Group inference of default-mode networks from functional magnetic resonance imaging data: Comparison of random- and mixed-effects group statistics

Yong Hwan Kim, Jong-Hwan Lee

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Default-mode network (DMN) activity measured with functional magnetic resonance imaging (fMRI) represents dominant intrinsic neuronal activations of the human brain during rest as opposed to task periods. Previous studies have demonstrated the utility of DMNs in identifying characteristic traits such as hyperactivation and hypoactivation from group-level fMRI data. However, these group-level spatial patterns (SPs) were mostly based on random-effect (RFX) statistics determined using only the intersubject variability. To reduce the potentially significant level of variability in group-level SPs in RFX due to intrasubject variability, we were motivated to adopt a mixed-effects (MFX) statistics that is using both intrasubject and intersubject variability. Publicly available group fMRI database during resting state was analyzed using a temporal concatenation-based group independent component (IC) analysis, and DMN-related ICs at the group-level were automatically selected. The individual-level SPs of these DMN-related ICs were subsequently estimated using a dual-regression approach. Using these individual-level SPs, we evaluated the reproducibility and potential variability of the DMNs from the RFX and MFX statistics using performance measures including (1) neuronal activation levels, (2) percentages of overlap, (3) Pearson's spatial correlation coefficients, and (4) the distances between center-of-clusters. The resulting SPs from the MFX-based group inference showed a significantly greater level of reproducibility than those from the RFX-based group inference as tested in a bootstrapping framework Family-wise error (FWE)-corrected p < 10 -10, one-way analysis of variance (ANOVA)). The reported findings may provide a valuable supplemental option for investigating the neuropsychiatric group- or condition-dependent characteristic traits implicated in DMNs.

Original languageEnglish
Pages (from-to)121-131
Number of pages11
JournalInternational Journal of Imaging Systems and Technology
Volume22
Issue number2
DOIs
Publication statusPublished - 2012 Jun 1

Fingerprint

Statistics
Chemical activation
Independent component analysis
Analysis of variance (ANOVA)
Brain
Magnetic Resonance Imaging

Keywords

  • default-mode network
  • functional magnetic resonance imaging
  • mixed-effects
  • random-effect
  • reproducibility

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Computer Vision and Pattern Recognition
  • Software

Cite this

@article{c48e138f3f4642c080e84b524b3cc5d9,
title = "Group inference of default-mode networks from functional magnetic resonance imaging data: Comparison of random- and mixed-effects group statistics",
abstract = "Default-mode network (DMN) activity measured with functional magnetic resonance imaging (fMRI) represents dominant intrinsic neuronal activations of the human brain during rest as opposed to task periods. Previous studies have demonstrated the utility of DMNs in identifying characteristic traits such as hyperactivation and hypoactivation from group-level fMRI data. However, these group-level spatial patterns (SPs) were mostly based on random-effect (RFX) statistics determined using only the intersubject variability. To reduce the potentially significant level of variability in group-level SPs in RFX due to intrasubject variability, we were motivated to adopt a mixed-effects (MFX) statistics that is using both intrasubject and intersubject variability. Publicly available group fMRI database during resting state was analyzed using a temporal concatenation-based group independent component (IC) analysis, and DMN-related ICs at the group-level were automatically selected. The individual-level SPs of these DMN-related ICs were subsequently estimated using a dual-regression approach. Using these individual-level SPs, we evaluated the reproducibility and potential variability of the DMNs from the RFX and MFX statistics using performance measures including (1) neuronal activation levels, (2) percentages of overlap, (3) Pearson's spatial correlation coefficients, and (4) the distances between center-of-clusters. The resulting SPs from the MFX-based group inference showed a significantly greater level of reproducibility than those from the RFX-based group inference as tested in a bootstrapping framework Family-wise error (FWE)-corrected p < 10 -10, one-way analysis of variance (ANOVA)). The reported findings may provide a valuable supplemental option for investigating the neuropsychiatric group- or condition-dependent characteristic traits implicated in DMNs.",
keywords = "default-mode network, functional magnetic resonance imaging, mixed-effects, random-effect, reproducibility",
author = "Kim, {Yong Hwan} and Jong-Hwan Lee",
year = "2012",
month = "6",
day = "1",
doi = "10.1002/ima.22012",
language = "English",
volume = "22",
pages = "121--131",
journal = "International Journal of Imaging Systems and Technology",
issn = "0899-9457",
publisher = "John Wiley and Sons Inc.",
number = "2",

