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Ahmadi, Hessam, Fatemizadeh, Emad, Motie Nasrabadi, Ali. (1402). A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer’s Patients. سامانه مدیریت نشریات علمی, 13(2), 125-134. doi: 10.31661/jbpe.v0i0.2007-1134
Hessam Ahmadi; Emad Fatemizadeh; Ali Motie Nasrabadi. "A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer’s Patients". سامانه مدیریت نشریات علمی, 13, 2, 1402, 125-134. doi: 10.31661/jbpe.v0i0.2007-1134
Ahmadi, Hessam, Fatemizadeh, Emad, Motie Nasrabadi, Ali. (1402). 'A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer’s Patients', سامانه مدیریت نشریات علمی, 13(2), pp. 125-134. doi: 10.31661/jbpe.v0i0.2007-1134
Ahmadi, Hessam, Fatemizadeh, Emad, Motie Nasrabadi, Ali. A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer’s Patients. سامانه مدیریت نشریات علمی, 1402; 13(2): 125-134. doi: 10.31661/jbpe.v0i0.2007-1134

A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer’s Patients

Journal of Biomedical Physics and Engineering
دوره 13، شماره 2، خرداد و تیر 2023، صفحه 125-134    اصل مقاله (929.08 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2007-1134
نویسندگان
Hessam Ahmadi1؛ Emad Fatemizadeh* 2؛ Ali Motie Nasrabadi3
1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
3Department of Biomedical Engineering, Shahed University, Tehran, Iran
چکیده
Background: Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging tool, used in brain function research and is also a low-frequency signal, showing brain activation by means of Oxygen consumption. 
Objective: One of the reliable methods in brain functional connectivity analysis is the correlation method. In correlation analysis, the relationship between two time-series has been investigated. In fMRI analysis, the Pearson correlation is used while there are other methods. This study aims to investigate the different correlation methods in functional connectivity analysis.
Material and Methods: In this analytical research, based on fMRI signals of Alzheimer’s Disease (AD) and healthy individuals from the ADNI database, brain functional networks were generated using correlation techniques, including Pearson, Kendall, and Spearman. Then, the global and nodal measures were calculated in the whole brain and in the most important resting-state network called Default Mode 
Network (DMN). The statistical analysis was performed using non-parametric permutation test. 
Results: Results show that although in nodal analysis, the performance of correlation methods was almost similar, in global features, the Spearman and Kendall were better in distinguishing AD subjects. Note that, nodal analysis reveals that the functional connectivity of the posterior areas in the brain was more damaged because of AD in comparison to frontal areas. Moreover, the functional connectivity of the dominant hemisphere was disrupted more.  
Conclusion: Although the Pearson method has limitations in capturing non-linear relationships, it is the most prevalent method. To have a comprehensive analysis, investigating non-linear methods such as distance correlation is recommended.
کلیدواژه‌ها
Functional Connectivity؛ Correlation؛ Brain Networks؛ Fmri؛ Graph Measures؛ DMN Network؛ Alzheimer Disease؛ Brain؛ Neuroimaging
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مراجع
  1. Bijsterbosch J, Smith SM, Beckmann CF. An introduction to resting state fMRI functional connectivity. Oxford University Press; 2017.
  2. Rossini PM, Di Iorio R, Bentivoglio M, Bertini G, Ferreri F, Gerloff C, et al. Methods for analysis of brain connectivity: An IFCN-sponsored review. Clin Neurophysiol. 2019;130(10):1833-58. doi: 10.1016/j.clinph.2019.06.006. PubMed PMID: 31401492.
  3. Hull JV, Dokovna LB, Jacokes ZJ, Torgerson CM, Irimia A, Van Horn JD. Resting-state functional connectivity in autism spectrum disorders: a review. Front Psychiatry. 2017;7:205. doi: 10.3389/fpsyt.2016.00205. PubMed PMID: 28101064. PubMed PMCID: PMC5209637.
  4. Baggio HC, Junqué C. Functional MRI in Parkinson’s Disease Cognitive Impairment. Int Rev Neurobiol. 2019;144:29-58. doi: 10.1016/bs.irn.2018.09.010. PubMed PMID: 30638456.
