Interactions between areas of the cortical grasping network

Davare, Marco; Kraskov, Alexander; Rothwell, John C.; Lemon, Roger

doi:10.1016/j.conb.2011.05.021

Cited by 186 publications

(150 citation statements)

References 52 publications

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“…The PPC has classically been linked to object orientated actions (Castiello, 2005;Grafton, 2010;Davare et al, 2011). While posteromedial regions of the intraparietal sulcus (IPS) contribute to the planning of reaching movements toward an object (Davare et al, 2012), anterolateral parts of the IPS integrate grasp-related information about the object (Tunik et al, 2005;Davare et al, 2007;Davare et al, 2010), with a gradient or interactions among these areas likely to reflect the degree of online control required by the action (Grol et al, 2007;Verhagen et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Stimulation of PPC Affects the Mapping between Motion and Force Signals for Stiffness Perception But Not Motion Control

et al. 2016

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How motion and sensory inputs are combined to assess an object's stiffness is still unknown. Here, we provide evidence for the existence of a stiffness estimator in the human posterior parietal cortex (PPC). We showed previously that delaying force feedback with respect to motion when interacting with an object caused participants to underestimate its stiffness. We found that applying theta-burst transcranial magnetic stimulation (TMS) over the PPC, but not the dorsal premotor cortex, enhances this effect without affecting movement control. We explain this enhancement as an additional lag in force signals. This is the first causal evidence that the PPC is not only involved in motion control, but also has an important role in perception that is disassociated from action. We provide a computational model suggesting that the PPC integrates position and force signals for perception of stiffness and that TMS alters the synchronization between the two signals causing lasting consequences on perceptual behavior.

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Section: Discussionmentioning

confidence: 99%

Stimulation of PPC Affects the Mapping between Motion and Force Signals for Stiffness Perception But Not Motion Control

et al. 2016

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“…This raises the question of whether rTMS to M1 in our task would also suppress the effect of the previous trial on digit placement. If supported, this would suggest that M1 is involved with the retrieval not only of digit force sensorimotor memories but also of a high-level representation of manipulation that combines digit forces and position built through previous hand-object interactions (Davare et al 2011).…”

Section: Neural Bases Of Action Planning In Response To Uncertainty Omentioning

confidence: 99%

Grasping uncertainty: effects of sensorimotor memories on high-level planning of dexterous manipulation

Lukos

Choi

Santello

2013

Journal of Neurophysiology

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Lukos JR, Choi JY, Santello M. Grasping uncertainty: effects of sensorimotor memories on high-level planning of dexterous manipulation. J Neurophysiol 109: 2937-2946, 2013. First published April 3, 2013 doi:10.1152/jn.00060.2013.-For successful object manipulation, the central nervous system must appropriately coordinate digit placement and force distribution. It is known that digit force planning is significantly influenced by previous manipulations even when object properties cannot be predicted on a trial-to-trial basis. We sought to determine whether this effect extends beyond force control to the coordination of digit placement and force. Subjects grasped and lifted an object whose center of mass (CM) was changed unpredictably across trials. Grasp planning was quantified by measuring the torque generated on the object at lift onset. We found that both digit placement and force were systematically affected by the CM experienced on the previous trial. Additionally, the negative covariation between digit forces and positions typically found for predictable CM presentations was also found for unpredictable CM trials. A follow-up experiment revealed that these effects were not dependent on visual feedback of object roll during object lift on the previous trial. We conclude that somatosensory feedback from previous grasp experience alone can affect high-level grasp planning by constraining the relation between digit force and position even when the task behavioral consequences cannot be reliably predicted. As learning of manipulations often involves interactions with objects in novel environments, the present findings are an important step to understanding the control strategies associated with the integration of sensorimotor memories and motor planning.

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“…These results have demonstrated that the movementrelated discharge of the PMd neurons is correlated with kinematic parameters such as reaching directions and amplitudes [27]. However, the contribution of the distal forelimb movements to PMd discharge was rarely reported.…”

Section: Resultsmentioning

confidence: 78%

“…Actually, PMd has a strong connection with other cortical areas in grasp cortical network, such as PMv, M1 and area V6A [31,32]. Compared with the classical dorsal lateral AIP-F5 grasp pathway, PMd is involved in a dorsal medial pathway, which contains area V6A, medial intraparietal area (MIP), mesial parietal areas (PEc and PGm) and PMd [9,27]. The high grasp type decoding accuracy using PMd neurons in our result also provided the evidence that PMd played an important role in grasp movement.…”

Section: Resultsmentioning

confidence: 99%

Decoding grasp movement from monkey premotor cortex for real-time prosthetic hand control

Hao

Zhang

et al. 2013

Chin. Sci. Bull.

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Brain machine interfaces (BMIs) have demonstrated lots of successful arm-related reach decoding in past decades, which provide a new hope for restoring the lost motor functions for the disabled. On the other hand, the more sophisticated hand grasp movement, which is more fundamental and crucial for daily life, was less referred. Current state of arts has specified some grasp related brain areas and offline decoding results; however, online decoding grasp movement and real-time neuroprosthetic control have not been systematically investigated. In this study, we obtained neural data from the dorsal premotor cortex (PMd) when monkey reaching and grasping one of four differently shaped objects following visual cues. The four grasp gesture types with an additional resting state were classified asynchronously using a fuzzy k-nearest neighbor model, and an artificial hand was controlled online using a shared control strategy. The results showed that most of the neurons in PMd are tuned by reach and grasp movement, using which we get a high average offline decoding accuracy of 97.1%. In the online demonstration, the instantaneous status of monkey grasping could be extracted successfully to control the artificial hand, with an event-wise accuracy of 85.1%. Overall, our results inspect the neural firing along the time course of grasp and for the first time enables asynchronous neural control of a prosthetic hand, which underline a feasible hand neural prosthesis in BMIs. The loss of the hand results in a serious reduction of the functional autonomy of a person in his daily living. Prosthesis is the most common way to restore the lost function, however, there are several barriers existing trough a successful prosthesis use, and the most important seems regarding the development of a reliable interface capable to decode the intention of the disabled to the prosthetic device. Brain machine interfaces (BMIs) provide a new hope for restoring motor functions of the severely disabled through controlling prostheses with intentional commands extracted from brain signals. Decoding motor cortex activities for robotic arm and screen cursor control in two or three dimensions has been examined successfully in human or non-human primates in past decades [1][2][3]. However, there are few investigations of movement decoding for restoring hand function, which is a great challenge with a higher degrees of freedom (DoFs). The hand is a marvelous example of how a complex biomechanism can be implemented, with effective combinations of mechanisms, sensing, actuation and cortical control system coordinated in 38 muscles and 22 DoFs [4]. With these complex architectures, recent studies have shown that how dexterous grasps are represented and transformed into motor commands in distinct brain regions. Several cortical

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Interactions between areas of the cortical grasping network

Cited by 186 publications

References 52 publications

Stimulation of PPC Affects the Mapping between Motion and Force Signals for Stiffness Perception But Not Motion Control

Stimulation of PPC Affects the Mapping between Motion and Force Signals for Stiffness Perception But Not Motion Control

Grasping uncertainty: effects of sensorimotor memories on high-level planning of dexterous manipulation

Decoding grasp movement from monkey premotor cortex for real-time prosthetic hand control

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