Skip to main content

Advertisement

SpringerLink
Log in

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data

  • ORIGINAL ARTICLE
  • Published:
GeroScience Aims and scope Submit manuscript

Abstract

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal–Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.

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
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

All the data used are from public databases and described and referenced properly in the manuscript.

References

  1. Krampe RT. Aging, expertise and fine motor movement. Neurosci Biobehav Rev. 2002;26(7):769–76.

    Article  PubMed  Google Scholar 

  2. Grady C. The cognitive neuroscience of ageing. Nat Rev Neurosci. 2012;13(7):491–505.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Franke K, Ziegler G, Klöppel S, Gaser C, Initiative ADN. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage. 2010;50(3):883–92.

    Article  PubMed  Google Scholar 

  4. Varangis E, Habeck CG, Razlighi QR, Stern Y. The effect of aging on resting state connectivity of predefined networks in the brain. Front Aging Neurosci. 2019;11:234.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Roceanu A, Onu M, Badea L, Bajenaru O. Imaging brain networks—short presentation of new techniques. Rom J Neurol. 2013;12(4):180.

    Article  Google Scholar 

  6. Finotelli P, et al. Exploring resting-state functional connectivity invariants across the lifespan in healthy people by means of a recently proposed graph theoretical model. PLoS One. 2018;13(11):e0206567.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Calhoun VD, Miller R, Pearlson G, Adalı T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron. 2014;84(2):262–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tafreshi TF, Daliri MR, Ghodousi M. Functional and effective connectivity based features of EEG signals for object recognition. Cogn Neurodyn. 2019;13(6):555–66.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Cao M, et al. Topological organization of the human brain functional connectome across the lifespan. Dev Cogn Neurosci. 2014;7:76–93.

    Article  PubMed  Google Scholar 

  10. Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp. 1994;2(1–2):56–78.

    Article  Google Scholar 

  11. Wang L, Su L, Shen H, Hu D. Decoding lifespan changes of the human brain using resting-state functional connectivity MRI. PLoS ONE. 2012;7(8):e44530.

  12. Fair DA, et al. Functional brain networks develop from a ‘local to distributed’ organization. PLoS Comput Biol. 2009;5(5):e1000381.

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  13. Qiu A, Lee A, Tan M, Chung MK. Manifold learning on brain functional networks in aging. Med Image Anal. 2015;20(1):52–60.

    Article  PubMed  Google Scholar 

  14. Cai B, et al. Refined measure of functional connectomes for improved identifiability and prediction. Hum Brain Mapp. 2019;40(16):4843–58.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cui Z, et al. Individual variation in functional topography of association networks in youth. Neuron. 2020;106(2):340–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Finn E, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015;18:1664–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Xiao L, et al. Distance correlation-based brain functional connectivity estimation and non-convex multi-task learning for developmental fMRI studies. IEEE Trans Biomed Eng. 2022;69(10):3039–50.

  18. Rubinov M. Rubinov and sporns-2010—complex network measures of brain connectivity. Neuroimage. 2010;52:1059–69.

    Article  PubMed  Google Scholar 

  19. Song J, et al. Age-related reorganizational changes in modularity and functional connectivity of human brain networks. Brain Connect. 2014;4(9):662–76.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sporns O. Graph theory methods: applications in brain networks. Dialogues Clin Neurosci. 2018;20(2):111–21.

  21. Varangis E, Habeck CG, Stern Y. Task-based functional connectivity in aging: how task and connectivity methodology affect discovery of age effects. Brain Behav. 2021;11(1):e01954.

    Article  PubMed  Google Scholar 

  22. Javaid H, Kumarnsit E, Chatpun S. Age-related alterations in EEG network connectivity in healthy aging. Brain Sci. 2022;12(2):218.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, Wu-Minn HCP Consortium et al. The WU-Minn human connectome project: an overview. Neuroimage. 2013;80:62–79.

  24. Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, Consortium W-MH, et al. Neuroimage. 2013;80:62.

  25. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17(2):825–41.

