Publications by authors named "Chethan Pandarinath"

28 Publications

  • Page 1 of 1

Estimating muscle activation from EMG using deep learning-based dynamical systems models.

J Neural Eng 2022 05 19;19(3). Epub 2022 May 19.

Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States of America.

. To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent variable modeling methods to electromyographic (EMG) recordings. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded EMG signals. Common approaches estimate each muscle's activation independently or require manual tuning of model hyperparameters to preserve behaviorally-relevant features.. Here, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks to model the spatial and temporal regularities that underlie multi-muscle activation.. We first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also applied AutoLFADS to monkey forearm muscle activity recorded during an isometric wrist force task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than were other tested approaches.This method leverages dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles. Ultimately, the approach can be used for further studies of multi-muscle coordination and its control by upstream brain areas, and for improving brain-machine interfaces that rely on myoelectric control signals.
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http://dx.doi.org/10.1088/1741-2552/ac6369DOI Listing
May 2022

The science and engineering behind sensitized brain-controlled bionic hands.

Physiol Rev 2022 04 20;102(2):551-604. Epub 2021 Sep 20.

Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois.

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.
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http://dx.doi.org/10.1152/physrev.00034.2020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742729PMC
April 2022

The Representation of Finger Movement and Force in Human Motor and Premotor Cortices.

eNeuro 2020 Jul/Aug;7(4). Epub 2020 Aug 17.

Department of Neurology, Northwestern University, Chicago, IL 60611.

The ability to grasp and manipulate objects requires controlling both finger movement kinematics and isometric force in rapid succession. Previous work suggests that these behavioral modes are controlled separately, but it is unknown whether the cerebral cortex represents them differently. Here, we asked the question of how movement and force were represented cortically, when executed sequentially with the same finger. We recorded high-density electrocorticography (ECoG) from the motor and premotor cortices of seven human subjects performing a movement-force motor task. We decoded finger movement [0.7 ± 0.3 fractional variance accounted for (FVAF)] and force (0.7 ± 0.2 FVAF) with high accuracy, yet found different spatial representations. In addition, we used a state-of-the-art deep learning method to uncover smooth, repeatable trajectories through ECoG state space during the movement-force task. We also summarized ECoG across trials and participants by developing a new metric, the neural vector angle (NVA). Thus, state-space techniques can help to investigate broad cortical networks. Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90 ± 6%). Thus, finger movement and force appear to have distinct representations in motor/premotor cortices. These results inform our understanding of the neural control of movement, as well as the design of grasp brain-machine interfaces (BMIs).
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http://dx.doi.org/10.1523/ENEURO.0063-20.2020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438059PMC
June 2021

From unstable input to robust output.

Nat Biomed Eng 2020 07;4(7):665-667

Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.

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http://dx.doi.org/10.1038/s41551-020-0587-9DOI Listing
July 2020

Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model.

Sci Rep 2019 06 20;9(1):8881. Epub 2019 Jun 20.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.

Decoders optimized offline to reconstruct intended movements from neural recordings sometimes fail to achieve optimal performance online when they are used in closed-loop as part of an intracortical brain-computer interface (iBCI). This is because typical decoder calibration routines do not model the emergent interactions between the decoder, the user, and the task parameters (e.g. target size). Here, we investigated the feasibility of simulating online performance to better guide decoder parameter selection and design. Three participants in the BrainGate2 pilot clinical trial controlled a computer cursor using a linear velocity decoder under different gain (speed scaling) and temporal smoothing parameters and acquired targets with different radii and distances. We show that a user-specific iBCI feedback control model can predict how performance changes under these different decoder and task parameters in held-out data. We also used the model to optimize a nonlinear speed scaling function for the decoder. When used online with two participants, it increased the dynamic range of decoded speeds and decreased the time taken to acquire targets (compared to an optimized standard decoder). These results suggest that it is feasible to simulate iBCI performance accurately enough to be useful for quantitative decoder optimization and design.
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http://dx.doi.org/10.1038/s41598-019-44166-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586941PMC
June 2019

Publisher Correction: Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements.

Sci Rep 2019 Mar 28;9(1):5528. Epub 2019 Mar 28.

