Abstract
Graph theory has been extensively used to investigate brain network topology and its changes in disease cohorts. However, many graph theoretic analysis-based brain network studies focused on the shortest paths or, more generally, cost-efficiency. In this work, we use two new concepts, connectedness and 2-connectedness, to measure different global properties compared to the previously widely adopted ones. We apply them to unravel interesting characteristics in the brain, such as redundancy design and further conduct a time-varying brain functional network analysis for characterizing the progression of Alzheimer’s disease (AD). Specifically, we define different connectedness and 2-connectedness states and evaluate their dynamics in AD and its preclinical stage, mild cognitive impairment (MCI), compared to the normal controls (NC). Results indicate that, compared to MCI and NC, brain networks of AD tend to be more frequently connected at a sparse level. For MCI, we found that their brains are more likely to be 2-connected in the minimal connected state as well indicating increasing redundancy in brain connectivity. Such a redundant design could ensure maintained connectedness of the MCI’s brain network in the case that pathological damages break down any link or silenced any node, making it possible to preserve cognitive abilities. Our study suggests that the redundancy in the brain functional chronnectome could be altered in the preclinical stage of AD. The findings can be successfully replicated in a retest study and with an independent MCI dataset. Characterizing redundancy design in the brain chronnectome using connectedness and 2-connectedness analysis provides a unique viewpoint for understanding disease affected brain networks.
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Availability of Data and Material
The time series data from all the subjects as well as the calculated redundancy measurements that support our claims are publicly available at https://github.com/mghanba/Maryam Ghanbari Repository/tree/master, upon the manuscript is entering review process.
Code Availability
The software we used to calculate connectedness and 2-connectedness is SAGE 8.6 (https://www.sagemath.org). The core function for calculating dynamic redundancy statuses and their transitions are publicly available at https://github.com/mghanba/Maryam Ghanbari Repository/tree /master, upon the manuscript is entering review process.
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Funding
M.G. was supported by the National Institutes of Health grants (EB022880 and AG041721). Z.Z., L.-M.H., P.-T.Y. and D.S. were supported by the National Institutes of Health grant (EB022880). Y.H. and Y.S. were supported by National Natural Science Foundation of China (Grants 61633018, 31371007). H.Z. was supported by the National Institutes of Health grants (EB022880, AG041721, AG049371, and AG042599).
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H.Z. and D.S. designed and conceptualized the study and revised the manuscript. M.G. drafted and edited the manuscript, analyzed data, interpreted results. H.Z. played a major role in the interpretation of the results and revision of the manuscript. L.-M.H. analyzed the data and revised the manuscript. Z.Z. and P.-T.Y. analyzed data and revised the manuscript. Y.H. and Y.S. collected and analyzed part of the data and revised the manuscript. All authors read and approved the final manuscript.
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The experiments and data collection were approved by the local ethics committees, as mentioned in ADNI data sharing website http://ad ni.loni.usc.edu. For the Xuanwu hospital’s data, ethical approval has been obtained from the medical research ethics committee and institutional review board of XuanWu Hospital, Capital Medical University (approval number: [2014]011).
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Ghanbari, M., Zhou, Z., Hsu, LM. et al. Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer’s Disease. Neuroinform 20, 391–403 (2022). https://doi.org/10.1007/s12021-021-09554-3
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DOI: https://doi.org/10.1007/s12021-021-09554-3