A multivariate, spatiotemporal analysis of electromagnetic time-frequency data of recognition memory

Electromagnetic indices of "fast" (above 12 Hz) oscillating brain activity are much more likely to be considerably attenuated by time-averaging across multiple trials than "slow" (below 12 Hz) oscillating brain activity. To the extent that both types of oscillations represent the activity of temporally and topographically separable neural populations, time averaging can cause a loss of brain activity information that is important both conceptually and for multimodal integration with hemodynamic techniques. To address this issue for recognition memory, simultaneous electroencephalography (EEG) and whole-head magnetoencephalography (MEG) recordings of explicit word recognition from 11 healthy subjects were analyzed in two different ways. First, the time course of neural oscillations ranging from theta (4.5 Hz) to gamma (42 Hz) frequencies were identified using single-trial continuous wavelet transforms. Second, traditional analyses of amplitude variations of time-averaged EEG and MEG signals, event-related potentials (ERPs), and fields (ERFs) were performed and submitted to distributed source analyses. To identify data patterns that covaried with the difference between correctly recognized studied (old) words and correctly rejected nonstudied (new) words, a multivariate statistical tool, partial least squares (PLS), was applied to both types of analyses. The results show that ERPs and ERFs are mainly displaying those neural indices of recognition memory that oscillate in the theta (4.5-7.5 Hz), alpha (8-11.5), and to some extent in the beta1 (12-19.5 Hz) frequency range. The sources of the ERPs/ERFs were in good agreement with the topography of theta/alpha/beta 1 oscillations in being confined to the anterior temporal lobe at 400 ms and being distributed across temporal, parietal, and occipital areas between 500 and 700 ms. Gamma oscillations covaried either positively or negatively with theta/alpha/beta1 oscillations. A positive covariance, for instance, was detected over left anterior temporal sensors as early as 200-350 ms and is compatible with studies in rodents showing that gamma and theta oscillations emerge together out of the interaction of the hippocampus and the entorhinal and perirhinal cortices. Fast beta oscillations (20-29.5 Hz), on the other hand, did not strongly covary with slow oscillations and were likely to arise from neural populations not adequately represented in ERPs/ERFs. In summary, by providing a more comprehensive description of electromagnetic signals, time-frequency data are of potential benefit for integrating electrophysiological and hemodynamic indices of brain activity and also for integrating human and animal electrophysiology.