Network-based biomarkers in Alzheimer's disease: Review and future directions

Frontiers in Aging Neuroscience

Volumn 6, Issue FEB, 2014, Pages

  Gomez Ramirez, Jaime ^a,b   Wu, Jinglong ^b  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b OKAYAMA UNIVERSITY   (Japan)

Author keywords

Alzheimer's disease; Default mode network DMN; Network degeneration hypothesis; Network based biomarkers; Resting state functional connectivity

Indexed keywords

BIOLOGICAL MARKER;

ALZHEIMER DISEASE; BRAIN FUNCTION; FUNCTIONAL ASSESSMENT; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; NETWORK BASED BIOMARKER APPROACH; NEUROLOGICAL AND SENSORIAL PROCEDURES; NONHUMAN; RESTING STATE FUNCTIONAL MAGNETIC RESONANCE IMAGING; REVIEW;

EID: 84895781080     PISSN: None     EISSN: 16634365     Source Type: Journal    
DOI: 10.3389/fnagi.2014.00012     Document Type: Review

References (72)

1. Ahn, A. C., Tewari, M., Poon, C.-S., and Phillips, R. S. (2006). The limits of reductionism in medicine: could systems biology offer an alternative? PLoS Med. 3:e208. doi:10.1371/journal.pmed.0030208
2. Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., et al. (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, 270-279. doi:10.1016/j.jalz.2011.03.008
3. Balthazar, M. L. F., De Campos, B. M., Franco, A. R., Damasceno, B. P., and Cendes, F. (2014). Whole cortical and default mode network mean functional connectivity as potential biomarkers for mild Alzheimer's disease. Psychiatry Res. 221, 37-42. doi:10.1016/j.pscychresns.2013.10.010
4. Barabási, A.-L., Gulbahce, N., and Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56-68. doi:10.1038/nrg2918
5. Bateman, R. J., Xiong, C., Benzinger, T. L. S., Fagan, A. M., Goate, A., Fox, N. C., et al. (2012). Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N. Engl. J. Med. 367, 795-804. doi:10.1056/NEJMoa1202753
6. Bertram, L., and Tanzi, R. E. (2004). The current status of Alzheimer's disease genetics: what do we tell the patients? Pharmacol. Res. 50, 385-396. doi:10.1016/j.phrs.2003.11.018
7. Biomarkers Definitions Working Group. (2001). Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69, 89-95. doi:10.1067/mcp.2001.113989
8. Biswal, B., Yetkin, F. Z., Haughton, V. M., and Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537-541. doi:10.1002/mrm.1910340409
9. Buckner, R. L., Andrews-Hanna, J. R., and Schacter, D. L. (2008). The brain's default network. Ann. N. Y. Acad. Sci. 1124, 1-38. doi:10.1196/annals.1440.011
10. Buntine, W. L. (1996). A guide to the literature on learning probabilistic networks from data. IEEE Trans. Knowl. Data Eng. 8, 195-210. doi:10.1109/69.494161
11. Chhatwal, J. P., Schultz, A. P., Johnson, K., Benzinger, T. L. S., Jack, C., Ances, B. M., et al. (2013). Impaired default network functional connectivity in autosomal dominant Alzheimer disease. Neurology 81, 736-744. doi:10.1212/WNL.0b013e3182a1aafe
12. Cole, D. M., Smith, S. M., and Beckmann, C. F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front. Syst. Neurosci. 4. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2854531/doi:10.3389/fnsys.2010.00008
13. Comon, P. (1994). Independent component analysis, a new concept? Signal Processing 36, 287-314. doi:10.1016/0165-1684(94)90029-9
14. Corbett, A., and Ballard, C. (2012). New and emerging treatments for Alzheimer's disease. Expert Opin. Emerg. Drugs 17, 147-156. doi:10.1517/14728214.2012.675327
15. Damoiseaux, J. S. (2012). Resting-state fMRI as a biomarker for Alzheimer's disease? Alzheimers Res. Ther. 4, 8. doi:10.1186/alzrt106
16. Damoiseaux, J. S., Prater, K. E., Miller, B. L., and Greicius, M. D. (2012). Functional connectivity tracks clinical deterioration in Alzheimer's disease. Neurobiol. Aging 33, 828.e19-.e30. doi:10.1016/j.neurobiolaging.2011.06.024
