Custom-fit radiolucent cranial implants for neurophysiological recording and stimulation.

BACKGROUND: Recording and manipulating neural activity in awake behaving animal models requires long-term implantation of cranial implants that must address a variety of design considerations, which include preventing infection, minimizing tissue damage, mechanical strength of the implant, and MRI compatibility. NEW METHOD: Here we address these issues by designing legless, custom-fit cranial implants using structural MRI-based reconstruction of the skull and that are made from carbon-reinforced PEEK. RESULTS: We report several novel custom-fit radiolucent implant designs, which include a legless recording chamber, a legless stimulation chamber, a multi-channel microdrive and a head post. The fit to the skull was excellent in all cases, with no visible gaps between the base of the implants and the skull. The wound margin was minimal in size and showed no sign of infection or skin recession. COMPARISON WITH EXISTING METHODS: Cranial implants used for neurophysiological investigation in awake behaving animals often employ methyl methacrylate (MMA) to serve as a bonding agent to secure the implant to the skull. Other designs rely on radially extending legs to secure the implant. Both of these methods have significant drawbacks. MMA is toxic to bone and frequently leads to infection while radially extending legs cause the skin to recede away from the implant, ultimately exposing bone and proliferating granulation tissue. CONCLUSIONS: These radiolucent implants constitute a set of technologies suitable for reliable long-term recording, which minimize infection and tissue damage.

Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.

Cortical neural prosthetics extract command signals from the brain with the goal to restore function in paralyzed or amputated patients. Continuous control signals can be extracted from the motor cortical areas, whereas neural activity from posterior parietal cortex (PPC) can be used to decode cognitive variables related to the goals of movement. Because typical activities of daily living comprise both continuous control tasks such as reaching, and tasks benefiting from discrete control such as typing on a keyboard, availability of both signals simultaneously would promise significant increases in performance and versatility. Here, we show that PPC can provide 3D hand trajectory information under natural conditions that would be encountered for prosthetic applications, thus allowing simultaneous extraction of continuous and discrete signals without requiring multisite surgical implants. We found that limb movements can be decoded robustly and with high accuracy from a small population of neural units under free gaze in a complex 3D point-to-point reaching task. Both animals' brain-control performance improved rapidly with practice, resulting in faster target acquisition and increasing accuracy. These findings disprove the notion that the motor cortical areas are the only candidate areas for continuous prosthetic command signals and, rather, suggests that PPC can provide equally useful trajectory signals in addition to discrete, cognitive variables. Hybrid use of continuous and discrete signals from PPC may enable a new generation of neural prostheses providing superior performance and additional flexibility in addressing individual patient needs.

Cognitive neural prosthetics.

The cognitive neural prosthetic (CNP) is a very versatile method for assisting paralyzed patients and patients with amputations. The CNP records the cognitive state of the subject, rather than signals strictly related to motor execution or sensation. We review a number of high-level cortical signals and their application for CNPs, including intention, motor imagery, decision making, forward estimation, executive function, attention, learning, and multi-effector movement planning. CNPs are defined by the cognitive function they extract, not the cortical region from which the signals are recorded. However, some cortical areas may be better than others for particular applications. Signals can also be extracted in parallel from multiple cortical areas using multiple implants, which in many circumstances can increase the range of applications of CNPs. The CNP approach relies on scientific understanding of the neural processes involved in cognition, and many of the decoding algorithms it uses also have parallels to underlying neural circuit functions.

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 monkey's 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.

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.