Marzi Chiara, Scheda Riccardo, Salvadori Emilia, Giorgio Antonio, De Stefano Nicola, Poggesi Anna, Inzitari Domenico, Pantoni Leonardo, Mascalchi Mario, Diciotti Stefano
Department of Statistics, Computer Science, Applications "Giuseppe Parenti, " University of Florence, Florence, Italy.
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi, " University of Bologna, Cesena, Italy.
Front Hum Neurosci. 2023 Sep 26;17:1231513. doi: 10.3389/fnhum.2023.1231513. eCollection 2023.
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 T hyperintensities in the cerebral WM, also known as leukoaraiosis.
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, T 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-T 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.
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.
Our 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.
脑白质(WM)和皮质灰质(GM)变化对轻度认知障碍(MCI)患者向痴呆转变的相对贡献尚未明确。在这项纵向研究中,我们旨在分析可能预测MCI患者以及脑白质T2高信号(也称为脑白质疏松症)患者向痴呆转变的MRI特征。
64名患有MCI和中度至重度脑白质疏松症的参与者在2年期间接受了基线MRI检查和年度神经心理学测试。痴呆的诊断基于既定标准。我们将基线时的人口统计学、神经心理学和一些MRI特征评估为临床转变的预测指标。MRI特征包括视觉评估的MRI特征,如腔隙、微出血和扩张的血管周围间隙的数量,以及定量MRI特征,如皮质灰质、海马体的体积、T2高信号和脑白质的扩散指数。此外,我们还研究了高级定量特征,如皮质灰质和白质的分形维数(FD),它代表了从三维T加权图像得出的组织结构复杂性指数。为了评估向痴呆转变的预测情况,我们采用了基于XGBoost的机器学习系统,并使用Shapley加性解释(SHAP)值为机器学习模型提供可解释性。
2年后,18名(28.1%)参与者从MCI转变为痴呆。接受者操作特征曲线下面积为0.69(0.53,0.85)[平均值(90%置信区间)]。皮质灰质分形维数成为转变的首要预测特征。此外,在预测向痴呆的转变方面,综合定量神经影像特征优于视觉评估的MRI特征。
我们的研究结果证实了皮质灰质和白质变化在MCI和脑白质疏松症患者痴呆发展中作为潜在因素的互补作用。分形维数似乎是一种可能比其他脑特征更敏感的生物标志物。