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Decoding Brain States for Planning Functional Grasps of Tools: A Functional Magnetic Resonance Imaging Multivoxel Pattern Analysis Study

Published online by Cambridge University Press:  10 September 2018

Mikolaj Buchwald ,
Łukasz Przybylski  and
Gregory Króliczak Open the ORCID record for Gregory Króliczak [Opens in a new window]
Mikolaj Buchwald*
Affiliation:
Action & Cognition Laboratory, Institute of Psychology, Faculty of Social Sciences, Adam Mickiewicz University in Poznań, Poland
Łukasz Przybylski
Affiliation:
Section of Logic and Cognitive Science, Institute of Psychology, Faculty of Social Sciences, Adam Mickiewicz University in Poznań, Poland
Gregory Króliczak
Affiliation:
Action & Cognition Laboratory, Institute of Psychology, Faculty of Social Sciences, Adam Mickiewicz University in Poznań, Poland
*
Correspondence and reprint requests to: Mikolaj Buchwald, Instytut Psychologii UAM, ul. Szamarzewskiego 89, 60-568 Poznań, Poland. E-mail: [email protected]
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Abstract

Objectives: We used multivoxel pattern analysis (MVPA) to investigate neural selectivity for grasp planning within the left-lateralized temporo-parieto-frontal network of areas (praxis representation network, PRN) typically associated with tool-related actions, as studied with traditional neuroimaging contrasts. Methods: We used data from 20 participants whose task was to plan functional grasps of tools, with either right or left hands. Region of interest and whole-brain searchlight analyses were performed to show task-related neural patterns. Results: MVPA revealed significant contributions to functional grasp planning from the anterior intraparietal sulcus (aIPS) and its immediate vicinities, supplemented by inputs from posterior subdivisions of IPS, and the ventral lateral occipital complex (vLOC). Moreover, greater local selectivity was demonstrated in areas near the superior parieto-occipital cortex and dorsal premotor cortex, putatively forming the dorso-dorsal stream. Conclusions: A contribution from aIPS, consistent with its role in prospective grasp formation and/or encoding of relevant tool properties (e.g., potential graspable parts), is likely to accompany the retrieval of manipulation and/or mechanical knowledge subserved by the supramarginal gyrus for achieving action goals. An involvement of vLOC indicates that MVPA is particularly sensitive to coding of object properties, their identities and even functions, for a support of grip formation. Finally, the engagement of the superior parieto-frontal regions as revealed by MVPA is consistent with their selectivity for transient features of tools (i.e., variable affordances) for anticipatory hand postures. These outcomes support the notion that, compared to traditional approaches, MVPA can reveal more fine-grained patterns of neural activity. (JINS, 2018, 24, 1013–1025)

Keywords

MVPATool useAffordancesMotor controlPraxisFunctional neuroimaging
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 24 , Issue 10 , November 2018 , pp. 1013 - 1025
Copyright
Copyright © The International Neuropsychological Society 2018 

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References

REFERENCES

Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics, 8, 14. doi: 10.3389/fninf.2014.00014 Google Scholar
Almeida, J., Fintzi, A.R., & Mahon, B.Z. (2013). Tool manipulation knowledge is retrieved by way of the ventral visual object processing pathway. Cortex, 49(9), 2334–2344. doi: 10.1016/j.cortex.2013.05.004 Google Scholar
Andersen, R.A., & Buneo, C.A. (2002). Intentional maps in posterior parietal cortex. Annual Review of Neuroscience, 25, 189–220. doi: 10.1146/annurev.neuro.25.112701.142922 Google Scholar
