Reliability of single-subject neural activation patterns in speech production tasks

Traditional group fMRI (functional magnetic resonance imaging) analyses are not designed to detect individual differences that may be crucial to better understanding speech disorders. Single-subject research could therefore provide a richer characterization of the neural substrates of speech production in development and disease. Before this line of research can be tackled, however, it is necessary to evaluate whether healthy individuals exhibit reproducible brain activation across multiple sessions during speech production tasks. In the present study, we evaluated the reliability and discriminability of cortical functional magnetic resonance imaging data from twenty neurotypical subjects who participated in two experiments involving reading aloud mono- or bisyllabic speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability, with exceptions likely due to increased head motion in the scanner. Further, this level of reliability for speech production was not directly correlated with reliable patterns in the underlying average blood oxygenation level dependent signal across the brain. Finally, we found that a novel machine-learning subject classifier could identify these individuals by their speech activation patterns with 97% accuracy from among a dataset of seventy-five subjects. These results suggest that single-subject speech research would yield valid results and that investigations into the reliability of speech activation in people with speech disorders are warranted.Accepted manuscrip

Surface electromyographic control of a novel phonemic interface for speech synthesis

Many individuals with minimal movement capabilities use AAC to communicate. These individuals require both an interface with which to construct a message (e.g., a grid of letters) and an input modality with which to select targets. This study evaluated the interaction of two such systems: (a) an input modality using surface electromyography (sEMG) of spared facial musculature, and (b) an onscreen interface from which users select phonemic targets. These systems were evaluated in two experiments: (a) participants without motor impairments used the systems during a series of eight training sessions, and (b) one individual who uses AAC used the systems for two sessions. Both the phonemic interface and the electromyographic cursor show promise for future AAC applications.F31 DC014872 - NIDCD NIH HHS; R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HHS; T90 DA032484 - NIDA NIH HHShttps://www.ncbi.nlm.nih.gov/pubmed/?term=Surface+electromyographic+control+of+a+novel+phonemic+interface+for+speech+synthesishttps://www.ncbi.nlm.nih.gov/pubmed/?term=Surface+electromyographic+control+of+a+novel+phonemic+interface+for+speech+synthesisPublished versio

Anomalous morphology in left hemisphere motor and premotor cortex of children who stutter

Stuttering is a neurodevelopmental disorder that affects the smooth flow of speech production. Stuttering onset occurs during a dynamic period of development when children first start learning to formulate sentences. Although most children grow out of stuttering naturally, ∼1% of all children develop persistent stuttering that can lead to significant psychosocial consequences throughout one’s life. To date, few studies have examined neural bases of stuttering in children who stutter, and even fewer have examined the basis for natural recovery versus persistence of stuttering. Here we report the first study to conduct surface-based analysis of the brain morphometric measures in children who stutter. We used FreeSurfer to extract cortical size and shape measures from structural MRI scans collected from the initial year of a longitudinal study involving 70 children (36 stuttering, 34 controls) in the 3–10-year range. The stuttering group was further divided into two groups: persistent and recovered, based on their later longitudinal visits that allowed determination of their eventual clinical outcome. A region of interest analysis that focused on the left hemisphere speech network and a whole-brain exploratory analysis were conducted to examine group differences and group × age interaction effects. We found that the persistent group could be differentiated from the control and recovered groups by reduced cortical thickness in left motor and lateral premotor cortical regions. The recovered group showed an age-related decrease in local gyrification in the left medial premotor cortex (supplementary motor area and and pre-supplementary motor area). These results provide strong evidence of a primary deficit in the left hemisphere speech network, specifically involving lateral premotor cortex and primary motor cortex, in persistent developmental stuttering. Results further point to a possible compensatory mechanism involving left medial premotor cortex in those who recover from childhood stuttering.This study was supported by Award Numbers R01DC011277 (SC) and R01DC007683 (FG) from the National Institute on Deafness and other Communication Disorders (NIDCD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDCD or the National Institutes of Health. (R01DC011277 - National Institute on Deafness and other Communication Disorders (NIDCD); R01DC007683 - National Institute on Deafness and other Communication Disorders (NIDCD))Accepted manuscrip

Reliability of single-subject neural activation patterns in speech production tasks

Speech neuroimaging research targeting individual speakers could help elucidate differences that may be crucial to understanding speech disorders. However, this research necessitates reliable brain activation across multiple speech production sessions. In the present study, we evaluated the reliability of speech-related brain activity measured by functional magnetic resonance imaging data from twenty neuro-typical subjects who participated in two experiments involving reading aloud simple speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability. We also found that a novel machine-learning subject classifier could identify these individuals by their speech activation patterns with 97% accuracy from among a dataset of seventy-five subjects. These results suggest that single-subject speech research would yield valid results and that investigations into the reliability of speech activation in people with speech disorders are warranted.R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HHS; T32 DC013017 - NIDCD NIH HHSAccepted manuscrip

Determining fracture energy parameters of concrete from the modified compact tension test

