Regions of Interest as nodes of dynamic functional brain networks

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Regions of Interest as nodes of dynamic functional brain networks. / Ryyppö, Elisa; Glerean, Enrico; Brattico, Elvira; Saramäki, Jari; Korhonen, Onerva.

In: Network neuroscience (Cambridge, Mass.), Vol. 2, No. 4, 2018, p. 513-535.

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

Harvard

Ryyppö, E, Glerean, E, Brattico, E, Saramäki, J & Korhonen, O 2018, 'Regions of Interest as nodes of dynamic functional brain networks', Network neuroscience (Cambridge, Mass.), vol. 2, no. 4, pp. 513-535. https://doi.org/10.1162/netn_a_00047

APA

Ryyppö, E., Glerean, E., Brattico, E., Saramäki, J., & Korhonen, O. (2018). Regions of Interest as nodes of dynamic functional brain networks. Network neuroscience (Cambridge, Mass.), 2(4), 513-535. https://doi.org/10.1162/netn_a_00047

CBE

Ryyppö E, Glerean E, Brattico E, Saramäki J, Korhonen O. 2018. Regions of Interest as nodes of dynamic functional brain networks. Network neuroscience (Cambridge, Mass.). 2(4):513-535. https://doi.org/10.1162/netn_a_00047

MLA

Ryyppö, Elisa et al. "Regions of Interest as nodes of dynamic functional brain networks". Network neuroscience (Cambridge, Mass.). 2018, 2(4). 513-535. https://doi.org/10.1162/netn_a_00047

Vancouver

Ryyppö E, Glerean E, Brattico E, Saramäki J, Korhonen O. Regions of Interest as nodes of dynamic functional brain networks. Network neuroscience (Cambridge, Mass.). 2018;2(4):513-535. https://doi.org/10.1162/netn_a_00047

Author

Ryyppö, Elisa ; Glerean, Enrico ; Brattico, Elvira ; Saramäki, Jari ; Korhonen, Onerva. / Regions of Interest as nodes of dynamic functional brain networks. In: Network neuroscience (Cambridge, Mass.). 2018 ; Vol. 2, No. 4. pp. 513-535.

Bibtex

@article{62407cc59f404eb8a8b7f388754144f3,
title = "Regions of Interest as nodes of dynamic functional brain networks",
abstract = "The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), predetermined groupings of fMRI measurement voxels. Earlier, we demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIs in commonly used brain atlases. Here, we ask how ROIs behave as nodes of dynamic brain networks. To this end, we use two measures: spatiotemporal consistency measures changes in spatial consistency across time and network turnover quantifies the changes in the local network structure around an ROI. We find that spatial consistency varies non-uniformly in space and time, which is reflected in the variation of spatiotemporal consistency across ROIs. Furthermore, we see time-dependent changes in the network neighborhoods of the ROIs, reflected in high network turnover. Network turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover. Finally, we reveal that there is rich voxel-level correlation structure inside ROIs. Because the internal structure and the connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate. Therefore, network neuroscience would greatly benefit from node definition strategies tailored for dynamical networks.",
author = "Elisa Ryypp{\"o} and Enrico Glerean and Elvira Brattico and Jari Saram{\"a}ki and Onerva Korhonen",
year = "2018",
doi = "10.1162/netn_a_00047",
language = "English",
volume = "2",
pages = "513--535",
journal = "Network neuroscience (Cambridge, Mass.)",
issn = "2472-1751",
number = "4",

}

RIS

TY - JOUR

T1 - Regions of Interest as nodes of dynamic functional brain networks

AU - Ryyppö, Elisa

AU - Glerean, Enrico

AU - Brattico, Elvira

AU - Saramäki, Jari

AU - Korhonen, Onerva

PY - 2018

Y1 - 2018

N2 - The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), predetermined groupings of fMRI measurement voxels. Earlier, we demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIs in commonly used brain atlases. Here, we ask how ROIs behave as nodes of dynamic brain networks. To this end, we use two measures: spatiotemporal consistency measures changes in spatial consistency across time and network turnover quantifies the changes in the local network structure around an ROI. We find that spatial consistency varies non-uniformly in space and time, which is reflected in the variation of spatiotemporal consistency across ROIs. Furthermore, we see time-dependent changes in the network neighborhoods of the ROIs, reflected in high network turnover. Network turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover. Finally, we reveal that there is rich voxel-level correlation structure inside ROIs. Because the internal structure and the connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate. Therefore, network neuroscience would greatly benefit from node definition strategies tailored for dynamical networks.

AB - The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), predetermined groupings of fMRI measurement voxels. Earlier, we demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIs in commonly used brain atlases. Here, we ask how ROIs behave as nodes of dynamic brain networks. To this end, we use two measures: spatiotemporal consistency measures changes in spatial consistency across time and network turnover quantifies the changes in the local network structure around an ROI. We find that spatial consistency varies non-uniformly in space and time, which is reflected in the variation of spatiotemporal consistency across ROIs. Furthermore, we see time-dependent changes in the network neighborhoods of the ROIs, reflected in high network turnover. Network turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover. Finally, we reveal that there is rich voxel-level correlation structure inside ROIs. Because the internal structure and the connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate. Therefore, network neuroscience would greatly benefit from node definition strategies tailored for dynamical networks.

U2 - 10.1162/netn_a_00047

DO - 10.1162/netn_a_00047

M3 - Journal article

C2 - 30294707

VL - 2

SP - 513

EP - 535

JO - Network neuroscience (Cambridge, Mass.)

JF - Network neuroscience (Cambridge, Mass.)

SN - 2472-1751

IS - 4

ER -