Andersen, R.A., Musallam, S., & Pesaran, B. (2004). Selecting the signals for a brain-machine interface. Current Opinion in Neurobiology, 14(6), 720–726. doi: 10.1016/j.conb.2004.10.005 Google Scholar
Bernier, P.M., Cieslak, M., & Grafton, S.T. (2012). Effector selection precedes reach planning in the dorsal parietofrontal cortex. Journal of Neurophysiology, 108(1), 57–68. doi: 10.1152/jn.00011.2012 Google 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 Google Scholar
Biduła, S.P., Przybylski, L., Pawlak, M.A., & Kroliczak, G. (2017). Unique neural characteristics of atypical lateralization of language in healthy individuals. Frontiers in Neuroscience, 11(525), 1–21. doi: 10.3389/fnins.2017.00525 Google Scholar
Binkofski, F., & Buxbaum, L.J. (2013). Two action systems in the human brain. Brain and Language, 127(2), 222–229. doi: 10.1016/j.bandl.2012.07.007 Google Scholar
Binkofski, F., Dohle, C., Posse, S., Stephan, K.M., Hefter, H., Seitz, R.J.,& Freund, H.J. (1998). Human anterior intraparietal area subserves prehension: A combined lesion and functional MRI activation study. Neurology, 50(5), 1253–1259. doi: 10.1212/WNL.50.5.1253 Google Scholar
Bracci, S., Cavina-Pratesi, C., Ietswaart, M., Caramazza, A., & Peelen, M.V. (2012). Closely overlapping responses to tools and hands in left lateral occipitotemporal cortex. Journal of Neurophysiology, 107(5), 1443–1456. doi: 10.1152/jn.00619.2011 Google Scholar
Bracci, S., Ritchie, J.B., & de Beeck, H.O. (2017). On the partnership between neural representations of object categories and visual features in the ventral visual pathway. Neuropsychologia, 105, 153–164. doi: 10.1016/j.neuropsychologia.2017.06.010 Google Scholar
Brandi, M.L., Wohlschlager, A., Sorg, C., & Hermsdorfer, J. (2014). The neural correlates of planning and executing actual tool use. The Journal of Neuroscience, 34(39), 13183–13194. doi: 10.1523/JNEUROSCI.0597-14.2014 Google Scholar
Buxbaum, L.J. (2001). Ideomotor apraxia: A call to action. Neurocase, 7(6), 445–458.Google Scholar
Buxbaum, L.J. (2017). Learning, remembering, and predicting how to use tools: Distributed neurocognitive mechanisms: Comment on Osiurak and Badets (2016). Psychological Review, 124(3), 346–360.Google Scholar
Buxbaum, L.J., & Kalenine, S. (2010). Action knowledge, visuomotor activation, and embodiment in the two action systems. Annals of the New York Academy of Sciences, 1191, 201–218. doi: 10.1111/j.1749-6632.2010.05447.x Google Scholar
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. doi: 10.1016/j.neuroimage.2006.06.054 Google Scholar
Cavina-Pratesi, C., Goodale, M.A., & Culham, J.C. (2007). FMRI reveals a dissociation between grasping and perceiving the size of real 3D objects. PLoS One, 2, e424. doi: 10.1371/journal.pone.0000424 Google Scholar
Chao, L.L., & Martin, A. (1999). Cortical regions associated with perceiving, naming, and knowing about colors. Journal of Cognitive Neuroscience, 11(1), 25–35.Google Scholar
Chen, J., Snow, J.C., Culham, J.C., & Goodale, M.A. (2018). What role does “elongation” play in “tool-specific” activation and connectivity in the dorsal and ventral visual streams? Cerebral Cortex, 28(4), 1117–1131. doi: 10.1093/cercor/bhx017 Google Scholar
Choi, H.J., Zilles, K., Mohlberg, H., Schleicher, A., Fink, G.R., Armstrong, E.,& Amunts, K. (2006). Cytoarchitectonic identification and probabilistic mapping of two distinct areas within the anterior ventral bank of the human intraparietal sulcus. Journal of Comparative Neurology, 495(1), 53–69. doi: 10.1002/cne.20849 Google Scholar
Cross, E.S., Hamilton, A.F.C., Cohen, N.R., & Grafton, S.T. (2017). Learning to tie the knot: The acquisition of functional object representations by physical and observational experience. PLoS One, 12(10), e0185044. doi: 10.1371/journal.pone.0185044 Google Scholar
Dassonville, P., Zhu, X.H., Uurbil, K., Kim, S.G., & Ashe, J. (1997). Functional activation in motor cortex reflects the direction and the degree of handedness. Proceedings of the National Academy of Sciences of the United States of America, 94(25), 14015–14018.Google Scholar
