The hand is one of the most fascinating and sophisticated biological motor systems. The complex biomechanical and neural architecture of the hand poses challenging questions for understanding the ...control strategies that underlie the coordination of finger movements and forces required for a wide variety of behavioral tasks, ranging from multidigit grasping to the individuated movements of single digits. Hence, a number of experimental approaches, from studies of finger movement kinematics to the recording of electromyographic and cortical activities, have been used to extend our knowledge of neural control of the hand. Experimental evidence indicates that the simultaneous motion and force of the fingers are characterized by coordination patterns that reduce the number of independent degrees of freedom to be controlled. Peripheral and central constraints in the neuromuscular apparatus have been identified that may in part underlie these coordination patterns, simplifying the control of multi-digit grasping while placing certain limitations on individuation of finger movements. We review this evidence, with a particular emphasis on how these constraints extend through the neuromuscular system from the behavioral aspects of finger movements and forces to the control of the hand from the motor cortex.
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 3 ...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.
The macaque flexor digitorum profundus (FDP) consists of a muscle belly with four neuromuscular regions and a complex insertion tendon that divides to serve all five digits of the hand. To determine ...the extent to which compartments within FDP act on single versus multiple digits, we stimulated the primary nerve branch innervating each neuromuscular region while recording the tension in all five distal insertion tendons. Stimulation of each primary nerve branch activated a distinct region of the muscle belly, so that each primary nerve branch and the muscle region innervated can be considered a neuromuscular compartment. Although each neuromuscular compartment provided a distinct distribution of tension across the five distal tendons, none acted on only one digital tendon. Most of the distribution of tension to multiple digits could be attributed to passive biomechanical interactions in the complex insertion tendon, although for the larger compartments a wider distribution resulted from the broad insertion of the muscle belly. Nerve ligations excluded contributions of spinal reflexes or distal axon reflexes to the distribution of tension to multiple digits. We conclude that the macaque FDP consists of four neuromuscular compartments, each of which provides a distinct distribution of tension to multiple digits.
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.
We studied the extent to which mechanical coupling and neuromuscular control limit finger independence by studying passive and active individuated finger movements in healthy adults. For passive ...movements, subjects relaxed while each finger was rotated into flexion and extension by a custom-built device. For active movements, subjects moved each finger into flexion and extension while attempting to keep the other, noninstructed fingers still. Active movements were performed through approximately the same joint excursions and at approximately the same speeds as the passive movements. We quantified how mechanical coupling limited finger independence from the passive movements, and quantified how neuromuscular control limited finger independence using an analysis that subtracted the indices obtained in the passive condition from those obtained in the active condition. Finger independence was generally similar during passive and active movements, but showed a trend toward less independence in the middle, ring, and little fingers during active, large-arc movements. Mechanical coupling limited the independence of the index, middle, and ring fingers to the greatest degree, followed by the little finger, and placed only negligible limitations on the independence of the thumb. In contrast, neuromuscular control primarily limited the independence of the ring, and little fingers during large-arc movements, and had minimal effects on the other fingers, especially during small-arc movements. For the movement conditions tested here, mechanical coupling between the fingers appears to be a major factor limiting the complete independence of finger movement.
Spike-triggered averaging of EMG is a useful experimental technique for revealing functional connectivity from central neurons to motoneurons. Because EMG waveforms constitute time series, ...statistical analysis of spike-triggered averages is complicated. Empirical methods generally have been employed to detect the presence of post-spike effects (PSEs), since, as we argue in this report, it is not feasible to develop a rigorous yet sensitive statistical test that detects PSEs in a single grand average of rectified EMG. We have developed a method of multiple fragment statistical analysis (MFSA) of PSEs, based on dividing an experimental record into a large numbers of non-overlapping fragments. The calculations necessary to obtain accurate
P-values using the multiple fragment method are simple and efficient, and therefore preliminary results can be obtained while recording. In this report, we present the rationale for MFSA, and give examples of its application. We found MFSA to have considerable utility in accurately testing the significance of small PSEs, and in detecting PSEs in shorter recordings. Statistical corrections that should be used when recording multiple channels simultaneously are discussed. MFSA could be implemented for statistical analysis of other waveforms averaged, such as evoked potentials, movement-related cortical potentials, or event-related desychronizations.
Mercuric iodide (HgI
2) and lead iodide (PbI
2) thin polycrystalline films have been under development for several years as direct converter layers for digital X-ray imaging. In this paper, we cover ...the basic electrical and physical characteristics of these materials and compare to other X-ray sensitive photoconductive materials. Both lead iodide and mercuric iodide were vacuum deposited on a-Si TFT arrays with 127
μm pixel pitch. This coating technology is scalable to sizes required in common X-ray imaging applications, as proved by the recent 10
cm×10
cm and 20
cm×25
cm imager results. A difficult challenge of both lead iodide and mercuric iodide detectors is higher than the desired leakage current. Minimizing the leakage current must also be achieved without adversely affecting charge transport, which plays a large role in gain and is also influenced by these parameters. New deposition technologies have been developed through which the leakage current has now decreased by more than an order of magnitude while showing no negative effects on gain. The improvement in dark current correlates with more perfect (single crystalline like) structures as shown by X-ray diffraction data in HgI
2 films. The imagers were evaluated for both radiographic and fluoroscopic imaging. MTF was measured as a function of the spatial frequency and results were compared to values for indirect detectors (CsI). The ability to operate at moderate voltages (∼0.2–1.0
V/μm) provides adequate dark current for most applications and allows low voltage electronics design. Image lag characteristics of mercuric iodide appear adequate for fluoroscopic rates.
PDF
