Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue.

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.

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

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.

Quantitative Assessment of the Training Improvement in a Motor-Cognitive Task by Using EEG, ECG and EOG Signals.

Generally, the training evaluation methods consist in experts supervision and qualitative check of the operator's skills improvement by asking them to perform specific tasks and by verifying the final performance. The aim of this work is to find out if it is possible to obtain quantitative information about the degree of the learning process throughout the training period by analyzing neuro-physiological signals, such as the electroencephalogram, the electrocardiogram and the electrooculogram. In fact, it is well known that such signals correlate with a variety of cognitive processes, e.g. attention, information processing, and working memory. A group of 10 subjects have been asked to train daily with the NASA multi-attribute-task-battery. During such training period the neuro-physiological, behavioral and subjective data have been collected. In particular, the neuro-physiological signals have been recorded on the first (T1), on the third (T3) and on the last training day (T5), while the behavioral and subjective data have been collected every day. Finally, all these data have been compared for a complete overview of the learning process and its relations with the neuro-physiological parameters. It has been shown how the integration of brain activity, in the theta and alpha frequency bands, with the autonomic parameters of heart rate and eyeblink rate could be used as metric for the evaluation of the learning progress, as well as the final training level reached by the subjects, in terms of request of cognitive resources.

Topological Changes in the Brain Network Induced by the Training on a Piloting Task: An EEG-Based Functional Connectome Approach.

Training is a process to improve one's capacity or performance through the acquisition of knowledge or skills specific for the trained task. Although behavioral performance would be improved monotonically and reach a plateau as the learning progresses, neurophysiological signal shows different patterns like a U-shaped curve. One possible account for the phenomenon is that the brain first works hard to learn how to use task-relevant areas, followed by improvement in the efficiency derived from disuse of irrelevant brain areas for good task performance. Here, we hypothesize that topology of the brain network would show U-shaped changes during the training on a piloting task. To test this hypothesis, graph theoretical metrics quantifying global and local characteristics of the network were investigated. Our results demonstrated that global information transfer efficiency of the functional network in a high frequency band first decreased and then increased during the training while other measures such as local information transfer efficiency and small-worldness showed opposite patterns. Additionally, the centrality of nodes changed due to the training at frontal and temporal sites. Our results suggest network metrics can be used as biomarkers for quantifying the training progress, which can be differed depending on network efficiency of the brain.

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 t + Δt. 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.

A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal.

Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG-fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.

Comparison method for community detection on brain networks from neuroimaging data.

The brain is a complex system consisting of regions dedicated to different brain functions, and higher cognitive functions are realized via information flow between distant brain areas communicating with each other. As such, it is natural to shift towards brain network analysis from mapping of brain functions, for deeper understanding of the brain system. The graph theoretical network metrics measure global or local properties of network topology, but they do not provide any information about the intermediate scale of the network. Community structure analysis is a useful approach to investigate the mesoscale organization of brain network. However, the community detection schemes are yet to be established. In this paper, we propose a method to compare different community detection schemes for neuroimaging data from multiple subjects. To the best of our knowledge, our method is the first attempt to evaluate community detection from multiple-subject data without "ground truth" community and any assumptions about the original network features. To show its feasibility, three community detection algorithms and three different brain atlases were examined using resting-state fMRI functional networks. As it is crucial to find a single group-based community structure as a representative for a group of subjects to allow discussion about brain areas and connections in different conditions on common ground, a number of community detection schemes based on different approaches have been proposed. A non-parametric permutation test on similarity between group-based community structures and individual community structures was used to determine which algorithm or atlas provided the best representative structure of the group. The Normalized Mutual Information (NMI) was computed to measure the similarity between the community structures. We also discuss further issues on community detection using the proposed method.

Spatio-temporal dynamics of the visual system revealed in binocular rivalry.

From the evolutionary viewpoint, animals need to monitor the surrounding environment and capture salient features, such as motion, for survival. The visual system is highly developed for monitoring a wide area of visual field and capturing such salient features. In humans and primates, there is a wide binocular field, suggesting a necessity of integrating the images from the two eyes. Binocular rivalry [R. Blake, A neural theory of binocular rivalry, Psychol. Rev. 96 (1989) 145-167; R. Blake, N.K. Logothetis, Visual competition, Nat. Rev. Neurosci. 3 (2002) 13-21], where incompatible inputs from the two eyes compete to emerge in the subject's visual percept, has been shown to exhibit highly adaptive behavior [I. Kovacs, T.V. Parathomas, M. Yang, A. Feher, When the brain changes its mind: interocular grouping during binocular rivalry. Proc. Natl. Acad. Sci. U.S.A. 93 (1996) 15508-15511; N.K. Logothetis, Single units and conscious vision, Philos. Trans. R. Soc. Lond. B. Biol. Sci. 353 (1998) 1801-1818]. Here we investigated the spatio-temporal dynamics of the ocular dominance pattern in binocular rivalry under conditions where conflicting salient features were presented in a temporally varying manner. We found a striking example of the detailed structure of the dominance wave propagation, by using a spatio-temporal sampling method. The data show in detail the ability of the visual system to dynamically adapt to the changing stimuli in the context of the massively parallel visual field. We show by model prediction that the globally coherent dominance change in the presence of multiple stimuli can be explained by a mechanism based on local saliency comparison.

