Graph coarse-graining reveals differences in the module-level structure of functional brain networks

Tutkimustuotos: Lehtiartikkeli

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@article{e381903c2cc240aea24d0a53bc3b09a6,
title = "Graph coarse-graining reveals differences in the module-level structure of functional brain networks",
abstract = "Networks have become a standard tool for analyzing functional magnetic resonance imaging (fMRI) data. In this approach, brain areas and their functional connections are mapped to the nodes and links of a network. Even though this mapping reduces the complexity of the underlying data, it remains challenging to understand the structure of the resulting networks due to the large number of nodes and links. One solution is to partition networks into modules and then investigate the modules' composition and relationship with brain functioning. While this approach works well for single networks, understanding differences between two networks by comparing their partitions is difficult and alternative approaches are thus necessary. To this end, we present a coarse-graining framework that uses a single set of data-driven modules as a frame of reference, enabling one to zoom out from the node- and link-level details. As a result, differences in the module-level connectivity can be understood in a transparent, statistically verifiable manner. We demonstrate the feasibility of the method by applying it to networks constructed from fMRI data recorded from 13 healthy subjects during rest and movie viewing. While independently partitioning the rest and movie networks is shown to yield little insight, the coarse-graining framework enables one to pinpoint differences in the module-level structure, such as the increased number of intra-module links within the visual cortex during movie viewing. In addition to quantifying differences due to external stimuli, the approach could also be applied in clinical settings, such as comparing patients with healthy controls.",
keywords = "Community detection, Functional connectivity, Functional magnetic resonance imaging, Modularity",
author = "Rainer Kujala and Enrico Glerean and Pan, {Raj Kumar} and J{\"a}{\"a}skel{\"a}inen, {Iiro P.} and Mikko Sams and Jari Saram{\"a}ki",
year = "2016",
month = "11",
day = "1",
doi = "10.1111/ejn.13392",
language = "English",
volume = "44",
pages = "2673--2684",
journal = "European Journal of Neuroscience",
issn = "0953-816X",
publisher = "WILEY-BLACKWELL",
number = "9",

}

RIS - Lataa

TY - JOUR

T1 - Graph coarse-graining reveals differences in the module-level structure of functional brain networks

AU - Kujala, Rainer

AU - Glerean, Enrico

AU - Pan, Raj Kumar

AU - Jääskeläinen, Iiro P.

AU - Sams, Mikko

AU - Saramäki, Jari

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Networks have become a standard tool for analyzing functional magnetic resonance imaging (fMRI) data. In this approach, brain areas and their functional connections are mapped to the nodes and links of a network. Even though this mapping reduces the complexity of the underlying data, it remains challenging to understand the structure of the resulting networks due to the large number of nodes and links. One solution is to partition networks into modules and then investigate the modules' composition and relationship with brain functioning. While this approach works well for single networks, understanding differences between two networks by comparing their partitions is difficult and alternative approaches are thus necessary. To this end, we present a coarse-graining framework that uses a single set of data-driven modules as a frame of reference, enabling one to zoom out from the node- and link-level details. As a result, differences in the module-level connectivity can be understood in a transparent, statistically verifiable manner. We demonstrate the feasibility of the method by applying it to networks constructed from fMRI data recorded from 13 healthy subjects during rest and movie viewing. While independently partitioning the rest and movie networks is shown to yield little insight, the coarse-graining framework enables one to pinpoint differences in the module-level structure, such as the increased number of intra-module links within the visual cortex during movie viewing. In addition to quantifying differences due to external stimuli, the approach could also be applied in clinical settings, such as comparing patients with healthy controls.

AB - Networks have become a standard tool for analyzing functional magnetic resonance imaging (fMRI) data. In this approach, brain areas and their functional connections are mapped to the nodes and links of a network. Even though this mapping reduces the complexity of the underlying data, it remains challenging to understand the structure of the resulting networks due to the large number of nodes and links. One solution is to partition networks into modules and then investigate the modules' composition and relationship with brain functioning. While this approach works well for single networks, understanding differences between two networks by comparing their partitions is difficult and alternative approaches are thus necessary. To this end, we present a coarse-graining framework that uses a single set of data-driven modules as a frame of reference, enabling one to zoom out from the node- and link-level details. As a result, differences in the module-level connectivity can be understood in a transparent, statistically verifiable manner. We demonstrate the feasibility of the method by applying it to networks constructed from fMRI data recorded from 13 healthy subjects during rest and movie viewing. While independently partitioning the rest and movie networks is shown to yield little insight, the coarse-graining framework enables one to pinpoint differences in the module-level structure, such as the increased number of intra-module links within the visual cortex during movie viewing. In addition to quantifying differences due to external stimuli, the approach could also be applied in clinical settings, such as comparing patients with healthy controls.

KW - Community detection

KW - Functional connectivity

KW - Functional magnetic resonance imaging

KW - Modularity

UR - http://www.scopus.com/inward/record.url?scp=84988883713&partnerID=8YFLogxK

U2 - 10.1111/ejn.13392

DO - 10.1111/ejn.13392

M3 - Article

VL - 44

SP - 2673

EP - 2684

JO - European Journal of Neuroscience

JF - European Journal of Neuroscience

SN - 0953-816X

IS - 9

ER -

ID: 8712871