David Putrino

A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity

The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neurons spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.

Telerehabilitation and emerging virtual reality approaches to stroke rehabilitation.

PURPOSE OF REVIEW Stroke is the leading cause of permanent motor disability in the United States, and the rapidly aging population makes finding large-scale treatment solutions to this problem a national priority. Telerehabilitation is an emerging approach that is being used for the effective treatment of multiple diseases, and is beginning to show promise for stroke. The purpose of this review is to identify and highlight the areas of telerehabilitation that require the most research attention. RECENT FINDINGS Although there are many different forms of telerehabilitation approaches being attempted for stroke, the only approach that is currently showing moderate-strong evidence for efficacy is videogame-driven telerehabilitation (VGDT). However, targeted research is still required to determine the feasibility of VGDT: metrics regarding system usability, cost-effectiveness, and data privacy concerns still require major attention. SUMMARY VGDT is an emerging approach that shows enormous promise for stroke rehabilitation. Future studies should focus less on developing custom task controllers and therapy games and more on developing innovative, online data acquisition and analytics pipelines, as well as understanding the patient population so that the rehabilitation experience can be better customized.

Validation of the Leap Motion Controller using markered motion capture technology

The Leap Motion Controller (LMC) is a low-cost, markerless motion capture device that tracks hand, wrist and forearm position. Integration of this technology into healthcare applications has begun to occur rapidly, making validation of the LMC׳s data output an important research goal. Here, we perform a detailed evaluation of the kinematic data output from the LMC, and validate this output against gold-standard, markered motion capture technology. We instructed subjects to perform three clinically-relevant wrist (flexion/extension, radial/ulnar deviation) and forearm (pronation/supination) movements. The movements were simultaneously tracked using both the LMC and a marker-based motion capture system from Motion Analysis Corporation (MAC). Adjusting for known inconsistencies in the LMC sampling frequency, we compared simultaneously acquired LMC and MAC data by performing Pearson׳s correlation (r) and root mean square error (RMSE). Wrist flexion/extension and radial/ulnar deviation showed good overall agreement (r=0.95; RMSE=11.6°, and r=0.92; RMSE=12.4°, respectively) with the MAC system. However, when tracking forearm pronation/supination, there were serious inconsistencies in reported joint angles (r=0.79; RMSE=38.4°). Hand posture significantly influenced the quality of wrist deviation (P<0.005) and forearm supination/pronation (P<0.001), but not wrist flexion/extension (P=0.29). We conclude that the LMC is capable of providing data that are clinically meaningful for wrist flexion/extension, and perhaps wrist deviation. It cannot yet return clinically meaningful data for measuring forearm pronation/supination. Future studies should continue to validate the LMC as updated versions of their software are developed.

Dynamic cortical lateralization during olfactory discrimination learning

Odour discrimination and memory involve changes in the primary olfactory (piriform) cortex. The results obtained in the present study suggest that there is an asymmetry in piriform cortical change, with learning‐related changes in cortical oscillations emerging with different time courses over the course of multiday training in the left and right piriform cortices in rats. There is an initial decrease in coherence between the left and right piriform cortices during the early stages of the odour discrimination task, which recovers as the animals approach criterion performance. This decreased coherence is expressed when the animals are performing the task relative to when they are in their home cage. The results suggest a transient cortical asymmetry during learning and raise new questions about the functions and mechanisms of cerebral lateralization.

Differential Involvement of Excitatory and Inhibitory Neurons of Cat Motor Cortex in Coincident Spike Activity Related to Behavioral Context

To assess temporal associations in spike activity between pairs of neurons in the primary motor cortex (MI) related to different behaviors, we compared the incidence of coincident spiking activity of task-related (TR) and non-task-related (NTR) neurons during a skilled motor task and sitting quietly in adult cats (Felis domestica). Chronically implanted microwires were used to record spike activity of MI neurons in four animals (two male and two female) trained to perform a skilled reaching task or sit quietly. Neurons were identified as TR if spike activity was modulated during the task (and NTR if not). Based on spike characteristics, they were also classified as either regular-spiking (RS, putatively excitatory) or fast-spiking (FS, putatively inhibitory) neurons. Temporal associations in the activities of simultaneously recorded neurons were evaluated using shuffle-corrected cross-correlograms. Pairs of NTR and TR neurons showed associations in their firing patterns over wide areas of MI (representing forelimb and hindlimb movements) during quiet sitting, more commonly involving RS neurons. During skilled task performance, however, significantly coincident firing was seen almost exclusively between TR neurons in a smaller part of MI (representing forelimb movements), involving mainly FS neurons. The findings of this study show evidence for widespread interactions in MI when the animal sits quietly, which changes to a more specific and restricted pattern of interactions during task performance. Different populations of excitatory and inhibitory neurons appear to be synchronized during skilled movement and quiet sitting.

