In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson's disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provide evidence of their validity.

中文翻译：

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帕金森病代谢网络的光谱引导稀疏逆协方差估计

在神经退行性疾病中，更清楚地了解潜在的异常网络有助于寻找有效的治疗目标和潜在的治疗方法。脑代谢的 [18F]-氟脱氧葡萄糖 (FDG) 正电子发射断层扫描 (PET) 成像数据反映了已知与神经活动直接相关的葡萄糖消耗的分布。在 FDG PET 静息状态代谢数据中，使用多元空间协方差的主成分分析在各种神经退行性疾病的组分析中确定了与疾病相关的特征模式。值得注意的是，在几种帕金森综合征中，已确定的帕金森病相关模式 (PDRP) 已在全球独立群体中反复验证为 PD 的成像生物标志物。尽管该网络的主要节点关联是已知的，但尚未完全了解其连接性。在这里，我们描述了一种通过直接在全脑数据的疾病相关 PC 分区层内执行图理论稀疏网络推导而不是通过在全脑稀疏表示中追溯搜索关联来阐明功能主成分 (PC) 网络连接的新方法。分别使用每个重叠 PC 分区层的稀疏逆协方差估计，与全脑数据分析中更多的空间模块化分割相比，每个层都检测到单个相干网络。使用这种方法，可以识别 PD 疾病网络的主要节点枢纽，并在基底神经节内清楚地区分它们的特征功能通路，中脑和顶叶区域。使用邻接矩阵的拉普拉斯谱分析进一步阐明网络关联。此外，图派生网络的特征向量中心性在区分 PD 与健康外部数据方面的先天判别能力提供了其有效性的证据。