Detecting directional influence in fMRI connectivity analysis using PCA based Granger causality
Open accessAbstract
An fMRI connectivity analysis approach combining both principal component analysis (PCA) and Granger causality method (GCM) is proposed to study directional influence between functional brain regions. Both simulated data and human fMRI data obtained during behavioral tasks were used to validate this method. If PCA is first used to reduce number of fMRI time series, then more energy and information features in the signal can be preserved than using averaged values from brain regions of interest. Subsequently, GCM can be applied to principal components extracted in order to further investigate effective connectivity. The simulation demonstrated that by using GCM with PCA, between-region causalities were better represented than using GCM with average values. Furthermore, after localizing an emotion task-induced activation in the anterior cingulate cortex, inferior frontal sulcus and amygdala, the directional influences among these brain regions were resolved using our new approach. These results indicate that using PCA may improve upon application of existing GCMs in study of human brain effective connectivity.
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Cites
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989 · see citing works
Goodall, C. R., & Jolliffe, I. T. (1988) · Technometrics · doi:10.2307/1270093 · Cited by 8,677 · see citing works
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256 · see citing works
The brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761 · see citing works
The cognitive control of emotion
OCHSNER, K., & GROSS, J. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.03.010 · Cited by 4,495 · see citing works
Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1
Boynton, G., Engel, S., Glover, G., & Heeger, D. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-13-04207.1996 · Cited by 2,522 · see citing works
Oxygenation‐sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields
Ogawa, S., Lee, T., Nayak, A. S., & Glynn, P. (1990) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910140108 · Cited by 2,269 · see citing works
Measurement of Linear Dependence and Feedback between Multiple Time Series
Geweke, J. (1982) · Journal of the American Statistical Association · doi:10.1080/01621459.1982.10477803 · Cited by 2,060 · see citing works
Pain and emotion interactions in subregions of the cingulate gyrus
Vogt, B. A. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1704 · Cited by 1,810 · see citing works
Etkin, A., Egner, T., Peraza, D. M., Kandel, E. R., & Hirsch, J. (2006) · Neuron · doi:10.1016/j.neuron.2006.07.029 · Cited by 1,360 · see citing works
Mayberg, H. S. (2003) · British Medical Bulletin · doi:10.1093/bmb/65.1.193 · Cited by 1,166 · cited 1× · see citing works
“Furthermore, to assign significance levels to the computed measures a permutation procedure was applied (Nichols and Holmes, 2002; Chen et al., 2006).”
Brovelli, A., Ding, M., Ledberg, A., Chen, Y., Nakamura, R., & Bressler, S. L. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308538101 · Cited by 1,065 · see citing works
Self-citationMapping directed influence over the brain using Granger causality and fMRI
Roebroeck, A. F., Formisano, E., & Goebel, R. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.11.017 · Cited by 1,012 · see citing works
Kamiński, M., Ding, M., Truccolo, W., & Bressler, S. L. (2001) · Biological Cybernetics · doi:10.1007/s004220000235 · Cited by 990 · see citing works
Self-citationAnalyzing brain networks with PCA and conditional Granger causality
Zhou, Z., Chen, Y., Ding, M., Wright, P. K., Lu, Z., & Liu, Y. (2008) · Human Brain Mapping · doi:10.1002/hbm.20661 · Cited by 978 · see citing works
Self-citationQuirk, G. J., Likhtik, E., Pelletier, J. G., & Paré, D. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-25-08800.2003 · Cited by 927 · see citing works
Measures of Conditional Linear Dependence and Feedback Between Time Series
Geweke, J. F. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477110 · Cited by 769 · see citing works
Kherif, F., & Latypova, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00012-2 · Cited by 726 · see citing works
Goebel, R., Roebroeck, A. F., Kim, D., & Formisano, E. (2003) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2003.08.026 · Cited by 678 · see citing works
Ding, M., Bressler, S. L., Yang, W., & Liang, H. (2000) · Biological Cybernetics · doi:10.1007/s004229900137 · Cited by 582 · see citing works
Self-citationBeyond Phrenology: What Can Neuroimaging Tell Us About Distributed Circuitry?
