Motor Cortical Representation of Hand Translation and Rotation during Reaching

Wang, Wei; Chan, Sherwin S; Heldman, Dustin A.; Moran, Daniel W.

doi:10.1523/jneurosci.3742-09.2010

Cited by 38 publications

(35 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiplicative neuronal integration, known as gain fields, was first reported for neurons in posterior parietal cortex (LIP and 7a) (Andersen and Mountcastle, 1983;Andersen et al, 1985) and later found in many other areas in the brain, including subcortical structures (Lal and Friedlander, 1990;Van Opstal et al, 1995;Boussaoud et al, 1998;Wang et al, 2010). It has been theorized that such gain field encoding is beneficial for sensorimotor transformation from multiple neuronal inputs (Pouget and Sejnowski, 1997;Salinas and Sejnowski, 2001).…”

Section: Gain Field Encoding In the Primitivesmentioning

confidence: 99%

Gain Field Encoding of the Kinematics of Both Arms in the Internal Model Enables Flexible Bimanual Action

Yokoi¹,

Hirashima²,

Nozaki³

2011

J. Neurosci.

View full text Add to dashboard Cite

Bimanual action requires the neural controller (internal model) for each arm to predictively compensate for mechanical interactions resulting from movement of both that arm and its counterpart on the opposite side of the body. Here, we demonstrate that the brain may accomplish this by constructing the internal model with primitives multiplicatively encoding information from the kinematics of both arms. We had human participants adapt to a novel force field imposed on one arm while both arms were moving in particular directions and examined the generalization pattern of motor learning when changing the movement directions of both arms. The generalization pattern was consistent with the pattern predicted from the multiplicative encoding scheme. As proposed by previous theoretical studies, the strength of multiplicative encoding was manifested in the observation that participants could adapt reaching movements to complicated force fields depending nonlinearly on the movement directions of both arms. These results indicate that multiplicative neuronal influence of the kinematics of the opposing arm on the internal models enables the brain to control bimanual movement by providing great flexible ability to handle arbitrary dynamical environments resulting from the interactions of both arms.

show abstract

Section: Gain Field Encoding In the Primitivesmentioning

confidence: 99%

Gain Field Encoding of the Kinematics of Both Arms in the Internal Model Enables Flexible Bimanual Action

Yokoi¹,

Hirashima²,

Nozaki³

2011

J. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Grasp parameters, such as preshape, aperture, wrist orientation, grasp force and feedback control, should be involved in sensorimotor cortex to obtain a flexible grasp movement. Besides the grip type discussed in this paper, how grasp force [1,30], wrist orientation [17,33] and grasp aperture [12] are encoded in the brain were also studied before. However, a combination of these grasp factors was rarely mentioned, which are pivotal for dexterous control of prosthesis in BMI applications.…”

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.

View full text Add to dashboard Cite

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

show abstract

“…Using similar techniques and after being presented with a 3D brain control task (note: the subject had never seen a 3D center-out task prior to the closed-loop experiments), the subject gained very fast and accurate control in a manner of weeks. Finally, the subject was presented with a 4D task [83] where it had to control both the 3D translation and 1D rotation of a computer cursor. In a little over a month, the subject gained accurate control over a 4D cursor.…”

Section: Brain-computer Interfacesmentioning

confidence: 99%

Proceedings of the Third International Workshop on Advances in Electrocorticography

Ritaccio

Beauchamp²,

Bosman

et al. 2012

Epilepsy & Behavior

View full text Add to dashboard Cite

The Third International Workshop on Advances in Electrocorticography (ECoG) was convened in Washington, DC, on November 10-11, 2011. As in prior meetings, a true multidisciplinary fusion of clinicians, scientists, and engineers from many disciplines gathered to summarize contemporary experiences in brain surface recordings. The proceedings of this meeting serve as evidence of a very robust and transformative field, but will yet again require revision for the advances that the following year will surely bring.

show abstract

Motor Cortical Representation of Hand Translation and Rotation during Reaching

Cited by 38 publications

References 17 publications

Gain Field Encoding of the Kinematics of Both Arms in the Internal Model Enables Flexible Bimanual Action

Gain Field Encoding of the Kinematics of Both Arms in the Internal Model Enables Flexible Bimanual Action

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

Proceedings of the Third International Workshop on Advances in Electrocorticography

Contact Info

Product

Resources

About