ObjectiveThe study’s primary objective was to compare the effectiveness of intra-articular platelet-rich plasma injections versus corticosteroid injections for the treatment of cervical facetogenic pain. Secondary aims were to compare self-rated disability, pain self-efficacy, and the safety of the procedure between groups.MethodsA single-site randomized double-blind controlled trial with 40 participants assigned to receive either leucocyte-poor, low-concentrate platelet-rich plasma injections or corticosteroid injection without local anesthetic into the cervical facet joint under fluoroscopy. Outcomes were collected via telephone at 1, 3, and 6 months to determine treatment effectiveness.ResultsLow-concentrate platelet-rich plasma and corticosteroid injections had similar effects on cervical facetogenic pain intensity over a 6-month period post injection as demonstrated by a non-significant group-by-time interaction for Numeric Rating Scale scores (p>0.05). However, both groups showed a statistically significant decrease in cervical facetogenic pain intensity 1 month post treatment compared with baseline (p=0.02), while the platelet-rich plasma group also demonstrated a clinically significant decrease in pain intensity at the same time point. There was a significant interaction at 1 month post intervention for pain self-efficacy (p=0.04), with the platelet-rich plasma injection group showing a larger increase in pain self-efficacy compared with the corticosteroid injection group. No significant interaction was observed for self-rated disability; however, significant reductions were shown at 3 and 6 months post treatment compared with baseline in both groups (p<0.01). No significant differences between groups were reported for adverse events; however, those receiving platelet-rich plasma injection reported significantly less procedural pain (p=0.02).ConclusionBoth platelet-rich plasma and corticosteroid injections induced similar improvements in cervical facetogenic pain intensity (1 month post) and self-rated disability (3 and 6 months post). Pain self-efficacy demonstrated a significant interaction with platelet-rich plasma injection showing greater improvement 1 month post. Additionally, both treatments exhibited a similar low prevalence of adverse events; however, those receiving platelet-rich plasma injection reported less procedural pain.
Surface electromyography (sEMG) has shown potential for intuitive control of wearable assistive technologies in individuals with motor impairments. However, accurately classifying sEMG signals in stroke patients, particularly those with severe impairments and no visible voluntary hand movement, is difficult due to altered neuromuscular patterns. While robust machine learning models require large and diverse datasets, high variability among stroke patients means that even extensive data may fail to capture individual differences. To address this challenge, this study applies a transfer learning approach that leverages richer sEMG signals from healthy individuals to pretrain a model, which is then adapted to the altered patterns of stroke patients. This process enables effective classification even with limited patient-specific data. Ten stroke patients wore a Myo armband, a wearable sEMG acquisition device, and performed hand-closing, rest, and hand-opening gestures across up to 26 arm postures. Healthy sEMG data were obtained from the Ninapro DB5 dataset. Multiple transfer learning configurations were evaluated by varying the number of frozen layers. The best-performing configuration achieved an overall accuracy of 93.6% across all participants, with 84.32% for severe, 97.42% for moderate, and 99.07% for low impairments. In comparison, training the model solely on stroke data without transfer learning resulted in a considerably lower accuracy of 46%. These findings highlight the value of incorporating healthy sEMG data for pattern recognition in stroke patients and demonstrate the potential of transfer learning to support inclusive and adaptive wearable sEMG-based control systems for individuals with varying levels of post-stroke motor impairment.
Background and objectives The application of non-invasive brain stimulation (NIBS) often relies on proxy estimates of cortical excitability (CE), such as motor evoked potentials (MEPs), measured through transcranial magnetic stimulation (TMS). However, measuring an MEP is not always possible, as it requires an intact corticospinal pathway for neural signals to travel from the cortex to the periphery. To broaden the application of NIBS there is a need for additional CE measures. Methods Fifty-nine healthy individuals participated in three experiments (Experiment 1: n = 11; Experiment 2: n = 20; Experiment 3: n = 28). Experiments 1 and 2 combined TMS and eye-tracking using a within-subject, sham controlled design, to measure TMS-induced pupil dilations at five different TMS intensities. Experiment 3 compared TMS-induced pupil dilations before and after inhibitory transcranial direct current stimulation (tDCS) CE neuromodulation. Results We present Bayesian evidence (Experiment 1: Bayes Factor; BF > 9; Experiment 2: BF > 46; Experiment 3: BFs > 6) that TMS-induced pupil dilations serve as an objective CE proxy, with larger pupil size reflecting higher CE. We also show that these effects are not due to auditory, muscle, or sensory confounds. Conclusion The introduction of this novel measure paves the way for a deeper understanding of CE by enabling objective NIBS measures beyond the motor cortex. Moving away from the reliance on the motor cortex would allow NIBS research and therapy to become more inclusive, allowing access to populations that are affected by damage along the corticospinal pathway.
Digital phenotyping promises to transform psychiatry by using multimodal, densely sampled data. However, its potential is hindered by the lack of focus on identifying and validating digital biomarkers that accurately reflect mental states before evaluating their impact on outcomes. This longitudinal study used explainable machine learning to analyze multivariate, densely sampled data from 133 bipolar disorder (BD) participants over a median of 251 days, identifying robust digital biomarkers defining depressive episodes. The analysis included features from email-based daily self-reported mood, energy, and anxiety, as well as passively collected activity and sleep data using an Oura ring. The most robust descriptors of depressive episodes were lower daily mood variability, lower daily activity variability, and higher daily sleep onset latency variability. Self-reported daily mood features achieved the highest performance (AU-ROC: 0.82 ± 0.03). Our results establish the value of multimodal data and represent a critical first step toward automated detection and prediction of illness episodes in BD.
The level of difficulty of a secondary cognitive task (DT) can affect gait and cortical activity distinctly in individuals with Parkinson’s disease (PD). During a simpler ST, individuals with PD may use a compensatory neural mechanism by reallocating neural resources to preserve gait performance; for difficult DT, this compensation may not be the case. However, whether different levels of difficulty of a single-domain DT would distinctively affect gait and cortical activity in individuals with PD compared to neurologically healthy individuals is still unknown. Fourteen individuals with PD and 14 healthy individuals performed walking trials at self-selected speed, under six conditions of walking with an auditory DT and varying levels of difficulty (very easy: VE-SCT, easy: E-SCT, moderate: M-SCT, difficult: D-SCT, and very difficult: VD-SCT). Gait kinematics and cortical activity data were recorded. RM-ANOVAs identified that individuals with PD showed higher DT cost for both step length and step velocity when the cognitive task was D-SCT or VD-SCT, compared to easier tasks (p < 0.005). Cortical activity showed a different pattern. During more difficult tasks (M-SCT, D-SCT, VD-SCT), PD individuals had a lower DT cost in delta frequency (frontal and motor areas) and beta frequency (parietal area) compared to the easier tasks (VE-SCT, E-SCT) (p < 0.005). These findings suggest that individuals with PD exhibit a distinct pattern of cognitive-motor interaction during dual-task walking, characterized by increased cortical dual-task cost in lower vs. greater gait deterioration in higher task demands. These findings suggest that individuals with PD over-engage cognitive resources while walking with relatively easier DT.
