Extracting information from functional connectivity maps via function-on-scalar regression

Philip T. Reiss; Maarten Mennes; Eva Petkova; Lei Huang; Matthew J. Hoptman; Bharat B. Biswal; Stanley J. Colcombe; Xi Nian Zuo; Michael P. Milham

doi:10.1016/j.neuroimage.2011.01.071

Extracting information from functional connectivity maps via function-on-scalar regression

Philip T. Reiss, Maarten Mennes, Eva Petkova, Lei Huang, Matthew J. Hoptman, Bharat B. Biswal, Stanley J. Colcombe, Xi Nian Zuo, Michael P. Milham

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

Abstract

Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel's BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship that has attracted much recent interest. Next, these profile functions are regressed on predictors of interest, whether categorical (e.g., acquisition site or diagnostic group) or continuous (e.g., age). This procedure can provide insight into the roles of multiple sources of variation, and detect large-scale patterns not easily available from conventional analyses. We illustrate the proposed methods with a resting state data set pooled across four imaging sites.

Original language	English
Pages (from-to)	140-148
Number of pages	9
Journal	NeuroImage
Volume	56
Issue number	1
DOIs	https://doi.org/10.1016/j.neuroimage.2011.01.071
State	Published - 1 May 2011
Externally published	Yes

Bibliographical note

Funding Information:
The authors wish to express their gratitude to the referees, whose incisive comments led to a much improved paper, and to Clare Kelly, for very helpful discussions. This research was partially supported by grants from the National Institute of Mental Health ( R01MH083246 and K23MH087770 ), Autism Speaks , the Stavros Niarchos Foundation , and the Leon Levy Foundation , and gifts from Joseph P. Healy, Linda and Richard Schaps, Jill and Bob Smith, and the endowment provided by Phyllis Green and Randolph Cōwen. Reiss's research was supported in part by National Science Foundation grant DMS-0907017 and National Institutes of Health (NIH) grant R01 EB009744-01A . Hoptman's research was supported in part by NIH grants R21 MH084031 and R01 MH064783 .

Keywords

Functional connectivity
Functional data analysis
Model selection
Quantile regression
Resting state
Seed region

ASJC Scopus subject areas

Neurology
Cognitive Neuroscience

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10.1016/j.neuroimage.2011.01.071

Cite this

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title = "Extracting information from functional connectivity maps via function-on-scalar regression",

abstract = "Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel's BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship that has attracted much recent interest. Next, these profile functions are regressed on predictors of interest, whether categorical (e.g., acquisition site or diagnostic group) or continuous (e.g., age). This procedure can provide insight into the roles of multiple sources of variation, and detect large-scale patterns not easily available from conventional analyses. We illustrate the proposed methods with a resting state data set pooled across four imaging sites.",

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author = "Reiss, {Philip T.} and Maarten Mennes and Eva Petkova and Lei Huang and Hoptman, {Matthew J.} and Biswal, {Bharat B.} and Colcombe, {Stanley J.} and Zuo, {Xi Nian} and Milham, {Michael P.}",

note = "Funding Information: The authors wish to express their gratitude to the referees, whose incisive comments led to a much improved paper, and to Clare Kelly, for very helpful discussions. This research was partially supported by grants from the National Institute of Mental Health ( R01MH083246 and K23MH087770 ), Autism Speaks , the Stavros Niarchos Foundation , and the Leon Levy Foundation , and gifts from Joseph P. Healy, Linda and Richard Schaps, Jill and Bob Smith, and the endowment provided by Phyllis Green and Randolph Cōwen. Reiss's research was supported in part by National Science Foundation grant DMS-0907017 and National Institutes of Health (NIH) grant R01 EB009744-01A . Hoptman's research was supported in part by NIH grants R21 MH084031 and R01 MH064783 . ",

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AU - Colcombe, Stanley J.

AU - Zuo, Xi Nian

AU - Milham, Michael P.

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Extracting information from functional connectivity maps via function-on-scalar regression

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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