Dexterous Finger Movements Research Articles

Decoding Dexterous Finger Movements in a Neural Prosthesis Model Approaching Real-World Conditions

Dexterous finger movements can be decoded from neuronal action potentials acquired from a nonhuman primate using a chronically implanted Utah Electrode Array. We have developed an algorithm that can, after training, detect and classify individual and combined finger movements without any a priori knowledge of the data, task, or behavior. The algorithm is based on changes in the firing rates of individual neurons that are tuned for one or more finger movement types. Nine different movement types, which consisted of individual flexions, individual extensions, and combined flexions of the thumb, index finger, and middle finger, were decoded. The algorithm performed reliably on data recorded continuously during movement tasks, including a no-movement state, with an overall average sensitivity and specificity that were both > 92%. These results demonstrate a viable algorithm for decoding dexterous finger movements under conditions similar to those required for a real-world neural prosthetic application.

1P1-Q10 装着型ピアノ演奏教示装置の研究・開発 : 手首・手指駆動機構の設計・製作(アミューズメント・エンタテイナーロボット)

This paper proposes a wearable teaching system for finger's dexterous movement to play the piano. The teaching system is an exoskeleton type mounted on a hand and a wrist part of a piano player and then pushes his fingers to tap one or multiple key. Exoskeleton parts of fingers and wrist are activated by fifteen DC motors. A player can have an experience with more skillful and dexterous finger movements than what he has. As a preliminary test, simple tapping of five fingers are introduced in this paper.

Projections of the hand field of the macaque ventral premotor area F5 to the brainstem and spinal cord

In the present study we first assessed that the hand motor field of the macaque ventral premotor area F5, involved in visuomotor control of hand actions, is connected to both the hand field of the primary motor cortex (M1) and the spinal cord. We then injected retroanterograde tracers in this field to completely illustrate its possible descending motor projections. In the brainstem the F5 hand motor field projects to the intermediate and deep layers of the superior colliculus (SC) and to sectors of the mesencephalic, pontine, and bulbar reticular formation, which are the sources of spinal projections. In the spinal cord, labeled terminals were virtually all confined to the C2-T1 segments, mostly contralaterally. At C6-T1 levels the labeling was weaker and mostly clustered laterally in the intermediate zone. At C2-C5 levels, labeled terminals were much denser and diffusely distributed over the mid-dorsal part of the intermediate zone where a propriospinal system that directly controls hand muscle motoneurons and mediates commands for the control of dexterous finger movements is located (Isa et al. [2007] Physiology 22:145-152). Thus, the F5 hand motor field has a weaker direct access and a stronger indirect access to spinal segments where hand muscle motoneurons are located, suggesting a role of this field in the generation and control of hand movements not only at the M1 level, but also at the spinal cord level. These projections may represent the neural substrate for the F5 hand motor field's role in the recovery of manual dexterity after M1 lesions.

Correction to "Asynchronous Decoding of Dexterous Finger Movements Using M1 Neurons" [Feb 08 3-14

In the above titled paper (ibid., vol. 16, no. 1, pp. 3-14, Feb 08), there were a few errors. Corrections are presented here.

A common subcortical oscillatory network contributes to recovery after spinal cord injury

AbstractRecent studies in monkeys showed that when the direct cortico-motoneuronal connection was transected at mid-cervical segments, remaining, indirect cortico-motoneuronal pathways compensated for finger dexterity within one to three months. To elucidate the changes in dynamic properties of neural circuits during the recovery, we investigated the cortico-muscular and inter-muscular couplings of activities throughout the recovery course. Activities of antagonist muscle pairs showed co-activation during the second postoperative week, and oscillated coherently at frequencies of 30-46 Hz (gamma-band) by one month postoperatively. Such gamma-band inter-muscular coherence was not observed preoperatively, but became prominent and distributed widely over proximal and distal muscles with the recovery. Neither the gamma-band cortico-muscular coupling (14-30 Hz) observed before lesion, nor a gamma-band oscillation was observed in bilateral motor cortex after lesion. Thus, we propose that an unknown, subcortical oscillator, independent of cortical oscillation, commonly recruits hand/arm muscles and may underlie functional recovery of dexterous finger movements.

