Electroencephalographic interbrain synchronization in children with disabilities, their parents, and neurologic music therapists.

As with typically developing children, children with cerebral palsy and autism spectrum disorder develop important socio-emotional rapport with their parents and healthcare providers. However, the neural mechanisms underlying these relationships have been less studied. By simultaneously measuring the brain activity of multiple individuals, interbrain synchronization could serve as a neurophysiological marker of social-emotional responses. Music evokes emotional and physiological responses and enhances social cohesion. These characteristics of music have fostered its deployment as a therapeutic medium in clinical settings. Therefore, this study investigated two aspects of interbrain synchronization, namely, its phase and directionality, in child-parent (CP) and child-therapist (CT) dyads during music and storytelling sessions (as a comparison). A total of 17 participants (seven cerebral palsy or autism spectrum disorder children [aged 12-18 years], their parents, and three neurologic music therapists) completed this study, comprising seven CP and seven CT dyads. Each music therapist worked with two or three children. We found that session type, dyadic relationship, frequency band, and brain region were significantly related to the degree of interbrain synchronization and its directionality. Particularly, music sessions and CP dyads were associated with higher interbrain synchronization and stronger directionality. Delta (.5-4 Hz) range showed the highest phase locking value in both CP and CT dyads in frontal brain regions. It appears that synchronization is directed predominantly from parent to child, that is, parents and music therapists' brain activity tended to influence a child's. Our findings encourage further research into neural synchrony in children with disabilities, especially in musical contexts, and its implications for social and emotional development.

Automated movement recognition to predict motor impairment in high-risk infants: a systematic review of diagnostic test accuracy and meta-analysis.

AIM: To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high-risk infants. METHOD: We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high-risk infants to predict motor impairment, including cerebral palsy (CP) and non-CP motor impairments. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2. Meta-analyses were performed using hierarchical summary receiver operating characteristic models. RESULTS: Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5 months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval [CI] 0.68-0.77) and 0.70 (95% CI 0.65-0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74-100) and 91% (95% CI 83-93) respectively. Sensor-based technologies had higher specificity (0.88, 95% CI 0.80-0.93). INTERPRETATION: Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta-analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.

Quantifying fast optical signal and event-related potential relationships during a visual oddball task.

Event-related potentials (ERPs) have previously been used to confirm the existence of the fast optical signal (FOS) but validation methods have mainly been limited to exploring the temporal correspondence of FOS peaks to those of ERPs. The purpose of this study was to systematically quantify the relationship between FOS and ERP responses to a visual oddball task in both time and frequency domains. Near-infrared spectroscopy (NIRS) and electroencephalography (EEG) sensors were co-located over the prefrontal cortex while participants performed a visual oddball task. Fifteen participants completed 2 data collection sessions each, where they were instructed to keep a mental count of oddball images. The oddball condition produced a positive ERP at 200 ms followed by a negativity 300-500 ms after image onset in the frontal electrodes. In contrast to previous FOS studies, a FOS response was identified only in DC intensity signals and not in phase delay signals. A decrease in DC intensity was found 150-250 ms after oddball image onset with a 400-trial average in 10 of 15 participants. The latency of the positive 200 ms ERP and the FOS DC intensity decrease were significantly correlated for only 6 (out of 15) participants due to the low signal-to-noise ratio of the FOS response. Coherence values between the FOS and ERP oddball responses were found to be significant in the 3-5 Hz frequency band for 10 participants. A significant Granger causal influence of the ERP on the FOS oddball response was uncovered in the 2-6 Hz frequency band for 7 participants. Collectively, our findings suggest that, for a majority of participants, the ERP and the DC intensity signal of the FOS are spectrally coherent, specifically in narrow frequency bands previously associated with event-related oscillations in the prefrontal cortex. However, these electro-optical relationships were only found in a subset of participants. Further research on enhancing the quality of the event-related FOS signal is required before it can be practically exploited in applications such as brain-computer interfacing.

Long term consistency of handwriting grip kinetics in adults.

