Dinesh Kumbhare

Increased ankle co-contraction is common in older adults and individuals with neurological disorders and is often interpreted as a compensatory strategy to increase apparent joint stiffness and enhance joint stability. However, increased joint stiffness does not necessarily translate into improved postural stability; greater co-contraction is frequently accompanied by larger postural sway. Because voluntary co-contraction involves concurrent changes in neural control, attention, and postural strategy, its mechanical contribution remains unclear. We used neuromuscular electrical stimulation (NMES) to impose ankle co-contraction, independent of voluntary motor commands, and investigated its influence on postural stability during quiet standing. Seventeen healthy adults completed a control condition and three NMES-imposed co-contraction conditions targeting the soleus (SOL) and tibialis anterior (TA). Shear wave elastography quantified muscle stiffness, ankle joint quasi-stiffness was estimated using established biomechanical methods, and postural sway was assessed using the standard deviation of center-of-mass acceleration. NMES-imposed co-contraction significantly increased SOL and TA stiffness and ankle joint quasi-stiffness by 32–40%. Despite these increases, postural sway increased by 35–60% (p < 0.001), demonstrating a stiffness–instability trade-off. Repeated-measures correlations revealed significant positive associations among muscle stiffness, ankle joint quasi-stiffness, and postural sway. Linear mixed-effects models further showed that TA stiffness was more strongly associated with ankle joint quasi-stiffness and postural sway than SOL stiffness. These findings indicate that NMES-imposed ankle co-contraction can increase postural sway despite increasing ankle joint quasi-stiffness. This mechanical–functional dissociation may help explain why elevated co-contraction is frequently associated with impaired balance in older adults and individuals with neurological disorders.

Background: Neuromuscular electrical stimulation (NMES) is widely used in rehabilitation, yet its effects on muscle stiffness and the relationship between stiffness and joint torque remain unclear. We quantified NMES-induced changes in soleus (SOL) and tibialis anterior (TA) stiffness and ankle torque during voluntary, electrically evoked, and combined activation. Methods: Thirteen healthy adults performed isometric plantarflexion and dorsiflexion with ankle fixed at 90°. Shear wave elastography measured muscle stiffness during voluntary contractions (10–100% maximum voluntary contraction, MVC), NMES (motor threshold to 100% maximum tolerable stimulation, MTS), and combined voluntary activation (10–30% MVC) plus NMES (80% MTS). Results: NMES increased SOL and TA stiffness and ankle torque with stimulation intensity (p < 0.05). At 100% MTS, SOL stiffness was comparable to MVC, whereas TA stiffness remained lower (p < 0.05). Electrically evoked torque reached approximately 35% of MVC. Combined activation increased stiffness and torque beyond voluntary activation alone, but responses were consistently lower than predicted by linear summation. Muscle stiffness remained strongly associated with joint torque across activation modes. Conclusions: NMES produces graded, muscle-specific mechanical responses while preserving the overall stiffness–torque relationship. Combined voluntary and electrically evoked responses are non-additive, highlighting distinct muscle- and joint-level mechanical effects of NMES.

Postural stability during standing depends on interactions among muscle mechanics, ankle joint behavior, and neural control. Functional electrical stimulation (FES) combined with visual feedback balance training has been developed as a standing balance therapy for individuals with incomplete spinal cord injury; however, the immediate orthotic effects of ankle-targeted stimulation during standing remain unclear. This study investigated the effects of therapy-relevant ankle FES on soleus (SOL) and tibialis anterior (TA) muscle stiffness, ankle joint quasi-stiffness, and postural sway during quiet standing. Seventeen healthy adults performed quiet-standing trials under no-stimulation, SOL stimulation, TA stimulation, and simultaneous SOL+TA stimulation conditions. For each stimulation condition, submaximal and maximum tolerable stimulation intensities were tested. Muscle stiffness was quantified using shear wave elastography, and ankle quasi-stiffness and postural sway were quantified using force-plate and motion-capture measurements. Stimulation-induced changes in muscle stiffness, ankle quasi-stiffness, and postural sway were examined across stimulation conditions, and repeated-measures correlation and linear mixed-effects analyses were used to assess coupling among muscle-level, joint-level, and postural outcomes. Therapy-relevant ankle FES increased ankle muscle stiffness, ankle joint quasi-stiffness, and postural sway during quiet standing. However, the relationships among these variables depended on the stimulated muscle condition. In the primary analyses, SOL stimulation showed the strongest and most consistent coupling between changes in muscle stiffness, ankle quasi-stiffness, and postural sway, whereas TA stimulation showed weaker and less consistent associations. Simultaneous SOL+TA stimulation also produced significant coupling between muscle stiffness and postural sway, but these relationships were not consistently stronger than those observed during SOL stimulation alone. These findings indicate that the immediate orthotic effects of ankle FES are muscle-specific and are not determined solely by the magnitude of stimulation-induced stiffness. Rather, the pathway through which stiffness is introduced into the ankle system appears to influence how muscle-level changes are reflected in joint mechanics and postural behavior. This mechanistic insight may help interpret the role of ankle-targeted FES during standing balance therapy and guide the design of FES-based rehabilitation interventions.