Asynchronous Decoding of Dexterous Finger Movements Using M1 Neurons

Previous efforts in brain-machine interfaces (BMI) have looked at decoding movement intent or hand and arm trajectory, but current cortical control strategies have not focused on the decoding of dexterous [corrected] actions such as finger movements. The present work demonstrates the asynchronous decoding (i.e., where cues indicating the onset of movement are not known) of individual and combined finger movements. Single-unit activities were recorded sequentially from a population of neurons in the M1 hand area of trained rhesus monkeys during flexion and extension movements of each finger and the wrist. Nonlinear filters were designed to detect the onset of movement and decode the movement type from randomly selected neuronal ensembles (assembled from individually recorded single-unit activities). Average asynchronous decoding accuracies as high as 99.8%, 96.2%, and 90.5%, were achieved for individuated finger and wrist movements with three monkeys. Average decoding accuracy was still 92.5% when combined movements of two fingers were included. These results demonstrate that it is possible to asynchronously decode dexterous finger movements from a neuronal ensemble with high accuracy. This work takes an important step towards the development of a BMI for direct neural control of a state-of-the-art, multifingered hand prosthesis.

Decoding Individuated Finger Movements Using Volume-Constrained Neuronal Ensembles in the M1 Hand Area

Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were simulated by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial neural network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p < 0.01) in decoding accuracy as a function of the location of the recording volume. The results suggest that a brain-machine interface (BMI) for dexterous control of individuated fingers and the wrist can be implemented using microelectrode arrays placed broadly in the M1 hand area.

Skilled digit movements in feline and primate – recovery after selective spinal cord lesions

Recovery of voluntary movements after partial spinal cord injury depends, in part, on a take-over of function via unlesioned pathways. Using precise forelimb movements in the cat as model, spinal pathways contributing to motor restitution have been investigated in more detail. The food-taking movement by which the cat graSPS a morsel of food with the digits and brings it to the mouth is governed by interneurones in the forelimb segments (C6-Th1) and is normally controlled via the cortico- and rubrospinal tracts. Food-taking disappears after transection of these pathways in the dorsal part of the lateral funiculus (DLF) in C5/C6, but then recovers during a period of 2-3 weeks. Experiments with double lesions showed that the recovery depends on a take-over via ipsilateral ventral systems; a ventrally descending pathway, most probably cortico-reticulospinal, and a pathway via propriospinal neurones in the C3-C4 segments. It is postulated that the recovery involves a plastic reorganization of these systems. Dexterous finger movements in the macaque monkey are generally considered to depend on the monosynaptic cortico-motoneuronal (CM) connexion, which is lacking in the cat. Such movements are abolished after pyramidotomy at the level of the trapezoid body. However, experiments with transection of the corticospinal tract in the DLF and partly ventral part of the lateral funiculus in C5, showed a fast (1-28 days) recovery of precision grip and, to some extent, independent finger movements. Deficits in preshaping during the final approach to the morsel as well as lack of force were observed. A C5 DLF lesion spares corticofugal pathways to the brainstem and upper cervical segments. It is suggested that indirect corticomotoneuronal pathways may provide for recovery of dexterous finger movements and that the role of CM pathways for such movements should be broadened to include not only the monosynaptic connexion.

Direct and Indirect Cortico-Motoneuronal Pathways and Control of Hand/Arm Movements

Recent studies from our group have demonstrated the existence of a disynaptic excitatory cortico-motoneuronal (CM) pathway in macaque monkeys via propriospinal neurons in the midcervical segments. Results from behavioral studies with lesion of the direct pathway suggest that the indirect CM pathway can mediate the command for dexterous finger movements.

Modular Organization of Finger Movements by the Human Central Nervous System

The motor system may generate automated movements, such as walking, by combining modular spinal motor synergies. However, it remains unknown whether a modular neuronal architecture is sufficient to generate the unique flexibility of human finger movements, which rely on cortical structures. Here we show that finger movements evoked by transcranial magnetic stimulation (TMS) of the primary motor cortex reproduced distinctive features of the spatial representation of voluntary movements as identified in previous neuroimaging studies, consistent with naturalistic activation of neuronal elements. Principal component analysis revealed that the dimensionality of TMS-evoked movements was low. Principal components extracted from TMS-induced finger movements resembled those derived from end-postures of voluntary movements performed to grasp imagined objects, and a small subset of them was sufficient to reconstruct these movements with remarkable fidelity. The motor system may coordinate even the most dexterous movements by using a modular architecture involving cortical components.

Cortico-motoneuronal system and dexterous finger movements

A recent paper in the Journal of Neurophysiology by [Sasaki et al. (2004)][1] reports that in macaque monkeys, skilled movements of the digits can survive a large unilateral lesion of the lateral corticospinal tract (LSCT) at the C5 level. This important report forces us to reconsider our views