Comprehensive assessment and classification of upper and lower limb pain in athletes: a scoping review.

BACKGROUND: Upper and lower limb (peripheral) pain is prevalent in athletes. Contemporary research prioritises multidimensional pain assessment and classification. This study aims to review comprehensive athlete pain assessment practices against the reference standard (International Olympic Committee, IOC Athlete Pain framework), identifying trends and highlighting gaps. METHODS AND ANALYSIS: Six databases were searched using a comprehensive search strategy. This review followed the Joanna Briggs Institute standardised methodology for scoping reviews and is reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Title and abstract, full-text screening and data charting were completed by two independent reviewers. INCLUSION CRITERIA: Original research, systematic reviews and clinical practice guidelines reporting assessment or classification of pain in athletes of any age with chronic or acute peripheral pain in English on human participants from database inception. RESULTS: 470 studies with 175 different pain assessment tools were mapped against the IOC Athlete Pain Framework. Papers included tools from neurophysiological (470/100%), biomechanical (425/90%), affective (103/22%), cognitive (59/13%) and socioenvironmental (182/39%) domains. Pain classification was included in 108 studies (23%). 4 studies (0.85%) defined pain. Athletes with physical disability were included in 13 (3%) studies and no studies included athletes with intellectual disabilities. Socioeconomic factors were addressed in 29 (6%) studies. DISCUSSION: Neurophysiological and biomechanical domains are frequently addressed. Affective, socioenvironmental and cognitive tools are under-represented. Potential tools for use by researchers and clinicians are highlighted. Defining and classifying pain and determining predominant pain mechanisms is needed in both research and clinical practice. More work on underrepresented populations is needed. CONCLUSION: This review informs researchers and clinicians working with athletes in pain how pain assessment and classification is currently conducted and highlights future priorities.

Adaptive data collection for intraindividual studies affected by adherence.

Recently, the use of mobile technologies in ecological momentary assessments (EMAs) and interventions has made it easier to collect data suitable for intraindividual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subject. In this paper, we address this problem for a specific EMA setting that aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection that considers both the individual variability of the self-reported variable and of the adherence. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon signed rank test shows that the algorithm tends to have both a higher F1 score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially in studies where data collection is impacted by adherence.

Differences in Learning and Persistency Characterizing Behavior in Chronic Pain for the Iowa Gambling Task: Web-Based Laboratory-in-the-Field Study.

BACKGROUND: Chronic pain is a significant worldwide health problem. It has been reported that people with chronic pain experience decision-making impairments, but these findings have been based on conventional laboratory experiments to date. In such experiments, researchers have extensive control of conditions and can more precisely eliminate potential confounds. In contrast, there is much less known regarding how chronic pain affects decision-making captured via laboratory-in-the-field experiments. Although such settings can introduce more experimental uncertainty, collecting data in more ecologically valid contexts can better characterize the real-world impact of chronic pain. OBJECTIVE: We aim to quantify decision-making differences between individuals with chronic pain and healthy controls in a laboratory-in-the-field environment by taking advantage of internet technologies and social media. METHODS: A cross-sectional design with independent groups was used. A convenience sample of 45 participants was recruited through social media: 20 (44%) participants who self-reported living with chronic pain, and 25 (56%) people with no pain or who were living with pain for <6 months acting as controls. All participants completed a self-report questionnaire assessing their pain experiences and a neuropsychological task measuring their decision-making (ie, the Iowa Gambling Task) in their web browser at a time and location of their choice without supervision. RESULTS: Standard behavioral analysis revealed no differences in learning strategies between the 2 groups, although qualitative differences could be observed in the learning curves. However, computational modeling revealed that individuals with chronic pain were quicker to update their behavior than healthy controls, which reflected their increased learning rate (95% highest-posterior-density interval [HDI] 0.66-0.99) when fitted to the Values-Plus-Perseverance model. This result was further validated and extended on the Outcome-Representation Learning model as higher differences (95% HDI 0.16-0.47) between the reward and punishment learning rates were observed when fitted to this model, indicating that individuals with chronic pain were more sensitive to rewards. It was also found that they were less persistent in their choices during the Iowa Gambling Task compared with controls, a fact reflected by their decreased outcome perseverance (95% HDI -4.38 to -0.21) when fitted using the Outcome-Representation Learning model. Moreover, correlation analysis revealed that the estimated parameters had predictive value for the self-reported pain experiences, suggesting that the altered cognitive parameters could be potential candidates for inclusion in chronic pain assessments. CONCLUSIONS: We found that individuals with chronic pain were more driven by rewards and less consistent when making decisions in our laboratory-in-the-field experiment. In this case study, it was demonstrated that, compared with standard statistical summaries of behavioral performance, computational approaches offered superior ability to resolve, understand, and explain the differences in decision-making behavior in the context of chronic pain outside the laboratory.

