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: | Sina Khezri |
| Institution: | TUMS |
| Department: | Neurosychology |
| Country: | |
| Proposed Analysis: | In this study, we will use ML algorithms conducted through python on Alzheimer's disease(AD) dataset to both classify the subjects and estimate mild cognitive impairment (MCI) to AD progression. The subjects can be classified into four different groups: 1. NC, 2. progressive MCI, 3. None-progressive MCI and 4. AD. Our first goal is to classify the subject into one of the mentioned groups. Then if the subject is in the progressive group, we will try to estimate the MCI to AD progress. To make this possible, different ML algorithms will be used. The data set which is going to be used is the ADNI which provides different variety of data sets including MRI images, PET scan images, biomarkers, cognitive tests, and the demographic data MRI and PET scan images are simply 2D images that the first one contains the structural and the second one includes the functional data of brain. the demographic data set contains sex, age, demographic data of the patients. For the classification problem, we will try deep learning methods including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other classic machine learning methods like K Nearest Neighbors (KNN), Randomized KNN, Support Vector Machine Classifiers (SVM), Naive Bayes, Decision Tree and Random Forest also will be used. After training the machine with these algorithms ensemble learning methods will be used to gain better results (that in our study is to have lower False-positive score). The next step is to calculate the probability which a MCI subject will convert to AD in a given time. Our prposed approach is using the sequence estimator methods like RNN to estimate the next statues given the previous ones. Each time we estimate the next status, the estimated status can be classified using the classifiers. Now in each step, we can measure the accuracy of classifiers and as it reaches a threshold, we can regard the status as converged to AD. The number of sequence estimators now can be regarded as the time at which an MCI will turn to AD. To make it clear, suppose that the data set of MRI images in ADNI are taken with 3-month steps. And the status of an imaginary subject will be AD after 4 sequence estimates. So, in fact, we can estimate that after 12 months (4 middle estimates * 3-month step for each) the status of the subject will turn to AD. |
| Additional Investigators | |
| Investigator's Name: | Ali Fele paranj |
| Proposed Analysis: | In this study, we will use ML algorithms conducted through python on Alzheimer's disease(AD) dataset to both classify the subjects and estimate mild cognitive impairment (MCI) to AD progression. The subjects can be classified into four different groups: 1. NC, 2. progressive MCI, 3. None-progressive MCI and 4. AD. Our first goal is to classify the subject into one of the mentioned groups. Then if the subject is in the progressive group, we will try to estimate the MCI to AD progress. To make this possible, different ML algorithms will be used. The data set which is going to be used is the ADNI which provides different variety of data sets including MRI images, PET scan images, biomarkers, cognitive tests, and the demographic data MRI and PET scan images are simply 2D images that the first one contains the structural and the second one includes the functional data of brain. the demographic data set contains sex, age, demographic data of the patients. For the classification problem, we will try deep learning methods including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other classic machine learning methods like K Nearest Neighbors (KNN), Randomized KNN, Support Vector Machine Classifiers (SVM), Naive Bayes, Decision Tree and Random Forest also will be used. After training the machine with these algorithms ensemble learning methods will be used to gain better results (that in our study is to have lower False-positive score). The next step is to calculate the probability which a MCI subject will convert to AD in a given time. Our prposed approach is using the sequence estimator methods like RNN to estimate the next statues given the previous ones. Each time we estimate the next status, the estimated status can be classified using the classifiers. Now in each step, we can measure the accuracy of classifiers and as it reaches a threshold, we can regard the status as converged to AD. The number of sequence estimators now can be regarded as the time at which an MCI will turn to AD. To make it clear, suppose that the data set of MRI images in ADNI are taken with 3-month steps. And the status of an imaginary subject will be AD after 4 sequence estimates. So, in fact, we can estimate that after 12 months (4 middle estimates * 3-month step for each) the status of the subject will turn to AD. |
| Investigator's Name: | Zahra Amjadi |