Electrical Engineering Department, Stanford University, Stanford, CA, USA.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.
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http://dx.doi.org/10.1038/s41598-018-37930-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437210PMC
March 2019

Volitional control of single-electrode high gamma local field potentials by people with paralysis.

J Neurophysiol 2019 04 20;121(4):1428-1450. Epub 2019 Feb 20.

Department of Neuroscience, Brown University , Providence, Rhode Island.

Intracortical brain-computer interfaces (BCIs) can enable individuals to control effectors, such as a computer cursor, by directly decoding the user's movement intentions from action potentials and local field potentials (LFPs) recorded within the motor cortex. However, the accuracy and complexity of effector control achieved with such "biomimetic" BCIs will depend on the degree to which the intended movements used to elicit control modulate the neural activity. In particular, channels that do not record distinguishable action potentials and only record LFP modulations may be of limited use for BCI control. In contrast, a biofeedback approach may surpass these limitations by letting the participants generate new control signals and learn strategies that improve the volitional control of signals used for effector control. Here, we show that, by using a biofeedback paradigm, three individuals with tetraplegia achieved volitional control of gamma LFPs (40-400 Hz) recorded by a single microelectrode implanted in the precentral gyrus. Control was improved over a pair of consecutive sessions up to 3 days apart. In all but one session, the channel used to achieve control lacked distinguishable action potentials. Our results indicate that biofeedback LFP-based BCIs may potentially contribute to the neural modulation necessary to obtain reliable and useful control of effectors. NEW & NOTEWORTHY Our study demonstrates that people with tetraplegia can volitionally control individual high-gamma local-field potential (LFP) channels recorded from the motor cortex, and that this control can be improved using biofeedback. Motor cortical LFP signals are thought to be both informative and stable intracortical signals and, thus, of importance for future brain-computer interfaces.
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http://dx.doi.org/10.1152/jn.00131.2018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485734PMC
April 2019

Cortical control of a tablet computer by people with paralysis.

PLoS One 2018 21;13(11):e0204566. Epub 2018 Nov 21.

Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America.

General-purpose computers have become ubiquitous and important for everyday life, but they are difficult for people with paralysis to use. Specialized software and personalized input devices can improve access, but often provide only limited functionality. In this study, three research participants with tetraplegia who had multielectrode arrays implanted in motor cortex as part of the BrainGate2 clinical trial used an intracortical brain-computer interface (iBCI) to control an unmodified commercial tablet computer. Neural activity was decoded in real time as a point-and-click wireless Bluetooth mouse, allowing participants to use common and recreational applications (web browsing, email, chatting, playing music on a piano application, sending text messages, etc.). Two of the participants also used the iBCI to "chat" with each other in real time. This study demonstrates, for the first time, high-performance iBCI control of an unmodified, commercially available, general-purpose mobile computing device by people with tetraplegia.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204566PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248919PMC
April 2019

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements.

Sci Rep 2018 11 5;8(1):16357. Epub 2018 Nov 5.

Electrical Engineering Department, Stanford University, Stanford, CA, USA.

Brain-machine interfaces (BMIs) that decode movement intentions should ignore neural modulation sources distinct from the intended command. However, neurophysiology and control theory suggest that motor cortex reflects the motor effector's position, which could be a nuisance variable. We investigated motor cortical correlates of BMI cursor position with or without concurrent arm movement. We show in two monkeys that subtracting away estimated neural correlates of position improves online BMI performance only if the animals were allowed to move their arm. To understand why, we compared the neural variance attributable to cursor position when the same task was performed using arm reaching, versus arms-restrained BMI use. Firing rates correlated with both BMI cursor and hand positions, but hand positional effects were greater. To examine whether BMI position influences decoding in people with paralysis, we analyzed data from two intracortical BMI clinical trial participants and performed an online decoder comparison in one participant. We found only small motor cortical correlates, which did not affect performance. These results suggest that arm movement and proprioception are the major contributors to position-related motor cortical correlates. Cursor position visual feedback is therefore unlikely to affect the performance of BMI-driven prosthetic systems being developed for people with paralysis.
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http://dx.doi.org/10.1038/s41598-018-34711-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6218537PMC
November 2018

Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces.