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach.

Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.

Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra.

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky-Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky-Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

Cognitive Performance, Quality and Quantity of Movement Reflect Psychological Symptoms in Adolescents.

The presentation of unhealthy psychological symptoms are rising sharply in adolescents. Detrimental lifestyle behaviours are proposed as both possible causes and consequences. This study set out to compare selected measures of quality and quantity of movement between adolescents with and without unhealthy psychological symptoms. Using a cross sectional design, 96 participants completed the study from a whole year group of 166, age (13.36 ± 0.48) male 50.6% from a secondary school in Oxfordshire, England as a part of a larger study (EPIC) between January and April 2018. Measures were taken of quality and quantity of movement: reaction/movement time, gait pattern & physical activity, alongside psychological symptoms. Differences in movement behaviour in relation to psychological symptom and emotional problem presentation were determined using ANOVA. In the event of a significant result for the main factor of each parameter, a Bonferroni -corrected post hoc test was conducted to show the difference between categories in each group. Results for both unhealthy psychological symptoms and emotional problems were grouped into four categories ('Close to average', 'slightly raised', 'high' and 'very high'). Early adolescents with very high unhealthy psychological symptoms had 16.79% slower reaction times (p = 0.003, ηp2 = 0.170), 13.43% smaller walk ratio (p = 0.007, ηp2 = 0.152), 7.13% faster cadence (p = 0.005, ηp2 = 0.149), 6.95% less step time (p = 0.007, ηp2 = 0.153) and 1.4% less vigorous physical activity (p = 0.04, ηp2 = 0.102) than children with close to average psychological symptoms. Early adolescents with very high emotional problems had 12.25% slower reaction times (p = 0.05, ηp2 = 0.081), 10.61% smaller walk ratio (p = 0.02, ηp2 = 0.108), 6.03% faster cadence (p = 0.01, ηp2 = 0.134), 6.07% shorter step time (p = 0.007, ηp2 = 0.141) and 1.78% less vigorous physical activity (p = 0.009, ηp2 = 0.136) than children with close to average emotional problems. Different movement quality and quantity of was present in adolescents with unhealthy psychological symptoms and emotional problems. We propose movement may be used to both monitor symptoms, and as a novel therapeutic behavioural approach. Further studies are required to confirm our findings.

Multicomponent analysis using a confocal Raman microscope.

Measuring the concentration of multiple chemical components in a low-volume aqueous mixture by Raman spectroscopy has received significant interest in the literature. All of the contributions to date focus on the design of optical systems that facilitate the recording of spectra with high signal-to-noise ratio by collecting as many Raman scattered photons as possible. In this study, the confocal Raman microscope setup is investigated for multicomponent analysis. Partial least-squares regression is used to quantify physiologically relevant aqueous mixtures of glucose, lactic acid, and urea. The predicted error is 17.81 mg/dL for glucose, 10.6 mg/dL for lactic acid, and 7.6 mg/dL for urea, although this can be improved with increased acquisition times. A theoretical analysis of the method is proposed, which relates the numerical aperture and the magnification of the microscope objective, as well as the confocal pinhole size, to the performance of the technique.

Technology in Strength and Conditioning Tracking Lower-Limb Exercises With Wearable Sensors.

Strength and conditioning (S&C) coaches offer expert guidance to help those they work with achieve their personal fitness goals. However, because of cost and availability issues, individuals are often left training without expert supervision. Recent developments in inertial measurement units (IMUs) and mobile computing platforms have allowed for the possibility of unobtrusive motion tracking systems and the provision of real-time individualized feedback regarding exercise performance. These systems could enable S&C coaches to remotely monitor sessions and help gym users record workouts. One component of these IMU systems is the ability to identify the exercises completed. In this study, IMUs were positioned on the lumbar spine, thighs, and shanks on 82 healthy participants. Participants completed 10 repetitions of the squat, lunge, single-leg squat, deadlift, and tuck jump with acceptable form. Descriptive features were extracted from the IMU signals for each repetition of each exercise, and these were used to train an exercise classifier. The exercises were detected with 99% accuracy when using signals from all 5 IMUs, 99% when using signals from the thigh and lumbar IMUs and 98% with just a single IMU on the shank. These results indicate that a single IMU can accurately distinguish between 5 common multijoint exercises.

