Elevated Risk of Stroke in Young Adults After Traumatic Brain Injury: A Nationwide Study of 1 Million Individuals.

BACKGROUND: Although stroke is commonly perceived as occurring in older adults, traumatic brain injury, one of the risk factors for stroke, is a leading cause of death in the younger adults. This study evaluated stroke risk in young-to-middle-aged adults based on traumatic brain injury severity and stroke subtypes. METHODS AND RESULTS: For this retrospective, population-based, cohort study, data of adults aged 18 to 49 years who were diagnosed with traumatic brain injury were obtained from the Korean National Health Insurance Service between 2010 and 2017. Traumatic brain injury history was measured based on the International Classification of Diseases, Tenth Revision (ICD-10), codes. Posttraumatic brain injury stroke risk was analyzed using a time-dependent Cox regression model. At baseline, 518423 patients with traumatic brain injury and 518 423 age- and sex-matched controls were included. The stroke incidence rate per 1000 person-years was 3.82 in patients with traumatic brain injury and 1.61 in controls. Stroke risk was approximately 1.89 times as high in patients with traumatic brain injury (hazard ratio, 1.89 [95% CI, 1.84-1.95]). After excluding stroke cases that occurred within 12 months following traumatic brain injury, these significant associations remained. In the subgroup analysis, patients with brain injury other than concussion had an approximately 9.34-fold risk of intracerebral hemorrhage than did the controls. CONCLUSIONS: Stroke prevention should be a priority even in young-to-middle-aged adult patients with traumatic brain injury. Managing stroke risk factors through regular health checkups and modifying health-related behaviors is necessary to prevent stroke.

Reliability of Surface Electromyography From the Lower-limb Muscles During Maximal and Submaximal Voluntary Isometric Contractions in In-bed Healthy Individuals and Patients With Subacute Stroke.

This study aims to develop maximal voluntary isometric contraction (MVIC) and submaximal voluntary isometric contraction (subMVIC) methods and to assess the reliability of the developed methods for in-bed healthy individuals and patients with subacute stroke. The electromyography (EMG) activities from the lower-limb muscles including the tensor fascia lata (TFL), rectus femoris (RF), tibialis anterior (TA), and gastrocnemius (GC) on both sides were recorded during MVIC and subMVIC using surface EMG sensors in 20 healthy individuals and 20 subacute stroke patients. In inter-trial reliability, both MVIC and subMVIC methods demonstrated excellent reliability for all the measured muscles at baseline and follow-up evaluations in both healthy individuals and stroke patients. In inter-day reliability, MVIC showed good reliability for the TFL and moderate reliability for the RF, TA, and GC, while subMVIC showed good reliability for the TFL, RF, and GC and poor reliability for the TA in healthy individuals. In conclusion, the MVIC and subMVIC methods of EMG activities were feasible in in-bed healthy individuals and patients with subacute stroke. The results can serve as a basis for the clinical evaluation of muscular activities using quantitative EMG signals on the lower-limb muscles in stroke patients with impaired mobility.

AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale.

BACKGROUND: The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients' walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor's empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial. OBJECTIVE: To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale. METHODS: Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing. RESULTS: The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively. CONCLUSION: This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification.

INTRODUCTION: Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images. METHODS: 1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC). RESULTS: Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did. CONCLUSIONS: The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

Effects of Transcutaneous Auricular Vagus Nerve Stimulation on Cortical Excitability in Healthy Adults.