Drager, B., Jansen, A., Bruchmann, S., Forster, A.F., Pleger, B., Zwitserlood, P.,& Knecht, S. (2004). How does the brain accommodate to increased task difficulty in word finding? A functional MRI study. NeuroImage, 23(3), 1152–1160. doi: 10.1016/j.neuroimage.2004.07.005 Google Scholar
Durand, J.B., Peeters, R., Norman, J.F., Todd, J.T., & Orban, G.A. (2009). Parietal regions processing visual 3D shape extracted from disparity. NeuroImage, 46(4), 1114–1126. doi: 10.1016/j.neuroimage.2009.03.023 Google Scholar
Elsinger, C.L., Harrington, D.L., & Rao, S.M. (2006). From preparation to online control: Reappraisal of neural circuitry mediating internally generated and externally guided actions. NeuroImage, 31(3), 1177–1187. doi: 10.1016/j.neuroimage.2006.01.041 Google Scholar
Etzel, J.A. (2017). MVPA significance testing when just above chance, and related properties of permutation tests. 2017 International Workshop on Pattern Recognition in Neuroimaging . PRNI 2017, 49, 1–4. doi: 10.1109/PRNI.2017.7981498 Google Scholar
Fabbri, S., Stubbs, K.M., Cusack, R., & Culham, J.C. (2016). Disentangling representations of object and grasp properties in the human brain. The Journal of Neuroscience, 36(29), 7648–7662. doi: 10.1523/JNEUROSCI.0313-16.2016 Google Scholar
Freud, E., Macdonald, S.N., Chen, J., Quinlan, D.J., Goodale, M.A., & Culham, J.C. (2018). Getting a grip on reality: Grasping movements directed to real objects and images rely on dissociable neural representations. Cortex, 98, 34–48. doi: 10.1016/j.cortex.2017.02.020 Google Scholar
Frey, S.H. (2007). What puts the how in where? Tool use and the divided visual streams hypothesis. Cortex, 43(3), 368–375. doi: 10.1016/S0010-9452(08)70462-3 Google Scholar
Frey, S.H. (2008). Tool use, communicative gesture and cerebral asymmetries in the modern human brain. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363(1499), 1951–1957. doi: 10.1098/rstb.2008.0008 Google Scholar
Gallivan, J.P., Cant, J.S., Goodale, M.A., & Flanagan, J.R. (2014). Representation of object weight in human ventral visual cortex. Current Biology, 24(16), 1866–1873. doi: 10.1016/j.cub.2014.06.046 Google Scholar
Gallivan, J.P., Cavina-Pratesi, C., & Culham, J.C. (2009). Is that within reach? fMRI reveals that the human superior parieto-occipital cortex encodes objects reachable by the hand. The Journal of Neuroscience, 29(14), 4381–4391. doi: 10.1523/JNEUROSCI.0377-09.2009 Google Scholar
Gallivan, J.P., & Culham, J.C. (2015). Neural coding within human brain areas involved in actions. Current Opinion in Neurobiology, 33, 141–149. doi: 10.1016/j.conb.2015.03.012 Google Scholar
Gallivan, J.P., McLean, D.A., Valyear, K.F., & Culham, J.C. (2013). Decoding the neural mechanisms of human tool use. Elife, 2, e00425. doi: 10.7554/eLife.00425 Google Scholar
Gallivan, J.P., McLean, D.A., Valyear, K.F., Pettypiece, C.E., & Culham, J.C. (2011). Decoding action intentions from preparatory brain activity in human parieto-frontal networks. The Journal of Neuroscience, 31(26), 9599–9610. doi: 10.1523/JNEUROSCI.0080-11.2011 Google Scholar
Garcea, F.E., Kristensen, S., Almeida, J., & Mahon, B.Z. (2016). Resilience to the contralateral visual field bias as a window into object representations. Cortex, 81, 14–23. doi: 10.1016/j.cortex.2016.04.006 Google Scholar
Garcea, F.E., & Mahon, B.Z. (2014). Parcellation of left parietal tool representations by functional connectivity. Neuropsychologia, 60, 131–143. doi: 10.1016/j.neuropsychologia.2014.05.018 Google Scholar
Gertz, H., Lingnau, A., & Fiehler, K. (2017). Decoding movement goals from the fronto-parietal reach network. Frontiers in Human Neuroscience, 11, 84. doi: 10.3389/fnhum.2017.00084 Google Scholar
