Dataset of middle cerebral artery blood flow stability in response to high-definition transcranial electrical stimulation.

This supplementary dataset is supportive of the randomized sham-controlled, double-blind, crossover clinical trial investigating polarity- and intensity-dependent effects of high-definition transcranial electrical stimulation (HD-tDCS) applied over the right temporo-parietal junction on mean middle cerebral artery blood flow velocity (MCA-BFv) bilaterally. Data of eleven healthy right-handed adults (6 women, 5 men; mean age 31 ± 5.6 years old) were analyzed for MCA-BFv, assessed using transcranial doppler ultrasound on the stimulated and the contralateral hemisphere concomitantly, during and after 3 blocks of 2 min HD-tDCS at 1, 2, and 3 mA. Participants received three electrical stimulation conditions (anode center, cathode center, and sham) randomly ordered across different days. The collected data is publicly available at Mendeley Data. This article and the data will inform future related investigations and safety analysis of transcranial non-invasive brain stimulation.

Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning.

Learning techniques involve unraveling regression structures, which aim to analyze in a probabilistic frame the associations across variables of interest. Thus, analyzing fraction and/or proportion data may not be adequate with standard regression procedures, since the linear regression models generally assume that the dependent (outcome) variable is normally distributed. In this manner, we propose a statistical model called unit-Lindley regression model, for the purpose of Statistical Process Control (SPC). As a result, a new control chart tool was proposed, which targets the water monitoring dynamic, as well as the monitoring of relative humidity, per minute, of Copiapó city, located in Atacama Desert (one of the driest non-polar places on Earth), north of Chile. Our results show that variables such as wind speed, 24-hour temperature variation, and solar radiation are useful to describe the amount of relative humidity in the air. Additionally, Information Visualization (InfoVis) tools help to understand the time seasonality of the water particle phenomenon of the region in near real-time analysis. The developed methodology also helps to label unusual events, such as Camanchaca, and other water monitoring-related events.

Middle cerebral artery blood flow stability in response to high-definition transcranial electrical stimulation: A randomized sham-controlled clinical trial.

Since neuronal activity is coupled with neurovascular activity, we aimed to analyze the cerebral blood flow hemodynamics during and following high-definition transcranial direct current stimulation (HD-tDCS). We assessed the mean middle cerebral artery blood flow velocity (MCA-BFv) bilaterally using transcranial doppler ultrasound, during and after HD-tDCS, in eleven right-handed healthy adult participants (6 women, 5 men; mean age 31 ± 5.6 years old), with no evidence of brain or cardiovascular dysfunction. The HD-tDCS electrode montage was centered over the right temporo-parietal junction. The stimulation protocol comprised 3 blocks of 2 min at each current intensity (1, 2, and 3 mA) and an inter-stimulus interval of 5 min between blocks. Participants received three electrical stimulation conditions (anode center, cathode center, and sham) on three different days, with an interval of at least 24 h. Stimulation was well tolerated across HD-tDCS conditions tested, and the volunteers reported no significant discomfort related to stimulation. There was no significant difference in the right or the left MCA-BFv during or after the stimulation protocol across all stimulation conditions. We conclude that at a range of intensities, vascular reaction assessed using middle cerebral artery blood flow is not significantly altered during or after HD-tDCS both locally and remotely, which provides further evidence for the safety of HD-tDCS.

BrainWave Nets: Are Sparse Dynamic Models Susceptible to Brain Manipulation Experimentation?

Sparse time series models have shown promise in estimating contemporaneous and ongoing brain connectivity. This paper was motivated by a neuroscience experiment using EEG signals as the outcome of our established interventional protocol, a new method in neurorehabilitation toward developing a treatment for visual verticality disorder in post-stroke patients. To analyze the [complex outcome measure (EEG)] that reflects neural-network functioning and processing in more specific ways regarding traditional analyses, we make a comparison among sparse time series models (classic VAR, GLASSO, TSCGM, and TSCGM-modified with non-linear and iterative optimizations) combined with a graphical approach, such as a Dynamic Chain Graph Model (DCGM). These dynamic graphical models were useful in assessing the role of estimating the brain network structure and describing its causal relationship. In addition, the class of DCGM was able to visualize and compare experimental conditions and brain frequency domains [using finite impulse response (FIR) filter]. Moreover, using multilayer networks, the results corroborate with the susceptibility of sparse dynamic models, bypassing the false positives problem in estimation algorithms. We conclude that applying sparse dynamic models to EEG data may be useful for describing intervention-relocated changes in brain connectivity.

