Wearable and sensor-based monitoring technologies are increasingly used to support objective, longitudinal assessment in Parkinson’s disease (PD). However, the global development, knowledge structure, and emerging research frontiers of this interdisciplinary field remain incompletely mapped. Bibliographic records on wearable and sensor-based monitoring technologies in PD were retrieved from the Web of Science Core Collection (WoSCC) and Scopus for the period from 1 January 2005 to 2 February 2026. Searches were conducted in title, abstract, and author keyword fields. Microsoft Excel, VOSviewer, CiteSpace, and the bibliometrix R package were used to analyze publication trends, collaboration networks, co-citation patterns, keyword co-occurrence, clustering, citation bursts, historiographic patterns, and source impact. A total of 3,332 publications were included. Publication output increased rapidly over the study period, particularly after 2019, while 2026 represented an incomplete publication year because the search was conducted on 2 February 2026. The United States (U.S.), China, and European countries constituted the main knowledge-production and collaboration structure. Thematic analyses suggested a gradual shift from engineering validation and objective motor-symptom quantification toward AI-enabled digital biomarkers, remote monitoring, multimodal monitoring, and closed-loop neuromodulation. These patterns reflect research activity and citation structures rather than direct evidence of clinical efficacy. This bibliometric analysis provides science-mapping evidence on the evolution of wearable and sensor-based monitoring technologies in PD. Future work should prioritize standardization, multicenter real-world validation, clinical workflow integration, multimodal data fusion, and cautious evaluation of closed-loop applications.
Juan Carlos Villagomez-Garcia, Juan Martínez-Miranda, Ansel Y. Rodríguez-González
Suicide is a multifactorial phenomenon influenced by social, cultural, biological, and psychological factors, whose early detection remains challenging. According to the World Health Organization, suicide is the third leading cause of death among individuals aged 15–29 years, representing a major public health concern for young populations worldwide. In first-contact clinical settings, screening opportunities are often constrained by limited time, sparse patient information, and operational workflows. This study evaluates the feasibility of detecting suicidal behavior from short Spanish-language texts collected during initial screening interviews. The dataset comprises 374 anonymized records labeled as “no suicidal behavior,” “ideation,” “planning,” or “attempt,” with a strong imbalance toward “attempt,” reflecting operational clinical usage rather than population-representative sampling. To address data scarcity, we applied data augmentation methods (Easy Data Augmentation for Spanish, backtranslation, and large language model-based synthesis), validated through statistical analysis and clinician-based plausibility assessments. Exploratory analyses revealed highly constrained and overlapping lexical patterns across classes, indicating the challenging nature of the task. A Support Vector Machine achieved 0.53 macro F 1 in binary classification (“suicidal behavior” vs. “no behavior”), while BETO, a Spanish BERT model, reached 0.36 macro F 1 in multiclass classification. Under the same text-only conditions, human specialists achieved macro F 1 scores of 0.29 and 0.15 respectively; however, these results were based on only 20 real texts evaluated with double annotation across five specialists and should therefore be interpreted cautiously. Although performance is modest compared to studies using full clinical records, these findings suggest that short-text machine learning could support nonspecialists in first-contact settings. Future work should expand datasets and explore multimodal approaches, including voice and text, to improve clinical applicability and early detection.
Ying Hu, Xiaojun Wang, Pei Sun, Yuan Shen, Tingwei Quan
BVLab-Analyzer is a standalone graphical software tool for end-to-end analysis of terabyte-scale whole-brain microscopy images. Operating through a zero-code interface, it handles data import (TIFF, BioImageVision, OME-Zarr), deep learning-based semantic segmentation, 3D skeleton reconstruction, and morphological statistical analysis within a unified pipeline. The software supports three target structures—cells, neurons and vasculature—and exports their morphological reconstructions in standard SWC format. To accelerate training data preparation, Smart Annotation tools provide sub-volume interference isolation, coarse-vessel ray tracing, and skeleton auto-interpolation. For inference, optimized block-wise strategies enable a standard 3D U-Net to predict whole-brain volumes up to 12 TB on a single consumer-grade GPU, while an interrupt-and-resume engine ensures fault tolerance during extended computations. By integrating these features, BVLab-Analyzer lowers the hardware barrier for whole-brain analysis and unifies previously fragmented toolchains—from annotation and segmentation to reconstruction and quantification—into a single, accessible software environment.
