IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

Transcranial Focused Ultrasound Stimulation (tFUS) is a promising non-invasive technique capable of modulating brain activity with high spatial precision. However, its efficacy for seizure suppression requires further exploration. This study aims to address whether tFUS of white matter can suppress seizures non-invasively. Repeated injections of a 4-Aminopyridine (4-AP) cocktail into the right somatosensory cortex (S1) induced cortical seizures in male rats under anesthesia with recording of both EEG and intracranial signals. Approximately one hour of tFUS was applied to the corpus callosum (CC) using a 128-element random-array transducer with 20 ms pulse duration, 1 Hz pulse repetition frequency, 2% duty cycle, and ~127 kPa pressure. Another 2-3 hours were used to assess post-stimulation effects. Seizure duration, seizure count, percent time in seizure, and inter-seizure interval were compared to a sham control for quantifying efficacy. The absolute frequency power, asymmetry index (AI), and phase lag index (PLI) were calculated to analyze brain activity changes induced by tFUS. CC tFUS can significantly reduce percent time in seizure, seizure duration, and seizure count, as well as increase inter-seizure interval. These effects extended up to 2 hours post-stimulation. We also observed a decrease in absolute power of the beta band and changes in the brain network, as evidenced by a decrease in synchronization and an improvement in interhemispheric balance. Our study is the first to show that white matter tFUS can significantly suppress seizures with a lasting post stimulation effect, potentially providing a safer alternative for drug-resistant epilepsy patients.

The present study aimed to characterize cortical activation and connectivity patterns in individuals post-stroke during digital upper limb motor tasks using functional near-infrared spectroscopy (fNIRS). We enrolled 10 individuals with chronic impairment subsequent to stroke (seven men; mean age, $64.3~pm ~9.2$ years; mean time since stroke, $108.2~pm ~60.5$ months). All participants had a unilateral lesion and moderate-to-mild upper limb dysfunction. The fNIRS data were recorded using a 16-source and 16-detector system, with 51 channels sampled at 5.1 Hz. The participants performed four motor tasks. Each task session followed a block design consisting of four 90-s block cycles (60 s of task execution followed by 30 s of rest). From these recordings, 200 activation and connectivity maps were extracted across the task blocks. K-means clustering was applied to identify distinct cortical activation patterns. The following three patterns were identified: Cluster 1, widespread activation and strong connectivity, higher Fugl-Meyer Assessment Upper Extremity (FMA-UE) scores, and better task accuracy; Cluster 2, moderate activation and connectivity, suggesting balanced task engagement; Cluster 3, limited activation and weak connectivity, linked to lower motor function and greater task difficulty. Multinomial logistic regression showed that higher FMA-UE scores increased the likelihood of being classified into Cluster 1. These findings suggest that clustering of cortical patterns reflects motor capacity and task performance for individuals post-stroke. With further validation, this approach may serve as a biomarker for real-time task adaptation and personalized rehabilitation strategies.

This study introduces a new Reinforcement Learning Assist-as-Needed (RL-AAN) controller intended for robot-assisted upper-limb rehabilitation after stroke, which leverages a modified action-dependent heuristic dynamic programming (ADHDP) framework. Unlike conventional adaptive assist-as-needed controllers based on Iterative Learning Control (ILC-AAN), the proposed RL-AAN controller autonomously adjusts the trade-off between movement errors and robot assistance in response to the user's recent performance, in real-time, while relying on a small set of high-level tunable parameters that do not require subject-specific manual adjustments. The RL-AAN controller was implemented on a cable-driven, end-effector type rehabilitation robot and validated against a conventional ILC-AAN controller through perturbation-based reaching tasks involving a group of healthy individuals. Compared to ILC-AAN, the RL-AAN controller significantly reduced the amount of robot assistance required during training, promoting user active participation and task performance. Following training with the RL-AAN controller, retention tests showed more accurate arm-reaching trajectories compared to ILC-AAN training, highlighting the potential of RL-AAN for future use in exercise-based rehabilitation. Overall, this work contributes to ongoing research into developing control strategies that enable personalization in physical human-robot interaction (pHRI) and robot-assisted rehabilitation.

The aperiodic component of brain field potentials (EEG, LFP, intracortical recordings) is increasingly being recognized as an important topic in both basic and clinical neuroscience. Aperiodic activity is modeled as a power law of the power spectral density, with the aperiodic exponent proposed as a marker of the balance between excitatory and inhibitory activity. While an ideal power law would apply across frequencies, recent evidence suggests that low- and high-frequency ranges may not present the same aperiodic exponent. To test this, here we analyzed human resting-state intracortical recordings from 62 patients, estimating aperiodic parameters with two complementary estimation methods, Specparam and IRASA. We further validate these results using synthetic data. We systematically observed that the aperiodic exponent depends on its estimation frequency range: low frequencies displayed flatter spectra than high frequencies. This was consistent across estimation methods. The capacity of both methods to accurately estimate aperiodic exponents that vary across frequency ranges was demonstrated in silico. Our results show that the aperiodic exponent depends on its estimation frequency range, highlighting the need for caution when comparing exponents across studies and encouraging further research on the functional meaning of frequency-specific aperiodic estimates.

Alzheimer's disease and related dementias (ADRD) are growing global health challenges, projected to affect over 82 million people by 2030. Early diagnosis is essential, offering the potential to extend life expectancy by over 50% and reduce healthcare costs by up to ${$}$ 150,000 per patient. Dual-task (DT) testing-evaluating motor performance under cognitive load-has emerged as a promising, non-invasive method for early ADRD detection. This review provides a comprehensive synthesis of DT-based ADRD assessments from January 2010 to October 2025, integrating insights from engineering and clinical neuroscience. We explore a broad range of DT paradigms (e.g., gait, balance, upper-limb function), sensing technologies (e.g., wearable sensors, electronic walkways, infrared/depth cameras, video, tablets, and brain imaging tools like fMRI and fNIRS), and analytic approaches, from traditional statistics to deep learning. Emerging tools, including eye-tracking and AI-based video pose estimation, are also discussed. We critically examine methodological trends, highlight key findings, and identify current limitations. Emphasizing the need for equitable, scalable, and clinically viable DT systems, this review highlights the role of modern sensor and AI technologies in enhancing early ADRD detection. It serves as a key resource for engineers, data scientists, and clinicians developing technology-driven tools for early detection and monitoring of neurodegenerative diseases.