| Title: |
Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis |
| Authors: |
Marzi C.; Scheda R.; Salvadori E.; Giorgio A.; De Stefano N.; Poggesi A.; Inzitari D.; Pantoni L.; Mascalchi M.; Diciotti S. |
| Contributors: |
Marzi, C.; Scheda, R.; Salvadori, E.; Giorgio, A.; De Stefano, N.; Poggesi, A.; Inzitari, D.; Pantoni, L.; Mascalchi, M.; Diciotti, S. |
| Publication Year: |
2023 |
| Collection: |
Università degli Studi di Siena: USiena air |
| Subject Terms: |
dementia; fractal dimension; gray matter; leukoaraiosi; mild cognitive impairment; MRI; white matter |
| Description: |
Background: The relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T2 hyperintensities in the cerebral WM, also known as leukoaraiosis. Methods: Sixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T2 hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T1 weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model. Results: After 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia. Discussion: Our findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
ELETTRONICO |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/37822707; info:eu-repo/semantics/altIdentifier/wos/WOS:001078025300001; volume:17; numberofpages:12; journal:FRONTIERS IN HUMAN NEUROSCIENCE; https://hdl.handle.net/11365/1282221; https://pmc.ncbi.nlm.nih.gov/articles/PMC10562576/ |
| DOI: |
10.3389/fnhum.2023.1231513 |
| DOI: |
10.3389/fnhum.2023.1231513/full |
| Availability: |
https://hdl.handle.net/11365/1282221; https://doi.org/10.3389/fnhum.2023.1231513; https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2023.1231513/full; https://pmc.ncbi.nlm.nih.gov/articles/PMC10562576/ |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.71C5522 |
| Database: |
BASE |