AUTHOR=Marzi Chiara , Scheda Riccardo , Salvadori Emilia , Giorgio Antonio , De Stefano Nicola , Poggesi Anna , Inzitari Domenico , Pantoni Leonardo , Mascalchi Mario , Diciotti Stefano 

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

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2023.1231513

DOI=10.3389/fnhum.2023.1231513

ISSN=1662-5161

ABSTRACT=Background The relative contribution of changes of 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 examination and annual neuropsychological testing for two years. Diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological and several MRI features at baseline as predictors of the clinical transition. These 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 like 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 SHAP (SHapley Additive exPlanations) values to provide explainability to the machine learning model.
Results After two years, 18 (28.1%) participants had converted 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 dementia development in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more than other brain features.