Fumihiko Taya

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.

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.

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.

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.

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

Comparison method for community detection on brain networks from neuroimaging data

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Single trial EEG classification of lower-limb movements using improved regularized common spatial pattern

. 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.

Cooperation driven coherence: Brains working hard together

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.

Training-induced changes in information transfer efficiency of the brain network: A functional connectome approach

Brain computer interface (BCI) is a direct communication pathway between the human central nervous system and external devices primarily aiming at restoring damaged functions such as sight, hearing and movement. Although great achievements have been made for the development of reliable BCI systems to assist people with upper-limb disabilities, researches on BCI development related to lower-limb are still rudimentary. In the current study, based on the regularized common spatial pattern analysis (R-CSP) method and statistical dependency, we have developed an improved feature selection method for lower-limb movement pattern classification. High-resolution electroencephalogram (EEG) signals were recorded from four healthy male subjects undergoing real lower-limb movements. Compared to the conventional CSP, R-CSP, and PCA methods, the proposed method achieved the best average accuracy of 83.5% for single trial classification of left and right lower-limb movement. Our findings thereby have insightful implications for developing practical BCI systems for lower-limb movement.

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

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.