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How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure

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How structure sculpts function : Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure. / Bettinardi, R G; Deco, G; Karlaftis, V M; Van Hartevelt, T J; Fernandes, H M; Kourtzi, Z; Kringelbach, M L; Zamora-López, G.

In: Chaos, Vol. 27, No. 4, 04.2017, p. 047409.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Bettinardi, RG, Deco, G, Karlaftis, VM, Van Hartevelt, TJ, Fernandes, HM, Kourtzi, Z, Kringelbach, ML & Zamora-López, G 2017, 'How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure', Chaos, vol. 27, no. 4, pp. 047409. https://doi.org/10.1063/1.4980099

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Author

Bettinardi, R G ; Deco, G ; Karlaftis, V M ; Van Hartevelt, T J ; Fernandes, H M ; Kourtzi, Z ; Kringelbach, M L ; Zamora-López, G. / How structure sculpts function : Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure. In: Chaos. 2017 ; Vol. 27, No. 4. pp. 047409.

Bibtex

@article{274b45743eef4cfbaff352dcff4f0684,
title = "How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure",
abstract = "Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.",
keywords = "Journal Article",
author = "Bettinardi, {R G} and G Deco and Karlaftis, {V M} and {Van Hartevelt}, {T J} and Fernandes, {H M} and Z Kourtzi and Kringelbach, {M L} and G Zamora-L{\'o}pez",
year = "2017",
month = apr,
doi = "10.1063/1.4980099",
language = "English",
volume = "27",
pages = "047409",
journal = "Chaos",
issn = "1054-1500",
publisher = "American Institute of Physics",
number = "4",

}

RIS

TY - JOUR

T1 - How structure sculpts function

T2 - Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure

AU - Bettinardi, R G

AU - Deco, G

AU - Karlaftis, V M

AU - Van Hartevelt, T J

AU - Fernandes, H M

AU - Kourtzi, Z

AU - Kringelbach, M L

AU - Zamora-López, G

PY - 2017/4

Y1 - 2017/4

N2 - Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.

AB - Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.

KW - Journal Article

U2 - 10.1063/1.4980099

DO - 10.1063/1.4980099

M3 - Journal article

C2 - 28456160

VL - 27

SP - 047409

JO - Chaos

JF - Chaos

SN - 1054-1500

IS - 4

ER -