Junhua Li

The effects of a mid-task break on the brain connectome in healthy participants: A resting-state functional MRI study

ABSTRACT Although rest breaks are commonly administered as a countermeasure to reduce mental fatigue and boost cognitive performance, the effects of taking a break on behavior are not consistent. Moreover, our understanding of the underlying neural mechanisms of rest breaks and how they modulate mental fatigue is still rudimentary. In this study, we investigated the effects of receiving a rest break on the topological properties of brain connectivity networks via a two‐session experimental paradigm, in which one session comprised four successive blocks of a mentally demanding visual selective attention task (No‐rest session), whereas the other contained a rest break between the second and third task blocks (Rest session). Functional brain networks were constructed using resting‐state functional MRI data recorded from 20 healthy adults before and after the performance of the task blocks. Behaviorally, subjects displayed robust time‐on‐task (TOT) declines, as reflected by increasingly slower reaction time as the test progressed and lower post‐task self‐reported ratings of engagement. However, we did not find a significant effect on task performance due to administering a mid‐task break. Compared to pre‐task measurements, post‐task functional brain networks demonstrated an overall decrease of optimal small‐world properties together with lower global efficiency. Specifically, we found TOT‐related reduced nodal efficiency in brain regions that mainly resided in the subcortical areas. More interestingly, a significant block‐by‐session interaction was revealed in local efficiency, attributing to a significant post‐task decline in No‐rest session and a preserved local efficiency when a mid‐task break opportunity was introduced in the Rest session. Taken together, these findings augment our understanding of how the resting brain reorganizes following the accumulation of prolonged task, suggest dissociable processes between the neural mechanisms of fatigue and recovery, and provide some of the first quantitative insights into the cognitive neuroscience of work and rest. HighlightsWe tested the effect of a break on time‐on‐task (TOT) and the brain connectome.TOT impaired small‐world topology, as seen in less global efficiency.TOT reduced regional communication efficiency, mainly in subcortical areas.A short break did not have significant effects on task performance.Improved local efficiency occurred at the end of blocks following a break.

Modular-level alterations of structure-function coupling in schizophrenia connectome.

Convergent evidences have revealed that schizophrenia is associated with brain dysconnectivity, which leads to abnormal network organization. However, discrepancies were apparent between the structural connectivity (SC) and functional connectivity (FC) studies, and the relationship between structural and functional deficits in schizophrenia remains largely unknown. In this study, resting‐state functional magnetic resonance imaging and structural diffusion tensor imaging were performed in 20 patients with schizophrenia and 20 matched healthy volunteers (patients/controls = 19/17 after head motion rejection). Functional and structural brain networks were obtained for each participant. Graph theoretical approaches were employed to parcellate the FC networks into functional modules. The relationships between the entries of SC and FC were estimated within each module to identify group differences and their correlations with clinical symptoms. Although five common functional modules (including the default mode, occipital, subcortical, frontoparietal, and central modules) were identified in both groups, the patients showed a significantly reduced modularity in comparison with healthy participants. Furthermore, we found that schizophrenia‐related aberrations of SC–FC coupling exhibited complex patterns among modules. Compared with controls, patients showed an increased SC–FC coupling in the default mode and the central modules. Moreover, significant SC–FC decoupling was demonstrated in the occipital and the subcortical modules, which was associated with longer duration of illness and more severe clinical manifestations of schizophrenia. Taken together, these findings demonstrated that altered module‐dependent SC–FC coupling may underlie abnormal brain function and clinical symptoms observed in schizophrenia and highlighted the potential for using new multimodal neuroimaging biomarkers for diagnosis and severity evaluation of schizophrenia. Hum Brain Mapp 38:2008–2025, 2017.

