Original Article

On the search for convergence of functional brain patterns across neuroimaging studies: A coordinate-based meta-analysis using Gibbs point process


Introduction: Coordinate-based meta-analysis (CBMA) is a standard method for integrating brain functional patterns in neuroimaging studies. CBMA aims to identify convergency in activated brain regions across studies using coordinates of the peak activation (foci). Here, we aimed to introduce a new application of the Gibbs models for the meta-regression of the neuroimaging studies.

Methods: We used a dataset acquired from 31 studies by previous work. For each study as well as foci, study features such as SD duration and the average age were extracted. Two widely Gibbs models, Area-interaction and Geyer saturation were fitted on the foci. These models can quantify and test evidence for clusters in foci using an interaction parameter. We included study features in the models to identify their contribution to foci distribution and hence determine sources of the heterogeneity.

Results: Our results revealed that latent study-specific features have a moderate contribution to the heterogeneity of foci distribution. However, the effect of age and SD duration was not significant (p<0.001). Additionally, the estimated interaction parameter was 1.34 (p<0.001) which denotes strong evidence of clusters in foci.

Conclusions: Overall, this study highlighted the role of the interaction parameter in CBMA. The results of this work suggest that Gibbs models can be considered as a promising tool for neuroimaging meta-analysis.

1. Salimi-Khorshidi G, Smith SM, Keltner JR, Wager TD, Nichols TE. Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. Neuroimage. 2009 Apr 15;45(3):810–23.
2. Samartsidis P, Montagna S, Nichols TE, Johnson TD. The coordinate-based meta-analysis of neuroimaging data. Stat Sci. 2017;32(4):580–99.
3. Baddeley A, Rubak E, Turner R. Spatial point patterns: methodology and applications with R. Chapman and Hall/CRC; 2015.
4. Venkatraman V, Chuah YML, Huettel SA, Chee MWL. Sleep deprivation elevates expectation of gains and attenuates response to losses following risky decisions. Sleep. 2007 May;30(5):603–9.
5. Menz MM, Büchel C, Peters J. Sleep deprivation is associated with attenuated parametric valuation and control signals in the midbrain during value-based decision making. J Neurosci. 2012 May 16;32(20):6937–46.
6. Medic G, Wille M, Hemels ME. Short- and long-term health consequences of sleep disruption. Nat Sci Sleep. 2017 May 19;9:151–61.
7. Khazaie H, Tahmasian M, Ghadami MR, Safaei H, Ekhtiari H, Samadzadeh S, et al. The Effects of Chronic Partial Sleep Deprivation on Cognitive Functions of Medical Residents. Iran J Psychiatry. 2010;5(2):74–7.
8. Javaheripour N, Shahdipour N, Noori K, Zarei M, Camilleri JA, Laird AR, et al. Functional brain alterations in acute sleep deprivation: An activation likelihood estimation meta-analysis. Sleep Medicine Reviews. 2019 Aug 1;46:64–73.
9. Ripley BD, Kelly FP. Markov point processes. Journal of the London Mathematical Society. 1977;2(1):188–92.
10. Baddeley A, Turner R. spatstat: An R Package for Analyzing Spatial Point Patterns. Journal of Statistical Software. 2005 Jan 26;12(1):1–42.
11. Yeo BTT, Tandi J, Chee MWL. Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation. NeuroImage. 2015 May 1;111:147–58.
IssueVol 8 No 3 (2022) QRcode
SectionOriginal Article(s)
Gibbs point process Meta-regression Coordinate-based meta-analysis Heterogeneity Sleep deprivation

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Mohammadzadeh M, Tahmasian M, Rasekhi A. On the search for convergence of functional brain patterns across neuroimaging studies: A coordinate-based meta-analysis using Gibbs point process. JBE. 2022;8(3):295-303.