Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task.

Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectro-spatial features of the resting-state electroencephalograms (EEG). In this study, ten healthy volunteers have participated in fixed-sequence, varying-duration sessions of sustained attention to response task (SART) for over 100 minutes. A novel and adaptive cumulative vigilance scoring (CVS) scheme is proposed based on tonic performance and response time. Multiple linear regression (MLR) using feature relevance analysis has shown that average CVS, average response time, and variabilities of these scores can be predicted (p < 0.05) from the resting-state band-power ratios of EEG signals. Cross-validated neural networks also captured different associations for narrow-band beta and wide-band gamma and differences between the high- and low-attention networks in temporal regions. The proposed framework and these first findings on stable and significant attention predictors from the power ratios of resting-state EEG can be useful in brain-computer interfacing and vigilance monitoring applications.

Prediction of Response Time and Vigilance Score in a Sustained Attention Task from Pre-trial Phase Synchrony using Deep Neural Networks.

A real-time assessment of sustained attention requires a continuous performance measure ideally obtained objectively and without disrupting the ongoing behavioral patterns. In this work, we investigate whether the phasic functional connectivity patterns from short- and long-range attention networks can predict the tonic performance in a long Sustained Attention to Response Task (SART). Pre-trial phase synchrony indices (PSIs) from individual experiment blocks are used as features for assessment of the proposed average cumulative vigilance score (CVS) and hit response time (HRT). Deep neural networks (DNNs) with the mean-squared-error (MSE) loss function outperformed the ones with mean-absolute-error (MAE) in 4-fold cross-validations. PSI features from the 16-20 Hz beta sub-band obtained the lowest RMSE of 0.043 and highest correlation of 0.806 for predicting the average CVS, and the alpha oscillation PSIs resulted in an RMSE of 51.91 ms and a correlation of 0.903 for predicting the mean HRT. The proposed system can be used for monitoring performance of users susceptible to hypo- or hyper-vigilance and the subsequent system adaptation without implemented eye trackers. To the best of our knowledge, functional connectivity features in general and phase locking values in particular have not been used for regression models of vigilance variations with neural networks.

Dendritic spine classification using shape and appearance features based on two-photon microscopy.

BACKGROUND: Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. The first step towards understanding the structure-function relationships is to classify spine shapes into the main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is mostly performed manually, which is a time-intensive task and prone to subjectivity. NEW METHOD: We propose an automated method to classify dendritic spines using shape and appearance features based on challenging two-photon laser scanning microscopy (2PLSM) data. Disjunctive Normal Shape Models (DNSM) is a recently proposed parametric shape representation. We perform segmentation of spine images by applying DNSM and use the resulting representation as shape features. Furthermore, we use Histogram of oriented gradients (HOG) to extract appearance features. In this context, we propose a kernel density estimation (KDE) based framework for dendritic spine classification, which uses these shape and appearance features. RESULTS: Our shape and appearance features based approach combined with Neural Network (NN) correctly classifies 87.06% of spines on a dataset of 456 spines. COMPARISON WITH EXISTING METHODS: Our proposed method outperforms standard morphological feature based approaches. Our KDE based framework also enables neuroscientists to analyze the separability of spine shape classes in the likelihood ratio space, which leads to further insights about nature of the spine shape analysis problem. CONCLUSIONS: Results validate that performance of our proposed approach is comparable to a human expert. It also enable neuroscientists to study shape statistics in the likelihood ratio space.