OBJECTIVE: Vagus nerve stimulation (VNS) has recently been reported to exert additional benefits for functional recovery in patients with brain injury. However, the mechanisms underlying these effects have not yet been elucidated. This study examined the effects of transcutaneous auricular VNS (taVNS) on cortical excitability in healthy adults. MATERIALS AND METHODS: We recorded subthreshold and suprathreshold single- and paired-pulse motor-evoked potentials (MEPs) in the right-hand muscles of 16 healthy adults by stimulating the left primary motor cortex. Interstimulus intervals were set at 2 milliseconds and 3 milliseconds for intracortical inhibition (ICI), and 10 milliseconds and 15 milliseconds for intracortical facilitation (ICF). taVNS was applied to the cymba conchae of both ears for 30 minutes. The intensity of taVNS was set to a maximum tolerable level of 1.95 mA. MEPs were measured before stimulation, 20 minutes after the beginning of the stimulation, and 10 minutes after the cessation of stimulation. RESULTS: The participants' age was 33.25 ± 7.08 years, and nine of 16 were male. No statistically significant changes were observed in the mean values of the single-pulse MEPs before, during, or after stimulation. Although the ICF showed an increasing trend after stimulation, the changes in ICI and ICF were not significant, primarily because of the substantial interindividual variability. CONCLUSIONS: The effect of taVNS on cortical excitability varied in healthy adults. An increase in ICF was observed after taVNS, although the difference was not statistically significant. Our findings contribute to the understanding of the mechanisms by which taVNS is effective in patients with brain disorders.

Design and validation of a wearable dynamometry system for knee extension-flexion torque measurement.

Muscle strength assessments are vital in rehabilitation, orthopedics, and sports medicine. However, current methods used in clinical settings, such as manual muscle testing and hand-held dynamometers, often lack reliability, and isokinetic dynamometers (IKD), while reliable, are not easily portable. The aim of this study was to design and validate a wearable dynamometry system with high accessibility, accuracy, and reliability, and to validate the device. Therefore, we designed a wearable dynamometry system (WDS) equipped with knee joint torque sensors. To validate this WDS, we measured knee extension and flexion strength in 39 healthy adults using both the IKD and WDS. Comparing maximal isometric torque measurements, WDS and IKD showed strong correlation and good reliability for extension (Pearson's r: 0.900; intraclass correlation coefficient [ICC]: 0.893; standard error of measurement [SEM]: 9.85%; minimal detectable change [MDC]: 27.31%) and flexion (Pearson's r: 0.870; ICC: 0.857; SEM: 11.93%; MDC: 33.07%). WDS demonstrated excellent inter-rater (Pearson's r: 0.990; ICC: 0.993; SEM: 4.05%) and test-retest (Pearson's r: 0.970; ICC: 0.984; SEM: 6.15%) reliability during extension/flexion. User feedback from 35 participants, including healthcare professionals, underscores WDS's positive user experience and clinical potential. The proposed WDS is a suitable alternative to IKD, providing high accuracy, reliability, and potentially greater accessibility.

Changes of neural coupling between cognitive and motor networks associated with dual-task performance in Parkinson's disease.

BACKGOUND: Although cognitive control is essential for efficient gait, the associations between cognitive and motor networks regarding gait in individuals with Parkinson's disease (PD) remain to be determined. Herein, we enrolled 28 PD and 28 controls to compare internetwork coupling among cognitive and motor networks and examine its relationship with single- and dual-task gait performance in PD. METHODS: The dorsal attention network (DAN), left and right frontoparietal control networks (FPNs), sensorimotor network, and lateral motor network were identified using resting-state functional magnetic resonance imaging data. The time taken to complete a 10-m walk test during cognitive or physical dual-tasks in PD was calculated representing gait performance. RESULTS: We observed that the internetwork couplings between the DAN and motor networks and between the motor networks decreased whereas those between the left FPN and DAN and motor networks increased in PD compared to controls using a permutation test. There was no significant correlation between the internetwork couplings and single- and dual-task gait performance in PD. Nevertheless, improved cognitive dual-task performance showed a positive correlation with the DAN and left FPN coupling and a negative correlation with the DAN and lateral motor network coupling in a good performance group. The opposite relationship was observed in the poor cognitive dual-task performance group. CONCLUSION: Our findings suggest a neural mechanism of cognitive control on gait to compensate for reduced goal-directed attention in PD who maintain cognitive dual-task performance.