While there is growing interest in clinical applications of handwriting grip kinetics, the consistency of these forces over time is not well-understood at present. In this study, we investigated the short- and long-term intra-participant consistency and inter-participant differences in grip kinetics associated with adult signature writing. Grip data were collected from 20 adult participants using a digitizing tablet and an instrumented pen. The first phase of data collection occurred over 10 separate days within a three week period. To ascertain long-term consistency, a second phase of data collection followed, one day per month over several months. In both phases, data were collected three times a day. After pre-processing and feature extraction, nonparametric statistical tests were used to compare the within-participant grip force variation between the two phases. Participant classification based on grip force features was used to determine the relative magnitude of inter-participant versus intra-participant differences. The misclassification rate for the longitudinal data were used as an indication of long term kinetic consistency. Intra-participant analysis revealed significant changes in grip kinetic features between the two phases for many participants. However, the misclassification rate, on average, remained stable, despite different demarcations of training, and testing data. This finding suggests that while signature writing grip forces may evolve over time, inter-participant kinetic differences consistently exceeds within-participant force changes in the long-term. These results bear implications on the collection, modeling and interpretation of grip kinetics in clinical applications.

Phosphorylation regulates NCC stability and transporter activity in vivo.

A T60M mutation in the thiazide-sensitive sodium chloride cotransporter (NCC) is common in patients with Gitelman's syndrome (GS). This mutation prevents Ste20-related proline and alanine-rich kinase (SPAK)/oxidative stress responsive kinase-1 (OSR1)-mediated phosphorylation of NCC and alters NCC transporter activity in vitro. Here, we examined the physiologic effects of NCC phosphorylation in vivo using a novel Ncc T58M (human T60M) knock-in mouse model. Ncc(T58M/T58M) mice exhibited typical features of GS with a blunted response to thiazide diuretics. Despite expressing normal levels of Ncc mRNA, these mice had lower levels of total Ncc and p-Ncc protein that did not change with a low-salt diet that increased p-Spak. In contrast to wild-type Ncc, which localized to the apical membrane of distal convoluted tubule cells, T58M Ncc localized primarily to the cytosolic region and caused an increase in late distal convoluted tubule volume. In MDCK cells, exogenous expression of phosphorylation-defective NCC mutants reduced total protein expression levels and membrane stability. Furthermore, our analysis found diminished total urine NCC excretion in a cohort of GS patients with homozygous NCC T60M mutations. When Wnk4(D561A/+) mice, a model of pseudohypoaldosteronism type II expressing an activated Spak/Osr1-Ncc, were crossed with Ncc(T58M/T58M) mice, total Ncc and p-Ncc protein levels decreased and the GS phenotype persisted over the hypertensive phenotype. Overall, these data suggest that SPAK-mediated phosphorylation of NCC at T60 regulates NCC stability and function, and defective phosphorylation at this residue corrects the phenotype of pseudohypoaldosteronism type II.

Autonomic responses to correct outcomes and interaction errors during single-switch scanning among children with severe spastic quadriplegic cerebral palsy.

BACKGROUND: The combination of single-switch access technology and scanning is the most promising means of augmentative and alternative communication for many children with severe physical disabilities. However, the physical impairment of the child and the technology's limited ability to interpret the child's intentions often lead to false positives and negatives (corresponding to accidental and missed selections, respectively) occurring at rates that frustrate the user and preclude functional communication. Multiple psychophysiological studies have associated cardiac deceleration and increased phasic electrodermal activity with self-realization of errors among able-bodied individuals. Thus, physiological measurements have potential utility at enhancing single-switch access, provided that such prototypical autonomic responses exist in persons with profound disabilities. METHODS: The present case series investigated the autonomic responses of three pediatric single-switch users with severe spastic quadriplegic cerebral palsy, in the context of a single-switch letter matching activity. Each participant exhibited distinct autonomic responses to activity engagement. RESULTS: Our analysis confirmed the presence of the autonomic response pattern of cardiac deceleration and increased phasic electrodermal activity following true positives, false positives and false negatives errors, but not subsequent to true negative outcomes. CONCLUSIONS: These findings suggest that there may be merit in complementing single-switch input with autonomic measurements to improve augmentative and alternative communications for pediatric access technology users.

A pediatric correlational study of stride interval dynamics, energy expenditure and activity level.

