Functional Magnetic Resonance Imaging Signal Modelling and Contrasts: an Example of Manual Praxis Tasks
Poznan Supercomputing and Networking Center
ul. Jana Pawła II 10, 61-139 Poznań, PolandE-mail: mikolaj.buchwald@man.poznan.pl
Additional materials:
Supplementary materials – fMRI signal modelling on the example of praxis tasks.zip
Received:
Received: 2 December 2021; revised: 14 December 2021; accepted: 16 December 2021; published online: 29 December 2021
DOI: 10.12921/cmst.2021.0000033
Abstract:
The goal of neuroscience as a discipline is to understand how the neural system is organized in the brain, giving rise to mental processes and the control of behavior. One of the most frequently utilized methods in neuroscientific studies is the functional magnetic resonance imaging (fMRI), which is a non-invasive technique for quantifying brain processes dynamics. In a standard fMRI procedure, the hypothesis of the correlation between a cognitive task and the observed physiological signal is tested. This way, a certain computational model of a given brain mechanism can be validated. The procedure of modelling fMRI signal time course will be explained in this article as exemplified by planning functional grasps of tools. Subsequently, the results of contrasting model parameter estimates will be presented for a different experiment on manual praxis skills, i.e., bimanual tool grasps and manipulations.
Key words:
References:
[1] S. Ogawa, T.M. Lee, A.R. Kay, D.W. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation, Proc. Natl. Acad. Sci. U. S. A. 87(24), 9868–9872 (1990).
[2] M.A. Lindquist, J. Meng Loh, L.Y. Atlas, T.D. Wager, Modeling the hemodynamic response function in fMRI: efficiency, bias and mis-modeling, Neuroimage 45(1), S187–S198 (2009).
[3] M.F. Glasser, S.M. Smith, D.S. Marcus, J.L.R. Andersson, E.J. Auerbach, T.E.J. Behrens, T.S. Coalson, M.P. Harms, M. Jenkinson, S. Moeller, E.C. Robinson, S.N. Sotiropoulos, J. Xu, E. Yacoub, K. Ugurbil, D.C. Van Essen, The Human Connectome Project’s neuroimaging approach, Nat. Neurosci. 19(9), 1175–1187 (2016).
[4] D.S. Marcus, J. Harwell, T. Olsen, M. Hodge, M.F. Glasser, F. Prior, M. Jenkinson, T. Laumann, S.W. Curtiss, D.C. Van Essen, Informatics and Data Mining Tools and Strategies for the Human Connectome Project, Front. Neuroinform. 5, 1–12 (2011).
[5] L.E. Suárez, R.D. Markello, R.F. Betzel, B. Misic, Linking Structure and Function in Macroscale Brain Networks, Trends Cogn. Sci. 24(4), 302–315 (2020).
[6] A. Gramfort, M. Luessi, E. Larson, D.A. Engemann, D. Strohmeier, C. Brodbeck, R. Goj, M. Jas, T. Brooks, L. Parkkonen, M. Hämäläinen, MEG and EEG data analysis with MNE-Python, Front. Neurosci. 7, 1–13 (2013).
[7] S. Tak, J.C. Ye, Statistical analysis of fNIRS data: A comprehensive review, Neuroimage 85, 72–91 (2014).
[8] S. Na, J.J. Russin, L. Lin, X. Yuan, P. Hu, K.B. Jann, L. Yan, K. Maslov, J. Shi, D.J. Wang, C.Y. Liu, L.V. Wang, Massively parallel functional photoacoustic computed tomography of the human brain, Nat. Biomed. Eng. (2021).
[9] K.E. Bouchard, J.B. Aimone, M. Chun, T. Dean, M. Denker, M. Diesmann, D.D. Donofrio, L.M. Frank, N. Kasthuri, C. Koch, O. Ruebel, H.D. Simon, F.T. Sommer, Prabhat, High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination, Neuron 92(3), 628–631 (2016).
[10] A.S. Shatil, S. Younas, H. Pourreza, C.R. Figley, Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing, Magn. Reson. Insights 8, MRI.S23558 (2015).
[11] K.E. Bouchard, J.B. Aimone, M. Chun, T. Dean, M. Denker, M. Diesmann, D.D. Donofrio, L.M. Frank, N. Kasthuri, C. Koch, O. Rübel, H.D. Simon, F.T. Sommer, Prabhat, International Neuroscience Initiatives through the Lens of High-Performance Computing, Computer (Long. Beach. Calif). 51(4), 50–59 (2018).
[12] M. Kumar, C.T. Ellis, Q. Lu, H. Zhang, M. Capota, T.L. Willke, P.J. Ramadge, N.B. Turk-Browne, K.A. Norman, BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis, PLoS Comput. Biol. 16(1), e1007549 (2020).
[13] K. Amunts, T. Lippert, Brain research challenges supercomputing, Science 374(6571), 1054–1055 (2021).
[14] A. Tahmassebi, A.H. Gandomi, A. Meyer-Bäse, High Performance GP-Based Approach for fMRI Big Data Classification, Proc. Pract. Exp. Adv. Res. Comput. 2017 Sustain. Success Impact – PEARC17, 1–4 (2017).
[15] H. Markram, E. Muller, S. Ramaswamy et al., Reconstruction and Simulation of Neocortical Microcircuitry, Cell 163(2), 456–492 (2015).
[16] Ł. Przybylski, G. Kroliczak, Planning Functional Grasps of Simple Tools Invokes the Hand-independent Praxis Representation Network: An fMRI Study, J. Int. Neuropsychol. Soc. 23(2), 108–120 (2017).
[17] G. Kroliczak, M. Buchwald, P. Kleka, M. Klichowski, W. Potok, A.M. Nowik, J. Randerath, B.J. Piper, Manual praxis and language-production networks, and their links to handedness, Cortex 140, 110–127 (2021).
[18] S.H. Johnson-Frey, R. Newman-Norlund, S.T. Grafton, A distributed left hemisphere network active during planning of everyday tool use skills, Cereb. Cortex 15(6), 681–695 (2005).
[19] G.A. Orban, S. Ferri, fMRI Techniques and Protocols, 41(1) (2009).
[20] J.H. Zar, Biostatistical Analysis: Data transformation, Pearson Prentice Hall, USA (1996).
[21] R.A. Johnson, I. Miller, J.E. Freund, Probability and statistics for engineers, Pearson Education London (2000).
[22] M. Buchwald, Neural substrates underlying planning interactions with bimanual tools: a functional magnetic resonance imaging study, PhD Thesis, Adam Mickiewicz University in Poznan´, Poland (2021).
[23] M. Jenkinson, C.F. Beckmann, T.E.J. Behrens, M.W. Woolrich, S.M. Smith, FSL, Neuroimage 62(2), 782–790 (2012).
[24] M.W. Woolrich, B.D. Ripley, M. Brady, S.M. Smith, Temporal autocorrelation in univariate linear modeling of FMRI data, Neuroimage 14(6), 1370–1386 (2001).
[25] M. Buchwald, Ł. Przybylski, G. Kroliczak, Decoding Brain States for Planning Functional Grasps of Tools: A Functional Magnetic Resonance Imaging Multivoxel Pattern Analysis Study, J. Int. Neuropsychol. Soc. 24(10), 1013–1025 (2018).
[26] S. Anzellotti, M.N. Coutanche, Beyond Functional Connectivity: Investigating Networks of Multivariate Representations, Trends Cogn. Sci. 22(3), 258–269 (2018).
[27] J.V. Haxby, A.C. Connolly, J.S. Guntupalli, Decoding Neural Representational Spaces Using Multivariate Pattern Analysis, Annu. Rev. Neurosci. 37(1), 435–456 (2014).
[28] W.D. Penny, K.J. Friston, J.T. Ashburner, S.J. Kiebel, T.E. Nichols, Statistical parametric mapping: the analysis of functional brain images, Elsevier (2011).
[29] K. Gorgolewski, C.D. Burns, C. Madison, D. Clark, Y.O. Halchenko, M.L. Waskom, S.S. Ghosh, Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python, Front. Neuroinform. 5 (2011).
[30] A. Abraham, F. Pedregosa, M. Eickenberg, P. Gervais, A. Mueller, J. Kossaifi, A. Gramfort, B. Thirion, G. Varoquaux, Machine Learning for Neuroimaging with Scikit-Learn, Front. Neuroinform. 8(14), 1–15 (2014).