Technology in Strength and Conditioning: Assessing Bodyweight Squat Technique With Wearable Sensors.

O'Reilly, MA, Whelan, DF, Ward, TE, Delahunt, E, and Caulfield, BM. Technology in strength and conditioning: assessing bodyweight squat technique with wearable sensors. J Strength Cond Res 31(8): 2303-2312, 2017-Strength and conditioning (S&C) coaches offer expert guidance to help those they work with achieve their personal fitness goals. However, it is not always practical to operate under the direct supervision of an S&C coach and consequently individuals are often left training without expert oversight. Recent developments in inertial measurement units (IMUs) and mobile computing platforms have allowed for the possibility of unobtrusive motion tracking systems and the provision of real-time individualized feedback regarding exercise performance. These systems could enable S&C coaches to remotely monitor sessions and help individuals record their workout performance. One aspect of such technologies is the ability to assess exercise technique and detect common deviations from acceptable exercise form. In this study, we investigate this ability in the context of a bodyweight (BW) squat exercise. Inertial measurement units were positioned on the lumbar spine, thighs, and shanks of 77 healthy participants. Participants completed repetitions of BW squats with acceptable form and 5 common deviations from acceptable BW squatting technique. Descriptive features were extracted from the IMU signals for each BW squat repetition, and these were used to train a technique classifier. Acceptable or aberrant BW squat technique can be detected with 98% accuracy, 96% sensitivity, and 99% specificity when using features derived from all 5 IMUs. A single IMU system can also distinguish between acceptable and aberrant BW squat biomechanics with excellent accuracy, sensitivity, and specificity. Detecting exact deviations from acceptable BW squatting technique can be achieved with 80% accuracy using a 5 IMU system and 72% accuracy when using a single IMU positioned on the right shank. These results suggest that IMU-based systems can distinguish between acceptable and aberrant BW squat technique with excellent accuracy with a single IMU system. Identification of exact deviations is also possible but multi-IMU systems outperform single IMU systems.

Effects of Anodal High-Definition Transcranial Direct Current Stimulation on Bilateral Sensorimotor Cortex Activation During Sequential Finger Movements: An fNIRS Study.

Transcranial direct current stimulation (tDCS) is a non-invasive electrical brain stimulation technique that can modulate cortical neuronal excitability and activity. This study utilized functional near infrared spectroscopy (fNIRS) neuroimaging to determine the effects of anodal high-definition (HD)-tDCS on bilateral sensorimotor cortex (SMC) activation. Before (Pre), during (Online), and after (Offline) anodal HD-tDCS (2 mA, 20 min) targeting the left SMC, eight healthy subjects performed a simple finger sequence (SFS) task with their right or left hand in an alternating blocked design (30-s rest and 30-s SFS task, repeated five times). In order to determine the level of bilateral SMC activation during the SFS task, an Oxymon MkIII fNIRS system was used to measure from the left and right SMC, changes in oxygenated (O2Hb) and deoxygenated (HHb) haemoglobin concentration values. The fNIRS data suggests a finding that compared to the Pre condition both the "Online" and "Offline" anodal HD-tDCS conditions induced a significant reduction in bilateral SMC activation (i.e., smaller decrease in HHb) for a similar motor output (i.e., SFS tap rate). These findings could be related to anodal HD-tDCS inducing a greater efficiency of neuronal transmission in the bilateral SMC to perform the same SFS task.

Expectations induced by natural-like temporal fluctuations are independent of attention decrement: evidence from behavior and early visual evoked potentials.

Temporal expectations and attention decrement affect human behavior in opposing ways: the former positively, the latter negatively yet both exhibit similar neural signatures - i.e., reduction in the early event-related potential components' amplitude - despite different underlying mechanisms. Furthermore, there is a significant and growing debate in the literature regarding the putative role of attention in the encoding of expectations in perception. The question then arises as to what are the behavioral and neural consequences, if any, of attention decrement on temporal expectations and related enhancement of sensory information processing. Here, we investigated behavioral performance and visual N1a, N1p and P1 components during a sustained attention reaction time task inducing attention decrement under two conditions. In one condition, the inter-stimulus intervals (ISIs) were randomly distributed to impede expectation effects while for the other, the ISI exhibited natural-like long-term correlations supposed to induce temporal expectations. Behavioral results show that natural-like fluctuations in ISI indeed induced faster RT due to temporal expectations. These temporal expectations were beneficial even under attention decrement circumstances. Further, temporal expectations were associated with reduced N1a amplitude while attention decrement was associated with reduced N1p amplitude. Our findings provide evidence that the effects of temporal expectations and attention decrement induced in a single task can be independent at the behavioral level, and are supported at separate information processing stages at the neural level in vision.