J Neurosci 2018 10;38(44):9390-9401

Department of Electrical and Computer Engineering, and.

In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the "latent factors" underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.
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http://dx.doi.org/10.1523/JNEUROSCI.1669-18.2018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209846PMC
October 2018

Inferring single-trial neural population dynamics using sequential auto-encoders.

Nat Methods 2018 10 17;15(10):805-815. Epub 2018 Sep 17.

Department of Electrical Engineering, Stanford University, Stanford, CA, USA.

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.
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http://dx.doi.org/10.1038/s41592-018-0109-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380887PMC
October 2018

Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces.

IEEE Trans Biomed Eng 2018 08 21;65(8):1771-1784. Epub 2017 Nov 21.

Objective: Brain-computer interfaces (BCIs) aim to help people with impaired movement ability by directly translating their movement intentions into command signals for assistive technologies. Despite large performance improvements over the last two decades, BCI systems still make errors that need to be corrected manually by the user. This decreases system performance and is also frustrating for the user. The deleterious effects of errors could be mitigated if the system automatically detected when the user perceives that an error was made and automatically intervened with a corrective action; thus, sparing users from having to make the correction themselves. Our previous preclinical work with monkeys demonstrated that task-outcome correlates exist in motor cortical spiking activity and can be utilized to improve BCI performance. Here, we asked if these signals also exist in the human hand area of motor cortex, and whether they can be decoded with high accuracy.

Methods: We analyzed posthoc the intracortical neural activity of two BrainGate2 clinical trial participants who were neurally controlling a computer cursor to perform a grid target selection task and a keyboard-typing task.

Results: Our key findings are that: 1) there exists a putative outcome error signal reflected in both the action potentials and local field potentials of the human hand area of motor cortex, and 2) target selection outcomes can be classified with high accuracy (70-85%) of errors successfully detected with minimal (0-3%) misclassifications of success trials, based on neural activity alone.

Significance: These offline results suggest that it will be possible to improve the performance of clinical intracortical BCIs by incorporating a real-time error detect-and-undo system alongside the decoding of movement intention.
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http://dx.doi.org/10.1109/TBME.2017.2776204DOI Listing
August 2018

A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain-Computer Interfaces.

IEEE Trans Biomed Eng 2018 09 14;65(9):2066-2078. Epub 2017 Dec 14.

Objective: Recent reports indicate that making better assumptions about the user's intended movement can improve the accuracy of decoder calibration for intracortical brain-computer interfaces. Several methods now exist for estimating user intent, including an optimal feedback control model, a piecewise-linear feedback control model, ReFIT, and other heuristics. Which of these methods yields the best decoding performance?

Methods: Using data from the BrainGate2 pilot clinical trial, we measured how a steady-state velocity Kalman filter decoder was affected by the choice of intention estimation method. We examined three separate components of the Kalman filter: dimensionality reduction, temporal smoothing, and output gain (speed scaling).

Results: The decoder's dimensionality reduction properties were largely unaffected by the intention estimation method. Decoded velocity vectors differed by <5% in terms of angular error and speed vs. target distance curves across methods. In contrast, the smoothing and gain properties of the decoder were greatly affected (> 50% difference in average values). Since the optimal gain and smoothing properties are task-specific (e.g. lower gains are better for smaller targets but worse for larger targets), no one method was better for all tasks.

Conclusion: Our results show that, when gain and smoothing differences are accounted for, current intention estimation methods yield nearly equivalent decoders and that simple models of user intent, such as a position error vector (target position minus cursor position), perform comparably to more elaborate models. Our results also highlight that simple differences in gain and smoothing properties have a large effect on online performance and can confound decoder comparisons.
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http://dx.doi.org/10.1109/TBME.2017.2783358DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6043406PMC
September 2018

Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals.

J Neurophysiol 2018 07 25;120(1):343-360. Epub 2018 Apr 25.

Carney Institute for Brain Science, Brown University , Providence, Rhode Island.

