Chimera States in a Two-Population Network of Coupled Pendulum-Like Elements

More than a decade ago, a surprising coexistence of synchronous and asynchronous behavior called the chimera state was discovered in networks of nonlocally coupled identical phase oscillators. In later years, chimeras were found to occur in a variety of theoretical and experimental studies of chemical and optical systems, as well as models of neuron dynamics. In this work, we study two coupled populations of pendulum-like elements represented by phase oscillators with a second derivative term multiplied by a mass parameter $m$ and treat the first order derivative terms as dissipation with parameter $\epsilon>0$. We first present numerical evidence showing that chimeras do exist in this system for small mass values $0<m<<1$. We then proceed to explain these states by reducing the coherent population to a single damped pendulum equation driven parametrically by oscillating averaged quantities related to the incoherent population

Brain functional connectivity in unconstrained walking with and without an exoskeleton

An exoskeleton is utilized to effectively restore the motor function of amputees’ limbs and is frequently employed in motor rehabilitation training during convalescence. Understanding of exoskeleton impact on the brain is required in order to better and more efficiently use the exoskeleton. Almost all previous studies investigated the exoskeleton effect on the brain in a situation with constraints such as predefined walking speed, which could lead to findings differed from that obtained in an unconstrained situation. We, therefore, performed an experiment of unconstrained walking with and without an exoskeleton. Both individual connections and graph metrics were explored and compared among walking conditions. We found that low-order functional connections and associated high-order functional connections mainly between the left centroparietal region and right frontal region were significantly different among walking conditions. Generally speaking, connective strength was enhanced in LOFC and was decreased in aHOFC when assistant force was provided by the exoskeleton. Further, we proposed connection length investigation and revealed the large majority of these connections were long-distance connectivity. Graph metric investigation discovered higher connectivity clustering in the walking with low exoskeleton-aided force compared to the walking without the exoskeleton. This study expanded the existing knowledge of the effect of exoskeleton on the brain and is of implications on new exoskeleton development and exoskeleton-aided rehabilitation training

Growing functional modules from a seed protein via integration of protein interaction and gene expression data

<p>Abstract</p> <p>Background</p> <p>Nowadays modern biology aims at unravelling the strands of complex biological structures such as the protein-protein interaction (PPI) networks. A key concept in the organization of PPI networks is the existence of dense subnetworks (functional modules) in them. In recent approaches clustering algorithms were applied at these networks and the resulting subnetworks were evaluated by estimating the coverage of well-established protein complexes they contained. However, most of these algorithms elaborate on an unweighted graph structure which in turn fails to elevate those interactions that would contribute to the construction of biologically more valid and coherent functional modules.</p> <p>Results</p> <p>In the current study, we present a method that corroborates the integration of protein interaction and microarray data via the discovery of biologically valid functional modules. Initially the gene expression information is overlaid as weights onto the PPI network and the enriched PPI graph allows us to exploit its topological aspects, while simultaneously highlights enhanced functional association in specific pairs of proteins. Then we present an algorithm that unveils the functional modules of the weighted graph by expanding a kernel protein set, which originates from a given 'seed' protein used as starting-point.</p> <p>Conclusion</p> <p>The integrated data and the concept of our approach provide reliable functional modules. We give proofs based on yeast data that our method manages to give accurate results in terms both of structural coherency, as well as functional consistency.</p

Mining cross-frequency coupling microstates (CFCμstates) from EEG recordings during resting state and mental arithmetic tasks

The functional brain connectivity has been studied by analyzing synchronization between dynamic oscillations of identical frequency or between different frequencies of distinct brain areas. It has been hypothesized that cross-frequency coupling (CFC) between different frequency bands is the carrier mechanism for the coordination of global and local neural processes and hence supports the distributed information processing in the brain. In the present study, we attempt to study the dynamic evolution of CFC at resting-state and during a mental task. The concept of CFC microstates (CFCμstates) is introduced as emerged short-lived patterns of CFC. We analyzed dynamic CFC (dCFC) at resting-state and during a comparison task by adopting a phase-amplitude coupling (PAC) estimator for [δ phase-γ-amplitude] coupling at every sensor. Modifying a well-established framework for mining brain dynamics, we show that a small sized repertoire of CFCμstates can be derived so as to encapsulate connectivity variations and further provide novel insights into network's functional reorganization. By analyzing the transition dynamics among CFCμstates, in both tasks, we provided a clear evidence about intrinsic networks that may play a crucial role in information integration

