Chaovalitwongse W Art, Won Daehan, Seref Onur, Borghesani Paul, Askren M Katie, Willis Sherry, Grabowski Thomas J
IEEE Trans Neural Syst Rehabil Eng. 2017 Jul;25(7):1079-1089. doi: 10.1109/TNSRE.2017.2679056. Epub 2017 Mar 7.
The rapid aging of the world's population is causing an increase in the prevalence of cognitive decline and degenerative brain disease in the elderly. Current diagnoses of amnestic and nonamnestic mild cognitive impairment, which may represent early stage Alzheimer's disease or related degenerative conditions, are based on clinical grounds. The recent emergence of advanced network analyses of functional magnetic resonance imaging (fMRI) data taken at cognitive rest has provided insight that declining functional connectivity of the default mode network (DMN) may be correlated with neurological disorders, and particularly prodromal Alzheimer's disease. The goal of this paper is to develop a network analysis technique using fMRI data to characterize transition stages from healthy brain aging to cognitive decline. Previous studies primarily focused on inter-nodal connectivity of the DMN and often assume functional homogeneity within each DMN region. In this paper, we develop a technique that focuses on identifying critical intra-nodal DMN connectivity by incorporating sparsity into connectivity modeling of the k -cardinality tree (KCT) problem. Most biological networks are efficient and formed by sparse connections, and the KCT can potentially reveal sparse connectivity patterns that are biologically informative. The KCT problem is NP-hard, and existing solution approaches are mostly heuristic. Mathematical formulations of the KCT problem in the literature are not compact and do not provide good solution bounds. This paper presents new KCT formulations and a fast heuristic approach to efficiently solve the KCT models for large DMN regions. The results in this paper demonstrate that traditional fMRI group analysis on DMN regions cannot detect any statistically significant connectivity differences between normal aging and cognitively impaired subjects in DMN regions, and the proposed KCT approaches are more sensitive than the state-of-the-art regional homogeneity approach in detecting significant differences in both left and right medial temporal regions of the DMN.
全球人口的快速老龄化导致老年人认知能力下降和退行性脑疾病的患病率上升。目前对遗忘型和非遗忘型轻度认知障碍的诊断（可能代表早期阿尔茨海默病或相关退行性疾病）基于临床依据。最近，对认知静息状态下的功能磁共振成像（fMRI）数据进行的高级网络分析表明，默认模式网络（DMN）功能连接性下降可能与神经疾病相关，尤其是前驱期阿尔茨海默病。本文的目标是开发一种利用fMRI数据的网络分析技术，以表征从健康脑老化到认知衰退的过渡阶段。以往的研究主要集中在DMN的节点间连接性，并且通常假设每个DMN区域内功能同质。在本文中，我们开发了一种技术，通过将稀疏性纳入k基数树（KCT）问题的连接性建模，专注于识别关键的节点内DMN连接性。大多数生物网络是高效的，由稀疏连接形成，KCT可能揭示具有生物学信息的稀疏连接模式。KCT问题是NP难问题，现有解决方案大多是启发式的。文献中KCT问题的数学公式不紧凑，也没有提供良好的解边界。本文提出了新的KCT公式和一种快速启发式方法，以有效地求解大DMN区域的KCT模型。本文的结果表明，对DMN区域进行传统的fMRI组分析无法检测到正常老化和认知受损受试者在DMN区域之间的任何统计学显著连接性差异，并且所提出的KCT方法在检测DMN左右内侧颞叶区域的显著差异方面比现有最先进的区域同质性方法更敏感。
