Faculty Bibliography

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.

The size and curvature of the macaque brain present challenges for two photon laser scanning microscopy (2P-LSM). General access to the cortex requires 5-axis positioning over a range of motion wider than existing designs offer. In addition, movement artifacts due to physiological pulsations and bodily movement present particular challenges. We present a microscope and implant platform that allows for repeatable, motorized positioning and stable imaging at any point on the dorsal convexity of macaque cortex. While testing the system to image neurons expressing fluorescent proteins in an awake macaque, motion artifacts were limited to several microns.

Neural circuitry can be investigated and manipulated using a variety of techniques, including electrical and optical recording and stimulation. At present, most neural interfaces are designed to accommodate a single mode of neural recording and/or manipulation, which limits the amount of data that can be extracted from a single population of neurons. To overcome these technical limitations, we developed a chronic, multi-scale, multi-modal chamber-based neural implant for use in non-human primates that accommodates electrophysiological recording and stimulation, optical manipulation, and wide-field imaging. We present key design features of the system and mechanical validation. We also present sample data from two non-human primate subjects to validate the efficacy of the design in vivo.

A key element needed in a brain-machine interface (BMI) decoder is the encoding model, which relates the neural activity to intended movement. The vast majority of work have used a representational encoding model, which assumes movement parameters are directly encoded in neural activity. Recent work have in turn suggested the existence of neural dynamics that represent behavior. This recent evidence motivates developing dynamical encoding models with hidden states that encode movement. Regardless of their type, encoding models have vastly characterized a single scale of activity, e.g., either spikes or local field potentials (LFP). In our recent work we developed a multiscale representational encoding model to simultaneously characterize and decode discrete spikes and continuous field activity. However, learning a multiscale dynamical model from simultaneous spike-field recordings in the presence of hidden states is challenging. Here we present an unsupervised learning algorithm for estimating a multiscale state-space model with hidden states and validate it using spike-LFP activity during a reaching movement. We use the learned multiscale statespace model and a corresponding decoder to identify hidden states from spike-LFP activity. We then decode the movement trajectories using these hidden states. We find that the identified states can accurately decode the trajectories. Moreover, we demonstrate that adding LFP to spikes improves the decoding accuracy, suggesting that our unsupervised learning algorithm incorporates information across scales. This learning algorithm could serve as a new tool to study encoding across scales and to enhance future BMI systems.

Reaching is an essential behavior that allows primates to interact with the environment. Precise reaching to visual targets depends on our ability to localize and foveate the target. Despite this, how the saccade system contributes to improvements in reach accuracy remains poorly understood. To assess spatial contributions of eye movements to reach accuracy, we performed a series of behavioral psychophysics experiments in nonhuman primates (Macaca mulatta). We found that a coordinated saccade with a reach to a remembered target location increases reach accuracy without target foveation. The improvement in reach accuracy was similar to that obtained when the subject had visual information about the location of the current target in the visual periphery and executed the reach while maintaining central fixation. Moreover, we found that the increase in reach accuracy elicited by a coordinated movement involved a spatial coupling mechanism between the saccade and reach movements. We observed significant correlations between the saccade and reach errors for coordinated movements. In contrast, when the eye and arm movements were made to targets in different spatial locations, the magnitude of the error and the degree of correlation between the saccade and reach direction were determined by the spatial location of the eye and the hand targets. Hence, we propose that coordinated movements improve reach accuracy without target foveation due to spatial coupling between the reach and saccade systems. Spatial coupling could arise from a neural mechanism for coordinated visual behavior that involves interacting spatial representations.NEW & NOTEWORTHY How visual spatial representations guiding reach movements involve coordinated saccadic eye movements is unknown. Temporal coupling between the reach and saccade system during coordinated movements improves reach performance. However, the role of spatial coupling is unclear. Using behavioral psychophysics, we found that spatial coupling increases reach accuracy in addition to temporal coupling and visual acuity. These results suggest that a spatial mechanism to couple the reach and saccade systems increases the accuracy of coordinated movements.

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies.

Recordings from invasive implants can degrade over time, resulting in a loss of spiking activity for some electrodes. For brain-machine interfaces (BMI), such a signal degradation lowers control performance. Achieving reliable performance over time is critical for BMI clinical viability. One approach to improve BMI longevity is to simultaneously use spikes and other recording modalities such as local field potentials (LFP), which are more robust to signal degradation over time. We have developed a multiscale decoder that can simultaneously model the different statistical profiles of multi-scale spike/LFP activity (discrete spikes vs. continuous LFP). This decoder can also run at multiple time-scales (millisecond for spikes vs. tens of milliseconds for LFP). Here, we validate the multiscale decoder for estimating the movement of 7 major upper-arm joint angles in a non-human primate (NHP) during a 3D reach-to-grasp task. The multiscale decoder uses motor cortical spike/LFP recordings as its input. We show that the multiscale decoder can improve decoding accuracy by adding information from LFP to spikes, while running at the fast millisecond time-scale of the spiking activity. Moreover, this improvement is achieved using relatively few LFP channels, demonstrating the robustness of the approach. These results suggest that using multiscale decoders has the potential to improve the reliability and longevity of BMIs.