A modular chemigenetic calcium indicator enables in vivo functional imaging with near-infrared light.

Genetically encoded fluorescent calcium indicators have revolutionized neuroscience and other biological fields by allowing cellular-resolution recording of physiology during behavior. However, we currently lack bright, genetically targetable indicators in the near infrared that can be used in animals. Here, we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains that can be genetically targeted to specific cell populations. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several dye-ligands that efficiently label the central nervous system in animals. When bound to a near-infrared dye-ligand, WHaloCaMP1a is more than twice as bright as jGCaMP8s, and shows a 7× increase in fluorescence intensity and a 2.1 ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a with near-infrared fluorescence emission to image Ca2+ responses in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae, and to quantitate calcium concentration using fluorescence lifetime imaging microscopy (FLIM).

Glutamate indicators with improved activation kinetics and localization for imaging synaptic transmission.

The fluorescent glutamate indicator iGluSnFR enables imaging of neurotransmission with genetic and molecular specificity. However, existing iGluSnFR variants exhibit low in vivo signal-to-noise ratios, saturating activation kinetics and exclusion from postsynaptic densities. Using a multiassay screen in bacteria, soluble protein and cultured neurons, we generated variants with improved signal-to-noise ratios and kinetics. We developed surface display constructs that improve iGluSnFR's nanoscopic localization to postsynapses. The resulting indicator iGluSnFR3 exhibits rapid nonsaturating activation kinetics and reports synaptic glutamate release with decreased saturation and increased specificity versus extrasynaptic signals in cultured neurons. Simultaneous imaging and electrophysiology at individual boutons in mouse visual cortex showed that iGluSnFR3 transients report single action potentials with high specificity. In vibrissal sensory cortex layer 4, we used iGluSnFR3 to characterize distinct patterns of touch-evoked feedforward input from thalamocortical boutons and both feedforward and recurrent input onto L4 cortical neuron dendritic spines.

A Large-Scale Interface for Optogenetic Stimulation and Recording in Nonhuman Primates.

While optogenetics offers great potential for linking brain function and behavior in nonhuman primates, taking full advantage of that potential will require stable access for optical stimulation and concurrent monitoring of neural activity. Here we present a practical, stable interface for stimulation and recording of large-scale cortical circuits. To obtain optogenetic expression across a broad region, here spanning primary somatosensory (S1) and motor (M1) cortices, we used convection-enhanced delivery of the viral vector, with online guidance from MRI. To record neural activity across this region, we used a custom micro-electrocorticographic (µECoG) array designed to minimally attenuate optical stimuli. Lastly, we demonstrated the use of this interface to measure spatiotemporal responses to optical stimulation across M1 and S1. This interface offers a powerful tool for studying circuit dynamics and connectivity across cortical areas, for long-term studies of neuromodulation and targeted cortical plasticity, and for linking these to behavior.

Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys.

Advances in techniques for recording large-scale brain activity contribute to both the elucidation of neurophysiological principles and the development of brain-machine interfaces (BMIs). Here we describe a neurophysiological paradigm for performing tethered and wireless large-scale recordings based on movable volumetric three-dimensional (3D) multielectrode implants. This approach allowed us to isolate up to 1,800 neurons (units) per animal and simultaneously record the extracellular activity of close to 500 cortical neurons, distributed across multiple cortical areas, in freely behaving rhesus monkeys. The method is expandable, in principle, to thousands of simultaneously recorded channels. It also allows increased recording longevity (5 consecutive years) and recording of a broad range of behaviors, such as social interactions, and BMI paradigms in freely moving primates. We propose that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research while providing a framework for the development and testing of clinically relevant neuroprostheses.

Subcortical neuronal ensembles: an analysis of motor task association, tremor, oscillations, and synchrony in human patients.

Deep brain stimulation (DBS) has expanded as an effective treatment for motor disorders, providing a valuable opportunity for intraoperative recording of the spiking activity of subcortical neurons. The properties of these neurons and their potential utility in neuroprosthetic applications are not completely understood. During DBS surgeries in 25 human patients with either essential tremor or Parkinson's disease, we acutely recorded the single-unit activity of 274 ventral intermediate/ventral oralis posterior motor thalamus (Vim/Vop) neurons and 123 subthalamic nucleus (STN) neurons. These subcortical neuronal ensembles (up to 23 neurons sampled simultaneously) were recorded while the patients performed a target-tracking motor task using a cursor controlled by a haptic glove. We observed that modulations in firing rate of a substantial number of neurons in both Vim/Vop and STN represented target onset, movement onset/direction, and hand tremor. Neurons in both areas exhibited rhythmic oscillations and pairwise synchrony. Notably, all tremor-associated neurons exhibited synchrony within the ensemble. The data further indicate that oscillatory (likely pathological) neurons and behaviorally tuned neurons are not distinct but rather form overlapping sets. Whereas previous studies have reported a linear relationship between power spectra of neuronal oscillations and hand tremor, we report a nonlinear relationship suggestive of complex encoding schemes. Even in the presence of this pathological activity, linear models were able to extract motor parameters from ensemble discharges. Based on these findings, we propose that chronic multielectrode recordings from Vim/Vop and STN could prove useful for further studying, monitoring, and even treating motor disorders.

High-side digitally current controlled biphasic bipolar microstimulator.

Electrical stimulation of nervous tissue has been extensively used as both a tool in experimental neuroscience research and as a method for restoring of neural functions in patients suffering from sensory and motor disabilities. In the central nervous system, intracortical microstimulation (ICMS) has been shown to be an effective method for inducing or biasing perception, including visual and tactile sensation. ICMS also holds promise for enabling brain-machine-brain interfaces (BMBIs) by directly writing information into the brain. Here we detail the design of a high-side, digitally current-controlled biphasic, bipolar microstimulator, and describe the validation of the device in vivo. As many applications of this technique, including BMBIs, require recording as well as stimulation, we pay careful attention to isolation of the stimulus channels and parasitic current injection. With the realized device and standard recording hardware-without active artifact rejection-we are able to observe stimulus artifacts of less than 2 ms in duration.

Future developments in brain-machine interface research.

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Future developments in brain-machine interface research

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

A brain-machine interface instructed by direct intracortical microstimulation.

Brain-machine interfaces (BMIs) establish direct communication between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a) vibrotactile stimulation of the monkey's hands or (b) multi-channel intracortical microstimulation (ICMS) delivered to the primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PP) in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine-brain recursive input. After 2 weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices.

Unscented Kalman filter for brain-machine interfaces.

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation.

The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.

Current demonstrations of brain-machine interfaces (BMIs) have shown the potential for controlling neuroprostheses under pure motion control. For interaction with objects, however, pure motion control lacks the information required for versatile manipulation. This paper investigates the idea of applying impedance control in a BMI system. An extraction algorithm incorporating a musculoskeletal arm model was developed for this purpose. The new algorithm, called the muscle activation method (MAM), was tested on cortical recordings from a behaving monkey. The MAM was found to predict motion parameters with as much accuracy as a linear filter. Furthermore, it successfully predicted limb interactions with novel force fields, which is a new and significant capability lacking in other algorithms.