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Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples
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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“GIMME is one of the only methods to date that can recover both the direction and presence of directed functional connectivity maps in heterogeneous data, making it an ideal place to start since it addresses the problem of heterogeneity.”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [10]
In the citing paper
“This has been shown to vastly improve reliability and precision of individual-level connections [10].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [10]
In the citing paper
“As seen previously [10], GIMME reliably recovered the presence and true direction of connections at the group and individual levels and is one of the few approaches that can do so [46].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [10]
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Ramsey, J., Sánchez-Romero, R., & Glymour, C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.062 · Cited by 48
A large-scale structural and functional connectome of social mentalizing
Wang, Y., Metoki, A., Xia, Y., Zang, Y., Yong He, A., & Olson, I. R. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118115 · Cited by 48
Gao, Y., Linke, A. C., Keehn, R. J. J., Punyamurthula, S., Jahedi, A., Gates, K. M., Fishman, I., & Müller, R. (2019) · Autism Research · doi:10.1002/aur.2171 · Cited by 52
Self-citationLatent variable GIMME using model implied instrumental variables (MIIVs).
Gates, K. M., Fisher, Z. F., & Bollen, K. A. (2019) · Psychological Methods · doi:10.1037/met0000229 · Cited by 50
Self-citationComparing directed functional connectivity between groups with confirmatory subgrouping GIMME
Henry, T. R., Feczko, E., Cordova, M., Earl, E., Williams, S., Nigg, J. T., Fair, D. A., & Gates, K. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.040 · Cited by 50
Self-citationWoods, W. C., Arizmendi, C., Gates, K. M., Stepp, S. D., Pilkonis, P. A., & Wright, A. G. C. (2020) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000472 · Cited by 48
Self-citationDealing with Multiple Solutions in Structural Vector Autoregressive Models
Beltz, A. M., & Molenaar, P. C. M. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1151333 · Cited by 45
Self-citationInsights from Personalized Models of Brain and Behavior for Identifying Biomarkers in PsychiatryPreprint
Kraus, B. T., Zinbarg, R. E., Braga, R. M., Nusslock, R., Mittal, V. A., & Gratton, C. (2023) · PsyArXiv (OSF Preprints) · doi:10.1016/j.neubiorev.2023.105259 · Cited by 48
Heshmati, S., Oravecz, Z., Brick, T. R., & Roeser, R. W. (2020) · Applied Developmental Science · doi:10.1080/10888691.2020.1766356 · Cited by 43
Metadata
- Authors
- Gates, K. M., & Molenaar, P. C. M.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Gates, Kathleen M.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 63
- Issue
- 1
- Pages
- 310-319
- ISSN
- 1053-8119 (ISSN-L)
- 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
- 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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