Restoring communication for people with locked-in syndrome remains a challenging clinical problem without a reliable solution. Recent studies have shown that people with paralysis can use brain-computer interfaces (BCIs) based on intracortical spiking activity to efficiently type messages. However, due to neuronal signal instability, most intracortical BCIs have required frequent calibration and continuous assistance of skilled engineers to maintain performance. Here, an individual with locked-in syndrome due to brain stem stroke and an individual with tetraplegia secondary to amyotrophic lateral sclerosis (ALS) used a simple communication BCI based on intracortical local field potentials (LFPs) for 76 and 138 days, respectively, without recalibration and without significant loss of performance. BCI spelling rates of 3.07 and 6.88 correct characters/minute allowed the participants to type messages and write emails. Our results indicate that people with locked-in syndrome could soon use a slow but reliable LFP-based BCI for everyday communication without ongoing intervention from a technician or caregiver. NEW & NOTEWORTHY This study demonstrates, for the first time, stable repeated use of an intracortical brain-computer interface by people with tetraplegia over up to four and a half months. The approach uses local field potentials (LFPs), signals that may be more stable than neuronal action potentials, to decode participants' commands. Throughout the several months of evaluation, the decoder remained unchanged; thus no technical interventions were required to maintain consistent brain-computer interface operation.
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http://dx.doi.org/10.1152/jn.00493.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6093965PMC
July 2018

Rapid calibration of an intracortical brain-computer interface for people with tetraplegia.

J Neural Eng 2018 04;15(2):026007

Neuroscience Graduate Program, Brown University, Providence, RI, United States of America. Department of Neuroscience, Brown University, Providence, RI, United States of America. Brown Institute for Brain Science, Brown University, Providence, RI, United States of America. Department of Surgery (Neurosurgery), Dalhousie University, Halifax, NS, Canada.

Objective: Brain-computer interfaces (BCIs) can enable individuals with tetraplegia to communicate and control external devices. Though much progress has been made in improving the speed and robustness of neural control provided by intracortical BCIs, little research has been devoted to minimizing the amount of time spent on decoder calibration.

Approach: We investigated the amount of time users needed to calibrate decoders and achieve performance saturation using two markedly different decoding algorithms: the steady-state Kalman filter, and a novel technique using Gaussian process regression (GP-DKF).

Main Results: Three people with tetraplegia gained rapid closed-loop neural cursor control and peak, plateaued decoder performance within 3 min of initializing calibration. We also show that a BCI-naïve user (T5) was able to rapidly attain closed-loop neural cursor control with the GP-DKF using self-selected movement imagery on his first-ever day of closed-loop BCI use, acquiring a target 37 s after initiating calibration.

Significance: These results demonstrate the potential for an intracortical BCI to be used immediately after deployment by people with paralysis, without the need for user learning or extensive system calibration.
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http://dx.doi.org/10.1088/1741-2552/aa9ee7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5823702PMC
April 2018

High performance communication by people with paralysis using an intracortical brain-computer interface.

Elife 2017 02 21;6. Epub 2017 Feb 21.

Department of Neurosurgery, Stanford University, Stanford, United States.

Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O'Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4-4.2) and information throughput (by a factor of 2.2-4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function.Clinical Trial No: NCT00912041.
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http://dx.doi.org/10.7554/eLife.18554DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319839PMC
February 2017

Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law.

J Neural Eng 2017 04 8;14(2):026010. Epub 2017 Feb 8.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America. Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, United States of America.

Objective: Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]).

Approach: Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law.

Main Results: We found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder.

Significance: The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.
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http://dx.doi.org/10.1088/1741-2552/aa5990DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5371026PMC
April 2017

Feedback control policies employed by people using intracortical brain-computer interfaces.

J Neural Eng 2017 02 30;14(1):016001. Epub 2016 Nov 30.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. Louis Stokes Cleveland Department of Veterans Affairs Medical Center, FES Center of Excellence, Rehab. R&D Service, Cleveland, OH, USA.

Objective: When using an intracortical BCI (iBCI), users modulate their neural population activity to move an effector towards a target, stop accurately, and correct for movement errors. We call the rules that govern this modulation a 'feedback control policy'. A better understanding of these policies may inform the design of higher-performing neural decoders.