17. Dickerson, B. C., Salat, D. H., Bates, J. F., Atiya, M., Killiany, R. J., Greve, D. N., et al. (2004). Medial temporal lobe function and structure in mild cognitive impairment. Ann. Neurol. 56, 27-35. doi:10.1002/ana.20163
18. Doecke, J. D., Laws, S. M., Faux, N. G., Wilson, W., Burnham, S. C., Lam, C.-P., et al. (2012). Blood-based protein biomarkers for diagnosis of Alzheimer disease. Arch. Neurol. 69, 1318-1325. doi:10.1001/archneurol.2012.1282
19. Dubois, B., Feldman, H. H., Jacova, C., Cummings, J. L., Dekosky, S. T., Barberger-Gateau, P., et al. (2010). Revising the definition of Alzheimer's disease: a new lexicon. Lancet Neurol. 9, 1118-1127. doi:10.1016/S1474-4422(10)70223-4
20. Dubois, B., Feldman, H. H., Jacova, C., Dekosky, S. T., Barberger-Gateau, P., Cummings, J., et al. (2007). Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 6, 734-746. doi:10.1016/S1474-4422(07)70178-3
21. Eidelberg, D., and Martin, W. (2013). Different β-amyloid binding patterns in Alzheimer and Parkinson diseases: it's the network! Neurology 81, 516-517. doi:10.1212/WNL.0b013e31829e703e
22. Erler, J. T., and Linding, R. (2010). Network-based drugs and biomarkers. J. Pathol. 220, 290-296. doi:10.1002/path.2646
23. Faria, A. V., Joel, S. E., Zhang, Y., Oishi, K., Van Zjil, P. C. M., Miller, M. I., et al. (2012). Atlas-based analysis of resting-state functional connectivity: evaluation for reproducibility and multi-modal anatomy-function correlation studies. Neuroimage 61, 613-621. doi:10.1016/j.neuroimage.2012.03.078
24. Fransson, P. (2005). Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum. Brain Mapp. 26, 15-29. doi:10.1002/hbm.20113
25. Friston, K. J. (1998). Modes or models: a critique on independent component analysis for fMRI. Trends Cogn. Sci. (Regul. Ed.) 2, 373-375. doi:10.1016/S1364-6613(98)01227-3
26. Gomar, J. J., Bobes-Bascaran, M. T., Conejero-Goldberg, C., Davies, P., Goldberg, T. E., Alzheimer's Disease Neuroimaging Initiative. (2011). Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer's disease neuroimaging initiative. Arch. Gen. Psychiatry 68, 961-969. doi:10.1001/archgenpsychiatry.2011.96
27. Gomez-Ramirez, J., and Sanz, R. (2013). On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology. Prog. Biophys. Mol. Biol. 113, 80-91. doi:10.1016/j.pbiomolbio.2013.03.008
28. Greicius, M. D., Srivastava, G., Reiss, A. L., and Menon, V. (2004). Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. U.S.A. 101, 4637-4642. doi:10.1073/pnas.0308627101
29. Habeck, C., Risacher, S., Lee, G. J., Glymour, M. M., Mormino, E., Mukherjee, S., et al. (2012). Relationship between baseline brain metabolism measured using [18F]FDG PET and memory and executive function in prodromal and early Alzheimer's disease. Brain Imaging Behav. 6, 568-583. doi:10.1007/s11682-012-9208-x
30. Hafkemeijer, A., Van der Grond, J., and Rombouts, S. A. R. B. (2012). Imaging the default mode network in aging and dementia. Biochim. Biophys. Acta 1822, 431-441. doi:10.1016/j.bbadis.2011.07.008
31. Hampel, H., Wilcock, G., Andrieu, S., Aisen, P., Blennow, K., Broich, K., et al. (2011). Biomarkers for Alzheimer's disease therapeutic trials. Prog. Neurobiol. 95, 579-593. doi:10.1016/j.pneurobio.2010.11.005
32. He, Y., Wang, J., Wang, L., Chen, Z. J., Yan, C., Yang, H., et al. (2009). Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS ONE 4:e5226. doi:10.1371/journal.pone.0005226
33. Henriksen, K., O'Bryant, S. E., Hampel, H., Trojanowski, J. Q., Montine, T. J., Jeromin, A., et al. (2014). The future of blood-based biomarkers for Alzheimer's disease. Alzheimers Dement. 10, 115-131. doi:10.1016/j.jalz.2013.01.013
34. Hye, A., Lynham, S., Thambisetty, M., Causevic, M., Campbell, J., Byers, H. L., et al. (2006). Proteome-based plasma biomarkers for Alzheimer's disease. Brain 129(Pt 11), 3042-3050. doi:10.1093/brain/awl279
35. Jack, C. R. (2012). Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play. Radiology 263, 344-361. doi:10.1148/radiol.12110433