Gibson, J.J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing. Toward an ecological psychology (pp. 67–82). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Glasser, M.F., Coalson, T.S., Robinson, E.C., Hacker, C.D., Harwell, J., Yacoub, E.,& Van Essen, D.C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178. doi: 10.1038/nature18933 Google Scholar
Goldenberg, G. (2017). Facets of pantomime. Journal of the International Neuropsychological Society, 23(2), 121–127. doi: 10.1017/S1355617716000989 Google Scholar
Goldenberg, G., & Hagmann, S. (1998). Tool use and mechanical problem solving in apraxia. Neuropsychologia, 36(7), 581–589.Google Scholar
Goodale, M.A., Jakobson, L.S., & Keillor, J.M. (1994). Differences in the visual control of pantomimed and natural grasping movements. Neuropsychologia, 32(10), 1159–1178. doi: 10.1016/0028-3932(94)90100-7 Google Scholar
Goodale, M.A., Kroliczak, G., & Westwood, D.A. (2005). Dual routes to action: Contributions of the dorsal and ventral streams to adaptive behavior. Progress in Brain Research, 149, 269–283. doi: 10.1016/S0079-6123(05)49019-6 Google Scholar
Goodale, M.A., & Milner, A.D. (1992). Separate visual pathways for perception and action. Trends in Neuroscience, 15(1), 20–25. doi: 10.1016/0166-2236(92)90344-8 Google Scholar
Grill-Spector, K., & Weiner, K.S. (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature Reviews. Neuroscience, 15(8), 536–548. doi: 10.1038/nrn3747 Google Scholar
Hamilton, A.F., & Grafton, S.T. (2008). Action outcomes are represented in human inferior frontoparietal cortex. Cerebral Cortex, 18(5), 1160–1168.Google Scholar
Haxby, J.V., Connolly, A.C., & Guntupalli, J.S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual Review of Neuroscience, 37, 435–456. doi: 10.1146/annurev-neuro-062012-170325 Google Scholar
Haynes, J.D., & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews. Neuroscience, 7(7), 523–534. doi: 10.1038/nrn1931 Google Scholar
Hermsdorfer, J., Terlinden, G., Muhlau, M., Goldenberg, G., & Wohlschlager, A.M. (2007). Neural representations of pantomimed and actual tool use: Evidence from an event-related fMRI study. NeuroImage, 36(Suppl. 2), T109–T118. doi: 10.1016/j.neuroimage.2007.03.037 Google Scholar
Hutchison, R.M., Culham, J.C., Flanagan, J.R., Everling, S., & Gallivan, J.P. (2015). Functional subdivisions of medial parieto-occipital cortex in humans and nonhuman primates using resting-state fMRI. NeuroImage, 116, 10–29. doi: 10.1016/j.neuroimage.2015.04.068 Google Scholar
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., & Smith, S.M. (2012).Fsl. NeuroImage, 62(2), 782–790. doi: 10.1016/j.neuroimage.2011.09.015 Google Scholar
Johnson-Frey, S.H., Newman-Norlund, R., & Grafton, S.T. (2005). A distributed left hemisphere network active during planning of everyday tool use skills. Cerebral Cortex, 15(6), 681–695. doi: 10.1093/cercor/bhh169 Google Scholar
Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103(10), 3863–3868. doi: 10.1073/pnas.0600244103 Google Scholar
Kristensen, S., Garcea, F.E., Mahon, B.Z., & Almeida, J. (2016). Temporal frequency tuning reveals interactions between the dorsal and ventral visual streams. Journal of Cognitive Neuroscience, 28(9), 1295–1302. doi: 10.1162/jocn_a_00969 Google Scholar
Kroliczak, G., Buchwald, M., Potok, W., & Przybylski, L. (2018). Handedness, praxis and language: A tricky triad revisited. Polish Psychological Forum [Polskie Forum Psychologiczne], 23(1), 22–34. doi: 10.14656/PFP20180102 Google Scholar
Kroliczak, G., Cavina-Pratesi, C., Goodman, D.A., & Culham, J.C. (2007). What does the brain do when you fake it? An FMRI study of pantomimed and real grasping. Journal of Neurophysiology, 97(3), 2410–2422. doi: 10.1152/jn.00778.2006 Google Scholar
Kroliczak, G., & Frey, S.H. (2009). A common network in the left cerebral hemisphere represents planning of tool use pantomimes and familiar intransitive gestures at the hand-independent level. Cerebral Cortex, 19(10), 2396–2410. doi: 10.1093/cercor/bhn261 Google Scholar
