Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1.R. A. Poldrack, “Region of interest analysis for fMRI,” Soc. Cognit. Affective Neurosci. 2, 6770 (2007).
2.Y. Wang and T. Q. Li, “Analysis of whole-brain resting-state fMRI data using hierarchical clustering approach,” PLoS One 8, e76315 (2013).
3.K. J. K. Duncan and J. T. Devlin, “Improving the reliability of functional localizers,” NeuroImage 57, 10221030 (2011).
4.M. Desco, J. Pascau, S. Reig, J. D. Gispert, A. Santos, C. Benito, V. Molina, and P. Garcia-Barreno, “Multimodality image quantification using Talairach grid,” Proc. SPIE 4322, 13851392 (2001).
5.J. A. Etzel, V. Gazzola, and C. Keysers, “An introduction to anatomical ROI-bas fMRI classification analysis,” Brain Res. 1282, 114125 (2009).
6.R. Saxe, M. Brett, and N. Kanwisher, “Divide and conquer: A defense of functional localizers,” NeuroImage 30, 10881096 (2006).
7.K. J. Friston, P. Rotshtein, J. J. Geng, P. Sterzer, and R. N. Henson, “A critique of functional localisers,” NeuroImage 30, 10771087 (2006).
8.K. J. Friston, A. Holmes, J. B. Poline, C. J. Price, and C. D. Frith, “Detecting activations in PET and fMRI: Levels of inference and power,” NeuroImage 4, 223235 (1996).
9.A. P. Holmes, R. C. Blair, J. Watson, and I. Ford, “Nonparametric analysis of statistic images from functional mapping experiments,” J. Cereb. Blood Flow Metab. 16, 722 (1996).
10.T. E. Nichols and A. P. Holmes, “Nonparametric permutation tests for functional neuroimaging: A primer with examples,” Hum. Brain Mapp. 15, 125 (2002).
11.R. A. Poldrack and J. A. Mumford, “Independence in ROI analysis: Where is the voodoo?,” Soc. Cognit. Affective Neurosci. 4, 208213 (2009).
12.R. A. Poldrack and J. T. Devlin, “On the fundamental role of anatomy in functional imaging: Reply to commentaries on “In praise of tedious anatomy”,” NeuroImage 37, 10661068 (2007).
13.B. Fischl, A. van der Kouwe, and C. Destrieux, “Automatically parcellating the human cerebral cortex,” Cereb. Cortex 14, 1122 (2004).
14.A. Nieto-Castanon, S. S. Ghosh, J. A. Tourville, and F. H. Guenther, “Region of interest based analysis of functional imaging data,” NeuroImage 19, 13031316 (2003).
15.R. A. Poldrack, P. C. Fletcher, R. N. Henson, K. J. Worsley, M. Brett, and T. E. Nichols, “Guidelines for reporting an fMRI experiment,” NeuroImage 40, 409414 (2008).
16.T. E. Nichols, “Multiple testing corrections, nonparametric methods, and random field theory,” NeuroImage 62, 811815 (2012).
17.M. Jenkinson, C. F. Beckmann, T. E. Behrens, M. W. Woolrich, and S. M. Smith, “FSL,” NeuroImage 62, 782790 (2012).
18.B. Ng, R. Abugharbieh, S. J. Palmer, and M. J. Mckeown, “Joint spatial denoising and active region of interest delineation in functional magnetic resonance imaging,” in Proceedings of IEEE Engineering in Medicine and Biology Society (IEEE, Lyon, France, 2007), pp. 34043407.
19.J. G. Parker, E. J. Zalusky, and C. Kirbas, “Functional MRI mapping of visual function and selective attention for performance assessment and pre-surgical planning using conjunctive visual search,” Brain Behav. 4, 227237 (2014).
20.J. L. Lancaster, D. Tordessillas-Gutierrez, M. Martinez, F. Salinas, A. Evans, K. Zilles, J. C. Mazziotta, and P. T. Fox, “Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template,” Hum. Brain Mapp. 28, 11941205 (2007).
21.M. Jenkinson and S. M. Smith, “A global optimisation method for robust affine registration of brain images,” Med. Image Anal. 5, 143156 (2001).
22.M. Jenkinson, P. R. Bannister, J. M. Brady, and S. M. Smith, “Improved optimisation for the robust and accurate linear registration and motion correction of brain images,” NeuroImage 17, 825841 (2002).
23.S. M. Smith, M. Jenkinson, M. W. Woolrich, C. F. Beckman, T. J. Behrens, H. Johansen-Berg, P. R. Bannister, and M. De Luca, “Advances in functional and structural MR image analysis and implementation as FSL,” NeuroImage 23, 208219 (2004).
24.M. W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckman, M. Jenkinson, and S. M. Smith, “Bayesian analysis of neuroimaging data in FSL,” NeuroImage 45, 7378 (2009).
25.D. L. Collins, C. J. Holmes, and T. M. Peters, “Automatic 3-D model-based neuroanatomical segmentation,” Hum. Brain Mapp. 3, 190208 (1995).
26.J. Mazziotta, A. Toga, A. Evans, P. Fox, J. Lancaster, K. Zilles, R. Woods, and T. Paus, “A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM),” Philos. Trans. R. Soc., B 356, 12931322 (2001).
27. Mathworks “Multiple comparison test—matlab,” 2013, online available:, accessed 19 January 2014.
28.E. Vul, C. Harris, P. Winkielman, and H. Pashler, “Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition,” Perspect. Psychol. Sci. 4, 274290 (2009).
29.E. Vul and H. Pashler, “Voodoo and circularity errors,” NeuroImage 62, 945948 (2012).
30.M. Brett, K. Christoff, R. Cusack, and J. Lancaster, “Using the Talairach atlas with the MNI template,” NeuroImage 13, 85 (2001).
31.P. Carmack, J. Spense, R. Gunst, W. Schucany, W. Woodward, and R. Haley, “Improved agreement between Talairach and MNI coordinate spaces in deep brain regions,” NeuroImage 22, 367371 (2004).
32.W. Chau and A. McIntosh, “The Talairach coordinate of a point in the MNI space: How to interpret it,” NeuroImage 25, 408416 (2005).
33.A. W. Toga and P. M. Thompson, “What is where and why it is important,” NeuroImage 37, 10451068 (2007).
34.R. Heller, D. Stanley, D. Yekutieli, N. Rubin, and Y. Benjamini, “Cluster-based analysis of FMRI data,” NeuroImage 33, 599608 (2006).

