Stroke is a central nervous system disease that causes structural lesions and functional impairments of the brain, resulting in varying types, and degrees of dysfunction. The bimodal balance-recovery model (interhemispheric competition model and vicariation model) has been proposed as the mechanism of functional recovery after a stroke. We analyzed how combinations of motor observation treatment approaches, transcranial electrical (TES) or magnetic (TMS) stimulation and peripheral electrical (PES) or magnetic (PMS) stimulation techniques can be taken as accessorial physical therapy methods on symptom reduction of stroke patients. We suggest that top-down and bottom-up stimulation techniques combined with action observation treatment synergistically might develop into valuable physical therapy strategies in neurorehabilitation after stroke. We explored how TES or TMS intervention over the contralesional hemisphere or the lesioned hemisphere combined with PES or PMS of the paretic limbs during motor observation followed by action execution have super-additive effects to potentiate the effect of conventional treatment in stroke patients. The proposed paradigm could be an innovative and adjunctive approach to potentiate the effect of conventional rehabilitation treatment, especially for those patients with severe motor deficits.

Traumatic brain injury, cardiac arrest, intracerebral hemorrhage, and ischemic stroke may cause disorders of consciousness (DoC). Repetitive transcranial magnetic stimulation (rTMS) has been used to promote the recovery of disorders of consciousness (DoC) patients. In this meta-analysis, we examined whether rTMS can relieve DoC patient symptoms. We searched through journal articles indexed in PubMed, the Web of Science, Embase, Scopus, and the Cochrane Library until 20 April 2023. We assessed whether studies used rTMS as an intervention and reported the pre- and post-rTMS coma recovery scale-revised (CRS-R) scores. A total of 207 patients from seven trials were included. rTMS significantly improved the recovery degree of patients; the weighted mean difference (WMD) of the change in the CRS-R score was 1.89 (95% confidence interval (CI): 1.39–2.39; p < 0.00001) in comparison with controls. The subgroup analysis showed a significant improvement in CRS-R scores in rTMS over the dorsolateral prefrontal cortex (WMD = 2.24; 95% CI: 1.55–2.92; p < 0.00001; I² = 31%) and the primary motor cortex (WMD = 1.63; 95% CI: 0.69–2.57; p = 0.0007; I² = 14%). Twenty-hertz rTMS significantly improved CRS-R scores in patients with DoC (WMD = 1.61; 95% CI: 0.39–2.83; p = 0.010; I² = 31%). Furthermore, CRS-R scores in rTMS over 20 sessions significantly improved (WMD = 1.75; 95% CI: 0.95–2.55; p < 0.0001; I² = 12%). rTMS improved the symptoms of DoC patients; however, the available evidence remains limited and inadequate.

Alzheimer's disease (AD) is a degenerative mental disorder of the central nervous system that affects people's ability of daily life. Unfortunately, there is currently no known cure for AD. Thus, the early detection of AD plays a key role in preventing and controlling its progression. Magnetic resonance imaging (MRI)-based measures of cerebral atrophy are regarded as valid markers of the AD state. As one of representative methods for measuring brain atrophy, image registration technique has been widely adopted for AD diagnosis. However, AD detection is sensitive to the accuracy of image registration. To address this problem, an AD assistant diagnosis framework based on joint registration and classification is proposed. Specifically, in order to capture more local deformation information, we propose a novel patch-based joint brain image registration and classification network (RClaNet) to estimate the local dense deformation fields (DDF) and disease risk probability maps that explain high-risk areas for AD patients. RClaNet consists of a registration network and a classification network, in which the deformation field from registration network is fed into the classification network to enhance the prediction accuracy of the disease. Then, the exponential distance weighting method is used to obtain the global DDF and the global disease risk probability map, and it can remove grid-like artifacts by uniformly weighting method. Finally, the global classification network uses the global disease risk probability map for the early detection of AD. We evaluate the proposed method on the OASIS-3, AIBL and ADNI datasets, and experimental results show that the proposed RClaNet achieves superior registration performances than several state-of-the-art methods. Early diagnosis of AD using the global disease risk probability map also yielded competitive results. To demonstrate the generality, we also evaluate the proposed method on a COVID-19 dataset and achieve decent registration and classification results. These experiments prove that the deformation information in the registration process can be used to characterize subtle changes of degenerative diseases and further assist clinicians in diagnosis.

Objective: The aim of this study was to analyze the research hotspots and frontiers in the field of autistic traits (AT) between 1997 and 2024.Methods: The Web of Science Core Collection (WOSCC) is used as a data source in the study, analyzing 1,044 academic articles on AT published between 1997 and 2024. The included articles were visually analyzed using CiteSpace 6.2.R4 software, which is used to map keywords and core literature related to AT and to analyze research progress and emerging hotspots in the fieldResults: A total of 1,044 articles have been included in the analysis, and the total number of articles has demonstrated an upward trajectory. The nations of England, the USA, and Australia are at the vanguard of this body of literature. With regard to research institutions, the University of London has attracted considerable attention as a result of its substantial contributions to the field of AT. The most relevant research is published in the Journal of Autism and Developmental Disorders.Conclusion: The scope of AT research has expanded considerably, encompassing psychology, education, and other disciplines. The research dimensions extend beyond behavioral, genetic, cognitive, and neural aspects to include environmental and hormonal factors. Furthermore, the study population has evolved from twins and the general population to focus on specific groups, such as individuals with psychiatric disorders. This broadening of focus has led to a significant increase in AT research in recent years. In sum, this study provides a comprehensive perspective that can inform and guide further in-depth research on AT.