36. Kjelvik, G., Sando, S. B., Aasly, J., Engedal, K. A., and White, L. R. (2007). Use of the Brief Smell Identification Test for olfactory deficit in a Norwegian population with Alzheimer's disease. Int. J. Geriatr. Psychiatry 22, 1020-1024. doi:10.1002/gps.1783
37. Knopman, D. S., DeKosky, S. T., Cummings, J. L., Chui, H., Corey-Bloom, J., Relkin, N., et al. (2001). Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 56, 1143-1153. doi:10.1212/WNL.56.9.1143
38. Lee, M. H., Smyser, C. D., and Shimony, J. S. (2013). Resting-state fMRI: a review of methods and clinical applications. AJNR Am. J. Neuroradiol. 34, 1866-1872. doi:10.3174/ajnr. A3263
39. Li, X., Li, T.-Q., Andreasen, N., Wiberg, M. K., Westman, E., and Wahlund, L.-O. (2013). Ratio of Aβ42/P-tau181p in CSF is associated with aberrant default mode network in AD. Sci. Rep. 3, 1339. doi:10.1038/srep01339
40. Liu, Y., Yu, C., Zhang, X., Liu, J., Duan, Y., Alexander-Bloch, A. F., et al. (2013). Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease. Cereb. Cortex doi:10.1093/cercor/bhs410
41. Mattila, J., Koikkalainen, J., Virkki, A., Simonsen, A., Van Gils, M., Waldemar, G., et al. (2011). A disease state fingerprint for evaluation of Alzheimer's disease. J. Alzheimers Dis. 27, 163-176. doi:10.3233/JAD-2011-110365
42. Mattila, J., Soininen, H., Koikkalainen, J., Rueckert, D., Wolz, R., Waldemar, G., et al. (2012). Optimizing the diagnosis of early Alzheimer's disease in mild cognitive impairment subjects. J. Alzheimers Dis. 32, 969-979. doi:10.3233/JAD-2012-120934
43. Mayeux, R., and Schupf, N. (2011). Blood-based biomarkers for Alzheimer's disease: plasma Aβ40 and Aβ42, and genetic variants. Neurobiol. Aging 32(Suppl. 1), S10-S19. doi:10.1016/j.neurobiolaging.2011.09.004
44. 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, 939-939. doi:10.1212/WNL.34.7.939
45. Migon, H.-S., and Gamerman, D. (1999). Statistical Inference: An Integrated Approach. London: CRC press.
46. Pievani, M., De Haan, W., Wu, T., Seeley, W. W., and Frisoni, G. B. (2011). Functional network disruption in the degenerative dementias. Lancet Neurol. 10, 829-843. doi:10.1016/S1474-4422(11)70158-2
47. Raichle, M. E., and Gusnard, D. A. (2005). Intrinsic brain activity sets the stage for expression of motivated behavior. J. Comp. Neurol. 493, 167-176. doi:10.1002/cne.20752
48. Rombouts, S. A. R. B., Barkhof, F., Goekoop, R., Stam, C. J., and Scheltens, P. (2005). Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: an fMRI study. Hum. Brain Mapp. 26, 231-239. doi:10.1002/hbm.20160
49. Rosen, W. G., Mohs, R. C., and Davis, K. L. (1984). A new rating scale for Alzheimer's disease. Am. J. Psychiatry 141, 1356-1364.
50. Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A. R. B., Maris, E., Barkhof, F., et al. (2010). Loss of "small-world" networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity. PLoS ONE 5:e13788. doi:10.1371/journal.pone.0013788
51. Saumier, D., Aisen, P. S., Gauthier, S., Vellas, B., Ferris, S. H., Duong, A., et al. (2009). Lessons learned in the use of volumetric MRI in therapeutic trials in Alzheimer's disease: the ALZHEMED (Tramiprosate) experience. J. Nutr. Health Aging 13, 370-372. doi:10.1007/s12603-009-0047-4
52. Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., and Greicius, M. D. (2009). Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 42-52. doi:10.1016/j.neuron.2009.03.024
53. Serby, M., Larson, P., and Kalkstein, D. (1991). The nature and course of olfactory deficits in Alzheimer's disease. Am. J. Psychiatry 148, 357-360.
54. Sheline, Y. I., Barch, D. M., Price, J. L., Rundle, M. M., Vaishnavi, S. N., Snyder, A. Z., et al. (2009). The default mode network and self-referential processes in depression. Proc. Natl. Acad. Sci. U.S.A. 106, 1942-1947. doi:10.1073/pnas.0812686106
