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  • Review Article
  • Published:

Genetic variants in Alzheimer disease — molecular and brain network approaches

Nature Reviews Neurology volume 12, pages 413–427 (2016)Cite this article

Subjects

Key Points

  • Genetic findings in late-onset Alzheimer disease (AD) have not yet resulted in strategies to prevent or treat AD

  • Examining the position of genes carrying disease-associated variants in large-scale molecular networks can aid identification of coherent disease mechanisms

  • Not all network approaches are equal: recent approaches involve networks that are directed (with causal links), and specific to the tissue and disease state

  • AD neuropathology and AD-associated genetic variants decrease efficiency of information transfer in the brain connectome, which can be quantified by measuring structural and functional patterns in brain networks

  • Construction of multiscale models of the effects of AD-associated genetic variants with large neuronal simulations is now feasible, and will be useful to understand the effects of such variants and to screen therapeutics in silico

Abstract

Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.

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Figure 1: Effects of Alzheimer disease genetic variants on molecular networks and global brain topology.
Figure 2: Components of a multiscale model of the effects of genetic variants.

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References

  1. Ferri, C. P. et al. Global prevalence of dementia: a Delphi consensus study. Lancet 366, 2112–2117 (2006).

    Article  Google Scholar 

  2. Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).

    Article  CAS  PubMed  Google Scholar 

  3. Jack, C. R. et al. Introduction to the recommendations from the National Institute on Aging–Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 7, 257–262 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Dunkin, J. J. & Anderson-Hanley, C. Dementia caregiver burde: a review of the literature and guidelines for assessment and intervention. Neurology 51, S53–S60 (1998).

    Article  CAS  PubMed  Google Scholar 

  5. Hurd, M. D., Martorell, P., Delavande, A., Mullen, K. J. & Langa, K. M. Monetary costs of dementia in the United States. N. Engl. J. Med. 368, 1326–1334 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Langa, K. M. et al. National estimates of the quantity and cost of informal caregiving for the elderly with dementia. J. Gen. Intern. Med. 16, 770–778 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hebert, L. E., Weuve, J., Scherr, P. A. & Evans, D. A. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology 80, 1778–1783 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Gatz, M. et al. Heritability for Alzheimer's disease: the study of dementia in Swedish twins. J. Gerontol. A Biol. Sci. Med. Sci. 52, M117–M125 (1997).

    Article  CAS  PubMed  Google Scholar 

  9. Janssen, J. et al. Early onset familial Alzheimer's disease mutation frequency in 31 families. Neurology 60, 235–239 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Campion, D. et al. Mutations of the presenilin I gene in families with early-onset Alzheimer's disease. Hum. Mol. Genet. 4, 2373–2377 (1995).

    Article  CAS  PubMed  Google Scholar 

  11. Barnes, D. E. & Yaffe, K. The projected effect of risk factor reduction on Alzheimer's disease prevalence. Lancet Neurol. 10, 819–828 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lambert, J.-C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat. Genet. 45, 1452–1458 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Karch, C. M., Cruchaga, C. & Goate, A. M. Alzheimer's disease genetics: from the bench to the clinic. Neuron 83, 11–26 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bertram, L. & Tanzi, R. E. Thirty years of Alzheimer's disease genetics: the implications of systematic meta-analyses. Nat. Rev. Neurosci. 9, 768–778 (2008).

    Article  CAS  PubMed  Google Scholar 

  15. Schellenberg, G. D. & Montine, T. J. The genetics and neuropathology of Alzheimer's disease. Acta Neuropathol. 124, 305–323 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Sleegers, K. et al. The pursuit of susceptibility genes for Alzheimer's disease: progress and prospects. Trends Genet. 26, 84–93 (2010).

    Article  CAS  PubMed  Google Scholar 

  17. Gillis, J. & Pavlidis, P. The impact of multifunctional genes on 'guilt by association' analysis. PLoS ONE 6, e17258 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gillis, J. & Pavlidis, P. Assessing identity, redundancy and confounds in Gene Ontology annotations over time. Bioinformatics 29, 476–482 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Khatri, P., Sirota, M. & Butte, A. J. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 8, e1002375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Parikshak, N. N., Gandal, M. J. & Geschwind, D. H. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat. Rev. Genet. 16, 441–458 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gaiteri, C., Ding, Y., French, B., Tseng, G. C. & Sibille, E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 13, 13–24 (2014). This article summarizes the origin of coexpression networks — one of the most common types of molecular network studied in the context of AD.

