A5101728858- Neurological disorders and treatments
- Functional Brain Connectivity Studies
- Advanced MRI Techniques and Applications
- Advanced Neuroimaging Techniques and Applications
- Genetic Neurodegenerative Diseases
- Diabetic Foot Ulcer Assessment and Management
- Peptidase Inhibition and Analysis
- Orthopedic Infections and Treatments
- Vascular Malformations Diagnosis and Treatment
- EEG and Brain-Computer Interfaces
- Cerebrovascular and Carotid Artery Diseases
- Streptococcal Infections and Treatments
- Intracranial Aneurysms: Treatment and Complications
- Cell Image Analysis Techniques
- Neuroscience and Neuropharmacology Research
- Neuroscience and Neural Engineering
- Photoreceptor and optogenetics research
- Acute Ischemic Stroke Management
The Affiliated Yongchuan Hospital of Chongqing Medical University
2015-2025
Chongqing Medical University
2015-2025
The clinical benefits of targeting the ventral intermediate nucleus (VIM) for treatment tremors in essential tremor (ET) patients suggest that VIM is a key hub network generation and propagation can be considered as seed region to study network.However, little known about central ET patients.Twentysix 26 matched healthy controls (HCs) were included this study.After considering structural head-motion factors establishing accuracy our region, seedbased functional connectivity (FC) analysis...
ABSTRACT Introduction The heterogeneous clinical features of essential tremor indicate that the dysfunctions this syndrome are not confined to motor networks, but extend nonmotor networks. Currently, these neural network in remain unclear. In study, independent component analysis resting‐state functional MRI was used study mechanisms. Methods Thirty‐five patients and 35 matched healthy controls with neuropsychological tests were included, eight networks identified. After considering...
Although depression is one of the most common neuropsychiatric symptoms in essential tremor (ET), diagnosis biomarker and intrinsic brain activity remain unclear. We aimed to combine multivariate pattern analysis (MVPA) with local functional connectivity identify depressed ET. Based on individual voxel-level (regional homogeneity, ReHo) mapping from 41 ET, 43 non-depressed 45 healthy controls (HCs), binary support vector machine (BSVM) multiclass Gaussian Process Classification (MGPC)...
Abstract Background Depression in essential tremor (ET) has been constantly studied and reported, while the associated brain activity changes remain unclear. Recently, regional homogeneity (ReHo), a voxel-wise local functional connectivity (FC) analysis of resting-state magnetic resonance imaging, provided promising way to observe spontaneous activity. Methods Local FC analyses were performed forty-one depressed ET patients, 49 non-depressed patients 43 healthy controls (HCs), then matrix...
Background Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging a promising way to identify ET patients from healthy controls (HCs) and further explore spontaneous change mechanisms build potential diagnostic biomarker in patients. Methods The histogram features Resting-state functional magnetic resonance (Rs-fMRI) data were extracted 133 135 well-matched HCs as input features. Then, two-sample t-test, mutual...
Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping resting-state fMRI has provided a promising way to identify patients depressed ET at individual level help reveal brain network ET. Methods: Based on global (GBC) from 41 ET, 49 non-depressed 45 primary depression, 43...
Currently, machine-learning algorithms have been considered the most promising approach to reach a clinical diagnosis at individual level. This study aimed investigate whether whole-brain resting-state functional connectivity (RSFC) metrics combined with could be used identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET-related brain network pathogenesis establish potential diagnostic biomarkers. The RSFC obtained 127 ET 120 HCs were as input features,...
Background Due to the absence of biomarkers, misdiagnosis essential tremor (ET) with other diseases and enhanced physiologic is very common in practice. Combined radiomics based on diffusion tensor imaging (DTI) three-dimensional T1-weighted (3D-T1) machine learning (ML) give a most promising way identify at individual level further reveal potential biomarkers. Methods Radiomics features were extracted from 3D-T1 DTI 103 ET patients age-and sex-matched healthy controls (HCs). After data...
<title>Abstract</title> Background Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging has a promising way to identify ET patients from healthy controls (HCs) and further explore spontaneous changes build potential diagnostic biomarker in patients. Methods The histogram features extracted 133 135 well-matched HCs as input features. Then, two-sample t-test, mutual information, least absolute shrinkage selection...