Abstract
Spatial registration is an essential step in the analysis of fMRI data because it enables between-subject analyses of brain activity, measured either during task performance or in the resting state. In this study, we investigated how anatomical registration with MRTOOL affects the reliability of task-related fMRI activity. We used as a benchmark the results from two other spatial registration methods implemented in SPM12: the Unified Segmentation algorithm and the DARTEL toolbox. Structural alignment accuracy and the impact on functional activation maps were assessed with high-resolution T1-weighted images and a set of task-related functional volumes acquired in 10 healthy volunteers. Our findings confirmed that anatomical registration is a crucial step in fMRI data processing, contributing significantly to the total inter-subject variance of the activation maps. MRTOOL and DARTEL provided greater registration accuracy than Unified Segmentation. Although DARTEL had superior gray matter and white matter tissue alignment than MRTOOL, there were no significant differences between DARTEL and MRTOOL in test–retest reliability. Likewise, we found only limited differences in BOLD activation morphology between MRTOOL and DARTEL. The test–retest reliability of task-related responses was comparable between MRTOOL and DARTEL, and both proved superior to Unified Segmentation. We conclude that MRTOOL, which is suitable for single-subject processing of structural and functional MR images, is a valid alternative to other SPM12-based approaches that are intended for group analysis. MRTOOL now includes a normalization module for fMRI data and is freely available to the scientific community.
Similar content being viewed by others
References
Acosta-Cabronero, J., Williams, G. B., Pereira, J. M. S., Pengas, G., & Nestor, P. J. (2008). The impact of skull-stripping and radio-frequency bins correction on grey-matter segmentation for voxel-based morphometry. Neuroimage, 39(4), 1654–1665. https://doi.org/10.1016/j.neuroimage.2007.10.051.
Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H. B. M., & Zilles, K. (1999). Broca’s region revisited: Cytoarchitecture and intersubject variability. Journal of Comparative Neurology, 412(2), 319–341. https://doi.org/10.1002/(SICI)1096-9861(19990920)412:2<319::AID-CNE10>3.0.CO;2-7.
Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95–113. https://doi.org/10.1016/j.neuroimage.2007.07.007.
Ashburner, J., & Friston, K. J. (2005). Unified segmentation. Neuroimage, 26(3), 839–851. https://doi.org/10.1016/j.neuroimage.2005.02.018.
Bennett, C. M., & Miller, M. B. (2010). How reliable are the results from functional magnetic resonance imaging? Annals of the New York Academy of Sciences, 1191, 133–155. https://doi.org/10.1111/j.1749-6632.2010.05446.x.
Boisgontier, M. P., Cheval, B., van Ruitenbeek, P., Cuypers, K., Leunissen, I., Sunaert, S., Meesen, R., Zivari Adab, H., Renaud, O., & Swinnen, S. P. (2018). Cerebellar gray matter explains bimanual coordination performance in children and older adults. Neurobiology of Aging, 65, 109–120. https://doi.org/10.1016/j.neurobiolaging.2018.01.016.
Bullmore, E. (2012). The future of functional MRI in clinical medicine. NeuroImage, 62, 1267–1271. https://doi.org/10.1016/j.neuroimage.2012.01.026.
Caceres, A., Hall, D. L., Zelaya, F. O., Williams, S. C. R., & Mehta, M. A. (2009). Measuring fMRI reliability with the intra-class correlation coefficient. NeuroImage, 45, 758–768. https://doi.org/10.1016/j.neuroimage.2008.12.035.
Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., & Zilles, K. (2006). The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability. NeuroImage, 33(2), 430–448. https://doi.org/10.1016/j.neuroimage.2006.06.054.
Chumbley, J. R., & Friston, K. J. (2009). False discovery rate revisited: FDR and topological inference using Gaussian random fields. Neuroimage, 44(1), 62–70. https://doi.org/10.1016/j.neuroimage.2008.05.021.
Crinion, J., Ashbumer, J., Leff, A., Brett, M., Price, C., & Friston, K. J. (2007). Spatial normalization of lesioned brains: Performance evaluation and impact on fMRI analyses. Neuroimage, 37(3), 866–875. https://doi.org/10.1016/j.neuroimage.2007.04.065.