Kroliczak, G., McAdam, T.D., Quinlan, D.J., & Culham, J.C. (2008). The human dorsal stream adapts to real actions and 3D shape processing: A functional magnetic resonance imaging study. Journal of Neurophysiology, 100(5), 2627–2639. doi: 10.1152/jn.01376.2007 Google Scholar
Kroliczak, G., Piper, B.J., & Frey, S.H. (2016). Specialization of the left supramarginal gyrus for hand-independent praxis representation is not related to hand dominance. Neuropsychologia, 93, 501–512. doi: 10.1016/j.neuropsychologia.2016.03.023 Google Scholar
Kroliczak, G., Westwood, D.A., & Goodale, M.A. (2006). Differential effects of advance semantic cues on grasping, naming, and manual estimation. Experimental Brain Research, 175(1), 139–152. doi: 10.1007/s00221-006-0524-5 Google Scholar
Kubiak, A., & Kroliczak, G. (2016). Left extrastriate body area is sensitive to the meaning of symbolic gesture: Evidence from fMRI repetition suppression. Scientific Reports, 6, 31064. doi: 10.1038/srep31064 Google Scholar
Lesourd, M., Budriesi, C., Osiurak, F., Nichelli, P.F., & Bartolo, A. (2017). Mechanical knowledge does matter to tool use even when assessed with a non-production task: Evidence from left brain-damaged patients. Journal of Neuropsychology (doi: 10.1111/jnp.12140 Google Scholar
Lesourd, M., Osiurak, F., Navarro, J., & Reynaud, E. (2017). Involvement of the left supramarginal gyrus in manipulation judgment tasks: Contributions to theories of tool use. Journal of the International Neuropsychological Society, 23(8), 685–691. doi: 10.1017/S1355617717000455 Google Scholar
Mahon, B.Z., Kumar, N., & Almeida, J. (2013). Spatial frequency tuning reveals interactions between the dorsal and ventral visual systems. Journal of Cognitive Neuroscience, 25(6), 862–871.Google Scholar
Makoshi, Z., Kroliczak, G., & van Donkelaar, P. (2011). Human supplementary motor area contribution to predictive motor planning. Journal of Motor Behavior, 43(4), 303–309. doi: 10.1080/00222895.2011.584085 Google Scholar
Marangon, M., Kubiak, A., & Kroliczak, G. (2016). Haptically guided grasping. fMRI shows right-hemisphere parietal stimulus encoding, and bilateral dorso-ventral parietal gradients of object- and action-related processing during grasp execution. Frontiers in Human Neuroscience, 9, 691. doi: 10.3389/fnhum.2015.00691 Google Scholar
Marcus, D.S., Harwell, J., Olsen, T., Hodge, M., Glasser, M.F., Prior, F.,& Van Essen, D.C. (2011). Informatics and data mining tools and strategies for the human connectome project. Frontiers in Neuroinformatics, 5, 4. doi: 10.3389/fninf.2011.00004 Google Scholar
McDowell, T., Holmes, N.P., Sunderland, A., & Schurmann, M. (2018). TMS over the supramarginal gyrus delays selection of appropriate grasp orientation during reaching and grasping tools for use. Cortex, 103, 117–129. doi: 10.1016/j.cortex.2018.03.002 Google Scholar
Miezin, F.M., Maccotta, L., Ollinger, J.M., Petersen, S.E., & Buckner, R.L. (2000). Characterizing the hemodynamic response: Effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. NeuroImage, 11(6), 735–759. doi: 10.1006/nimg.2000.0568 Google Scholar
Milner, A.D., & Goodale, M.A. (2008). Two visual systems re-viewed. Neuropsychologia, 46(3), 774–785. doi: 10.1016/j.neuropsychologia.2007.10.005 Google Scholar
Misaki, M., Kim, Y., Bandettini, P.A., & Kriegeskorte, N. (2010). Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage, 53(1), 103–118. doi: 10.1016/j.neuroimage.2010.05.051 Google Scholar
Mizelle, J.C., Kelly, R.L., & Wheaton, L.A. (2013). Ventral encoding of functional affordances: A neural pathway for identifying errors in action. Brain and Cognition, 82(3), 274–282. doi: 10.1016/j.bandc.2013.05.002 Google Scholar
