Background: To evaluate the effect of repetitive transcranial magnetic stimulation (rTMS) combined with task-oriented training (TOT) on upper limb function in stroke patients with hemiplegia. Methods: A systematic review and meta-analysis was performed using PRISMA guidelines. Computer searches of PubMed, Cochrane Library, Embase, Web of science, China Knowledge Network, Wanfang, and Wipu databases were conducted from the time of database creation to October 27, 2022. Clinical trials meeting the inclusion criteria were screened, with rTMS combined with TOT in the test group and other therapies in the control group. Literature screening and data extraction were performed independently by 2 investigators, and meta-analysis was performed using Stata software after quality evaluation of the literature. Results: Meta-analysis results showed that repeated transcranial magnetic stimulation combined with TOT was more effective in box and block test (I2 = 0%, P = .820, 95% confidence interval [CI] [−0.20, 0.88]), Fugl-Meyer Assessment (I ² = 0%, P = .569, 95% CI [0.88, 1.26]), and modified Barthel Index (I ² = 39.9%, P = .189, 95% CI [0.45, 1.03]) were not significantly different from controls, and the efficacy was significantly better in motor evoked potentials (I ² = 86.5%, P < .001, 95% CI [−1.38, −0.83]). Conclusions: Data analysis clarified the efficacy of rTMS) combined with TOT on upper extremity motor function disorders after stroke, but there was no significant difference between the efficacy in box and block test, Fugl-Meyer Assessment, and modified Barthel Index and the efficacy in motor evoked potentials between rTMS and the control group, suggesting that the neuro plasticizing effect of rTMS may translate into functional improvement by promoting neuro electrical signaling.

Odonate is a new, intelligent three-dimensional gait analysis system based on binocular depth cameras and neural networks, but its accuracy has not been validated. Twenty-six healthy subjects and sixteen patients with post-stroke were recruited to investigate the validity and reliability of Odonate for gait analysis and examine its ability to discriminate abnormal gait patterns. The repeatability tests of different raters and different days showed great consistency. Compared with the results measured by Vicon, gait velocity, cadence, step length, cycle time, and sagittal hip and knee joint angles measured by Odonate showed high consistency, while the consistency of the gait phase division and the sagittal ankle joint angle was slightly lower. In addition, the stages with statistical differences between healthy subjects and patients during a gait cycle measured by the two systems were consistent. In conclusion, Odonate has excellent inter/intra-rater reliability, and has strong validity in measuring some spatiotemporal parameters and the sagittal joint angles, except the gait phase division and the ankle joint angle. Odonate is comparable to Vicon in its ability to identify abnormal gait patterns in patients with post-stroke. Therefore, Odonate has the potential to provide accessible and objective measurements for clinical gait assessment.

Background: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of sensor-acquired isokinetic muscle strength data, combined with machine learning techniques, to predict the GDI in hemiplegic patients. This study utilizes data captured from sensors embedded in the Biodex dynamometry system and the Vicon 3D motion capture system, highlighting the integration of sensor technology in clinical gait analysis. Methods: This study was a cross-sectional, observational study that included a cohort of 150 post-stroke hemiplegic patients. The sensor data included measurements such as peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. The GDI was calculated using data from a Vicon 3D motion capture system. This study employed four machine learning models—Lasso Regression, Random Forest (RF), Support Vector regression (SVR), and BP Neural Network—to model and validate the sensor data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R² of 0.89, and an MAE of 2.99. In contrast, the Lasso Regression model yielded an MSE of 22.29, an R² of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R² of 0.82, and an MAE of 7.68, while the BP Neural Network model exhibited the poorest performance with an MSE of 50.38, an R² of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical sensor-derived features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusions: This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. The developed GDI prediction model, based on sensor-acquired isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application.

Background: Chronic cerebral hypoperfusion (CCH) is a major hemodynamic substrate of vascular cognitive impairment and vascular dementia, yet effective therapies with multi‐target neuroprotective potential remain limited. CCH triggers a complex pathological cascade characterized by neurovascular dysfunction, white matter injury, neuroinflammation, oxidative stress, and synaptic impairment. Methods: In this review, we summarize the major pathophysiological mechanisms of CCH and discuss current evidence supporting neuromodulation as a promising therapeutic strategy. We focus primarily on central stimulation modalities, including repetitive transcranial magnetic stimulation, transcranial electrical stimulation, photobiomodulation, and ultrasound‐based neuromodulation, while also addressing selected peripheral physical modulation approaches and emerging multimodal combination strategies. Results: Available evidence suggests that these interventions may mitigate hypoperfusion‐related brain injury and cognitive impairment by targeting multiple pathophysiological mechanisms, ultimately restoring synaptic plasticity and promoting circuit‐ and network‐level recovery. We further discuss current challenges to clinical translation, including limited deep‐target engagement, insufficient individualized dosing, and the underuse of rhythm‐based biomarkers. Conclusion: Neuromodulation holds promise as a mechanistically informed and multi‐target neuroprotective strategy for CCH‐related cognitive impairment.

Background: To investigate whether the common clinical practice of setting transcranial magnetic stimulation (TMS) parameters by scaling from the motor threshold (MT) measured at the primary motor cortex (M1) can be expected to yield comparable stimulation at complex brain regions such as the dorsolateral prefrontal cortex (DLPFC). Methods: Personalized head models were constructed from T1- and T2-weighted magnetic resonance imaging (MRI) scans from 20 healthy elderly adults from a public Chinese dataset. SimNIBS 4.1 was used for tissue segmentation, mesh generation, and TMS electric field (E-field) simulations. A MagVenture MC-B70 figure-of-eight coil was used to simulate stimulation over the M1 and DLPFC (C3/F3, 10–10 electroencephalography (EEG) system). First, both targets were simulated at a fixed intensity of 60% of maximum stimulator output (MSO) to isolate anatomical contributions. Second, an MT-equivalent reference output framework was implemented by calibrating subject-specific output to an M1 reference E-field level of 60 mV/mm, using two calibration tracks (Mean-based and Max-based), and the DLPFC was evaluated at 80–120% of the subject-specific reference output. Results: Under fixed-output stimulation, the mean and robust maximum E-field magnitudes on the gray matter surface and within gray matter volume were significantly lower in the DLPFC region of interest (ROI) than in the M1 ROI (PFDR < 0.05). The normal (surface-perpendicular) component did not differ significantly between targets (PFDR > 0.05), whereas the tangential (surface-parallel) component was significantly weaker at the DLPFC (PFDR < 0.05). Under MT-equivalent scaling, increasing intensity reduced the overall magnitude gap, with approximate parity around 110% of the reference output, but a component trade-off persisted: scaling that brought tangential fields closer to the M1 reference tended to increase normal fields beyond the M1 reference. Conclusion: Fixed-output simulations show target-wise differences in both E-field magnitude and component balance between the M1 and DLPFC. An M1-referenced, MT-equivalent scaling framework can reduce magnitude differences but does not necessarily align component balance at the DLPFC. These findings support target-specific, E-field–informed parameter selection and should be tested in prospective studies linking modeled E-fields to experimental readouts and clinical outcomes.