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Adaptive smoothing as inference strategy

Marijke Welvaert (UGent) , Karsten Tabelow, Ruth Seurinck (UGent) and Yves Rosseel (UGent)
(2013) NEUROINFORMATICS. 11(4). p.435-445
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Abstract
Although spatial smoothing of fMRI data can serve multiple purposes, increasing the sensitivity of activation detection is probably its greatest benefit. However, this increased detection power comes with a loss of specificity when non-adaptive smoothing (i.e. the standard in most software packages) is used. Simulation studies and analysis of experimental data was performed using the R packages neuRosim and fmri. In these studies, we systematically investigated the effect of spatial smoothing on the power and number of false positives in two particular cases that are often encountered in fMRI research: (1) Single condition activation detection for regions that differ in size, and (2) multiple condition activation detection for neighbouring regions. Our results demonstrate that adaptive smoothing is superior in both cases because less false positives are introduced by the spatial smoothing process compared to standard Gaussian smoothing or FDR inference of unsmoothed data.
Keywords
fMRI, Power, False positive rate, IMAGES, MAXIMA, ACTIVATION, FMRI ANALYSIS, FALSE DISCOVERY RATE, RANDOM-FIELDS, EXCURSION SETS, Structural adaptive segmentation, Gaussian smoothing

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MLA
Welvaert, Marijke, et al. “Adaptive Smoothing as Inference Strategy.” NEUROINFORMATICS, vol. 11, no. 4, 2013, pp. 435–45, doi:10.1007/s12021-013-9196-z.
APA
Welvaert, M., Tabelow, K., Seurinck, R., & Rosseel, Y. (2013). Adaptive smoothing as inference strategy. NEUROINFORMATICS, 11(4), 435–445. https://doi.org/10.1007/s12021-013-9196-z
Chicago author-date
Welvaert, Marijke, Karsten Tabelow, Ruth Seurinck, and Yves Rosseel. 2013. “Adaptive Smoothing as Inference Strategy.” NEUROINFORMATICS 11 (4): 435–45. https://doi.org/10.1007/s12021-013-9196-z.
Chicago author-date (all authors)
Welvaert, Marijke, Karsten Tabelow, Ruth Seurinck, and Yves Rosseel. 2013. “Adaptive Smoothing as Inference Strategy.” NEUROINFORMATICS 11 (4): 435–445. doi:10.1007/s12021-013-9196-z.
Vancouver
1.
Welvaert M, Tabelow K, Seurinck R, Rosseel Y. Adaptive smoothing as inference strategy. NEUROINFORMATICS. 2013;11(4):435–45.
IEEE
[1]
M. Welvaert, K. Tabelow, R. Seurinck, and Y. Rosseel, “Adaptive smoothing as inference strategy,” NEUROINFORMATICS, vol. 11, no. 4, pp. 435–445, 2013.
@article{4235044,
  abstract     = {{Although spatial smoothing of fMRI data can serve multiple purposes, increasing the sensitivity of activation detection is probably its greatest benefit. However, this increased detection power comes with a loss of specificity when non-adaptive smoothing (i.e. the standard in most software packages) is used. Simulation studies and analysis of experimental data was performed using the R packages neuRosim and fmri. In these studies, we systematically investigated the effect of spatial smoothing on the power and number of false positives in two particular cases that are often encountered in fMRI research: (1) Single condition activation detection for regions that differ in size, and (2) multiple condition activation detection for neighbouring regions. Our results demonstrate that adaptive smoothing is superior in both cases because less false positives are introduced by the spatial smoothing process compared to standard Gaussian smoothing or FDR inference of unsmoothed data.}},
  author       = {{Welvaert, Marijke and Tabelow, Karsten and Seurinck, Ruth and Rosseel, Yves}},
  issn         = {{1539-2791}},
  journal      = {{NEUROINFORMATICS}},
  keywords     = {{fMRI,Power,False positive rate,IMAGES,MAXIMA,ACTIVATION,FMRI ANALYSIS,FALSE DISCOVERY RATE,RANDOM-FIELDS,EXCURSION SETS,Structural adaptive segmentation,Gaussian smoothing}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{435--445}},
  title        = {{Adaptive smoothing as inference strategy}},
  url          = {{http://doi.org/10.1007/s12021-013-9196-z}},
  volume       = {{11}},
  year         = {{2013}},
}
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