Skip to main content

Advertisement

SpringerLink
Log in

Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer’s Disease

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig.1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

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.

References

  • Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neurosci, 26(1), 63–72.

    Article  CAS  Google Scholar 

  • Adeli, H., Ghosh-Dastidar, S., & Dadmehr, N. (2005a). Alzheimer’s disease and models of computation: Imaging, classification, and neural models. Journal of Alzheimer’s Disease, 7(3), 187–199.

    Article  PubMed  Google Scholar 

  • Adeli, H., Ghosh-Dastidar, S., & Dadmehr, N. (2005b). Alzheimer’s disease: Models of computation and analysis of EEGs. Clinical EEG and Neurosci, 36(3), 131–140.

    Article  Google Scholar 

  • Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018). Communication dynamics in complex brain networks. Nature Reviews Neuroscience, 19(1), 17.

    Article  CAS  Google Scholar 

  • Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17(10), 502–509.

    Article  PubMed  Google Scholar 

  • Binnewijzend, M. A., Schoonheim, M. M., Sanz-Arigita, E., Wink, A. M., van der Flier, W. M., Tolboom, N., Adriaanse, S. M., Damoiseaux, J. S., Scheltens, P., van Berckel, B. N., et al. (2012). Resting-state fMRI changes in alzheimer’s disease and mild cognitive impairment. Neurobiology of Aging, 33(9), 2018–2028.

    Article  PubMed  Google Scholar 

  • Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neurosci, 10(3), 186.

    Article  CAS  Google Scholar 

  • Cabeza, R., Albert, M., Belleville, S., Craik, F. I., Duarte, A., Grady, C. L., Lindenberger, U., Nyberg, L., Park, D. C., Reuter-Lorenz, P. A., & Rugg, M. D. (2018). Maintenance, reserve and compensation: The cognitive neuroscience of healthy ageing. Nature Reviews Neuroscience, 19(11), 701–710.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cascone, A. D., Langella, S., Sklerov, M., & Dayan, E. (2021). Frontoparietal network resilience is associated with protection against cognitive decline in Parkinson’s disease. Communications Biology, 4(1), 1–10.

    Article  Google Scholar 

  • Chang, C., & Glover, G. H. (2010). Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage, 50(1), 81–98.

    Article  PubMed  Google Scholar 

  • Chavez, M., Valencia, M., Latora, V., & Martinerie, J. (2010). Complex networks: New trends for the analysis of brain connectivity. International J of Bifurcation and Chaos, 20(06), 1677–1686.

    Article  Google Scholar 

  • Chen, G. Q., Sheng, C., Li, Y. X., Yu, Y., Wang, X. N., Sun, Y., Li, H. Y., Li, X. Y., Xie, Y. Y., & Han, Y. (2016). Neuroimaging basis in the conversion of aMCI patients with apoe-ε4 to ad: Study protocol of a prospective diagnostic trial. BMC Neurology, 16(1), 64.

    Article  PubMed  PubMed Central  Google Scholar 

  • Corson, F. (2010). Fluctuations and redundancy in optimal transport networks. Physical Review Letters, 104(4), 048703.

  • Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.

    Article  CAS  PubMed  Google Scholar 

  • Cribben, I., Haraldsdottir, R., Atlas, L. Y., Wager, T. D., & Lindquist, M. A. (2012). Dynamic connectivity regression: Determining state-related changes in brain connectivity. NeuroImage, 61(4), 907–920.

    Article  PubMed  Google Scholar 

  • Dai, Z., Lin, Q., Li, T., Wang, X., Yuan, H., Yu, X., He, Y., & Wang, H. (2019). Disrupted structural and functional brain networks in alzheimer’s disease. Neurobiology of Aging, 75, 71–82.

    Article  CAS  PubMed  Google Scholar 

  • Damaraju, E., Allen, E. A., Belger, A., Ford, J. M., McEwen, S., Mathalon, D., Mueller, B., Pearlson, G., Potkin, S., Preda A, et al. (2014). Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage: Clinical, 5, 298–308.

  • Demirtas, M., Tornador, C., Falcon, C., Lopez-Sola, M., Hernandez-Ribas, R., Pujol, J., Menchon, J. M., Ritter, P., Cardoner, N., Soriano-Mas, C., et al. (2016). Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder. Human Brain Mapping, 37(8), 2918–2930.

