The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets
Abstract
The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.
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Cites
The Theory of Stochastic Processes
Cox, D. R., Miller, H. D., Weiss, G., & Shuler, K. (1966) · Physics Today · doi:10.1063/1.3047872 · Cited by 3,324
Large-scale neurocognitive networks and distributed processing for attention, language, and memory
Mesulam, M. (1990) · Annals of Neurology · doi:10.1002/ana.410280502 · Cited by 2,684
Simplified neuron model as a principal component analyzer
Oja, E. (1982) · Journal of Mathematical Biology · doi:10.1007/bf00275687 · Cited by 2,309
Pardo, J. V., Pardo, P. J., Janer, K. W., & Raichle, M. E. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.1.256 · Cited by 1,495
Investigating a network model of word generation with positron emission tomography
(1991) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rspb.1991.0057 · Cited by 165
Self-citation
Cited by
Items 101–120 of 120
Sensory-motor brain network connectivity for speech comprehension
Londei, A., D'Ausilio, A., Basso, D., Sestieri, C., Gratta, C. D., Romani, G., & Belardinelli, M. O. (2010) · Human Brain Mapping · doi:10.1002/hbm.20888 · Cited by 90
Teles, M. C., Almeida, O., Lopes, J. S., & Oliveira, R. F. (2015) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2015.1099 · Cited by 87
Sex differences in the functional connectivity of the amygdalae in association with cortisol
Kogler, L., Müller, V. I., Seidel, E.-M., Boubela, R., Kalcher, K., Moser, E., Habel, U., Gur, R. C., Eickhoff, S. B., & Derntl, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.064 · Cited by 78
Fripp, J., Bourgeat, P., Acosta, O., Raniga, P., Modat, M., Pike, K. E., Jones, G., O'Keefe, G., Masters, C. L., Ames, D., et al. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.07.053 · Cited by 80
Cocchi, L., Harrison, B. J., Pujol, J., Harding, I. H., Fornito, A., Pantelis, C., & Yücel, M. (2012) · Human Brain Mapping · doi:10.1002/hbm.21270 · Cited by 79
Social cues shift functional connectivity in the hypothalamus
Hoke, K. L., Ryan, M. J., & Wilczynski, W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502361102 · Cited by 76
Bettinardi, R. G., Tort-Colet, N., Ruiz-Mejias, M., Sanchez-Vives, M. V., & Deco, G. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.037 · Cited by 68
Bottger, J., Schafer, A., Lohmann, G., Villringer, A., & Margulies, D. S. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.114 · Cited by 59
McCarthy, P., Benuskova, L., & Franz, E. A. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00301 · Cited by 48
Morelli, N., Johnson, N. F., Kaiser, K., Andreatta, R. D., Heebner, N. R., & Hoch, M. C. (2021) · Brain Injury · doi:10.1080/02699052.2021.1972339 · Cited by 38
Karbe, H., Herholz, K., Weber-Luxenburger, G., Ghaemi, M., & Heiss, W.-D. (1998) · Brain and Language · doi:10.1006/brln.1997.1937 · Cited by 37
Aberrant spontaneous and task-dependent functional connections in the anxious brain
MacNamara, A., DiGangi, J., & Phan, K. L. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2015.12.004 · Cited by 34
Brain networks governing the golf swing in professional golfers
Kim, J. H., Han, J. K., Kim, B.-N., & Han, D. H. (2015) · Journal of Sports Sciences · doi:10.1080/02640414.2015.1022570 · Cited by 30
Barnes, J. J., Woolrich, M. W., Baker, K., Colclough, G. L., & Astle, D. E. (2016) · Developmental Science · doi:10.1111/desc.12297 · Cited by 32
Liu, G., Ma, F., Liu, G., Zhao, H., Guo, J., & Cao, J. (2019) · Sustainability · doi:10.3390/su11123345 · Cited by 26
Poon, J. A., Niehaus, C. E., Thompson, J. C., & Chaplin, T. M. (2019) · Hormones and Behavior · doi:10.1016/j.yhbeh.2019.02.015 · Cited by 24
“In order to examine functional connectivity between NAcc and our other regions of interest during the win-neutral trials, we performed psycho-physiological interaction (PPI) analyses using activity in the left or right NAcc and the win-neutral condition as a covariate (Friston et al., 1993).”
BackgroundMethodPopp, J. L., Thiele, J. A., Faskowitz, J., Seguin, C., Sporns, O., & Hilger, K. (2025) · Communications Biology · doi:10.1038/s42003-025-08231-4 · Cited by 17
Naumer, M. J., van den Bosch, J. J. F., Wibral, M., Kohler, A., Singer, W., Kaiser, J., van de Ven, V., & Muckli, L. (2011) · Experimental Brain Research · doi:10.1007/s00221-011-2669-0 · Cited by 11
Sun, Y., Xu, Y., Lv, J., & Liu, Y. (2022) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2022.108282 · Cited by 11
Neural correlates of fatigue after traumatic brain injury
Anliker, A. E., Chauvigné, L. A. S., Allaman, L., & Guggisberg, A. G. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf082 · Cited by 1
Metadata
- Authors
- Friston, K. J., Frith, C. D., Liddle, P. F., & Frackowiak, R. S. J.
- Venue
- Journal of Cerebral Blood Flow & Metabolism
- Publisher
- SAGE Publications
- Published
- 1993-01
- Volume
- 13
- Issue
- 1
- Pages
- 5-14
- ISSN
- 0271-678X (ISSN-L)
- 1559-7016
- OpenAlex
- W2060899536
- Cited by
- 1,978
- References
- 5
- FWCI
- 19.02
- Type
- journal article
- Language
- en
- Open access
- closed
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