Mumford, J.A., Turner, B.O., Ashby, F.G., & Poldrack, R.A. (2012). Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage, 59(3), 2636–2643. doi: 10.1016/j.neuroimage.2011.08.076 Google Scholar
Nastase, S.A., Connolly, A.C., Oosterhof, N.N., Halchenko, Y.O., Guntupalli, J.S., Visconti di Oleggio Castello, M., & Haxby, J.V. (2017). Attention selectively reshapes the geometry of distributed semantic representation. Cerebral Cortex, 27(8), 4277–4291. doi: 10.1093/cercor/bhx138 Google Scholar
Norman, K.A., Polyn, S.M., Detre, G.J., & Haxby, J.V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Science, 10(9), 424–430. doi: 10.1016/j.tics.2006.07.005 Google Scholar
Oldfield, R.C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9, 97–113. doi: 10.1016/0028-3932(71)90067-4 Google Scholar
Oliphant, T.E. (2007). Python for scientific computing. Computing in Science & Engineering, 9(3), 10–20. doi: 10.1109/MCSE.2007.58 Google Scholar
Oosterhof, N.N., Wiggett, A.J., Diedrichsen, J., Tipper, S.P., & Downing, P.E. (2010). Surface-based information mapping reveals crossmodal vision-action representations in human parietal and occipitotemporal cortex. Journal of Neurophysiology, 104(2), 1077–1089. doi: 10.1152/jn.00326.2010 Google Scholar
Orban, G.A. (2016). Functional definitions of parietal areas in human and non-human primates. Proceedings Biological Sciences, 283(1828 doi: 10.1098/rspb.2016.0118 Google Scholar
Orban, G.A., & Caruana, F. (2014). The neural basis of human tool use. Frontiers in Psychology, 5, 310. doi: 10.3389/fpsyg.2014.00310 Google Scholar
Orban, G.A., Van Essen, D., & Vanduffel, W. (2004). Comparative mapping of higher visual areas in monkeys and humans. Trends in Cognitive Sciences, 8(7), 315–324. doi: 10.1016/j.tics.2004.05.009 Google Scholar
Osiurak, F., & Badets, A. (2016). Tool use and affordance: Manipulation-based versus reasoning-based approaches. Psychological Review, 123(5), 534–568. doi: 10.1037/rev0000027 Google Scholar
Osiurak, F., Jarry, C., Allain, P., Aubin, G., Etcharry-Bouyx, F., Richard, I.,& Le Gall, D. (2009). Unusual use of objects after unilateral brain damage: The technical reasoning model. Cortex, 45(6), 769–783. doi: 10.1016/j.cortex.2008.06.013 Google Scholar
Osiurak, F., Jarry, C., Lesourd, M., Baumard, J., & Le Gall, D. (2013). Mechanical problem-solving strategies in left-brain damaged patients and apraxia of tool use. Neuropsychologia, 51(10), 1964–1972. doi: 10.1016/j.neuropsychologia.2013.06.017 Google Scholar
Osiurak, F., Rossetti, Y., & Badets, A. (2017). What is an affordance? 40 years later. Neuroscience and Biobehavioral Reviews, 77, 403–417. doi: 10.1016/j.neubiorev.2017.04.014 Google Scholar
Peelen, M.V., Bracci, S., Lu, X., He, C., Caramazza, A., & Bi, Y. (2013). Tool selectivity in left occipitotemporal cortex develops without vision. Journal of Cognitive Neuroscience, 25(8), 1225–1234. doi: 10.1162/jocn_a_00411 Google Scholar
Peeters, R.R., Rizzolatti, G., & Orban, G.A. (2013). Functional properties of the left parietal tool use region. NeuroImage, 78, 83–93. doi: 10.1016/j.neuroimage.2013.04.023 Google Scholar
Picard, N., & Strick, P.L. (2003). Activation of the supplementary motor area (SMA) during performance of visually guided movements. Cerebral Cortex, 13(9), 977–986.Google Scholar
Przybylski, L., & Kroliczak, G. (2017). Planning functional grasps of simple tools invokes the hand-independent praxis representation network: An fMRI study. Journal of the International Neuropsychological Society, 23(2), 108–120. doi: 10.1017/S1355617716001120 Google Scholar
Quadflieg, S., Etzel, J.A., Gazzola, V., Keysers, C., Schubert, T.W., Waiter, G.D.,& Macrae, C.N. (2011). Puddles, parties, and professors: Linking word categorization to neural patterns of visuospatial coding. Journal of Cognitive Neuroscience, 23(10), 2636–2649. doi: 10.1162/jocn.2011.21628 Google Scholar