Approach: We studied how three participants in the BrainGate2 pilot clinical trial used an iBCI to control a cursor in a 2D target acquisition task. Participants used a velocity decoder with exponential smoothing dynamics. Through offline analyses, we characterized the users' feedback control policies by modeling their neural activity as a function of cursor state and target position. We also tested whether users could adapt their policy to different decoder dynamics by varying the gain (speed scaling) and temporal smoothing parameters of the iBCI.

Main Results: We demonstrate that control policy assumptions made in previous studies do not fully describe the policies of our participants. To account for these discrepancies, we propose a new model that captures (1) how the user's neural population activity gradually declines as the cursor approaches the target from afar, then decreases more sharply as the cursor comes into contact with the target, (2) how the user makes constant feedback corrections even when the cursor is on top of the target, and (3) how the user actively accounts for the cursor's current velocity to avoid overshooting the target. Further, we show that users can adapt their control policy to decoder dynamics by attenuating neural modulation when the cursor gain is high and by damping the cursor velocity more strongly when the smoothing dynamics are high.

Significance: Our control policy model may help to build better decoders, understand how neural activity varies during active iBCI control, and produce better simulations of closed-loop iBCI movements.
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http://dx.doi.org/10.1088/1741-2560/14/1/016001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5239755PMC
February 2017

Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface.

Sci Transl Med 2015 Nov;7(313):313ra179

Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908, USA. Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA. School of Engineering, Brown University, Providence, RI 02912, USA. Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA. Department of Neurology, Harvard Medical School, Boston, MA 02115, USA.

Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs. We demonstrate that signal nonstationarity in an intracortical BCI can be mitigated automatically in software, enabling long periods (hours to days) of self-paced point-and-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user's self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.
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http://dx.doi.org/10.1126/scitranslmed.aac7328DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765319PMC
November 2015

Clinical translation of a high-performance neural prosthesis.

Nat Med 2015 Oct 28;21(10):1142-5. Epub 2015 Sep 28.

Department of Neurosurgery, Stanford University, Stanford, California, USA.

Neural prostheses have the potential to improve the quality of life of individuals with paralysis by directly mapping neural activity to limb- and computer-control signals. We translated a neural prosthetic system previously developed in animal model studies for use by two individuals with amyotrophic lateral sclerosis who had intracortical microelectrode arrays placed in motor cortex. Measured more than 1 year after implant, the neural cursor-control system showed the highest published performance achieved by a person to date, more than double that of previous pilot clinical trial participants.
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http://dx.doi.org/10.1038/nm.3953DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4805425PMC
October 2015

Neural population dynamics in human motor cortex during movements in people with ALS.

Elife 2015 Jun 23;4:e07436. Epub 2015 Jun 23.

Department of Electrical Engineering, Stanford University, Stanford, United States.

The prevailing view of motor cortex holds that motor cortical neural activity represents muscle or movement parameters. However, recent studies in non-human primates have shown that neural activity does not simply represent muscle or movement parameters; instead, its temporal structure is well-described by a dynamical system where activity during movement evolves lawfully from an initial pre-movement state. In this study, we analyze neuronal ensemble activity in motor cortex in two clinical trial participants diagnosed with Amyotrophic Lateral Sclerosis (ALS). We find that activity in human motor cortex has similar dynamical structure to that of non-human primates, indicating that human motor cortex contains a similar underlying dynamical system for movement generation.
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http://dx.doi.org/10.7554/eLife.07436DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4475900PMC
June 2015

Recruitment of PfSET2 by RNA polymerase II to variant antigen encoding loci contributes to antigenic variation in P. falciparum.

PLoS Pathog 2014 Jan 2;10(1):e1003854. Epub 2014 Jan 2.

Department of Microbiology and Immunology, Weill Medical College of Cornell University, New York, New York, United States of America.

