Login Register Cart 0
Generic placeholder image

Current Alzheimer Research

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe Translate in Chinese
Research Article

Directed Network Defects in Alzheimer's Disease Using Granger Causality and Graph Theory

Author(s): Man Sun, Hua Xie and Yan Tang*

Volume 17, Issue 10, 2020

Page: [939 - 947] Pages: 9

DOI: 10.2174/1567205017666201215140625

Price: $65

Abstract

Background: Few works studied the directed whole-brain interaction between different brain regions of Alzheimer’s disease (AD). Here, we investigated the whole-brain effective connectivity and studied the graph metrics associated with AD.

Methods: Large-scale Granger causality analysis was conducted to explore abnormal whole-brain effective connectivity of patients with AD. Moreover, graph-theoretical metrics including smallworldness, assortativity, and hierarchy, were computed from the effective connectivity network. Statistical analysis identified the aberrant network properties of AD subjects when compared against healthy controls.

Results: Decreased small-worldness, and increased characteristic path length, disassortativity, and hierarchy were found in AD subjects.

Conclusion: This work sheds insight into the underlying neuropathological mechanism of the brain network of AD individuals such as less efficient information transmission and reduced resilience to a random or targeted attack.

Keywords: Alzheimer`s disease, effective connectivity, large-scale granger causality, functional connectomes, assortativity, hierarchy.