    Article  CAS  PubMed  Google Scholar 

  22. Karch, C. M. & Goate, A. M. Alzheimer's disease risk genes and mechanisms of disease pathogenesis. Biol. Psychiatry 77, 43–51 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. Goate, A. et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer's disease. Nature 349, 704–706 (1991).

    Article  CAS  PubMed  Google Scholar 

  24. Levy-Lahad, E. et al. Candidate gene for the chromosome 1 familial Alzheimer's disease locus. Science 269, 973–977 (1995).

    Article  CAS  PubMed  Google Scholar 

  25. Rogaev, E. et al. Familial Alzheimer's disease in kindreds with missense mutations in a gene on chromosome 1 related to the Alzheimer's disease type 3 gene. Nature 376, 775–778 (1995).

    Article  CAS  PubMed  Google Scholar 

  26. Corder, E. et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science 261, 921–923 (1993).

    Article  CAS  PubMed  Google Scholar 

  27. Cummings, J. L., Morstorf, T. & Zhong, K. Alzheimer's disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res. Ther. 6, 37 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Reiman, E. M. et al. Alzheimer's Prevention Initiative: a plan to accelerate the evaluation of presymptomatic treatments. J. Alzheimers Dis. 26, 321 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Bateman, R. J. et al. Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N. Engl. J. Med. 367, 795–804 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Roses, A. D. et al. New applications of disease genetics and pharmacogenetics to drug development. Curr. Opin. Pharmacol. 14, 81–89 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. Reisberg, B. et al. A 24-week open-label extension study of memantine in moderate to severe Alzheimer disease. Arch. Neurol. 63, 49–54 (2006).

    Article  PubMed  Google Scholar 

  32. Schneider, L. S., Dagerman, K. S., Higgins, J. P. & McShane, R. Lack of evidence for the efficacy of memantine in mild Alzheimer disease. Arch. Neurol. 68, 991–998 (2011).

    Article  PubMed  Google Scholar 

  33. Doody, R. S. et al. Phase 3 trials of solanezumab for mild-to-moderate Alzheimer's disease. N. Engl. J. Med. 370, 311–321 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Salloway, S. et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer's disease. N. Engl. J. Med. 370, 322–333 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Holmes, C. et al. Long-term effects of Aβ42 immunisation in Alzheimer's disease: follow-up of a randomised, placebo-controlled phase I trial. Lancet 372, 216–223 (2008).

    Article  CAS  PubMed  Google Scholar 

  36. Rinne, J. O. et al. 11C-PiB PET assessment of change in fibrillar amyloid-β load in patients with Alzheimer's disease treated with bapineuzumab: a phase 2, double-blind, placebo-controlled, ascending-dose study. Lancet Neurol. 9, 363–372 (2010).

    Article  CAS  PubMed  Google Scholar 

  37. Jack, C. R. Jr. et al. Age-specific population frequencies of cerebral β-amyloidosis and neurodegeneration among people with normal cognitive function aged 50–89 years: a cross-sectional study. Lancet Neurol. 13, 997–1005 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Boyle, P. A. et al. Much of late life cognitive decline is not due to common neurodegenerative pathologies. Ann. Neurol. 74, 478–489 (2013). This article shows that AD and age-related neuropathologies do not fully account for cognitive decline observed with ageing.

    Article  PubMed  Google Scholar 

  39. Murray, M. E. et al. Clinicopathologic and 11C-Pittsburgh compound B implications of Thal amyloid phase across the Alzheimer's disease spectrum. Brain http://dx.doi.org/10.1093/brain/awv050 (2015).

  40. Sperling, R. A. et al. The A4 study: stopping AD before symptoms begin? Science Transl. Med. 6, 228fs213 (2014).

    Article  CAS  Google Scholar 

  41. Villemagne, V. L. et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. Lancet Neurol. 12, 357–367 (2013). This work detected a long prodrome of amyloid build-up prior to the onset of cognitive symptoms of AD.

    Article  CAS  PubMed  Google Scholar 

  42. Dean, D. C. et al. Brain differences in infants at differential genetic risk for late-onset Alzheimer disease: a cross-sectional imaging study. JAMA Neurol. 71, 11–22 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Jansen, W. J. et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA 313, 1924–1938 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Escott-Price, V. et al. Gene-wide analysis detects two new susceptibility genes for Alzheimer's disease. PLoS ONE 9, e94661 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Naj, A. C. et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nat. Genet. 43, 436–441 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sabuncu, M. R. et al. The association between a polygenic Alzheimer score and cortical thickness in clinically normal subjects. Cereb. Cortex 22, 2653–2661 (2012).

