Functional disorganization of small-world brain networks in mild Alzheimer's disease and amnestic Mild cognitive impairment: An EEG study using Relative Wavelet Entropy (RWE)

Frontiers in Aging Neuroscience

Volumn 6, Issue AUG, 2014, Pages

  Frantzidis, Christos A ^a   Vivas, Ana B ^b   Tsolaki, Anthoula ^a,c   Klados, Manousos A ^a   Tsolaki, Magda ^d   Bamidis, Panagiotis D ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b UNIVERSITY OF SHEFFIELD   (Greece)

^c Greek Association of Alzheimer's Disease and Related Disorders   (Greece)

^d ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Alzheimer disease; Amnestic mild cognitive impairment; Electroencephalography; Graph analysis; Relative wavelet entropy

Indexed keywords

AGED; ALZHEIMER DISEASE; ARTICLE; CLINICAL ARTICLE; COMPARATIVE STUDY; CONTROLLED STUDY; DISORIENTATION; ELECTROENCEPHALOGRAM; ELECTROENCEPHALOGRAPH; ELECTROENCEPHALOGRAPH ELECTRODE; ENTROPY; FEMALE; FUNCTIONAL DISORGANIZATION; HEMISPHERE; HUMAN; INFORMATION PROCESSING; MALE; MENTAL DETERIORATION; MILD COGNITIVE IMPAIRMENT; MINI MENTAL STATE EXAMINATION; NERVE CELL NETWORK; NERVE DEGENERATION;

EID: 84906217583     PISSN: None     EISSN: 16634365     Source Type: Journal    
DOI: 10.3389/fnagi.2014.00224     Document Type: Article

References (55)

