Name
Abstract | ||
|---|---|---|
Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression.Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database.Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices.Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1089/brain.2020.0903 | BRAIN CONNECTIVITY |
Keywords | DocType | Volume |
aging, Alzheimer's disease, brain connectivity, conductance, diffusion MRI | Journal | 11 |
Issue | ISSN | Citations |
7 | 2158-0014 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aina Frau-Pascual | 1 | 0 | 0.34 |
| Jean Augustinack | 2 | 0 | 0.34 |
| Divya Varadarajan | 3 | 0 | 0.34 |
| Anastasia Yendiki | 4 | 0 | 0.34 |
| David H Salat | 5 | 0 | 0.34 |
| Bruce Fischl | 6 | 0 | 0.34 |
| Iman Aganj | 7 | 195 | 18.93 |