Friston, K. (2002) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.25.112701.142846 · Cited by 465 · see citing works
Hesse, W., Möller, E., Arnold, M., & Schack, B. (2003) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(02)00366-7 · Cited by 382 · see citing works
Extending animal models of fear conditioning to humans
Delgado, M. R., Olsson, A., & Phelps, E. A. (2006) · Biological Psychology · doi:10.1016/j.biopsycho.2006.01.006 · Cited by 366 · see citing works
Chen, Y., Bressler, S. L., & Ding, M. (2005) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.06.011 · Cited by 344 · see citing works
Self-citationEstimating brain functional connectivity with sparse multivariate autoregression
Valdes-Sosa, P. A., Sánchez‐Bornot, J. M., Lage‐Castellanos, A., Vega‐Hernández, M., Bosch‐Bayard, J., Melie‐García, L., & Canales-Rodríguez, E. J. (2005) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2005.1654 · Cited by 318 · see citing works
Disgust and the insula: fMRI responses to pictures of mutilation and contamination
Wright, P. K., He, G., Shapira, N. A., Goodman, W. K., & Liu, Y. (2004) · Neuroreport · doi:10.1097/00001756-200410250-00009 · Cited by 244 · see citing works
Self-citationA report of the functional connectivity workshop, Dusseldorf 2002
Lee, L., Harrison, L. M., & Mechelli, A. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00062-4 · Cited by 231 · see citing works
Sato, J. R., Amaro, E., Takahashi, D. Y., Felix, M. D. M., Brammer, M. J., & Morettin, P. A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.11.039 · Cited by 121 · see citing works
Neutral faces activate the amygdala during identity matching
Wright, P. K., & Liu, Y. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.07.047 · Cited by 89 · see citing works
Self-citationPolat, K., & GUNES, S. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.06.004 · Cited by 42 · see citing works
Brain network for passive word listening as evaluated with ICA and Granger causality
Londei, A., D’Ausilio, A., Basso, D., Sestieri, C., Gratta, C. D., Romani, G. L., & Belardinelli, M. O. (2007) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2007.01.008 · Cited by 36 · see citing works
Principal component analysis for classifying passive sonar signals
Soares-Filho, W., de Seixas, J. M., & Calôba, L. P. (2002) · doi:10.1109/iscas.2001.921380 · Cited by 31 · see citing works
Robust PCA based on neural networks
Wang, S., & Xia, S. (2002) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1997.650676 · Cited by 17 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Ekman P Friesen WV 1976 Pictures of Facial Affect Consulting Psychologists Press
- Johansen-Berg H Behrens TEJ Matthews PM Katz E Metwalli N Lozano A 2006 Connectivity of a subgenual cingulate target for treatment-resistant depression Proceedings of 12th Human Brain Mapping Annual Meeting
Cited by
Sorted by citations
Altered resting-state amygdala functional connectivity in men with posttraumatic stress disorder
Sripada, R. K., King, A. P., Garfinkel, S. N., Wang, X., Sripada, C. S., Welsh, R. C., & Liberzon, I. (2012) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.110069 · Cited by 352 · see citing works
Top-down regulation of default mode activity in spatial visual attention
Wen, X., Liu, Y., Yao, L., & Ding, M. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4939-12.2013 · Cited by 150 · see citing works
Self-citationParr, A. C., Calabro, F., Larsen, B., Tervo-Clemmens, B., Elliot, S., Foran, W., Olafsson, V., & Luna, B. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.101997 · Cited by 60 · see citing works
- Authors
- Zhou, Z., Ding, M., Chen, Y., Wright, P., Lu, Z., & Liu, Y.
- Affiliations
- Columbia University (US)
- University of Florida (US)
- Southeast University (CN)
- Venue
- Brain Research
- Publisher
- Elsevier BV
- Published
- 2009-09
- Volume
- 1289
- Pages
- 22-29
- ISSN
- 0006-8993 (ISSN-L)
- PMC
- PMC2730503
- OpenAlex
- W2078515955
- Cited by
- 54
- References
- 33
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 3 · 2020: 1 · 2019: 2 · 2018: 6 · 2017: 2 · 2016: 3 · 2015: 3 · 2014: 6 · 2013: 5 · 2012: 6
- FWCI
- 2.58
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Brain Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (MH072776, R03MH072776)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- Granger causality, principal component analysis, effective connectivity, dimensionality reduction, anterior cingulate cortex, inferior frontal sulcus, amygdala, brain regions of interest
- MeSH terms
- Adolescent, Adult, Brain, Brain Mapping, Computer Simulation, Emotions, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neural Pathways, Psychomotor Performance, Imaging, Three-Dimensional, Principal Component Analysis
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