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Pooling fMRI results across studies: a comparison of two hierarchical models.

Han Bossier (UGent)
(2017)
Author
Organization
Abstract
Neuroscientists are increasingly aware of the limited reproducibility of functional Magnetic Resonance Imaging (fMRI) studies due to an inherent low signal to noise ratio, small sample sizes, questionable research practices and a strong focus on minimizing type I errors (Durnez et al., 2014). To overcome this, researchers now recognize the value of open science initiatives which leads to an increased amount of open access data and a higher inclusion of null results (Pernet and Poline, 2016). This in turn enables the integration of data across studies to increase the understanding of human brain function. In this contribution, we investigate two approaches of pooling data across studies. Traditionally, researchers can rely on methods for meta-analysis to aggregate effect sizes while accounting for within and between study variability. A second approach is to use a three-level linear model with an additional study level, an extension of the two-stage mixed effects model commonly used in fMRI to aggregate trials and subjects within individual studies. We compare both procedures in terms of the average standardized bias, length and coverage of confidence intervals using both simulations and resting state fMRI (under the null hypothesis). Results indicate that the three-level model does not outperform meta-analysis techniques.

Citation

Please use this url to cite or link to this publication:

Chicago
Bossier, Han. 2017. “Pooling fMRI Results Across Studies: a Comparison of Two Hierarchical Models.” In .
APA
Bossier, H. (2017). Pooling fMRI results across studies: a comparison of two hierarchical models. Presented at the The 6th Channel Network Conference of the International Biometric Society.
Vancouver
1.
Bossier H. Pooling fMRI results across studies: a comparison of two hierarchical models. 2017.
MLA
Bossier, Han. “Pooling fMRI Results Across Studies: a Comparison of Two Hierarchical Models.” 2017. Print.
@inproceedings{8552057,
  abstract     = {Neuroscientists are increasingly aware of the limited reproducibility of functional Magnetic Resonance Imaging (fMRI) studies due to an inherent low signal to noise ratio, small sample sizes, questionable research practices and a strong focus on minimizing type I errors (Durnez et al., 2014). To overcome this, researchers now recognize the value of open science initiatives which leads to an increased amount of open access data and a higher inclusion of null results (Pernet and Poline, 2016). This in turn enables the integration of data across studies to increase the understanding of human brain function. In this contribution, we investigate two approaches of pooling data across studies. Traditionally, researchers can rely on methods for meta-analysis to aggregate effect sizes while accounting for within and between study variability. A second approach is to use a three-level linear model with an additional study level, an extension of the two-stage mixed effects model commonly used in fMRI to aggregate trials and subjects within individual studies. We compare both procedures in terms of the average standardized bias, length and coverage of confidence intervals using both simulations and resting state fMRI (under the null hypothesis). Results indicate that the three-level model does not outperform meta-analysis techniques.},
  author       = {Bossier, Han},
  language     = {eng},
  location     = {Hasselt},
  title        = {Pooling fMRI results across studies: a comparison of two hierarchical models.},
  url          = {https://www.uhasselt.be/UH/ibschannel2017/Programme-IBSchannel2017.html},
  year         = {2017},
}