Corriveau-Lecavalier Nick, Gunter Jeffrey L, Kamykowski Michael, Dicks Ellen, Botha Hugo, Kremers Walter K, Graff-Radford Jonathan, Wiepert Daniela A, Schwarz Christopher G, Yacoub Essa, Knopman David S, Boeve Bradley F, Ugurbil Kamil, Petersen Ronald C, Jack Clifford R, Terpstra Melissa J, Jones David T
Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA.
Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Brain Commun. 2023 Mar 8;5(2):fcad058. doi: 10.1093/braincomms/fcad058. eCollection 2023.
From a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer's disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals ( = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer's disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic ( = 8) or dysexecutive ( = 10) Alzheimer's disease from the normative cohort at the patient level, as well as between Alzheimer's disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer's disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer's disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer's disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer's disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer's disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer's disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.
从复杂系统的角度来看，神经退行性疾病所呈现的临床综合征被认为是由错误折叠蛋白聚集体与协调认知现象基础功能运作的大规模网络失衡之间的多尺度相互作用所导致的。在阿尔茨海默病的所有综合征表现中，默认模式网络与年龄相关的破坏会因淀粉样蛋白沉积而加速。相反，综合征的变异性可能反映了支持特定认知能力的模块化网络的选择性神经退行性变。在本研究中，我们利用人类连接组计划 - 衰老队列（n = 724）中未患痴呆症个体的广度作为一个正常队列，来评估阿尔茨海默病中默认模式网络功能障碍生物标志物——网络失败商数在整个衰老范围内的稳健性。然后，我们在患者层面以及阿尔茨海默病不同表型之间，检验了网络失败商数和神经退行性变的局灶性标志物区分遗忘型（n = 8）或执行功能障碍型（n = 10）阿尔茨海默病患者与正常队列的能力。重要的是，所有参与者和患者均使用人类连接组计划 - 衰老方案进行扫描，从而能够获取高分辨率结构成像以及更长的静息态连接采集时间。通过一个回归框架，我们发现网络失败商数与正常人类连接组计划 - 衰老队列中的年龄、全脑和局灶性皮质厚度、海马体积以及认知相关，这重复了梅奥诊所衰老研究使用不同扫描方案得出的先前结果。然后，我们使用分位数曲线和组间比较来表明，网络失败商数通常能将执行功能障碍型和遗忘型阿尔茨海默病患者与正常队列区分开来。相比之下，局灶性神经退行性变标志物更具表型特异性，其中顶叶 - 额叶区域的神经退行性变与执行功能障碍型阿尔茨海默病相关，而海马和颞叶区域的神经退行性变与遗忘型阿尔茨海默病相关。利用一个大型正常队列和优化的成像采集方案，我们强调了一个反映衰老、执行功能障碍型和遗忘型阿尔茨海默病共有的系统水平病理生理机制的默认模式网络失败生物标志物，以及反映遗忘型和执行功能障碍型阿尔茨海默病表型不同特征性过程的局灶性神经退行性变生物标志物。这些发现提供了证据，表明阿尔茨海默病个体间认知障碍的变异性可能与模块化网络退化和默认模式网络破坏都有关。这些结果为推进认知衰老和退化的复杂系统方法、扩大可用于辅助诊断、监测疾病进展和为临床试验提供信息的生物标志物库提供了重要信息。