The strength of time-dependent correlations known as stride interval (SI) dynamics has been proposed as an indicator of neurologically healthy gait. Most recently, it has been hypothesized that these dynamics may be necessary for gait efficiency although the supporting evidence to date is scant. The current study examines over-ground SI dynamics, and their relationship with the cost of walking and physical activity levels in neurologically healthy children aged nine to 15 years. Twenty participants completed a single experimental session consisting of three phases: 10 min resting, 15 min walking and 10 min recovery. The scaling exponent (α) was used to characterize SI dynamics while net energy cost was measured using a portable metabolic cart, and physical activity levels were determined based on a 7-day recall questionnaire. No significant linear relationships were found between a and the net energy cost measures (r < .07; p > .25) or between α and physical activity levels (r = .01, p = .62). However, there was a marked reduction in the variance of α as activity levels increased. Over-ground stride dynamics do not appear to directly reflect energy conservation of gait in neurologically healthy youth. However, the reduction in the variance of α with increasing physical activity suggests a potential exercise-moderated convergence toward a level of stride interval persistence for able-bodied youth reported in the literature. This latter finding warrants further investigation.

Physiotherapy-assisted overground exoskeleton use: mixed methods feasibility study protocol quantifying the user experience, as well as functional, neural, and muscular outcomes in children with mobility impairments.

Background: Early phase research suggests that physiotherapy paired with use of robotic walking aids provides a novel opportunity for children with severe mobility challenges to experience active walking. The Trexo Plus is a pediatric lower limb exoskeleton mounted on a wheeled walker frame, and is adjustable to fit a child's positional and gait requirements. It guides and powers the child's leg movements in a way that is individualized to their movement potential and upright support needs, and can provide progressive challenges for walking within a physiotherapy-based motor learning treatment paradigm. Methods: This protocol outlines a single group mixed-methods study that assesses the feasibility of physiotherapy-assisted overground Trexo use in school and outpatient settings during a 6-week physiotherapy block. Children ages 3-6 years (n = 10; cerebral palsy or related disorder, Gross Motor Function Classification System level IV) will be recruited by circle of care invitations to participate. Study indicators/outcomes will focus on evaluation of: (i) clinical feasibility, safety, and acceptability of intervention; (ii) pre-post intervention motor/functional outcomes; (iii) pre-post intervention brain structure characterization and resting state brain connectivity; (iv) muscle activity characterization during Trexo-assisted gait and natural assisted gait; (v) heart rate during Trexo-assisted gait and natural assisted gait; and (vi) user experience and perceptions of physiotherapists, children, and parents. Discussion: This will be the first study to investigate feasibility indicators, outcomes, and experiences of Trexo-based physiotherapy in a school and outpatient context with children who have mobility challenges. It will explore the possibility of experience-dependent neuroplasticity in the context of gait rehabilitation, as well as associated functional and muscular outcomes. Finally, the study will address important questions about clinical utility and future adoption of the device from the physiotherapists' perspective, comfort and engagement from the children's perspective, and the impressions of parents about the value of introducing this technology as an early intervention. Clinical trial registration: https://clinicaltrials.gov, identifier NCT05463211.

Artificial intelligence-enabled electrocardiography contributes to hyperthyroidism detection and outcome prediction.

BACKGROUND: Hyperthyroidism is frequently under-recognized and leads to heart failure and mortality. Timely identification of high-risk patients is a prerequisite to effective antithyroid therapy. Since the heart is very sensitive to hyperthyroidism and its electrical signature can be demonstrated by electrocardiography, we developed an artificial intelligence model to detect hyperthyroidism by electrocardiography and examined its potential for outcome prediction. METHODS: The deep learning model was trained using a large dataset of 47,245 electrocardiograms from 33,246 patients at an academic medical center. Patients were included if electrocardiograms and measurements of serum thyroid-stimulating hormone were available that had been obtained within a three day period. Serum thyroid-stimulating hormone and free thyroxine were used to define overt and subclinical hyperthyroidism. We tested the model internally using 14,420 patients and externally using two additional test sets comprising 11,498 and 596 patients, respectively. RESULTS: The performance of the deep learning model achieves areas under the receiver operating characteristic curves (AUCs) of 0.725-0.761 for hyperthyroidism detection, AUCs of 0.867-0.876 for overt hyperthyroidism, and AUC of 0.631-0.701 for subclinical hyperthyroidism, superior to a traditional features-based machine learning model. Patients identified as hyperthyroidism-positive by the deep learning model have a significantly higher risk (1.97-2.94 fold) of all-cause mortality and new-onset heart failure compared to hyperthyroidism-negative patients. This cardiovascular disease stratification is particularly pronounced in subclinical hyperthyroidism, surpassing that observed in overt hyperthyroidism. CONCLUSIONS: An innovative algorithm effectively identifies overt and subclinical hyperthyroidism and contributes to cardiovascular risk assessment.

