Matthew E. Phillips

Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training

Skill acquisition requires distributed learning both within (online) and across (offline) days to consolidate experiences into newly learned abilities. In particular, piloting an aircraft requires skills developed from extensive training and practice. Here, we tested the hypothesis that transcranial direct current stimulation (tDCS) can modulate neuronal function to improve skill learning and performance during flight simulator training of aircraft landing procedures. Thirty-two right-handed participants consented to participate in four consecutive daily sessions of flight simulation training and received sham or anodal high-definition-tDCS to the right dorsolateral prefrontal cortex (DLPFC) or left motor cortex (M1) in a randomized, double-blind experiment. Continuous electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were collected during flight simulation, n-back working memory, and resting-state assessments. tDCS of the right DLPFC increased midline-frontal theta-band activity in flight and n-back working memory training, confirming tDCS-related modulation of brain processes involved in executive function. This modulation corresponded to a significantly different online and offline learning rates for working memory accuracy and decreased inter-subject behavioral variability in flight and n-back tasks in the DLPFC stimulation group. Additionally, tDCS of left M1 increased parietal alpha power during flight tasks and tDCS to the right DLPFC increased midline frontal theta-band power during n-back and flight tasks. These results demonstrate a modulation of group variance in skill acquisition through an increasing in learned skill consistency in cognitive and real-world tasks with tDCS. Further, tDCS performance improvements corresponded to changes in electrophysiological and blood-oxygenation activity of the DLPFC and motor cortices, providing a stronger link between modulated neuronal function and behavior.

Using precise word timing information improves decoding accuracy in a multiband-accelerated multimodal reading experiment

ABSTRACT The blood-oxygen-level-dependent (BOLD) signal measured in functional magnetic resonance imaging (fMRI) experiments is generally regarded as sluggish and poorly suited for probing neural function at the rapid timescales involved in sentence comprehension. However, recent studies have shown the value of acquiring data with very short repetition times (TRs), not merely in terms of improvements in contrast to noise ratio (CNR) through averaging, but also in terms of additional fine-grained temporal information. Using multiband-accelerated fMRI, we achieved whole-brain scans at 3-mm resolution with a TR of just 500 ms at both 3T and 7T field strengths. By taking advantage of word timing information, we found that word decoding accuracy across two separate sets of scan sessions improved significantly, with better overall performance at 7T than at 3T. The effect of TR was also investigated; we found that substantial word timing information can be extracted using fast TRs, with diminishing benefits beyond TRs of 1000 ms.

A Neurostimulation-based Advanced Training System for Human Performance Augmentation

Transcranial direct current stimulation (tDCS) is an emerging therapeutic tool for many neuropsychiatric disorders with applications in enhancing learning and memory. Despite human clinical trials showing promising results for memory retention and recall, the underlying cellular mechanisms that promote DC stimulationinduced plasticity have not been characterized. tDCS produces a weak electric field (0.1 V/m per mA current applied) across the brain, which results in incremental passive membrane polarization (up to 0.4 mV polarization per V/m in cortical pyramidal cells). The change in membrane potential alters current flow through voltagegated ion channels that can modify synaptic transmission and synaptic plasticity. We have previously demonstrated in vitro that somatic polarization by DC stimulation modulates neuronal output, specifically firing rate and spike timing. Changes in synaptic input can also have profound effects on neuronal output (firing rate and spike timing). In this study we show that DC stimulation of the rat hippocampal CA1 modifies the neuronal input-output function. Our results indicate DC stimulation can change the threshold and latency of population spikes as a function of the EPSP amplitude, electric field magnitude and duration. Additionally, we demonstrate in vitro that DC stimulation can amplify neuronal output by modulating synaptic efficacy. These results indicate an important role of DC stimulation in modifying the neural code, synaptic computation and ultimately facilitating plasticity.

Neuromorphic and early warning behavior-based authentication for mobile devices

Finding the balance between security, privacy, and usability for mobile authentication has been an active area of research for the past several years. Many researchers have taken advantage of the availability of multiple sensors on mobile devices and have used these data to train classifiers to authenticate users. For example, implicit authentication algorithms have been developed based on behavior patterns identified from a combination of sensors including location, co-location, application usage, biometric measurements, continuity of interaction between the user and the phone, and possession of the phone [1,2,3,4,5]. Furthermore, the onboard sensors of mobile devices have previously been used to identify users based on touch [6] and fusions of touch and speech inputs [7]. However, a system utilizing low-power onboard electronics for anomaly detection and user classification is lacking. Here, we report on the performance of two subsystems tested in a controlled use scenario to classify and authenticate users of a mobile device. The overall system utilizes two subsystems for anomaly detection and user classification: (1) a neuromorphic chip for continuous, low-power, online monitoring and classification, and (2) an early warning system (EWS) algorithm for longer duration time-series behavioral and biometric classification.

The neural basis of decision-making during sensemaking: Implications for human-system interaction

We have created a high-fidelity model of 9 regions of the brain involved in making sense of complex and uncertain situations. Sense making is a proactive form of situation awareness requiring sifting through information of various types to form hypotheses about evolving situations. The MINDS model (Mirroring Intelligence in a Neural Description of Sensemaking) reveals the neural principles and cognitive tradeoffs that explain weaknesses in human reasoning and decision-making.

Does Neurotechnology Produce a Better Brain

Neurotechnologies in clinical applications can image the brain noninvasively, but they typically require surgical insertion to stimulate it. Although an increasingly popular alternative is to use noninvasive stimulation to enhance nervous system functions, questions about its effectiveness and ethical use remain unanswered.

Investigation of Biases and Compensatory Strategies Using a Probabilistic Variant of the Wisconsin Card Sorting Test

The Wisconsin Card Sorting Test (WCST) evaluates a subject’s ability to shift to a new pattern of behavior in response to the presentation of unexpected negative feedback. The present study introduces a novel version of the traditional WCST by integrating a probabilistic component into its traditional rule shifting to add uncertainty to the task, as well as the option to forage for information during any particular trial. These changes transformed a task that is trivial for neurotypical individuals into a challenging environment useful for evaluating biases and compensatory strategizing. Sixty subjects performed the probabilistic WCST at four uncertainty levels to determine the effect of uncertainty on subject performance and strategy. Results revealed that increasing the level of uncertainty during a run of trials correlated with a reduction in rational strategizing in favor of both random choice and information foraging, evoking biases and suboptimal strategies such as satisfaction of search, negativity bias, and probability matching.

Neuromorphic and Early Warning behavior-based authentication in common theft scenarios

With increased rates of smartphone theft over the past decade, mobile authentication systems that operate on a continual basis are a necessity to meet increasing demands for user privacy, device usage, and authentication accuracy. Rather than forcing end users to continually self-authenticate via password pins or through other means on a time-interval basis [2–7], a system that continuously authenticates users provides a more frictionless relationship between a users device and its physical security. Such a system, if effectively operating on a low-powered, unobtrusive, and secure basis, would make it viable for most consumer mobile devices. In this paper, we build upon our work in [1] to provide a novel authentication scheme that meets these requirements for a commonly adopted system. Our system, iSentinel, hopes to provide an unobtrusive, low-powered solution for detecting and responding to common theft scenarios by continuously authenticating mobile devices in use cases such as walking, texting, and driving.