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

Frontiers in Human Neuroscience

Human Neuroscience

Leonardo Pantoni¹  Riccardo Scheda²  Stefano Diciotti³  Mario Mascalchi⁴  Antonio Giorgio⁵  Nicola De Stefano⁵  Chiara Marzi⁶  Anna Poggesi⁷  Domenico Inzitari⁷  Emilia Salvadori⁷ 

[1] Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy;Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi, ” University of Bologna, Cesena, Italy;Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi, ” University of Bologna, Cesena, Italy;Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy;Department of Experimental and Clinical Biomedical Sciences “Mario Serio, ” University of Florence, Florence, Italy;Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and Network in Oncology (ISPRO), Florence, Italy;Department of Medicine, Surgery, and Neuroscience, University of Siena, Siena, Italy;Department of Statistics, Computer Science, Applications “Giuseppe Parenti, ” University of Florence, Florence, Italy;NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy;

关键词: dementia;    fractal dimension;    gray matter;    leukoaraiosis;    mild cognitive impairment;    MRI;    white matter;   

DOI  :  10.3389/fnhum.2023.1231513

 received in 2023-05-30, accepted in 2023-08-31,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundThe 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.MethodsSixty-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.ResultsAfter 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.DiscussionOur findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features.

【 授权许可】

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Copyright © 2023 Marzi, Scheda, Salvadori, Giorgio, De Stefano, Poggesi, Inzitari, Pantoni, Mascalchi and Diciotti.