Grosch Maximilian, Beyer Leonie, Lindner Magdalena, Kaiser Lena, Ahmadi Seyed-Ahmad, Stockbauer Anna, Bartenstein Peter, Dieterich Marianne, Brendel Matthias, Zwergal Andreas, Ziegler Sibylle
German Center for Vertigo and Balance Disorders, DSGZ, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistrasse 15, D-81377 Munich, Germany; Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany.
Department of Nuclear Medicine, University Hospital, LMU Munich, Munich Germany.
Neuroimage. 2021 Jul 15;235:118007. doi: 10.1016/j.neuroimage.2021.118007. Epub 2021 Apr 6.
Metabolic connectivity patterns on the basis of [F]-FDG positron emission tomography (PET) are used to depict complex cerebral network alterations in different neurological disorders and therefore may have the potential to support diagnostic decisions. In this study, we established a novel statistical classification method taking advantage of differential time-dependent states of whole-brain metabolic connectivity following unilateral labyrinthectomy (UL) in the rat and explored its classification accuracy. The dataset consisted of repeated [F]-FDG PET measurements at baseline and 1, 3, 7, and 15 days (= maximum of 5 classes) after UL with 17 rats per measurement day. Classification in different stages after UL was performed by determining connectivity patterns for the different classes by Pearson's correlation between uptake values in atlas-based segmented brain regions. Connections were fitted with a linear function, with which different thresholds on the correlation coefficient (r = [0.5, 0.85]) were investigated. Rats were classified by determining the congruence of their PET uptake pattern with the fitted connectivity patterns in the classes. Overall, the classification accuracy with this method was 84.3% for 3 classes, 75.0% for 4 classes, and 54.1% for 5 classes and outperformed random classification as well as machine learning classification on the same dataset. The optimal classification thresholds of the correlation coefficient and distance-to-fit were found to be |r| > 0.65 and d = 4 when using Siegel's slope estimator for fitting. This connectivity-based classification method can compete with machine learning classification and may have methodological advantages when applied to support PET-based diagnostic decisions in neurological network disorders (such as neurodegenerative syndromes).
基于[F]-FDG正电子发射断层扫描（PET）的代谢连接模式用于描绘不同神经疾病中复杂的脑网络改变，因此可能有潜力辅助诊断决策。在本研究中，我们利用大鼠单侧迷路切除术（UL）后全脑代谢连接的不同时间依赖性状态，建立了一种新的统计分类方法，并探索了其分类准确性。数据集包括在基线以及UL后1、3、7和15天（=最多5个类别）重复进行的[F]-FDG PET测量，每个测量日有17只大鼠。通过基于图谱分割的脑区摄取值之间的Pearson相关性确定不同类别的连接模式，对UL后不同阶段进行分类。连接用线性函数拟合，并研究了相关系数（r = [0.5, 0.85]）的不同阈值。通过确定大鼠PET摄取模式与类中拟合的连接模式的一致性对大鼠进行分类。总体而言，该方法对3个类别的分类准确率为84.3%，对4个类别的分类准确率为75.0%，对5个类别的分类准确率为54.1%，在同一数据集上优于随机分类以及机器学习分类。当使用Siegel斜率估计器进行拟合时，相关系数和拟合距离的最佳分类阈值分别为|r| > 0.65和d = 4。这种基于连接性的分类方法可以与机器学习分类相竞争，并且在应用于辅助基于PET的神经网络疾病（如神经退行性综合征）诊断决策时可能具有方法学优势。
