There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Xuewei Cheng |
| Institution: | Central South University |
| Department: | School of mathematics and statistics |
| Country: | |
| Proposed Analysis: | ADNI (Alzheimer's Disease Neuroimaging Initiative) is a large-scale study of brain aging and Alzheimer's disease that aims to identify biomarkers for early detection, diagnosis, and monitoring of the disease. The study collects data from multiple sources, including magnetic resonance imaging (MRI), positron emission tomography (PET), cerebrospinal fluid (CSF), and clinical assessments. To analyze the ADNI data, we can use a variety of statistical and machine learning techniques to explore relationships between different variables and potentially discover new biomarkers for Alzheimer's disease. Here are some specific analysis methods that could be used: Exploratory data analysis: We can start by examining the data using descriptive statistics, visualization, and dimensionality reduction techniques such as PCA (Principal Component Analysis). This will help us understand the structure of the data, identify any patterns or outliers, and determine which variables are most important for predicting Alzheimer's disease. Feature engineering: Based on the exploratory analysis, we can create new features that capture important information about the data. For example, we could extract texture and shape features from MRI images using image processing techniques, or calculate ratios of different protein levels in CSF to identify potential biomarkers. Machine learning models: We can use various machine learning algorithms to build models that predict Alzheimer's disease based on the available data. Some possible approaches include logistic regression, random forest, support vector machines, and neural networks. We can also use ensemble methods such as stacking and boosting to combine multiple models and improve their performance. Network analysis: Another approach is to use graph theory and network analysis to explore the functional connectivity of the brain and identify biomarkers based on the topology of the network. This involves constructing a graph representation of the brain using connectivity data from fMRI or PET scans, and then analyzing the graph properties such as degree distribution, clustering coefficient, and centrality measures. Longitudinal analysis: Since the ADNI data includes longitudinal measurements of patients over time, we can use longitudinal analysis techniques such as mixed-effects models and growth curve analysis to examine how different variables change over time and how they are related to Alzheimer's disease progression. Overall, the ADNI data provides a rich source of information for studying Alzheimer's disease and developing new diagnostic tools and treatments. By applying advanced analysis techniques to this data, we can gain insights into the underlying mechanisms of the disease and potentially identify new targets for intervention. |
| Additional Investigators | |
| Investigator's Name: | Shujie Ma |
| Proposed Analysis: | ADNI (Alzheimer's Disease Neuroimaging Initiative) is a large-scale study of brain aging and Alzheimer's disease that collects data from multiple sources, including magnetic resonance imaging (MRI), positron emission tomography (PET), cerebrospinal fluid (CSF), and clinical assessments. In general, the aim of analyzing ADNI data is to identify biomarkers for early detection, diagnosis, and monitoring of Alzheimer's disease. Here are some proposed analysis methods: Exploratory Data Analysis: Use descriptive statistics and visualization techniques to get an overview of the data and identify any patterns or outliers. Feature Engineering: Create new features based on the extracted data. For example, we could extract texture and shape features from MRI images using image processing techniques, calculate ratios of different protein levels in CSF, or use NLP (natural language processing) to analyze clinical notes. Machine Learning Models: Use machine learning algorithms to build models that predict Alzheimer's disease or other outcomes based on the available data. Some possible approaches include logistic regression, random forest, support vector machines, and neural networks. Network Analysis: Create a graph representation of the brain using connectivity data from fMRI or PET scans, and then analyze the graph properties such as degree distribution, clustering coefficient, and centrality measures. Longitudinal Analysis: Examine how different variables change over time and how they are related to Alzheimer's disease progression using longitudinal analysis techniques such as mixed-effects models and growth curve analysis. Biomarker Identification: Identify potential biomarkers for Alzheimer's disease by examining correlations between different types of data (e.g., MRI, PET, CSF) and looking for specific biological markers that may be associated with disease onset or progression. Deep Learning Models: Use deep learning techniques, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to automatically extract features from the data and improve model performance. Interpretability: Develop methods to interpret the models and identify which variables are most important for predicting Alzheimer's disease. Overall, analyzing ADNI data requires a combination of statistical, machine learning, and domain expertise. By applying advanced analysis techniques to this data, we can gain insights into the underlying mechanisms of Alzheimer's disease and potentially identify new targets for intervention. |