Patterns of spatio-temporal correlations in the neural activity of the cat motor cortex during trained forelimb movements

In order to study how neurons in the primary motor cortex (MI) are dynamically linked together during skilled movement, we recorded simultaneously from many cortical neurons in cats trained to perform a reaching and retrieval task using their forelimbs. Analysis of task-related spike activity in the MI of the hemisphere contralateral to the reaching forelimb (in identified forelimb or hindlimb representations) recorded through chronically implanted microwires, was followed by pairwise evaluation of temporally correlated activity in these neurons during task performance using shuffle corrected cross-correlograms. Over many months of recording, a variety of task-related modulations of neural activities were observed in individual efferent zones. Positively correlated activity (mainly narrow peaks at zero or short latencies) was seen during task performance frequently between neurons recorded within the forelimb representation of MI, rarely within the hindlimb area of MI, and never between forelimb and hindlimb areas. Correlated activity was frequently observed between neurons with different patterns of task-related activity or preferential activity during different task elements (reaching, feeding, etc.), and located in efferent zones with dissimilar representation as defined by intracortical microstimulation. The observed synchronization of action potentials among selected but functionally varied groups of MI neurons possibly reflects dynamic recruitment of network connections between efferent zones during skilled movement.

A training platform for many-dimensional prosthetic devices using a virtual reality environment

Brain machine interfaces (BMIs) have the potential to assist in the rehabilitation of millions of patients worldwide. Despite recent advancements in BMI technology for the restoration of lost motor function, a training environment to restore full control of the anatomical segments of an upper limb extremity has not yet been presented. Here, we develop a virtual upper limb prosthesis with 27 independent dimensions, the anatomical dimensions of the human arm and hand, and deploy the virtual prosthesis as an avatar in a virtual reality environment (VRE) that can be controlled in real-time. The prosthesis avatar accepts kinematic control inputs that can be captured from movements of the arm and hand as well as neural control inputs derived from processed neural signals. We characterize the system performance under kinematic control using a commercially available motion capture system. We also present the performance under kinematic control achieved by two non-human primates (Macaca Mulatta) trained to use the prosthetic avatar to perform reaching and grasping tasks. This is the first virtual prosthetic device that is capable of emulating all the anatomical movements of a healthy upper limb in real-time. Since the system accepts both neural and kinematic inputs for a variety of many-dimensional skeletons, we propose it provides a customizable training platform for the acquisition of many-dimensional neural prosthetic control.

Neural integration of reaching and posture: interhemispheric spike correlations in cat motor cortex

To study the interlimb coordination of reaching and postural movements, chronically implanted microelectrodes were used to record single unit activity from the primary motor cortex (MI) of cats during performance of a trained reaching task. Recordings were made from both cerebral hemispheres to record neurons that modulated their activity during reaching (reach-related neurons) and supportive (posture-related neurons) movements of either forelimb. Evidence of temporal associations in the activities of simultaneously recorded reach- and posture-related neurons was evaluated using shuffle-corrected cross correlograms. The spike activity of approximately 34% of reach-related neurons was temporally correlated with the spike activity of simultaneously recorded posture-related neurons in the opposite motor cortex. Significant associations in the spike activity of neurons recorded from homotopic representational areas of the motor cortex in opposite hemispheres have not previously been reported. These interactions may have an important role in the coordination of opposite forelimbs during reaching movements and postural actions.

A regularized point process generalized linear model for assessing the functional connectivity in the cat motor cortex

Identification of multiple simultaneously recorded neural spike train recordings is an important task in understanding neuronal dependency, functional connectivity, and temporal causality in neural systems. An assessment of the functional connectivity in a group of ensemble cells was performed using a regularized point process generalized linear model (GLM) that incorporates temporal smoothness or contiguity of the solution. An efficient convex optimization algorithm was then developed for the regularized solution. The point process model was applied to an ensemble of neurons recorded from the cat motor cortex during a skilled reaching task. The implications of this analysis to the coding of skilled movement in primary motor cortex is discussed.

Using data from the Microsoft Kinect 2 to determine postural stability in healthy subjects: A feasibility trial

The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy subjects. Twelve subjects were recruited for the project, and were instructed to perform a sequence of simple postural stability tasks. The movement sequence was performed as subjects were seated on top of a force platform, and the MK2 was positioned in front of them. This sequence of tasks was performed by each subject under three different postural conditions: “both feet on the ground” (1), “One foot off the ground” (2), and “both feet off the ground” (3). We compared force platform and MK2 data to quantify the degree to which the MK2 was returning reliable data across subjects. We then applied a novel machine-learning paradigm to the MK2 data in order to determine the extent to which data from the MK2 could be used to reliably classify different postural conditions. Our initial comparison of force plate and MK2 data showed a strong agreement between the two devices, with strong Pearson correlations between the trunk centroids “Spine_Mid” (0.85 ± 0.06), “Neck” (0.86 ± 0.07) and “Head” (0.87 ± 0.07), and the center of pressure centroid inferred by the force platform. Mean accuracy for the machine learning classifier from MK2 was 97.0%, with a specific classification accuracy breakdown of 90.9%, 100%, and 100% for conditions 1 through 3, respectively. Mean accuracy for the machine learning classifier derived from the force platform data was lower at 84.4%. We conclude that data from the MK2 has sufficient information content to allow us to classify sequences of tasks being performed under different levels of postural stability. Future studies will focus on validating this protocol on large populations of individuals with actual balance impairments in order to create a toolkit that is clinically validated and available to the medical community.