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Your search keyword '"Lu, Weizhao"' showing total 10 results
Start Over Author "Lu, Weizhao" Publisher wiley
10 results on '"Lu, Weizhao"'

5. Age-related changes in fiber tracts in healthy adult brains: A generalized q-sampling and connectometry study

Author

Lu Weizhao, Dong Cui, Xiaodong Gao, Zilong Yuan, Fenglian Zheng, Jindong Xie, Jianfeng Qiu, Xiaojing Liu, Li Zhang, and Chen Zhang

Subjects
education.field_of_study, Genu of the corpus callosum, business.industry, Fornix, Population, Biology, Corpus callosum, 030218 nuclear medicine & medical imaging, White matter, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, Multiple comparisons problem, Fractional anisotropy, medicine, Radiology, Nuclear Medicine and imaging, Nuclear medicine, business, education, 030217 neurology & neurosurgery, Diffusion MRI
Abstract

BACKGROUND Generalized q-sampling (GQI) and connectometry analysis provide new indices, i.e., quantitative anisotropy (QA) and spin distribution function (SDF) in comparison with diffusion tensor imaging (DTI). They may provide more age-related changes in white matter (WM) in aging. PURPOSE To investigate the feasibility of using GQI and connectometry analysis to determine WM properties changes in aging. STUDY TYPE Cross-cohort research. POPULATION Fifty normal adults (27 females) aged 21-71 years. FIELD STRENGTH/SEQUENCE T1 -weighted images (T1 WI) and high angular resolution diffusion imaging (HARDI) images were acquired at 1.5T. ASSESSMENT HARDI data were analyzed using DTI and GQI to obtain fractional anisotropy (FA), QA, fiber numbers, and fiber lengths for tract analysis and using q-space diffeomorphic reconstruction (QSDR) for the connectometry analysis. We compared differences of DTI, GQI, and connectometry analysis to reflect WM changes in aging. STATISTICAL TESTS Associations between FA, QA, and fiber numbers and lengths and age were analyzed using Pearson's correlation coefficients. The connectometry analysis was conducted using a multiple linear regression analysis, including age and gender as factors. Uncorrected P-value/false discovery rate (FDR) (corrected for multiple comparisons) 6 regions, P 7 regions, P < 0.05). However, DTI and GQI analyses revealed consistent results in some regions, including the genu of the corpus callosum (GCC), body of the corpus callosum (BCC), fornix (Fx), and anterior coronal radiation (ACR) (P < 0.05). The connectometry analysis showed more tract changes associated with age at an FDR of 0.05, which partially overlapped with the FA and QA. DATA CONCLUSION GQI and connectometry provide more information about age-related tracts and complement the DTI findings. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018;48:369-381.

Published
2018

10. Identifying individuals with attention-deficit/hyperactivity disorder based on multisite resting-state functional magnetic resonance imaging: A radiomics analysis.

Author

Liu G, Lu W, Qiu J, and Shi L

Subjects
Humans, Magnetic Resonance Imaging methods, Brain diagnostic imaging, Brain Mapping methods, Magnetic Resonance Spectroscopy, Attention Deficit Disorder with Hyperactivity diagnostic imaging
Abstract

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age-inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting-state functional magnetic resonance (rs-fMRI) have more discriminative power for the diagnosis of ADHD. The rs-fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD-200 Consortium. A total of four preprocessed rs-fMRI images including regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), voxel-mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve = 0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs-fMRI information to distinguish ADHD from healthy controls. The rs-fMRI-based radiomics features have the potential to be neuroimaging biomarkers for ADHD., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2023
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