Inferring sleep disturbance from text messages of suicide attempt survivors: A pilot study.

OBJECTIVE: Identifying digital markers of sleep disturbance-a known suicide risk factor-may aid in the detection of imminent suicide risk. This study examined sleep-related communication and texting patterns in personal text messages (N = 86,705) of suicide attempt survivors. METHOD: Twenty-six participants provided dates of past suicide attempts and 2-week periods of positive mood, depressed mood, or suicidal ideation. Linguistic Inquiry Word Count was used to identify sleep-related texts via a custom dictionary. Mixed effect models were fitted to test the association between suicide/mood episode type (e.g., attempt versus ideation) and three outcomes: likelihood of a text including sleep-related content, nightly count of texts sent from midnight to 5:00 AM, and sum of unique hour bins from midnight to 5:00 AM with outgoing texts. RESULTS: Analyses with a sleep dictionary that was manually revised to be more accurate (but not the original unedited dictionary) showed sleep-related communication was more likely during depressed mood episodes than positive mood episodes. Otherwise, there were no significant differences in sleep-related communication or objective texting patterns across episode type. CONCLUSIONS: Although we did not detect differences in sleep-related communication tied to suicidal thoughts or behaviors, sleep-related communication may differ as a function of within-person mood level.

Which variations of a brief cognitive bias modification session for interpretations lead to the strongest effects?

BACKGROUND: Brief computerized programs that train less threatening interpretations (termed Cognitive Bias Modification for Interpretations, or CBM-I) can shift interpretation biases and subsequent anxiety symptoms. However, results have been inconsistent, particularly for studies conducted over the Internet. METHODS: The current exploratory study tests 13 variations of a single brief session of CBM-I, a non-CBM-I cognitive flexibility condition, a neutral condition, and a no task control condition in an analogue sample with moderate to severe anxiety. RESULTS: Results suggest that all conditions, except the neutral scenarios condition and the alternative way to improve cognitive flexibility, led to changes in interpretations (when compared to the no task control condition). Only conditions geared toward increasing imagery during CBM-I and targeting flexibility related to emotional material differed from the no task control condition on other post-training measures. CONCLUSIONS: Presenting valenced interpretations of ambiguous information during brief CBM-I, regardless of the format, can lead to changes in interpretation bias. However, most conditions did not differ from the no task control condition on other post-training assessments (and differences that did occur may be due to chance). Future trials should consider further testing of CBM-I that targets flexibility related to emotional material, and should include an increased number of sessions and trials.

Gut microbiota composition is associated with newborn functional brain connectivity and behavioral temperament.

The gut microbiome appears to play an important role in human health and disease. However, only little is known about how variability in the gut microbiome contributes to individual differences during early and sensitive stages of brain and behavioral development. The current study examined the link between gut microbiome, brain, and behavior in newborn infants (N = 63; M [age] = 25 days). Infant gut microbiome diversity was measured from stool samples using metagenomic sequencing, infant functional brain network connectivity was assessed using a resting state functional near infrared spectroscopy (rs-fNIRS) procedure, and infant behavioral temperament was assessed using parental report. Our results show that gut microbiota composition is linked to individual variability in brain network connectivity, which in turn mediated individual differences in behavioral temperament, specifically negative emotionality, among infants. Furthermore, virulence factors, possibly indexing pathogenic activity, were associated with differences in brain network connectivity linked to negative emotionality. These findings provide novel insights into the early developmental origins of the gut microbiome-brain axis and its association with variability in important behavioral traits. This suggests that the gut microbiome is an important biological factor to consider when studying human development and health.

Evaluating Longitudinal Anchoring Methods for Rasch Models.

Because modern, simultaneously estimated longitudinal Rasch models are unable to handle many timepoints, new methods of producing person and item estimates and evaluating test function are necessary. Longitudinal anchoring, in which a common scale of item parameters is used to estimate trait levels over multiple occasions, is a potential solution. With proper anchoring procedures, person and item estimates can be obtained without limiting the number of timepoints that can be analyzed. A simulation study examining the performance of six longitudinal anchoring methods (Floated, Racked, Time One, Mean, Random, and Stacked) was conducted. The Mean and the Stacked anchoring methods best recovered the population change over time, person and item estimates, and model fit. The Racked method could not produce reliable change estimates and should be avoided. Longitudinal anchoring is an easily implemented solution when analyzing large longitudinal datasets and shows promise as a low-computation method of producing latent trait estimates.