Rubbert Christian, Mathys Christian, Jockwitz Christiane, Hartmann Christian J, Eickhoff Simon B, Hoffstaedter Felix, Caspers Svenja, Eickhoff Claudia R, Sigl Benjamin, Teichert Nikolas A, Südmeyer Martin, Turowski Bernd, Schnitzler Alfons, Caspers Julian
1University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany.
2Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, Germany.
Br J Radiol. 2019 Sep;92(1101):20180886. doi: 10.1259/bjr.20180886. Epub 2019 May 14.
Evaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI).
Whole-brain rs-fMRI (EPI, TR = 2.2 s, TE = 30 ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1 mm, acquisition time ≈ 11 min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance.
Over the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 - 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 - 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 - 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks.
A rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting.
Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.
评估一种基于数据驱动和模型的分类方法，该方法基于全脑静息态功能磁共振成像（rs-fMRI）中的网络间连接性，以区分特发性帕金森病（PD）患者与健康对照（HC）。
对42例处于药物未服用状态的PD患者和47例年龄及性别匹配的HC进行全脑rs-fMRI评估（回波平面成像，重复时间=2.2秒，回波时间=30毫秒，翻转角=90°，分辨率=3.1×3.1×3.1毫米，采集时间≈11分钟）。基于两个队列在不同粒度水平的标准功能网络架构（人类连接组计划：15/25/50/100/200个网络；1000大脑计划：15/25/50/70个网络），为每个受试者计算基于完全和L2正则化偏相关测量的网络间连接性。使用具有10个外层和10个内层折的嵌套交叉验证（CV）在相关矩阵上训练一个增强逻辑回归模型，以进行无偏性能估计，并将标准功能网络架构和相关类型视为超参数。增强迭代次数固定为100。在内层折上具有最高平均准确率的模型使用整个数据集上的非嵌套10折20次重复CV进行训练，以确定特征重要性。
在外层折上，平均准确率为76.2%（中位数77.8%，标准差18.2，四分位距69.4 - 87.1%）。平均敏感性为81%（中位数80%，标准差21.1，四分位距75 - 100%），平均特异性为72.7%（中位数75%，标准差20.4，四分位距66.7 - 80%）。使用完全相关性的1000大脑计划50网络分割在内层折上表现最佳。顶级特征主要包括感觉运动以及感觉网络。
一种基于rs-fMRI全脑连接性、数据驱动和模型的方法，用于区分PD患者与健康对照，显示出非常好的准确率和高敏感性。鉴于该方法的高敏感性，它可能在筛查环境中有用。
静息态功能磁共振成像可能被证明是一种用于神经退行性疾病的有价值的非侵入性神经影像学生物标志物。当前基于模型的数据驱动方法，利用全脑网络间连接性来区分帕金森病患者与健康对照，显示出有前景的结果，具有非常好的准确率和非常高的敏感性。
