Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA; Program in Medical Neuroscience, Paul and Carole Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA.
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
Neuroimage Clin. 2019;22:101687. doi: 10.1016/j.nicl.2019.101687. Epub 2019 Jan 22.
Alzheimer's disease is considered a disconnection syndrome, motivating the use of brain network measures to detect changes in whole-brain resting state functional connectivity (FC). We investigated changes in FC within and among resting state networks (RSN) across four different stages in the Alzheimer's disease continuum. FC changes were examined in two independent cohorts of individuals (84 and 58 individuals, respectively) each comprising control, subjective cognitive decline, mild cognitive impairment and Alzheimer's dementia groups. For each participant, FC was computed as a matrix of Pearson correlations between pairs of time series from 278 gray matter brain regions. We determined significant differences in FC modular organization with two distinct approaches, network contingency analysis and multiresolution consensus clustering. Network contingency analysis identified RSN sub-blocks that differed significantly across clinical groups. Multiresolution consensus clustering identified differences in the stability of modules across multiple spatial scales. Significant modules were further tested for statistical association with memory and executive function cognitive domain scores. Across both analytic approaches and in both participant cohorts, the findings converged on a pattern of FC that varied systematically with diagnosis within the frontoparietal network (FP) and between the FP network and default mode network (DMN). Disturbances of modular organization were manifest as greater internal coherence of the FP network and stronger coupling between FP and DMN, resulting in less segregation of these two networks. Our findings suggest that the pattern of interactions within and between specific RSNs offers new insight into the functional disruption that occurs across the Alzheimer's disease spectrum.
阿尔茨海默病被认为是一种连接中断综合征，这促使人们使用脑网络测量来检测整个大脑静息状态功能连接（FC）的变化。我们研究了阿尔茨海默病连续体的四个不同阶段中静息状态网络（RSN）内和之间的 FC 变化。在两个独立的个体队列中检查了 FC 的变化（分别有 84 名和 58 名个体），每个队列都包括对照组、主观认知下降、轻度认知障碍和阿尔茨海默病痴呆组。对于每个参与者，FC 是通过计算来自 278 个灰质脑区的成对时间序列之间的 Pearson 相关系数矩阵来计算的。我们使用两种不同的方法，网络关联分析和多分辨率一致聚类，来确定 FC 模块化组织的显著差异。网络关联分析确定了在临床组之间存在显著差异的 RSN 子块。多分辨率一致聚类确定了模块在多个空间尺度上稳定性的差异。进一步对显著模块进行了统计关联测试，以评估其与记忆和执行功能认知域评分的关系。在这两种分析方法和两个参与者队列中，研究结果都集中在 FC 模式上，该模式在额顶网络（FP）内和 FP 网络与默认模式网络（DMN）之间的诊断中呈现出系统性变化。模块化组织的紊乱表现为 FP 网络的内部一致性增加，FP 和 DMN 之间的耦合增强，导致这两个网络的分离减少。我们的研究结果表明，特定 RSN 内和之间的相互作用模式为阿尔茨海默病谱中发生的功能障碍提供了新的见解。
