Cited by 478Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

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Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples

Gates, Kathleen M., Molenaar, Peter C.M.·2012·NeuroImage

DOI 10.1016/j.neuroimage.2012.06.026

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Abstract

At its best, connectivity mapping can offer researchers great insight into how spatially disparate regions of the human brain coordinate activity during brain processing. A recent investigation conducted by Smith and colleagues (2011) on methods for estimating connectivity maps suggested that those which attempt to ascertain the direction of influence among ROIs rarely provide reliable results. Another problem gaining increasing attention is heterogeneity in connectivity maps. Most group-level methods require that the data come from homogeneous samples, and misleading findings may arise from current methods if the connectivity maps for individuals vary across the sample (which is likely the case). The utility of maps resulting from effective connectivity on the individual or group levels is thus diminished because they do not accurately inform researchers. The present paper introduces a novel estimation technique for fMRI researchers, Group Iterative Multiple Model Estimation (GIMME), which demonstrates that using information across individuals assists in the recovery of the existence of connections among ROIs used by Smith and colleagues (2011) and the direction of the influence. Using heterogeneous in-house data, we demonstrate that GIMME offers a unique improvement over current approaches by arriving at reliable group and individual structures even when the data are highly heterogeneous across individuals comprising the group. An added benefit of GIMME is that it obtains reliable connectivity map estimates equally well using the data from resting state, block, or event-related designs. GIMME provides researchers with a powerful, flexible tool for identifying directed connectivity maps at the group and individual levels.

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Authors: Gates, K. M., & Molenaar, P. C. M.
Affiliations: Pennsylvania State University (US)
Corresponding author: Gates, Kathleen M.
ORCID: Gates, Kathleen M.
Molenaar, Peter C.M.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2012-10
Volume: 63
Issue: 1
Pages: 310-319
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2012.06.026
OpenAlex: W2085423000
Cited by: 478
References: 30
Citations by year: 2026: 31 · 2025: 50 · 2024: 53 · 2023: 45 · 2022: 61 · 2021: 51 · 2020: 39 · 2019: 29 · 2018: 34 · 2017: 24 · 2016: 24 · 2015: 15 · 2014: 10 · 2013: 10 · 2012: 2
FWCI: 6.22
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation (0852147, 1157220)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords: effective connectivity, GIMME, directed connectivity, heterogeneous samples, resting-state fMRI, connectivity mapping
MeSH terms: Algorithms, Artificial Intelligence, Brain, Brain Mapping, Data Interpretation, Statistical, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Nerve Net, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Sample Size, Connectome

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Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples

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