
Clustering of dependent components: a new paradigm for fMRI signal detection
Meyer‐Baese, A., Theis, F. J., Lange, O., & Wismüller, A. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1380910 · Cited by 1
Data driven analysis of brain activity and functional connectivity in fMRI
Dodel, S. (2003) · doi:10.53846/goediss-2655 · Cited by 1
Optimized Independent Component Analysis Algorithms and Applications
Yu, X., Hu, D., & Xu, J. (2014) · doi:10.1002/9781118679852.ch13
Fusar‐Poli, P., & McGuire, P. K. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511642197.020
Arbabshirani, M. R. (2014) · UNM’s Digital Repository (University of New Mexico)
Development of a unique whole-brain model for upper extremity neuroprosthetic control
Nathan, D. E. (2010) · e-Publications@Marquette (Marquette University)
Clustering analysis of default mode network in fMRI data
Liu, D. (2014) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2014.7002761
Urban, K. J. (2014) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/26855
Khaliq, A. A., Qureshi, I. M., Shah, J. A., & Abdullah, S. (2013)
DOPAMINE-REGULATING GENES, EXECUTIVE CONTROL, AND THE NETWORK STRUCTURE OF THE HUMAN BRAIN
Gordon, E. M. (2013) · DigitalGeorgetown (Georgetown University Library)
Original contributions Assessing functional connectivity in the human brain by fMRI B
Rogers, B. P., Morgan, V. L., Newton, A. T., & Gore, J. C. (2007)
Functional imaging of schizophrenia
Pearlson, G. D. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511782091.003
Investigating Brain Dynamics and Connectivity with Functional MRI
Duff, E. P., Egan, G. F., Cunnington, R., Mareels, I., Wang, B., Fox, P. T., & Xiong, J. (2005) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2004.1417523
Michalopoulos, K. (2014) · OhioLink ETD Center (Ohio Library and Information Network)
Jaisal, P. K., Kumar, S., & Shukla, S. (2014)
Statistical methods for fMRI data analysis
Xia, J. (2011) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Fishburn, F. A. (2017) · DigitalGeorgetown (Georgetown University Library)
(2001) · NMR in Biomedicine · doi:10.1002/nbm.668
Herron, R. E. (2008) · Open University of Cape Town (University of Cape Town)
An Examination of Task‐Evoked fMRI Data Processing in Functional Connectivity
Giubergia, A., Mascheretti, S., Lampis, V., Ciceri, T., Villa, M., Andreola, C., Arrigoni, F., Bertoldo, A., & Peruzzo, D. (2026) · Journal of Neuroscience Research · doi:10.1002/jnr.70132
Function and structure of the mirror neuron system in autism
Perkins, T. J. (2014)
Metadata
- Authors
- Arfanakis, K., Cordes, D., Haughton, V. M., Moritz, C. H., Quigley, M. A., & Meyerand, M. E.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Arfanakis, Konstantinos
- Venue
- Magnetic Resonance Imaging
- Publisher
- Elsevier BV
- Published
- 2000-10
- Volume
- 18
- Issue
- 8
- Pages
- 921-930
- ISSN
- 0730-725X (ISSN-L)
- OpenAlex
- W2066906403
- Cited by
- 191
- References
- 30
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 4 · 2021: 4 · 2020: 3 · 2019: 2 · 2018: 6 · 2017: 3 · 2016: 4 · 2015: 7 · 2014: 15 · 2013: 16 · 2012: 9
- FWCI
- 2.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- 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
- independent component analysis, correlation analysis, task-related brain activation, resting state, low-frequency fluctuations
- MeSH terms
- Adult, Auditory Cortex, Brain, Brain Mapping, Hemodynamics, Humans, Magnetic Resonance Imaging, Models, Neurological, Motor Cortex, Research, Time Factors, Visual Cortex, Databases as Topic
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