Computation of headmodel and sourcemodel from the subject's MRI scan is an essential step for source localization of magnetoencephalography (MEG) (or EEG) sensor signals. In the absence of a real MRI scan, pseudo MRI (i.e., associated headmodel and sourcemodel) is often approximated from an available standard MRI template or pool of MRI scans considering the subject's digitized head surface. In the present study, we approximated two types of pseudo MRI (i.e., associated headmodel and sourcemodel) using an available pool of MRI scans with the focus on MEG source imaging. The first was the first rank pseudo MRI; that is, the MRI scan in the dataset having the lowest objective registration error (ORE) after being registered (rigid body transformation with isotropic scaling) to the subject's digitized head surface. The second was the averaged rank pseudo MRI that is generated by averaging of headmodels and sourcemodels from multiple MRI scans respectively, after being registered to the subject's digitized head surface. Subject level analysis showed that the mean upper bound of source location error for the approximated sourcemodel in reference to the real one was 10 ± 3 mm for the averaged rank pseudo MRI, which was significantly lower than the first rank pseudo MRI approach. Functional group source response in the brain to visual stimulation in the form of event-related power (ERP) at the time latency of peak amplitude showed noticeably identical source distribution for first rank pseudo MRI, averaged rank pseudo MRI, and real MRI. The source localization error for functional peak response was significantly lower for averaged rank pseudo MRI compared to first rank pseudo MRI. We conclude that it is feasible to use approximated pseudo MRI, particularly the averaged rank pseudo MRI, as a substitute for real MRI without losing the generality of the functional group source response.

Every day, we face situations that involve multi-tasking. How our brain utilizes cortical resources during multi-tasking is one of many interesting research topics. In this study, we tested whether a dual-task can be differentiated in the neural and behavioral responses of healthy subjects with varying degree of working memory capacity (WMC). We combined word recall and oculomotor tasks because they incorporate common neural networks including the fronto-parietal (FP) network. Three different types of oculomotor tasks (eye fixation, Fix-EM; predictive and random smooth pursuit eye movement, P-SPEM and R-SPEM) were combined with two memory load levels (low-load: five words, high-load: 10 words) for a word recall task. Each of those dual-task combinations was supposed to create varying cognitive loads on the FP network. We hypothesize that each dual-task requires different cognitive strategies for allocating the brain’s limited cortical resources and affects brain oscillation of the FP network. In addition, we hypothesized that groups with different WMC will show differential neural and behavioral responses. We measured oscillatory brain activity with simultaneous MEG and EEG recordings and behavioral performance by word recall. Prominent frontal midline (FM) theta (4–6 Hz) synchronization emerged in the EEG of the high-WMC group experiencing R-SPEM with high-load conditions during the early phase of the word maintenance period. Conversely, significant parietal upper alpha (10–12 Hz) desynchronization was observed in the EEG and MEG of the low-WMC group experiencing P-SPEM under high-load conditions during the same period. Different brain oscillatory patterns seem to depend on each individual’s WMC and varying attentional demands from different dual-task combinations. These findings suggest that specific brain oscillations may reflect different strategies for allocating cortical resources during combined word recall and oculomotor dual-tasks.

The somatic marker hypothesis proposes that the cortical representation of visceral signals is a crucial component of emotional processing. No previous study has investigated the information flow among brain regions that process visceral information during emotional perception. In this magnetoencephalography study of 32 healthy subjects of either sex, heartbeat-evoked responses (HERs), which reflect the cortical processing of heartbeats, were modulated by the perception of a sad face. The modulation effect was localized to the prefrontal cortices, the globus pallidus, and an interoceptive network including the right anterior insula (RAI) and dorsal anterior cingulate cortex (RdACC). Importantly, our Granger causality analysis provides the first evidence for the increased flow of heartbeat information from the RAI to the RdACC during sad face perception. Moreover, using a surrogate R-peak analysis, we have shown that this HER modulation effect was time-locked to heartbeats. These findings advance the understanding of brain-body interactions during emotional processing.

Phase-amplitude coupling (PAC) plays an important role in neural communication and computation. Interestingly, recent studies have indicated the presence of ubiquitous PAC phenomenon even during the resting state. Despite the importance of PAC phenomenon, estimation of significant physiological PAC is challenging because of the lack of appropriate surrogate measures to control false positives caused by non-physiological PAC. Therefore, in the present study, we evaluated PAC phenomenon during resting-state magnetoencephalography (MEG) signal and considered various surrogate measures and computational approaches widely used in the literature in addition to proposing new ones. We evaluated PAC phenomenon over the entire length of the MEG signal and for multiple shorter time segments. The results indicate that the extent of PAC phenomenon mainly depends on the surrogate measures and PAC computational methods used, as well as the evaluation approach. After a careful and critical evaluation, we found that resting-state MEG signals failed to exhibit ubiquitous PAC phenomenon, contrary to what has been suggested previously.