Brain enhancement through cognitive training: a new insight from brain connectome.

Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners' learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals' cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive functions.

Assessing small-worldness of dynamic functional brain connectivity during complex tasks.

The development of network theory has introduced new approaches to understand the brain as a complex system. Currently the time-variant functional connectivity of brain networks under complex tasks is still being investigated. To explore connectivity during complex cognitive and motor tasks, this study focused on the relevance of small-worldness to human workloads using EEG signals from a dynamic analytic approach. Experiments were designed to investigate the small-worldness under two types of flight simulation tasks at two levels of difficulty - easy and hard. The results demonstrated a consistent small-world architecture of brain connectivity with time-based variance during complex tasks. We noticed an increased small-world effect especially at the alpha band when performing hard tasks compared to easy tasks, which relate to high and low workload respectively. Our results show the potential of dynamic brain network analysis in exploring time-variant and task-dependent brain connectivity during complex tasks.

Cooperation driven coherence: Brains working hard together.

The current study aims to look at the difference in coupling of EEG activity of participant pairs while they perform a cooperative, concurrent, independent yet different task at high and low difficulty levels. Participants performed the National Aeronautics and Space Administration (NASA) designed Multi-Attribute Task Battery (MATB-II) task which simulates a pilot and copilot operating an aircraft. Each participant in the pair was responsible for 2 out of 4 subtasks which were independent and different from one another while all tasks occurs concurrently in real time with difficulty levels being the frequency that adjustments are required for each subtask. We found that as the task become more difficult, there was more coupling between the pilot and copilot.

Manipulation detection and preference alterations in a choice blindness paradigm.

OBJECTIVES: It is commonly believed that individuals make choices based upon their preferences and have access to the reasons for their choices. Recent studies in several areas suggest that this is not always the case. In choice blindness paradigms, two-alternative forced-choice in which chosen-options are later replaced by the unselected option, individuals often fail to notice replacement of their chosen option, confabulate explanations for why they chose the unselected option, and even show increased preferences for the unselected-but-replaced options immediately after choice (seconds). Although choice blindness has been replicated across a variety of domains, there are numerous outstanding questions. Firstly, we sought to investigate how individual- or trial-factors modulated detection of the manipulations. Secondly, we examined the nature and temporal duration (minutes vs. days) of the preference alterations induced by these manipulations. METHODS: Participants performed a computerized choice blindness task, selecting the more attractive face between presented pairs of female faces, and providing a typewritten explanation for their choice on half of the trials. Chosen-face cue manipulations were produced on a subset of trials by presenting the unselected face during the choice explanation as if it had been selected. Following all choice trials, participants rated the attractiveness of each face individually, and rated the similarity of each face pair. After approximately two weeks, participants re-rated the attractiveness of each individual face online. RESULTS: Participants detected manipulations on only a small proportion of trials, with detections by fewer than half of participants. Detection rates increased with the number of prior detections, and detection rates subsequent to first detection were modulated by the choice certainty. We show clear short-term modulation of preferences in both manipulated and non-manipulated explanation trials compared to choice-only trials (with opposite directions of effect). Preferences were altered in the direction that subjects were led to believe they selected.

Topological changes of the effective connectivity during the working memory training.

Working memory (WM) refers to the retention of information over a short period of time. Accumulated evidence showed that training WM would lead to beneficial effects in untrained tasks, which could be attributed to the strengthening of the functional connections between brain regions through repeated training task. In this proof of concept investigation, we applied a graph theoretical approach to analyze the early changes of functional connectivity from two subjects undergoing a spatial n-back WM training task for three continuous days. A significant decreased clustering coefficient and normalized shortest path length was revealed, suggesting a reduced local efficiency with an increased global efficiency after WM training. Our findings thereby provide insightful implications for understanding the mechanisms of brain dynamics in cognitive training.