The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS.

Human language units are hierarchical, and reading acquisition involves integrating multisensory information (typically from auditory and visual modalities) to access meaning. However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in auditory and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory, visual, or combined audio-visual modalities while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e. characters/monosyllabic words) and higher-level linguistic structures (i.e. phrases and sentences) across the 3 modalities separately. We found that audio-visual integration occurs in all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.

Abnormal functional connectivity of the reward circuit associated with early satiety in patients with postprandial distress syndrome.

Postprandial distress syndrome (PDS) is the most common functional dyspepsia (FD) subtype. Early satiety is one of the cardinal symptoms of the PDS subtype in FD patients. The heterogeneity of symptoms in FD patients hampered therapy for patients based on specific symptoms, necessitating a symptom-based understanding of the pathophysiology of FD. To investigate the correlation between reward circuit and symptom severity of PDS patients, seed (Nucleus accumbens, NAc, a key node in the reward circuit) based resting-state functional connectivity (FC) was applied in the neuroimaging data analysis. The results demonstrated that the patients with PDS manifested strengthened FC between NAc and the caudate, putamen, pallidum, amygdala, hippocampus, thalamus, anterior cingulate cortex (ACC), and insula. Moreover, the FC between NAc and ACC, insula, thalamus, and hippocampus exhibited significant positive associations with symptom severity. More importantly, the strengthened FC between NAc and the ACC, insula, amygdala, and hippocampus were found associated with the early satiety symptom of patients with PDS. This study indicated that the altered FC of reward circuit regions may play a role in the pathophysiology of patients with PDS, and some of the aberrant NAc-based FC within the reward circuit were more related to the early satiety of patients with PDS. These findings improve our symptom-based understanding of the central pathophysiology of FD, lay the groundwork for an objective diagnosis of FD, and shed light on the precise prescription for treating FD based on symptoms.

Single-Trial Recognition of Imagined Forces and Speeds of Hand Clenching Based on Brain Topography and Brain Network.

To provide optional force and speed control parameters for brain-computer interfaces (BCIs), an effective feature extraction method of imagined force and speed of hand clenching based on electroencephalography (EEG) was explored. Twenty subjects were recruited to participate in the experiment. They were instructed to perform three different actual/imagined hand clenching force tasks (4 kg, 10 kg, and 16 kg) and three different hand clenching speed tasks (0.5 Hz, 1 Hz, and 2 Hz). Topographical maps parameters and brain network parameters of EEG were calculated as new features of imagined force and speed of hand clenching, which were classified by three classifiers: linear discrimination analysis, extreme learning machines and support vector machines. Topographical maps parameters were better for recognition of the hand clenching force task, while brain network parameters were better for recognition of the hand clenching speed task. After a combination of five types of features (energy, power spectrum of the autoregressive model, wavelet packet coefficients, topographical maps parameters and brain network parameters), the recognition rate of the hand clenching force task was 97%, and that of the hand clenching speed task was as high as 100%. The brain topographical and the brain network parameters are expected to improve the accuracy of decoding the EEG signal of imagined force and speed of hand clenching. A more efficient brain network may facilitate the recognition of force/speed of hand clenching. Combined features could significantly improve the single-trial recognition rate of imagined forces and speeds of hand clenching. The current study provides a new idea for the imagined force and speed of hand clenching that offers more control intention instructions for BCIs.

[Design and experiment of a multi-modal electroencephalogram-near infrared spectroscopy helmet for simultaneously acquiring at the same brain area].

Multi-modal brain-computer interface and multi-modal brain function imaging are developing trends for the present and future. Aiming at multi-modal brain-computer interface based on electroencephalogram-near infrared spectroscopy (EEG-NIRS) and in order to simultaneously acquire the brain activity of motor area, an acquisition helmet by NIRS combined with EEG was designed and verified by the experiment. According to the 10-20 system or 10-20 extended system, the diameter and spacing of NIRS probe and EEG electrode, NIRS probes were aligned with C3 and C4 as the reference electrodes, and NIRS probes were placed in the middle position between EEG electrodes to simultaneously measure variations of NIRS and the corresponding variation of EEG in the same functional brain area. The clamp holder and near infrared probe were coupled by tightening a screw. To verify the feasibility and effectiveness of the multi-modal EEG-NIRS helmet, NIRS and EEG signals were collected from six healthy subjects during six mental tasks involving the right hand clenching force and speed motor imagery. These signals may reflect brain activity related to hand clenching force and speed motor imagery in a certain extent. The experiment showed that the EEG-NIRS helmet designed in the paper was feasible and effective. It not only could provide support for the multi-modal motor imagery brain-computer interface based on EEG-NIRS, but also was expected to provide support for multi-modal brain functional imaging based on EEG-NIRS.

The correlation between upper body grip strength and resting-state EEG network.