| Proposed Analysis: | In this study, we will use ML algorithms conducted through python on Alzheimer's disease(AD) dataset to both classify the subjects and estimate mild cognitive impairment (MCI) to AD progression. The subjects can be classified into four different groups: 1. NC, 2. progressive MCI, 3. None-progressive MCI and 4. AD. Our first goal is to classify the subject into one of the mentioned groups. Then if the subject is in the progressive group, we will try to estimate the MCI to AD progress. To make this possible, different ML algorithms will be used. The data set which is going to be used is the ADNI which provides different variety of data sets including MRI images, PET scan images, biomarkers, cognitive tests, and the demographic data MRI and PET scan images are simply 2D images that the first one contains the structural and the second one includes the functional data of brain. the demographic data set contains sex, age, demographic data of the patients. For the classification problem, we will try deep learning methods including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other classic machine learning methods like K Nearest Neighbors (KNN), Randomized KNN, Support Vector Machine Classifiers (SVM), Naive Bayes, Decision Tree and Random Forest also will be used. After training the machine with these algorithms ensemble learning methods will be used to gain better results (that in our study is to have lower False-positive score). The next step is to calculate the probability which a MCI subject will convert to AD in a given time. Our prposed approach is using the sequence estimator methods like RNN to estimate the next statues given the previous ones. Each time we estimate the next status, the estimated status can be classified using the classifiers. Now in each step, we can measure the accuracy of classifiers and as it reaches a threshold, we can regard the status as converged to AD. The number of sequence estimators now can be regarded as the time at which an MCI will turn to AD. To make it clear, suppose that the data set of MRI images in ADNI are taken with 3-month steps. And the status of an imaginary subject will be AD after 4 sequence estimates. So, in fact, we can estimate that after 12 months (4 middle estimates * 3-month step for each) the status of the subject will turn to AD. |
| Investigator's Name: | Mahdie Karimzade |
| Proposed Analysis: | In this study, we will use ML algorithms conducted through python on Alzheimer's disease(AD) dataset to both classify the subjects and estimate mild cognitive impairment (MCI) to AD progression. The subjects can be classified into four different groups: 1. NC, 2. progressive MCI, 3. None-progressive MCI and 4. AD. Our first goal is to classify the subject into one of the mentioned groups. Then if the subject is in the progressive group, we will try to estimate the MCI to AD progress. To make this possible, different ML algorithms will be used. The data set which is going to be used is the ADNI which provides different variety of data sets including MRI images, PET scan images, biomarkers, cognitive tests, and the demographic data MRI and PET scan images are simply 2D images that the first one contains the structural and the second one includes the functional data of brain. the demographic data set contains sex, age, demographic data of the patients. For the classification problem, we will try deep learning methods including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other classic machine learning methods like K Nearest Neighbors (KNN), Randomized KNN, Support Vector Machine Classifiers (SVM), Naive Bayes, Decision Tree and Random Forest also will be used. After training the machine with these algorithms ensemble learning methods will be used to gain better results (that in our study is to have lower False-positive score). The next step is to calculate the probability which a MCI subject will convert to AD in a given time. Our prposed approach is using the sequence estimator methods like RNN to estimate the next statues given the previous ones. Each time we estimate the next status, the estimated status can be classified using the classifiers. Now in each step, we can measure the accuracy of classifiers and as it reaches a threshold, we can regard the status as converged to AD. The number of sequence estimators now can be regarded as the time at which an MCI will turn to AD. To make it clear, suppose that the data set of MRI images in ADNI are taken with 3-month steps. And the status of an imaginary subject will be AD after 4 sequence estimates. So, in fact, we can estimate that after 12 months (4 middle estimates * 3-month step for each) the status of the subject will turn to AD. |
| Investigator's Name: | Narjes(Elahe) Mollaheidar |
| Proposed Analysis: | In this study, we will use ML algorithms conducted through python on Alzheimer's disease(AD) dataset to both classify the subjects and estimate mild cognitive impairment (MCI) to AD progression. The subjects can be classified into four different groups: 1. NC, 2. progressive MCI, 3. None-progressive MCI and 4. AD. Our first goal is to classify the subject into one of the mentioned groups. Then if the subject is in the progressive group, we will try to estimate the MCI to AD progress. To make this possible, different ML algorithms will be used. The data set which is going to be used is the ADNI which provides different variety of data sets including MRI images, PET scan images, biomarkers, cognitive tests, and the demographic data MRI and PET scan images are simply 2D images that the first one contains the structural and the second one includes the functional data of brain. the demographic data set contains sex, age, demographic data of the patients. For the classification problem, we will try deep learning methods including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other