Objective: This review examines the evidence that deep brain stimulation (DBS) has extensive impact on nonmotor symptoms (NMSs) of patients with Parkinson's disease (PD). Data Sources: We retrieved information from the PubMed database up to September, 2015, using various search terms and their combinations including PD, NMSs, DBS, globus pallidus internus (GPi), subthalamic nucleus (STN), and ventral intermediate thalamic nucleus. Study Selection: We included data from peer-reviewed journals on impacts of DBS on neuropsychological profiles, sensory function, autonomic symptoms, weight changes, and sleep disturbances. For psychological symptoms and cognitive impairment, we tried to use more reliable proofs: Random, control, multicenter, large sample sizes, and long period follow-up clinical studies. We categorized the NMSs into four groups: those that would improve definitively following DBS; those that are not significantly affected by DBS; those that remain controversial on their surgical benefit; and those that can be worsened by DBS. Results: In general, it seems to be an overall beneficial effect of DBS on NMSs, such as sensory, sleep, gastrointestinal, sweating, cardiovascular, odor, urological symptoms, and sexual dysfunction, GPi-DBS may produce similar results; Both STN and Gpi-DBS are safe with regard to cognition and psychology over long-term follow-up, though verbal fluency decline is related to DBS; The impact of DBS on behavioral addictions and dysphagia is still uncertain. Conclusions: As the motor effects of STN-DBS and GPi-DBS are similar, NMSs may determine the target choice in surgery of future patients.

Objective: To examine cognitive deficits and associated brain activity in fragile X-associated tremor/ataxia syndrome (FXTAS) patients with parkinsonism (FXTp+), in relation to FXTAS patients without parkinsonism (FXTp-), and normal elderly controls (NC). Methods: Retrospective reviews were performed in 65 FXTAS patients who participated in the event-related brain potential (ERP) study and also had either a videotaped neurological examination or a neurological examination for extrapyramidal signs. Parkinsonism was defined as having bradykinesia with at least one of the following: rest tremor, postural instability, hypermyotonia, or rigidity. Eleven FXTp+ patients were identified and compared to 11 matched FXTp- and 11 NC. Main ERP measures included the N400 congruity effect, N400 repetition effect, and the late positive component (LPC) repetition effect. Results: When compared with FXTp- and NC, the FXTp+ group showed more severe deficits in executive function, cued-recall, recognition memory, along with a significantly reduced N400 repetition effect (thought to index semantic processing and verbal learning/memory) which was correlated with poorer verbal memory. Across all patients, FMR1 mRNA levels were inversely correlated with delayed recall on the California Verbal Learning Test (CVLT). Interpretation: The findings of more prominent executive dysfunction and verbal learning/memory deficits in FXTp+ than FXTp- are consistent with findings in Parkinson’s disease (PD), and may indicate that concomitant and/or synergistic pathogenetic mechanisms associated with PD play a role in FXTAS. These results have implications not only for understanding the cognitive impairments associated with the parkinsonism subtype of FXTAS, but also for the development of new interventions for these patients.

