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Abstract: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract We describe a method to construct directed networks from multivariate time series which has several advantages over the widely accepted methods. This method is based on an information theoretic reduction of linear (auto-regressive) models. The models are called reduced auto-regressive (RAR) models. The procedure of the proposed method is composed of three steps: (i) each time series is treated as a basic node of a network, (ii) multivariate RAR models are built and the constituent information in the models is summarized, and (iii) nodes are connected with a directed link based on that summary information. The proposed method is demonstrated for numerical data generated by known systems, and applied to several actual time series of special interest. Although the proposed method can identify connectivity, there are three points to keep in mind: (1) the proposed method cannot always identify nonlinear relationships among components, (2) as constructing RAR models is NP-hard, the network constructed by the proposed method might be near-optimal network when we cannot perform an exhaustive search, and (3) it is difficult to construct appropriate networks when the observational noise is large. Highlights • A method to construct directed networks from multivariate time series is described. • The method is based on an information theoretic reduction of linear auto-regressive models. • The method enables to reveal hidden relationships among multivariate time series data. • The method is generic and can be applied to any kind of multivariate time series data in principle. • The method is applied to two real time series to verify its effectiveness. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]