Individualized Prediction of Task Performance Decline Using Pre-Task Resting-State Functional Connectivity.

As a common complaint in contemporary society, mental fatigue is a key element in the deterioration of the daily activities known as time-on-task (TOT) effect, making the prediction of fatigue-related performance decline exceedingly important. However, conventional group-level brain-behavioral correlation analysis has the limitation of generalizability to unseen individuals and fatigue prediction at individual-level is challenging due to the significant differences between individuals both in task performance efficiency and brain activities. Here, we introduced a cross-validated data-driven analysis framework to explore, for the first time, the feasibility of utilizing pre-task idiosyncratic resting-state functional connectivity (FC) on the prediction of fatigue-related task performance degradation at individual level. Specifically, two behavioral metrics, namely ∆RT (between the most vigilant and fatigued states) and TOTslope over the course of the 15-min sustained attention task, were estimated among three sessions from 37 healthy subjects to represent fatigue-related individual behavioral impairment. Then, a connectome-based prediction model was employed on pre-task resting-state FC features, identifying the network-related differences that contributed to the prediction of performance deterioration. As expected, prominent populational TOT-related performance declines were revealed across three sessions accompanied with substantial inter-individual differences. More importantly, we achieved significantly high accuracies for individualized prediction of both TOT-related behavioral impairment metrics using pre-task neuroimaging features. Despite the distinct patterns between both behavioral metrics, the identified top FC features contributing to the individualized predictions were mainly resided within/between frontal, temporal and parietal areas. Overall, our results of individualized prediction framework extended conventional correlation/classification analysis and may represent a promising avenue for the development of applicable techniques that allow precaution of the TOT-related performance declines in real-world scenarios.

Brain Network Reorganization During Visual Search Task Revealed by a Network Analysis of Fixation-Related Potential.

Visual search is ubiquitous in daily life and has attracted substantial research interest over the past decades. Although accumulating evidence has suggested complex neurocognitive processes underlying visual search, the neural communication across the brain regions remains poorly understood. The present work aimed to fill this gap by investigating functional networks of fixation-related potential (FRP) during the visual search task. Multi-frequency electroencephalogram (EEG) networks were constructed from 70 university students (male/female = 35/35) using FRPs time-locked to target and non-target fixation onsets, which were determined by concurrent eye-tracking data. Then graph theoretical analysis (GTA) and a data-driven classification framework were employed to quantitatively reveal the divergent reorganization between target and non-target FRPs. We found distinct network architectures between target and non-target mainly in the delta and theta bands. More importantly, we achieved a classification accuracy of 92.74% for target and non-target discrimination using both global and nodal network features. In line with the results of GTA, we found that the integration corresponding to target and non-target FRPs significantly differed, while the nodal features contributing most to classification performance primarily resided in the occipital and parietal-temporal areas. Interestingly, we revealed that females exhibited significantly higher local efficiency in delta band when focusing on the search task. In summary, these results provide some of the first quantitative insights into the underlying brain interaction patterns during the visual search process.

Modulating break types induces divergent low band EEG processes during post-break improvement: A power spectral analysis.

Conventional wisdom suggests mid-task rest as a potential approach to relieve the time-on-task (TOT) effect while accumulating evidence indicated that acute exercise might also effectively restore mental fatigue. However, few studies have explored the neural mechanism underlying these different break types, and the results were scattered. This study provided one of the first looks at how different types of fatigue-recovery break exerted influence on the cognitive processes by evaluating the corresponding behavioral improvement and neural response (EEG power spectral) in a sustained attention task. Specifically, 19 participants performed three sessions of psychomotor vigilance tasks (PVT), with one session including a continuous 30-min PVT while the other two sessions additionally inserted a 15-min mid-task cycling and rest break, respectively. For behavioral performance, both types of break could restore objective vigilance transiently, while subjective feeling was only maintained after mid-task rest. Moreover, divergent patterns of EEG change were observed during post-break improvement. In detail, relative theta decreased and delta increased immediately after mid-task exercise, while decreased delta was found near the end of the rest-inserted task. Meanwhile, theta and delta could serve as neurological indicators to predict the reaction time change for exercise and rest intervention, respectively. In sum, our findings provided novel evidence to demonstrate divergent neural patterns following the mid-task exercise and rest intervention to counter TOT effects, which might lead to new insights into the nascent field of neuroergonomics for mental fatigue restoration.

Dynamic Reorganization of Functional Connectivity During Post-Break Task Reengagement.

Because of the undesired fatigue-related consequences, accumulating efforts have been made to find an effective intervention to alleviate the suboptimal cognitive function caused by mental fatigue. Nonetheless, limitations of intervention and evaluation methods may hinder the revealing of underlying neural mechanisms of fatigue recovery. Through the newly-developed dynamic functional connectivity (FC) analysis framework, this study aims to investigate the effects of two types of mid-task interventions (i.e., rest-break and moderate-intensity exercise-break) on the dynamic reorganization of FC during the execution of psychomotor vigilance test (PVT). Using a sliding window approach, temporal brain networks within each frequency band (i.e., δ , θ , α , & ß ) were estimated before and immediately after the intervention, and towards the end of the task to investigate the immediate and delayed effects respectively during post-break task reengagement. Behaviourally, similar beneficial effects of exercise- and rest-break on performance were observed, manifested by the immediate improvements after both interventions and a long-lasting influence towards the end of tasks. Moreover, temporal brain networks assessment showed significant immediate decreases of fluctuability, which was followed by an increase of fluctuability towards the end of intervention tasks. Furthermore, the temporal nodal measure revealed the channels with significant differences across tasks were mainly resided in the fronto-parietal areas that exhibited interesting frequency-dependent distribution. The observations of immediate and delayed dynamic FC reorganizations extend previous fatigue-related intervention and static FC studies, and provide new insight into the dynamic characteristics of FC during post-break task reengagement.