Histone modifications are important regulators of gene expression in all eukaryotes. In Plasmodium falciparum, these epigenetic marks regulate expression of genes involved in several aspects of host-parasite interactions, including antigenic variation. While the identities and genomic positions of many histone modifications have now been cataloged, how they are targeted to defined genomic regions remains poorly understood. For example, how variant antigen encoding loci (var) are targeted for deposition of unique histone marks is a mystery that continues to perplex the field. Here we describe the recruitment of an ortholog of the histone modifier SET2 to var genes through direct interactions with the C-terminal domain (CTD) of RNA polymerase II. In higher eukaryotes, SET2 is a histone methyltransferase recruited by RNA pol II during mRNA transcription; however, the ortholog in P. falciparum (PfSET2) has an atypical architecture and its role in regulating transcription is unknown. Here we show that PfSET2 binds to the unphosphorylated form of the CTD, a property inconsistent with its recruitment during mRNA synthesis. Further, we show that H3K36me3, the epigenetic mark deposited by PfSET2, is enriched at both active and silent var gene loci, providing additional evidence that its recruitment is not associated with mRNA production. Over-expression of a dominant negative form of PfSET2 designed to disrupt binding to RNA pol II induced rapid var gene expression switching, confirming both the importance of PfSET2 in var gene regulation and a role for RNA pol II in its recruitment. RNA pol II is known to transcribe non-coding RNAs from both active and silent var genes, providing a possible mechanism by which it could recruit PfSET2 to var loci. This work unifies previous reports of histone modifications, the production of ncRNAs, and the promoter activity of var introns into a mechanism that contributes to antigenic variation by malaria parasites.
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http://dx.doi.org/10.1371/journal.ppat.1003854DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3879369PMC
January 2014

A system for optically controlling neural circuits with very high spatial and temporal resolution.

Proc IEEE Int Symp Bioinformatics Bioeng 2013 Nov;2013

Optogenetics offers a powerful new approach for controlling neural circuits. It has a vast array of applications in both basic and clinical science. For basic science, it opens the door to unraveling circuit operations, since one can perturb specific circuit components with high spatial (single cell) and high temporal (millisecond) resolution. For clinical applications, it allows new kinds of selective treatments, because it provides a method to inactivate or activate specific components in a malfunctioning circuit and bring it back into a normal operating range [1-3]. To harness the power of optogenetics, though, one needs stimulating tools that work with the same high spatial and temporal resolution as the molecules themselves, the channelrhodopsins. To date, most stimulating tools require a tradeoff between spatial and temporal precision and are prohibitively expensive to integrate into a stimulating/recording setup in a laboratory or a device in a clinical setting [4, 5]. Here we describe a Digital Light Processing (DLP)-based system capable of extremely high temporal resolution (sub-millisecond), without sacrificing spatial resolution. Furthermore, it is constructed using off-the-shelf components, making it feasible for a broad range of biology and bioengineering labs. Using transgenic mice that express channelrhodopsin-2 (ChR2), we demonstrate the system's capability for stimulating channelrhodopsin-expressing neurons in tissue with single cell and sub-millisecond precision.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331115PMC
http://dx.doi.org/10.1109/bibe.2013.6701707DOI Listing
November 2013

Retinal prosthetic strategy with the capacity to restore normal vision.

Proc Natl Acad Sci U S A 2012 Sep 13;109(37):15012-7. Epub 2012 Aug 13.

Department of Physiology and Biophysics, Weill Medical College of Cornell University, New York, NY 10065, USA.

Retinal prosthetics offer hope for patients with retinal degenerative diseases. There are 20-25 million people worldwide who are blind or facing blindness due to these diseases, and they have few treatment options. Drug therapies are able to help a small fraction of the population, but for the vast majority, their best hope is through prosthetic devices [reviewed in Chader et al. (2009) Prog Brain Res 175:317-332]. Current prosthetics, however, are still very limited in the vision that they provide: for example, they allow for perception of spots of light and high-contrast edges, but not natural images. Efforts to improve prosthetic capabilities have focused largely on increasing the resolution of the device's stimulators (either electrodes or optogenetic transducers). Here, we show that a second factor is also critical: driving the stimulators with the retina's neural code. Using the mouse as a model system, we generated a prosthetic system that incorporates the code. This dramatically increased the system's capabilities--well beyond what can be achieved just by increasing resolution. Furthermore, the results show, using 9,800 optogenetically stimulated ganglion cell responses, that the combined effect of using the code and high-resolution stimulation is able to bring prosthetic capabilities into the realm of normal image representation.
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http://dx.doi.org/10.1073/pnas.1207035109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3443127PMC
September 2012

Symmetry breakdown in the ON and OFF pathways of the retina at night: functional implications.