    Article  PubMed  Google Scholar 

  47. Harris, S. E. et al. Polygenic risk for Alzheimer's disease is not associated with cognitive ability or cognitive aging in non-demented older people. J. Alzheimers Dis. 39, 565–574 (2014).

    Article  PubMed  Google Scholar 

  48. Ridge, P. G., Mukherjee, S., Crane, P. K., Kauwe, J. S. & Alzheimer's Disease Genetics Consortium. Alzheimer's disease: analyzing the missing heritability. PLoS ONE 8, e79771 (2013). This study considers the origin of the 'missing heritability' of AD, which appears when traditional additive models are used.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cruchaga, C. et al. Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer's disease. Nature 505, 550–554 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. Vardarajan, B. N. et al. Coding mutations in SORL1 and Alzheimer disease. Ann. Neurol. 77, 215–227 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kobolt, D. et al. Exome-sequencing in a large dataset of late-onset families with Alzheimer's disease. Alzheimers Dement. 11, 359 (2015).

    Article  Google Scholar 

  52. Farfel, J. et al. Relation of genomic variants for Alzheimer's disease dementia to common neuropathologies. Neurology (in the press) (2016).

  53. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. He, X., Qian, W., Wang, Z., Li, Y. & Zhang, J. Prevalent positive epistasis in Escherichia coli and Saccharomyces cerevisiae metabolic networks. Nat. Genet. 42, 272–276 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hu, T. et al. Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinformatics 12, 364 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pandey, A. et al. Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder. Transl. Psychiatry 2, e154 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Darabos, C. & Moore, J. H. in Epistasis 269–283 (Springer, 2015).

    Google Scholar 

  58. Moore, J. H. & Williams, S. M. Epistasis and its implications for personal genetics. Am. J. Hum. Genet. 85, 309–320 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Combarros, O., Cortina-Borja, M., Smith, A. D. & Lehmann, D. J. Epistasis in sporadic Alzheimer's disease. Neurobiol. Aging 30, 1333–1349 (2009).

    Article  CAS  PubMed  Google Scholar 

  60. Carrasquillo, M. M. et al. Replication of BIN1 association with Alzheimer's disease and evaluation of genetic interactions. J. Alzheimers Dis. 24, 751–758 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Gusareva, E. S. et al. Genome-wide association interaction analysis for Alzheimer's disease. Neurobiol. Aging 35, 2436–2443 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bullock, J. M. et al. Discovery by the Epistasis Project of an epistatic interaction between the GSTM3 gene and the HHEX/IDE/KIF11 locus in the risk of Alzheimer's disease. Neurobiol. Aging 34, 1309.e1–1309.e7 (2013).

    Article  CAS  Google Scholar 

  63. Meda, S. A. et al. Genetic interactions associated with 12-month atrophy in hippocampus and entorhinal cortex in Alzheimer's Disease Neuroimaging Initiative. Neurobiol. Aging 34, 1518.e9–1518.e18 (2013).

    Article  CAS  Google Scholar 

  64. Hibar, D. P. et al. Genome-wide interaction analysis reveals replicated epistatic effects on brain structure. Neurobiol. Aging 36, S151–S158 (2015).

    Article  CAS  PubMed  Google Scholar 

  65. Zhong, Q. et al. Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol. 5, 321 (2009). This work found that edge-specific (interface-specific) protein interactions, as opposed to all protein interactions, can be disrupted by many disease-related genetic variants.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Mosca, R. et al. dSysMap: exploring the edgetic role of disease mutations. Nat. Methods 12, 167–168 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Guo, Y. et al. Dissecting disease inheritance modes in a three-dimensional protein network challenges the 'guilt-by-association' principle. Am. J. Hum. Genet. 93, 78–89 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Vardarajan, B. N. et al. Rare coding mutations identified by sequencing of Alzheimer disease genome-wide association studies loci. Ann. Neurol. 78, 487–498 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Jin, S. C. et al. Coding variants in TREM2 increase risk for Alzheimer's disease. Hum. Mol. Genet. 23, 5838–5846 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Feldman, I., Rzhetsky, A. & Vitkup, D. Network properties of genes harboring inherited disease mutations. Proc. Natl Acad. Sci. USA 105, 4323–4328 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Mitra, K., Carvunis, A.-R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719–732 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Goh, K.-I. et al. The human disease network. Proc. Natl Acad. Sci. USA 104, 8685–8690 (2007). This study found widespread coherence in disease organization through the use of phenotypic similarity or gene signature overlap.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Köhler, S., Bauer, S., Horn, D. & Robinson, P. N. Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82, 949–958 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Raj, T. et al. Alzheimer disease susceptibility loci: evidence for a protein network under natural selection. Am. J. Hum. Genet. 90, 720–726 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. O'Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012). This group used protein networks to show that, despite being seemingly sporadic, de novo mutations associated with autism are connected in a functionally related network that contains genes previously implicated in the disease.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Gulsuner, S. et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154, 518–529 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Hormozdiari, F., Penn, O., Borenstein, E. & Eichler, E. E. The discovery of integrated gene networks for autism and related disorders. Genome Res. 25, 142–154 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Pierson, E. et al. Sharing and specificity of co-expression networks across 35 human tissues. PLoS Comput. Biol. 11, e1004220 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  83. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  84. Miller, J. A., Woltjer, R. L., Goodenbour, J. M., Horvath, S. & Geschwind, D. H. Genes and pathways underlying regional and cell type changes in Alzheimer's disease. Genome Med. 5, 48 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Neumann, H. & Daly, M. J. Variant TREM2 as risk factor for Alzheimer's disease. N. Engl. J. Med. 368, 182–184 (2013).

    Article  CAS  PubMed  Google Scholar 

  87. Hofree, M., Shen, J. P., Carter, H., Gross, A. & Ideker, T. Network-based stratification of tumor mutations. Nat. Methods 10, 1108–1115 (2013). In this study, protein interaction networks were used to organize mutations from individual patients.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Gligorijevic, V., Malod-Dognin, N. & Przulj, N. Patient-specific data fusion for cancer stratification and personalized treatment. Pac. Symp. Biocomput. 21, 321–332 (2016).

    PubMed  Google Scholar 

  89. Leiserson, M. D. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106–114 (2015).

    Article  CAS  PubMed  Google Scholar 

  90. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008).

    Article  CAS  PubMed  Google Scholar 

  91. Tasaki, S. et al. Bayesian network reconstruction using systems genetics data: comparison of MCMC methods. Genetics 199, 973–989 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  92. de la Fuente, A. From 'differential expression'to 'differential networking' — identification of dysfunctional regulatory networks in diseases. Trends Genet. 26, 326–333 (2010).

    Article  CAS  PubMed  Google Scholar 

  93. Rhinn, H. et al. Alternative α-synuclein transcript usage as a convergent mechanism in Parkinson's disease pathology. Nat. Commun. 3, 1084 (2012).

    Article  CAS  PubMed  Google Scholar 

  94. Amar, D., Safer, H. & Shamir, R. Dissection of regulatory networks that are altered in disease via differential co-expression. PLoS Comput. Biol. 9, e1002955 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Miller, J. A., Horvath, S. & Geschwind, D. H. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc. Natl Acad. Sci. USA 107, 12698–12703 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Schneider, J. A., Arvanitakis, Z., Leurgans, S. E. & Bennett, D. A. The neuropathology of probable Alzheimer disease and mild cognitive impairment. Ann. Neurol. 66, 200–208 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Beecham, G. W. et al. Genome-wide association meta-analysis of neuropathologic features of Alzheimer's disease and related dementias. 10, e1004606 (2014).

  98. Bennett, D. et al. Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology 66, 1837–1844 (2006).

    Article  CAS  PubMed  Google Scholar 

  99. Schneider, J. A., Arvanitakis, Z., Bang, W. & Bennett, D. A. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology 69, 2197–2204 (2007). Here, the researchers found that multiple types of pathology, in addition to classic AD pathology, account for clinical dementia in an older population.

    Article  PubMed  Google Scholar 

  100. De Jager, P. L. & Bennett, D. A. An inflection point in gene discovery efforts for neurodegenerative diseases: from syndromic diagnoses toward endophenotypes and the epigenome. JAMA Neurol. 70, 719–726 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Nelson, P. T. et al. ABCC9 gene polymorphism is associated with hippocampal sclerosis of aging pathology. Acta Neuropathol. 127, 825–843 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Hibar, D. P. et al. Common genetic variants influence human subcortical brain structures. Nature 520, 224–229 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Weiner, M. W. et al. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement. 8, S1–S68 (2012).