Crivello, F., Schormann, T., Tzourio-Mazoyer, N., Roland, P. E., Zilles, K., & Mazoyer, B. M. (2002). Comparison of spatial normalization procedures and their impact on functional maps. Human Brain Mapping, 16(4), 228–250. https://doi.org/10.1002/hbm.10047.
de Bertoldi, F., Finos, L., Maieron, M., Weis, L., Campanella, M., Ius, T., & Fadiga, L. (2015). Improving the reliability of single-subject fMRI by weighting intra-run variability. NeuroImage, 114, 287–293. https://doi.org/10.1016/j.neuroimage.2015.03.076.
Demirci, O., & Calhoun, V. D. (2009). Functional magnetic resonance imaging - implications for detection of schizophrenia. European Neurological Review, 4(2), 103–106. https://doi.org/10.17925/ENR.2009.04.02.103.
Detre, J. A. (2006). Clinical applicability of functional MRI. Journal of Magnetic Resonance Imaging, 23, 808–815. https://doi.org/10.1002/jmri.20585.
Dong, Y., Dobkin, B. H., Cen, S. Y., Wu, A. D., & Winstein, C. J. (2006). Motor cortex activation during treatment may predict therapeutic gains in paretic hand function after stroke. Stroke, 37(6), 1552–1555. https://doi.org/10.1161/01.STR.0000221281.69373.4e.
Dubois, J., & Adolphs, R. (2016). Building a science of individual differences from fMRI. Trends in Cognitive Sciences, 20, 425–443. https://doi.org/10.1016/j.tics.2016.03.014.
Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005). A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage, 25(4), 1325–1335. https://doi.org/10.1016/j.neuroimage.2004.12.034.
Eickhoff, S. B., Heim, S., Zilles, K., & Amunts, K. (2006). Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps. NeuroImage, 32(2), 570–582. https://doi.org/10.1016/j.neuroimage.2006.04.204.
Eickhoff, S. B., Paus, T., Caspers, S., Grosbras, M. H., Evans, A. C., Zilles, K., & Amunts, K. (2007). Assignment of functional activations to probabilistic cytoarchitectonic areas revisited. NeuroImage, 36(3), 511–521. https://doi.org/10.1016/j.neuroimage.2007.03.060.
Fein, G., Landman, B., Tran, H., Barakos, J., Moon, K., Di Sclafani, V., & Shumway, R. (2006). Statistical parametric mapping of brain morphology: Sensitivity is dramatically increased by using brain-extracted images as inputs. Neuroimage, 30(4), 1187–1195. https://doi.org/10.1016/j.neuroimage.2005.10.054.
Fischmeister, F. P. S., Hollinger, I., Klinger, N., Geissler, A., Wurnig, M. C., Matt, E., et al. (2013). The benefits of skull stripping in the normalization of clinical fMRI data. Neuroimage-Clinical, 3, 369–380. https://doi.org/10.1016/j.nicl.2013.09.007.
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. P., Frith, C. D., & Frackowiak, R. S. J. (1994). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2(4), 193–1097. https://doi.org/10.1002/hbm.460020402.
Ganzetti, M., Liu, Q., & Mantini, D. (2018). A spatial registration toolbox for structural MR imaging of the aging brain. Neuroinformatics, pp., 16, 1–13. https://doi.org/10.1007/s12021-018-9355-3.
Geyer, S., Ledberg, A., Schleicher, A., Kinomura, S., Schormann, T., Burgel, U., et al. (1996). Two different areas within the primary motor cortex of man. Nature, 382(6594), 805–807. https://doi.org/10.1038/382805a0.
Giussani, C., Roux, F. E., Ojemann, J., Sganzerla, E. P., & Pirillo, D. (2010). Is preoperative functional magnetic resonance imaging reliable for language areas mapping in brain tumor surgery? Review of language functional magnetic resonance imaging and direct cortical stimulation correlation studies. Neurosurgery. http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=emed12&NEWS=N&AN=358341166.
Gorgolewski, K., Storkey, A. J., Bastin, M. E., & Pernet, C. (2012). Adaptive thresholding for reliable topological inference in single subject fMRI analysis. Frontiers in Human Neuroscience, 6. https://doi.org/10.3389/fnhum.2012.00245.
