Effects of anodal multichannel transcranial direct current stimulation (tDCS) on social-cognitive performance in healthy subjects: A randomized sham-controlled crossover pilot study.

Recent studies suggest that temporoparietal junction (TPJ) modulation can influence attention and social cognition performance. Nevertheless, no studies have used multichannel transcranial direct current stimulation (tDCS) over bilateral TPJ to estimate the effects on these neuropsychological functions. The project STIPED is using optimized multichannel stimulation as an innovative treatment approach for chronic pediatric neurodevelopmental disorders, namely in children/adolescents with Autism Spectrum Disorder (ASD). In this pilot study, we aim to explore whether anodal multichannel tDCS coupled with a Joint Attention Task (JAT) influences social-cognitive task performance relative to sham stimulation, both in an Emotion Recognition Task (ERT) and in a Mooney Faces Detection Task (MFDT), as well as to evaluate this technique's safety and tolerability. Twenty healthy adults were enrolled in a randomized, single-blinded, sham-controlled, crossover study. During two sessions, participants completed the ERT and the MFDT before and after 20min of sham or anodal tDCS over bilateral TPJ. No significant differences on performance accuracy and reaction time were found between stimulation conditions for all tasks, including the JAT. A significant main time effect for overall accuracy and reaction time was found for the MFDT. Itching was the most common side effect and stimulation conditions detection was at chance level. Results suggest that multichannel tDCS over bilateral TPJ does not affect performance of low-level emotional recognition tasks in healthy adults. Although preliminary safety and tolerability are demonstrated, further studies over longer periods will be pursued to investigate the clinical efficacy in children/adolescents with ASD, where social cognition impairments are preponderant.

Cardiovascular risk analysis by means of pulse morphology and clustering methodologies.

The purpose of this study was the development of a clustering methodology to deal with arterial pressure waveform (APW) parameters to be used in the cardiovascular risk assessment. One hundred sixteen subjects were monitored and divided into two groups. The first one (23 hypertensive subjects) was analyzed using APW and biochemical parameters, while the remaining 93 healthy subjects were only evaluated through APW parameters. The expectation maximization (EM) and k-means algorithms were used in the cluster analysis, and the risk scores (the Framingham Risk Score (FRS), the Systematic COronary Risk Evaluation (SCORE) project, the Assessing cardiovascular risk using Scottish Intercollegiate Guidelines Network (ASSIGN) and the PROspective Cardiovascular Münster (PROCAM)), commonly used in clinical practice were selected to the cluster risk validation. The result from the clustering risk analysis showed a very significant correlation with ASSIGN (r=0.582, p<0.01) and a significant correlation with FRS (r=0.458, p<0.05). The results from the comparison of both groups also allowed to identify the cluster with higher cardiovascular risk in the healthy group. These results give new insights to explore this methodology in future scoring trials.

Machine learning techniques for arterial pressure waveform analysis.

The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.

Characterization of a double probe for local pulse wave velocity assessment.

Local pulse-wave velocity (PWV) is an accurate indicator of the degree of arteriosclerosis (stiffness) in an artery, providing a direct characterization of the properties of its wall. Devices currently available for local PWV measurement are mainly based on ultrasound systems and have not yet been generalized to clinical practice since they require high technical expertise and most of them are limited in precision, due to the lack of reliable signal processing methods. The present work describes a new type of probe, based on a double-headed piezoelectric (PZ) sensor. The principle of PWV measurement involves determination of the pulse transit time between the signals acquired simultaneously by both PZs, placed 23 mm apart. The double probe (DP) characterization is accomplished in three main studies, carried out in a dedicated test bench system, capable of reproducing a range of clinically relevant properties of the cardiovascular system. The first study refers to determination of the impulse response (IR) for each PZ sensor, whereas the second one explores the existence of crosstalk between both transducers. In the last one, DP time resolution is inferred from a set of three different algorithms based on (a) the maximum of cross-correlation function, (b) the maximum amplitude detection and (c) the zero-crossing point identification. These values were compared with those obtained by the reference method, which consists of the simultaneous acquisition of pressure waves by means of two pressure sensors. The new probe demonstrates good performance on the test bench system and results show that the signals do not exhibit crosstalk. A good agreement was also verified between the PWV obtained from the DP signals (19.55 ± 2.02 ms(-1)) and the PWV determined using the reference method (19.26 ± 0.04 ms(-1)). Although additional studies are still required, this probe seems to be a valid alternative to local PWV stand-alone devices.