Data & Media loading...


Article metrics loading...



In the quantification of functional neuroimaging data, region-of-interest (ROI) analysis can be used to assess a variety of properties of the activation signal, but taken alone these properties are susceptible to noise and may fail to accurately describe overall regional involvement. Here, the authors present and evaluate an automated method for quantification and localization of functional neuroimaging data that combines multiple properties of the activation signal to generate rank-order lists of regional activation results.

The proposed automated quantification method, referred to as neuroimaging results decomposition (NIRD), begins by decomposing an activation map into a hierarchical list of ROIs using a digital atlas. In an intermediate step, the ROIs are rank-ordered according to extent, mean intensity, and total intensity. A final rank-order list () is created by sorting the ROIs according to the average of their ranks from the intermediate step. The authors hypothesized that would have improved regional quantification accuracy compared to all other quantitative metrics, including methods based on properties of statistical clusters. To test their hypothesis, NIRD rankings were directly compared to three common cluster-based methods using simulated fMRI data both with and without realistic head motion.

For both the no-motion and motion datasets, an analysis of variance found that significant differences between the quantification methods existed ( = 64.8, < 0.0001 for no motion; = 55.2, < 0.0001 for motion), and a post-hoc test found that was the most accurate quantification method tested ( < 0.05 for both datasets). Furthermore, all variants of the NIRD method were found to be significantly more accurate than the cluster-based methods in all cases.

These results confirm their hypothesis and demonstrate that the proposed NIRD methodology provides improved regional quantification accuracy compared to cluster-based methods.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd