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Association between Motor Planning and the Frontoparietal Network in Children: An Exploratory Multimodal Study

Published online by Cambridge University Press:  22 October 2021

Ranila Bhoyroo Open the ORCID record for Ranila Bhoyroo [Opens in a new window] ,
Beth Hands ,
Karen Caeyenberghs ,
Alberto de Luca ,
Alexander Leemans ,
Adam Wigley  and
Christian Hyde
Ranila Bhoyroo*
Affiliation:
Institute for Health Research, School of Health Sciences, University of Notre Dame Australia, Perth, WA, Australia School of Psychology and Exercise Science, Murdoch University, Perth, WA, Australia Faculty of Health Sciences, Curtin School of Population Health, Curtin University, Perth, WA, Australia
Beth Hands
Affiliation:
Institute for Health Research, School of Health Sciences, University of Notre Dame Australia, Perth, WA, Australia
Karen Caeyenberghs
Affiliation:
Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
Alberto de Luca
Affiliation:
Image Sciences Institute, University Medical Utrecht, Utrecht, The Netherlands
Alexander Leemans
Affiliation:
Image Sciences Institute, University Medical Utrecht, Utrecht, The Netherlands
Adam Wigley
Affiliation:
Institute for Health Research, School of Health Sciences, University of Notre Dame Australia, Perth, WA, Australia
Christian Hyde
Affiliation:
Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
*
*Correspondence and reprint requests to: Dr Ranila Bhoyroo, Institute for Health Research, The University of Notre Dame Australia, 19 Mouat Street (PO Box 1225), Fremantle, WA, 6959, Australia. E-mail: [email protected]
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Abstract

Objective:

Evidence from adult literature shows the involvement of cortical grey matter areas of the frontoparietal lobe and the white matter bundle, the superior longitudinal fasciculus (SLF) in motor planning. This is yet to be confirmed in children.

Method:

A multimodal study was designed to probe the neurostructural basis of childhood motor planning. Behavioural (motor planning), magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) data were acquired from 19 boys aged 8–11 years. Motor planning was assessed using the one and two colour sequences of the octagon task. The MRI data were preprocessed and analysed using FreeSurfer 6.0. Cortical thickness and cortical surface area were extracted from the caudal middle frontal gyrus (MFG), superior frontal gyrus (SFG), precentral gyrus (PcG), supramarginal gyrus (SMG), superior parietal lobe (SPL) and the inferior parietal lobe (IPL) using the Desikan–Killiany atlas. The DWI data were preprocessed and analysed using ExploreDTI 4.8.6 and the white matter tract, the SLF was reconstructed.

Results:

Motor planning of the two colour sequence was associated with cortical thickness of the bilateral MFG and left SFG, PcG, IPL and SPL. The right SLF was related to motor planning for the two colour sequence as well as with the left cortical thickness of the SFG.

Conclusion:

Altogether, morphology within frontodorsal circuity, and the white matter bundles that support communication between them, may be associated with individual differences in childhood motor planning.

Keywords

Superior longitudinal fasciculusMagnetic resonance imagingDiffusion weighted imagingEnd-state-comfortFrontal lobeParietal lobe
Type
Research Article
Information
Journal of the International Neuropsychological Society , Volume 28 , Issue 9 , October 2022 , pp. 926 - 936
Copyright
Copyright © INS. Published by Cambridge University Press, 2021

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References

REFERENCES

Andersen, R. A., & Cui, H. (2009). Review: Intention, action planning, and decision making in parietal-frontal circuits. Neuron, 63, 568–583. doi: 10.1016/j.neuron.2009.08.028 CrossRefGoogle ScholarPubMed
Battaglia-Mayer, A., & Caminiti, R. (2018). Parieto-frontal networks for eye–hand coordination and movements. 151, 499–524. doi: 10.1016/B978-0-444-63622-5.00026-7 CrossRefGoogle Scholar
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x CrossRefGoogle Scholar
Beurze, S. M., de Lange, F. P., Toni, I., & Medendorp, W. P. (2007). Integration of target and effector information in the human brain during reach planning. Journal of Neurophysiology, 97(1), 188–199. doi: 10.1152/jn.00456.2006 CrossRefGoogle ScholarPubMed
Bhoyroo, R., Hands, B., Steenbergen, B., & Wigley, C. A. (2020). Examining complexity in grip selection tasks and consequent effects on planning for end-state-comfort in children with developmental coordination disorder: A systematic review and meta-analysis. Child Neuropsychology, 26(4), 534–559. doi: 10.1080/09297049.2019.1695768 CrossRefGoogle ScholarPubMed
Bhoyroo, R., Hands, B., Wilmut, K., Hyde, C., & Wigley, A. (2018). Investigating motor planning in children with DCD: Evidence from simple and complex grip-selection tasks. Human Movement Science, 61, 42–51. doi: 10.1016/j.humov.2018.07.006 CrossRefGoogle ScholarPubMed
Bhoyroo, R., Hands, B., Wilmut, K., Hyde, C., & Wigley, A. (2019). Motor planning with and without motor imagery in children with Developmental Coordination Disorder. Acta Psychologica, 199, 102902. doi: 10.1016/j.actpsy.2019.102902 CrossRefGoogle ScholarPubMed
Biotteau, M., Peran, P., Vayssiere, N., Tallet, J., Albaret, J. M., & Chaix, Y. (2017). Neural changes associated to procedural learning and automatization process in Developmental Coordination Disorder and/or Developmental Dyslexia. European Journal of Paediatric Neurology, 21(2), 286–299. doi: 10.1016/j.ejpn.2016.07.025 CrossRefGoogle ScholarPubMed
Budisavljevic, S., Dell’Acqua, F., Zanatto, D., Begliomini, C., Miotto, D., Motta, R., & Castiello, U. (2017). Asymmetry and structure of the fronto-parietal networks underlie visuomotor processing in humans. 27, 1532–1544. doi: 10.1093/cercor/bhv348 CrossRefGoogle Scholar
Chen, R., Gerloff, C., Hallett, M., & Cohen, L. G. (1997). Involvement of the ipsilateral motor cortex in finger movements of different complexities. Annals of neurology, 41(2), 247–254. doi: 10.1002/ana.410410216 CrossRefGoogle ScholarPubMed
Decety, J., & Grezes, J. (1999). Neural mechanisms subserving the perception of human actions. Trends in Cognitive Sciences, 3(5), 172–178. doi: 10.1016/S1364-6613(99)01312-1 CrossRefGoogle ScholarPubMed
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., … Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31, 968–980. doi: 10.1016/j.neuroimage.2006.01.021 CrossRefGoogle ScholarPubMed
Farquharson, S. & Tournier, J. D. (2016). High angular resolution diffusion imaging. In W. Van Hecke, L. Emsell, & S. Sunaert (Eds.), Diffusion tensor imaging, (pp. 383–406). New York, NY: Springer.Google Scholar
Farquharson, S., Tournier, J. D., Calamante, F., Fabinyi, G., Schneider-Kolsky, M., Jackson, G. D., & Connelly, A. (2013). White matter fiber tractography: Why we need to move beyond DTI. Journal of Neurosurgery, 118(6), 1367–1377. doi: 10.3171/2013.2.Jns121294 CrossRefGoogle ScholarPubMed
Fassbender, C., Murphy, K., Foxe, J. J., Wylie, G. R., Javitt, D. C., Robertson, I. H., & Garavan, H. (2004). A topography of executive functions and their interactions revealed by functional magnetic resonance imaging. Cognitive Brain Research, 20(2), 132–143. doi: 10.1016/j.cogbrainres.2004.02.007 CrossRefGoogle ScholarPubMed
Filimon, F. (2010). Human cortical control of hand movements: Parietofrontal networks for reaching, grasping, and pointing. The Neuroscientist, 16(4), 388–407. doi: 10.1177/1073858410375468 CrossRefGoogle ScholarPubMed
Floris, D. L., & Howells, H. (2018). Atypical structural and functional motor networks in autism. 238, 207–248. doi: 10.1016/bs.pbr.2018.06.010 CrossRefGoogle Scholar
Fuelscher, I., Caeyenberghs, K., Enticott, P. G., Williams, J., Lum, J., & Hyde, C. (2018). Differential activation of brain areas in children with developmental coordination disorder during tasks of manual dexterity: An ALE meta-analysis. Neuroscience and Biobehavioral Reviews, 86, 77–84. doi: 10.1016/j.neubiorev.2018.01.002 CrossRefGoogle ScholarPubMed
Fuelscher, I., Williams, J., Wilmut, K., Enticott, P. G., & Hyde, C. (2016). Modeling the maturation of grip selection planning and action representation: Insights from typical and atypical motor development. Frontiers in Psychology, 7(108). doi: 10.3389/fpsyg.2016.00108 CrossRefGoogle ScholarPubMed
Gilbert, S. J., & Burgess, P. W. (2008). Executive function. Current Biology, 18(3), R110–R114. doi: 10.1016/j.cub.2007.12.014 CrossRefGoogle ScholarPubMed
Hanakawa, T., Dimyan, M. A., & Hallett, M. (2008). Motor planning, imagery, and execution in the distributed motor network: A time-course study with functional MRI. Cerebral Cortex, 18(12), 2775–2788. doi: 10.1093/cercor/bhn036 CrossRefGoogle ScholarPubMed
Hecht, E. E., Gutman, D. A., Bradley, B. A., Preuss, T. M., & Stout, D. (2015). Virtual dissection and comparative connectivity of the superior longitudinal fasciculus in chimpanzees and humans. NeuroImage, 108, 124–137. doi: 10.1016/j.neuroimage.2014.12.039 CrossRefGoogle ScholarPubMed
Hétu, S., Grégoire, M., Saimpont, A., Coll, M.-P., Eugène, F., Michon, P.-E., & Jackson, P. L. (2013). The neural network of motor imagery: An ALE meta-analysis. Neuroscience and Biobehavioral Reviews, 37(5), 930–949. doi: 10.1016/j.neubiorev.2013.03.017 CrossRefGoogle ScholarPubMed
Hyde, C., Fuelscher, I., Enticott, P. G., Jones, D. K., Farquharson, S., Silk, T. J., … Caeyenberghs, K. (2019). White matter organization in developmental coordination disorder: A pilot study exploring the added value of constrained spherical deconvolution. NeuroImage: Clinical, 21, 101625. doi: 10.1016/j.nicl.2018.101625 CrossRefGoogle ScholarPubMed
Jeurissen, B., Leemans, A., Jones, D. K., Tournier, J.-D., & Sijbers, J. (2011). Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Human Brain Mapping, 32(3), 461–479. doi: 10.1002/hbm.21032 CrossRefGoogle ScholarPubMed
Jones, D. K., Knösche, T. R., & Turner, R. (2013). White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. NeuroImage, 73, 239–254. doi: 10.1016/j.neuroimage.2012.06.081 CrossRefGoogle ScholarPubMed
Johnson-Frey, S., McCarty, M., & Keen, R. (2004). Reaching beyond spatial perception: Effects of intended future actions on visually guided prehension. Visual Cognition, 11(2–3), 371–399. doi: 10.1080/13506280344000329 CrossRefGoogle Scholar
Kashiwagi, M., Iwaki, S., Narumi, Y., Tamai, H., & Suzuki, S. (2009). Parietal dysfunction in developmental coordination disorder: A functional MRI study. NeuroReport, 20(15), 1319–1324. doi: 10.1097/WNR.0b013e32832f4d87 CrossRefGoogle ScholarPubMed
Koch, G., Cercignani, M., Pecchioli, C., Versace, V., Oliveri, M., Caltagirone, C., … Bozzali, M. (2010). In vivo definition of parieto-motor connections involved in planning of grasping movements. NeuroImage, 51(1), 300–312. doi: 10.1016/j.neuroimage.2010.02.022 CrossRefGoogle ScholarPubMed
Langevin, L. M., Macmaster, F. P., Crawford, S., Lebel, C., & Dewey, D. (2014). Common white matter microstructure alterations in pediatric motor and attention disorders. The Journal of Pediatrics, 164(5), 1157–1164. doi: 10.1016/j.jpeds.2014.01.018 CrossRefGoogle ScholarPubMed
Langevin, L. M., MacMaster, F. P., & Dewey, D. (2015). Distinct patterns of cortical thinning in concurrent motor and attention disorders. Developmental Medicine & Child Neurology, 57(3), 257–264. doi: 10.1111/dmcn.12561 CrossRefGoogle ScholarPubMed
Leemans, A., Jeurissen, B., Sijbers, J., & Jones, D. (2009). ExploreDTI: A graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Paper presented at the 17th Annual Meeting of International Society of Magnetic Resonance Imaging.Google Scholar
Omata, K., Ito, S., Takata, Y., & Ouchi, Y. (2018). Similar neural correlates of planning and execution to inhibit continuing actions. 12(951). doi: 10.3389/fnins.2018.00951 CrossRefGoogle Scholar
Parlatini, V., Radua, J., Dell’Acqua, F., Leslie, A., Simmons, A., Murphy, D. G., … Thiebaut de Schotten, M. (2017). Functional segregation and integration within fronto-parietal networks. NeuroImage, 367. doi: 10.1016/j.neuroimage.2016.08.031 CrossRefGoogle ScholarPubMed
Raffelt, D., Tournier, J. D., Rose, S., Ridgway, G. R., Henderson, R., Crozier, S., … Connelly, A. (2012). Apparent fibre density: A novel measure for the analysis of diffusion-weighted magnetic resonance images. NeuroImage, 59(4), 3976–3994. doi: 10.1016/j.neuroimage.2011.10.045 CrossRefGoogle ScholarPubMed
Rosenbaum, D. A., Herbort, O., van der Wei, R., & Weiss, D. J. (2014). What’s in a grasp? Simple acts of picking up a water glass or turning a handle are the product of multilayered cognitive plans and sophisticated neural computations. American Scientist, 102(5), 366–373. doi: 10.1511/2014.110.366 Google Scholar
Rosenbaum, D. A., Vaughan, J., Barnes, H. J., & Jorgensen, M. J. (1992). Time course of movement planning: Selection of handgrips for object manipulation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(5), 1058–1073. doi: 10.1037/0278-7393.18.5.1058 Google ScholarPubMed
Shadmehr, R., & Krakauer, J. W. (2008). A computational neuroanatomy for motor control. Experimental Brain Research, 185(3), 359–381. doi: 10.1007/s00221-008-1280-5 CrossRefGoogle ScholarPubMed
Tax, C. M. W., Otte, W. M., Viergever, M. A., Dijkhuizen, R. M., & Leemans, A. (2015). REKINDLE: Robust extraction of kurtosis INDices with linear estimation. Magnetic Resonance in Medicine, 73(2), 794–808. doi: 10.1002/mrm.25165 CrossRefGoogle ScholarPubMed
Thiebaut de Schotten, M., Ffytche, D. H., Bizzi, A., Dell’Acqua, F., Allin, M., Walshe, M., … Catani, M. (2011). Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography. NeuroImage, 54(1), 49–59. doi: 10.1016/j.neuroimage.2010.07.055 CrossRefGoogle ScholarPubMed
Tournier, J. D., Yeh, C.-H., Calamante, F., Cho, K.-H., Connelly, A., & Lin, C.-P. (2008). Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data. NeuroImage, 42(2), 617–625. doi: 10.1016/j.neuroimage.2008.05.002 CrossRefGoogle ScholarPubMed
Tunik, E., Lo, O.-Y., & Adamovich, S. V. (2008). Transcranial magnetic stimulation to the frontal operculum and supramarginal gyrus disrupts planning of outcome-based hand–object interactions. The Journal of Neuroscience, 28(53), 14422. doi: 10.1523/JNEUROSCI.4734-08.2008 CrossRefGoogle Scholar
Urger, S. E., De Bellis, M. D., Hooper, S. R., Woolley, D. P., Chen, S. D., & Provenzale, J. (2015). The superior longitudinal fasciculus in typically developing children and adolescents: diffusion tensor imaging and neuropsychological correlates. Journal of Child Neurology, 30(1), 9–20. doi: 10.1177/0883073813520503 CrossRefGoogle ScholarPubMed
Verstynen, T., & Ivry, R. B. (2011). Network dynamics mediating ipsilateral motor cortex activity during unimanual actions. Journal of Cognitive Neuroscience, 23(9), 2468–2480. doi: 10.1162/jocn.2011.21612 CrossRefGoogle ScholarPubMed
Wagner, A. D., Maril, A., Bjork, R. A., & Schacter, D. L. (2001). Prefrontal contributions to executive control: fMRI evidence for functional distinctions within lateral Prefrontal cortex. NeuroImage, 14(6), 1337–1347. doi: 10.1006/nimg.2001.0936 CrossRefGoogle ScholarPubMed
Wakana, S., Caprihan, A., Panzenboeck, M. M., Fallon, J. H., Perry, M., Gollub, R. L., … Mori, S. (2007). Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage, 36(3), 630–644. doi: 10.1016/j.neuroimage.2007.02.049 CrossRefGoogle ScholarPubMed
Williams, J., Kashuk, S. R., Wilson, P. H., Thorpe, G., & Egan, G. F. (2017). White matter alterations in adults with probable developmental coordination disorder: An MRI diffusion tensor imaging study. NeuroReport, 28(2), 87–92. doi: 10.1097/WNR.0000000000000711 CrossRefGoogle ScholarPubMed
Wilmut, K., & Byrne, M. (2014a). Grip selection for sequential movements in children and adults with and without Developmental Coordination Disorder. Human Movement Science, 36, 272–284. doi: 10.1016/j.humov.2013.07.015 CrossRefGoogle ScholarPubMed
Wilmut, K., & Byrne, M. (2014b). Influences of grasp selection in typically developing children. Acta Psychologica, 148, 181–187. doi: 10.1016/j.actpsy.2014.02.005 CrossRefGoogle ScholarPubMed
Wolpert, D. M., & Flanagan, J. R. (2001). Motor prediction. Current Biology, 11(18), R729–732. doi: 10.1016/S0960-9822(01)00432-8 CrossRefGoogle ScholarPubMed
Wunsch, K., Henning, A., Aschersleben, G., & Weigelt, M. (2013). A systematic review of the end-state comfort effect in normally developing children and in children with developmental disorders. Journal of Motor Learning and Development, 1(3), 59–76. doi: 10.1123/jmld.1.3.59 CrossRefGoogle Scholar
Zimmermann, M., Meulenbroek, R. J. G., & de Lange, F. P. (2012). Motor planning is facilitated by adopting an action‘s goal posture: An fMRI study. Cerebral Cortex, (1), 122. doi: 10.1093/cercor/bhr098 CrossRefGoogle ScholarPubMed