Psychiatric symptoms are often accompanied by somatic symptoms induced by the activity of the autonomic nervous system (ANS). The aim of this study was to calculate the time lag between electroencephalography (EEG) and electrocardiography (ECG) responses, to clarify the changes in the relationship between the cerebral cortex (CC) and the sympathetic nervous system (SNS) during emotional recall processing. Twenty-two healthy young adults were examined. Their EEG and ECG data were simultaneously recorded during emotional audiovisual recall tasks using pleasant and unpleasant stimuli for 180 s, with three repetitions (Epochs 1 & 2 and Epoch 3). The EEG data were analyzed using a fast Fourier transform (FFT) to obtain a time series of relative power spectra, X E , in the theta 1, theta 2, alpha 1, alpha 2, alpha 3, beta 1, beta 2, and beta 3 bands. Time series of RR (inter-beat) intervals (time intervals between successive R waves) derived from the ECG spectral analysis using FFT was applied to the resampled time series of RR intervals over about 60 s to obtain a time series of power spectra for the ratio low frequency/high frequency (LH/HF), X C , which reflects the activity of the sympathetic nervous function. The time lag between X E and X C was calculated using wavelet-crosscorrelation analysis. The results demonstrated that the brain responded to unfamiliar emotionally pleasant stimuli in Epochs 1 & 2 earlier than the SNS, whereas the brain and SNS responded to unfamiliar unpleasant stimuli nearly simultaneously. The brain was activated rapidly in response to familiar unpleasant stimuli, although SNS responded more rapidly to familiar pleasant stimuli than the brain in Epoch 3. Our results quantitatively describe the relationship between the CC and the ANS during emotional recall.

An emotional event can provoke somatic symptoms as a consequence of brain responses accompanying reactions of the autonomic nervous system (ANS). ANS consists of the sympathetic nervous system and the parasympathetic nervous system, and their anatomical and physiological mechanisms are different. The aim of this study was to evaluate the time lag between the responses of electroencephalography (EEG) and the parasympathetic nervous system (from electrocardiography; ECG) during emotional recall. EEG and ECG of healthy young adults were simultaneously recorded during the recall of emotionally pleasant and unpleasant audiovisual stimuli, with three repetitions. A time series (TS) of relative power spectra from EEG and a TS of power spectral values in high frequency (HF) of RR intervals in ECG were obtained using fast Fourier transform. The time lag between the TS of EEG and HF was calculated using wavelet cross‐correlation analysis. The results indicated that the cerebral cortex responded earlier than the parasympathetic nervous system to the pleasant stimuli. In contrast, the parasympathetic nervous system responded earlier than the cerebral cortex to the unpleasant stimuli. These events constantly appeared in both unfamiliar and familiar situations. This study quantitatively shows that the parasympathetic nervous system quickly and constantly reacts to emotionally unpleasant stimuli. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Interictal paroxysmal rhythmic activity can appear in electroencephalography (EEG) in patients with mental disorders. The abnormal EEG were analyzed by wavelet-crosscorrelation analysis. The EEG were categorized as following: 2-4 seconds (A) and 0-2 seconds (B) before the appearance of the abnormal EEG; 2 seconds during the abnormal EEG (C); 0-2 seconds (D) and 2-4 seconds (E) after the abnormal EEG. Wavelet-crosscorrelation coefficients (WCC) in the theta and alpha bands for each segment were calculated in all patients. The results in the theta band showed that the abnormal connections between the sites in the brain could continue although the EEG seemingly recovered from the interictal paroxysmal rhythmic activity. The results in the alpha band showed that the normal connections between the sites in the brain could be weakened in the 2 seconds prior to the beginning of the interictal paroxysmal rhythmic activity in the EEG.

Graph theoretical analysis has recently been used to study brain function. This study aims to compare the functional brain networks derived from electroencephalography (EEG) of 10 patients suffering from epilepsy with 10 healthy subjects based on graph theory. Five epochs per healthy subject, and ten epochs (during epileptiform discharge and non-discharge) per patient were selected and analyzed using wavelet-crosscorrelation analysis. The clustering coefficient, characteristic path length, small-worldness, and nodal betweenness centrality were calculated using graph analysis. The results showed that in the patients, Wavelet-crosscorrelation Coefficients (WCC) were significantly higher, and clustering and path length were significantly lower during discharge compared with the healthy subjects, along with alterations in the hub regions. These results suggest a loss of small-world topology in the functional brain network of epilepsy patients. A loss of small-world topology was found even during non-discharge, therefore network indices might aid to distinguish epilepsy patients from healthy individuals.

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