Guo Alexander Y, Laporte John P, Singh Kavita, Bae Jonghyun, Bergeron Keagan, de Rouen Angelique, Fox Noam Y, Zhang Nathan, Carino-Bazan Isabel, Faulkner Mary E, de Cabo Rafael, Benjamini Dan, Gong Zhaoyuan, Bouhrara Mustapha
National Institute on Aging National Institutes of Health Baltimore Maryland USA.
Alzheimers Dement (Amst). 2025 Sep 11;17(3):e70182. doi: 10.1002/dad2.70182. eCollection 2025 Jul-Sep.
Machine learning applied to neuroimaging can help with medical diagnosis and early detection by identifying biomarkers of subtle changes in brain structure and function. The effectiveness of advanced diffusion MRI (dMRI) methods for pre-dementia classification remains largely unexplored, particularly when combined with CSF biomarkers.
We implemented XGBoost machine learning models to evaluate the classification potential of dMRI parameters (derived using NODDI, C-NODDI, MAP, or SMI), CSF biomarkers of Alzheimer's pathology (Tau, pTau, Aβ42, Aβ40), and pairwise dMRI + CSF combinations in distinguishing cognitive normality from mild cognitive impairment.
MAP-RTAP (AUC = 0.78) and pTau/Aβ42 (AUC = 0.76) were the best performing individual biomarkers. Combining C-NDI derived using C-NODDI and Aβ42/Aβ40 achieved the highest performance (AUC = 0.84) and accuracy (0.84), while other combinations optimized either sensitivity (0.93) or specificity (0.88).
dMRI biomarkers demonstrate comparable performance to CSF biomarkers, with notable improvements achieved when combined. This study highlights dMRI's effectiveness for enhancing early AD detection.
Advanced multishell diffusion MRI provides equivalent performance as CSF biomarkers in classifying MCICombining diffusion MRI and CSF biomarkers improves classification performanceStatistical diffusion MRI models perform best when used individually to classify MCIThe pTau/Aβ42 ratio outperforms other individual CSF biomarkers in MCI diagnosisBiophysical diffusion MRI models achieve the best performance when combined with CSF data.
应用于神经影像学的机器学习可通过识别脑结构和功能细微变化的生物标志物，助力医学诊断和早期检测。先进的扩散磁共振成像（dMRI）方法在痴呆前期分类中的有效性在很大程度上仍未得到探索，尤其是与脑脊液生物标志物联合使用时。
我们实施了XGBoost机器学习模型，以评估dMRI参数（使用NODDI、C-NODDI、MAP或SMI得出）、阿尔茨海默病病理的脑脊液生物标志物（Tau、pTau、Aβ42、Aβ40）以及dMRI+脑脊液成对组合在区分认知正常与轻度认知障碍方面的分类潜力。
MAP-RTAP（AUC=0.78）和pTau/Aβ42（AUC=0.76）是表现最佳的单个生物标志物。将使用C-NODDI得出的C-NDI与Aβ42/Aβ40相结合，实现了最高性能（AUC=0.84）和准确率（0.84），而其他组合则优化了敏感性（0.93）或特异性（0.88）。
dMRI生物标志物表现出与脑脊液生物标志物相当的性能，联合使用时显著改善。本研究突出了dMRI在增强早期阿尔茨海默病检测方面的有效性。
先进的多壳层扩散磁共振成像在对轻度认知障碍进行分类时提供了与脑脊液生物标志物相当的性能。联合扩散磁共振成像和脑脊液生物标志物可提高分类性能。统计扩散磁共振成像模型单独用于对轻度认知障碍进行分类时表现最佳。在轻度认知障碍诊断中，pTau/Aβ42比值优于其他单个脑脊液生物标志物。生物物理扩散磁共振成像模型与脑脊液数据联合使用时性能最佳。
