Sam Musallam

Cognitive control signals for neural prosthetics

Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices. In this study, higher level signals related to the goals of movements were decoded from three monkeys and used to position cursors on a computer screen without the animals emitting any behavior. Their performance in this task improved over a period of weeks. Expected value signals related to fluid preference, the expected magnitude, or probability of reward were decoded simultaneously with the intended goal. For neural prosthetic applications, the goal signals can be used to operate computers, robots, and vehicles, whereas the expected value signals can be used to continuously monitor a paralyzed patients preferences and motivation.

Selecting the signals for a brain-machine interface

Brain-machine interfaces are being developed to assist paralyzed patients by enabling them to operate machines with recordings of their own neural activity. Recent studies show that motor parameters, such as hand trajectory, and cognitive parameters, such as the goal and predicted value of an action, can be decoded from the recorded activity to provide control signals. Neural prosthetics that use simultaneously a variety of cognitive and motor signals can maximize the ability of patients to communicate and interact with the outside world. Although most studies have recorded electroencephalograms or spike activity, recent research shows that local field potentials (LFPs) offer a promising additional signal. The decode performances of LFPs and spike signals are comparable and, because LFP recordings are more long lasting, they might help to increase the lifetime of the prosthetics.

Decoding Trajectories from Posterior Parietal Cortex Ensembles

High-level cognitive signals in the posterior parietal cortex (PPC) have previously been used to decode the intended endpoint of a reach, providing the first evidence that PPC can be used for direct control of a neural prosthesis (Musallam et al., 2004). Here we expand on this work by showing that PPC neural activity can be harnessed to estimate not only the endpoint but also to continuously control the trajectory of an end effector. Specifically, we trained two monkeys to use a joystick to guide a cursor on a computer screen to peripheral target locations while maintaining central ocular fixation. We found that we could accurately reconstruct the trajectory of the cursor using a relatively small ensemble of simultaneously recorded PPC neurons. Using a goal-based Kalman filter that incorporates target information into the state-space, we showed that the decoded estimate of cursor position could be significantly improved. Finally, we tested whether we could decode trajectories during closed-loop brain control sessions, in which the real-time position of the cursor was determined solely by a monkeys neural activity in PPC. The monkey learned to perform brain control trajectories at 80% success rate (for 8 targets) after just 4–5 sessions. This improvement in behavioral performance was accompanied by a corresponding enhancement in neural tuning properties (i.e., increased tuning depth and coverage of encoding parameter space) as well as an increase in off-line decoding performance of the PPC ensemble.

Cognitive Neural Prosthetics

Research on neural prosthetics has focused largely on using activity related to hand trajectories recorded from motor cortical areas. An interesting question revolves around what other signals might be read out from the brain and used for neural prosthetic applications. Recent studies indicate that goals and expected value are among the high-level cognitive signals that can be used and will potentially enhance the ability of paralyzed patients to communicate with the outside world. Other new findings show that local field potentials provide an excellent source of information about the cognitive state of the subject and are much easier to record and maintain than spike activity. Finally, new movable probe technologies will enable recording electrodes to seek out automatically the best signals for decoding cognitive variables.

Forward estimation of movement state in posterior parietal cortex

During goal-directed movements, primates are able to rapidly and accurately control an online trajectory despite substantial delay times incurred in the sensorimotor control loop. To address the problem of large delays, it has been proposed that the brain uses an internal forward model of the arm to estimate current and upcoming states of a movement, which are more useful for rapid online control. To study online control mechanisms in the posterior parietal cortex (PPC), we recorded from single neurons while monkeys performed a joystick task. Neurons encoded the static target direction and the dynamic movement angle of the cursor. The dynamic encoding properties of many movement angle neurons reflected a forward estimate of the state of the cursor that is neither directly available from passive sensory feedback nor compatible with outgoing motor commands and is consistent with PPC serving as a forward model for online sensorimotor control. In addition, we found that the space–time tuning functions of these neurons were largely separable in the angle–time plane, suggesting that they mostly encode straight and approximately instantaneous trajectories.

NeuroMEMS: Neural Probe Microtechnologies

Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimers, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultra-long multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

A floating metal microelectrode array for chronic implantation.

Implantation of multi-electrode arrays is becoming increasingly more prevalent within the neuroscience research community and has become important for clinical applications. Many of these studies have been directed towards the development of sensory and motor prosthesis. Here, we present a multi-electrode system made from biocompatible material that is electrically and mechanically stable, and employs design features allowing flexibility in the geometric layout and length of the individual electrodes within the array. We also employ recent advances in laser machining of thin ceramic substrates, application of ultra-fine line gold conductors to ceramic, fabrication of extremely flexible cables, and fine wire management techniques associated with juxtaposing metal microelectrodes within a few hundred microns of each other in the development of a floating multi-electrode array (FMA). We implanted the FMA in rats and show that the FMA is capable of recording both spikes and local field potentials.

Low-Power Circuits for Brain–Machine Interfaces

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinsons disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode-recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented.

Parietal Reach Region Encodes Reach Depth Using Retinal Disparity and Vergence Angle Signals

Performing a visually guided reach requires the ability to perceive the egocentric distance of a target in three-dimensional space. Previous studies have shown that the parietal reach region (PRR) encodes the two-dimensional location of frontoparallel targets in an eye-centered reference frame. To investigate how a reach target is represented in three dimensions, we recorded the spiking activity of PRR neurons from two rhesus macaques trained to fixate and perform memory reaches to targets at different depths. Reach and fixation targets were configured to explore whether neural activity directly reflects egocentric distance as the amplitude of the required motor command, which is the absolute depth of the target, or rather the relative depth of the target with reference to fixation depth. We show that planning activity in PRR represents the depth of the reach target as a function of disparity and fixation depth, the spatial parameters important for encoding the depth of a reach goal in an eye centered reference frame. The strength of modulation by disparity is maintained across fixation depth. Fixation depth gain modulates disparity tuning while preserving the location of peak tuning features in PRR neurons. The results show that individual PRR neurons code depth with respect to the fixation point, that is, in eye centered coordinates. However, because the activity is gain modulated by vergence angle, the absolute depth can be decoded from the population activity.

Local field potentials.

Research on neural prosthetics has focused largely on using activity related to hand trajectories recorded from motor cortical areas. An interesting question revolves around what other signals might be read out from the brain and used for neural prosthetic applications. Recent studies indicate that goals and expected value are among the high-level cognitive signals that can be used and will potentially enhance the ability of paralyzed patients to communicate with the outside world. Other new findings show that local field potentials provide an excellent source of information about the cognitive state of the subject and are much easier to record and maintain than spike activity. Finally, new movable probe technologies will enable recording electrodes to seek out automatically the best signals for decoding cognitive variables.