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01.10.2016 | Software Original Article

PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI

verfasst von: Huiyuan Huang, Zhongxiang Ding, Dewang Mao, Jianhua Yuan, Fangmei Zhu, Shuda Chen, Yan Xu, Lin Lou, Xiaoyan Feng, Le Qi, Wusi Qiu, Han Zhang, Yu-Feng Zang

Erschienen in: Neuroinformatics | Ausgabe 4/2016

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Abstract

The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp. This toolbox can reveal eloquent areas using comprehensive methods and various complementary fMRI modalities. For example, PreSurgMapp supports both model-based (general linear model, GLM, and seed correlation) and data-driven (independent component analysis, ICA) methods and processes both task-based and resting-state fMRI data. PreSurgMapp is designed for highly automatic and individualized functional mapping with a user-friendly graphical user interface (GUI) for time-saving pipeline processing. For example, sensorimotor and language-related components can be automatically identified without human input interference using an effective, accurate component identification algorithm using discriminability index. All the results generated can be further evaluated and compared by neuro-radiologists or neurosurgeons. This software has substantial value for clinical neuro-radiology and neuro-oncology, including application to patients with low- and high-grade brain tumors and those with epilepsy foci in the dominant language hemisphere who are planning to undergo a temporal lobectomy.

http://www.fil.ion.ucl.ac.uk/spm

http://www.restfmri.net (Song et al. 2011)

http://rfmri.org/DPARSF (Yan and Zang 2010)

http://icatb.sourceforge.net/

http://www.nitrc.org/projects/cogicat (Zhang et al. 2010)

http://www.mricro.com/

Abou-Elseoud, A., Starck, T., Remes, J., Nikkinen, J., Tervonen, O., & Kiviniemi, V. (2010). The effect of model order selection in group PICA. Human Brain Mapping, 31(8), 1207–1216.PubMed

Arfanakis, K., Cordes, D., Haughton, V. M., Moritz, C. H., Quigley, M. A., & Meyerand, M. E. (2000). Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets. Magnetic Resonance Imaging, 18(8), 921–930.PubMedCrossRef

Atlas, S. W., Howard, R. S., Maldjian, J., Alsop, D., Detre, J. A., Listerud, J., et al. (1996). Functional magnetic resonance imaging of regional brain activity in patients with intracerebral gliomas: findings and implications for clinical management. Neurosurgery, 38(2), 329–338.PubMedCrossRef

Bartels, A., & Zeki, S. (2004). The chronoarchitecture of the human brain—natural viewing conditions reveal a time-based anatomy of the brain. NeuroImage, 22(1), 419–433.PubMedCrossRef

Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 360(1457), 1001–1013.PubMedPubMedCentralCrossRef

Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.PubMedCrossRef

Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic Resonance in Medicine, 34(4), 537–541.PubMedCrossRef

Böttger, J., Margulies, D. S., Horn, P., Thomale, U. W., Podlipsky, I., Shapira-Lichter, I., et al. (2011). A software tool for interactive exploration of intrinsic functional connectivity opens new perspectives for brain surgery. Acta Neurochirurgica, 153(8), 1561–1572.PubMedCrossRef

Briganti, C., Sestieri, C., Mattei, P., Esposito, R., Galzio, R., Tartaro, A., et al. (2012). Reorganization of functional connectivity of the language network in patients with brain gliomas. American Journal of Neuroradiology, 33(10), 1983–1990.PubMedCrossRef

Coello, A. F., Moritz-Gasser, S., Martino, J., Martinoni, M., Matsuda, R., & Duffau, H. (2013). Selection of intraoperative tasks for awake mapping based on relationships between tumor location and functional networks: a review. Journal of Neurosurgery, 119(6), 1380–1394.CrossRef

Cordes, D., Haughton, V. M., Arfanakis, K., Carew, J. D., Turski, P. A., Moritz, C. H., et al. (2001). Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. American Journal of Neuroradiology, 22(7), 1326–1333.PubMed

DeCarlo, L. T. (1998). Signal detection theory and generalized linear models. Psychological Methods, 3(2), 186.CrossRef

Fesl, G., Moriggl, B., Schmid, U., Naidich, T., Herholz, K., & Yousry, T. (2003). Inferior central sulcus: variations of anatomy and function on the example of the motor tongue area. NeuroImage, 20(1), 601–610.PubMedCrossRef

Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews. Neuroscience, 8(9), 700–711.PubMedCrossRef

Fransson, P., Merboldt, K.-D., Petersson, K. M., Ingvar, M., & Frahm, J. (2002). On the effects of spatial filtering—a comparative fMRI study of episodic memory encoding at high and low resolution. NeuroImage, 16(4), 977–984.PubMedCrossRef

