The relationship between spatial configuration and functional connectivity of brain regions revisited

Janine Diane Bijsterbosch; Christian F. Beckmann; Mark W. Woolrich; Stephen M. Smith; Samuel J. Harrison

doi:10.7554/eLife.44890

The relationship between spatial configuration and functional connectivity of brain regions revisited

Janine Diane Bijsterbosch, Christian F. Beckmann, Mark W. Woolrich, Stephen M. Smith, Samuel J. Harrison

Research output: Contribution to journal › Article › peer-review

38 Scopus citations

Abstract

Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.

Original language	English
Article number	e44890
Journal	eLife
Volume	8
DOIs	https://doi.org/10.7554/eLife.44890
State	Published - May 2019

Access to Document

10.7554/eLife.44890

Cite this

@article{b4a091d4af0e4a9a8d4fb7d8ed24eec2,

title = "The relationship between spatial configuration and functional connectivity of brain regions revisited",

abstract = "Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.",

author = "Bijsterbosch, {Janine Diane} and Beckmann, {Christian F.} and Woolrich, {Mark W.} and Smith, {Stephen M.} and Harrison, {Samuel J.}",

note = "Publisher Copyright: {\textcopyright} Bijsterbosch et al.",

year = "2019",

month = may,

doi = "10.7554/eLife.44890",

language = "English",

volume = "8",

journal = "eLife",

issn = "2050-084X",

}

TY - JOUR

T1 - The relationship between spatial configuration and functional connectivity of brain regions revisited

AU - Bijsterbosch, Janine Diane

AU - Beckmann, Christian F.

AU - Woolrich, Mark W.

AU - Smith, Stephen M.

AU - Harrison, Samuel J.

N1 - Publisher Copyright: © Bijsterbosch et al.

PY - 2019/5

Y1 - 2019/5

N2 - Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.

AB - Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.

UR - http://www.scopus.com/inward/record.url?scp=85067269307&partnerID=8YFLogxK

U2 - 10.7554/eLife.44890

DO - 10.7554/eLife.44890

M3 - Article

C2 - 31066676

AN - SCOPUS:85067269307

SN - 2050-084X

VL - 8

JO - eLife

JF - eLife

M1 - e44890

ER -

The relationship between spatial configuration and functional connectivity of brain regions revisited

Abstract

Access to Document

Other files and links

Fingerprint

Cite this