A5100336783- Dementia and Cognitive Impairment Research
- Functional Brain Connectivity Studies
- Radiomics and Machine Learning in Medical Imaging
- Mathematical Biology Tumor Growth
- Migraine and Headache Studies
- Cancer, Stress, Anesthesia, and Immune Response
- Traumatic Brain Injury and Neurovascular Disturbances
- Epilepsy research and treatment
- Traumatic Brain Injury Research
- Glioma Diagnosis and Treatment
- Advanced Electron Microscopy Techniques and Applications
- Machine Learning in Healthcare
- Fault Detection and Control Systems
- Electron and X-Ray Spectroscopy Techniques
- Pharmacological Effects and Toxicity Studies
- Gene Regulatory Network Analysis
- Bayesian Modeling and Causal Inference
- Trigeminal Neuralgia and Treatments
- Health, Environment, Cognitive Aging
- Advanced Mathematical Modeling in Engineering
- Alzheimer's disease research and treatments
- Risk and Safety Analysis
- Integrated Circuits and Semiconductor Failure Analysis
- Advanced Statistical Process Monitoring
- Cell Image Analysis Techniques
Georgia Institute of Technology
2003-2025
Sichuan University
2013-2025
Shenzhen Technology University
2025
Shanghai University
2024
Anhui Medical University
2016-2024
University of Washington
2023-2024
Anhui University
2024
Seattle University
2024
Third Affiliated Hospital of Harbin Medical University
2024
First Affiliated Hospital of Anhui Medical University
2016-2024
Nanostructures of wurtzite‐structured zinc sulfide , which is unlikely to be stable at room temperature under conventional conditions, have been synthesized by a simple physical process. Morphologies with belt‐ (see Figure), saw‐, comb‐, and windmill‐like nanostructures observed characterized using variety imaging techniques.
The International Classification of Headache Disorders provides criteria for the diagnosis and subclassification migraine. Since there is no objective gold standard by which to test these diagnostic criteria, are based on consensus opinion content experts. Accurate migraine classifiers consisting brain structural measures could serve as an revise criteria. objectives this study were utilize magnetic resonance imaging structure constructing classifiers: (1) that accurately identify...
Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods included 58 (mean age = 36.3 years; SD 11.5) 50 controls 35.9 11.0). The connections of 33 seeded pain-related regions were as input for a brain classification algorithm tested the accuracy determining whether an MRI...
Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which unable to precisely identify tumor cell invasion, impairing effective surgery radiation planning. We present novel hybrid model, based on multiparametric intensities, combines machine learning (ML) with mechanistic model of growth provide spatially resolved density predictions. The ML component an data-driven graph-based semi-supervised we use the...
Effective diagnosis of Alzheimer's disease (AD) is primary importance in biomedical research. Recent studies have demonstrated that neuroimaging parameters are sensitive and consistent measures AD. In addition, genetic demographic information also been successfully used for detecting the onset progression The research so far has mainly focused on studying one type data source only. It expected integration heterogeneous (neuroimages, demographic, measures) will improve prediction accuracy...
ABSTRACT Background: Very few recent studies are available that compare caregiver burden, sleep quality, and stress in caregivers of different types dementia. We aimed to investigate patients with frontotemporal lobar degeneration dementia Lewy bodies, as compared Alzheimer's disease. Methods: This study was carried out from March 2011 January 2014. In total, 492 dyads patient (frontotemporal degeneration, n = 131; 36; disease, 325) participated this study. respect the Neuropsychiatric...
Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional biopsies, including 111 from NE, 68 HGG patients. Whole exome RNA sequencing uncover unique genomic alterations to unresectable NE tumor, subclonal events, which inform...
Quantitative two-dimensional maps of electrostatic potential in device structures are obtained using off-axis electron holography with a spatial resolution 6 nm and sensitivity 0.17 V. Estimates junction depth variation by holography, process simulation, secondary ion mass spectroscopy show close agreement. Measurement artifacts due to sample charging surface "dead layers" do not need be considered provided that proper care is taken preparation. The results demonstrate could become an...
Neuroimaging techniques hold the promise that they may one day aid clinical assessment of individual psychiatric patients. However, vast majority studies published so far have been based on average differences between groups. This study employed a multivariate approach to examine potential resting-state functional magnetic resonance imaging (MRI) data for making accurate predictions about psychopathology in survivors 2008 Sichuan earthquake at an level. Resting-state MRI was acquired 121...
Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications genetics and brain sciences, accurate efficient large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose Sparse Network (SBN) structure algorithm that employs novel formulation involving one L1-norm penalty term to impose sparsity another ensure the learned Directed Acyclic Graph--a required property BNs....
Presently, visual and quantitative approaches for image-supported diagnosis of dementing disorders rely on regional intensity rather than connectivity measurements. Here, we test metabolic differentiation between Alzheimer's disease frontotemporal lobar degeneration. Positron emission tomography with 18F-fluorodeoxyglucose was conducted in 47 patients mild disease, 52 degeneration, 45 healthy elderly subjects. Sparse inverse covariance estimation selection were used to identify patterns...
<h3>BACKGROUND AND PURPOSE:</h3> Because sinonasal inverted papilloma can harbor squamous cell carcinoma, differentiating these tumors is relevant. The objectives of this study were to determine whether MR imaging–based texture analysis accurately classify cases noncoexistent carcinoma and compare classification performance with neuroradiologists9 review. <h3>MATERIALS METHODS:</h3> Adult patients who had or resected eligible (coexistent excluded). Inclusion required tumor size >1.5 cm...
To determine the prevalence of dementia in past two decades and provide updated estimates about older people (aged ≥60 years) with China from 2015 to 2050.The English Chinese databases were retrieved. Published epidemiology surveys 1990-2018 screened. Meta-analysis was used calculate their pooled prevalence. The age-moving method estimate population aged years 2020, 2030, 2040 2050 based on data sampling survey 1% released by National Bureau Statistics. three age groups (60-69, 70-79, ≥80...
Telemonitoring is the use of electronic devices to remotely monitor patients. Taking Parkinson's disease (PD) as an example, at-home testing device (AHTD) enables remote, internet-based measurement PD vocal symptoms. Translating AHTD into a unified rating scale (UPDRS) through predictive analytics cost-effective, convenient, and close tracking progression. Building model between UPDRS not straightforward because patients are highly heterogeneous, which requires patient-specific models....
This study aims to evaluate the intervention effect of intermittent Theta burst stimulation (iTBS) on bilateral dorsomedial prefrontal cortex (DMPFC) for negative symptoms in schizophrenia using functional near-infrared spectroscopy (fNIRS) confirm therapeutic significance DMPFC treating and provide new evidence treatment research. Thirty-nine patients with mild cognitive impairment were randomly divided into a group (n=20) control (n=19). The received iTBS DMPFC. sham treatment. Negative...