Reynaud, E., Lesourd, M., Navarro, J., & Osiurak, F. (2016). On the neurocognitive origins of human tool use: A critical review of neuroimaging data. Neuroscience and Biobehavioral Reviews, 64, 421–437. doi: 10.1016/j.neubiorev.2016.03.009 Google Scholar
Rizzolatti, G., & Matelli, M. (2003). Two different streams form the dorsal visual system: Anatomy and functions. Experimental Brain Research, 153(2), 146–157. doi: 10.1007/s00221-003-1588-0 Google Scholar
Rossit, S., Malhotra, P., Muir, K., Reeves, I., Duncan, G., & Harvey, M. (2011). The role of right temporal lobe structures in off-line action: Evidence from lesion-behavior mapping in stroke patients. Cerebral Cortex, 21(12), 2751–2761. doi: 10.1093/cercor/bhr073 Google Scholar
Rossit, S., McAdam, T., McLean, D.A., Goodale, M.A., & Culham, J.C. (2013). fMRI reveals a lower visual field preference for hand actions in human superior parieto-occipital cortex (SPOC) and precuneus. Cortex, 49(9), 2525–2541. doi: 10.1016/j.cortex.2012.12.014 Google Scholar
Sakreida, K., Effnert, I., Thill, S., Menz, M.M., Jirak, D., Eickhoff, C.R.,& Binkofski, F. (2016). Affordance processing in segregated parieto-frontal dorsal stream sub-pathways. Neuroscience and Biobehavioral Reviews, 69, 89–112. doi: 10.1016/j.neubiorev.2016.07.032 Google Scholar
Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and Statistical Modeling with Python. Proceedings of the 9th Python in Science Conference, (Scipy), 57–61. Retrieved from http://conference.scipy.org/proceedings/scipy2010/seabold.html.Google Scholar
Shay, E.A., Chen, Q., Garcea, F.E., & Mahon, B.Z. (2018). Decoding intransitive actions in primary motor cortex using fMRI: Toward a componential theory of ‘action primitives’ in motor cortex. Cognitive Neuroscience, 1–7. doi: 10.1080/17588928.2018.1453491 Google Scholar
Striem-Amit, E., Vannuscorps, G., & Caramazza, A. (2017). Sensorimotor-independent development of hands and tools selectivity in the visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 114(18), 4787–4792. doi: 10.1073/pnas.1620289114 Google Scholar
Valyear, K.F., Gallivan, J.P., McLean, D.A., & Culham, J.C. (2012). fMRI repetition suppression for familiar but not arbitrary actions with tools. Journal of Neuroscience, 32(12), 4247–4259. doi: 10.1523/JNEUROSCI.5270-11.2012 Google Scholar
Van Essen, D.C., Drury, H.A., Dickson, J., Harwell, J., Hanlon, D., & Anderson, C.H. (2001). An integrated software suite for surface-based analyses of cerebral cortex. Journal of the American Medical Informatics Association, 8(5), 443–459. doi: 10.1136/jamia.2001.0080443 Google Scholar
Vingerhoets, G. (2014). Contribution of the posterior parietal cortex in reaching, grasping, and using objects and tools. Frontiers in Psychology, 5, 151. doi: 10.3389/fpsyg.2014.00151 Google Scholar
Vingerhoets, G., & Clauwaert, A. (2015). Functional connectivity associated with hand shape generation: Imitating novel hand postures and pantomiming tool grips challenge different nodes of a shared neural network. Human Brain Mapping, 36(9), 3426–3440. doi: 10.1002/hbm.22853 Google Scholar
Vingerhoets, G., Nys, J., Honore, P., Vandekerckhove, E., & Vandemaele, P. (2013). Human left ventral premotor cortex mediates matching of hand posture to object use. PLoS One, 8(7), e70480. doi: 10.1371/journal.pone.0070480 Google Scholar
Vingerhoets, G., Vandekerckhove, E., Honore, P., Vandemaele, P., & Achten, E. (2011). Neural correlates of pantomiming familiar and unfamiliar tools: Action semantics versus mechanical problem solving? Human Brain Mapping, 32(6), 905–918. doi: 10.1002/hbm.21078 Google Scholar
Wiestler, T., & Diedrichsen, J. (2013). Skill learning strengthens cortical representations of motor sequences. Elife, 2, e00801. doi: 10.7554/eLife.00801 Google Scholar
Wurm, M.F., & Lingnau, A. (2015). Decoding actions at different levels of abstraction. The Journal of Neuroscience, 35(20), 7727–7735. doi: 10.1523/JNEUROSCI.0188-15.2015 Google Scholar