}

TY - JOUR

T1 - Group inference of default-mode networks from functional magnetic resonance imaging data

T2 - Comparison of random- and mixed-effects group statistics

AU - Kim, Yong Hwan

AU - Lee, Jong-Hwan

PY - 2012/6/1

Y1 - 2012/6/1

N2 - Default-mode network (DMN) activity measured with functional magnetic resonance imaging (fMRI) represents dominant intrinsic neuronal activations of the human brain during rest as opposed to task periods. Previous studies have demonstrated the utility of DMNs in identifying characteristic traits such as hyperactivation and hypoactivation from group-level fMRI data. However, these group-level spatial patterns (SPs) were mostly based on random-effect (RFX) statistics determined using only the intersubject variability. To reduce the potentially significant level of variability in group-level SPs in RFX due to intrasubject variability, we were motivated to adopt a mixed-effects (MFX) statistics that is using both intrasubject and intersubject variability. Publicly available group fMRI database during resting state was analyzed using a temporal concatenation-based group independent component (IC) analysis, and DMN-related ICs at the group-level were automatically selected. The individual-level SPs of these DMN-related ICs were subsequently estimated using a dual-regression approach. Using these individual-level SPs, we evaluated the reproducibility and potential variability of the DMNs from the RFX and MFX statistics using performance measures including (1) neuronal activation levels, (2) percentages of overlap, (3) Pearson's spatial correlation coefficients, and (4) the distances between center-of-clusters. The resulting SPs from the MFX-based group inference showed a significantly greater level of reproducibility than those from the RFX-based group inference as tested in a bootstrapping framework Family-wise error (FWE)-corrected p < 10 -10, one-way analysis of variance (ANOVA)). The reported findings may provide a valuable supplemental option for investigating the neuropsychiatric group- or condition-dependent characteristic traits implicated in DMNs.

AB - Default-mode network (DMN) activity measured with functional magnetic resonance imaging (fMRI) represents dominant intrinsic neuronal activations of the human brain during rest as opposed to task periods. Previous studies have demonstrated the utility of DMNs in identifying characteristic traits such as hyperactivation and hypoactivation from group-level fMRI data. However, these group-level spatial patterns (SPs) were mostly based on random-effect (RFX) statistics determined using only the intersubject variability. To reduce the potentially significant level of variability in group-level SPs in RFX due to intrasubject variability, we were motivated to adopt a mixed-effects (MFX) statistics that is using both intrasubject and intersubject variability. Publicly available group fMRI database during resting state was analyzed using a temporal concatenation-based group independent component (IC) analysis, and DMN-related ICs at the group-level were automatically selected. The individual-level SPs of these DMN-related ICs were subsequently estimated using a dual-regression approach. Using these individual-level SPs, we evaluated the reproducibility and potential variability of the DMNs from the RFX and MFX statistics using performance measures including (1) neuronal activation levels, (2) percentages of overlap, (3) Pearson's spatial correlation coefficients, and (4) the distances between center-of-clusters. The resulting SPs from the MFX-based group inference showed a significantly greater level of reproducibility than those from the RFX-based group inference as tested in a bootstrapping framework Family-wise error (FWE)-corrected p < 10 -10, one-way analysis of variance (ANOVA)). The reported findings may provide a valuable supplemental option for investigating the neuropsychiatric group- or condition-dependent characteristic traits implicated in DMNs.

KW - default-mode network

KW - functional magnetic resonance imaging

KW - mixed-effects

KW - random-effect

KW - reproducibility

UR - http://www.scopus.com/inward/record.url?scp=84862110829&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862110829&partnerID=8YFLogxK

U2 - 10.1002/ima.22012

DO - 10.1002/ima.22012

M3 - Article

AN - SCOPUS:84862110829

VL - 22

SP - 121

EP - 131

JO - International Journal of Imaging Systems and Technology

JF - International Journal of Imaging Systems and Technology

SN - 0899-9457

IS - 2

ER -