The modified compact tension (MCT) test, though not yet recognized as a valid test for determining fracture energy of concrete, is believed to represent a plausible and suitable alternative versus other well established procedures, such as the wedge-splitting test (WST) and the three point (3PB) or four point bending (4PB) tests, due to its simplicity and low cost. The aim of the paper is twofold: Firstly, to demonstrate the necessary correspondence between the experimental MCT test setup and finite element simulations and secondly, to initiate the way of establishing the desirable conversion between the fracture energy parameter values resulting from the MCT test and the standard conventional procedures. MCT tests are carried out and compared with the numerical results from 2-D and 3-D finite element calculations using the commercial codesABAQUS and ATENA, the latter being specifically developed for applications on concrete structures andelements. In this way, the usability of the modified compact tension test for practical purposes is confirmed

El uso de las Fintech por la Generación Millenial en México

The Fintech sector in Mexico has gained great relevance, this sector has shown an important 14% growth during 2019, with 660 companies and the generation of 60,000 job positions. This research aims to analyze the adoption of Fintech by the Millennials generation in the State of San Luis Potosí, Mexico. A convenience sample of 90 people was composed, their age range is between 20 and 50 years. A 30 question instrument related to the knowledge, use and management of Fintech was applied. The study is exploratory and a quantitative-correlational approach is adopted. The results show that Millennials adopt these technologies to a greater extent; one of the determining factors being the affordable cost compared to traditional banking. Respondents exhibit a preference for technological platforms to carry out their financial operations, instead of going to a bank branch.El sector Fintech en México ha cobrado gran relevancia, y muestra de ello ha sido su crecimiento del 14% durante 2019, con 441 empresas y la generación de 60,000 empleos. El objetivo de esta investigación es analizar la adopción de las Fintech por la generación Millenial en el Estado de San Luis Potosí, México. Se conformó una muestra seleccionada a conveniencia de 90 personas, cuyo rango de edad se encuentra entre los 20 y 50 años, y a la cual se le aplicó un instrumento de 30 preguntas relacionadas al conocimiento, uso y manejo de las Fintech. El trabajo es de carácter exploratorio y se adopta un enfoque cuantitativo-correlacional. Los resultados ponen de manifiesto que los Millenials adoptan en mayor medida estas tecnologías, siendo uno de los factores determinantes el costo asequible que ofrecen en comparación con la banca tradicional. Los encuestados manifestaron una preferencia para realizar sus transacciones financieras por medio de plataformas tecnológicas, en lugar de acudir a una sucursal

Data-driven region-of-interest selection without inflating Type I error rate

In event-related potentials (ERP) and other large multi-dimensional neuroscience datasets, researchers often select regions-of-interest (ROIs) for analysis. The method of ROI selection can critically affect the conclusions of a study by causing the researcher to miss effects in the data or to detect spurious effects. In practice, to avoid inflating Type I error rate (i.e., false positives), ROIs are often based on a priori hypotheses or independent information. However, this can be insensitive to experiment-specific variations in effect location (e.g. latency shifts) reducing power to detect effects. Data-driven ROI selection, in contrast, is non-independent and uses the data under analysis to determine ROI positions. Therefore, it has potential to select ROIs based on experiment-specific information and increase power for detecting effects. However, data driven methods have been criticized because they can substantially inflate Type I error rate. Here we demonstrate, using simulations of simple ERP experiments, that data-driven ROI selection can indeed be more powerful than a priori hypotheses or independent information. Furthermore, we show that data-driven ROI selection using the aggregate-grand-average from trials (AGAT), despite being based on the data at hand, can be safely used for ROI selection under many circumstances. However, when there is a noise difference between conditions, using the AGAT can inflate Type 1 error and should be avoided. We identify critical assumptions for use of the AGAT and provide a basis for researchers to use, and reviewers to assess, data-driven methods of ROI localization in ERP and other studies

Clinical applications of the respiratory equation of motion to guide decision-making in the patient under invasive mechanical ventilation

Introduction: Mechanical ventilation is a common practice in intensive care units and anesthesiology with both therapeutic and potentially harmful implications for the respiratory system and distant organs, that is why it is of utmost importance to continually monitor ventilation parameters. Objective: To describe the equation of motion of the respiratory system and its clinical applications in the patient under invasive mechanical ventilation. Main: The equation of motion of the respiratory system integrates the dynamic forces generated by the ventilator with the intrinsic properties of the lung and chest wall. It expresses the pressure in the respiratory system in relation to volume, elastance, resistance, air flow and pressures generated by the ventilator and the patient. Elevated pressures in the respiratory system during mechanical ventilation are associated with greater mortality, that is why the identification of the components responsible for elevation of pressures through the equation of motion of the respiratory system allows to modify ventilator programmed parameters to maintain a protective ventilation. Conclusion: Decision-making based on the equation of motion of the respiratory system allows to modify ventilatory parameters according to the characteristics and diseases of the patient under mechanical ventilation