Longitudinal changes of day-time and night-time gross motor activity in clinical responders and non-responders of major depression

Psychomotor retardation (PR) is among the most important features of depression. This study investigates the development of day- and night-time as well as intensity and quantity of circadian motor activity during a 4-week course of treatment among 27 patients with depression compared to 27 healthy controls. A diagnosis of major depression was made using SCID. Motor activity was continuously measured with an actigraph during the study and clinical course of depression with HAM-D-21. Motor activity was described as the quantity and intensity of movements during day- and night- time. Clinically improved patients had significantly intensified movements after 4 weeks, compared to subjects with <50% improvement on HAM-D. While the measures of day-time level of movements captured the clinical improvement of depression, clinical improvement was not reflected by the night-time measurements. This study demonstrates that the separated analysis of level and quantity of movements supports a better understanding of the nature of psychomotor retardation during depression. The subdivision in day- and night-time activity objectively measured with actigraphy captures distinct patterns of motor activity and represents prognostic factors in the treatment outcome of depression. The study also highlights the importance of studying the intensity of movements separately from the quantity of movements in relation to treatment outcome.Doron Todder, Serdal Caliskan & Bernhard T. Baun

VoPo leverages cellular heterogeneity for predictive modeling of single-cell data

High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters