| Title: | Identify biomarkers for distinguishing different mental disorders using brain images and their associations with genetic risk |
| Lead Institution: | Georgia State University |
| Principal investigator: | Dr Yuhui Du |
Application 34175
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About
We aim to investigate neuroimage-based biomarkers and disease-specific genetic variants for distinguishing different brain disorders since some mental disorders currently diagnosed using clinical symptoms have similar symptoms and shared risk genes. We will work on data of T1 and T2 brain MRI, resting and task functional brain MRI, diffusion brain MRI as well as genetic data from patients with schizophrenia (SZ), bipolar disorder (BP), schizoaffective disorder (SAD), autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy controls (HC). We expect to reveal shared brain abnormalities across these disorders and identify disease-specific brain alterations and genetic variants for each disorder. Our goal is to improve the diagnosis, prediction, prevention and treatment of brain disorders by using measures from brain images and genes. Therefore, our project accords with the aims of UK Biobank project. In our project, biologically meaningful measures obtained from brain functional and structural imaging data as well as genetics are expected to provide biological markers for distinguishing different mental disorders. We will compute voxel-based morphometry using structure MRI, functional networks using functional MRI and PET, white-matter mapping using diffusion images, genetic association, as well as the fusion of the above measures. Based on these measures, we will identify markers and classify brain disorders, which will in turn help to understand the mechanism of disorders and diagnose the brain disease. In the project, we expect to include all the subjects with imaging data in BioBank as it becomes available, although we may primarily focus on patients with mental disorders. We want to include the full imaged cohort of all available data. In addition, we would get both self-report and medical record data if we can. The timeline is as follows. Month 1: Download, organize and preprocess all the imaging and genetic data. Month 2-3: Extract measures from the structural, diffusion, functional MRI data and genetic variants. Month 4-5: Identify markers, investigate associations, and classify individual subjects.6 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 12293 | A functional connectome signature of blood pressure in >30 000 participants from the UK biobank. | Rongtao Jiang (+6) | 2022 | Cardiovascular Research |
| 7826 | Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study (N > 6,000) | Yuhui Du (+2) | 2023 | Frontiers in Aging Neuroscience |
| 7363 | Associations between grip strength, brain structure, and mental health in > 40,000 participants from the UK Biobank | Rongtao Jiang (+8) | 2022 | BMC Medicine |
| 13031 | Common and unique brain aging patterns between females and males quantified by large-scale deep learning | Yuhui Du (+3) | 2024 | Human Brain Mapping |
| 10449 | Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network | Shile Qi (+15) | 2022 | Nature Communications |
| 9379 | dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping | Debbrata K. Saha (+4) | 2023 | Human Brain Mapping |
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