  5. Hua J, Blair NI, Paez A, Choe A, et al. Altered functional connectivity between sub-regions in the thalamus and cortex in schizophrenia patients measured by resting state BOLD fMRI at 7T. Schizophr Res. 2019;206:370-7. doi: 10.1016/j.schres.2018.10.016. PubMed PMID: 30409697. PubMed PMCID: PMC6500777.
  6. Farahani FV, Karwowski W, Lighthall NR. Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review. Front Neurosci. 2019;13:585. doi: 10.3389/fnins.2019.00585. PubMed PMID: 31249501. PubMed PMCID: PMC6582769.
  7. Alzheimer’s Association. 2019 Alzheimer’s disease facts and figures. Alzheimer’s Dementia. 2019;15(3):321-87. doi: 10.1016/j.jalz.2019.01.010.
  8. Forouzannezhad P, Abbaspour A, Fang C, et al. A survey on applications and analysis methods of functional magnetic resonance imaging for Alzheimer’s disease. J Neurosci Methods. 2019;317:121-40. doi: 1016/j.jneumeth.2018.12.012. PubMed PMID: 30593787.
  9. Dennis EL, Thompson PM. Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol Rev. 2014;24(1):49-62. doi: 10.1007/s11065-014-9249-6. PubMed PMID: 24562737. PubMed PMCID: PMC4109887.
  10. Gao Y, Li M, Zu Z, Rogers BP, Anderson AW, Ding Z, Gore JC. Progressive degeneration of white matter functional connectivity in Alzheimer’s Disease. Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; San Diego, California, United States: SPIE; 2019. doi: 10.1117/12.2512919.
  11. De Vos F, Koini M, Schouten TM, Seiler S, et al. A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer’s disease. Neuroimage. 2018;167:62-72. doi: 10.1016/j.neuroimage.2017.11.025. PubMed PMID: 29155080.
  12. Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. 2017;155:530-48. doi: 10.1016/j.neuroimage.2017.03.057. PubMed PMID: 28414186. PubMed PMCID: PMC5511557.
  13. Meszlényi RJ, Buza K, Vidnyánszky Z. Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture. Front Neuroinform. 2017;11:61. doi: 10.3389/fninf.2017.00061. PubMed PMID: 29089883. PubMed PMCID: PMC5651030.
  14. Franzmeier N, Caballero MA, Taylor AN, Simon-Vermot L, Buerger K, et al. Resting-state global functional connectivity as a biomarker of cognitive reserve in mild cognitive impairment. Brain Imaging Behav. 2017;11(2):368-82. doi: 10.1007/s11682-016-9599-1. PubMed PMID: 27709513.
  15. Cai S, Chong T, Peng Y, Shen W, Li J, Von Deneen KM, Huang L. Altered functional brain networks in amnestic mild cognitive impairment: a resting-state fMRI study. Brain Imaging Behav. 2017;11(3):619-31. doi: 10.1007/s11682-016-9539-0. PubMed PMID: 26972578.
  16. Dai Z, Lin Q, Li T, Wang X, Yuan H, et al. Disrupted structural and functional brain networks in Alzheimer’s disease. Neurobiol Aging. 2019;75:71-82. doi: 10.1016/j.neurobiolaging.2018.11.005.m. PubMed PMID: 30553155.
  17. Hlinka J, Paluš M, Vejmelka M, Mantini D, Corbetta M. Functional connectivity in resting-state fMRI: is linear correlation sufficient? Neuroimage. 2011;54(3):2218-25. doi: 10.1016/j.neuroimage.2010.08.042. PubMed PMID: 20800096. PubMed PMCID: PMC4139498.
  18. Bowyer SM. Coherence a measure of the brain networks: past and present. Neuropsychiatric Electrophysiology. 2016;2(1):1-2. doi: 10.1186/s40810-015-0015-7.
  19. Chirles TJ, Reiter K, Weiss LR, Alfini AJ, et al. Exercise training and functional connectivity changes in mild cognitive impairment and healthy elders. J Alzheimers Dis. 2017;57(3):845-56. doi: 10.3233/JAD-161151. PubMed PMID: 28304298. PubMed PMCID: PMC6472271.
