Search

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, XE, 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), XC, which reflects the activity of the sympathetic nervous function. The time lag between XE and XC 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., Competing Interests: The authors have declared that no competing interests exist.

Purpose: The occurrence of unwanted afterdischarges (ADs) impedes cortical stimulation for mapping purposes. We investigated the safety of several stimulation paradigms., Methods: We compared the incidence of ADs and behavioral responses of two stimulation frequencies (50 and 100 Hz), at two intensities (1 and 0.2 ms pulse widths)., Results: Stimulation with 100 Hz was more likely to cause ADs than 50 Hz, and stimulation using 1 ms pulse width was more likely to cause ADs than 0.2 ms., Conclusions: Stimulation using 50 Hz frequency with a pulse width of 0.2 ms might be safer during cortical mapping.

Objective: Wavelet-crosscorrelation analysis is a new application of wavelet analysis used to show the propagation of epileptiform discharges and to localize the corresponding lesions. We have shown previously that this analysis can help predict brain conditions statistically (Mizuno-Matsumoto et al. 2002). Our objective was to assess whether wavelet-crosscorrelation analysis reveals the initiation and propagation of epileptiform activity in human patients., Methods: The data obtained from three patients with simple partial seizures (SPS) using whole-head magnetoencephalography (MEG) were analyzed by the wavelet-crosscorrelation method. Wavelet-crosscorrelation coefficients (WCC), the coherent structure of each possible pair of signals from 64 MEG channels forvarious periods, and the time lag (TL) in two related signals, were ascertained., Results: We clearly demonstrated both localization of the irritative zone and propagation of the epileptiform discharges., Conclusions: Wavelet-crosscorrelation analysis can help reveal and visualize the dynamic changes of brain conditions. The method of this analysis can compensate for other existing methods for the analysis of MEG, electroencephalography (EEG) or Elecotrocorticography (ECoG)., Significance: Our proposed method suggests that revealing and visualizing the dynamic changes of brain conditions can help clinicians and even patients themselves better understand such conditions.

Purpose: We previously reported that brief pulses of electrical stimulation (BPSs) can terminate afterdischarges (ADs) during cortical stimulation. We investigated conditions under which BPS is more likely to suppress ADs., Methods: We analyzed parameters altering BPS effectiveness on 200 ADs in seven patients with implanted subdural electrodes., Results: The odds of BPSs stopping ADs was 8.6 times greater at primary sites (directly stimulated electrodes) than at secondary sites (adjacent electrodes) (p = 0.016). BPS applied within 4.5 s after onset of AD had 2 times greater odds of stopping ADs (p = 0.014). BPS applied when AD voltage was negative was 1.9 times more likely to stop ADs (p = 0.012). ADs with rhythmic pattern responded best (p < 0.0001). BPS stopped 100% of ADs not starting immediately after localization stimulus (LS) versus 29% of those starting immediately (p < 0.0001)., Conclusions: BPS is more likely to terminate ADs at primary electrodes, if given early, if applied to the negative peak of the AD waveform, if AD has a rhythmic pattern, and if AD did not start immediately after LS.

Objective: Extraoperative cortical localizing stimulation (LS) is a standard clinical tool used to assess brain function before epilepsy surgery. However, LS can produce unwanted afterdischarges (ADs). We previously have shown that brief pulses of electrical stimulation (BPS) can terminate ADs caused by cortical stimulation. Our objective was to assess whether wavelet-crosscorrelation analysis could help predict the conditions under which BPS would be most likely to terminate ADs., Methods: We used wavelet-crosscorrelation analysis to get wavelet-correlation coefficients (WCC), and determine time lag (TL) and absolute value of TL (ATL) between two electrodes. For Analysis-1, we compared WCC and ATL in epoch 1 which was before LS, epoch 2 which was after LS but before BPS, and epoch 3 which was after BPS. For Analysis 2, we compared WCC and ATL during epoch 1 under 4 conditions: epochs when ADs subsequently terminated within 2 s after the end of BPS (1A), terminated within 2-5 s (1B), did not terminate within 5 s (1C), and when ADs did not appear (1D)., Results: We found that BPS efficacy in terminating ADs was predicted by (1) low correlation and (2) slow propagation speed between electrode pairs in the 2-10 s period before stimulation., Conclusions: Wavelet-crosscorrelation analysis can help predict conditions during which BPS can abort ADs. It is possible that similar analyses could help predict when BPS or other interventions could abort clinical seizures.