Abstract
Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer’s disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer’s Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer’s disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This work was supported by the National Institute on Aging (R01 AG019771, P30 AG10133, K01 AG049050, R01 AG061788), the National Institute for Complementary and Integrative Health (R01 AT009036-03), the Indiana University Network Science Institute, the Department of Radiology and Imaging Sciences, the Indiana University Health-Indiana University School of Medicine Strategic Research Initiative, and the Indiana Clinical and Translational Sciences Institute (CTSI). Part of this research was also supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute, and in part by the Indiana METACyt Initiative. The Indiana METACyt Initiative at Indiana University was also supported in part by Lilly Endowment, Inc. This material is based upon work supported by the National Science Foundation under grant no. CNS-0521433.
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Footnotes
Revisions include additional supplementary analyses, and well as some additional elaborations on methods and results throughout the manuscript.
Data Availability
Data access can be obtained by contacting the Indiana Alzheimer's Disease Center Data Core.