« Previous Next »
[1]
Blennow K, de Leon MJ, Zetterberg H. Alzheimer’s disease. Lancet 2006; 368(9533): 387-403.
[http://dx.doi.org/10.1016/S0140-6736(06)69113-7 ] [PMID: 16876668]
[2]
Costa P T. Recognition and initial assessment of Alzheimer's disease and related dementias 1996.
[3]
Chatterjee A, Strauss ME, Smyth KA, Whitehouse PJ. Personality changes in Alzheimer’s disease. Arch Neurol 1992; 49(5): 486-91.
[http://dx.doi.org/10.1001/archneur.1992.00530290070014 ] [PMID: 1580810]
[4]
Association AS. Alzheimer’s disease facts and figures. Alzheimers Dement 2018; 14: 367-429.
[5]
Jia J, Wei C, Chen S, et al. The cost of Alzheimer’s disease in China and re-estimation of costs worldwide. Alzheimers Dement 2018; 14(4): 483-91.
[http://dx.doi.org/10.1016/j.jalz.2017.12.006 ] [PMID: 29433981]
[6]
Greicius M. Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol 2008; 21(4): 424-30.
[http://dx.doi.org/10.1097/WCO.0b013e328306f2c5 ] [PMID: 18607202]
[7]
Friston KJ. Functional and effective connectivity: a review. Brain Connect 2011; 1(1): 13-36.
[http://dx.doi.org/10.1089/brain.2011.0008 ] [PMID: 22432952]
[8]
Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 1993; 13(1): 5-14.
[http://dx.doi.org/10.1038/jcbfm.1993.4 ] [PMID: 8417010]
[9]
Agosta F, Pievani M, Geroldi C, Copetti M, Frisoni GB, Filippi M. Resting state fMRI in Alzheimer’s disease: beyond the default mode network. Neurobiol Aging 2012; 33(8): 1564-78.
[http://dx.doi.org/10.1016/j.neurobiolaging.2011.06.007 ] [PMID: 21813210]
[10]
Jalilianhasanpour R, Beheshtian E, Sherbaf G, Sahraian S, Sair HI. Functional connectivity in neurodegenerative disorders: Alzheimer’s disease and frontotemporal dementia. Top Magn Reson Imaging 2019; 28(6): 317-24.
[http://dx.doi.org/10.1097/RMR.0000000000000223] [PMID: 31794504]
[11]
J L , Testa N, Jordan R, et al. Functional connectivity between the resting-state olfactory network and the hippocampus in Alzheimer’s disease. Brain Sci 2019; 9(12): 338.
[12]
Zhao S, Rangaprakash D, Venkataraman A, Liang P, Deshpande G. Investigating focal connectivity deficits in alzheimer’s disease using directional brain networks derived from resting-state fMRI. Front Aging Neurosci 2017; 9: 211.
[http://dx.doi.org/10.3389/fnagi.2017.00211]
[13]
Scherr M, Utz L, Tahmasian M, et al. Effective connectivity in the default mode network is distinctively disrupted in Alzheimer’s disease-a simultaneous resting-state FDG-PET/fMRI study. Hum Brain Mapp 2019.
[http://dx.doi.org/10.1002/hbm.24517]
[14]
Liu J, Ji J, Jia X, Zhang A. Learning brain effective connectivity network structure using ant colony optimization combining with voxel activation information. IEEE J Biomed Health Inform 2020; 24(7): 2028-40.
[15]
Zhong Y, Huang L, Cai S, et al. Alzheimer’s Disease Neuroimaging Initiative. Altered effective connectivity patterns of the default mode network in Alzheimer’s disease: an fMRI study. Neurosci Lett 2014; 578: 171-5.
[http://dx.doi.org/10.1016/j.neulet.2014.06.043 ] [PMID: 24996191]
[16]
Chen G, Ward BD, Chen G, Li S-J. Decreased effective connectivity from cortices to the right parahippocampal gyrus in Alzheimer’s disease subjects. Brain Connect 2014; 4(9): 702-8.
[http://dx.doi.org/10.1089/brain.2014.0295 ] [PMID: 25132215]
[17]
Tang Y, Liu B, Yang Y, et al. Identifying mild-moderate Parkinson’s disease using whole-brain functional connectivity. Clin Neurophysiol 2018; 129(12): 2507-16.
[http://dx.doi.org/10.1016/j.clinph.2018.09.006 ] [PMID: 30347309]
[18]
Friston K, Moran R, Seth AK. Analysing connectivity with Granger causality and dynamic causal modelling. Curr Opin Neurobiol 2013; 23(2): 172-8.
[http://dx.doi.org/10.1016/j.conb.2012.11.010 ] [PMID: 23265964]
[19]
Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Alzheimer’s Disease Neuroimaging Initiative. Classification of patients with MCI and AD from healthy controls using directed graph measures of restingstate fMRI. Behav Brain Res 2017; 322(Pt B): 339-50.
[http://dx.doi.org/10.1016/j.bbr.2016.06.043] [PMID: 27345822]
[20]
Seth AK, Barrett AB, Barnett LJJN. Granger causality analysis in neuroscience and neuroimaging. J Neurosci 2015; 35(8): 3293-7.
[21]
Ide JS, Chiang-Shan RL. A cerebellar thalamic cortical circuit for error-related cognitive control. Neuroimage 2011; 54: 455-64.
[22]
Hu S, Job M, Jenks SK, Chao HH, et al. Imaging the effects of age on proactive control in healthy adults. Brain Imaging Behav 2019; 13(6): 1526-37.
[23]
Schmidt C, Pester B, Schmid-Hertel N, Witte H, Wismüller A, Leistritz L. A multivariate granger causality concept towards full brain functional connectivity. PLoS One 2016; 11(4): e0153105.
[http://dx.doi.org/10.1371/journal.pone.0153105 ] [PMID: 27064897]
[24]
Wismüller A, Nagarajan MB, Witte H, Pester B, Leistritz L. Pair-wise clustering of large scale Granger causality index matrices for revealing communities. Proc SPIE Int Soc Opt Eng 2014; 9038: 90381R.
[25]
Nigro S, Riccelli R, Passamonti L, et al. Characterizing structural neural networks in de novo Parkinson disease patients using diffusion tensor imaging. Hum Brain Mapp 2016; 37(12): 4500-10.
[http://dx.doi.org/10.1002/hbm.23324 ] [PMID: 27466157]
[26]
DSouza AM, Abidin AZ, Leistritz L, Wismüller A. Exploring connectivity with large-scale Granger causality on resting-state functional MRI. J Neurosci Methods 2017; 287: 68-79.
[http://dx.doi.org/10.1016/j.jneumeth.2017.06.007 ] [PMID: 28629720]
[27]
Whitlow CT, Casanova R, Maldjian JA. Effect of resting-state functional MR imaging duration on stability of graph theory metrics of brain network connectivity. Radiology 2011; 259(2): 516-24.