55. Sheline, Y. I., Raichle, M. E., Snyder, A. Z., Morris, J. C., Head, D., Wang, S., et al. (2010). Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol. Psychiatry 67, 584-587. doi:10.1016/j.biopsych.2009.08.024
56. Sperling, R. (2011). Potential of functional MRI as a biomarker in early Alzheimer's disease. Neurobiol. Aging 32(Suppl. 1), S37-S43. doi:10.1016/j.neurobiolaging.2011.09.009
57. Sperling, R. A., Dickerson, B. C., Pihlajamaki, M., Vannini, P., LaViolette, P. S., Vitolo, O. V., et al. (2010). Functional alterations in memory networks in early Alzheimer's disease. Neuromolecular Med. 12, 27-43. doi:10.1007/s12017-009-8109-7
58. Stone, J. V. (2002). Independent component analysis: an introduction. Trends Cogn. Sci. (Regul. Ed.) 6, 59-64. doi:10.1016/S1364-6613(00)01813-1
59. Toledo, J. B., Shaw, L. M., and Trojanowski, J. Q. (2013). Plasma amyloid beta measurements-a desired but elusive Alzheimer's disease biomarker. Alzheimers Res. Ther. 5, 8. doi:10.1186/alzrt162
60. Trushina, E., Dutta, T., Persson, X.-M. T., Mielke, M. M., and Petersen, R. C. (2013). Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer's disease using metabolomics. PLoS ONE 8:e63644. doi:10.1371/journal.pone.0063644
61. Van den Heuvel, M. P., and HulshoffPol, H. E. (2010). Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20, 519-534. doi:10.1016/j.euroneuro.2010.03.008
62. Vanderstichele, H., Van Kerschaver, E., Hesse, C., Davidsson, P., Buyse, M. A., Andreasen, N., et al. (2000). Standardization of measurement of beta-amyloid(1-42) in cerebrospinal fluid and plasma. Amyloid 7, 245-258. doi:10.3109/13506120009146438
63. Wang, J., Wang, L., Zang, Y., Yang, H., Tang, H., Gong, Q., et al. (2009). Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum. Brain Mapp. 30, 1511-1523. doi:10.1002/hbm.20623
64. Wang, J., Zuo, X., and He, Y. (2010). Graph-based network analysis of resting-state functional MRI. Front. Syst. Neurosci. 4. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2893007/doi:10.3389/fnsys.2010.00016
65. Wang, L., Brier, M. R., Snyder, A. Z., Thomas, J. B., Fagan, A. M., Xiong, C., et al. (2013). Cerebrospinal fluid Aβ42, phosphorylated Tau181, and resting-state functional connectivity. JAMA Neurol. 70, 1242-1248. doi:10.1001/jamaneurol.2013.3253
66. Washington, S. D., Gordon, E. M., Brar, J., Warburton, S., Sawyer, A. T., Wolfe, A., et al. (2013). Dysmaturation of the default mode network in autism. Hum. Brain Mapp. doi:10.1002/hbm.22252
67. Wee, C.-Y., Yap, P.-T., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., et al. (2012). Resting-state multi-spectrum functional connectivity networks for identification of MCI patients. PLoS ONE 7:e37828. doi:10.1371/journal.pone.0037828
68. Wu, J., Anwyl, R., and Rowan, M. J. (1995). Beta-Amyloid selectively augments NMDA receptor-mediated synaptic transmission in rat hippocampus. Neuroreport 6, 2409-2413. doi:10.1097/00001756-199511270-00031
69. Wu, X., Li, R., Fleisher, A. S., Reiman, E. M., Guan, X., Zhang, Y., et al. (2011). Altered default mode network connectivity in Alzheimer's disease-a resting functional MRI and Bayesian network study. Hum. Brain Mapp. 32, 1868-1881. doi:10.1002/hbm.21153
70. Yang, J., Ogasa, T., Ohta, Y., Abe, K., and Wu, J. (2010). Decline of human tactile angle discrimination in patients with mild cognitive impairment and Alzheimer's disease. J. Alzheimers Dis. 22, 225-234. doi:10.3233/JAD-2010-100723
71. Yoshiyama, Y., Lee, V. M. Y., and Trojanowski, J. Q. (2012). Therapeutic strategies for tau mediated neurodegeneration. J. Neurol. Neurosurg. Psychiatry 84, 784-795. doi:10.1136/jnnp-2012-303144
72. Zhu, D. C., Majumdar, S., Korolev, I. O., Berger, K. L., and Bozoki, A. C. (2013). Alzheimer's disease and amnestic mild cognitive impairment weaken connections within the default-mode network: a multi-modal imaging study. J. Alzheimers Dis. 34, 969-984. doi:10.3233/JAD-121879