ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Paul Dufort |
| Institution: | University Health Network |
| Department: | Joint Department of Medical Imaging |
| Country: | |
| Proposed Analysis: | The vast majority of current research into the dynamics and connectivity of resting state fMRI data implicitly assumes a linear, multivariate normal model for the overall pattern of the BOLD signal. Very recently, some groups have begun to explore deviations from this model in order to reveal further as yet unidentified structure that is not consistent with this simple representation. Our proposed work is the latest in the evolution of these recent efforts. Its importance derives from the fact that every robust, repeatable pattern that is found in fMRI data of healthy controls provides a potential new biomarker for the detection of disease states. Our goal is to develop new methods for parsing and understanding the dynamical patterns observed in the BOLD signal of both healthy controls and AD patients, and ultimately to use the insights gained in this process to identify robust functional signatures of brains developing AD both earlier and more accurately than is currently possible using conventional structural MRI classifier techniques. Our method will be to discover and dissect predictive dynamical rules that are capable of anticipating the fluctuating changes in the resting state BOLD signal using novel machine learning approaches. Our hypothesis is that the rules that best predict the spatiotemporal patterns of the BOLD signal from one measurement to the next will be different in AD patients versus healthy controls. Our primary interest at the outset will be in the fMRI data that we understand will be (or is already?) available from phase 2 of the ADNI project. Thank you for your time. |
| Additional Investigators | |
| Investigator's Name: | David Mikulis |
| Proposed Analysis: | The vast majority of current research into the dynamics and connectivity of resting state fMRI data implicitly assumes a linear, multivariate normal model for the overall pattern of the BOLD signal. Very recently, some groups have begun to explore deviations from this model in order to reveal further as yet unidentified structure that is not consistent with this simple representation. Our proposed work is the latest in the evolution of these recent efforts. Its importance derives from the fact that every robust, repeatable pattern that is found in fMRI data of healthy controls provides a potential new biomarker for the detection of disease states. Our goal is to develop new methods for parsing and understanding the dynamical patterns observed in the BOLD signal of both healthy controls and AD patients, and ultimately to use the insights gained in this process to identify robust functional signatures of brains developing AD both earlier and more accurately than is currently possible using conventional classifier techniques. Our method will be to discover and dissect predictive dynamical rules that are capable of anticipating the fluctuating changes in the resting state BOLD signal using novel machine learning techniques. Our hypothesis is that the rules that best predict the spatiotemporal patterns of the BOLD signal from one measurement to the next will be different in AD patients versus healthy controls. Our primary interest at the outset will be in the fMRI data that we understand will be (or is already?) available from phase 2 of the ADNI project. Thank you for your time. |
| Investigator's Name: | Adrian Crawley |
| Proposed Analysis: | The vast majority of current research into the dynamics and connectivity of resting state fMRI data implicitly assumes a linear, multivariate normal model for the overall pattern of the BOLD signal. Very recently, some groups have begun to explore deviations from this model in order to reveal further as yet unidentified structure that is not consistent with this simple representation. Our proposed work is the latest in the evolution of these recent efforts. Its importance derives from the fact that every robust, repeatable pattern that is found in fMRI data of healthy controls provides a potential new biomarker for the detection of disease states. Our goal is to develop new methods for parsing and understanding the dynamical patterns observed in the BOLD signal of both healthy controls and AD patients, and ultimately to use the insights gained in this process to identify robust functional signatures of brains developing AD both earlier and more accurately than is currently possible using conventional classifier techniques. Our method will be to discover and dissect predictive dynamical rules that are capable of anticipating the fluctuating changes in the resting state BOLD signal using novel machine learning techniques. Our hypothesis is that the rules that best predict the spatiotemporal patterns of the BOLD signal from one measurement to the next will be different in AD patients versus healthy controls. Our primary interest at the outset will be in the fMRI data that we understand will be (or is already?) available from phase 2 of the ADNI project. Thank you for your time. |