Current research in the field of neuroscience primarily focuses on the analysis of electroencephalogram (EEG) activities associated with movement within the central nervous system. However, there is a dearth of studies investigating the impact of prolonged individual strength training on the resting state of the brain. Therefore, it is crucial to examine the correlation between upper body grip strength and resting-state EEG networks. In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p < 0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands, with correlation coefficients greater than 0.60 (p < 0.01). Additionally, predicted MVC positively correlated with actual MVC, with a coefficient of 0.70 and root mean square error of 5.67 (p < 0.01). The results show that the resting-state EEG network is closely related to upper body grip strength, which can indirectly reflect an individual's muscle strength through the resting brain network.

The long-term effect of modulated acoustic stimulation on alteration in EEG brain network of chronic tinnitus patients: An exploratory study.

Acoustic stimulation is one of the most influential techniques for distressing tinnitus, while how it functions to reverse neural changes associated with tinnitus remains undisclosed. In this study, our objective is to investigate alterations in brain networks to shed light on the enigma of acoustic intervention for tinnitus. We designed a 75-day long-term acoustic intervention experiment, during which chronic tinnitus patients received daily modulated acoustic stimulation with each session lasting 15 days. Every 15 days, professional tinnitus assessments were conducted, collecting both electroencephalogram (EEG) and tinnitus handicap inventory (THI) data from the patients. Thereafter, we investigated the changes in EEG network organizations during continuous acoustic stimulation and their progressive evolution throughout long-term therapy, alongside exploring the associations between the evolving changes of the network alterations and THI. Our current study findings reveal reorganization in alpha/beta long-range frontal-parietal-occipital connections as well as local frontal and parietal-occipital regions induced by acoustic stimulation. Furthermore, we observed a decrease in modulation effects as therapy sessions progressed. These alterations in brain networks reflect the reversal of tinnitus-related neural activities, particularly distress and perception; thus contributing to tinnitus rehabilitation through long-term modulation effects. This study provides unique insights into how long-term acoustic intervention affects the network organizations of tinnitus patients and deepens our understanding of the pathophysiological mechanisms underlying tinnitus rehabilitation.

Dynamic corticomuscular multi-regional modulations during finger movement revealed by time-varying network analysis.

Objective.A body movement involves the complicated information exchange between the central and peripheral systems, which is characterized by the dynamical coupling patterns between the multiple brain areas and multiple muscle units. How the central and peripheral nerves coordinate multiple internal brain regions and muscle groups is very important when accomplishing the action.Approach.In this study, we extend the adaptive directed transfer function to construct the time-varying networks between multiple corticomuscular regions, and divided the movement duration into different stages by the time-varying corticomuscular network patterns.Main results.The inter dynamical corticomuscular network demonstrated the different interaction patterns between the central and peripheral systems during the different hand movement stages. The muscles transmit bottom-up movement information in the preparation stage, but the brain issues top-down control commands and dominates in the execution stage, and finally the brain's dominant advantage gradually weakens in the relaxation stage. When classifying the different movement stages based on time-varying corticomuscular network indicators, an average accuracy above 74% could be reliably achieved.Significance.The findings of this study help deepen our knowledge of central-peripheral nerve pathways and coordination mechanisms, and also provide opportunities for monitoring and regulating movement disorders.

Constructing Time-Varying Directed EEG Network by Multivariate Nonparametric Dynamical Granger Causality.

Time-varying directed electroencephalography (EEG) network is the potential tool for studying the dynamical causality among brain areas at a millisecond level; which conduces to understanding how our brain effectively adapts to information processing, giving inspiration to causality- and brain-inspired machine learning. Currently, its construction still mainly relies on the parametric approach such as multivariate adaptive autoregressive (MVAAR), represented by the most widely used adaptive directed transfer function (ADTF). Restricted by the model assumption, the corresponding performance largely depends on the MVAAR modeling which usually encounters difficulty in fitting complex spectral features. In this study, we proposed to construct EEG directed network with multivariate nonparametric dynamical Granger causality (mndGC) method that infers the causality of a network, instead, in a data-driven way directly and therefore avoids the trap in the model-dependent parametric approach. Comparisons between mndGC and ADTF were conducted both with simulation and real data application. Simulation study demonstrated the superiority of mndGC both in noise resistance and capturing the instantaneous directed network changes. When applying to the real motor imagery (MI) data set, distinguishable network characters between left- and right-hand MI during different MI stages were better revealed by mndGC. Our study extends the nonparametric causality exploration and provides practical suggestions for the time-varying directed EEG network analysis.

A survey of brain network analysis by electroencephalographic signals.

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Auditory Dominance in Processing Chinese Semantic Abnormalities in Response to Competing Audio-visual Stimuli.

Language is a remarkable cognitive ability that can be expressed through visual (written language) or auditory (spoken language) modalities. When visual characters and auditory speech convey conflicting information, individuals may selectively attend to either one of them. However, the dominant modality in such a competing situation and the neural mechanism underlying it are still unclear. Here, we presented participants with Chinese sentences in which the visual characters and auditory speech convey conflicting information, while behavioral and electroencephalographic (EEG) responses were recorded. Results showed a prominent auditory dominance when audio-visual competition occurred. Specifically, higher accuracy (ACC), larger N400 amplitudes and more linkages in the posterior occipital-parietal areas were demonstrated in the auditory mismatch condition compared to that in the visual mismatch condition. Our research illustrates the superiority of the auditory speech over the visual characters, extending our understanding of the neural mechanisms of audio-visual competition in Chinese.

Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder.

Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.

Predicting individual muscle fatigue tolerance by resting-state EEG brain network.

Objective.Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and fatigue tolerance varies across individuals. Various studies have emphasized the close relationships between muscle fatigue and the brain. However, the relationships between the resting-state electroencephalogram (rsEEG) brain network and individual muscle fatigue tolerance remain unexplored.Approach.Eighteen elite water polo athletes took part in our experiment. Five-minute before- and after-fatigue-exercise rsEEG and fatiguing task (i.e. elbow flexion and extension) electromyography (EMG) data were recorded. Based on the graph theory, we constructed the before- and after-task rsEEG coherence network and compared the network differences between them. Then, the correlation between the before-fatigue rsEEG network properties and the EMG fatigue indexes when a subject cannot keep on exercising anymore was profiled. Finally, a prediction model based on the before-fatigue rsEEG network properties was established to predict fatigue tolerance.Main results. Results of this study revealed the significant differences between the before- and after-exercise rsEEG brain network and found significant high correlations between before-exercise rsEEG network properties in the beta band and individual muscle fatigue tolerance. Finally, an efficient support vector regression (SVR) model based on the before-exercise rsEEG network properties in the beta band was constructed and achieved the accurate prediction of individual fatigue tolerance. Similar results were also revealed on another 30 subject swimmer data set further demonstrating the reliability of predicting fatigue tolerance based on the rsEEG network.Significance.Our study investigates the relationship between the rsEEG brain network and individual muscle fatigue tolerance and provides a potential objective physiological biomarker for tolerance prediction and the regulation of muscle fatigue.

Correlation Analysis of EEG Brain Network With Modulated Acoustic Stimulation for Chronic Tinnitus Patients.

The acoustic stimulation influences of the brain is still unveiled, especially from the brain network point, which can reveal how interaction is propagated and integrated between different brain zones for chronic tinnitus patients. We specifically designed a paradigm to record the electroencephalograms (EEGs) for tinnitus patients when they were treated with consecutive acoustic stimulation neuromodulation therapy for up to 75 days, using the tinnitus handicap inventory (THI) to evaluate the tinnitus severity or the acoustic stimulation treatment efficacy, and the EEG to record the brain activities every 2 weeks. Then, we used an EEG-based coherence analysis to investigate if the changes in brain network consistent with the clinical outcomes can be observed during 75-days acoustic treatment. Finally, correlation analysis was conducted to study potential relationships between network properties and tinnitus handicap inventory score change. The EEG network became significantly weaker after long-term periodic acoustic stimulation treatment, and tinnitus handicap inventory score changes or the acoustic stimulation treatment efficacy are strongly correlated with the varying brain network properties. Long-term acoustic stimulation neuromodulation intervention can improve the rehabilitation of chronic tinnitus patients, and the EEG network provides a relatively reliable and quantitative analysis approach for objective evaluation of tinnitus clinical diagnosis and treatment.

Antidiabetic effect of an active fraction extracted from dragon's blood (Dracaena Cochinchinensis).

The active fraction extracted from dragon's blood displayed an inhibitory effect on alpha-glucosidase activity with an IC50 of 0.152 microg/mL, which is nearly half of the crude material. Its inhibition on alpha-glucosidase was noncompetitive. In addition, when this fraction was orally administered to mice dosed with Acarbose (20 mg/kg), the active fraction (100, 300, 500 mg/kg) significantly suppressed increase of blood glucose levels after sucrose loading in a dose-dependent manner. These results suggest that this extract from dragon's blood exerts an anti-diabetic effect by suppressing intestinal carbohydrate absorption and thereby reducing the postprandial increase of blood glucose.

Identification of Neuromuscular Causal Relationship Between Brain and Muscles in Limb Movement by Using Ensemble Empirical Mode Decomposition based Causal Decomposition.

This paper proposes the potential extension of Ensemble Empirical Mode Decomposition based Causal Decomposition (EEMD-CD) to the physiological system. The neural basis of Volitional Motor Control (VMC), resulting in skilled motor behaviors through a connected interaction between limb biomechanical properties and Central Neural System (CNS), has been well documented. Specifically, the Primary Motor Cortex (M1) contributes volitional and goal-directed limb movements in terms of motor planning and motor behavior. The actual applications of causality detection approaches were still dominated by the prediction concept, i.e., Granger Causality (GC). This study concerns clearly some of components of M1 regulating motor properties of upper limbs, and holds the neuroscience finding from which the bi-directional causal interaction in brain and muscles has been concluded. The study performs an experiment by which Electromyography (EMG) of limb muscles and Electroencephalography (EEG) across from prefrontal cortex to M1, were synchronously acquired during wrist extensions. It also provides a valid example of how the casuality can be approached by EEMD-CD and offers a first step in the identification of casual relationship in mutual physiological systems.