Impaired performance on the rapid visual information processing task (RVIP) could be an endophenotype of schizophrenia

The aim of the present study was to investigate whether healthy first-degree relatives of schizophrenia patients show reduced sensitivity performance, higher intra-individual variability (IIV) in reaction time (RT), and a steeper decline in sensitivity over time in a sustained attention task. Healthy first-degree relatives of schizophrenia patients (n=23) and healthy control subjects (n=46) without a family history of schizophrenia performed a demanding version of the Rapid Visual Information Processing task (RVIP). RTs, hits, false alarms, and the sensitivity index A' were assessed. The relatives were significantly less sensitive, tended to have higher IIV in RT, but sustained the impaired level of sensitivity over time. Impaired performance on the RVIP is a possible endophenotype for schizophrenia. Higher IIV in RT, apparently caused by impaired context representations, might result in fluctuations in control and lead to more frequent attentional lapses.

Caloric vestibular stimulation modulates affective control and mood

BACKGROUND: Clinical evidence suggests a link between vestibular dysfunctions and mood disorders. No study has yet investigated mood and affective control during vestibular stimulation in healthy participants. OBJECTIVE: We predicted a modulating effect of caloric vestibular stimulation (CVS) on affective control measured in an affective Go/NoGo task (AGN). METHODS: Thirty-two participants performed an AGN task while they were exposed to cold left or right ear CVS (20 °C) and sham stimulation (37 °C). In each block, either positive or negative pictures (taken from the International Affective Picture System) were defined as targets. Participants had to respond to targets (Go), and withhold responses to distractors (NoGo). RESULTS: The sensitivity index d' (hits - false alarms) was used to measure affective control. Affective control improved during right ear CVS when viewing positive stimuli (P = .005), but decreased during left ear CVS when compared to sham stimulation (P = .009). CVS had a similar effect on positive mood ratings (Positive and Negative Affect Schedule). Positive mood ratings decreased during left ear CVS when compared to sham stimulation, but there was no effect after right ear CVS. DISCUSSION: The results suggest that CVS, depending on side of stimulation, has a modulating effect on mood and affective control. The results complement previous findings in manic patients and provide new evidence for the clinical potential of CVS.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.