EEG and Eye Tracking Demonstrate Vigilance Enhancement with Challenge Integration

Maintaining vigilance is possibly the first requirement for surveillance tasks where personnel are faced with monotonous yet intensive monitoring tasks. Decrement in vigilance in such situations could result in dangerous consequences such as accidents, loss of life and system failure. In this paper, we investigate the possibility to enhance vigilance or sustained attention using “challenge integration,” a strategy that integrates a primary task with challenging stimuli. A primary surveillance task (identifying an intruder in a simulated factory environment) and a challenge stimulus (periods of rain obscuring the surveillance scene) were employed to test the changes in vigilance levels. The effect of integrating challenging events (resulting from artificially simulated rain) into the task were compared to the initial monotonous phase. EEG and eye tracking data is collected and analyzed for n = 12 subjects. Frontal midline theta power and frontal theta to parietal alpha power ratio which are used as measures of engagement and attention allocation show an increase due to challenge integration (p < 0.05 in each case). Relative delta band power of EEG also shows statistically significant suppression on the frontoparietal and occipital cortices due to challenge integration (p < 0.05). Saccade amplitude, saccade velocity and blink rate obtained from eye tracking data exhibit statistically significant changes during the challenge phase of the experiment (p < 0.05 in each case). From the correlation analysis between the statistically significant measures of eye tracking and EEG, we infer that saccade amplitude and saccade velocity decrease with vigilance decrement along with frontal midline theta and frontal theta to parietal alpha ratio. Conversely, blink rate and relative delta power increase with vigilance decrement. However, these measures exhibit a reverse trend when challenge stimulus appears in the task suggesting vigilance enhancement. Moreover, the mean reaction time is lower for the challenge integrated phase (RTmean = 3.65 ± 1.4s) compared to initial monotonous phase without challenge (RTmean = 4.6 ± 2.7s). Our work shows that vigilance level, as assessed by response of these vital signs, is enhanced by challenge integration.

Dynamic Functional Segregation and Integration in Human Brain Network During Complex Tasks

The analysis of the topology and organization of brain networks is known to greatly benefit from network measures in graph theory. However, to evaluate dynamic changes of brain functional connectivity, more sophisticated quantitative metrics characterizing temporal evolution of brain topological features are required. To simplify conversion of time-varying brain connectivity to a static graph representation is straightforward but the procedure loses temporal information that could be critical in understanding the brain functions. To extend the understandings of functional segregation and integration to a dynamic fashion, we recommend dynamic graph metrics to characterise temporal changes of topological features of brain networks. This study investigated functional segregation and integration of brain networks over time by dynamic graph metrics derived from EEG signals during an experimental protocol: performance of complex flight simulation tasks with multiple levels of difficulty. We modelled time-varying brain functional connectivity as multi-layer networks, in which each layer models brain connectivity at time window

Eeg cortical connectivity analysis of working memory reveals topological reorganization in theta and alpha bands

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A robotic knee exoskeleton for walking assistance and connectivity topology exploration in EEG signal

. Dynamic graph metrics were calculated to quantify temporal and topological properties of the network. Results show that brain networks under the performance of complex tasks reveal a dynamic small-world architecture with a number of frequently connected nodes or hubs, which supports the balance of information segregation and integration in brain over time. The results also show that greater cognitive workloads caused by more difficult tasks induced a more globally efficient but less clustered dynamic small-world functional network. Our study illustrates that task-related changes of functional brain network segregation and integration can be characterized by dynamic graph metrics.

Mid-Task Break Improves Global Integration of Functional Connectivity in Lower Alpha Band.

Numerous studies have revealed various working memory (WM)-related brain activities that originate from various cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the brain network in WM in the cortical space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize the topological properties of the brain functional network in the theta and alpha frequency bands during WM tasks. Twenty-eight subjects performed visual n-back tasks at two difficulty levels, i.e., 0-back (control task) and 2-back (WM task). After preprocessing, Electroencephalogram (EEG) signals were projected into the source space and 80 cortical brain regions were selected for further analysis. Subsequently, the theta- and alpha-band networks were constructed by calculating the Pearson correlation coefficients between the power series (obtained by concatenating the power values of all epochs in each session) of all pairs of brain regions. Graph theoretical approaches were then employed to estimate the topological properties of the brain networks at different WM tasks. We found higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task. Moreover, compared to the 0-back task, altered regional centrality was revealed in the 2-back task in various brain regions that mainly resided in the frontal, temporal and occipital lobes, with distinct presentations in the theta and alpha bands. In addition, significant negative correlations were found between the reaction time with the average path length of the theta-band network and the local clustering of the alpha-band network, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks.

