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: | Xiao Liu |
| Institution: | the Pennsylvania State University |
| Department: | Department of Biomedical engineering |
| Country: | |
| Proposed Analysis: | The overall objective of this proposal is to investigate the mechanism underlying Alzheimer’s disease (AD) by exploring cross-modality correlations among multimodal neuroimaging, clinical measures and biospecimen data collected by ADNI. We will also derive the biomarker for early detection of Alzheimer’s disease prior to serious symptoms. Specifically, by using the datasets from the ADNI, we have the following specific aims: 1. To identify specific functional magnetic resonance imaging (fMRI) marker based on temporal dynamics that differentiates the AD group, mild cognition impairment (MCI) group, and the normal healthy control group. Hypothesis: Temporal dynamics of recurring brain co-activation patterns (CAP) are tightly linked to AD pathological features. 2. To determine the role of arousal in accounting for the AD pathology. Hypothesis: The arousal level during day-time affects the occurrence and development of AD symptom. 3. To investigate the AD pathology on the basis of cross-modalities correspondence among neuroimaging, clinical measures, and biospecimen data. Hypothesis: Early impairments of brain structure and function related to AD occurs chronologically and can be revealed by multi-modal imaging and non-imaging data. We hypothesize that the causal relationships of those impairments are: the impairment of the coupling between gray matter activation and cerebrospinal fluid (CSF) (early brain functional impairment)->the enlargement of brain ventricle (unnormal brain atrophy)->the amyloid-beta and tau deposition in gray matter->memory deficit or dementia. 4. To predict the AD occurrence and development based on the AD markers. Hypothesis: The robust prediction model will be constructed based on various AD markers of present study and show its advantage in the accuracy of AD occurrence rate. |
| Additional Investigators | |
| Investigator's Name: | Feng Han |
| Proposed Analysis: | For the aim #3: 1. Calculate the baseline values and longitudinal changes of the coupling of gray matter and CSF fMRI signal, the volume of gray matter and white matter with structural MRI, the amyloid-beta and tau deposition (in gray matter) from positron emission tomography and a variety of behavior or symptom quantification, such as Mini-Mental State Exam (MMSE). 2. Check the correlation between the degree of the coupling of gray matter and CSF fMRI signal at baseline (or year 0) with the longitudinal changes of other modalities above. 3. Check the correlation between the baseline atrophy of gray matter and white matter with the longitudinal changes of gray matter deposition of amyloid-beta and tau, and various behavioral changes, like MMSE. 4. Summarize the relationship cross modalities based on the significant correlations. |
| Investigator's Name: | Yameng Gu |
| Proposed Analysis: | For the aim #3: Calculate the baseline values and longitudinal changes of the coupling of gray matter and CSF fMRI signal, the volume of gray matter and white matter with structural MRI, the amyloid-beta and tau deposition (in gray matter) from positron emission tomography and a variety of behavior or symptom quantification, such as Mini-Mental State Exam (MMSE).(collaborate with Feng Han) |
| Investigator's Name: | Jing Chen |
| Proposed Analysis: | For the SA #2: 1. Calculate a time course of arousal index by spatially correlating a global signal template, which has been shown to be related to brain arousal modulation previously. 2. Reveal the arousal level based on the averaged arousal index across each rsfMRI scanning. 3. Compare the arousal level difference among different groups of subjects. 4. Check the association between the AD symptoms and arousal level through correlating the arousal level (within AD group) with various pathologic features. |
| Investigator's Name: | Aaron Belkin-Rosen |
| Proposed Analysis: | For the SA #2: 1. Calculate a time course of arousal index by spatially correlating a global signal template, which has been shown to be related to brain arousal modulation previously. 2. Reveal the arousal level based on the averaged arousal index across each rsfMRI scanning. 3. Compare the arousal level difference among different groups of subjects. 4. Check the association between the AD symptoms and arousal level through correlating the arousal level (within AD group) with various pathologic features. |
