Terpstra, Melissa

AbstractFrom 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 (N = 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 (N = 8) or dysexecutive (N = 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.

AbstractBackgroundDisruption of the default mode network (DMN) is thought to be a common feature of aging and across Alzheimer’s disease (AD) clinical phenotypes, while syndromic variability could be reflected in selective degeneration of macro‐scale networks optimally supporting specific mental abilities (Fig 1). As part of a collaborative effort between Mayo Clinic and the Human Connectome Project‐Aging (HCP‐A), we aimed to delineate a biomarker of global network failure across aging and the dysexecutive (dAD) and amnestic phenotypes of AD, and biomarkers of focal degeneration specific to each AD phenotype.MethodWe used a regression framework to assess the relationships between a marker of DMN dysfunction, the network failure quotient (NFQ), and factors relevant to AD such as age, cortical thickness (global cortical thickness, inferior parietal lobe), hippocampal volume, and cognition (episodic memory, cognitive flexibility) in the HCP‐A cohort, which consists of 725 non‐demented older adults. We then fit centile curves to the HCP‐A cohort to assess whether the NFQ and focal degeneration markers could distinguish dAD (n=10) and amnestic AD (n=8) patients from the HCP‐A cohort at the group‐ and individual‐level. Participants were scanned using the HCP‐A protocol, allowing for the acquisition of high‐resolution multi‐echo structural images and longer resting‐state connectivity acquisition time compared to traditional protocols.ResultThe NFQ related to age, cortical thickness, hippocampal volume, and cognition in the HCP‐A cohort. It also strongly discriminated dAD and amnestic AD from the HCP‐A cohort but did not discriminate between AD phenotypes. In contrast, markers of focal degeneration were phenotype‐specific; parieto‐frontal areas yielded largest effect sizes for dAD patients, and hippocampal and temporal areas best discriminated patients with amnestic AD (Fig 2).ConclusionThe NFQ may reflect shared system‐level pathophysiological mechanisms across aging and the dysexecutive and amnestic phenotypes of AD whereas focal degeneration markers highlight distinct pathognomonic processes across AD phenotypes. These findings suggest that inter‐individual variability in cognitive impairment in AD would be closely tied to modular, phenotype‐specific network degeneration combined with a common global functional disruption of the DMN. Such information has crucial implications for clinical biomarkers and to provide a deeper understanding of heterogeneity in AD.