Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data.
Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data.
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DOI：
10.1038/s41598-022-08231-y
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发表时间：
2022-03-11
影响因子：
4.6
通讯作者：
Yamaguchi A
中科院分区：
文献类型：
--
作者：
Jitsuishi T;Yamaguchi A
在轻度认知功能障碍（MCI）阶段进行干预对预防阿尔茨海默病（AD）具有重要意义。本研究的目的是寻找最佳的机器学习（ML）模型来分类早期和晚期MCI（EMCI和LMCI）亚型使用多模态MRI数据。首先，基于轨迹的空间统计（TBSS）分析显示胼胝体中与LMCI相关的白色物质变化。基于ROI的纤维束成像通过受影响的胼胝体纤维解决了连接的皮质区域。然后，我们准备了两个特征子集ML通过测量静息状态功能连接（TBSS-RSFC方法）和图论指标（TBSS-Graph方法）在这些皮层区域，分别。我们还准备了TBSS分析检测到的LMCI相关白色物质改变区域的扩散参数的特征子集。使用这些特征子集，我们训练和测试了多个ML模型，用于交叉验证的EMCI/LMCI分类。结果表明，采用扩散参数特征子集的集成ML模型（AdaBoost）取得了更好的性能，平均准确率为70%。对分类有用的脑区包括额叶、顶叶、胼胝体、扣带回、丘脑和丘脑区。我们的研究结果表明，使用扩散参数的最佳ML模型可能是有效的区分LMCI从EMCI受试者在AD的前驱期。
The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer’s disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of AD.
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影响因子：
4.3
作者：
Achard S;Bullmore E
通讯作者：
Bullmore E
DOI：
10.1073/pnas.0308627101
发表时间：
2004-03-30
影响因子：
11.1
作者：
Greicius, MD;Srivastava, G;Menon, V
通讯作者：
Menon, V
使用联合独立成分分析绘制阿尔茨海默病的联合灰质和白质减少图
影响因子：
2.5
作者：
Guo, Xiaojuan;Han, Yuan;Yao, Li
通讯作者：
Yao, Li
影响因子：
3.2
作者：
Gupta, Yubraj;Lama, Ramesh Kumar;Kwon, Goo-Rak
通讯作者：
Kwon, Goo-Rak
影响因子：
2.5
作者：
Corbo, Daniele;Caiazzo, Giuseppina;Esposito, Fabrizio
通讯作者：
Esposito, Fabrizio