4 research outputs found
Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation
Objective. We investigated the neural correlates of workload buildup in a
fine visuomotor task called the boundary avoidance task (BAT). The BAT has been
known to induce naturally occurring failures of human-machine coupling in high
performance aircraft that can potentially lead to a crash; these failures are
termed pilot induced oscillations (PIOs). Approach. We recorded EEG and
pupillometry data from human subjects engaged in a flight BAT simulated within
a virtual 3D environment. Main results. We find that workload buildup in a BAT
can be successfully decoded from oscillatory features in the
electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma
spectral bands of the EEG all contribute to successful decoding, however gamma
band activity with a lateralized somatosensory topography has the highest
contribution, while theta band activity with a frontocentral topography has the
most robust contribution in terms of real world usability. We show that the
output of the spectral decoder can be used to predict PIO susceptibility. We
also find that workload buildup in the task induces pupil dilation, the
magnitude of which is significantly correlated with the magnitude of the
decoded EEG signals. These results suggest that PIOs may result from the
dysregulation of cortical networks such as the locus coeruleus (LC) anterior
cingulate cortex (ACC) circuit. Significance. Our findings may generalize to
similar control failures in other cases of tight man machine coupling where
gains and latencies in the control system must be inferred and compensated for
by the human operators. A closed-loop intervention using neurophysiological
decoding of workload buildup that targets the LC ACC circuit may positively
impact operator performance in such situations.Comment: Manuscript as initially submitted to Journal of Neural Engineering in
March, 201
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Fast Bootstrapping and Permutation Testing for Assessing Reproducibility and Interpretability of Multivariate fMRI Decoding Models
Multivariate decoding models are increasingly being applied to functional magnetic imaging (fMRI) data to interpret the distributed neural activity in the human brain. These models are typically formulated to optimize an objective function that maximizes decoding accuracy. For decoding models trained on full-brain data, this can result in multiple models that yield the same classification accuracy, though some may be more reproducible than others—i.e. small changes to the training set may result in very different voxels being selected. This issue of reproducibility can be partially controlled by regularizing the decoding model. Regularization, along with the cross-validation used to estimate decoding accuracy, typically requires retraining many (often on the order of thousands) of related decoding models. In this paper we describe an approach that uses a combination of bootstrapping and permutation testing to construct both a measure of cross-validated prediction accuracy and model reproducibility of the learned brain maps. This requires re-training our classification method on many re-sampled versions of the fMRI data. Given the size of fMRI datasets, this is normally a time-consuming process. Our approach leverages an algorithm called fast simultaneous training of generalized linear models (FaSTGLZ) to create a family of classifiers in the space of accuracy vs. reproducibility. The convex hull of this family of classifiers can be used to identify a subset of Pareto optimal classifiers, with a single-optimal classifier selectable based on the relative cost of accuracy vs. reproducibility. We demonstrate our approach using full-brain analysis of elastic-net classifiers trained to discriminate stimulus type in an auditory and visual oddball event-related fMRI design. Our approach and results argue for a computational approach to fMRI decoding models in which the value of the interpretation of the decoding model ultimately depends upon optimizing a joint space of accuracy and reproducibility
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Exposing Internal Attentional Brain States using Single-Trial EEG Analysis with Combined Imaging Modalities
The goal of this dissertation is to explore the neural correlates of endogenous task-related attentional modulations. Natural fluctuations in task engagement are challenging to study, primarily because they are by nature not event related and thus cannot be controlled experimentally. Here we exploit well-accepted links between attention and various measures of neural activity while subjects perform simple target detection tasks that leave their minds free to wander. We use multimodal neuroimaging, specifically simultaneous electroencephalograpy and functional magnetic resonance imaging (EEG-fMRI) and EEG-pupillometry, with data-driven machine learning methods and study activity across the whole brain.
We investigate BOLD fMRI correlates of EEG variability spanning each trial, enabling us to unravel a cascade of attention-related activations and determine their temporal ordering. We study activity during auditory and visual paradigms independently, and we also combine data to investigate supra modal attention systems. Without aiming to study known attention-related functional brain networks, we found correlates of attentional modulations in areas representative of the default mode network (DMN), ventral attention network (VAN), locus coeruleus norepinephrine (LC-NE) system, and regions implicated in generation of the extensively-studied P300 EEG response to target stimuli. Our results reveal complex interactions between known attentional systems, and do so non-invasively to study normal fluctuations of task engagement in the human brain