  20. Zhao J, Ding X, Du Y, Wang X, Men G. Functional connectivity between white matter and gray matter based on fMRI for Alzheimer’s disease classification. Brain Behav. 2019;9(10):e01407. doi: 10.1002/brb3.1407. PubMed PMID: 31512413. PubMed PMCID: PMC6790327.
  21. Yamashita KI, Uehara T, Prawiroharjo P, et al. Functional connectivity change between posterior cingulate cortex and ventral attention network relates to the impairment of orientation for time in Alzheimer’s disease patients. Brain Imaging Behav. 2019;13(1):154-61. doi: 10.1007/s11682-018-9860-x. PubMed PMID: 29644521.
  22. Mueller SG, Weiner MW, Thal LJ, et al. The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin N Am. 2005;15(4):869-77. doi: 10.1016/j.nic.2005.09.008. PubMed PMID: 16443497. PubMed PMCID: PMC2376747.
  23. Petersen RC, Aisen PS, Beckett LA, et al. Alzheimer’s disease neuroimaging initiative (ADNI): clinical characterization. 2010;74(3):201-9. doi: 10.1212/WNL.0b013e3181cb3e25. PubMed PMID: 20042704. PubMed PMCID: PMC2809036.
  24. Jack Jr CR, Bernstein MA, Fox NC, et al. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685-91. doi: 10.1002/jmri.21049. PubMed PMID: 18302232. PubMed PMCID: PMC2544629.
  25. Yan C, Zang Y. DPARSF: a MATLAB toolbox for” pipeline” data analysis of resting-state fMRI. Front Syst Neurosci. 2010;4:13. doi: 10.3389/fnsys.2010.00013. PubMed PMID: 20577591. PubMed PMCID: PMC2889691.
  26. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. 2002;15(1):273-89. doi: 10.1006/nimg.2001.0978. PubMed PMID: 11771995.
  27. Benesty J, Chen J, Huang Y, Cohen I. Pearson correlation coefficient. Noise Reduction in Speech Processing; Berlin, Heidelberg: Springer Topics in Signal Processing; 2009. p. 1-4.
  28. Myers L, Sirois MJ. Spearman correlation coefficients, differences between. Encyclopedia of Statistical Sciences. 2004. doi: 10.1002/0471667196.ess5050.pub2.
  29. Akoglu H. User’s guide to correlation coefficients. Turk J Emerg Med. 2018;18(3):91-3. doi: 10.1016/j.tjem.2018.08.001. PubMed PMID: 30191186. PubMed PMCID: PMC6107969.
  30. Ehtemami A, Scott R, Bernadin S. A Survey of FMRI Data Analysis Methods. SoutheastCon; USA: IEEE; 2018. p. 1-7.
  31. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10(3):186-98. doi: 10.1038/nrn2575. PubMed PMID: 19190637.
  32. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. 2010;52(3):1059-69. doi: 10.1016/j.neuroimage.2009.10.003. PubMed PMID: 19819337.
  33. Meghanathan N. Assortativity Analysis of Real-World Network Graphs based on Centrality Metrics. Computer and Information Science. 2016;9(3):7-25. doi: 5539/cis.v9n3p7.
  34. Qian W, Fischer C, Churchill N, Kumar S, et al. Decreased Default Mode Network Functional Connectivity in Alzheimer’s Disease Patients with Delusions. Am J Geriatr Psychiatry. 2019;27(10):1060-8. doi: 10.1016/j.jagp.2019.03.020. PubMed PMID: 31130416.
  35. Filippi M, Basaia S, Canu E, Galantucci S, et al. Structural and functional brain connectome architecture in Alzheimer’s disease and amnesic mild cognitive impairment patients (P4. 091). J Neurology. 2017;88(16 Supplement):P4.091.
  36. Liang Y, Chen Y, Li H, Zhao T, et al. Disrupted functional connectivity related to differential degeneration of the cingulum bundle in mild cognitive impairment patients. Curr Alzheimer Res. 2015;12(3):255-65. doi: 10.2174/1567205012666150302155336. PubMed PMID: 25731624.
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