Background: Amnestic mild cognitive impairment (aMCI) is a heterogeneous transitional cognitive state between normal aging and dementia. Aβ positivity elevates dementia conversion risk but does not equate to prodromal Alzheimer’s disease (AD), and not all Aβ-positive aMCI individuals progress to clinical AD dementia. Distinguishing Aβ-positive from Aβ-negative aMCI facilitates pathological subtyping rather than definitive early AD diagnosis. This cross-sectional study aimed to characterize scalp-level resting-state EEG and structural MRI signatures specific to Aβ-positive aMCI, explore their covariate-adjusted correlations with cognitive performance and regional Aβ burden, and develop an internally validated multimodal auxiliary classification model for Aβ stratification. Methods: Fifty enrolled aMCI participants (30 Aβ-positive, 20 Aβ-negative) and 35 cognitively normal controls (NC) completed unified 20-min eyes-closed resting-state scalp EEG, 3.0 T structural MRI, ¹⁸ F-AV45 amyloid PET and ¹⁸ F-FDG cerebral metabolism PET examinations. Artifact-free scalp EEG spectral power and regional θ/α ratio were calculated via Welch spectral analysis; voxel-based morphometry (VBM) was adopted to quantify whole-brain gray matter volume. Covariate-controlled partial correlation corrected for multiple comparisons was applied to evaluate cross-modal associations among cortical Aβ SUVR load, cerebral glucose metabolism and domain-specific cognitive scores. A binary logistic regression classification model combining screened EEG and MRI markers was established, with DeLong test for ROC curve comparison and 1,000-bootstrap internal validation. Results: (1) Relative to Aβ-negative aMCI and NC groups, the Aβ-positive subgroup presented significantly elevated whole-scalp θ relative power and scalp θ/α ratio, decreased scalp α and β relative power (all q < 0.05, FDR-corrected), with predominant scalp oscillatory alterations distributed over temporal, parietal and occipital scalp electrode clusters. The temporal scalp θ/α ratio yielded optimal classification performance for Aβ stratification (AUC = 0.875). (2) The Aβ-positive subgroup showed significant gray matter atrophy across 13 AD-vulnerable brain regions including bilateral fusiform gyrus, inferior parietal lobule, parahippocampal gyrus, precuneus and right entorhinal cortex (all q < 0.05), with the maximum effect size identified in the right precuneus (partial η ² = 0.477, AUC = 0.778). (3) Within the Aβ-positive subgroup, the temporal scalp θ/α ratio was positively correlated with medial temporal Aβ SUVR ( r = 0.501, 95% CI [0.146, 0.732], q = 0.030); right precuneus gray matter volume was negatively correlated with multi-region cortical Aβ burden ( q < 0.05). Left parahippocampal gyrus volume was positively correlated with MoCA memory subscores ( r = 0.479, 95% CI [0.118, 0.709], q < 0.05). No significant associations between scalp EEG metrics and FDG glucose metabolism survived FDR correction. (4) The multimodal logistic regression model integrating temporal scalp θ/α ratio and right precuneus gray matter volume achieved favorable classification efficacy for Aβ-positive aMCI differentiation (AUC = 0.852, 95% CI [0.748, 0.955]), with good calibration and clinical net benefit based solely on internal bootstrap validation. Conclusion: The temporal scalp θ/α ratio and right precuneus gray matter volume are promising candidate non-invasive auxiliary biomarkers for Aβ pathological stratification in aMCI, rather than validated clinical diagnostic biomarkers. The multimodal model provides supplementary subtyping value within this single-center cohort, while external multicenter validation is mandatory before clinical application. This cross-sectional dataset cannot support prediction of longitudinal progression to AD dementia.

Muscle synergy analysis has been widely applied in neuroscience, biomechanics, rehabilitation, and robotics. However, the reliability of muscle synergy extraction remains a critical issue, as results are highly sensitive to variations in experimental design, surface electromyography preprocessing, factorization algorithms, and the determination of the number of synergies. This narrative methodological review examines the key factors that influence the robustness and reliability of muscle synergies within the framework of limb neuromechanical controllers across anatomical regions and task contexts. By analyzing the current state of research in this field, we first summarize the conceptual and methodological foundations of muscle synergy analysis and then critically examine the factors that influence its robustness and reliability. This review provides practical recommendations to enhance methodological rigor, including greater methodological harmonization and transparent reporting of experimental protocols, the use of musculoskeletal simulation to evaluate muscle selection strategies, and the refinement of synergy extraction techniques. These insights aim to improve the reliability and robustness of muscle synergy analysis and promote its application in robotics, human–machine interfacing, and computational motor control.

Muscle synergies have emerged as a novel framework that offers a deeper understanding of the physiological basis of motor control and have been extensively applied in various fields. This study aims to investigate muscle synergies, involving the selection of the muscles of the lower limb while running, including the deep muscles. The muscle synergies were identified from the 23 muscle activations generated by the OpenSim simulation and experimented EMG, respectively, and these outcomes were compared. Our results revealed an additional synergy required to achieve a comparable variability accounting for (VAF) levels when considering 23 muscles compared to 10 muscles. This study highlights that the potential contribution of deep muscles to explaining the muscle synergies involved in running. These findings provide valuable insights for a comprehensive understanding of muscle synergy during running and the neuromotor control of human locomotion. Further optimization of the experimental protocols and extraction methods is necessary to enhance the results of muscle synergy analyses and promote their broader application across various fields in the future.