1. Albert, R., Jeong, H.and Barabási, A. L. (2000). Error and attack tolerance of complex networks. Nature 406(6794), 378-382.
2. Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W., Petersen, R. C., Snyder, P. J., Carrillo, M. C., Thies, B., and Phelps, C. H. (2011). The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 7(3), 270-279.
3. Amaral, L. A. N., Scala, A., Barthélémy, M., and Stanley, H. E. (2000). Classes of small-world networks. P. Natl. Acad. Sci. 97(21), 11149-11152.
4. Bamidis, P.D., Konstantinidis, E.I., Billis, A., Frantzidis, C., Tsolaki, M., Hlauschek, W., Kyriacou, E., Neofytou, M., and Pattichis, C.S. (2011). A Web services-based exergaming platform for senior citizens: The long lasting memories project approach to e-health care. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, art. no. 6090694, 2505-2509.
5. Bamidis et al, Long Lasting Memories Project Deliverable D1.4 Final report, http://www.longlastingmemories.eu/sites/default/files/LLM_D1.4_final_report_public_v2.2d oc.pdf, July 2012.
6. Bassett, D. S., and Bullmore, E. D. (2006). Small-world brain networks. Neuroscientist 12(6), 512- 523.
7. Buldú, J. M., Bajo, R., Maestú, F., Castellanos, N., Leyva, I., Gil, P., Sendina-Nadal, I., Almendral, J. A., Nevado, A., del-Pozo, F., and Boccaletti, S. (2011). Reorganization of functional networks in mild cognitive impairment. PLoS One, 6(5), e19584, doi: 10.1371/journal.pone.0019584.
8. Buerger, K., Ewers, M., Pirttilä, T., Zinkowski, R., Alafuzoff, I., Teipel, S. J., DeBernardis, J., Kerkman, D., McCulloch, C., Soininen, H., and Hampel, H. (2006). CSF phosphorylated tau protein correlates with neocortical neurofibrillary pathology in Alzheimer's disease. Brain 129(11), 3035-3041.
9. Bullmore, E., and Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186-198.
10. Cabeza, R., Anderson, N.D., Locantore, J.K., and McIntosh, A.R., ( 2002). Aging gracefully: compensatory brain activity in high-performing older adults. Neuroimage 17, 1394-1402.
11. Caselli, R. J., Reiman, E. M., Osborne, D., Hentz, J. G., Baxter, L. C., Hernandez, J. L., and Alexander, G. G. (2004). Longitudinal changes in cognition and behavior in asymptomatic carriers of the APOE e4 allele. Neurol. 62(11) 1990-1995.
12. Choo, I. H., Ni, R., Schöll, M., Wall, A., Almkvist, O., and Nordberg, A. (2013). Combination of 18 F-FDG PET and Cerebrospinal Fluid Biomarkers as a Better Predictor of the Progression to Alzheimer's Disease in Mild Cognitive Impairment Patients. J. Alzheimers Dis. 33(4), 929-939.
13. Citron, M. (2010). Alzheimer's disease: strategies for disease modification. Nat. Rev. Drug Discov. 9(5), 387-398.
14. De Haan, W., Van Der Flier, W. M., Koene, T., Smits, L. L., Scheltens, P., Stam, C. J. (2012). Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease. Neuroimage 59(4), 3085-3093.
15. De Haan, W., Pijnenburg, Y. A., Strijers, R. L., Van Der Made, Y., Van Der Flier, W. M., Scheltens, P., and Stam, C. J. (2009). Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory. BMC Neurosci. 10(1), 101, doi:10.1186/1471-2202-10-101.
16. Delbeuck, X., Van der Linden, M., and Collette, F., (2003). Alzheimer's Disease as a Disconnection Syndrome?. Neuropsychol Rev 13 (2), 79-92.
17. Delorme, A. and Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1), 9-21.
18. Drzezga, A., Becker, J. A., Van Dijk, K. R., Sreenivasan, A., Talukdar, T., Sullivan, C., Schultz, A. P., Sepulcre, J., Putcha, D., Greve, D., Johnson, K. A., and Sperling, R. A. (2011). Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain 134(6), 1635-1646.
19. Dubois, B., and Albert, M. L. (2004). Amnestic MCI or prodromal Alzheimer's disease?. Lancet Neurol. 3(4), 246-248.
20. Frantzidis, C. A., Bratsas, C., Papadelis, C. L., Konstantinidis, E., Pappas, C., and Bamidis, P. D. (2010). Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli. IEEE Trans. Inf. Technol. Biomed. 14(3), 589-597.
21. Frantzidis, C. A., Ladas, A.-K., I., Vivas, A. B., Tsolaki, M., and Bamidis, P. D. (2014). Cognitive and physical training for the elderly: evaluating outcome efficacy by means of neurophysiological synchronization. Int. J. Psychophysiol. doi: 10.1016/j.ijpsycho.2014.01.007.
22. Gonzalez-Palaua F, Franco M, Bamidis P.D, Losada R, Parra E, Papageorgiou S and Vivas AB. (2014). The effects of a computer-based cognitive and physical training program in a healthy and mildly cognitive impaired aging sample. Aging & Mental Health, In Press
23. Gudmundsson, S., Runarsson, T.P., Sigurdsson, S., Eiriksdottir, G., and Johnsen, K. (2007). Reliability of quantitative EEG features. Clin. Neurophysiol. 118 (10), 2162-2171
24. Hämälainen, A., Pihlajamäki, M., Tanila, H., Hänninen, T., Niskanen, E., Tervo, S., Karjalainen, P.A., Vanninen, R.L., and Soininen, H. (2007). Increased fMRI responses during encoding in mild cognitive impairment. Neurobiol. Aging 28, 1889-1903.
25. He, Y., Chen, Z., and Evans, A. (2008). Structural insights into aberrant topological patterns of large scale cortical networks in Alzheimer's disease. J. Neurosci. 28(18), 4756-4766.
26. Hsu, Y.F., Huang, Y.Z., Lin, Y.Y., Tang, C.W., Liao, K.K., Lee, P.L., Tsai, Y.A., Cheng, H.L., Cheng, H., Chem, C.M., Lee, I.H. (2012). Intermittent theta burst stimulation over ipsilesional primary motor cortex of subacute ischemic stroke patients: a pilot study. Brain Stimul. 6 (2), 166-174.
27. Lehmann, M., Koedam, E. L., Barnes, J., Bartlett, J. W., Barkhof, F., Wattjes, M. P., Schott, J. M., Scheltens, P., and Fox, N. C. (2013). Visual ratings of atrophy in MCI: prediction of conversion and relationship with CSF biomarkers. Neurobiol. Aging 34(1), 73-82.
28. Lithari, C., Klados, M. A., Pappas, C., Albani, M., Kapoukranidou, D., Kovatsi, L., Bamidis, P. D., and Papadelis, C. L. (2012). Alcohol Affects the Brain's Resting-State Network in Social Drinkers. PLoS One 7(10), e48641, doi: 10.1371/journal.pone.0048641.
29. McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., and Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer's disease Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 34(7), 939-939.
30. Mitchell, A. J., and Shiri-Feshki, M. (2009). Rate of progression of mild cognitive impairment to dementia-meta-analysis of 41 robust inception cohort studies. Acta Psychiatr. Scand., 119(4), 252-265.
31. Moretti, D. V., Frisoni, G. B., Fracassi, C., Pievani, M., Geroldi, C., Binetti, G., Rossini, P. M., and Zanetti, O. (2011). MCI patients' EEGs show group differences between those who progress and those who do not progress to AD. Neurobiol. Aging 32(4), 563-571.
32. Moretti, D. V., Prestia, A., Fracassi, C., Binetti, G., Zanetti, O., and Frisoni, G. B. (2012). Specific EEG changes associated with atrophy of hippocampus in subjects with mild cognitive impairment and Alzheimer's disease. Int. J. Alzheimers Dis., Vol. 2012, Article ID 253153, doi:10.1155/2012/253153.
33. Moretti, D. V. D., Paternicò, Binetti, G., Zanetti, O., and Frisoni, G. B. (2013). EEG upper/low alpha frequency power ratio relates to temporo-parietal brain atrophy and memory performances in mild cognitive impairment. Front. Aging Neurosci., doi: 10.3389/fnagi.2013.00063.
34. Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., and Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56(3), 303.
35. Petersen R C. Mild cognitive impairment as a diagnostic entity. J Intern. Med. (2004) 256(3): 183- 194.
36. Petersen, R. C., Roberts, R. O., Knopman, D. S., Boeve, B. F., Geda, Y. E., Ivnik, R. J., Smith, G. E., and Jack Jr, C. R. (2009). Mild cognitive impairment: ten years later. Arch. Neurol. 66(12), 1447.
37. Qi, Z., Wu, X., Wang, Z., Zhang, N., Dong, H., Yao, L., and Li, K. (2010). Impairment and compensation coexist in amnestic MCI default mode network. Neuroimage 50(1), 48-55.
38. Quian Quiroga, R., and Schürmann, M. (1999). Functions and sources of event-related EEG alpha oscillations studied with the Wavelet Transform. Clin Neurophysiol 110(4), 643-654.
39. Rosso, O. A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Schurmann, M., and Basar, E. (2001). Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J. Neurosci. Methods 105(1), 65-76.
40. Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A., Maris, E., Barkhof, F., Scheltens, P., and Stam, C. J. (2010). Loss of 'small-world'networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity. PLoS One, 5(11), e13788, doi: 10.1371/journal.pone.0013788.
41. Seo, E. H., Lee, D. Y., Lee, J. M., Park, J. S., Sohn, B. K., Lee, D. S., Choe, Y. M., Woo, J. I. (2013). Whole-brain Functional Networks in Cognitively Normal, Mild Cognitive Impairment, and Alzheimer's Disease. PloS one, 8(1), e53922, doi: 10.1371/journal.pone.0053922.
42. Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack Jr., C. R., Kaye, J., Montine, T. J., P ark, D. C., Reiman, E. M., Rowe, C. C., Siemers, E., Stern, Y., Yaffe, K., Carrillo, M. C., Thies, B., Morrison-Bogorad, M., Wagster, M. V., and Phelps, C. H. (2011). Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 7(3), 280-292.
43. Stam, C. J., Jones, B. F., Nolte, G., Breakspear, M., and Scheltens, P. (2007). Small-world networks and functional connectivity in Alzheimer's disease. Cereb. Cortex 17(1), 92-99.
44. Stam, C. J., and Reijneveld, J. C. (2007). Graph theoretical analysis of complex networks in the brain., Nonlinear Biomed. Phys. 1(1), 3.
45. Stam, C. J., De Haan, W., Daffertshofer, A., Jones, B. F., Manshanden, I., van Walsum, A. V. C., Montez, T., Verbunt, J. B. A., de Munck, J. C., van Dijk, B. W., Berendse, H. W., and Scheltens, P. (2009). Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain 132(1), 213-224.
46. Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS Comput. Biol. 4(6), e1000100, doi: 10.1371/journal.pcbi.1000100.
47. Toussaint, P. J., Perlbarg, V., Bellec, P., Desarnaud, S., Lacomblez, L., Doyon, J., Habert, M.-O., and Benali, H. (2012). Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses. NeuroImage 63(2), 936-946.
48. Unser, M., Akram A., and Murray E. (1992). On the asymptotic convergence of B-spline wavelets to Gabor functions. IEEE Trans Inf Theory, 38.2, 864-872.
49. Van Dijk, K. R., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., and Buckner, R. L. (2010). Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J. Neurophysiol. 103(1), 297-321.
50. Vanderstichele, H., Bibl, M., Engelborghs, S., Le Bastard, N., Lewczuk, P., Molinuevo, Parnetti, L., Perret-Liaudet, A., Shaw, L. M., Teunissen, C., Wouters, D., J., and Blennow, K. (2012). Standardization of preanalytical aspects of cerebrospinal fluid biomarker testing for Alzheimer's disease diagnosis: a consensus paper from the Alzheimer's Biomarkers Standardization Initiative. Alzheimers Dement. 8(1), 65-73.
51. Vos, S. J., van Rossum, I. A., Verhey, F., Knol, D. L., Soininen, H., Wahlund, L. O., Hampel, H., Tsolaki, M., Minthon, L., Frisoni, G. B., Froelich, L., Nobili, F., van der Flier, W., Blennow, K., Wolz, R., Scheltens, P., and Visser, P. J. (2013). Prediction of Alzheimer disease in subjects with amnestic and nonamnestic MCI. Neurol. 80(12), 1124-1132.
52. Wang, J., Zuo, X., Dai, Z., Xia, M., Zhao, Z., Zhao, X., Jia, J., Han, Y., and He, Y. (2013). Disrupted functional brain connectome in individuals at risk for Alzheimer's disease. Biol. Psychiatry 73(5), 472-481.
53. Watts, D. J.and Strogatz, S. H. (1998). Collective dynamics of 'small-world'networks. Nature 393(6684), 440-442.
54. Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C., and Jiang, T. (2010). Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Computat. Biol., 6(11), e1001006, doi: 10.1371/journal.pcbi.1001006.
55. Zhao, X., Liu, Y., Wang, X., Liu, B., Xi, Q., Guo, Q., Jiang, H., Jiang, T., Wang, P. (2012). Disrupted small-world brain networks in moderate Alzheimer's disease: a resting-state FMRI study. PLoS One, 7(3), e33540, doi:10.1371/journal.pone.0033540.