Oscillatory responses representing differential auditory processing in sleep.

The goal of the study was to investigate the contribution of the delta and theta responses to the peaks on the event-related potential waveform and specifically to find the possible cognitive correlates of these oscillatory responses in rapid eye movements (REM) sleep and Stage 2 (spindle sleep), Stage 3 (light sleep) and Stage 4 (deep sleep; slow wave sleep) of non-REM sleep. Data on overnight sleep was acquired from 12 healthy, young adult, volunteer males; those on awake stage were obtained from 19 matched males. Brain activity was obtained in response to auditory stimuli (2000 Hz deviant and 1000 Hz standard stimuli: 65 dB, 10 ms r/f time, 50 ms duration) under passive oddball paradigm in sleep, active and passive oddball (OB-a, OB-p, respectively) paradigms in wakefulness. The effect of the experimental variables (stimulus type, sleep stage) was studied using 2 x 4 analysis of variance for repeated measures and stepwise multiple regression analysis. Overall, three types of configurations were obtained for the oscillatory responses which varied according to sleep stage and stimulus type: Large amplitude, differentiated delta and distinct theta response of long duration; distinct theta response with short duration; distinct delta response. As in wakefulness, the morphology of the time-domain peaks was found to be due to the superposition of the delta and theta responses. The configuration in REM resembled the responses to the OB-p paradigm and that NREM stages resembled the responses to the OB-a paradigm in wakefulness. Auditory information processing selectively varied according to sleep stages and took longer in sleep. Comparable peaks were obtained at longer latencies and later components appeared that did not exist under wakefulness. With respect to the long-duration theta activity, and greater differentiation between the deviant- and standard-elicited stimuli, Stage 2 appeared to represent the more effortful cognitive processing.

Early gamma response of sleep is sensory/perceptual in origin.

The goal of the study was to investigate the gamma response of the brain and its functional correlates in rapid eye movements (REM) sleep and the three stages of non-REM sleep. Data on overnight sleep were acquired from 16 healthy, young adult, volunteer males. Neuroelectric activity was recorded from seven recording sites (Fz, Cz, Pz, F3, F4, P3, P4) in response to auditory stimuli (2000 Hz deviant and 1000 Hz standard stimuli: 65 dB, 10 ms r/f time, 50 ms duration) under passive oddball paradigm. Data were analyzed with the Fourier transform and digital filtering and also the recently developed technique of time-frequency component analysis (TFCA). TFCA displayed the gamma response under all stages of sleep. Statistical analysis did not reveal a significant effect of stimulus type, recording site or sleep stage on the three parameters of TFCA, which included maximum value of the time-frequency representation of the extracted gamma component, maximum magnitude of the time-domain representation of the component and the energy of this component. The gamma period included N1 and the early theta response, both of which are related to sensory-perceptual processing in the literature. According to these findings, the gamma response is possibly related, as in wakefulness, to early stimulus processing that also includes sensory/perceptual operations.

Early time-locked gamma response and gender specificity.

The aim was to investigate whether gender is a causative factor in the gamma status according to which some individuals respond with time-locked, early gamma response, G+, while the others do not show this response, G-. The sample consisted of 42 volunteer participants (between 19 and 37 years of age with at least 9 years of education). There were 22 females and 20 males. Data were collected under the oddball paradigm. Auditory stimulation (10 ms r/f time, 50 ms duration, 65 dB SPL) consisted of target (2000 Hz; p = .20) stimuli that occurred randomly within a series of standard stimuli (1000 Hz; p = .80). Gamma responses were studied in the amplitude frequency characteristics, in the digitally filtered event-related potentials (f-ERPs) and in the distributions which were obtained using the recently developed time-frequency component analysis (TFCA) technique. Participants were classified into G+ and G- groups with a criterion of full agreement between the results of an automated gamma detection technique and expert opinion. The 2 x 2 x 2 ANOVA on f-ERPs and 2 x 2 x 2 multivariate ANOVA on TFCA distributions showed the main effect of gamma status and gender as significant, and the interaction between gamma status and gender as nonsignificant. Accordingly, individual difference in gamma status is a reliable phenomenon, but this does not depend on gender. There are conflicting findings in the literature concerning the effect of gender on ERP components (N100, P300). The present study showed that if the gamma status is not included in research designs, it may produce a confounding effect on ERP parameters.

Time-frequency component analyser and its application to brain oscillatory activity.

Currently, event-related potential (ERP) signals are analysed in the time domain (ERP technique) or in the frequency domain (Fourier analysis and variants). In techniques of time-domain and frequency-domain analysis (short-time Fourier transform, wavelet transform) assumptions concerning linearity, stationarity, and templates are made about the brain signals. In the time-frequency component analyser (TFCA), the assumption is that the signal has one or more components with non-overlapping supports in the time-frequency plane. In this study, the TFCA technique was applied to ERPs. TFCA determined and extracted the oscillatory components from the signal and, simultaneously, localized them in the time-frequency plane with high resolution and negligible cross-term contamination. The results obtained by means of TFCA were compared with those obtained by means of other commonly used techniques of ERP analysis, such as bilinear time-frequency distributions and wavelet analysis. It is suggested that TFCA may serve as an appropriate tool for capturing the localized ERP components in the time-frequency domain and for studying the intricate, frequency-based dynamics of the human brain.

Discrimination of brain's neuroelectric responses by a decision-making function.

The aim of this work was to develop a mathematical decision-making procedure that might become a basis for real-time pattern recognition studies of the brain's neuroelectric responses. Data were collected from 77 volunteers under the auditory oddball paradigm with standard (1000 Hz) and deviant (2000 Hz) stimuli. The participants counted the deviants and reported them at the end of the experimental session. Event-related potentials (ERPs) were recorded and filtered with a bandpass between 0.16 and 70 Hz (3 dB down, 12 dB/octave) at Fz- and Pz-recording sites. The most significant potential values that discriminated the responses to the deviant stimuli group were at 136, 224, 328, 348, and 350 ms for Fz, and at 166, 220, and 350 ms for the Pz. The 328, 348, and 350 ms potential values define the curvature of the P300 peak; the 224 ms potential at Fz, and the 220 ms at Pz define that of the N2b peak. The differentiation between the deviant and standard group was checked through a discriminant function that allowed prediction of group membership; 98% of the ERP responses were correctly identified. The results showed that statistically derived time-points were congruent with the P300 and N2b ERP curvature.