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Principal Investigator  
Principal Investigator's Name: Kun Huang
Institution: Indiana University School of Medicine
Department: Hematology Oncology
Country:
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Additional Investigators  
Investigator's Name: Yi Zhao
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Investigator's Name: Huanmei Wu
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Investigator's Name: Sha Cao
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Investigator's Name: Chi Zhang
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Investigator's Name: Jun Wan
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Investigator's Name: Xiaowen Liu
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Investigator's Name: Jie Zhang
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Investigator's Name: Zhi Han
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Investigator's Name: Jie Zhang
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.
Investigator's Name: Li Chen
Proposed Analysis: Understanding disease pathology, identifying biological markers and suggesting early diagnosis and intervention strategies are critically important in AD research. In the project, we aim to use ADNI data to develop methodologies to gain better knowledge about the role of different data modalities in disease development and progression by integrating genomics, proteomics/metabolomics, neuroimaging and clinical data. In addition, in AD research, it is important to investigate longitudinal trajectories of variations in metabolism, brain structure and function, and neuropsychological behaviors. We propose to use ADNI data to develop novel methodologies to analyze longitudinal data.