Differential contributions of the superior and inferior parietal cortex to feedback versus feedforward control of tools

Damage to the superior and/or inferior parietal lobules (SPL, IPL) (Sirigu et al., 1996) or cerebellum (Grealy and Lee, 2011) can selectively disrupt motor imagery, motivating the hypothesis that these regions participate in predictive (i.e., feedforward) control. If so, then the SPL, IPL, and cerebellum should show greater activity as the demands on feedforward control increase from visually-guided execution (closed-loop) to execution without visual feedback (open-loop) to motor imagery. Using fMRI and a Fitts' reciprocal aiming task with tools directed at targets in far space, we found that the SPL and cerebellum exhibited greater activity during closed-loop control. Conversely, open-loop and imagery conditions were associated with increased activity within the IPL and prefrontal areas. These results are consistent with a superior-to-inferior gradient in the representation of feedback-to-feedforward control within the posterior parietal cortex. Additionally, the anterior SPL displayed greater activity when aiming movements were performed with a stick vs. laser pointer. This may suggest that it is involved in the remapping of far into near (reachable) space (Maravita and Iriki, 2004), or in distalization of the end-effector from hand to stick (Arbib et al., 2009).

[1]  A. Sirigu,et al.  The Mental Representation of Hand Movements After Parietal Cortex Damage , 1996, Science.

[2]  Manuel Rodríguez,et al.  Disturbance of motor imagery after cerebellar stroke. , 2005, Behavioral neuroscience.

[3]  A. Berti,et al.  When Far Becomes Near: Remapping of Space by Tool Use , 2000, Journal of Cognitive Neuroscience.

[4]  Catalina Llanos,et al.  How similar are motor imagery and movement? , 2008, Behavioral neuroscience.

[5]  Scott T. Grafton,et al.  A distributed left hemisphere network active during planning of everyday tool use skills. , 2004, Cerebral cortex.

[6]  P. Fitts.  The information capacity of the human motor system in controlling the amplitude of movement. , 1954, Journal of experimental psychology.

[7]  D. Chaffin,et al.  An investigation of fitts' law using a wide range of movement amplitudes. , 1976, Journal of motor behavior.

[8]  Stella F. Lourenco,et al.  On the nature of near space: Effects of tool use and the transition to far space , 2006, Neuropsychologia.

[9]  A. Maravita,et al.  Tools for the body (schema) , 2004, Trends in Cognitive Sciences.

[10]  S. Blakemore,et al.  Action prediction in the cerebellum and in the parietal lobe , 2003, Experimental Brain Research.

[11]  S. Glover,et al.  Separate visual representations in the planning and control of action , 2004, Behavioral and Brain Sciences.

[12]  Keith J. Worsley,et al.  Statistical analysis of activation images , 2001 .

[13]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[14]  A. Fuchs,et al.  Cortical and cerebellar activity of the human brain during imagined and executed unimanual and bimanual action sequences: a functional MRI study. , 2003, Brain research. Cognitive brain research.

[15]  Jody C Culham,et al.  Is That within Reach? fMRI Reveals That the Human Superior Parieto-Occipital Cortex Encodes Objects Reachable by the Hand , 2009, The Journal of Neuroscience.

[16]  M. Walton,et al.  Action sets and decisions in the medial frontal cortex , 2004, Trends in Cognitive Sciences.

[17]  M. Jeannerod.  The representing brain: Neural correlates of motor intention and imagery , 1994, Behavioral and Brain Sciences.

[18]  T Landis,et al.  So near yet so far: Neglect in far or near space depends on tool use , 2001, Annals of neurology.

[19]  Masao Ito.  The Cerebellum And Neural Control , 1984 .

[20]  M. Arbib,et al.  Tool use and the distalization of the end-effector , 2009, Psychological research.

[21]  Rick Grush,et al.  The emulation theory of representation: Motor control, imagery, and perception , 2004, Behavioral and Brain Sciences.

[22]  Scott T Grafton,et al.  From 'acting on' to 'acting with': the functional anatomy of object-oriented action schemata. , 2003, Progress in brain research.

[23]  M. Tanaka,et al.  Coding of modified body schema during tool use by macaque postcentral neurones. , 1996, Neuroreport.

[24]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[25]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[26]  M. Hallett,et al.  Cerebral Processes Related to Visuomotor Imagery and Generation of Simple Finger Movements Studied with Positron Emission Tomography , 1998, NeuroImage.

