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Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task. Twenty-three stroke survivors with no, mild, or moderate upper-limb impairment performed two repetitions of one-to-two minute-long continuous, random (i.e., patternless), voluntary upper-limb movements spanning the entire range of motion. The three-dimensional time-series of upper-limb movements were segmented into a series of one-dimensional submovements by employing a unique movement decomposition technique. An unsupervised clustering algorithm and a supervised regression model were used to estimate Fugl-Meyer Assessment (FMA) scores based on features extracted from these submovements. Our regression model estimated FMA scores with a normalized root mean square error of 18.2% (${r}^{{2}}={0.70}$) and needed as little as one minute of movement data to yield reasonable estimation performance. These results support the possibility of frequently monitoring stroke survivors’ rehabilitation outcomes, ultimately enabling the development of individually-tailored rehabilitation programs. [ABSTRACT FROM AUTHOR]

Background: Transcranial direct current stimulation (tDCS) is known to modulate spontaneous neural network excitability. The cognitive improvement observed in previous trials raises the potential of this technique as a possible therapeutic tool for use in attention-deficit/hyperactivity disorder (ADHD) population. However, to explore the potential of this technique as a treatment approach, the functional parameters of brain connectivity and the extent of its effects need to be more fully investigated. Objective: The aim of this study was to investigate a functional cortical network (FCN) model based on electroencephalographic activity for studying the dynamic patterns of brain connectivity modulated by tDCS and the distribution of its effects in individuals with ADHD. Methods: Sixty ADHD patients participated in a parallel, randomized, double-blind, sham-controlled trial. Individuals underwent a single session of sham or anodal tDCS at 1 mA of current intensity over the left dorsolateral prefrontal cortex for 20 min. The acute effects of stimulation on brain connectivity were assessed using the FCN model based on electroencephalography activity. Results: Comparing the weighted node degree within groups prior to and following the intervention, a statistically significant difference was found in the electrodes located on the target and correlated areas in the active group (p < 0.05), while no statistically significant results were found in the sham group (p ≥ 0.05; paired-sample Wilcoxon signed-rank test). Conclusion: Anodal tDCS increased functional brain connectivity in individuals with ADHD compared to data recorded in the baseline resting state. In addition, although some studies have suggested that the effects of tDCS are selective, the present findings show that its modulatory activity spreads. Further studies need to be performed to investigate the dynamic patterns and physiological mechanisms underlying the modulatory effects of tDCS. Trial Registration ClinicalTrials.gov NCT01968512.

Background: Current standardized treatments for cognitive impairment in attention-deficit/hyperactivity disorder remain limited and their efficacy restricted. Transcranial direct current stimulation (tDCS) is a promising tool for enhancing cognitive performance in several neuropsychiatric disorders. Nevertheless, the effects of tDCS in reducing cognitive impairment in patients with attention-deficit/hyperactivity disorder (ADHD) have not yet been investigated. Methods: A parallel, randomized, double-blind, sham-controlled trial was conducted to examine the efficacy of tDCS on the modulation of inhibitory control in adults with ADHD. Thirty patients were randomly allocated to each group and performed a go/no-go task before and after a single session of either anodal stimulation (1 mA) over the left dorsolateral prefrontal cortex or sham stimulation. Results: A nonparametric two-sample Wilcoxon rank-sum (Mann-Whitney) test revealed no significant differences between the two groups of individuals with ADHD (tDCS vs. sham) in regard to behavioral performance in the go/no go tasks. Furthermore, the effect sizes of group differences after treatment for the primary outcome measures—correct responses, impulsivity and omission errors—were small. No adverse events resulting from stimulation were reported. Conclusion: According to these findings, there is no evidence in support of the use of anodal stimulation over the left dorsolateral prefrontal cortex as an approach for improving inhibitory control in ADHD patients. To the best of our knowledge, this is the first clinical study to assess the cognitive effects of tDCS in individuals with ADHD. Further research is needed to assess the clinical efficacy of tDCS in this population. Trial Registration ClinicalTrials.gov NCT01968512

Dengue infection is a public health problem with a complex distribution. The physical means of propagation and the dynamics of diffusion of the disease between municipalities need to be analysed to direct efficient public policies to prevent dengue infection. The present study presents correlations of occurrences of reported cases of dengue infection among municipalities, self-organized criticality (SOC), and transportation between areas, identifying the municipalities that play an important role in the diffusion of dengue across the state of Bahia, Brazil. The significant correlation found between the correlation network and the SOC demonstrates that the pattern of intramunicipal diffusion of dengue is coupled to the pattern of synchronisation between the municipalities. Transportation emerges as influential in the dynamics of diffusion of epidemics by acting on the aforementioned variables. [ABSTRACT FROM AUTHOR]

Recently, we proposed a novel approach where movements are decomposed into sub-segments, termed movement elements. This approach, to date, provides a robust construct of how the brain may generate simple as well as complex movements. Here, we address the issue of motor variability during voluntary movements by applying an unsupervised clustering algorithm to group movement elements according to their morphological characteristics. We observed that most movement elements closely match the theoretical bell-shaped velocity profile expected from goal-directed movements. However, for those movement elements that deviate from this theoretical shape, a small number of defined patterns in their shape can be identified. Furthermore, we observed that the axis of the body from which the movement elements are extracted (i.e., medio-lateral, antero-posterior, and vertical) affect the proportion of the movement elements matching the theoretical model. These results provide novel insight into how the nervous system controls voluntary movements and may use variability in movement element properties to explore the environment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Daneault, Oubre, Miranda and Lee.)

Nonlinear analysis such as detrended fluctuation analysis (DFA) and power spectrum density are often used to describe the gait motor behavior. This is an interdisciplinary effort to understand and evaluate human movement by the complexity field lenses. However, there are conflicting interpretations about the measures. For instance, the same alpha value could be a better adaptation or sign of pathology. Therefore, the purposes of this scope review are: to map scientific production in the application of the scaling exponent for gait and running analysis, identify the scaling methods used in these studies and the results interpretation, and identify knowledge gaps for future studies. Eleven methods and six metrics associated with them were found. Most of the papers use DFA and explain the results through hypotheses about the supraspinal influence and origin of long-range correlations, adaptability and stability during gait and running. Comparing studies and interpretations, we found a broad designation of terms for the same metric. This reflects the lack of agreement and language uniformity in this literature.

Stroke is a leading cause of long-term disability that may lead to significant functional motor impairments in the upper limb (UL). Wrist-worn inertial sensors have emerged as an objective, minimally-obtrusive tool to monitor UL motor function in the real-world setting, such that rehabilitation interventions can be individually tailored to maximize functional performance. However, current wearable solutions focus on capturing the quantity of movement without considering the quality of movement. This paper introduces a novel approach to unobtrusively estimate the quality of UL movements in stroke survivors using a single wrist-worn inertial sensor during any type of voluntary UL movements. The proposed method exploits kinematic characteristics of voluntary limb movements that are optimized by the central nervous system during motor control. This work demonstrates that the proposed method could extract clinically important information during random UL movements in 16 stroke survivors, showing a statistically significant correlation to the Functional Ability Scale - a clinically validated score for movement quality.