Deep learning has transformed medical image and video analysis, but it usually requires large, well annotated datasets. In many clinical domains, especially when testing novel mechanistic hypotheses, such retrospective datasets are hard to obtain since acquiring adequate cohorts is time intensive, costly, and operationally difficult. This creates a critical translational gap: scientifically compelling early stage ideas may remain untested due to lack of sufficient sample size to support conventional deep learning pipelines. Developing data-efficient strategies for evaluating new hypotheses within small prospective cohorts is therefore essential to de-risk innovation before large-scale validation. Myofascial Pain Syndrome (MPS) exemplifies this challenge, as quantitative ultrasound imaging biomarkers for MPS remain underexplored. We investigated whether MPS in the upper trapezius can be detected from full B-mode ultrasound videos in a small prospective cohort (11 controls, 13 patients). Videos were automatically preprocessed and resampled using a sliding window strategy to expand training samples (404 clips). A self-supervised Video Diffusion Encoder (VDE) is developed to learn spatiotemporal representations without relying on extensive labeled data, and compared it with transfer-learning-based ResNet, VideoMAE, and SimCLR. Using subject-level stratified four-fold cross-validation, the VDE outperformed transfer learning baselines and achieved performance comparable to SimCLR, with subject-level AUC of 0.79 and accuracy of 0.86, and no significant differences between latent-only and combined trigger point analyses. These results demonstrate that self-supervised diffusion learning can support robust, data-efficient deep learning in small prospective studies, enabling early feasibility testing of innovative ultrasound biomarkers before large-scale clinical trials.

Chronic pain (CP) affects over 30% of the global population and is the leading cause of disability worldwide. Diagnosing and managing CP remains challenging due to overlapping pain mechanisms, i.e., nociceptive, neuropathic, and nociplastic, which frequently coexist within individual patients. This study introduces a novel, data-driven machine learning pipeline that integrates autoencoder-based feature extraction with Fuzzy C-Means clustering to identify, quantify, and interpret coexisting CP mechanisms using patient-reported data. Two retrospective datasets from tertiary pain clinics were analyzed (DADOS: 451 patients, 44 features; TRIC: 195 patients, 28 features), encompassing mixed-type clinical, psychosocial, and behavioral parameters. The proposed model achieved a Hamming loss of 0.43 in multi-label classification, outperforming both unsupervised (k-prototypes) and semi-supervised baselines; empirically validating the hypothesis that CP exists on a mechanistic continuum rather than as discrete categories. Compared with existing clustering studies that focus on single pain disorders or neglect overlapping mechanisms, our approach provides quantitative estimates of mechanistic dominance for each patient, enabling personalized, mechanism-based treatment decisions without reliance on imaging or invasive procedures. This work highlights the timeliness and clinical significance of computational approaches in CP research, offering a scalable, interpretable, and resource-efficient framework for advancing mechanism-based diagnosis and management.

Research suggests that skeletal muscle oxidative capacity in mixed-fiber muscle homogenates is greater in females than in males. In healthy, untrained individuals, we demonstrate by fiber-specific immunofluorescence that females have a greater proportionate area of type I muscle fibers but no difference in mitochondrial content or capillarization of type I or II fibers compared to males. These findings suggest that although females display a more oxidative fiber type composition, sex does not influence muscle fiber-specific oxidative capacity.

Myofascial pain syndrome (MPS) is a chronic regional pain condition characterized by trigger points—hyperirritable spots within taut bands of muscle fibers that cause both localized and referred pain. The pathogenesis, diagnostic criteria, and classification of MPS are still under investigation, which complicates the development of standardized treatment protocols. Although diagnostic tools have improved, MPS often remains underrecognized due to symptom overlap with other pain disorders, such as fibromyalgia, neuropathic pain, and joint disorders. Factors contributing to its onset and persistence include muscle overuse, postural imbalance, systemic conditions, and psychological and behavioral influences. This narrative review explores the primary risk factors, current hypotheses on pathogenesis, diagnosis and differential diagnosis, and both conventional and emerging treatments. Sufficient evidence supports the use of local anesthetic injections for MPS. Some evidence suggests that dry needling, acupuncture, magnetic stimulation, ultrasound therapy, laser therapy, extracorporeal shock wave therapy, and manual therapy may be effective, particularly compared to sham or placebo. However, non‐steroidal anti‐inflammatory drugs, diclofenac, botulinum toxin, and transcutaneous electrical nerve stimulation show insufficient evidence, while the effectiveness of muscle relaxants, antidepressants, gabapentin, opioids, topical lidocaine, capsaicin, EMLA cream, and kinesio taping remains inconclusive. Effective management of MPS requires a patient‐centered approach that integrates empirically supported and evidence‐based treatments tailored to individual needs. This review synthesizes the current understanding of MPS and highlights the need for high‐quality research to improve clinical decision‐making in managing this complex condition.