[27]  Mitsuo Kawato,et al.  Cerebellar Activity Evoked By Common Tool-Use Execution And Imagery Tasks: An Fmri Study , 2007, Cortex.

[28]  P. Strick,et al.  Motor areas of the medial wall: a review of their location and functional activation. , 1996, Cerebral cortex.

[29]  A. Damasio,et al.  Neural Correlates of Naming Actions and of Naming Spatial Relations , 2001, NeuroImage.

[30]  K. R. Ridderinkhof,et al.  Neurocognitive mechanisms of cognitive control: The role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning , 2004, Brain and Cognition.

[31]  David N. Lee,et al.  An automatic-voluntary dissociation and mental imagery disturbance following a cerebellar lesion , 2011, Neuropsychologia.

[32]  Scott T Grafton,et al.  Telling the Right Hand From the Left Hand , 2012, Psychological science.

[33]  D. Wolpert,et al.  Maintaining internal representations: the role of the human superior parietal lobe , 1998, Nature Neuroscience.

[34]  Scott H. Frey.  Forecasting the Long-range Consequences of Manual and Tool Use Actions , 2010 .

[35]  Andrew B. Templeman,et al.  Non-Linear Optimisation in Civil Engineering , 1982 .

[36]  Leslie G. Ungerleider,et al.  Neural correlates of category-specific knowledge , 1996, Nature.

[37]  E. Todorov.  Optimality principles in sensorimotor control , 2004, Nature Neuroscience.

[38]  M. Preul.  The Human Brain: Surface, Blood Supply, and Three-Dimensional Sectional Anatomy , 2001 .

[39]  Hiroshi Imamizu,et al.  Human cerebellar activity reflecting an acquired internal model of a new tool , 2000, Nature.

[40]  D. Wolpert,et al.  Internal models in the cerebellum , 1998, Trends in Cognitive Sciences.

[41]  Mark Jenkinson,et al.  Fast, automated, N‐dimensional phase‐unwrapping algorithm , 2003, Magnetic resonance in medicine.

[42]  G. Rizzolatti,et al.  Two different streams form the dorsal visual system: anatomy and functions , 2003, Experimental Brain Research.

[43]  J. Krakauer,et al.  A computational neuroanatomy for motor control , 2008, Experimental Brain Research.

[44]  David C. Van Essen,et al.  Application of Information Technology: An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex , 2001, J. Am. Medical Informatics Assoc..

[45]  Hanna Damasio,et al.  Premotor and Prefrontal Correlates of Category-Related Lexical Retrieval , 1998, NeuroImage.

[46]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[47]  Stephen M. Smith,et al.  General multilevel linear modeling for group analysis in FMRI , 2003, NeuroImage.

[48]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[49]  Kristen L. Macuga,et al.  Neural representations involved in observed, imagined, and imitated actions are dissociable and hierarchically organized , 2012, NeuroImage.

[50]  Gregory Króliczak,et al.  A common network in the left cerebral hemisphere represents planning of tool use pantomimes and familiar intransitive gestures at the hand-independent level. , 2009, Cerebral cortex.

[51]  D. Wolpert,et al.  Motor prediction , 2001, Current Biology.

[52]  Scott T. Grafton,et al.  Forward modeling allows feedback control for fast reaching movements , 2000, Trends in Cognitive Sciences.

[53]  Kristen L. Macuga,et al.  Motor imagery of tool use: relationship to actual use and adherence to Fitts’ law across tasks , 2012, Experimental Brain Research.

[54]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[55]  Mark W. Woolrich,et al.  Multilevel linear modelling for FMRI group analysis using Bayesian inference , 2004, NeuroImage.

[56]  Maurizio Corbetta,et al.  Neurological Principles and Rehabilitation of Action Disorders , 2011, Neurorehabilitation and neural repair.

[57]  Scott T. Grafton,et al.  Motor task difficulty and brain activity: investigation of goal-directed reciprocal aiming using positron emission tomography. , 1997, Journal of neurophysiology.

[58]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[59]  R. D Seidler,et al.  Feedforward and feedback processes in motor control , 2004, NeuroImage.

[60]  Konrad Paul Kording,et al.  Review TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Bayesian decision theory in sensorimotor control , 2022 .

[61]  David C. Van Essen,et al.  A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex , 2005, NeuroImage.