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: | junjie wang |
| Institution: | Nanjing Medical Unverisity |
| Department: | School of Biomedical Engineering and Informatics |
| Country: | |
| Proposed Analysis: | Data Collection: Obtain a dataset that consists of features related to Alzheimer's disease. This dataset may include demographic information, cognitive assessment scores, genetic data, neuroimaging data (such as MRI or PET scans), and other relevant variables. Data Preprocessing: Clean the dataset by handling missing values, outliers, and performing necessary transformations. Normalize or standardize the data if required. Exploratory Data Analysis: Perform exploratory data analysis to gain insights into the dataset. Analyze the distribution of variables, identify any patterns or correlations, and visualize the data using plots and charts. Feature Selection/Extraction: Select relevant features that are likely to contribute to Alzheimer's disease classification. This can be done using statistical methods, domain knowledge, or feature selection algorithms (e.g., recursive feature elimination, L1 regularization). Model Selection: Choose an appropriate classification algorithm for Alzheimer's disease classification. Commonly used algorithms include logistic regression, support vector machines (SVM), random forests, and deep learning models (such as convolutional neural networks or recurrent neural networks). Model Training: Split the dataset into training and testing sets. Train the selected classification model using the training data. Adjust hyperparameters, such as learning rate, regularization strength, or network architecture, using cross-validation techniques to optimize model performance. Model Evaluation: Evaluate the trained model using the testing set. Calculate relevant evaluation metrics such as accuracy, precision, recall, and F1-score. Additionally, analyze the model's performance using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics. Interpretation and Visualization: Interpret the trained model to understand the importance of features in Alzheimer's disease classification. Use techniques like feature importance analysis, SHAP values, or partial dependence plots to gain insights into the model's decision-making process. Performance Improvement: Fine-tune the model by adjusting parameters, trying different algorithms, or incorporating advanced techniques like ensemble learning or transfer learning to improve the classification performance. Validation and Reproducibility: Validate the model's performance on additional datasets or using cross-validation techniques to ensure the generalizability and reproducibility of the results. Documentation and Reporting: Summarize the analysis process, findings, and model performance in a comprehensive report. Communicate the results effectively, highlighting the implications of the analysis for Alzheimer's disease classification. |
| Additional Investigators |