An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy.

BACKGROUND: Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke. METHODS: 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data. RESULTS: Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset. CONCLUSIONS: This exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.

Objective.Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection.Approach.In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording.Main results.The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials.Significance.Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

A dataset of text prompts, videos and video quality metrics from generative text-to-video AI models.

Evaluating the quality of videos which have been automatically generated from text-to-video (T2V) models is important if the models are to produce plausible outputs that convince a viewer of their authenticity. This paper presents a dataset of 201 text prompts used to automatically generate 1,005 videos using 5 very recent T2V models namely Tune-a-Video, VideoFusion, Text-To-Video Synthesis, Text2Video-Zero and Aphantasia. The prompts are divided into short, medium and longer lengths. We also include the results of some commonly used metrics used to automatically evaluate the quality of those generated videos. These include each video's naturalness, the text similarity between the original prompt and an automatically generated text caption for the video, and the inception score which measures how realistic is each generated video. Each of the 1,005 generated videos was manually rated by 24 different annotators for alignment between the videos and their original prompts, as well as for the perception and overall quality of the video. The data also includes the Mean Opinion Scores (MOS) for alignment between the generated videos and the original prompts. The dataset of T2V prompts, videos and assessments can be reused by those building or refining text-to-video generation models to compare the accuracy, quality and naturalness of their new models against existing ones.

Dataset on comprehensive assessment & classification of upper & lower limb pain in athletes.

Data were charted as part of a scoping review which followed the Joanna Briggs Institute (JBI) evidence synthesis guidelines and the Preferred Reporting Items for Systematic Reviews and Meta Analysis Scoping Review extension (PRISMA-SCr) guidelines. Data was extracted from 470 articles that met the inclusion criteria for the scoping review; primary research articles of athletes where upper and/or lower limb pain since database inception. A draft data charting tool was developed by the research team and piloted for feasibility, accuracy and agreement. The charting tool was updated accordingly before being applied to the entire data set. Data collected included citation details, research context, participant information and pain assessment and classification tools, categories, and additional relevant information. The raw data set was filtered, and descriptive analysis of frequencies and counts were conducted. Researchers and clinicians interested in the range and applications of different pain assessment practices in athletes may reuse this data set. Data charting was comprehensive including aspects beyond the scope of the original research that offer clinical and research potential. These include information around recommended practice, (International Olympic Committee guidance) pain classifications and definitions and the use of multi-domain pain assessment tools.

Corrigendum: Motor learning in developmental coordination disorder: behavioral and neuroimaging study.

[This corrects the article DOI: 10.3389/fnins.2023.1187790.].

The Potential of Electrospinning to Enable the Realization of Energy-Autonomous Wearable Sensing Systems.

The market for wearable electronic devices is experiencing significant growth and increasing potential for the future. Researchers worldwide are actively working to improve these devices, particularly in developing wearable electronics with balanced functionality and wearability for commercialization. Electrospinning, a technology that creates nano/microfiber-based membranes with high surface area, porosity, and favorable mechanical properties for human in vitro and in vivo applications using a broad range of materials, is proving to be a promising approach. Wearable electronic devices can use mechanical, thermal, evaporative and solar energy harvesting technologies to generate power for future energy needs, providing more options than traditional sources. This review offers a comprehensive analysis of how electrospinning technology can be used in energy-autonomous wearable wireless sensing systems. It provides an overview of the electrospinning technology, fundamental mechanisms, and applications in energy scavenging, human physiological signal sensing, energy storage, and antenna for data transmission. The review discusses combining wearable electronic technology and textile engineering to create superior wearable devices and increase future collaboration opportunities. Additionally, the challenges related to conducting appropriate testing for market-ready products using these devices are also discussed.

Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial.