Geissler, A., Lanzenberger, R., Barth, M., Tahamtan, A. R., Milakara, D., Gartus, A., et al. (2005). Influence of fMRI smoothing procedures on replicability of fine scale motor localization. NeuroImage, 24(2), 323–331.PubMedCrossRef

Greicius, M. (2008). Resting-state functional connectivity in neuropsychiatric disorders. Current Opinion in Neurology, 21(4), 424–430.PubMedCrossRef

Håberg, A., Kvistad, K. A., Unsgård, G., & Haraldseth, O. (2004). Preoperative blood oxygen level-dependent functional magnetic resonance imaging in patients with primary brain tumors: clinical application and outcome. Neurosurgery, 54(4), 902–915.PubMedCrossRef

Hall, W. A., Liu, H., & Truwit, C. L. (2005). Functional magnetic resonance imaging–guided resection of low-grade gliomas. Surgical Neurology, 64(1), 20–27.PubMedCrossRef

Jia, W., Zhang, H., Liao, W., & Zang, Y. (2013). “Correct” sensorimotor network detected by independent component analysis on resting-state fMRI. In Paper presented at the Organization of Human Brain Mapping (OHBM). Seattle: USA.

Kiviniemi, V., Kantola, J.-H., Jauhiainen, J., Hyvärinen, A., & Tervonen, O. (2003). Independent component analysis of nondeterministic fMRI signal sources. NeuroImage, 19(2), 253–260.PubMedCrossRef

Kiviniemi, V., Starck, T., Remes, J., Long, X., Nikkinen, J., Haapea, M., et al. (2009). Functional segmentation of the brain cortex using high model order group PICA. Human Brain Mapping, 30(12), 3865–3886.PubMedCrossRef

Kokkonen, S.-M., Nikkinen, J., Remes, J., Kantola, J., Starck, T., Haapea, M., et al. (2009). Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI. Magnetic Resonance Imaging, 27(6), 733–740.PubMedCrossRef

Kristo, G., Rutten, G. J., Raemaekers, M., Gelder, B., Rombouts, S. A., & Ramsey, N. F. (2014). Task and task-free FMRI reproducibility comparison for motor network identification. Human Brain Mapping, 35(1), 340–352.PubMedCrossRef

Lee, M., Smyser, C., & Shimony, J. (2013). Resting-state fMRI: a review of methods and clinical applications. American Journal of Neuroradiology, 34(10), 1866–1872.PubMedCrossRef

Lehéricy, S., Duffau, H., Cornu, P., Capelle, L., Pidoux, B., Carpentier, A., et al. (2000). Correspondence between functional magnetic resonance imaging somatotopy and individual brain anatomy of the central region: comparison with intraoperative stimulation in patients with brain tumors. Journal of Neurosurgery, 92(4), 589–598.PubMedCrossRef

Li, Z., Dai, J., Jiang, T., Li, S., Sun, Y., Liang, X., et al. (2006). Function magnetic resonance imaging and diffusion tensor tractography in patients with brain gliomas involving motor areas: clinical application and outcome. Zhonghua wai ke za zhi [Chinese journal of surgery], 44(18), 1275–1279.

Li, Y.-O., Adalı, T., & Calhoun, V. D. (2007). Estimating the number of independent components for functional magnetic resonance imaging data. Human Brain Mapping, 28(11), 1251–1266. doi:10.1002/hbm.20359.PubMedCrossRef

Liu, H., Buckner, R. L., Talukdar, T., Tanaka, N., Madsen, J. R., & Stufflebeam, S. M. (2009). Task-free presurgical mapping using functional magnetic resonance imaging intrinsic activity: laboratory investigation. Journal of Neurosurgery, 111(4), 746.PubMedPubMedCentralCrossRef

Logothetis, N. K. (2003). The underpinnings of the BOLD functional magnetic resonance imaging signal. The Journal of Neuroscience, 23(10), 3963–3971.PubMed

Lu, H., Zuo, Y., Gu, H., Waltz, J. A., Zhan, W., Scholl, C. A., et al. (2007). Synchronized delta oscillations correlate with the resting-state functional MRI signal. Proceedings of the National Academy of Sciences, 104(46), 18265–18269.CrossRef

Majos, A., Tybor, K., Stefańczyk, L., & Góraj, B. (2005). Cortical mapping by functional magnetic resonance imaging in patients with brain tumors. European Radiology, 15(6), 1148–1158.PubMedCrossRef