Gorgolewski, K., Storkey, A. J., Bastin, M. E., Whittle, I., & Pernet, C. (2013a). Single subject fMRI test-retest reliability metrics and confounding factors. Neuroimage, 69, 231–243. https://doi.org/10.1016/j.neuroimage.2012.10.085.
Gorgolewski, K., Storkey, A., Bastin, M. E., Whittle, I. R., Wardlaw, J. M., & Pernet, C. R. (2013b). A test-retest fMRI dataset for motor, language and spatial attention functions. Gigascience, 2, Artn 6. https://doi.org/10.1186/2047-217x-2-6.
Hoffman, P., & Lambon Ralph, M. A. (2018). From percept to concept in the ventral temporal lobes: Graded hemispheric specialisation based on stimulus and task. Cortex, 101, 107–118. https://doi.org/10.1016/j.cortex.2018.01.015.
Hope, T. M. H., Jones, O. P., Grogan, A., Crinion, J., Rae, J., Ruffle, L., et al. (2015). Comparing language outcomes in monolingual and bilingual stroke patients. Brain, 138(4), 1070–1083. https://doi.org/10.1093/brain/awv020.
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M. C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., Song, J. H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R. P., Mann, J. J., & Parsey, R. V. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage, 46(3), 786–802. https://doi.org/10.1016/j.neuroimage.2008.12.037.
Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., et al. (2008). Automatic classification of MR scans in Alzheimer’s disease. Brain, 131(3), 681–689. https://doi.org/10.1093/brain/awm319.
Laatsch, L. K., Thulborn, K. R., Krisky, C. M., Shobat, D. M., & Sweeney, J. A. (2004). Investigating the neurobiological basis of cognitive rehabilitation therapy with fMRI. Brain Injury, 18(10), 957–974. https://doi.org/10.1080/02699050410001672369.
Lorio, S., Kherif, F., Ruef, A., Melie-Garcia, L., Frackowiak, R., Ashburner, J., Helms, G., Lutti, A., & Draganski, B. (2016). Neurobiological origin of spurious brain morphological changes: A quantitative MRI study. Human Brain Mapping, 37(5), 1801–1815. https://doi.org/10.1002/hbm.23137.
Michely, J., Volz, L. J., Hoffstaedter, F., Tittgemeyer, M., Eickhoff, S. B., Fink, G. R., & Grefkes, C. (2018). Network connectivity of motor control in the ageing brain. NeuroImage: Clinical, 18, 443–455. https://doi.org/10.1016/j.nicl.2018.02.001.
Morosan, P., Rademacher, J., Schleicher, A., Amunts, K., Schormann, T., & Zilles, K. (2001). Human primary auditory cortex: Cytoarchitectonic subdivisions and mapping into a spatial reference system. NeuroImage, 13(4), 684–701. https://doi.org/10.1006/nimg.2000.0715.
Morosan, P., Schleicher, A., Amunts, K., & Zilles, K. (2005). Multimodal architectonic mapping of human superior temporal gyrus. In Anatomy and Embryology (Vol. 210, pp. 401–406). https://doi.org/10.1007/s00429-005-0029-1.
Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009). The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? NeuroImage, 44(3), 893–905. https://doi.org/10.1016/j.neuroimage.2008.09.036.
Nahab, F. B., & Hallett, M. (2010). Current role of functional MRI in the diagnosis of movement disorders. Neuroimaging Clinics of North America, 20, 103–110. https://doi.org/10.1016/j.nic.2009.08.001.
Ossenkoppele, R., Mattsson, N., Teunissen, C. E., Barkhof, F., Pijnenburg, Y., Scheltens, P., van der Flier, W. M., & Rabinovici, G. D. (2015). Cerebrospinal fluid biomarkers and cerebral atrophy in distinct clinical variants of probable Alzheimer’s disease. Neurobiology of Aging, 36(8), 2340–2347. https://doi.org/10.1016/j.neurobiolaging.2015.04.011.
Pauwels, L., Vancleef, K., Swinnen, S. P., & Beets, I. A. M. (2015). Challenge to promote change: Both young and older adults benefit from contextual interference. Frontiers in Aging Neuroscience, 7(JUL). https://doi.org/10.3389/fnagi.2015.00157.