BACKGROUND: The number of people with cognitive deficits and diseases, such as stroke, dementia, or attention-deficit/hyperactivity disorder, is rising due to an aging, or in the case of attention-deficit/hyperactivity disorder, a growing population. Neurofeedback training using brain-computer interfaces is emerging as a means of easy-to-use and noninvasive cognitive training and rehabilitation. A novel application of neurofeedback training using a P300-based brain-computer interface has previously shown potential to improve attention in healthy adults. OBJECTIVE: This study aims to accelerate attention training using iterative learning control to optimize the task difficulty in an adaptive P300 speller task. Furthermore, we hope to replicate the results of a previous study using a P300 speller for attention training, as a benchmark comparison. In addition, the effectiveness of personalizing the task difficulty during training will be compared to a nonpersonalized task difficulty adaptation. METHODS: In this single-blind, parallel, 3-arm randomized controlled trial, 45 healthy adults will be recruited and randomly assigned to the experimental group or 1 of 2 control groups. This study involves a single training session, where participants receive neurofeedback training through a P300 speller task. During this training, the task's difficulty is progressively increased, which makes it more difficult for the participants to maintain their performance. This encourages the participants to improve their focus. Task difficulty is either adapted based on the participants' performance (in the experimental group and control group 1) or chosen randomly (in control group 2). Changes in brain patterns before and after training will be analyzed to study the effectiveness of the different approaches. Participants will complete a random dot motion task before and after the training so that any transfer effects of the training to other cognitive tasks can be evaluated. Questionnaires will be used to estimate the participants' fatigue and compare the perceived workload of the training between groups. RESULTS: This study has been approved by the Maynooth University Ethics Committee (BSRESC-2022-2474456) and is registered on ClinicalTrials.gov (NCT05576649). Participant recruitment and data collection began in October 2022, and we expect to publish the results in 2023. CONCLUSIONS: This study aims to accelerate attention training using iterative learning control in an adaptive P300 speller task, making it a more attractive training option for individuals with cognitive deficits due to its ease of use and speed. The successful replication of the results from the previous study, which used a P300 speller for attention training, would provide further evidence to support the effectiveness of this training tool. TRIAL REGISTRATION: ClinicalTrials.gov NCT05576649; https://clinicaltrials.gov/ct2/show/NCT05576649. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46135.

Semi-Supervised Mixture Learning for Graph Neural Networks With Neighbor Dependence.

A graph neural network (GNN) is a powerful architecture for semi-supervised learning (SSL). However, the data-driven mode of GNNs raises some challenging problems. In particular, these models suffer from the limitations of incomplete attribute learning, insufficient structure capture, and the inability to distinguish between node attribute and graph structure, especially on label-scarce or attribute-missing data. In this article, we propose a novel framework, called graph coneighbor neural network (GCoNN), for node classification. It is composed of two modules: GCoNN Γ and GCoNN Γ° . GCoNN Γ is trained to establish the fundamental prototype for attribute learning on labeled data, while GCoNN [Formula: see text] learns neighbor dependence on transductive data through pseudolabels generated by GCoNN Γ . Next, GCoNN Γ is retrained to improve integration of node attribute and neighbor structure through feedback from GCoNN [Formula: see text] . GCoNN tends to convergence iteratively using such an approach. From a theoretical perspective, we analyze this iteration process from a generalized expectation-maximization (GEM) framework perspective which optimizes an evidence lower bound (ELBO) by amortized variational inference. Empirical evidence demonstrates that the state-of-the-art performance of the proposed approach outperforms other methods. We also apply GCoNN to brain functional networks, the results of which reveal response features across the brain which are physiologically plausible with respect to known language and visual functions.

A Novel Approach of Customized Pelvic Implant Design Based on Symmetrical Analysis and 3D Printing.

In pelvic trauma patients, the mismatch of complex geometries between the pelvis and fixation implant is a fundamental cause of unstable and displaced pelvic ring disruption, in which secondary intervention is strongly considered. The geometrical matching in the current customized implant design and clinical practice is through the nonfractured hemi-pelvis for the fractured pelvis. This design philosophy overlooks the anatomical difference between the hemipelves, and further, the geometrical asymmetry at local area still remains unknown. This study analyzed the anatomical asymmetry of a patient's 3D pelvic models from 13 patients. The hemipelves of each patient were registered by using an iterative closet algorithm to an optimum position with minimum deviations. The high deviation regions were summarized between the hemipelves in each case, and a color map was drawn on a hemipelvis model that identified the areas that had a high possibility to be symmetrically different. A severe pelvic trauma case was used to comprehend the approach by designing a 3D printed implant. Each fracture was then registered to the mirrored uninjured hemipelvis by using the same algorithm, and customized fixation implants were designed with reference to the fractured model. The customized fixation plates showed that the implants had lower geometrical deviation when attached onto the re-stitched fracture side than onto the mirrored nonfractured bone. These results indicate that the symmetrical analysis of bone anatomy and the deviation color map can assist with implant selection and customized implant design given the geometrical difference between symmetrical bones. The novel approach provides a scientific reference that improves the accuracy and overall standard of 3D printed implants.