The back-projected independent components (BICs) of single-trial, auditory P300 and contingent negative variation (CNV) evoked potentials (EPs) were derived using independent component analysis (ICA) and cluster analysis. The method was tested in simulation including a study of the electric dipole equivalents of the signal sources. P300 data were obtained from healthy and Alzheimer's disease (AD) subjects. The BICs were of approximately 100 ms duration and approximated positive- and negative-going half-sinusoids. Some positively and negatively peaking BICs constituting the P300 coincided with known peaks in the averaged P300. However, there were trial-to-trial differences in their occurrences, particularly where a positive or a negative BIC could occur with the same latency in different trials, a fact which would be obscured by averaging them. These variations resulted in marked differences in the shapes of the reconstructed, artefact-free, single-trial P300s. The latencies of the BIC associated with the P3b peak differed between healthy and AD subjects (p < 0.01). More reliable evidence than that obtainable from single-trial or averaged P300s is likely to be found by studying the properties of the BICs over a number of trials. For the CNV, BICs corresponding to both the orienting and the expectancy components were found.
The objective was to characterize the non-oscillatory independent components (ICs) of the auditory event-related potential (ERP) waveform of an oddball task for normal and newly diagnosed Alzheimer's disease (AD) subjects, and to seek biomarkers for AD. Single trial ERP waveforms were analysed using independent components analysis (ICA) and k-means clustering. Two stages of clustering depended upon the magnitudes and latencies, and the scalp topographies of the non-oscillatory back-projected ICs (BICs) at electrode Cz. The electrical current dipole sources of the BICs were located using Low Resolution Electromagnetic Tomography (LORETA). Generally 3-10 BICs, of different latencies and polarities, occurred in each trial. Each peak was associated with positive and negative BICs. The trial-to-trial variations in their relative numbers and magnitudes may explain the variations in the averaged ERP reported, and the delay in the averaged P300 for AD patients. The BIC latencies, topographies and electrical current density maximum locations varied from trial-to-trial. Voltage foci in the BIC topographies identify the BIC source locations. Since statistical differences were found between the BICs in healthy and AD subjects, the method might provide reliable biomarkers for AD, if these findings are reproduced in a larger study, independently of other factors influencing the comparison of the two populations. The method can extract artefact- and EEG-free single trial ERP waveforms, offers improved ERP averages by selecting the trials on the basis of their BICs, and is applicable to other evoked potentials, conditions and diseases.
Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Neural reactivation was classified above chance in all participants when TMR was applied in SWS, and in 5 of the 14 participants to whom TMR was applied in N2. Classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be valuable for future investigations of memory consolidation.
Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Above chance classification was significantly higher in slow wave sleep than in stage 2, suggesting differential efficacy of TMR in these two sleep stages.Interestingly, classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be invaluable for future investigations of memory consolidation.not peer-reviewed)
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