    Article  PubMed  PubMed Central  Google Scholar 

  • Dennis, E. L., & Thompson, P. M. (2014). Functional brain connectivity using fMRI in aging and alzheimer’s disease. Neuropsychology Review, 24(1), 49–62.

    Article  PubMed  PubMed Central  Google Scholar 

  • Di Lanzo, C., Marzetti, L., Zappasodi, F., De Vico Fallani, F., Pizzella, V. (2012). Redundancy as a graph-based index of frequency specific meg functional connectivity. Computational and mathematical methods in medicine 2012.

  • Edmonds, E. C., McDonald, C. R., Marshall, A., Thomas, K. R., Eppig, J., Weigand, A. J., Delano-Wood, L., Galasko, D. R., Salmon, D. P., Bondi, M. W., et al. (2019). Early versus late MCI : Improved MCI staging using a neuropsychological approach. Alzheim & Dem, 15(5), 699–708.

    Article  Google Scholar 

  • Fallani, F. D. V., Rodrigues, F. A., da Fontoura, C. L., Astolfi, L., Cincotti, F., Mattia, D., Salinari, S., & Babiloni, F. (2011). Multiple pathways analysis of brain functional networks from EEG signals: An application to real data. Brain Topography, 23(4), 344–354.

    Article  Google Scholar 

  • Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C., Ritchie, K., Broich, K., Belleville, S., Brodaty, H., Bennett, D., Chertkow, H., et al. (2006). Mild cognitive impairment. The Lancet, 367(9518), 1262–1270.

    Article  Google Scholar 

  • Härkegård, O., Glad, S. T. (2005). Resolving actuator redundancy—optimal control vs. control allocation. Automatica, 41(1), 137–144.

  • van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696.

    Article  PubMed  Google Scholar 

  • Hojjati, S. H., Ebrahimzadeh, A., Khazaee, A., Babajani-Feremi, A., Initiative, A. D. N., et al. (2017). Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. Journal of Neurosci Methods, 282, 69–80.

    Article  Google Scholar 

  • Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360–378.

    Article  PubMed  Google Scholar 

  • Jack, C. R., Jr., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., Whitwell, L., J, Ward C, et al. (2008). The alzheimer’s disease neuroimaging initiative (ADNI ): MRI methods. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 27(4), 685–691.

    Article  Google Scholar 

  • Kambeitz, J., Kambeitz-Ilankovic, L., Cabral, C., Dwyer, D. B., Calhoun, V. D., Van Den Heuvel, M. P. (2016). Aberrant functional whole-brain network architecture in patients with schizophrenia: a meta-analysis. Schizophrenia Bulletin, 42(suppl_1), S13-S21.

  • Karwowski, W., Vasheghani Farahani, F., & Lighthall, N. (2019). Application of graph theory for identifying connectivity patterns in human brain networks: A systematic review. Frontiers in Neurosci, 13, 585.

    Article  Google Scholar 

  • Kasthurirathna, D., Piraveenan, M., & Thedchanamoorthy, G. (2013). On the influence of topological characteristics on robustness of complex networks. Journal of Artificial Intelligence and Soft Computing Research, 3(2), 89–100.

    Article  Google Scholar 

  • Latora, V., Marchiori, M. (2001), Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.

  • Leonardi, N., & Van De Ville, D. (2015). On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage, 104, 430–436.

    Article  PubMed  Google Scholar 

  • Lindquist, M. A., Xu, Y., Nebel, M. B., & Caffo, B. S. (2014). Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach. NeuroImage, 101, 531–546.

    Article  PubMed  Google Scholar 

  • Ma, X., Jiang, G., Fu, S., Fang, J., Wu, Y., Liu, M., Xu, G., & Wang, T. (2018). Enhanced network efficiency of functional brain networks in primary insomnia patients. Frontiers in Psychiatry, 9, 46.

    Article  PubMed  PubMed Central  Google Scholar 

  • Marusak, H. A., Calhoun, V. D., Brown, S., Crespo, L. M., Sala-Hamrick, K., Gotlib, I. H., & Thomason, M. E. (2017). Dynamic functional connectivity of neurocognitive networks in children. Human Brain Mapping, 38(1), 97–108.

