Selected rapporteur summaries from the XX World Congress of Psychiatric Genetics, Hamburg, Germany, October 14-18, 2012.

The XXth World Congress of Psychiatric Genetics (WCPG), sponsored by The International Society of Psychiatric Genetics (ISPG) took place in Hamburg, Germany on October 14-18, 2012. Approximately 600 participants gathered to discuss the latest findings in this rapidly advancing field. The following report was written by student travel awardees. Each was assigned sessions as rapporteurs. This manuscript represents topics covered in most, but not all, oral presentations during the conference, and some of the major notable new findings reported at this 2012 WCPG.

Integration of the circadian and stress systems: influence of neuropeptides and implications for alcohol consumption.

Disruptions in circadian rhythm and stress reactivity are associated with risks of developing neuropsychiatric disorders. The circadian system is organised in a hierarchical manner, whereby the master clock is located at the suprachiasmatic nucleus, a highly conserved brain region that coordinates the oscillations of peripheral clocks. Exposure to psychological stress leads to activation of the hypothalamic-pituitary-adrenal axis. There is growing evidence supporting the interactions between the circadian and stress systems. Anatomically, the circadian and stress signals converge at the paraventricular nucleus (PVN) in the hypothalamus. Genes that are involved in the operation of the circadian and stress systems, including Clock, Period and CRH are expressed in the PVN. In addition, several neuropeptides, including arginin-vasopressin, vasoactive intestinal polypeptide, pituitary adenylate cyclase-activating polypeptide and the neurotransmitter gamma-aminobutyric acid, are present in the PVN. In this review, we will discuss the interaction of circadian genes and stress-response genes at the molecular, neurotransmission and behavioural levels. We will place particular emphasis on the role of neuropeptides in mediating this interaction.

Estimating blood pressure trends and the nocturnal dip from photoplethysmography.

OBJECTIVE: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24 h blood pressure trends using a wrist-worn photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines. APPROACH: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 d during which 5111 reference values for blood pressure (BP) were obtained with a 24 h ambulatory BP monitor and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms. Long- and short term memory (LSTM) networks, dense networks, random forests and linear regression models were trained and evaluated in their capability of tracking trends in BP, as well as the estimation of the SBP dip. MAIN RESULTS: Best performance for estimating the SBP dip were obtained with a deep LSTM neural network with a root mean squared error (RMSE) of 3.12 [Formula: see text] 2.20 [Formula: see text] mmHg and a correlation of 0.69 [Formula: see text]. This dip was derived from trend estimates of BP which had an RMSE of 8.22 [Formula: see text] 1.49 mmHg for systolic and 6.55 [Formula: see text] 1.39 mmHg for diastolic BP (DBP). While other models had similar performance for the tracking of relative BP, they did not perform as well as the LSTM for the SBP dip. SIGNIFICANCE: The work provides first evidence for the unobtrusive estimation of the nocturnal SBP dip, a highly prognostic clinical parameter. It is also the first to evaluate unobtrusive BP measurement in a large data set of unconstrained 24 h measurements in free-living individuals and provides evidence for the utility of LSTM models in this domain.