Despite the apparent importance of mental fatigue detection, a reliable application is hindered due to the incomprehensive understanding of the neural mechanisms of mental fatigue. In this paper, we investigated the topological alterations of functional brain networks in the theta band (4 - 7 Hz) of electroencephalography (EEG) data from 40 male subjects undergoing two distinct fatigue-inducing tasks: a low-intensity one-hour simulated driving and a high-demanding half-hour sustained attention task [psychomotor vigilance task (PVT)]. Behaviorally, subjects demonstrated a robust mental fatigue effect, as reflected by significantly declined performances in cognitive tasks prior and post these two tasks. Furthermore, characteristic path length presented a positive correlation with task duration, which led to a significant increase between the first and the last five minutes of both tasks, indicating a fatigue-related disruption in information processing efficiency. However, significantly increased clustering coefficient was revealed only in the driving task, suggesting distinct network reorganizations between the two fatigue-inducing tasks. Moreover, high accuracy (92% for driving; 97% for PVT) was achieved for fatigue classification with apparently different discriminative functional connectivity features. These findings augment our understanding of the complex nature of fatigue-related neural mechanisms and demonstrate the feasibility of using functional connectivity as neural biomarkers for applicable fatigue monitoring.

Efficient classification of mental workload, an important issue in neuroscience, is limited, so far to single task, while cross-task classification remains a challenge. Furthermore, network approaches have emerged as a promising direction for studying the complex organization of the brain, enabling easier interpretation of various mental states. In this paper, using two mental tasks (N-back and mental arithmetic), we present a framework for cross- as well as within-task workload discrimination by utilizing multiband electroencephalography (EEG) cortical brain connectivity. In detail, we constructed functional networks in EEG source space in different frequency bands and considering the individual functional connections as classification features, we identified salient feature subsets based on a sequential feature selection algorithm. These connectivity subsets were able to provide accuracy of 87% for cross-task, 88% for N-back task, and 86% for mental arithmetic task. In conclusion, our method achieved to detect a small number of discriminative interactions among brain areas, leading to high accuracy in both within-task and cross-task classifications. In addition, the identified functional connectivity features, the majority of which were detected in frontal areas in theta and beta frequency bands, helped delineate the shared as well as the distinct neural mechanisms of the two mental tasks.

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Summary The PICKLE 3.0 upgrade refers to the enrichment of this human protein–protein interaction (PPI) meta-database with the mouse protein interactome. Experimental PPI data between mouse genetic entities are rather limited; however, they are substantially complemented by PPIs between mouse and human genetic entities. The relational scheme of PICKLE 3.0 has been amended to exploit the Mouse Genome Informatics mouse–human ortholog gene pair collection, enabling (i) the extension through orthology of the mouse interactome with potentially valid PPIs between mouse entities based on the experimental PPIs between mouse and human entities and (ii) the comparison between mouse and human PPI networks. Interestingly, 43.5% of the experimental mouse PPIs lacks a corresponding by orthology PPI in human, an inconsistency in need of further investigation. Overall, as primary mouse PPI datasets show a considerably limited overlap, PICKLE 3.0 provides a unique comprehensive representation of the mouse protein interactome. Availability and implementation PICKLE can be queried and downloaded at http://www.pickle.gr. Supplementary information Supplementary data are available at Bioinformatics online.

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Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine host response pathway interactome. To accomplish that, an ensemble of dynamic and time-varying Gene Regulatory Network Inference methodologies was recruited to set a confident interactome based on mouse time series transcriptome data (day 1-day 60). The proposed H1N1 interactome demonstrated significant transformations among activated and suppressed pathways in time. Enhanced interplay was observed at day 1, while the maximal network complexity was reached at day 8 (correlated with viral clearance and iBALT tissue formation) and one interaction was present at day 40. Next, we searched for common interactivity features between the murine-adapted PR8 strain and other influenza A subtypes/strains. For this, two other interactomes, describing the murine host response against H5N1 and H1N1pdm, were constructed, which in turn validated many of the observed interactions (in the period day 1-day 7). The H1N1 interactome revealed the role of cell cycle both in innate and adaptive immunity (day 1-day 14). Also, pathogen sensory pathways (e.g., RIG-I) displayed long-lasting association with cytokine/chemokine signaling (until day 8). Interestingly, the above observations were also supported by the H5N1 and H1N1pdm models. It also elucidated the enhanced coupling of the activated innate pathways with the suppressed PPAR signaling to keep low inflammation until viral clearance (until day 14). Further, it showed that interactions reflecting phagocytosis processes continued long after the viral clearance and the establishment of adaptive immunity (day 8-day 40). Additionally, interactions involving B cell receptor pathway were evident since day 1. These results collectively inform the emerging field of public health omics and future clinical studies aimed at deciphering dynamic host responses to infectious agents.

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