Cracking the code: using educational gaming for high-level thinking in physiology education.

The multidisciplinary nature of physiology requires students to acquire, retain, apply, and evaluate knowledge from different scientific disciplines. Optimal learning techniques, such as active learning, interleaving topics and conditions, and recall, can greatly enhance the speed and effectiveness with which students achieve this type of higher-order thinking. However, developing and implementing optimal learning techniques in the classroom can be both time-intensive and challenging for the instructor. In addition, students may be resistant or slow to accept novel learning processes. One way to potentially introduce these learning techniques in a fun and engaging way is through educational gaming, or using a game or game elements intentionally to support learning. In this article we present an easy-to-implement adaptation of the Codenames board game for the physiology classroom. The activity requires minimal preparation while addressing high-level learning outcomes. Postintervention surveys of students were collected in three different health-related academic programs, both graduate and undergraduate, at two different institutions. Results suggest that participating in the activity both actively engaged the students and pushed them toward high-level, integrative thinking regardless of class level.NEW & NOTEWORTHY An easy-to-implement word game (Codenames) was used to engage students in higher-level Bloom's thinking about physiology. The gameplay required students to recall, apply, evaluate, and debate as they developed and guessed clues as part of the game. Students found the activity fun, engaging, and challenging. The activity is relatively easy to implement both online and in person, requiring at minimum a simple list of vocabulary terms.

Cortico-Cortical Interactions during Acquisition and Use of a Neuroprosthetic Skill.

A motor cortex-based brain-computer interface (BCI) creates a novel real world output directly from cortical activity. Use of a BCI has been demonstrated to be a learned skill that involves recruitment of neural populations that are directly linked to BCI control as well as those that are not. The nature of interactions between these populations, however, remains largely unknown. Here, we employed a data-driven approach to assess the interaction between both local and remote cortical areas during the use of an electrocorticographic BCI, a method which allows direct sampling of cortical surface potentials. Comparing the area controlling the BCI with remote areas, we evaluated relationships between the amplitude envelopes of band limited powers as well as non-linear phase-phase interactions. We found amplitude-amplitude interactions in the high gamma (HG, 70-150 Hz) range that were primarily located in the posterior portion of the frontal lobe, near the controlling site, and non-linear phase-phase interactions involving multiple frequencies (cross-frequency coupling between 8-11 Hz and 70-90 Hz) taking place over larger cortical distances. Further, strength of the amplitude-amplitude interactions decreased with time, whereas the phase-phase interactions did not. These findings suggest multiple modes of cortical communication taking place during BCI use that are specialized for function and depend on interaction distance.

Distributed cortical adaptation during learning of a brain-computer interface task.

The majority of subjects who attempt to learn control of a brain-computer interface (BCI) can do so with adequate training. Much like when one learns to type or ride a bicycle, BCI users report transitioning from a deliberate, cognitively focused mindset to near automatic control as training progresses. What are the neural correlates of this process of BCI skill acquisition? Seven subjects were implanted with electrocorticography (ECoG) electrodes and had multiple opportunities to practice a 1D BCI task. As subjects became proficient, strong initial task-related activation was followed by lessening of activation in prefrontal cortex, premotor cortex, and posterior parietal cortex, areas that have previously been implicated in the cognitive phase of motor sequence learning and abstract task learning. These results demonstrate that, although the use of a BCI only requires modulation of a local population of neurons, a distributed network of cortical areas is involved in the acquisition of BCI proficiency.

Sleep spindles are locally modulated by training on a brain-computer interface.

The learning of a motor task is known to be improved by sleep, and sleep spindles are thought to facilitate this learning by enabling synaptic plasticity. In this study subjects implanted with electrocorticography (ECoG) arrays for long-term epilepsy monitoring were trained to control a cursor on a computer screen by modulating either the high-gamma or mu/beta power at a single electrode located over the motor or premotor area. In all trained subjects, spindle density in posttraining sleep was increased with respect to pretraining sleep in a remarkably spatially specific manner. The pattern of increased spindle activity reflects the functionally specific regions that were involved in learning of a highly novel and salient task during wakefulness, supporting the idea that sleep spindles are involved in learning to use a motor-based brain-computer interface device.

Stored-trace reactivation in rat prefrontal cortex is correlated with down-to-up state fluctuation density.

Spontaneous reactivation of previously stored patterns of neural activity occurs in hippocampus and neocortex during non-rapid eye movement (NREM) sleep. Notable features of the neocortical local field potential during NREM sleep are high-amplitude, low-frequency thalamocortical oscillations including K-complexes, low-voltage spindles, and high-voltage spindles. Using combined neuronal ensemble and local field potential recordings, we show that prefrontal stored-trace reactivation is correlated with the density of down-to-up state transitions of the population of simultaneously recorded cells, as well as K-complexes and low-voltage spindles in the local field potential. This result strengthens the connection between reactivation and learning, as these same NREM sleep features have been correlated with memory. Although memory trace reactivation is correlated with low-voltage spindles, it is not correlated with high-voltage spindles, indicating that despite their similar frequency characteristics, these two oscillations serve different functions.

Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition.

BACKGROUND: There is a broad need in neuroscience to understand and visualize large-scale recordings of neural activity, big data acquired by tens or hundreds of electrodes recording dynamic brain activity over minutes to hours. Such datasets are characterized by coherent patterns across both space and time, yet existing computational methods are typically restricted to analysis either in space or in time separately. NEW METHOD: Here we report the adaptation of dynamic mode decomposition (DMD), an algorithm originally developed for studying fluid physics, to large-scale neural recordings. DMD is a modal decomposition algorithm that describes high-dimensional dynamic data using coupled spatial-temporal modes. The algorithm is robust to variations in noise and subsampling rate; it scales easily to very large numbers of simultaneously acquired measurements. RESULTS: We first validate the DMD approach on sub-dural electrode array recordings from human subjects performing a known motor task. Next, we combine DMD with unsupervised clustering, developing a novel method to extract spindle networks during sleep. We uncovered several distinct sleep spindle networks identifiable by their stereotypical cortical distribution patterns, frequency, and duration. COMPARISON WITH EXISTING METHODS: DMD is closely related to principal components analysis (PCA) and discrete Fourier transform (DFT). We may think of DMD as a rotation of the low-dimensional PCA space such that each basis vector has coherent dynamics. CONCLUSIONS: The resulting analysis combines key features of performing PCA in space and power spectral analysis in time, making it particularly suitable for analyzing large-scale neural recordings.

Sequential activation of premotor, primary somatosensory and primary motor areas in humans during cued finger movements.

OBJECTIVE: Human voluntary movements are a final product of complex interactions between multiple sensory, cognitive and motor areas of central nervous system. The objective was to investigate temporal sequence of activation of premotor (PM), primary motor (M1) and somatosensory (S1) areas during cued finger movements. METHODS: Electrocorticography (ECoG) was used to measure activation timing in human PM, S1, and M1 neurons in preparation for finger movements in 5 subjects with subdural grids for seizure localization. Cortical activation was determined by the onset of high gamma (HG) oscillation (70-150Hz). The three cortical regions were mapped anatomically using a common brain atlas and confirmed independently with direct electrical cortical stimulation, somatosensory evoked potentials and detection of HG response to tactile stimulation. Subjects were given visual cues to flex each finger or pinch the thumb and index finger. Movements were captured with a dataglove and time-locked with ECoG. A windowed covariance metric was used to identify the rising slope of HG power between two electrodes and compute time lag. Statistical constraints were applied to the time estimates to combat the noise. Rank sum testing was used to verify the sequential activation of cortical regions across 5 subjects. RESULTS: In all 5 subjects, HG activation in PM preceded S1 by an average of 53±13ms (P=0.03), PM preceded M1 by 180±40ms (P=0.001) and S1 activation preceded M1 by 136±40ms (P=0.04). CONCLUSIONS: Sequential HG activation of PM, S1 and M1 regions in preparation for movements is reported. Activity in S1 prior to any overt body movements supports the notion that these neurons may encode sensory information in anticipation of movements, i.e., an efference copy. Our analysis suggests that S1 modulation likely originates from PM. SIGNIFICANCE: First electrophysiological evidence of efference copy in humans.

Mimicking muscle activity with electrical stimulation.

Functional electrical stimulation is a rehabilitation technology that can restore some degree of motor function in individuals who have sustained a spinal cord injury or stroke. One way to identify the spatio-temporal patterns of muscle stimulation needed to elicit complex upper limb movements is to use electromyographic (EMG) activity recorded from able-bodied subjects as a template for electrical stimulation. However, this requires a transfer function to convert the recorded (or predicted) EMG signals into an appropriate pattern of electrical stimulation. Here we develop a generalized transfer function that maps EMG activity into a stimulation pattern that modulates muscle output by varying both the pulse frequency and the pulse amplitude. We show that the stimulation patterns produced by this transfer function mimic the active state measured by EMG insofar as they reproduce with good fidelity the complex patterns of joint torque and joint displacement.

Evaluation of probabilistic methods to predict muscle activity: implications for neuroprosthetics.

Functional electrical stimulation (FES) involves artificial activation of muscles with surface or implanted electrodes to restore motor function in paralyzed individuals. Currently, FES-based prostheses produce only a limited range of movements due to the difficulty associated with identifying patterns of muscle activity needed to evoke more complex behaviour. Here we test three probability-based models (Bayesian density estimation, polynomial curve fitting and dynamic neural network) that use the trajectory of the hand to predict the electromyographic (EMG) activities of 12 arm muscles during complex two- and three-dimensional movements. Across most conditions, the neural network model yielded the best predictions of muscle activity. For three-dimensional movements, the predicted patterns of muscle activity using the neural network accounted for 40% of the variance in the actual EMG signals and were associated with an average root-mean-squared error of 6%. These results suggest that such probabilistic models could be used effectively to predict patterns of muscle stimulation needed to produce complex movements with an FES-based neuroprosthetic.

A navigation aid for the blind using tactile-visual sensory substitution.

The objective of this study is to improve the quality of life for the visually impaired by restoring their ability to self-navigate. In this paper we describe a compact, wearable device that converts visual information into a tactile signal. This device, constructed entirely from commercially available parts, enables the user to perceive distant objects via a different sensory modality. Preliminary data suggest that this device is useful for object avoidance in simple environments.