    Article  PubMed  Google Scholar 

  • MATLAB, Version 9.3.0.713579. (R2017b)., The MathWorks Inc., Natick (2017).

  • Meier, J., Tewarie, P., & Van Mieghem, P. (2015). The union of shortest path trees of functional brain networks. Brain Connectivity, 5(9), 575–581.

    Article  PubMed  Google Scholar 

  • Misra, C., Fan, Y., & Davatzikos, C. (2009). Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to ad: Results from ADNI. NeuroImage, 44(4), 1415–1422.

    Article  PubMed  Google Scholar 

  • Musso, F., Brinkmeyer, J., Mobascher, A., Warbrick, T., Winterer, G. (2010). Spontaneous brain activity and EEG microstates. a novel EEG/fMRI analysis approach to explore resting-state networks. NeuroImage, 52(4), 1149–1161.

  • Newman, M. E. (2002). Assortative mixing in networks. Physical Review Letters, 89(20), 208701.

  • Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582.

    Article  CAS  Google Scholar 

  • Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3), 183–194.

    Article  CAS  PubMed  Google Scholar 

  • Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001a). Current concepts in mild cognitive impairment. Archives of Neurol, 58(12), 1985–1992.

    Article  CAS  Google Scholar 

  • Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001b). Current concepts in mild cognitive impairment. Archives of Neurology, 58(12), 1985–1992.

    Article  CAS  PubMed  Google Scholar 

  • Petersen, R. C., Caracciolo, B., Brayne, C., Gauthier, S., Jelic, V., & Fratiglioni, L. (2014). Mild cognitive impairment: A concept in evolution. Journal of Internal Medicine, 275(3), 214–228.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341.

    Article  PubMed  Google Scholar 

  • Prasad, G., Joshi, S. H., Nir, T. M., Toga, A. W., Thompson, P. M., ADNI, et al. (2015). Brain connectivity and novel network measures for alzheimer’s disease classification. Neurobiology of Aging, 36, S121–S131.

    Article  PubMed  Google Scholar 

  • Quattrociocchi, W., Caldarelli, G., Scala, A. (2014a). Self-healing networks: redundancy and structure. PLoS One, 9(2).

  • Quattrociocchi, W., Caldarelli, G., Scala, A. (2014b). Self-healing networks: redundancy and structure. PloS One, 9(2), e87986.

  • Ravasz, E., Barabási, A. L. (2003). Hierarchical organization in complex networks. Physical Review E, 67(2), 026112.

  • Romero-Garcia, R., Atienza, M., & Cantero, J. L. (2016). Different scales of cortical organization are selectively targeted in the progression to alzheimer’s disease. International J of Neural Systems, 26(02), 1650003.

    Article  Google Scholar 

  • Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069.

    Article  PubMed  Google Scholar 

  • Sadiq, M. U., Langella, S., Giovanello, K. S., Mucha, P. J., Dayan, E. (2021). Accrual of functional redundancy along the lifespan and its effects on cognition. NeuroImage, 229, 117737.

  • Sakoğlu, Ü., Pearlson, G. D., Kiehl, K. A., Wang, Y. M., Michael, A. M., & Calhoun, V. D. (2010). A method for evaluating dynamic functional network connectivity and task-modulation: Application to schizophrenia. Magnetic Resonance Materials in Physics, Biology and Medicine, 23(5–6), 351–366.

    Article  Google Scholar 

  • Schwab, S., Afyouni, S., Chen, Y., Han, Z., Guo, Q., Dierks, T., Wahlund, L. O., Grieder, M. (2018). Functional connectivity alterations of the temporal lobe and hippocampus in semantic dementia and alzheimer’s disease. BioRxiv p 322131.

  • Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage, 82, 403–415.

    Article  CAS  PubMed  Google Scholar 

  • Sporns, O. (2013). Structure and function of complex brain networks. Dialogues in Clinical Neuroscience, 15(3), 247.

    Article  PubMed  PubMed Central  Google Scholar 

  • Stam, C. J., Jones, B., Nolte, G., Breakspear, M., & Scheltens, P. (2006). Small-world networks and functional connectivity in alzheimer’s disease. Cerebral Cortex, 17(1), 92–99.