EEG-Based Multi-Class Workload Identification Using Feature Fusion and Selection

The effectiveness of workload identification is one of the critical aspects in a monitoring instrument of mental state. In this field, the workload is usually recognised as binary classes. There are scarce studies towards multi-class workload identification because the challenge of the success of workload identification is much tough, even though one more workload class is added. Besides, most of the existing studies only utilized spectral power features from individual channels but ignoring abundant inter-channel features that represent the interactions between brain regions. In this study, we utilized features representing intra-channel information and inter-channel information to classify multiple classes of workload based on EEG. We comprehensively compared each category of features contributing to workload identification and elucidated the roles of feature fusion and feature selection for the workload identification. The results demonstrated that feature combination (83.12% in terms of accuracy) enhanced the classification performance compared to individual feature categories (i.e., band power features, 75.90%; connection features, 81.72%, in terms of accuracy). With the F-score feature selection, the classification accuracy was further increased to 83.47%. When the features of graph metric were fused, the accuracy was reached to 84.34%. Our study provided comprehensive performance comparisons between methods and feature categories for the multi-class workload identification and demonstrated that feature selection and fusion played an important role in the enhancement of workload identification. These results could facilitate further studies of multi-class workload identification and practical application of workload identification

Multi-Kernel Capsule Network for Schizophrenia Identification

Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. To overcome the aforementioned drawbacks, we proposed a multi-kernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match with partition sizes of brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of widely-used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. MKCapsnet is promising for schizophrenia identification. Our study first utilized capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multi-kernel capsule structure with consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification

Emergence of chimera-like states in prefrontal-cortex macaque intracranial recordings

Neural synchronization plays a crucial role in cog- nitive functions and in performing tasks as it facilitates the transmission of information among the various brain subregions, and thus their communication. In this paper, we use an approach for analyzing and quantifying the emergence of synchronization patterns used previously in the study of data from toy dynamical models, in neurophysiological signals from a macaque monkey and particularly, from prefrontal-cortex intracranial recordings. Specifically, we study the emergence of synchronization patterns in neural ensembles recorded in the macaque brain while the monkey is performing the same delayed saccade task successfully for a number of times. We quantify the emergence of chimera- like states, metastability and coalition entropy in the recordings coming from intracranial arrays implanted in the macaque’s brain. Our results show the emergence of spatio-temporal co- existing patterns of synchronized and desynchronized behavior, termed chimera-like states with small metastability during the stage where the target and the distractor appears on the screen and when the go cue appears on the screen for the monkey to report, namely the two most crucial stages of the trials to be termed successful. Finally, we perform a statistical hypothesis test on the calculated quantities over the successful trials and demonstrate that our findings are statistically significant in the sense that they cannot be attributed to randomness

Reliability of EEG Measures in Driving Fatigue

Reliability investigation of measures is important in studies of brain science and neuroengineering. Measures’ reliability hasn’t been investigated across brain states, leaving unknown how reliable the measures are in the context of the change from alert state to fatigue state during driving. To compensate for the lack, we performed a comprehensive investigation. A two-session experiment with an interval of approximately one week was designed to evaluate the reliability of the measures at both sensor and source levels. The results showed that the average intraclass correlation coefficients (ICCs) of the measures at the sensor level were generally higher than those at the source level, except for the directed between-region measures. Single-region measures generally exhibited higher average ICCs relative to between-region measures. The exploration of brain network topology showed that nodal metrics displayed highly varying ICCs across regions and global metrics varied associated with nodal metrics. Single-region measures displayed higher ICCs in the frontal and occipital regions while the between-region measures exhibited higher ICCs in the area involving frontal, central and occipital regions. This study provides an appraisal for the measures' reliability over a long interval, which is informative for measure selection in practical mental monitoring