Inferring the Individual Psychopathologic Deficits With Structural Connectivity in a Longitudinal Cohort of Schizophrenia.

The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis.

EEG Fingerprints of Task-Independent Mental Workload Discrimination.

In the nascent field of neuroergonomics, mental workload assessment is one of the most important issues and has an apparent significance in real-world applications. Although prior research has achieved efficient single-task classification, scatted studies on cross-task mental workload assessment usually result in unsatisfactory performance. Here, we introduce a data-driven analysis framework to overcome the challenges regarding task-independent workload assessment using a fusion of EEG spectral characteristics and unveil the common neural mechanisms underlying mental workload. Specifically, multi-frequency power spectrum and functional connectivity (FC) were estimated for two workload levels in two working-memory tasks performed by 40 healthy participants, subsequently being fed into a machine learning approach to obtain the importance of each feature vector and evaluate classification performance in a cross-task fashion. Our framework achieved a classification accuracy of 0.94 for task-independent mental workload discrimination. Further investigation of the designated features in terms of their spectral and localization properties revealed task-independent common patterns in the neural mechanisms governing workload. In particular, increased workload was associated with elevated frontal delta and theta power but reduced parietal alpha power, whereas FC exhibited complex frequency- and region-dependent alterations. By implication, the employment of the EEG feature fusion emphasized their utility in serving as promising indicators for different workload conditions applications.

Gibberellins modulate local auxin biosynthesis and polar auxin transport by negatively affecting flavonoid biosynthesis in the root tips of rice.

As critical signalling molecules, both gibberellin (GA) and auxin play essential roles in regulating root elongation, and many studies have been shown that auxin influences GA biosynthesis and signalling. However, the mechanism by which GA affects auxin in root elongation is still unknown. In this study, root elongation and DR5-GUS activity were analyzed in rice seedlings. Paclobutrazol-induced short root phenotypes could be partially reversed by co-treatment with IAA, and the inhibition of root elongation caused by naphthylphthalamic acid could be partially reversed when plants were co-treated with GA. DR5-GUS activity was increased in the presence of GA and was reduced at the root tip of paclobutrazol-treated seedlings, indicating that GA could regulate local auxin biosynthesis and polar auxin transport (PAT) in rice root tips. Our RNA-seq analysis showed that GA was involved in the regulation of flavonoid biosynthesis. Flavonoid accumulation level in ks1 root tips was significantly increased and negatively correlated with GA content in GA- and PAC-treated seedlings. GA also rescued the decreased DR5-GUS activity induced by quercetin in rice root tips, confirming that flavonoids act as an intermediary in GA-mediated auxin biosynthesis and PAT. Based on RNA-seq and qPCR analyses, we determined that GA regulates local auxin biosynthesis and polar auxin transport by modulating the expression of OsYUCCA6 and PIN. Our findings provide valuable new insights into the interactions between GA and auxin in the root tips of rice.

EEG Functional Connectivity Predicts Individual Behavioural Impairment During Mental Fatigue.

Mental fatigue deteriorates ability to perform daily activities - known as time-on-task (TOT) effect and becomes a common complaint in contemporary society. However, an applicable technique for fatigue detection/prediction is hindered due to substantial inter-subject differences in behavioural impairment and brain activity. Here, we developed a fully cross-validated, data-driven analysis framework incorporating multivariate regression model to explore the feasibility of utilizing functional connectivity (FC) to predict the fatigue-related behavioural impairment at individual level. EEG was recorded from 40 healthy adults as they performed a 30-min high-demanding sustained attention task. FC were constructed in different frequency bands using three widely-adopted methods (including coherence, phase log index (PLI), and partial directed coherence (PDC)) and contrasted between the most vigilant and fatigued states. The differences of individual FC (diff (FC)) were considered as features; whereas the TOT slop across the course of task and the differences of reaction time ( ∆ RT) between the most vigilant and fatigued states were chosen to represent behavioural impairments. Behaviourally, we found substantial inter-subject differences of impairments. Furthermore, we achieved significantly high accuracies for individualized prediction of behavioural impairments using diff(PDC). The identified top diff(PDC) features contributing to the individualized predictions were found mainly in theta and alpha bands. Further interrogation of diff(PDC) features revealed distinct patterns between the TOT slop and ∆ RT prediction models, highlighting the complex neural mechanisms of mental fatigue. Overall, the current findings extended conventional brain-behavioural correlation analysis to individualized prediction of fatigue-related behavioural impairments, thereby moving a step forward towards development of applicable techniques for quantitative fatigue monitoring in real-world scenarios.