Measuring Mental Health Recovery: An Application of Rasch Modeling to the Consumer Recovery Measure.

As the need for recovery-oriented outcomes increases, it is critical to understand how numeric recovery scores are developed. In the current article, the modern Rasch modeling techniques were applied to establish numeric scores of consumers' perceptions of recovery. A sample of 1,973 adult consumers at a community-based mental health center (57.5% male; average age of 47 years old) completed the 15-item Consumer Recovery Measure. A confirmatory factor analysis revealed the unidimensional nature of the Consumer Recovery Measure and provided construct validity evidence. The Rasch analysis displayed that the items produced acceptable model fit, reliability, and identified the difficulty of the items. The conclusion emphasizes the value of Rasch modeling regarding the measurement of recovery and its relevance to consumer-derived assessments in the clinical decision-making process.

Validation of SOBI components from high-density EEG.

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that can be used to decompose mixtures of signals into a set of components or putative recovered sources. Previously, SOBI, as well as other BSS algorithms, has been applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. These BSS algorithms have been shown to recover components that appear to be physiologically and neuroanatomically interpretable. While some proponents of these algorithms suggest that fundamental discoveries about the human brain might be made through the application of these techniques, validation of BSS components has not yet received sufficient attention. Here we present two experiments for validating SOBI-recovered components. The first takes advantage of the fact that noise sources associated with individual sensors can be objectively validated independently from the SOBI process. The second utilizes the fact that the time course and location of primary somatosensory (SI) cortex activation by median nerve stimulation have been extensively characterized using converging imaging methods. In this paper, using both known noise sources and highly constrained and well-characterized neuronal sources, we provide validation for SOBI decomposition of high-density EEG data. We show that SOBI is able to (1) recover known noise sources that were either spontaneously occurring or artificially induced; (2) recover neuronal sources activated by median nerve stimulation that were spatially and temporally consistent with estimates obtained from previous EEG, MEG, and fMRI studies; (3) improve the signal-to-noise ratio (SNR) of somatosensory-evoked potentials (SEPs); and (4) reduce the level of subjectivity involved in the source localization process.