Multi-item working memory (WM) is a complex cognitive function thought to arise from specific frequency band oscillations and their interactions. While some theories and consistent findings have been established, there is still a lot of unclarity about the sources, temporal dynamics, and roles of event-related fields (ERFs) and theta, alpha, and beta oscillations during WM activity. In this study, we performed an extensive whole-brain ERF and time-frequency analysis on n-back magnetoencephalography data from 38 healthy controls. We identified the previously unknown sources of the n-back M300, the right inferior temporal and parahippocampal gyrus and left inferior temporal gyrus, and frontal theta power increase, the orbitofrontal cortex.We shed new light on the role of the precuneus during n-back activity, based on an early ERF and theta power increase, and suggest it to be a crucial link between lower-level and higher-level information processing. In addition, we provide strong evidence for the central role of the hippocampus in multi-item WM behavior through the dynamics of theta and alpha oscillatory changes. Almost simultaneous alpha power decreases observed in the hippocampus and occipital fusiform gyri, regions known to be involved in letter processing, suggest that these regions together enable letter recognition, encoding and storage in WM. In summary, this study offers an extensive investigation into the spatial, temporal, and spectral characteristics of n-back multi-item WM activity. K E Y W O R D Shippocampus, magnetoencephalography, n-back, precuneus, working memory

This study validates the BICAMS in a Belgian Dutch-speaking population and facilitates the use of it in clinical practice, while providing evidence that including full versions of the CVLT-II and BVMT-R does not increase its psychometric qualities markedly.

We investigated the power of EEG as biomarker in differential diagnosis of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD). EEG was recorded from 106 patients with AD or FTLD, of which 37 had a definite diagnosis, and 40 controls. Dominant frequency peaks were extracted for all 19 channels, for each subject. The average frequency of the largest dominant frequency peaks (maxpeak) was significantly lower in AD than FTLD patients and controls. Based on ROC analysis, classification could be made with diagnostic accuracy of 78.9%. Our findings show that quantitative analysis of EEG maxpeak frequency is an easy and useful measure for differential dementia diagnosis.

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