[http://dx.doi.org/10.1148/radiol.11101708 ] [PMID: 21406628]
[28]
Zhao S, Rangaprakash D, Liang P, Deshpande GJB. Deterioration from healthy to mild cognitive impairment and Alzheimer’s disease mirrored in corresponding loss of centrality in directed brain networks. Brain Inform 2019; 6: 8.
[29]
Stam CJ, Jones BF, Nolte G, Breakspear M, Scheltens P. Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 2007; 17(1): 92-9.
[http://dx.doi.org/10.1093/cercor/bhj127 ] [PMID: 16452642]
[30]
Zhang Y, Zhang S, Ide JS, et al. Dynamic network dysfunction in cocaine dependence: Graph theoretical metrics and stop signal reaction time. Neuroimage Clin 2018; 18: 793-801.
[31]
Eickhoff SB, Stephan KE, Mohlberg H, et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 2005; 25(4): 1325-35.
[http://dx.doi.org/10.1016/j.neuroimage.2004.12.034 ] [PMID: 15850749]
[32]
Chao-Gan Y, Yu-Feng Z. DPARSF: a MATLAB toolbox for” pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 2010; 4: 13.
[PMID: 20577591]
[33]
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 2012; 59(3): 2142-54.
[http://dx.doi.org/10.1016/j.neuroimage.2011.10.018 ] [PMID: 22019881]
[34]
Fan L, Li H, Zhuo J, et al. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb Cortex 2016; 26(8): 3508-26.
[http://dx.doi.org/10.1093/cercor/bhw157 ] [PMID: 27230218]
[35]
Marinazzo D, Liao W, Chen H, Stramaglia S. Nonlinear connectivity by Granger causality. Neuroimage 2011; 58(2): 330-8.
[http://dx.doi.org/10.1016/j.neuroimage.2010.01.099 ] [PMID: 20132895]
[36]
Duggento A, et al. Multivariate Granger causality unveils directed parietal to prefrontal cortex connectivity during task-free MRI Sci rep-UK 2018; 8: 5571.
[37]
Ravasz E, Barabási A-L. Hierarchical organization in complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 2003; 67(2 Pt 2): 026112.
[http://dx.doi.org/10.1103/PhysRevE.67.026112 ] [PMID: 12636753]
[38]
Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010; 52(3): 1059-69.
[http://dx.doi.org/10.1016/j.neuroimage.2009.10.003 ] [PMID: 19819337]
[39]
Tang Y, Xiao X, Xie H, et al. Altered functional brain connectomes between sporadic and familial Parkinson’s patients. Front Neuroanat 2017; 11: 99.
[http://dx.doi.org/10.3389/fnana.2017.00099 ] [PMID: 29163072]
[40]
Bassett DS, Bullmore E, Verchinski BA, Mattay VS, Weinberger DR, Meyer-Lindenberg A. Hierarchical organization of human cortical networks in health and schizophrenia. J Neurosci 2008; 28(37): 9239-48.
[http://dx.doi.org/10.1523/JNEUROSCI.1929-08.2008 ] [PMID: 18784304]
[41]
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.
[http://dx.doi.org/10.1007/s11682-015-9448-7 ] [PMID: 26363784]
[42]
Sporns O, Zwi JD. The small world of the cerebral cortex. Neuroinformatics 2004; 2(2): 145-62.
[http://dx.doi.org/10.1385/NI:2:2:145 ] [PMID: 15319512]
[43]
Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 2006; 26(1): 63-72.
[http://dx.doi.org/10.1523/JNEUROSCI.3874-05.2006 ] [PMID: 16399673]
[44]
Zhao X, Liu Y, Wang X, et al. Disrupted small-world brain networks in moderate Alzheimer’s disease: a resting-state FMRI study. PLoS One 2012; 7(3): e33540.
[http://dx.doi.org/10.1371/journal.pone.0033540 ] [PMID: 22457774]
[45]
Sporns O, Chialvo DR, Kaiser M, Hilgetag CC. Organization, development and function of complex brain networks. Trends Cogn Sci 2004; 8(9): 418-25.
[http://dx.doi.org/10.1016/j.tics.2004.07.008 ] [PMID: 15350243]
[46]
Newman ME. The structure and function of complex networks. SIAM Rev 2003; 45: 167-256.
[http://dx.doi.org/10.1137/S003614450342480]
[47]
Selkoe DJJPR. Alzheimer’s disease: genes, proteins, and therapy. Physiol Rev 2001; 81(2): 741-66.
[http://dx.doi.org/10.1152/physrev.2001.81.2.741 ] [PMID: 11274343]
[48]
Lazarov O, Marr R A JEN. Neurogenesis and Alzheimer’s disease: at the crossroads. Exp Neurol 2010; 223: 267-81.
[49]
Davies RR, Kipps CM, Mitchell J, Kril JJ, Halliday GM, Hodges JR. Progression in frontotemporal dementia: identifying a benign behavioral variant by magnetic resonance imaging. Arch Neurol 2006; 63(11): 1627-31.
[http://dx.doi.org/10.1001/archneur.63.11.1627 ] [PMID: 17101833]
[50]
Newman ME. Mixing patterns in networks. Phys Rev E Stat Nonlin Soft Matter Phys 2003; 67(2 Pt 2): 026126.
[http://dx.doi.org/10.1103/PhysRevE.67.026126 ] [PMID: 12636767]
[51]
Park CH, Kim SY, Kim Y-H, Kim K. Comparison of the small-world topology between anatomical and functional connectivity in the human brain. Physica A 2008; 387: 5958-62.
[http://dx.doi.org/10.1016/j.physa.2008.06.048]
[52]
Foster JG, Foster DV, Grassberger P, Paczuski M. Edge direction and the structure of networks. Proc Natl Acad Sci USA 2010; 107(24): 10815-20.
[http://dx.doi.org/10.1073/pnas.0912671107 ] [PMID: 20505119]
[53]
Dong G, Yang L, Li CR, et al. Dynamic network connectivity predicts subjective cognitive decline: the Sino-Longitudinal Cognitive impairment and dementia study. Brain Imag Behav 2020; 14(6): 2692-707.
[http://dx.doi.org/10.1007/s11682-019-00220-6 ] [PMID: 32361946]
[54]
de Haan W, Pijnenburg YA, Strijers RL, et al. Functional neural network analysis in frontotemporal dementia and Alzheimer’s disease using EEG and graph theory. BMC Neurosci 2009; 10: 101.
[http://dx.doi.org/10.1186/1471-2202-10-101 ] [PMID: 19698093]
[55]
Dong G, et al. Dynamic network connectivity predicts subjective cognitive decline: the Sino-Longitudinal Cognitive impairment and dementia study. Brain Imag Behav 2020; 14(6): 2692-07.

Rights & Permissions Print Cite
Article Metrics
37
© 2024 Bentham Science Publishers | Privacy Policy