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Functional Topography: Multidimensional Scaling and Functional Connectivity in the Brain
Abstract
In neuroimaging, functional mapping usually implies mapping function into an anatomical space, for example, using statistical parametric mapping to identify activation foci, or the characterization of distributed changes with spatial modes (eigenimages or principal components) (Friston et al., 1993a). This article is about a complementary approach, namely, mapping anatomy into a functional space. We describe a simple variant of multidimensional scaling (principal coordinates analysis; Gower, 1966) that uses functional connectivity as its metric. The scaling transformation maps anatomy into a functional space. The topography, or proximity relationships, in this space embody the functional connectivity among brain regions. The higher the functional connectivity, the closer the regions. Functional connectivity is defined here as the correlation between remote neurophysiological events. The technique represents a descriptive characterization of anatomically distributed changes in the brain that reveals the structure of corticocortical interactions in terms of functional correlations. To illustrate the approach we have analyzed data from normal subjects and schizophrenic patients obtained with PET during the performance of word generation tasks. In particular, we focus on prefrontotemporal integration in normal subjects and show that, in schizophrenia, the left temporal regions and prefrontal cortex evidence abnormal functional connectivity.
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Cites
Circuitry of Primate Prefrontal Cortex and Regulation of Behavior by Representational Memory
Goldman‐Rakic, P. S. (1987) · Comprehensive Physiology · doi:10.1002/cphy.cp010509 · Cited by 1,960
Frith, C. D. (1987) · Psychological Medicine · doi:10.1017/s0033291700025873 · Cited by 655
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A neuronal model of a global workspace in effortful cognitive tasks
Dehaene, S., Kerszberg, M., & Changeux, J.-P. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.24.14529 · Cited by 1,347
Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring
Stephan, K. E., Friston, K. J., & Frith, C. D. (2009) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn176 · Cited by 1,134
Self-citationBush, G., Frazier, J. A., Rauch, S. L., Seidman, L. J., Whalen, P. J., Jenike, M. A., Rosen, B. R., & Biederman, J. (1999) · Biological Psychiatry · doi:10.1016/s0006-3223(99)00083-9 · Cited by 866
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Stephan, K. E., Baldeweg, T., & Friston, K. J. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.10.005 · Cited by 841
Self-citationBrain imaging of the central executive component of working memory
Collette, F., & Van der Linden, M. (2002) · Neuroscience & Biobehavioral Reviews · doi:10.1016/s0149-7634(01)00063-x · Cited by 617
The functional neuroanatomy of PTSD: a critical review
Liberzon, I., & Sripada, C. S. (2007) · Progress in Brain Research · doi:10.1016/s0079-6123(07)67011-3 · Cited by 594
Allen, P., Larøi, F., McGuire, P. K., & Aleman, A. (2008) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2007.07.012 · Cited by 579
Peterson, B. S., Skudlarski, P., Gatenby, J. C., Zhang, H., Anderson, A. W., & Gore, J. C. (1999) · Biological Psychiatry · doi:10.1016/s0006-3223(99)00056-6 · Cited by 530
Eickhoff, S. B., Bzdok, D., Laird, A. R., Roski, C., Caspers, S., Zilles, K., & Fox, P. T. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.021 · Cited by 484
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Collette, F., Hogge, M., Salmon, E., & Van der Linden, M. (2006) · Neuroscience · doi:10.1016/j.neuroscience.2005.05.035 · Cited by 464
Undirected graphs of frequency-dependent functional connectivity in whole brain networks
Salvador, R., Suckling, J., Schwarzbauer, C., & Bullmore, E. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1645 · Cited by 437
Villalobos, M. E., Mizuno, A., Dahl, B. C., Kemmotsu, N., & Müller, R.-A. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.022 · Cited by 312
Functional clustering: identifying strongly interactive brain regions in neuroimaging data
Tononi, G., McIntosh, A. R., Russell, D. P., & Edelman, G. M. (1998) · NeuroImage · doi:10.1006/nimg.1997.0313 · Cited by 283
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Garcia-Larrea, L., & Peyron, R. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.05.062 · Cited by 259
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LIBERZON, I., & MARTIS, B. (2006) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1364.009 · Cited by 198
Versace, A., Thompson, W. K., Zhou, D., Almeida, J. R. C., Hassel, S., Klein, C. R., Kupfer, D. J., & Phillips, M. L. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.11.025 · Cited by 194
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"Dynamic" Connectivity in Neural Systems
Breakspear, M. (2004) · Neuroinformatics · doi:10.1385/ni:2:2:205 · Cited by 113
Neurobiology of PTSD: A Review of Neuroimaging Findings
Garfinkel, S. N., & Liberzon, I. (2009) · Psychiatric Annals · doi:10.3928/00485713-20090527-01 · Cited by 102
Group analysis in functional neuroimaging: selecting subjects using similarity measures
Kherif, F. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.08.018 · Cited by 100
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He, H., & Liu, T. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.018 · Cited by 93
Stimulated changes in localized cerebral energy consumption under anesthesia
Shulman, R. G., Rothman, D. L., & Hyder, F. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.6.3245 · Cited by 93
On nodes and modes in resting state fMRI
Friston, K. J., Kahan, J., Razi, A., Stephan, K. E., & Sporns, O. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.056 · Cited by 85
Self-citationDistinct functional connectivities predict clinical response with emotion regulation therapy
Fresco, D. M., Roy, A. K., Adelsberg, S., Seeley, S., García-Lesy, E., Liston, C., & Mennin, D. S. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00086 · Cited by 43
“Single participant nuisance regression included linear and quadratic trends, 24 Friston motion parameters (Friston et al., 1996), and five CompCor signals (Behzadi et al., 2007).”
BackgroundInfluential
Metadata
- Authors
- Friston, K. J., Frith, C. D., Fletcher, P., Liddle, P. F., & Frackowiak, R. S. J.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 1996
- Volume
- 6
- Issue
- 2
- Pages
- 156-164
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2002692727
- Cited by
- 276
- References
- 2
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- closed
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