Functional magnetic resonance imaging (fMRI) serves as a pivotal tool for investigating the functional organization and dynamic connectivity of the human brain, indirectly reflecting neural activity through blood oxygen level-dependent (BOLD) signals. However, the high-dimensional spatiotemporal structure, prominent noise interference, and inter-individual variability of fMRI data pose significant challenges for traditional modeling methods in characterizing complex brain functional patterns. In recent years, the Transformer architecture has gained significant traction in fMRI data analysis, leveraging its self-attention mechanism to model global dependencies. It has demonstrated superior performance in tasks such as brain disease classification, functional connectivity modeling, and brain representation learning. Compared to convolutional and recurrent neural networks, Transformers can more effectively capture long-range spatiotemporal dependencies and offer stronger structural scalability and cross-task transfer potential. To systematically map the development of this field, this paper proposes a unified taxonomy for Transformer-fMRI methods based on modeling dimensions. It categorizes existing research into four primary paradigms: temporal modeling, spatial modeling, spatiotemporal joint modeling, and self-supervised learning, providing a comparative analysis of their structural characteristics and applicable scenarios. Furthermore, this paper summarizes the key challenges regarding computational complexity, data dependency, and interpretability, while exploring the integration of neuroscientific priors with large-scale pre-trained models. Finally, we discuss future directions for Transformers in the construction of brain foundation models and their clinical utility.
Lecanemab, a humanized immunoglobulin monoclonal antibody, has been approved for the treatment of Alzheimer’s disease (AD). Although clinical trials have demonstrated its favorable safety profile, real-world studies are crucial for identifying rare adverse events (AEs) and evaluating post-marketing safety. This study aimed to evaluate the safety of lecanemab by combining a clinical prospective, observational case cohort with data from the FAERS database, thereby providing a robust basis for the identification of potential safety signals. A prospective and observational study was conducted on patients with mild-to-moderate AD receiving lecanemab treatment at a tertiary medical center in China from July 2024 to February 2026. Extracted data included demographic characteristics, time to onset of AEs, and clinical symptoms. Exposure-adjusted incidence rates were calculated per 100 patient-years (PYs). Disproportionality analysis was performed using the US Food and Drug Administration Adverse Event Reporting System (FAERS), and safety signals for AEs observed in the cohort were evaluated utilizing reporting odds ratios (RORs) and proportional reporting ratios (PRRs). Additionally, data from these two real-world sources were compared and cross-validated. The single-center prospective study enrolled a total of 91 patients. The median duration of lecanemab treatment was 185 days. Thirteen AEs were recorded (56.48/100 PYs), with the most frequent being amyloid-related imaging abnormalities-hemosiderin deposition (ARIA-H; defined as microhemorrhages and hemosiderin deposition) (12.39/100 PYs) and infusion-related reactions (IRRs) (8.85/100 PYs). In the FAERS database, the most frequently reported preferred terms (PTs) included headache, amyloid-related imaging abnormalities-edema/effusion (ARIA-E), ARIA-H, and IRRs. The majority of AEs occurred within the first 1–2 months after initiating lecanemab treatment ( n = 393, 45.65%). Furthermore, FAERS analysis revealed a potential safety signal for cerebral infarction. However, this finding must be interpreted cautiously due to the lack of complete data on traditional stroke risk factors and concomitant antithrombotic use in spontaneous reports. In this single-center prospective observational study, lecanemab demonstrated a generally favorable safety profile. Patients with AD aged 65 to 85 years represent a high-risk demographic for AEs. Particular clinical vigilance is required regarding the occurrence of AEs within the first two months of lecanemab initiation. Notably, ARIA (including both ARIA-E and ARIA-H) can occur at any phase during the treatment course.