Manglore, S., Bharath, R. D., Panda, R., George, L., Thamodharan, A., & Gupta, A. K. (2013). Utility of resting fMRI and connectivity in patients with brain tumor. Neurology India, 61(2), 144–151. doi:10.4103/0028-3886.111120.PubMedCrossRef

Mannfolk, P., Nilsson, M., Hansson, H., Ståhlberg, F., Fransson, P., Weibull, A., et al. (2011). Can resting-state functional MRI serve as a complement to task-based mapping of sensorimotor function? A test–retest reliability study in healthy volunteers. Journal of Magnetic Resonance Imaging, 34(3), 511–517. doi:10.1002/jmri.22654.PubMedCrossRef

Matthews, P. M., Honey, G. D., & Bullmore, E. T. (2006). Applications of fMRI in translational medicine and clinical practice. Nature Reviews. Neuroscience, 7(9), 732–744.PubMedCrossRef

Moritz, C., & Haughton, V. (2003). Functional MR imaging: paradigms for clinical preoperative mapping. Magnetic Resonance Imaging Clinics of North America, 11(4), 529–542.PubMedCrossRef

Naidich, T. P., Valavanis, A. G., & Kubik, S. (1995). Anatomic relationships along the low-middle convexity: part I–normal specimens and magnetic resonance imaging. Neurosurgery, 36(3), 517–532.PubMedCrossRef

Naidich, T. P., Blum, J. T., & Firestone, M. I. (2001a). The parasagittal line: an anatomic landmark for axial imaging. American Journal of Neuroradiology, 22(5), 885–895.PubMed

Naidich, T. P., Hof, P. R., Yousry, T. A., & Yousry, I. (2001b). The motor cortex: anatomic substrates of function. Neuroimaging Clinics of North America, 11(2), 171–193.PubMed

Ohgaki, H., & Kleihues, P. (2005). Epidemiology and etiology of gliomas. Acta Neuropathologica, 109(1), 93–108. doi:10.1007/s00401-005-0991-y.PubMedCrossRef

Oldfield, R. C. (1971). The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia, 9(1), 97–113.PubMedCrossRef

Pillai, J. (2010). The evolution of clinical functional imaging during the past 2 decades and its current impact on neurosurgical planning. American Journal of Neuroradiology, 31(2), 219–225.PubMedCrossRef

Pillai, J. (2013). The significance of streamlined postprocessing approaches for clinical FMRI. American Journal of Neuroradiology, 34(6), 1194–1196.PubMedCrossRef

Price, C. J., & Friston, K. J. (1999). Scanning patients with tasks they can perform. Human Brain Mapping, 8(2–3), 102–108.PubMedCrossRef

Raichle, M. E., & Mintun, M. A. (2006). Brain work and brain imaging. Annual Review of Neuroscience, 29, 449–476.PubMedCrossRef

Rigolo, L., Stern, E., Deaver, P., Golby, A. J., & Mukundan, S. (2011). Development of a clinical functional magnetic resonance imaging service. Neurosurgery Clinics of North America, 22(2), 307–314.PubMedPubMedCentralCrossRef

Rössler, K., Donat, M., Lanzenberger, R., Novak, K., Geissler, A., Gartus, A., et al. (2005). Evaluation of preoperative high magnetic field motor functional MRI (3 tesla) in glioma patients by navigated electrocortical stimulation and postoperative outcome. Journal of Neurology, Neurosurgery, and Psychiatry, 76(8), 1152–1157.CrossRef

Roux, F.-E., Boulanouar, K., Lotterie, J.-A., Mejdoubi, M., LeSage, J. P., & Berry, I. (2003). Language functional magnetic resonance imaging in preoperative assessment of language areas: correlation with direct cortical stimulation. Neurosurgery, 52(6), 1335–1347.PubMedCrossRef

Schlosser, M. J., McCarthy, G., Fulbright, R. K., Gore, J. C., & Awad, I. A. (1997). Cerebral vascular malformations adjacent to sensorimotor and visual cortex functional magnetic resonance imaging studies before and after therapeutic intervention. Stroke, 28(6), 1130–1137.PubMedCrossRef

Schwartzbaum, J. A., Fisher, J. L., Aldape, K. D., & Wrensch, M. (2006). Epidemiology and molecular pathology of glioma. Nature Clinical Practice. Neurology, 2(9), 494–503.PubMedCrossRef

Shimony, J. S., Zhang, D., Johnston, J. M., Fox, M. D., Roy, A., & Leuthardt, E. C. (2009). Resting-state spontaneous fluctuations in brain activity: a new paradigm for presurgical planning using fMRI. Academic Radiology, 16(5), 578–583.PubMedPubMedCentralCrossRef

Shu, H., Cheng, Y., & Zhang, H. (1989). The naming consistency, familiarity, representation consistency and visual complexity of 235 pictures. Acta Psychologica Sinica, 21(4), 389–396.

Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., et al. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040–13045. doi:10.1073/pnas.0905267106.PubMedPubMedCentralCrossRef

Snodgrass, J. G., & Vanderwart, M. (1980). A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. Journal of Experimental Psychology: Human Learning and Memory, 6(2), 174.

Song, X. W., Dong, Z. Y., Long, X. Y., Li, S. F., Zuo, X. N., Zhu, C. Z., et al. (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PloS One, 6(9), e25031. doi:10.1371/journal.pone.0025031.PubMedPubMedCentralCrossRef

Tie, Y., Rigolo, L., Norton, I. H., Huang, R. Y., Wu, W., Orringer, D., et al. (2014). Defining language networks from resting-state fMRI for surgical planning—a feasibility study. Human Brain Mapping, 35(3), 1018–1030.PubMedCrossRef

Van Dijk, K. R., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010). Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of Neurophysiology, 103(1), 297–321.PubMedCrossRef

Vlieger, E.-J., Majoie, C. B., Leenstra, S., & den Heeten, G. J. (2004). Functional magnetic resonance imaging for neurosurgical planning in neurooncology. European Radiology, 14(7), 1143–1153.PubMedCrossRef

Wengenroth, M., Blatow, M., Guenther, J., Akbar, M., Tronnier, V., & Stippich, C. (2011). Diagnostic benefits of presurgical fMRI in patients with brain tumours in the primary sensorimotor cortex. European Radiology, 21(7), 1517–1525.PubMedPubMedCentralCrossRef

Yan, C., & Zang, Y. (2010). DPARSF: a MATLAB toolbox for” pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13.

Yetkin, F. Z., Mueller, W. M., Morris, G. L., McAuliffe, T. L., Ulmer, J. L., Cox, R. W., et al. (1997). Functional MR activation correlated with intraoperative cortical mapping. American Journal of Neuroradiology, 18(7), 1311–1315.PubMed

Zacà, D., Jovicich, J., Nadar, S. R., Voyvodic, J. T., & Pillai, J. J. (2014). Cerebrovascular reactivity mapping in patients with low grade gliomas undergoing presurgical sensorimotor mapping with BOLD fMRI. Journal of Magnetic Resonance Imaging, 40(2), 383–390.PubMedCrossRef

Zhang, D., & Raichle, M. E. (2010). Disease and the brain’s dark energy. Nature Reviews. Neurology, 6(1), 15–28.PubMedCrossRef

Zhang, D., Johnston, J. M., Fox, M. D., Leuthardt, E. C., Grubb, R. L., Chicoine, M. R., et al. (2009). Preoperative sensorimotor mapping in brain tumor patients using spontaneous fluctuations in neuronal activity imaged with fMRI: initial experience. Neurosurgery, 65(6 Suppl), 226.PubMedPubMedCentral

Zhang, H., Zuo, X. N., Ma, S. Y., Zang, Y. F., Milham, M. P., & Zhu, C. Z. (2010). Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis. NeuroImage, 51(4), 1414–1424. doi:10.1016/j.neuroimage.2010.03.039.PubMedCrossRef

Zhang, H., Jia, W., Liao, W., & Zang, Y. (2013). Automatic component identification method based on normalized sensitivity/specificity measurement. In Paper presented at the Oraganization of human brain mapping (OHBM). Seattle: USA.

Zhang, H., Lu, J., Mao, Y., Jia, W., Wu, J., & Zhou, L. (2014). Mapping language network pre- and intra-operatively using fMRI and electrophysiology: new method. In Paper presented at the Organization of Human Brain Mapping (OHBM). Germany: Berlin.

Zuo, X.-N., Di Martino, A., Kelly, C., Shehzad, Z. E., Gee, D. G., Klein, D. F., et al. (2010). The oscillating brain: complex and reliable. NeuroImage, 49(2), 1432–1445.PubMedCrossRef

Titel: PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI
verfasst von: Huiyuan Huang
Zhongxiang Ding
Dewang Mao
Jianhua Yuan
Fangmei Zhu
Shuda Chen
Yan Xu
Lin Lou
Xiaoyan Feng
Le Qi
Wusi Qiu
Han Zhang
Yu-Feng Zang
Publikationsdatum: 01.10.2016
Verlag: Springer US
Erschienen in: Neuroinformatics / Ausgabe 4/2016
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-016-9304-y

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