Eye tracking and EEG synchronization to analyze microsaccades during a workload task

Disability of lower limb affects voluntary movements and results in a low quality of life. Although people with disabled lower limb can restore their movements through assistive devices like wheelchair, such manner is not as natural as that healthy people perform locomotion. It would be better to regain movements by restoring the function of lower limb itself. Hence, an orthosis that can be worn on the disable lower limb might be a good choice. In addition, it is of interest to explore brain activities during walking with and without an orthosis. In this paper, we introduced a robotic knee exoskeleton that can provide assistive torque to facilitate walking. Three different conditions were employed here, namely free walking (FW, without exoskeleton), zero force (ZF, with the exoskeleton but no torque was provided), and assistive force (AF, torque was provided by the exoskeleton). During the walking, electrophysiological signals were simultaneously recorded. Partial directed coherence (PDC) was employed to measure connectivity strengths between channels and the obtained effective connectivity network was quantitatively assessed through a standard graph theoretical analysis framework. We found that the clustering coefficient was significantly increased when assistive torque was provided by the exoskeleton compared with FW and ZF. A decrease in path length was also found when assistive torque was provided. Our findings demonstrated that topological patterns of brain activities were distinctly different when people received assistive torque during walking. This study could be of meaningful significance on further orthosis development from the perspective of brain electrophysiological activity and insight into the understanding for brain plasticity and neural rehabilitation.

Identification of gait-related brain activity using electroencephalographic signals

Numerous efforts have been devoted to revealing neurophysiological mechanisms of mental fatigue, aiming to find an effective way to reduce the undesirable fatigue-related outcomes. Until recently, mental fatigue is thought to be related to functional dysconnectivity among brain regions. However, the topological representation of brain functional connectivity altered by mental fatigue is only beginning to be revealed. In the current study, we applied a graph theoretical approach to analyse such topological alterations in the lower alpha band (8~10 Hz) of EEG data from 20 subjects undergoing a two-session experiment, in which one session includes four successive blocks with visual oddball tasks (session 1) whereas a mid-task break was introduced in the middle of four task blocks in the other session (session 2). Phase lag index (PLI) was then employed to measure functional connectivity strengths for all pairs of EEG channels. Behavior and connectivity maps were compared between the first and last task blocks in both sessions. Inverse efficiency scores (IES = reaction time/response accuracy) were significantly increased in the last task block, showing a clear effect of time-on-task in participants. Furthermore, a significant block-by-session interaction was revealed in the IES, suggesting the effectiveness of the mid-task break on maintaining task performance. More importantly, a significant session-independent deficit of global integration and an increase of local segregation were found in the last task block across both sessions, providing further support for the presence of a reshaped topology in functional brain connectivity networks under fatigue state. Moreover, a significant block-by-session interaction was revealed in the characteristic path length, small-worldness, and global efficiency, attributing to the significantly disrupted network topology in session 1 in comparison of the maintained network structure in session 2. Specifically, we found increased nodal betweenness centrality in several channels resided in frontal regions in session 1, resembling the observations of more segregated global architecture under fatigue state. Taken together, our findings provide insights into the substrates of brain functional dysconnectivity patterns for mental fatigue and reiterate the effectiveness of the mid-task break on maintaining brain network efficiency.

A new perspective of noise removal from EEG

Electroencephalography (EEG) and eye tracking are two fields that have evolved independently to study topics such as mental workload, attention and fatigue in cognitive neuroscience. However, little research has been devoted to integrating data from these two fields. In this paper, we investigate the utility of a specific type of eye movement, microsaccades, to analyze cognitive activity. We assess mental workload using event related potentials (ERPs) correlated with microsaccades during experiments where task complexity is designed to be greater with an increase in visual degradation. We also develop a modified eye movement algorithm to identify microsaccades during tasks that permit regular saccades and blinks. We compare ERPs at microsaccade onset locked epochs to those of stimulus onset locked epochs. Our results show a clear correlation of ERP activations to both latency and activation areas. These findings provide important insights for analyzing sophisticated tasks in a non-invasive fashion.