classic machine learning methods like K Nearest Neighbors (KNN), Randomized KNN, Support Vector Machine Classifiers (SVM), Naive Bayes, Decision Tree and Random Forest also will be used. After training the machine with these algorithms ensemble learning methods will be used to gain better results (that in our study is to have lower False-positive score). The next step is to calculate the probability which a MCI subject will convert to AD in a given time. Our prposed approach is using the sequence estimator methods like RNN to estimate the next statues given the previous ones. Each time we estimate the next status, the estimated status can be classified using the classifiers. Now in each step, we can measure the accuracy of classifiers and as it reaches a threshold, we can regard the status as converged to AD. The number of sequence estimators now can be regarded as the time at which an MCI will turn to AD. To make it clear, suppose that the data set of MRI images in ADNI are taken with 3-month steps. And the status of an imaginary subject will be AD after 4 sequence estimates. So, in fact, we can estimate that after 12 months (4 middle estimates * 3-month step for each) the status of the subject will turn to AD. |
| Investigator's Name: | Mahshad Fadaei |
| Proposed Analysis: | In this study, we will use ML algorithms conducted through python on Alzheimer's disease(AD) dataset to both classify the subjects and estimate mild cognitive impairment (MCI) to AD progression. The subjects can be classified into four different groups: 1. NC, 2. progressive MCI, 3. None-progressive MCI and 4. AD. Our first goal is to classify the subject into one of the mentioned groups. Then if the subject is in the progressive group, we will try to estimate the MCI to AD progress. To make this possible, different ML algorithms will be used. The data set which is going to be used is the ADNI which provides different variety of data sets including MRI images, PET scan images, biomarkers, cognitive tests, and the demographic data MRI and PET scan images are simply 2D images that the first one contains the structural and the second one includes the functional data of brain. the demographic data set contains sex, age, demographic data of the patients. For the classification problem, we will try deep learning methods including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other classic machine learning methods like K Nearest Neighbors (KNN), Randomized KNN, Support Vector Machine Classifiers (SVM), Naive Bayes, Decision Tree and Random Forest also will be used. After training the machine with these algorithms ensemble learning methods will be used to gain better results (that in our study is to have lower False-positive score). The next step is to calculate the probability which a MCI subject will convert to AD in a given time. Our prposed approach is using the sequence estimator methods like RNN to estimate the next statues given the previous ones. Each time we estimate the next status, the estimated status can be classified using the classifiers. Now in each step, we can measure the accuracy of classifiers and as it reaches a threshold, we can regard the status as converged to AD. The number of sequence estimators now can be regarded as the time at which an MCI will turn to AD. To make it clear, suppose that the data set of MRI images in ADNI are taken with 3-month steps. And the status of an imaginary subject will be AD after 4 sequence estimates. So, in fact, we can estimate that after 12 months (4 middle estimates * 3-month step for each) the status of the subject will turn to AD. |
| Investigator's Name: | Mahdie Esmaeili |
| Proposed Analysis: | In this study, we will use ML algorithms conducted through python on Alzheimer's disease(AD) dataset to both classify the subjects and estimate mild cognitive impairment (MCI) to AD progression. The subjects can be classified into four different groups: 1. NC, 2. progressive MCI, 3. None-progressive MCI and 4. AD. Our first goal is to classify the subject into one of the mentioned groups. Then if the subject is in the progressive group, we will try to estimate the MCI to AD progress. To make this possible, different ML algorithms will be used. The data set which is going to be used is the ADNI which provides different variety of data sets including MRI images, PET scan images, biomarkers, cognitive tests, and the demographic data MRI and PET scan images are simply 2D images that the first one contains the structural and the second one includes the functional data of brain. the demographic data set contains sex, age, demographic data of the patients. For the classification problem, we will try deep learning methods including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other classic machine learning methods like K Nearest Neighbors (KNN), Randomized KNN, Support Vector Machine Classifiers (SVM), Naive Bayes, Decision Tree and Random Forest also will be used. After training the machine with these algorithms ensemble learning methods will be used to gain better results (that in our study is to have lower False-positive score). The next step is to calculate the probability which a MCI subject will convert to AD in a given time. Our prposed approach is using the sequence estimator methods like RNN to estimate the next statues given the previous ones. Each time we estimate the next status, the estimated status can be classified using the classifiers. Now in each step, we can measure the accuracy of classifiers and as it reaches a threshold, we can regard the status as converged to AD. The number of sequence estimators now can be regarded as the time at which an MCI will turn to AD. To make it clear, suppose that the data set of MRI images in ADNI are taken with 3-month steps. And the status of an imaginary subject will be AD after 4 sequence estimates. So, in fact, we can estimate that after 12 months (4 middle estimates * 3-month step for each) the status of the subject will turn to AD. |