J Neurosci 2010 Jul;30(30):10006-14

Departments of Physiology and Biophysics and Neurology and Neuroscience, Weill Medical College, Cornell University, 1300 York Avenue, New York, NY 10065, USA.

Several recent studies have shown that the ON and OFF channels of the visual system are not simple mirror images of each other, that their response characteristics are asymmetric (Chichilnisky and Kalmar, 2002; Sagdullaev and McCall, 2005). How the asymmetries bear on visual processing is not well understood. Here, we show that ON and OFF ganglion cells show a strong asymmetry in their temporal adaptation to photopic (day) and scotopic (night) conditions and that the asymmetry confers a functional advantage. Under photopic conditions, the ON and OFF ganglion cells show similar temporal characteristics. Under scotopic conditions, the two cell classes diverge-ON cells shift their tuning to low temporal frequencies, whereas OFF cells continue to respond to high. This difference in processing corresponds to an asymmetry in the natural world, one produced by the Poisson nature of photon capture and persists over a broad range of light levels. This work characterizes a previously unknown divergence in the ON and OFF pathways and its utility to visual processing. Furthermore, the results have implications for downstream circuitry and thus offer new constraints for models of downstream processing, since ganglion cells serve as building blocks for circuits in higher brain areas. For example, if simple cells in visual cortex rely on complementary interactions between the two pathways, such as push-pull interactions (Alonso et al., 2001; Hirsch, 2003), their receptive fields may be radically different under scotopic conditions, when the ON and OFF pathways are out of sync.
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http://dx.doi.org/10.1523/JNEUROSCI.5616-09.2010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2940838PMC
July 2010

A novel mechanism for switching a neural system from one state to another.

Front Comput Neurosci 2010 31;4. Epub 2010 Mar 31.

Department of Physiology and Biophysics, Weill Cornell Medical College, Cornell University New York, NY, USA.

An animal's ability to rapidly adjust to new conditions is essential to its survival. The nervous system, then, must be built with the flexibility to adjust, or shift, its processing capabilities on the fly. To understand how this flexibility comes about, we tracked a well-known behavioral shift, a visual integration shift, down to its underlying circuitry, and found that it is produced by a novel mechanism - a change in gap junction coupling that can turn a cell class on and off. The results showed that the turning on and off of a cell class shifted the circuit's behavior from one state to another, and, likewise, the animal's behavior. The widespread presence of similar gap junction-coupled networks in the brain suggests that this mechanism may underlie other behavioral shifts as well.
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http://dx.doi.org/10.3389/fncom.2010.00002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2856633PMC
July 2011

Ganglion cell adaptability: does the coupling of horizontal cells play a role?

PLoS One 2008 Mar 5;3(3):e1714. Epub 2008 Mar 5.

Department of Neurobiology, University of Oldenburg, Oldenburg, Germany.

Background: The visual system can adjust itself to different visual environments. One of the most well known examples of this is the shift in spatial tuning that occurs in retinal ganglion cells with the change from night to day vision. This shift is thought to be produced by a change in the ganglion cell receptive field surround, mediated by a decrease in the coupling of horizontal cells.

Methodology/principal Findings: To test this hypothesis, we used a transgenic mouse line, a connexin57-deficient line, in which horizontal cell coupling was abolished. Measurements, both at the ganglion cell level and the level of behavioral performance, showed no differences between wild-type retinas and retinas with decoupled horizontal cells from connexin57-deficient mice.

Conclusion/significance: This analysis showed that the coupling and uncoupling of horizontal cells does not play a dominant role in spatial tuning and its adjustability to night and day light conditions. Instead, our data suggest that another mechanism, likely arising in the inner retina, must be responsible.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0001714PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2246161PMC
March 2008
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