    Article  PubMed  Google Scholar 

  • Steiglitz, K., Weiner, P., & Kleitman, D. (1969). The design of minimum-cost survivable networks. IEEE Transactions on Circuit Theory, 16(4), 455–460.

    Article  Google Scholar 

  • Supekar, K., Menon, V., Rubin, D., Musen, M., Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in alzheimer’s disease. PLoS Computational Biology, 4(6), e1000100.

  • Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015). Gretna: A graph theoretical network analysis toolbox for imaging connectomics. Frontiers in Human Neurosci, 9, 386.

    CAS  Google Scholar 

  • Wang, J. H., Zuo, X. N., Gohel, S., Milham, M. P., Biswal, B. B., He, Y. (2011). Graph theoretical analysis of functional brain networks: test-retest evaluation on short-and long-term resting-state functional MRI data. PloS One, 6(7).

  • White, D. R., Newman, M. (2001). Fast approximation algorithms for finding node-independent paths in networks. Santa Fe Institute Working Papers Series

  • Williams, N. J., Daly, I., & Nasuto, S. (2018). Markov model-based method to analyse time-varying networks in EEG task-related data. Frontiers in Computational Neuroscience, 12, 76.

    Article  PubMed  PubMed Central  Google Scholar 

  • Yan, C., & Zang, Y. (2010). DPARSF: A MATLAB toolbox for” pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neurosci, 4, 13.

    Google Scholar 

  • Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C., Jiang, T., ADNI, et al. (2010). Abnormal cortical networks in mild cognitive impairment and alzheimer’s disease. PLoS Computational Biology, 6(11), e1001006.

  • Yao, Z., Hu, B., Chen, X., Xie, Y., Gutknecht, J., & Majoe, D. (2018). Learning metabolic brain networks in MCI and AD by robustness and leave-one-out analysis: An FDG-PET study. American Journal of Alzheimer’s Disease & Other Dementias, 33(1), 42–54.

    Article  Google Scholar 

  • Yoo, S. W., Han, C. E., Shin, J. S., Seo, S. W., Na, D. L., Kaiser, M., Jeong, Y., & Seong, J. K. (2015). A network flow-based analysis of cognitive reserve in normal ageing and alzheimer’s disease. Scientific Reports, 5, 10057.

    Article  Google Scholar 

  • Yuan, H., Zotev, V., Phillips, R., Drevets, W. C., & Bodurka, J. (2012). Spatiotemporal dynamics of the brain at rest—exploring EEG microstates as electrophysiological signatures of bold resting state networks. NeuroImage, 60(4), 2062–2072.

    Article  PubMed  Google Scholar 

  • Zhou, Y., Ge, Y., & Dougherty, J. (2011). Small world network properties changes in mild cognitive impairment and early alzheimer’s disease. Alzheim & Dem: THe Journal of the Alzheimer’s Association, 7(4), S729.

    Google Scholar 

  • Zippo, A. G., Castiglioni, I., Borsa, V. M., & Biella, G. E. (2015). The compression flow as a measure to estimate the brain connectivity changes in resting state fMRI and 18FDG-PET alzheimer’s disease connectomes. Frontiers in Computational Neurosci, 9, 148.

    Article  Google Scholar 

Download references

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).

Author information

Author notes

  1. Han Zhang

    Present address: School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

  2. Dinggang Shen

    Present address: Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

Authors and Affiliations

  1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Maryam Ghanbari, Zhen Zhou, Li-Ming Hsu, Pew-Thian Yap, Han Zhang & Dinggang Shen

  2. Department of National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China

    Ying Han

  3. Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China

    Ying Han

  4. Beijing Institute of Geriatrics, Beijing, 100053, China

    Ying Han

  5. Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China

    Ying Han & Yu Sun

  6. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

    Dinggang Shen

Authors
  1. Maryam Ghanbari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li-Ming Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pew-Thian Yap
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Han Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dinggang Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding authors

Correspondence to Han Zhang or Dinggang Shen.

Ethics declarations

Ethics Approval

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).

Consent to Participate

Data used from ADNI is publicly available, so this is not applicable. For the Xuanwu hospital’s data, all participation is based on written informed consent and the participants will be able to withdraw from the study at any time.

Consent for Publication

The publisher has the permission from the authors to publish the paper.

Conflicts of Interest/Competing Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 561 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-021-09554-3

Keywords

Search

Navigation