Entropy Analysis of High-Definition Transcranial Electric Stimulation Effects on EEG Dynamics.

A foundation of medical research is time series analysis-the behavior of variables of interest with respect to time. Time series data are often analyzed using the mean, with statistical tests applied to mean differences, and has the assumption that data are stationary. Although widely practiced, this method has limitations. Here we present an alternative statistical approach with sample analysis that provides a summary statistic accounting for the non-stationary nature of time series data. This work discusses the use of entropy as a measurement of the complexity of time series, in the context of Neuroscience, due to the non-stationary characteristic of the data. To elucidate our argument, we conducted entropy analysis on a sample of electroencephalographic (EEG) data from an interventional study using non-invasive electrical brain stimulation. We demonstrated that entropy analysis could identify intervention-related change in EEG data, supporting that entropy can be a useful "summary" statistic in non-linear dynamical systems.

Modeling traumatic brain injury lifetime data: Improved estimators for the Generalized Gamma distribution under small samples.

In this paper, from the practical point of view, we focus on modeling traumatic brain injury data considering different stages of hospitalization, related to patients' survival rates following traumatic brain injury caused by traffic accidents. From the statistical point of view, the primary objective is related to overcoming the limited number of traumatic brain injury patients available for studying by considering different estimation methods to obtain improved estimators of the model parameters, which can be recommended to be used in the presence of small samples. To have a general methodology, at least in principle, we consider the very flexible Generalized Gamma distribution. We compare various estimation methods using extensive numerical simulations. The results reveal that the penalized maximum likelihood estimators have the smallest mean square errors and biases, proving to be the most efficient method among the investigated ones, mainly to be used in the presence of small samples. The Simulated Annealing technique is used to avoid numerical problems during the optimization process, as well as the need for good initial values. Overall, we considered an amount of three real data sets related to traumatic brain injury caused by traffic accidents to demonstrate that the Generalized Gamma distribution is a simple alternative to be used in this type of applications for different occurrence rates and risks, and in the presence of small samples.

Manipulation of Human Verticality Using High-Definition Transcranial Direct Current Stimulation.

Background: Using conventional tDCS over the temporo-parietal junction (TPJ) we previously reported that it is possible to manipulate subjective visual vertical (SVV) and postural control. We also demonstrated that high-definition tDCS (HD-tDCS) can achieve substantially greater cortical stimulation focality than conventional tDCS. However, it is critical to establish dose-response effects using well-defined protocols with relevance to clinically meaningful applications. Objective: To conduct three pilot studies investigating polarity and intensity-dependent effects of HD-tDCS over the right TPJ on behavioral and physiological outcome measures in healthy subjects. We additionally aimed to establish the feasibility, safety, and tolerability of this stimulation protocol. Methods: We designed three separate randomized, double-blind, crossover phase I clinical trials in different cohorts of healthy adults using the same stimulation protocol. The primary outcome measure for trial 1 was SVV; trial 2, weight-bearing asymmetry (WBA); and trial 3, electroencephalography power spectral density (EEG-PSD). The HD-tDCS montage comprised a single central, and 3 surround electrodes (HD-tDCS3x1) over the right TPJ. For each study, we tested 3x2 min HD-tDCS3x1 at 1, 2 and 3 mA; with anode center, cathode center, or sham stimulation, in random order across days. Results: We found significant SVV deviation relative to baseline, specific to the cathode center condition, with consistent direction and increasing with stimulation intensity. We further showed significant WBA with direction governed by stimulation polarity (cathode center, left asymmetry; anode center, right asymmetry). EEG-PSD in the gamma band was significantly increased at 3 mA under the cathode. Conclusions: The present series of studies provide converging evidence for focal neuromodulation that can modify physiology and have behavioral consequences with clinical potential.