Pupillary response is associated with the reset and switching of functional brain networks during salience processing.

The interface between processing internal goals and salient events in the environment involves various top-down processes. Previous studies have identified multiple brain areas for salience processing, including the salience network (SN), dorsal attention network, and the locus coeruleus-norepinephrine (LC-NE) system. However, interactions among these systems in salience processing remain unclear. Here, we simultaneously recorded pupillometry, EEG, and fMRI during an auditory oddball paradigm. The analyses of EEG and fMRI data uncovered spatiotemporally organized target-associated neural correlates. By modeling the target-modulated effective connectivity, we found that the target-evoked pupillary response is associated with the network directional couplings from late to early subsystems in the trial, as well as the network switching initiated by the SN. These findings indicate that the SN might cooperate with the pupil-indexed LC-NE system in the reset and switching of cortical networks, and shed light on their implications in various cognitive processes and neurological diseases.

Exploring biological effects of MoS2 nanosheets on native structures of α-helical peptides.

Recent reports of mono- and few-layer molybdenum disulfide (MoS2), a representative transition metal dichacogenide (TMD), as antibacterial and anticancer agents have shed light on their potential in biomedical applications. To better facilitate these promising applications, one needs to understand the biological effects of these TMDs as well, such as their potential adverse effects on protein structure and function. Here, we sought to understand the interaction of MoS2 nanosheets with peptides using molecular dynamics simulations and a simple model polyalanine with various lengths (PAn, n = 10, 20, 30, and 40; mainly α - helices). Our results demonstrated that MoS2 monolayer has an exceptional capability to bind all peptides in a fast and strong manner. The strong attraction from the MoS2 nanosheet is more than enough to compensate the energy needed to unfold the peptide, regardless of the length, which induces drastic disruptions to the intra-peptide hydrogen bonds and subsequent secondary structures of α - helices. This universal phenomenon may point to the potential nanotoxicity of MoS2 when used in biological systems. Moreover, these results aligned well with previous findings on the potential cytotoxicity of TMD nanomaterials.

Pupil-linked arousal correlates with neural activity prior to sensorimotor decisions.

Objective.Sensorimotor decisions require the brain to process external information and combine it with relevant knowledge prior to actions. In this study, we explore the neural predictors of motor actions in a novel, realistic driving task designed to study decisions while driving.Approach.Through a spatiospectral assessment of functional connectivity during the premotor period, we identified the organization of visual cortex regions of interest into a distinct scene processing network. Additionally, we identified a motor action selection network characterized by coherence between the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC).Main results.We show that steering behavior can be predicted from oscillatory power in the visual cortex, DLPFC, and ACC. Power during the premotor periods (specific to the theta and beta bands) correlates with pupil-linked arousal and saccade duration.Significance.We interpret our findings in the context of network-level correlations with saccade-related behavior and show that the DLPFC is a key node in arousal circuitry and in sensorimotor decisions.

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

OBJECTIVE: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs). METHODS: EEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects. RESULTS: We show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification. CONCLUSION: The presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings. SIGNIFICANCE: We present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG.

A Recurrent Neural Network for Attenuating Non-cognitive Components of Pupil Dynamics.

There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition and performance. Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than for those having better performance on the post-viewing test, (2) the residuals of the RNN (LSTM) model had the highest correlation with subject post-viewing test scores and (3) the residuals had the highest discriminability (assessed via area under the ROC curve, AUC) for classifying high and low test performers, compared to the true pupil size or the RNN model predictions. This suggests that deep learning sequence models may be good for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal.

Molecular Structure and Dynamics of Water on Pristine and Strained Phosphorene: Wetting and Diffusion at Nanoscale.

Phosphorene, a newly fabricated two-dimensional (2D) nanomaterial, has emerged as a promising material for biomedical applications with great potential. Nonetheless, understanding the wetting and diffusive properties of bio-fluids on phosphorene which are of fundamental importance to these applications remains elusive. In this work, using molecular dynamics (MD) simulations, we investigated the structural and dynamic properties of water on both pristine and strained phosphorene. Our simulations indicate that the diffusion of water molecules on the phosphorene surface is anisotropic, with strain-enhanced diffusion clearly present, which arises from strain-induced smoothing of the energy landscape. The contact angle of water droplet on phosphorene exhibits a non-monotonic variation with the transverse strain. The structure of water on transverse stretched phosphorene is demonstrated to be different from that on longitudinal stretched phosphorene. Moreover, the contact angle of water on strained phosphorene is proportional to the quotient of the longitudinal and transverse diffusion coefficients of the interfacial water. These findings thereby offer helpful insights into the mechanism of the wetting and transport of water at nanoscale, and provide a better foundation for future biomedical applications of phosphorene.

Your eyes give you away: prestimulus changes in pupil diameter correlate with poststimulus task-related EEG dynamics.

Pupillary measures have been linked to arousal and attention as well as activity in the brainstem's locus coeruleus norepinephrine (LC-NE) system. Similarly, there is evidence that evoked EEG responses, such as the P3, might have LC-NE activity as their basis. Since it is not feasible to record electrophysiological data directly from the LC in humans due to its location in the brainstem, an open question has been whether pupillary measures and EEG variability can be linked in a meaningful way to shed light on the nature of the LC-NE role in attention and arousal. We used an auditory oddball task with a data-driven approach to learn task-relevant projections of the EEG, for windows of data spanning the entire trial. We investigated linear and quadratic relationships between the evoked EEG along these projections and both prestimulus (baseline) and poststimulus (evoked dilation) pupil diameter measurements. We found that baseline pupil diameter correlates with early (175-200 ms) and late (350-400 ms) EEG component variability, suggesting a linear relationship between baseline (tonic) LC-NE activity and evoked EEG. We found no relationships between evoked EEG and evoked pupil dilation, which is often associated with evoked (phasic) LC activity. After regressing out reaction time (RT), the correlation between EEG variability and baseline pupil diameter remained, suggesting that such correlation is not explainable by RT variability. We also investigated the relationship between these pupil measures and prestimulus EEG alpha activity, which has been reported as a marker of attentional state, and found a negative linear relationship with evoked pupil dilation. In summary, our results demonstrate significant relationships between prestimulus and poststimulus neural and pupillary measures, and they provide further evidence for tight coupling between attentional state and evoked neural activity and for the role of cortical and subcortical networks underlying the process of target detection.