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: | Aristeidis Sotiras |
| Institution: | Washington University in St. Louis |
| Department: | Radiology and Institute for Informatics |
| Country: | |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity. |
| Additional Investigators | |
| Investigator's Name: | Tom Earnest |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity. |
| Investigator's Name: | Sung Min Ha |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity. |
| Investigator's Name: | Abdalla Bani |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity. |
| Investigator's Name: | Shuyang (Shawn) Fan |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity. |
| Investigator's Name: | John Lee |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity |
| Investigator's Name: | Shuyang Fan |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity |
| Investigator's Name: | Braden Yang |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity. UPDATED (2021-07-08) :Our proposed analysis remains as above. We used ADNI to obtain preliminary data and have recently obtained NIH R01 funding to carry out the proposed analyses. |
| Investigator's Name: | Deydeep Kothapalli |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity. |
| Investigator's Name: | Peiwang Liu |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity. |
| Investigator's Name: | Tyler Powell |
| Proposed Analysis: | Use of unsupervised machine learning techniques (Sotiras et al, Neuroimage 2015) to identify covariance patterns in processed PET and MR data (Sotiras et al, Alzheimer's & Dementia 2018,2019). Identify regional patterns that reflect earlies signs of disease and stage in vivo the participants (Grothe et al, Neurology 17; Sotiras et al, Alzheimer's & Dementia 2018,2019). Evaluate associations of regional patterns integrity with cognitive performance, clinical status and CSF biomarkers. Use of supervised machine learning techniques, such as Support Vector Machines and Deep Learning, to identify associations, which have high predictive power, of regional pattern integrity with cognitive performance and clinical outcomes. Use of advanced machine learning techniques (such as HYDRA (Varol, Sotiras, Davatzikos, 2017) to investigate disease heterogeneity. |
