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: | Maryam Alsameen |
| Institution: | NIH |
| Department: | NIA |
| Country: | |
| Proposed Analysis: | Investigating the sensitivity of NODDI and sTE-NODDI of capturing differences in axonal density in mild cognitive impairment and Alzheimer’s disease Maryam H. Alsameen and Mustapha Bouhrara MRPAD Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH Postmortem histological investigations have shown that cerebral tissue undergoes continuous microstructural and architectural changes throughout the lifespan. It has been suggested that axonal degeneration is among the main sequelae of aging and several age-related disorders including mild cognitive impairment (MCI) and Alzheimer’s disease (AD), with concomitant motor and cognitive decline. Neurite orientation dispersion and density imaging (NODDI) is a magnetic resonance imaging technique that provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). NODDI is based on a multicompartmental signal model of water diffusion incorporating intracellular water, extracellular water, and a third compartment representing the isotropically diffusing water from the cerebrospinal fluid (CSF) volume. Although has gained rapid popularity and has been used extensively in studies of cerebral aging and degeneration, NODDI has recently been criticized, as it greatly overestimates the CSF water fraction while simultaneously providing physiologically unrealistic high NDI values in white matter (WM). Furthermore, it has recently been shown that derived NDI values are echo time (TE)-dependent. These limitations are believed to be due to the underlying assumption that all compartments exhibit similar transverse relaxation (T2) values in the original NODDI signal model. Although two successful approaches have been introduced to overcome these issues, they drastically extend the scan time, making them impractical in clinical setting. In our lab, we introduced a modification of the NODDI signal model, named single echo-time NODDI (STE-NODDI), that provides improved and TE-independent values of NDI and ODI with no extension of the scan time (paper provenly accepted in NeuroImage). Our approach is based on the modification of the NODDI signal model such that fiso is provided as an input (i.e., known) value in each voxel, instead of being estimated along with NDI and ODI. Here, the fiso map is derived from the NODDI T2-weighted image obtained at b-value of 0 s/mm2 using the hidden Markov random field model and the expectation-maximization algorithm, known as FAST as implemented in the FSL software. This fitting bicomponent model replaces the original tricomponent model used in NODDI; this reduction of the parameter space provides parameter estimates with higher accuracy and precision. Using the ADNI multishell diffusion images, our main goal is to conduct a voxel-wise analysis of differences in NDI or ODI between control, MCI, and AD subjects. Results derived using our approaches, SE-NODDI, and the original NODDI approach will be compared. We conjecture that sTE-NODDI will allow a better depiction of NDI differences between these three groups. To this end, we will be grateful to have access to the ADNI multishell diffusion data along with the structural images (MPRAGE and/or FLAIR) for image segmentation and registration, and the demographics and cognitive status of each participant. |
| Additional Investigators | |
| Investigator's Name: | Mustapha Bouhrara |
| Proposed Analysis: | Investigating the sensitivity of NODDI and sTE-NODDI of capturing differences in axonal density in mild cognitive impairment and Alzheimer’s disease Maryam H. Alsameen and Mustapha Bouhrara MRPAD Unit, Laboratory of Clinical Investigation, National Institute on Aging, NIH Postmortem histological investigations have shown that cerebral tissue undergoes continuous microstructural and architectural changes throughout the lifespan. It has been suggested that axonal degeneration is among the main sequelae of aging and several age-related disorders including mild cognitive impairment (MCI) and Alzheimer’s disease (AD), with concomitant motor and cognitive decline. Neurite orientation dispersion and density imaging (NODDI) is a magnetic resonance imaging technique that provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). NODDI is based on a multicompartmental signal model of water diffusion incorporating intracellular water, extracellular water, and a third compartment representing the isotropically diffusing water from the cerebrospinal fluid (CSF) volume. Although has gained rapid popularity and has been used extensively in studies of cerebral aging and degeneration, NODDI has recently been criticized, as it greatly overestimates the CSF water fraction while simultaneously providing physiologically unrealistic high NDI values in white matter (WM). Furthermore, it has recently been shown that derived NDI values are echo time (TE)-dependent. These limitations are believed to be due to the underlying assumption that all compartments exhibit similar transverse relaxation (T2) values in the original NODDI signal model. Although two successful approaches have been introduced to overcome these issues, they drastically extend the scan time, making them impractical in clinical setting. In our lab, we introduced a modification of the NODDI signal model, named single echo-time NODDI (STE-NODDI), that provides improved and TE-independent values of NDI and ODI with no extension of the scan time (paper provenly accepted in NeuroImage). Our approach is based on the modification of the NODDI signal model such that fiso is provided as an input (i.e., known) value in each voxel, instead of being estimated along with NDI and ODI. Here, the fiso map is derived from the NODDI T2-weighted image obtained at b-value of 0 s/mm2 using the hidden Markov random field model and the expectation-maximization algorithm, known as FAST as implemented in the FSL software. This fitting bicomponent model replaces the original tricomponent model used in NODDI; this reduction of the parameter space provides parameter estimates with higher accuracy and precision. Using the ADNI multishell diffusion images, our main goal is to conduct a voxel-wise analysis of differences in NDI or ODI between control, MCI, and AD subjects. Results derived using our approaches, SE-NODDI, and the original NODDI approach will be compared. We conjecture that sTE-NODDI will allow a better depiction of NDI differences between these three groups. To this end, we will be grateful to have access to the ADNI multishell diffusion data along with the structural images (MPRAGE and/or FLAIR) for image segmentation and registration, and the demographics and cognitive status of each participant. |
