Mofrad Samaneh Abolpour, Lundervold Arvid, Lundervold Alexander Selvikvåg
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Postbox 7030, 5020 Bergen, Norway; The Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
The Neural Networks and Microcircuits Research Group, Department of Biomedicine, University of Bergen, Bergen, Norway; The Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
Comput Med Imaging Graph. 2021 Jun;90:101910. doi: 10.1016/j.compmedimag.2021.101910. Epub 2021 Apr 2.
We present a framework for constructing predictive models of cognitive decline from longitudinal MRI examinations, based on mixed effects models and machine learning. We apply the framework to detect conversion from cognitively normal (CN) to mild cognitive impairment (MCI) and from MCI to Alzheimer's disease (AD), using a large collection of subjects sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Aging (AIBL). We extract subcortical segmentation and cortical parcellation from corresponding T1-weighted images using FreeSurfer v.6.0, select bilateral 3D regions of interest relevant to neurodegeneration/dementia, and fit their longitudinal volume trajectories using linear mixed effects models. Features describing these model-based trajectories are then used to train an ensemble of machine learning classifiers to distinguish stable CN from converters to MCI, and stable MCI from converters to AD. On separate test sets the models achieved an average of accuracy/precision/recall score of 69/73/60% for converted to MCI and 75/74/77% for converted to AD, illustrating the framework's ability to extract predictive imaging-based biomarkers from routine T1-weighted MRI acquisitions.
我们提出了一个基于混合效应模型和机器学习，从纵向MRI检查构建认知衰退预测模型的框架。我们将该框架应用于检测从认知正常（CN）到轻度认知障碍（MCI）以及从MCI到阿尔茨海默病（AD）的转变，使用了来自阿尔茨海默病神经成像倡议（ADNI）和澳大利亚衰老成像、生物标志物和生活方式旗舰研究（AIBL）的大量受试者。我们使用FreeSurfer v.6.0从相应的T1加权图像中提取皮质下分割和皮质分区，选择与神经退行性变/痴呆相关的双侧3D感兴趣区域，并使用线性混合效应模型拟合它们的纵向体积轨迹。然后，描述这些基于模型轨迹的特征被用于训练一组机器学习分类器，以区分稳定的CN与向MCI转变者，以及稳定的MCI与向AD转变者。在单独的测试集上，模型对于转变为MCI的平均准确率/精确率/召回率得分分别为69/73/60%，对于转变为AD的平均准确率/精确率/召回率得分分别为75/74/77%，这说明了该框架能够从常规T1加权MRI采集中提取基于成像的预测性生物标志物。
