Machine learning-based prediction of illness course in major depression: The relevance of risk factors.

Background: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are still rare and do not quantify the predictive value of established MDD recurrence risk factors.

Methods: We analyzed N = 571 MDD patients from the Marburg-Münster Affective Disorder Cohort Study (MACS).

Judged by your neighbors: Brain structural normativity profiles for large and heterogeneous samples.

The detection of norm deviations is fundamental to clinical decision making and impacts our ability to diagnose and treat diseases effectively. Current normative modeling approaches rely on generic comparisons and quantify deviations in relation to the population average. However, generic models interpolate subtle nuances and risk the loss of critical information, thereby compromising effective personalization of health care strategies.

Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.

Background: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time.

Experience and trust: the benefits of mate familiarity are realized through sex-specific specialization of parental roles in Cassin's auklet.

Maintaining a pair bond year after year (perennial monogamy) often enhances reproductive success, but what familiar pairs are doing differently to improve success is unknown. We tested the hypothesis that endocrine changes mediate improvements in parental attendance in known-age Cassin's auklets , for which we found limited evidence. Instead, we found sex-specific parental roles in familiar pairs.