Research progress and application of transfer entropy algorithm / 生物医学工程学杂志

In recent years, exploring the physiological and pathological mechanisms of brain functional integration from the neural network level has become one of the focuses of neuroscience research. Due to the non-stationary and nonlinear characteristics of neural signals, its linear characteristics are not sufficient to fully explain the potential neurophysiological activity mechanism in the implementation of complex brain functions. In order to overcome the limitation that the linear algorithm cannot effectively analyze the nonlinear characteristics of signals, researchers proposed the transfer entropy (TE) algorithm. In recent years, with the introduction of the concept of brain functional network, TE has been continuously optimized as a powerful tool for nonlinear time series multivariate analysis. This paper first introduces the principle of TE algorithm and the research progress of related improved algorithms, discusses and compares their respective characteristics, and then summarizes the application of TE algorithm in the field of electrophysiological signal analysis. Finally, combined with the research progress in recent years, the existing problems of TE are discussed, and the future development direction is prospected.

Research progress on analysis methods in electroencephalography-electromyography synchronous coupling / 生物医学工程学杂志

The motor nervous system transmits motion control information through nervous oscillations, which causes the synchronous oscillatory activity of the corresponding muscle to reflect the motion response information and give the cerebral cortex feedback, so that it can sense the state of the limbs. This synchronous oscillatory activity can reflect connectivity information of electroencephalography-electromyography (EEG-EMG) functional coupling. The strength of the coupling is determined by various factors including the strength of muscle contraction, attention, motion intention etc. It is very significant to study motor functional evaluation and control methods to analyze the changes of EEG-EMG synchronous coupling caused by different factors. This article mainly introduces and compares coherence and Granger causality of linear methods, the mutual information and transfer entropy of nonlinear methods in EEG-EMG synchronous coupling, and summarizes the application of each method, so that researchers in related fields can understand the current research progress on analysis methods of EEG-EMG synchronous systematically.

Cortical Information Transmission during Tic Suppression in Boys with Tourette's Disorder / 신경정신의학

OBJECTIVES: Tourette's Disorder (TD) is a chronic neuropsychiatric disorder characterized by multiple motor and vocal tics with onset in childhood. The aim of this study was to ascertain the increased cortical information transmission in frontal area during tic suppression in drug naive boys with TD using new nonlinear analysis of EEGs, be called Transfer Entropy (TE) which can detect the directed exchange of information between two systems. METHODS: Subjects were 11 drug naive boys with DSM-IV diagnosis of TD and 10 control boys. Clinical assessments were performed, and EEGs were recorded from 19 scalp loci of the international 10-20 systems. TE was estimated by EEG timeseries data after noise reduction. TE difference between TD and control during resting state and between tic suppression and resting state in TD were investigated. RESULTS: Elevated TE was found in extensive channels, including frontal, central and temporal channels (F7, Fz, F8,Cz, C3, P3, T3, and T4) in resting state of Tourette's disorder compared to normal controls. During tic suppression elevated TE was found in more extensive and asymmetrical channels especially prefrontal area (Fp1, Fp2, F3, Fz, F7, F8, Cz, C4, C5, T3, and T4). CONCLUSION: These findings suggest that pathogenesis of Tourette's disorder involve impaired cortical neuronal modulation in subcortical neural circuits. EEG analysis of TE may be a useful tool to investigation of cortical mechanism of psychiatric illness.