Computational analysis of functional connectivity between areas of primate cerebral cortex

Stephan, Klaas Ε.; Hilgetag, Claus C.; Burns, Gully; O’Neill, Mark A.; Young, Malcolm P.; Kötter, Rolf

doi:10.1098/rstb.2000.0552

Cited by 241 publications

(186 citation statements)

References 91 publications

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“…The connectivity of each area determines its functional role in a network (Stephan et al, 2000;Kotter et al, 2001), and our results suggest that this is also true for human PFC.…”

Section: Discussionmentioning

confidence: 58%

Quantitative Investigation of Connections of the Prefrontal Cortex in the Human and Macaque using Probabilistic Diffusion Tractography

Croxson¹,

Johansen‐Berg²,

Behrens³

et al. 2005

J. Neurosci.

377

318

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The functions of prefrontal cortex (PFC) areas are constrained by their anatomical connections. There is little quantitative information about human PFC connections, and, instead, our knowledge of primate PFC connections is derived from tracing studies in macaques. The connections of subcortical areas, in which white matter penetration and hence diffusion anisotropy are greatest, can be studied with diffusion-weighted imaging (DWI) tractography. We therefore used DWI tractography in four macaque and 10 human hemispheres to compare the connections of PFC regions with nine subcortical regions, including several fascicles and several subcortical nuclei. A distinct connection pattern was identified for each PFC and each subcortical region. Because some of the fascicles contained connections with posterior cortical areas, it was also possible to draw inferences about PFC connection patterns with posterior cortical areas. Notably, it was possible to identify similar circuits centered on comparable PFC regions in both species; PFC regions probably engage in similar patterns of regionally specific functional interaction with other brain areas in both species. In the case of one area traditionally assigned to the human PFC, the pars opercularis, the distribution of connections was not reminiscent of any macaque PFC region but, instead, resembled the pattern for macaque ventral premotor area. Some limitations to the DWI approach were apparent; the high diffusion anisotropy in the corpus callosum made it difficult to compare connection probability values in the adjacent cingulate region.

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“…The connectivity of each area determines its functional role in a network (Stephan et al, 2000;Kotter et al, 2001), and our results suggest that this is also true for human PFC.…”

Section: Discussionmentioning

confidence: 58%

Quantitative Investigation of Connections of the Prefrontal Cortex in the Human and Macaque using Probabilistic Diffusion Tractography

Croxson¹,

Johansen‐Berg²,

Behrens³

et al. 2005

J. Neurosci.

377

318

View full text Add to dashboard Cite

show abstract

“…The connectivity clusters found in cat and rhesus monkey cortex tend to follow functional subdivisions of these brains [32,38]. The groups of areas delineated by clustering are also broadly similar to clusters of semi-functional, neuronographic interactions [67]. Thus, it appears that structural clustering shapes at least some cortical activation patterns.…”

Section: And Global Network Designmentioning

confidence: 72%

Organization, development and function of complex brain networks

Sporns

Chialvo

Kaiser

et al. 2004

Trends in Cognitive Sciences

1,866

1,358

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“…Small-world properties were recently described empirically in neuroanatomical networks at a microscopic level, e.g., the neuronal network of Caenorhabditis elegans (5), and at the macroscopic level of interregional axonal connectivity of the cat and macaque monkey cortices (9,10). Neurophysiological networks inferred from patterns of correlated time-series activity in regions of monkey brains (11), regions of human brains (12,13), and voxels of human functional MRI data also demonstrate small-world topology (14,15). However, the frequency dependence of small-world brain functional networks, and their sensitivity to different behavioral states, has yet to be determined (16).…”

mentioning

confidence: 99%

Adaptive reconfiguration of fractal small-world human brain functional networks

Bassett

Meyer‐Lindenberg

Achard

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

740

560

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Brain function depends on adaptive self-organization of largescale neural assemblies, but little is known about quantitative network parameters governing these processes in humans. Here, we describe the topology and synchronizability of frequencyspecific brain functional networks using wavelet decomposition of magnetoencephalographic time series, followed by construction and analysis of undirected graphs. Magnetoencephalographic data were acquired from 22 subjects, half of whom performed a fingertapping task, whereas the other half were studied at rest. We found that brain functional networks were characterized by smallworld properties at all six wavelet scales considered, corresponding approximately to classical ␦ (low and high), , ␣, ␤, and ␥ frequency bands. Global topological parameters (path length, clustering) were conserved across scales, most consistently in the frequency range 2-37 Hz, implying a scale-invariant or fractal small-world organization. Dynamical analysis showed that networks were located close to the threshold of order/disorder transition in all frequency bands. The highest-frequency ␥ network had greater synchronizability, greater clustering of connections, and shorter path length than networks in the scaling regime of (lower) frequencies. Behavioral state did not strongly influence global topology or synchronizability; however, motor task performance was associated with emergence of long-range connections in both ␤ and ␥ networks. Long-range connectivity, e.g., between frontal and parietal cortex, at high frequencies during a motor task may facilitate sensorimotor binding. Human brain functional networks demonstrate a fractal small-world architecture that supports critical dynamics and task-related spatial reconfiguration while preserving global topological parameters. magnetoencephalography ͉ wavelet ͉ graph theory ͉ connectivity ͉ binding C oherent or correlated oscillation of large-scale, distributed neural networks is widely regarded as an important physiological substrate for motor, perceptual and cognitive representations in the brain (1, 2). The topological description of brain networks promises quantitative insight into functionally relevant parameters because their topology strongly influences their dynamic properties such as speed and specialization of information processing, learning, and robustness against pathological attack by disease (3).The topology of networks can range from entirely random to fully ordered (a lattice). In this spectrum, small-world topology is characteristic of complex networks that demonstrate both clustered or cliquish interconnectivity within groups of nodes sharing many nearest neighbors in common (like regular lattices), and a short path length between any two nodes in the network (like random graphs) (3). This is an attractive configuration, in principle, for the anatomical and functional architecture of the brain, because small-world networks are known to optimize information transfer, increase the rate of learning, and support both segregated and...

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Computational analysis of functional connectivity between areas of primate cerebral cortex

Cited by 241 publications

References 91 publications

Quantitative Investigation of Connections of the Prefrontal Cortex in the Human and Macaque using Probabilistic Diffusion Tractography

Quantitative Investigation of Connections of the Prefrontal Cortex in the Human and Macaque using Probabilistic Diffusion Tractography

Organization, development and function of complex brain networks

Adaptive reconfiguration of fractal small-world human brain functional networks

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