Poster No:
1702
Submission Type:
Abstract Submission
Authors:
Raffaele Costanzo1, Michelangelo Tani2, Krishnendu Vyas2, Federica Bencivenga3, Sabrina Pitzalis4, Gaspare Galati2
Institutions:
1University of Rome "Foro Italico", Lusciano, Caserta (CE), 2“Sapienza” University of Rome, Rome, Rome, 3University of Montréal, QC, Canada, Canada, Canada, 4Fondazione Santa Lucia, Roma, RM
First Author:
Co-Author(s):
Introduction:
The brain dynamically and flexibly controls and selects actions based on a competition process (Cisek & Kalaska, 2010). Hand reaching to grasp is allowed by the interaction between three pathways: the dorsal reaching system (Caminiti et al., 2017), involved in fast reaching and real-time control; the lateral reaching system, involved in continuous visuospatial analysis; and the lateral grasping system, involved in purposeful hand actions (Castiello & Begliomini, 2009; Borra and Luppino, 2018). In real life, we often have to dynamically adjust our motor plans when acting on an object, for example when trying to grasp a moving target. Here we aimed to study how the reaching-to-grasp systems interact when dynamically updating a grasping motor plan.
Methods:
We recorded fMRI data from 24 volunteers who were required to execute a 'precision' or 'power' grasp of a bottle in a Virtual Reality (VR) scenario. Arm, hand, and finger movements were tracked online using the MOTUM system, consisting of IR cameras, a haptic glove, VR software, and a binocular display, allowing real-time visual feedback of the performed grasping movement. Participants kept their tracked right hand in a rest position. In each trial, either of two virtual LEDs placed on the bottle's neck and body lit on to instruct a precision or power grasp, respectively. In one-third of trials, the other led lit on after the participant started the arm movement, requiring an online adjustment of the motor plan both in terms of the final location to be reached (bottle's neck or body) and in terms of the final requested hand shape (precision or power grip). In separate blocks, the participants were asked only to observe a similar grasping movement or to rest.
Images were acquired on a Siemens Prisma (TR=0.8s, TE=0.03s, 2.4 mm isotropic voxels, 60 slices). Grasping trials were compared to a rest baseline. Data were preprocessed using fMRIprep and analyzed with SPM12 with a conventional univariate random-effects analysis (p < 0.05 cluster FDR, cluster-forming threshold p < 0.001 uncorrected).
Results:
Reaching-to-grasp movements, as expected, activated the hand area in left (contralateral) M1, the bilateral cerebellum, and extensive portions of the posterior parietal and premotor cortex, including the superior parietal lobule (SPL), the anterior intraparietal sulcus (aIPS), the dorsal and ventral premotor cortex (dPMC, vPMC), and the supplementary motor area (SMA). Grasping trials requiring online correction, when compared to regular grasping trials, showed a significant increase of BOLD signal in the M1 left hand area, in the bilateral dorsal-lateral reaching system (SPL, dPMC, SMA), and in the bilateral cerebellum, but apparently not in the lateral grasping system (aIPS, vPMC). Interestingly, posterior to aIPS a bilateral region in the supramarginal gyrus (SMG), wider in the right (ipsilateral) hemisphere, was selectively recruited in correction trials.
Conclusions:
This study shows that regions of reach-to-grasp pathways, together with the cerebellum, are involved during the online target-updating of reaching-to-grasp actions. The dorso-lateral reaching system seems more directly involved than the lateral grasping system in dynamically reprogramming actions, compatibly with monkey studies linking the dorso-medial regions of the parietal cortex with the visual online control of movement. It's however possible that our experimental setup emphasized the adjustment of the reaching component of the action with respect to hand re-shaping. An unexpected, selective response was observed for the adjustment trials in the SMG, a region which has been associated to the recognition of errors and incongruency and the response to unexpected events.
Motor Behavior:
Motor Planning and Execution 1
Visuo-Motor Functions 2
Motor Behavior Other
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Perception, Attention and Motor Behavior:
Perception: Multisensory and Crossmodal
Keywords:
ADULTS
Experimental Design
FUNCTIONAL MRI
Motor
MRI
Systems
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
Other, Please list
-
fMRIprep
Provide references using APA citation style.
- Borra, E., & Luppino, G. (2019). Large-scale temporo–parieto–frontal networks for motor and cognitive motor functions in the primate brain. Cortex, 118, 19-37.
- Caminiti, R., Borra, E., Visco-Comandini, F., Battaglia-Mayer, A., Averbeck, B. B., & Luppino, G. (2017). Computational architecture of the parieto-frontal network underlying cognitive-motor control in monkeys. eneuro, 4(1).
- Castiello, U., & Begliomini, C. (2008). The cortical control of visually guided grasping. The Neuroscientist, 14(2), 157-170.
- Cisek, P., & Kalaska, J. F. (2010). Neural mechanisms for interacting with a world full of action choices. Annual review of neuroscience, 33(1), 269-298.
No