[31] R. Botvinik-Nezer, F. Holzmeister, C.F. Camerer et al., Variability in the analysis of a single neuroimaging dataset by many teams, Nature 582(7810), 84–88 (2020).
The goal of neuroscience as a discipline is to understand how the neural system is organized in the brain, giving rise to mental processes and the control of behavior. One of the most frequently utilized methods in neuroscientific studies is the functional magnetic resonance imaging (fMRI), which is a non-invasive technique for quantifying brain processes dynamics. In a standard fMRI procedure, the hypothesis of the correlation between a cognitive task and the observed physiological signal is tested. This way, a certain computational model of a given brain mechanism can be validated. The procedure of modelling fMRI signal time course will be explained in this article as exemplified by planning functional grasps of tools. Subsequently, the results of contrasting model parameter estimates will be presented for a different experiment on manual praxis skills, i.e., bimanual tool grasps and manipulations.
Key words:
References:
[1] S. Ogawa, T.M. Lee, A.R. Kay, D.W. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation, Proc. Natl. Acad. Sci. U. S. A. 87(24), 9868–9872 (1990).
[2] M.A. Lindquist, J. Meng Loh, L.Y. Atlas, T.D. Wager, Modeling the hemodynamic response function in fMRI: efficiency, bias and mis-modeling, Neuroimage 45(1), S187–S198 (2009).
[3] M.F. Glasser, S.M. Smith, D.S. Marcus, J.L.R. Andersson, E.J. Auerbach, T.E.J. Behrens, T.S. Coalson, M.P. Harms, M. Jenkinson, S. Moeller, E.C. Robinson, S.N. Sotiropoulos, J. Xu, E. Yacoub, K. Ugurbil, D.C. Van Essen, The Human Connectome Project’s neuroimaging approach, Nat. Neurosci. 19(9), 1175–1187 (2016).
[4] D.S. Marcus, J. Harwell, T. Olsen, M. Hodge, M.F. Glasser, F. Prior, M. Jenkinson, T. Laumann, S.W. Curtiss, D.C. Van Essen, Informatics and Data Mining Tools and Strategies for the Human Connectome Project, Front. Neuroinform. 5, 1–12 (2011).
[5] L.E. Suárez, R.D. Markello, R.F. Betzel, B. Misic, Linking Structure and Function in Macroscale Brain Networks, Trends Cogn. Sci. 24(4), 302–315 (2020).
[6] A. Gramfort, M. Luessi, E. Larson, D.A. Engemann, D. Strohmeier, C. Brodbeck, R. Goj, M. Jas, T. Brooks, L. Parkkonen, M. Hämäläinen, MEG and EEG data analysis with MNE-Python, Front. Neurosci. 7, 1–13 (2013).
[7] S. Tak, J.C. Ye, Statistical analysis of fNIRS data: A comprehensive review, Neuroimage 85, 72–91 (2014).
[8] S. Na, J.J. Russin, L. Lin, X. Yuan, P. Hu, K.B. Jann, L. Yan, K. Maslov, J. Shi, D.J. Wang, C.Y. Liu, L.V. Wang, Massively parallel functional photoacoustic computed tomography of the human brain, Nat. Biomed. Eng. (2021).
[9] K.E. Bouchard, J.B. Aimone, M. Chun, T. Dean, M. Denker, M. Diesmann, D.D. Donofrio, L.M. Frank, N. Kasthuri, C. Koch, O. Ruebel, H.D. Simon, F.T. Sommer, Prabhat, High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination, Neuron 92(3), 628–631 (2016).
[10] A.S. Shatil, S. Younas, H. Pourreza, C.R. Figley, Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing, Magn. Reson. Insights 8, MRI.S23558 (2015).
[11] K.E. Bouchard, J.B. Aimone, M. Chun, T. Dean, M. Denker, M. Diesmann, D.D. Donofrio, L.M. Frank, N. Kasthuri, C. Koch, O. Rübel, H.D. Simon, F.T. Sommer, Prabhat, International Neuroscience Initiatives through the Lens of High-Performance Computing, Computer (Long. Beach. Calif). 51(4), 50–59 (2018).