    Article  PubMed  Google Scholar 

  26. Tzourio-Mazoyer N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15(1):273–89.

    Article  CAS  PubMed  Google Scholar 

  27. Lee Rodgers J, Nicewander WA. Thirteen ways to look at the correlation coefficient. Am Stat. 1988;42(1):59–66.

    Article  Google Scholar 

  28. Gamboa OL, et al. Working memory performance of early MS patients correlates inversely with modularity increases in resting state functional connectivity networks. Neuroimage. 2014;94:385–95.

    Article  CAS  PubMed  Google Scholar 

  29. Ashtiani SNM, et al. Altered topological properties of brain networks in the early MS patients revealed by cognitive task-related fMRI and graph theory. Biomed Signal Process Control. 2018;40:385–95.

    Article  Google Scholar 

  30. Borgatti SP, Everett MG. A graph-theoretic perspective on centrality. Soc Networks. 2006;28(4):466–84.

    Article  Google Scholar 

  31. Tononi G, Edelman GM, Sporns O. Complexity and coherency: integrating information in the brain. Trends Cogn Sci. 1998;2(12):474–84.

    Article  CAS  PubMed  Google Scholar 

  32. Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci. 2006;103(23):8577–82.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Chiang S, Haneef Z. Graph theory findings in the pathophysiology of temporal lobe epilepsy. Clin Neurophysiol. 2014;125(7):1295–305.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87(19):198701.

    Article  ADS  CAS  PubMed  Google Scholar 

  35. Latora V, Marchiori M. Economic small-world behavior in weighted networks. Eur Phys J B-Condensed Matter Complex Syst. 2003;32(2):249–63.

    Article  CAS  Google Scholar 

  36. Mari SI, Lee YH, Memon MS, Park YS, Kim M. Adaptivity of complex network topologies for designing resilient supply chain networks. Int J Ind Eng. 2015;22(1).

  37. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69.

    Article  PubMed  Google Scholar 

  38. Humphries MD, Gurney K. Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS One. 2008;3(4):e0002051.

    Article  ADS  PubMed  Google Scholar 

  39. Zhang Z, et al. Altered functional–structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain. 2011;134(10):2912–28.

    Article  PubMed  Google Scholar 

  40. Onias H, et al. Brain complex network analysis by means of resting state fMRI and graph analysis: will it be helpful in clinical epilepsy? Epilepsy Behav. 2014;38:71–80.

    Article  PubMed  Google Scholar 

  41. Hagmann P, et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008;6(7):e159.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lohmann G, et al. Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PLoS One. 2010;5(4):e10232.

    Article  ADS  MathSciNet  PubMed  PubMed Central  Google Scholar 

  43. Bullmore ET, Bassett DS. Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol. 2011;7:113–40.

    Article  PubMed  Google Scholar 

  44. Kruschwitz JD, List D, Waller L, Rubinov M, Walter H. GraphVar: a user-friendly toolbox for comprehensive graph analyses of functional brain connectivity. J Neurosci Methods. 2015;245:107–15.

    Article  CAS  PubMed  Google Scholar 

  45. McKight PE, Najab J. Kruskal‐Wallis test. Corsini Encycl Psychol. 2010;1–1.

  46. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc. 1952;47(260):583–621.

    Article  Google Scholar 

  47. Cherrington M, Thabtah F, Lu J, Xu Q. Feature selection: filter methods performance challenges. In Int Conf Comput Inf Sci (ICCIS). 2019;2019:1–4.

    Google Scholar 

  48. Azarmi F, Ashtiani SNM, Shalbaf A, Behnam H, Daliri MR. Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI. Comput Biol Med. 2019;115:103495.

    Article  PubMed  Google Scholar 

  49. Zhao Z, Morstatter F, Sharma S, Alelyani S, Anand A, Liu H. Advancing feature selection research. ASU feature selection repository.  Repos. 2010;1–28.

  50. Spolaôr N, Cherman EA, Monard MC, Lee HD. ReliefF for multi-label feature selection. In Braz Conf Intell Syst. 2013;2013:6–11.

    Google Scholar 

  51. Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27(8):1226–38.

    Article  PubMed  Google Scholar 

  52. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97.

    Article  Google Scholar 

  53. Abe S. Support vector machines for pattern classification. London: Springer, 2005;(2).

  54. Song Y-Y, Ying LU. Decision tree methods: applications for classification and prediction. Shanghai Arch psychiatry. 2015;27(2):130.

