Nicholas G. Hatsopoulos

Instant neural control of a movement signal

The activity of motor cortex (MI) neurons conveys movement intent sufficiently well to be used as a control signal to operate artificial devices, but until now this has called for extensive training or has been confined to a limited movement repertoire. Here we show how activity from a few (7–30) MI neurons can be decoded into a signal that a monkey is able to use immediately to move a computer cursor to any new position in its workspace (14° × 14° visual angle). Our results, which are based on recordings made by an electrode array that is suitable for human use, indicate that neurally based control of movement may eventually be feasible in paralysed humans.

Brain-machine interface: Instant neural control of a movement signal

The activity of motor cortex (MI) neurons conveys movement intent sufficiently well to be used as a control signal to operate artificial devices, but until now this has called for extensive training or has been confined to a limited movement repertoire. Here we show how activity from a few (7–30) MI neurons can be decoded into a signal that a monkey is able to use immediately to move a computer cursor to any new position in its workspace (14° × 14° visual angle). Our results, which are based on recordings made by an electrode array that is suitable for human use, indicate that neurally based control of movement may eventually be feasible in paralysed humans.

Propagating waves mediate information transfer in the motor cortex

High-frequency oscillations in the beta range (10–45 Hz) are most active in motor cortex during motor preparation and are postulated to reflect the steady postural state or global attentive state of the animal. By simultaneously recording multiple local field potential signals across the primary motor and dorsal premotor cortices of monkeys (Macaca mulatta) trained to perform an instructed-delay reaching task, we found that these oscillations propagated as waves across the surface of the motor cortex along dominant spatial axes characteristic of the local circuitry of the motor cortex. Moreover, we found that information about the visual target to be reached was encoded in terms of both latency and amplitude of evoked waves at a time when the field phase-locked with respect to the target onset. These findings suggest that high-frequency oscillations may subserve intra- and inter-cortical information transfer during movement preparation and execution.

The Science of Neural Interface Systems

The ultimate goal of neural interface research is to create links between the nervous system and the outside world either by stimulating or by recording from neural tissue to treat or assist people with sensory, motor, or other disabilities of neural function. Although electrical stimulation systems have already reached widespread clinical application, neural interfaces that record neural signals to decipher movement intentions are only now beginning to develop into clinically viable systems to help paralyzed people. We begin by reviewing state-of-the-art research and early-stage clinical recording systems and focus on systems that record single-unit action potentials. We then address the potential for neural interface research to enhance basic scientific understanding of brain function by offering unique insights in neural coding and representation, plasticity, brain-behavior relations, and the neurobiology of disease. Finally, we discuss technical and scientific challenges faced by these systems before they are widely adopted by severely motor-disabled patients.

Elementary Computation of Object Approach by a Wide-Field Visual Neuron

An essential function of the brain is to detect threats, such as those posed by objects or predators on a collision course. A wide-field, movement-sensitive visual neuron in the brain of the locust was studied by presenting simulated approaching, receding, and translating objects. The neurons responses could be described simply by multiplying the velocity of the image edge (dθ/dt) with an exponential function of the size of the objects image on the retina (e−αθ). Because this product peaks before the image reaches its maximum size during approach, this neuron can anticipate collision. The neurons activity peaks approximately when the approaching object reaches a certain angular size. Because this neuron receives distinct inputs about image size and velocity, the dendritic tree of a single neuron may function as a biophysical device that can carry out a multiplication of two independent input signals.

Congruent Activity during Action and Action Observation in Motor Cortex

A variety of studies have shown that motor cortical areas can be activated by observation of familiar actions. Here, we describe single-neuron responses in monkey primary motor (MI) and dorsal premotor (PMd) cortices during passive observation and execution of a familiar task. We show that the spiking modulation, preferred directions, and encoded information of cells in MI and PMd remain consistent during both observation and movement. Furthermore, we find that the presence of a visual target is necessary to elicit this congruent neural activity during observation. These findings along with results from our analysis of the oscillatory power in the beta frequency of the local field potential are consistent with previous imaging and EEG studies that have suggested that congruence between observation and action is a general feature of the motor system, even outside of canonical “mirror” areas. Such congruent activity has proposed relevance to motor learning, mimicry, and communication and has practical applications for the development of motor-cortical neuroprostheses in paralyzed patients.

Estimating the directed information to infer causal relationships in ensemble neural spike train recordings

Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures—most of which are variants of Granger causality—with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger’s original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering causal relationships whose directionality are consistent with predictions made from the wave propagation of simultaneously recorded local field potentials.

Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control

The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain–machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkeys arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback.

Single-Unit Stability Using Chronically Implanted Multielectrode Arrays

The use of chronic intracortical multielectrode arrays has become increasingly prevalent in neurophysiological experiments. However, it is not obvious whether neuronal signals obtained over multiple recording sessions come from the same or different neurons. Here, we develop a criterion to assess single-unit stability by measuring the similarity of 1) average spike waveforms and 2) interspike interval histograms (ISIHs). Neuronal activity was recorded from four Utah arrays implanted in primary motor and premotor cortices in three rhesus macaque monkeys during 10 recording sessions over a 15- to 17-day period. A unit was defined as stable through a given day if the stability criterion was satisfied on all recordings leading up to that day. We found that 57% of the original units were stable through 7 days, 43% were stable through 10 days, and 39% were stable through 15 days. Moreover, stable units were more likely to remain stable in subsequent recording sessions (i.e., 89% of the neurons that were stable through four sessions remained stable on the fifth). Using both waveform and ISIH data instead of just waveforms improved performance by reducing the number of false positives. We also demonstrate that this method can be used to track neurons across days, even during adaptation to a visuomotor rotation. Identifying a stable subset of neurons should allow the study of long-term learning effects across days and has practical implications for pooling of behavioral data across days and for increasing the effectiveness of brain-machine interfaces.

On the sufficiency of the velocity field for perception of heading

All models of self-motion from optical flow assume the instantaneous velocity field as input. We tested this assumption for human observers using random-dot displays that simulated translational and circular paths of movement by manipulating the lifetime and displacement of individual dots. For translational movement, observers were equally accurate in judging direction of heading from a “velocity field” with a two-frame dot life and a “direction field” in which the magnitudes of displacement were randomized while the radial pattern of directions was preserved, but at chance with a “speed field” in which the directions were randomized, preserving only magnitude. Accuracy declined with increasing noise in vector directions, but remained below 2.6° with a 90° noise envelope. Thus, the visual system uses the radial morphology of vector directions to determine translational heading and can tolerate large amounts of noise in this pattern. For circular movement, observers were equally accurate with a 2-frame “velocity field”, 3-frame “acceleration” displays, and 2-frame and 3-frame “direction fields”, consistent with the use of the pattern of vector directions to locate the center of rotation. The results indicate that successive independent velocity fields are sufficient for perception of translational and circular heading.