[12] M. Kumar, C.T. Ellis, Q. Lu, H. Zhang, M. Capota, T.L. Willke, P.J. Ramadge, N.B. Turk-Browne, K.A. Norman, BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis, PLoS Comput. Biol. 16(1), e1007549 (2020).
[13] K. Amunts, T. Lippert, Brain research challenges supercomputing, Science 374(6571), 1054–1055 (2021).
[14] A. Tahmassebi, A.H. Gandomi, A. Meyer-Bäse, High Performance GP-Based Approach for fMRI Big Data Classification, Proc. Pract. Exp. Adv. Res. Comput. 2017 Sustain. Success Impact – PEARC17, 1–4 (2017).
[15] H. Markram, E. Muller, S. Ramaswamy et al., Reconstruction and Simulation of Neocortical Microcircuitry, Cell 163(2), 456–492 (2015).
[16] Ł. Przybylski, G. Kroliczak, Planning Functional Grasps of Simple Tools Invokes the Hand-independent Praxis Representation Network: An fMRI Study, J. Int. Neuropsychol. Soc. 23(2), 108–120 (2017).
[17] G. Kroliczak, M. Buchwald, P. Kleka, M. Klichowski, W. Potok, A.M. Nowik, J. Randerath, B.J. Piper, Manual praxis and language-production networks, and their links to handedness, Cortex 140, 110–127 (2021).
[18] S.H. Johnson-Frey, R. Newman-Norlund, S.T. Grafton, A distributed left hemisphere network active during planning of everyday tool use skills, Cereb. Cortex 15(6), 681–695 (2005).
[19] G.A. Orban, S. Ferri, fMRI Techniques and Protocols, 41(1) (2009).
[20] J.H. Zar, Biostatistical Analysis: Data transformation, Pearson Prentice Hall, USA (1996).
[21] R.A. Johnson, I. Miller, J.E. Freund, Probability and statistics for engineers, Pearson Education London (2000).
[22] M. Buchwald, Neural substrates underlying planning interactions with bimanual tools: a functional magnetic resonance imaging study, PhD Thesis, Adam Mickiewicz University in Poznan´, Poland (2021).
[23] M. Jenkinson, C.F. Beckmann, T.E.J. Behrens, M.W. Woolrich, S.M. Smith, FSL, Neuroimage 62(2), 782–790 (2012).
[24] M.W. Woolrich, B.D. Ripley, M. Brady, S.M. Smith, Temporal autocorrelation in univariate linear modeling of FMRI data, Neuroimage 14(6), 1370–1386 (2001).
[25] M. Buchwald, Ł. Przybylski, G. Kroliczak, Decoding Brain States for Planning Functional Grasps of Tools: A Functional Magnetic Resonance Imaging Multivoxel Pattern Analysis Study, J. Int. Neuropsychol. Soc. 24(10), 1013–1025 (2018).
[26] S. Anzellotti, M.N. Coutanche, Beyond Functional Connectivity: Investigating Networks of Multivariate Representations, Trends Cogn. Sci. 22(3), 258–269 (2018).
[27] J.V. Haxby, A.C. Connolly, J.S. Guntupalli, Decoding Neural Representational Spaces Using Multivariate Pattern Analysis, Annu. Rev. Neurosci. 37(1), 435–456 (2014).
[28] W.D. Penny, K.J. Friston, J.T. Ashburner, S.J. Kiebel, T.E. Nichols, Statistical parametric mapping: the analysis of functional brain images, Elsevier (2011).
[29] K. Gorgolewski, C.D. Burns, C. Madison, D. Clark, Y.O. Halchenko, M.L. Waskom, S.S. Ghosh, Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python, Front. Neuroinform. 5 (2011).
[30] A. Abraham, F. Pedregosa, M. Eickenberg, P. Gervais, A. Mueller, J. Kossaifi, A. Gramfort, B. Thirion, G. Varoquaux, Machine Learning for Neuroimaging with Scikit-Learn, Front. Neuroinform. 8(14), 1–15 (2014).
[31] R. Botvinik-Nezer, F. Holzmeister, C.F. Camerer et al., Variability in the analysis of a single neuroimaging dataset by many teams, Nature 582(7810), 84–88 (2020).