| Investigator's Name: | Tauseeq Fatema |
| Proposed Analysis: | In this study, we will use ML algorithms conducted through python on Alzheimer's disease(AD) dataset to both classify the subjects and estimate mild cognitive impairment (MCI) to AD progression. The subjects can be classified into four different groups: 1. NC, 2. progressive MCI, 3. None-progressive MCI and 4. AD. Our first goal is to classify the subject into one of the mentioned groups. Then if the subject is in the progressive group, we will try to estimate the MCI to AD progress. To make this possible, different ML algorithms will be used. The data set which is going to be used is the ADNI which provides different variety of data sets including MRI images, PET scan images, biomarkers, cognitive tests, and the demographic data MRI and PET scan images are simply 2D images that the first one contains the structural and the second one includes the functional data of brain. the demographic data set contains sex, age, demographic data of the patients. For the classification problem, we will try deep learning methods including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other classic machine learning methods like K Nearest Neighbors (KNN), Randomized KNN, Support Vector Machine Classifiers (SVM), Naive Bayes, Decision Tree and Random Forest also will be used. After training the machine with these algorithms ensemble learning methods will be used to gain better results (that in our study is to have lower False-positive score). The next step is to calculate the probability which a MCI subject will convert to AD in a given time. Our prposed approach is using the sequence estimator methods like RNN to estimate the next statues given the previous ones. Each time we estimate the next status, the estimated status can be classified using the classifiers. Now in each step, we can measure the accuracy of classifiers and as it reaches a threshold, we can regard the status as converged to AD. The number of sequence estimators now can be regarded as the time at which an MCI will turn to AD. To make it clear, suppose that the data set of MRI images in ADNI are taken with 3-month steps. And the status of an imaginary subject will be AD after 4 sequence estimates. So, in fact, we can estimate that after 12 months (4 middle estimates * 3-month step for each) the status of the subject will turn to AD. |
| Investigator's Name: | Abolfazl Torabi |
| Proposed Analysis: | In this study, we will use ML algorithms conducted through python on Alzheimer's disease(AD) dataset to both classify the subjects and estimate mild cognitive impairment (MCI) to AD progression. The subjects can be classified into four different groups: 1. NC, 2. progressive MCI, 3. None-progressive MCI and 4. AD. Our first goal is to classify the subject into one of the mentioned groups. Then if the subject is in the progressive group, we will try to estimate the MCI to AD progress. To make this possible, different ML algorithms will be used. The data set which is going to be used is the ADNI which provides different variety of data sets including MRI images, PET scan images, biomarkers, cognitive tests, and the demographic data MRI and PET scan images are simply 2D images that the first one contains the structural and the second one includes the functional data of brain. the demographic data set contains sex, age, demographic data of the patients. For the classification problem, we will try deep learning methods including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and other classic machine learning methods like K Nearest Neighbors (KNN), Randomized KNN, Support Vector Machine Classifiers (SVM), Naive Bayes, Decision Tree and Random Forest also will be used. After training the machine with these algorithms ensemble learning methods will be used to gain better results (that in our study is to have lower False-positive score). The next step is to calculate the probability which a MCI subject will convert to AD in a given time. Our prposed approach is using the sequence estimator methods like RNN to estimate the next statues given the previous ones. Each time we estimate the next status, the estimated status can be classified using the classifiers. Now in each step, we can measure the accuracy of classifiers and as it reaches a threshold, we can regard the status as converged to AD. The number of sequence estimators now can be regarded as the time at which an MCI will turn to AD. To make it clear, suppose that the data set of MRI images in ADNI are taken with 3-month steps. And the status of an imaginary subject will be AD after 4 sequence estimates. So, in fact, we can estimate that after 12 months (4 middle estimates * 3-month step for each) the status of the subject will turn to AD. |