    PubMed  PubMed Central  Google Scholar 

  55. Cunningham P, Delany SJ. K-nearest neighbour classifiers-a tutorial. ACM Comput Surv. 2021;54(6):1–25.

    Article  Google Scholar 

  56. Refaeilzadeh P, Tang L, Liu H. Cross-validation. Encycl database Syst. 2009;5:532–8.

    Article  Google Scholar 

  57. Geerligs L, Renken RJ, Saliasi E, Maurits NM, Lorist MM. A brain-wide study of age-related changes in functional connectivity. Cereb cortex. 2015;25(7):1987–99.

    Article  PubMed  Google Scholar 

  58. Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012;13(5):336–49.

    Article  CAS  PubMed  Google Scholar 

  59. Foo H et al. Age-and sex-related topological organization of human brain functional networks and their relationship to cognition. Front Aging Neurosci. 2021;(13):758–97.

  60. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech theory Exp. 2008;2008(10):P10008.

    Article  Google Scholar 

  61. Ajilore O, Lamar M, Kumar A. Association of brain network efficiency with aging, depression, and cognition. Am J Geriatr Psychiatry. 2014;22(2):102–10.

    Article  PubMed  Google Scholar 

  62. Geerligs L, Renken RJ, Saliasi E, Maurits NM, Lorist MM. A brain-wide study of age-related changes in functional connectivity. 2015;(July):1987–1999. https://doi.org/10.1093/cercor/bhu012

  63. Elsheikh S, Chimusa ER, Mulder N, Crimi A. Relating connectivity changes in brain networks to genetic information in Alzheimer patients. In 2018 IEEE 15th Int Symp Biomed Imaging (ISBI 2018). 2018;1390–1393.

  64. Mårtensson G, et al. Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease. Sci Rep. 2018;8(1):1–15.

    Article  ADS  Google Scholar 

  65. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ. Mapping the structural core of human cerebral cortex. 2008;6 (7). https://doi.org/10.1371/journal.pbio.0060159

  66. Talati A, Hirsch J. Functional specialization within the medial frontal gyrus for perceptual go/no-go decisions based on ‘what’,‘when’, and ‘where’ related information: an fMRI study. J Cogn Neurosci. 2005;17(7):981–93.

    Article  PubMed  Google Scholar 

  67. Amunts K, et al. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat Embryol (Berl). 2005;210(5):343–52.

    Article  CAS  PubMed  Google Scholar 

  68. Jacques PS, Dolcos F, Cabeza R. Effects of aging on functional connectivity of the amygdala during negative evaluation: a network analysis of fMRI data. Neurobiol Aging. 2010;31(2):315–27.

    Article  Google Scholar 

  69. Xu X, Kuang Q, Zhang Y, Wang H, Wen Z, Li M. Age-related changes in functional connectivity between young adulthood and late adulthood. Anal Methods. 2015;7(10):4111–22.

    Article  Google Scholar 

  70. Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease. Brain Imaging Behav. 2016;10(3):799–817.

    Article  PubMed  Google Scholar 

  71. Onoda K, Ishihara M, Yamaguchi S. Decreased functional connectivity by aging is associated with cognitive decline. J Cogn Neurosci. 2012;24(11):2186–98.

    Article  PubMed  Google Scholar 

  72. Yamaguchi S, Levy RM, Braga R. Decreased functional connectivity by aging is associated with cognitive decline. J Cogn Neurosci. 2012;24(11):2186-98.

  73. Li L, Cazzell M, Babawale OM, Liu H. Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults. Neurophotonics. 2016;3(4):45002.

    Article  Google Scholar 

  74. Ai J, Liu T, Wang K, Yan T, Zhang, Huang T. Alterations of brain functional networks in older adults: a resting-state fMRI study using graph theory. In 2020 13th Int Congr Image Signal Process, BioMed Eng Informa (CISP-BMEI). 2020;372–377.

Download references

Funding

Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Author information

Authors and Affiliations

  1. Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

    Sepideh Baghernezhad & Mohammad Reza Daliri

Authors
  1. Sepideh Baghernezhad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Reza Daliri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Reza Daliri.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baghernezhad, S., Daliri, M. Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data. GeroScience (2024). https://doi.org/10.1007/s11357-024-01128-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11357-024-01128-w

Keywords

Search

Navigation