Background and objective: Neurodegenerative diseases like Parkinson’s disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [¹⁸F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ).

Methods: We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination’s validity, we analyze FDG-PET data of Parkinson’s disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists.

Results: One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients.

Conclusion: We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully.

背景与目的：帕金森氏病等神经退行性疾病通常需要花费数年时间，才能根据临床依据对其进行可靠诊断。MRI等成像技术可用于检测解剖（结构）病理变化。但是，这些更改通常仅在开发后期才能看到。通过[ ¹⁸F]氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）可以提供有用的信息，但其解释更为困难。结果表明，与其他统计技术相比，规模化子档案模型主成分分析（SSM / PCA）提供了更多有用的信息。我们的目标是通过结合SSM / PCA和基于原型的广义矩阵学习矢量量化（GMLVQ）进一步提高性能。

方法：我们将SSM / PCA和GMLVQ组合使用作为分类器。为了证明组合的有效性，我们分析了在欧洲三个不同的神经影像学中心收集的帕金森氏病（PD）患者的FDG-PET数据。我们通过执行十次重复十次交叉验证来确定诊断性能。此外，还包括数据的判别可视化。通过利用SSM / PCA的线性，GMLVQ的原型和相关性被转换回原始的体素空间。然后由三位神经科医师评估了所得的原型和相关性概况。

结果：一项重要发现是，判别可视化可以帮助识别与疾病相关的特征以及因中心特定因素而引起的差异。其次，神经科医生评估了该方法的可解释性，并确认原型与PD患者的已知活动特征相似。

结论：我们已经表明，提出的SSM / PCA和GMLVQ的组合可以提供有用的手段，以评估和更好地理解PD患者和HCs的FDG-PET数据的特征差异。根据医学专家的评估和我们的计算分析结果，我们得出结论，成功地朝着诊断支持系统迈出了第一步。