Constructing Instrument Mapping Using Markov Boundary Techniques: A Case Study with Two Quality of Life Instruments In psychiatry, it is common to have multiple available instruments for a concept of interest. Different studies may choose to measure the same concept with different instruments for reasons such as time and resource constraints. This makes it difficult to compare findings between studies, since differences in findings may be due to the use of different instruments. In this paper, we described and demonstrated a two step process to identify a correspondence mapping between QLS and GAF, two quality of life instruments. Specifically, we applied a Markov Boundary discovery algorithm, HITONMB, to identify the Markov Boundary of GAF using different QLS subscales and items as candidate Markov Boundary members. The mapping function is then derived by regressing the identified Markov Boundary members to GAF. We applied the two step procedure to two independ datasets to examine the QLSGAF correspondence across different patient population and training conditions. We identified the intrapsychic foundation subscale of QLS to be a consistent Markov Boundary member of GAF. We also reported the quality of the derived mapping functions.

Within and betweenperson correlates of the temporal dynamics of resting EEG microstates Abstract   Microstates reflect transient brain states resulting from the synchronous activity of brain networks that predominate in the broadband EEG. There has been increasing interest in how the functional organization of the brain varies across individuals, or the extent to which its spatiotemporal dynamics are state dependent. However, little research has examined within and betweenperson correlates of microstate temporal parameters in healthy populations. In the present study, neuroelectric activity recorded during eyesclosed rest and during simple visual fixation was segmented into a time series of transient microstate intervals. It was found that five datadriven microstate configurations explained the preponderance of topographic variance in the EEG time series of the 374 recordings (from 187 participants) included in the study. We observed that the temporal dynamics of microstates varied within individuals to a greater degree than they differed between persons, with withinperson factors explaining a large portion of the variance in mean microstate duration and occurrence rate. Nevertheless, several individual differences were found to predict the temporal dynamics of microstates. Of these, age and gender were the most reliable. These findings suggest that not only do the rich temporal dynamics of wholebrain neuronal networks vary considerably withinindividuals, but that microstates appear to differentiate persons based on trait individual differences. The current findings suggest that rather than focusing exclusively on betweenperson differences in microstates as measures of brain function, researchers should turn their attention towards understanding the factors contributing to withinperson variation.