Hyperthyroidism occurs when the thyroid gland produces too much hormone and can cause various symptoms including faster heartbeat, weight loss, and nervousness. Diagnosis is often missed, which can lead to heart problems and even death. Measurements of the heart's electrical activity can be obtained using Electrocardiograms (ECGs). We made a computational model that can detect hyperthyroidism from ECGs. Our model was better able to identify people with hyperthyroidism than currently available methods, especially the more severe forms of the condition. If future work demonstrates our model is safe and accurate, it could potentially be used to detect hyperthyroidism sooner, enabling faster treatment and improved health of people with hyperthyroidism.

Towards a multimodal brain-computer interface: combining fNIRS and fTCD measurements to enable higher classification accuracy.

Previous brain-computer interface (BCI) research has largely focused on single neuroimaging modalities such as near-infrared spectroscopy (NIRS) or transcranial Doppler ultrasonography (TCD). However, multimodal brain-computer interfaces, which combine signals from different brain modalities, have been suggested as a potential means of improving the accuracy of BCI systems. In this paper, we compare the classification accuracies attainable using NIRS signals alone, TCD signals alone, and a combination of NIRS and TCD signals. Nine able-bodied subjects (mean age=25.7) were recruited and simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. Using Linear Discriminant Analysis, the verbal fluency task was classified at mean accuracies of 76.1±9.9%, 79.4±10.3%, and 86.5±6.0% using NIRS, TCD, and NIRS-TCD systems respectively. In five of nine participants, classification accuracies with the NIRS-TCD system were significantly higher (p<0.05) than with NIRS or TCD systems alone. Our results suggest that multimodal neuroimaging may be a promising method of improving the accuracy of future brain-computer interfaces.

Acute prolonged neurotoxicity associated with recommended doses of ertapenem in 2 patients with advanced renal failure.

Ertapenem, a novel carbapenem with long-acting antimicrobial activity, is predominantly eliminated by the kidneys. Acute prolonged neurotoxicity associated with recommended doses of ertapenem in patients with advanced renal failure not yet on dialysis has not been reported. Two patients with Stage 5 chronic kidney disease (CKD) developed progressive hallucinations, asterixis, myoclonic jerks, and cognitive impairment after receiving the recommended dose reduction for CKD of ertapenem (500 mg/d) for 4 days (Case 1: acute cholecystitis) and 5 days (Case 2: arteriovenous fistula infection). Exhaustive diagnostic workups were non-revealing. Plasma ertapenem level measured 24 h after the last dose in Patient 2 was 53.7 mg/l, much higher than the therapeutic MIC90 (2 mg/l). Despite the cessation of ertapenem and initiation of high-flux hemodialysis, their neurologic manifestations lasted for 2 weeks. The structural and pharmacokinetic characteristics of ertapenem such as its high lipophilicity, central nervous penetration, and volume of distribution contributed to sustained neurotoxicity even with significant reduction in plasma ertapenem levels by high-flux hemodialysis. Although ertapenem 500 mg/d has been recommended in patients with glomerular filtration rate less than 30 ml/min/1.73 m2, our 2 cases highlight that this dosage might be excessive for patients with Stage 5 CKD, especially those not yet on dialysis.

Knowledge translation in rehabilitation engineering research and development: a knowledge ecosystem framework.

Rehabilitation engineering is concerned with technology innovations and technology-mediated treatments for the improvement of quality of care and quality of life of individuals with disability. Unlike many other fields of health research, the knowledge translation (KT) cycle of rehabilitation engineering research and development (R&D) is often considered incomplete until a technology product or technology-facilitated therapy is available to target clientele. As such, the KT journey of rehabilitation engineering R&D is extremely challenging, necessarily involving knowledge exchange among numerous players across multiple sectors. In this article, we draw on recent literature about the knowledge trichotomy in technology-based rehabilitation R&D and propose a knowledge ecosystem to frame the rehabilitation engineering KT process from need to product. Identifying the principal process of the ecosystem as one of knowledge flow, we elucidate the roles of repository and networked knowledge, identify key consumers and producers in a trinity of communities of practice, and draw on knowledge management literature to describe different knowledge flows. The article concludes with instantiations of this knowledge ecosystem for 2 local rehabilitation engineering research-development-commercialization endeavors.

Noninvasive detection of thin-liquid aspiration using dual-axis swallowing accelerometry.

Aspiration (the entry of foreign contents into the upper airway) is a serious concern for individuals with dysphagia and can lead to pneumonia. However, overt signs of aspiration, such as cough, are not always present, making noninstrumental diagnosis challenging. Valid, reliable tools for detecting aspiration during clinical screening and assessment are needed. In this study we investigated the validity of a noninvasive accelerometry signal-processing classifier for detecting aspiration. Dual-axis cervical accelerometry signals were collected from 40 adults on thin-liquid swallowing tasks during videofluoroscopic swallowing examinations. Signal-processing algorithms were used to remove known sources of artifact and a classifier was trained to identify signals associated with penetration-aspiration. Validity was measured in comparison to blinded ratings of penetration-aspiration from the concurrently recorded videofluoroscopies. On a bolus-by-bolus basis, the accelerometry classifier had a 10 % false-negative rate (90 % sensitivity) and a 23 % false-positive rate (77 % specificity) for detecting penetration-aspiration. We conclude that accelerometry can be used to support valid, reliable, and efficient detection of aspiration risk in patients with suspected dysphagia.

A review of EEG-based brain-computer interfaces as access pathways for individuals with severe disabilities.

Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals. Considering the many differences in body functions and usage scenarios between individuals with disabilities and able-bodied individuals, involvement of the target population in BCI evaluation is necessary. In this review, 39 studies reporting EEG-oriented BCI assessment by individuals with disabilities were identified in the past decade. With respect to participant populations, a need for assessing BCI performance for the pediatric population with severe disabilities was identified as an important future direction. Acquiring a reliable communication pathway during early stages of development is crucial in avoiding learned helplessness in pediatric-onset disabilities. With respect to evaluation, augmenting traditional measures of system performance with those relating to contextual factors was recommended for realizing user-centered designs appropriate for integration in real-life. Considering indicators of user state and developing more effective training paradigms are recommended for future studies of BCI involving individuals with disabilities.

Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.

Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (classDistinct reflecting the degree of class separability and classStability reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted.

Corrigendum: Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.

[This corrects the article DOI: 10.3389/fncom.2023.1108889.].

Online Ternary Classification of Covert Speech by Leveraging the Passive Perception of Speech.

Brain-computer interfaces (BCIs) provide communicative alternatives to those without functional speech. Covert speech (CS)-based BCIs enable communication simply by thinking of words and thus have intuitive appeal. However, an elusive barrier to their clinical translation is the collection of voluminous examples of high-quality CS signals, as iteratively rehearsing words for long durations is mentally fatiguing. Research on CS and speech perception (SP) identifies common spatiotemporal patterns in their respective electroencephalographic (EEG) signals, pointing towards shared encoding mechanisms. The goal of this study was to investigate whether a model that leverages the signal similarities between SP and CS can differentiate speech-related EEG signals online. Ten participants completed a dyadic protocol where in each trial, they listened to a randomly selected word and then subsequently mentally rehearsed the word. In the offline sessions, eight words were presented to participants. For the subsequent online sessions, the two most distinct words (most separable in terms of their EEG signals) were chosen to form a ternary classification problem (two words and rest). The model comprised a functional mapping derived from SP and CS signals of the same speech token (features are extracted via a Riemannian approach). An average ternary online accuracy of 75.3% (60% chance level) was achieved across participants, with individual accuracies as high as 93%. Moreover, we observed that the signal-to-noise ratio (SNR) of CS signals was enhanced by perception-covert modeling according to the level of high-frequency ([Formula: see text]-band) correspondence between CS and SP. These findings may lead to less burdensome data collection for training speech BCIs, which could eventually enhance the rate at which the vocabulary can grow.

Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs.

Objective.While electroencephalography (EEG)-based brain-computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user's ability to perform mental control tasks and produce discernible EEG patterns. While classification accuracy has predominantly been used to assess user performance, limitations and criticisms of this approach have emerged, thus prompting the need to develop novel user assessment approaches with greater descriptive capability. Here, we propose a combination of unsupervised clustering and Markov chain models to assess and describe user skill.Approach.Using unsupervisedK-means clustering, we segmented the EEG signal space into regions representing pattern states that users could produce. A user's movement through these pattern states while performing different tasks was modeled using Markov chains. Finally, using the steady-state distributions and entropy rates of the Markov chains, we proposed two metricstaskDistinctandrelativeTaskInconsistencyto assess, respectively, a user's ability to (i) produce distinct task-specific patterns for each mental task and (ii) maintain consistent patterns during individual tasks.Main results.Analysis of data from 14 adolescents using a three-class BCI revealed significant correlations between thetaskDistinctandrelativeTaskInconsistencymetrics and classification F1 score. Moreover, analysis of the pattern states and Markov chain models yielded descriptive information regarding user performance not immediately apparent from classification accuracy.Significance.Our proposed user assessment method can be used in concert with classifier-based analysis to further understand the extent to which users produce task-specific, time-evolving EEG patterns. In turn, this information could be used to enhance user training or classifier design.

Quantitative classification of pediatric swallowing through accelerometry.

BACKGROUND: Dysphagia or swallowing disorder negatively impacts a child's health and development. The gold standard of dysphagia detection is videofluoroscopy which exposes the child to ionizing radiation, and requires specialized clinical expertise and expensive institutionally-based equipment, precluding day-to-day and repeated assessment of fluctuating swallowing function. Swallowing accelerometry is the non-invasive measurement of cervical vibrations during swallowing and may provide a portable and cost-effective bedside alternative. In particular, dual-axis swallowing accelerometry has demonstrated screening potential in older persons with neurogenic dysphagia, but the technique has not been evaluated in the pediatric population. METHODS: In this study, dual-axis accelerometric signals were collected simultaneous to videofluoroscopic records from 29 pediatric participants (age 6.8 ± 4.8 years; 20 males) previously diagnosed with neurogenic dysphagia. Participants swallowed 3-5 sips of barium-coated boluses of different consistencies (normally, from thick puree to thin liquid) by spoon or bottle. Videofluoroscopic records were reviewed retrospectively by a clinical expert to extract swallow timings and ratings. The dual-axis acceleration signals corresponding to each identified swallow were pre-processed, segmented and trimmed prior to feature extraction from time, frequency, time-frequency and information theoretic domains. Feature space dimensionality was reduced via principal components. RESULTS: Using 8-fold cross-validation, 16-17 dimensions and a support vector machine classifier with an RBF kernel, an adjusted accuracy of 89.6% ± 0.9 was achieved for the discrimination between swallows with and with out airway entry. CONCLUSIONS: Our results suggest that dual-axis accelerometry has merit in the non-invasive detection of unsafe swallows in children and deserves further consideration as a pediatric medical device.

Challenges of developing communicative interaction in individuals with congenital profound intellectual and multiple disabilities.

BACKGROUND: Physiological responses have been used in individuals with acquired disability to enable communicative interaction without motor movement. This study explored four autonomic nervous system (ANS) signals-electrodermal activity, skin temperature, cardiac patterns and respiratory patterns-to enable interaction with individuals born with profound intellectual and multiple disabilities (PIMD). METHOD: A series of case studies were conducted to teach a 15-year-old individual with PIMD to voluntarily control his ANS signals for the purposes of communicative interaction. Training was carried out according to an alternating treatment single-subject research design. RESULTS: Training was unsuccessful; however, ANS signal patterns revealed issues unique to people with PIMD: profound intrasubject variability, discrepancy between physiological responses and caregiver perspectives, and the participant's lack of contingency awareness. CONCLUSIONS: These three priority areas unique to people with congenital PIMD must be addressed before ANS signals can be used to enable communicative interaction with this population.