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Factors affecting the cerebral network in brain tumor patients.

Heimans JJ, Reijneveld JC - J. Neurooncol. (2012)

Bottom Line: The effect of a brain tumor on cerebral functioning is often more global than should be expected on the basis of the local character of the disease, and this is thought to be a consequence of disturbance of the cerebral network as a whole.Functional MRI, electroencephalography and especially magnetoencephalography are used to measure brain function and the signals that are being registered with these techniques can be analyzed with respect to network characteristics such as "synchronization" and "clustering".Evidence accumulates that loss of optimal neural network architecture negatively impacts complex cerebral functioning and also decreases the threshold to develop epileptic seizures.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. j.heimans@vumc.nl

ABSTRACT
Brain functions, including cognitive functions, are frequently disturbed in brain tumor patients. These disturbances may result from the tumor itself, but also from the treatment directed against the tumor. Surgery, radiotherapy and chemotherapy all may affect cerebral functioning, both in a positive as well as in a negative way. Apart from the anti-tumor treatment, glioma patients often receive glucocorticoids and anti-epileptic drugs, which both also have influence on brain functioning. The effect of a brain tumor on cerebral functioning is often more global than should be expected on the basis of the local character of the disease, and this is thought to be a consequence of disturbance of the cerebral network as a whole. Any network, whether it be a neural, a social or an electronic network, can be described in parameters assessing the topological characteristics of that particular network. Repeated assessment of neural network characteristics in brain tumor patients during their disease course enables study of the dynamics of neural networks and provides more insight into the plasticity of the diseased brain. Functional MRI, electroencephalography and especially magnetoencephalography are used to measure brain function and the signals that are being registered with these techniques can be analyzed with respect to network characteristics such as "synchronization" and "clustering". Evidence accumulates that loss of optimal neural network architecture negatively impacts complex cerebral functioning and also decreases the threshold to develop epileptic seizures. Future research should be focused on both plasticity of neural networks and the factors that have impact on that plasticity as well as the possible role of assessment of neural network characteristics in the determination of cerebral function during the disease course.

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Related in: MedlinePlus

Schematic drawing of a random, a regular, and a small-world network. In the regular network (left), each node is only connected to its neighbors. Therefore, it has both a high clustering coefficient (C) and a long path length (L), while the random network (right) combines a low C and a low L. The intermediate of the two: the so-called “small-world network” (middle) can be achieved by relocating but a few long-distance connections from the regular network, which causes L to decrease drastically but preserves a high C. Thus it combines “the best of two worlds”
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Fig1: Schematic drawing of a random, a regular, and a small-world network. In the regular network (left), each node is only connected to its neighbors. Therefore, it has both a high clustering coefficient (C) and a long path length (L), while the random network (right) combines a low C and a low L. The intermediate of the two: the so-called “small-world network” (middle) can be achieved by relocating but a few long-distance connections from the regular network, which causes L to decrease drastically but preserves a high C. Thus it combines “the best of two worlds”

Mentions: Random networks were first described in the 20th century and seemed promising to model complex networks [30, 31]. However, these graphs did not meet up to the expectations of explaining the abovementioned small-world characteristics of networks. Watts and Strogatz [32] provided an elegant way of modeling small-world networks. They proposed a very simple model of a one-dimensional network (see Fig. 1). In the “regular” network, each node or vertex is only connected to its ‘k’ nearest neighbors (k being the degree of the network). Next, with likelihood ‘p’, connections or edges are chosen at random and connected to other nodes, also chosen randomly. With increasing p, more and more edges become randomly reconnected and finally, for p = 1, the network is completely random. This comprehensible model allows investigation of all types of networks, ranging from completely regular to completely random.Fig. 1


Factors affecting the cerebral network in brain tumor patients.

Heimans JJ, Reijneveld JC - J. Neurooncol. (2012)

Schematic drawing of a random, a regular, and a small-world network. In the regular network (left), each node is only connected to its neighbors. Therefore, it has both a high clustering coefficient (C) and a long path length (L), while the random network (right) combines a low C and a low L. The intermediate of the two: the so-called “small-world network” (middle) can be achieved by relocating but a few long-distance connections from the regular network, which causes L to decrease drastically but preserves a high C. Thus it combines “the best of two worlds”
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3351596&req=5

Fig1: Schematic drawing of a random, a regular, and a small-world network. In the regular network (left), each node is only connected to its neighbors. Therefore, it has both a high clustering coefficient (C) and a long path length (L), while the random network (right) combines a low C and a low L. The intermediate of the two: the so-called “small-world network” (middle) can be achieved by relocating but a few long-distance connections from the regular network, which causes L to decrease drastically but preserves a high C. Thus it combines “the best of two worlds”
Mentions: Random networks were first described in the 20th century and seemed promising to model complex networks [30, 31]. However, these graphs did not meet up to the expectations of explaining the abovementioned small-world characteristics of networks. Watts and Strogatz [32] provided an elegant way of modeling small-world networks. They proposed a very simple model of a one-dimensional network (see Fig. 1). In the “regular” network, each node or vertex is only connected to its ‘k’ nearest neighbors (k being the degree of the network). Next, with likelihood ‘p’, connections or edges are chosen at random and connected to other nodes, also chosen randomly. With increasing p, more and more edges become randomly reconnected and finally, for p = 1, the network is completely random. This comprehensible model allows investigation of all types of networks, ranging from completely regular to completely random.Fig. 1

Bottom Line: The effect of a brain tumor on cerebral functioning is often more global than should be expected on the basis of the local character of the disease, and this is thought to be a consequence of disturbance of the cerebral network as a whole.Functional MRI, electroencephalography and especially magnetoencephalography are used to measure brain function and the signals that are being registered with these techniques can be analyzed with respect to network characteristics such as "synchronization" and "clustering".Evidence accumulates that loss of optimal neural network architecture negatively impacts complex cerebral functioning and also decreases the threshold to develop epileptic seizures.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. j.heimans@vumc.nl

ABSTRACT
Brain functions, including cognitive functions, are frequently disturbed in brain tumor patients. These disturbances may result from the tumor itself, but also from the treatment directed against the tumor. Surgery, radiotherapy and chemotherapy all may affect cerebral functioning, both in a positive as well as in a negative way. Apart from the anti-tumor treatment, glioma patients often receive glucocorticoids and anti-epileptic drugs, which both also have influence on brain functioning. The effect of a brain tumor on cerebral functioning is often more global than should be expected on the basis of the local character of the disease, and this is thought to be a consequence of disturbance of the cerebral network as a whole. Any network, whether it be a neural, a social or an electronic network, can be described in parameters assessing the topological characteristics of that particular network. Repeated assessment of neural network characteristics in brain tumor patients during their disease course enables study of the dynamics of neural networks and provides more insight into the plasticity of the diseased brain. Functional MRI, electroencephalography and especially magnetoencephalography are used to measure brain function and the signals that are being registered with these techniques can be analyzed with respect to network characteristics such as "synchronization" and "clustering". Evidence accumulates that loss of optimal neural network architecture negatively impacts complex cerebral functioning and also decreases the threshold to develop epileptic seizures. Future research should be focused on both plasticity of neural networks and the factors that have impact on that plasticity as well as the possible role of assessment of neural network characteristics in the determination of cerebral function during the disease course.

Show MeSH
Related in: MedlinePlus