Pohl, K. M., Fisher, J., Levitt, J. J., Shenton, M. E., Kikinis, R., Grimson, W. E. L., & Wells, W. M. (2005). A unifying approach to registration, segmentation, and intensity correction. Medical Image Computing and Computer-Assisted Intervention - Miccai 2005, Pt 1, 3749, 310–318.
Poldrack, R. A., & Gorgolewski, K. J. (2017). OpenfMRI: Open sharing of task fMRI data. Neuroimage, 144, 259–261. https://doi.org/10.1016/j.neuroimage.2015.05.073.
Rosen, B. R., & Savoy, R. L. (2012). FMRI at 20: Has it changed the world? NeuroImage, 62, 1316–1324. https://doi.org/10.1016/j.neuroimage.2012.03.004.
Savoy, R. L. (2005). Experimental design in brain activation MRI: Cautionary tales. In Brain Research Bulletin (Vol. 67, pp. 361–367). https://doi.org/10.1016/j.brainresbull.2005.06.008.
Shattuck, D. W., Sandor-Leahy, S. R., Schaper, K. A., Rottenberg, D. A., & Leahy, R. M. (2001). Magnetic resonance image tissue classification using a partial volume model. Neuroimage, 13(5), 856–876. https://doi.org/10.1006/nimg.2000.0730.
Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86, 420–428. https://doi.org/10.1037/0033-2909.86.2.420.
Smith, S. M., Beckmann, C. F., Ramnani, N., Woolrich, M. W., Bannister, P. R., Jenkinson, M., Matthews, P. M., & McGonigle, D. J. (2005). Variability in fMRI: A re-examination of inter-session differences. Human Brain Mapping, 24(3), 248–257. https://doi.org/10.1002/hbm.20080.
Solesio-Jofre, E., Beets, I. A. M., Woolley, D. G., Pauwels, L., Chalavi, S., Mantini, D., & Swinnen, S. P. (2018). Age-dependent modulations of resting state connectivity following motor practice. Frontiers in Aging Neuroscience, 10(FEB). https://doi.org/10.3389/fnagi.2018.00025.
Stephens, M. A. (1992). Introduction to Kolmogorov (1933) on the empirical determination of a distribution. In Breakthroughs in Statistics: Methodology and Distribution. https://doi.org/10.1007/978-1-4612-4380-9_9.
Uludağ, K., Uğurbil, K., & Berliner, L. (2015). fMRI: From nuclear spins to brain functions. fMRI: From nuclear spins to brain functions. https://doi.org/10.1007/978-1-4899-7591-1.
Van Leemput, K., Maes, F., Vandermeulen, D., & Suetens, P. (1999). Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging, 18(10), 897–908. https://doi.org/10.1109/42.811270.
Wei, X., Yoo, S. S., Dickey, C. C., Zou, K. H., Guttmann, C. R., & Panych, L. P. (2004). Functional MRI of auditory verbal working memory: Long-term reproducibility analysis. NeuroImage, 21, 1000–1008. https://doi.org/10.1016/j.neuroimage.2003.10.039.
Wengenroth, M., Blatow, M., Guenther, J., Akbar, M., Tronnier, V. M., & Stippich, C. (2011). Diagnostic benefits of presurgical fMRI in patients with brain tumours in the primary sensorimotor cortex. European Radiology, 21(7), 1517–1525. https://doi.org/10.1007/s00330-011-2067-9.
Woo, C. W., Krishnan, A., & Wager, T. D. (2014). Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations. Neuroimage, 91, 412–419. https://doi.org/10.1016/j.neuroimage.2013.12.058.
Funding
The study received funding from the FWO and European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie action (grant 665501) and the KU Leuven Special Research Fund (grant C16/15/070).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Author Marco Ganzetti declares that he has no conflict of interest. Author Gaia Amaranta Taberna declares that she has no conflict of interest. Author Dante Mantini declares that he has no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ganzetti, M., Taberna, G.A. & Mantini, D. fMRI data processing in MRTOOL: to what extent does anatomical registration affect the reliability of functional results?. Brain Imaging and Behavior 13, 1538–1553 (2019). https://doi.org/10.1007/s11682-018-9986-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-018-9986-x