    Article  PubMed  Google Scholar 

  104. Schroeter, M. L., Stein, T., Maslowski, N. & Neumann, J. Neural correlates of Alzheimer's disease and mild cognitive impairment: a systematic and quantitative meta-analysis involving 1351 patients. Neuroimage 47, 1196–1206 (2009).

    Article  PubMed  Google Scholar 

  105. Scahill, R. I., Schott, J. M., Stevens, J. M., Rossor, M. N. & Fox, N. C. Mapping the evolution of regional atrophy in Alzheimer's disease: unbiased analysis of fluid-registered serial MRI. Proc. Natl Acad. Sci. USA 99, 4703–4707 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Betzel, R. F., Fukushima, M., He, Y., Zuo, X.-N. & Sporns, O. Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks. Neuroimage 127, 287–297 (2016).

    Article  PubMed  Google Scholar 

  107. Fornito, A., Harrison, B. J., Zalesky, A. & Simons, J. S. Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proc. Natl Acad. Sci. USA 109, 12788–12793 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Sadaghiani, S., Poline, J.-B., Kleinschmidt, A. & D'Esposito, M. Ongoing dynamics in large-scale functional connectivity predict perception. Proc. Natl Acad. Sci. USA 112, 8463–8468 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T. & Bullmore, E. Adaptive reconfiguration of fractal small-world human brain functional networks. Proc. Natl Acad. Sci. USA 103, 19518–19523 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. van den Heuvel, M. P., Kahn, R. S., Goñi, J. & Sporns, O. High-cost, high-capacity backbone for global brain communication. Proc. Natl Acad. Sci. USA 109, 11372–11377 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. van den Heuvel, M. P., Stam, C. J., Kahn, R. S. & Pol, H. E. H. Efficiency of functional brain networks and intellectual performance. J. Neurosci. 29, 7619–7624 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Gallos, L. K., Makse, H. A. & Sigman, M. A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks. Proc. Natl Acad. Sci. USA 109, 2825–2830 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Watts, D. J. & Strogatz, S. H. Collective dynamics of 'small-world' networks. Nature 393, 440–442 (1998).

    Article  CAS  PubMed  Google Scholar 

  114. van den Heuvel, M. P. et al. Genetic control of functional brain network efficiency in children. Eur. Neuropsychopharmacol. 23, 19–23 (2013).

    Article  CAS  PubMed  Google Scholar 

  115. Bohlken, M. M. et al. Heritability of structural brain network topology: a DTI study of 156 twins. Hum. Brain Mapp. 35, 5295–5305 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Khazaee, A., Ebrahimzadeh, A. & Babajani-Feremi, A. Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory. Clin. Neurophysiol. 126, 2132–2141 (2015).

    Article  PubMed  Google Scholar 

  117. Wang, J. et al. Disrupted functional brain connectome in individuals at risk for Alzheimer's disease. Biol. Psychiatry 73, 472–481 (2013).

    Article  CAS  PubMed  Google Scholar 

  118. Nir, T. M. et al. Connectivity network measures predict volumetric atrophy in mild cognitive impairment. Neurobiol. Aging 36, S113–S120 (2015).

    Article  PubMed  Google Scholar 

  119. Fischer, F. U., Wolf, D., Scheurich, A., Fellgiebel, A. & Alzheimer's Disease Neuroimaging Initiative. Altered whole-brain white matter networks in preclinical Alzheimer's disease. Neuroimage Clin. 8, 660–666 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Stam, C., Jones, B., Nolte, G., Breakspear, M. & Scheltens, P. Small-world networks and functional connectivity in Alzheimer's disease. Cereb. Cortex 17, 92–99 (2007). This study found initial evidence for conceptualization of widespread connectivity changes in AD as an altered balance of functional modularity and integration.

    Article  CAS  PubMed  Google Scholar 

  121. Lo, C.-Y. et al. Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer's disease. J. Neurosci. 30, 16876–16885 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Myers, N. et al. Within-patient correspondence of amyloid-β and intrinsic network connectivity in Alzheimer's disease. Brain 137, 2052–2064 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Frantzidis, C. A. et al. 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). Front. Aging Neurosci. 6, 224 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Stam, C. et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain 132, 213–224 (2009).

    Article  CAS  PubMed  Google Scholar 

  125. He, Y., Chen, Z. & Evans, A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J. Neurosci. 28, 4756–4766 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Langer, N. et al. Functional brain network efficiency predicts intelligence. Hum. Brain Mapp. 33, 1393–1406 (2012).

    Article  PubMed  Google Scholar 

  127. Liang, P., Wang, Z., Yang, Y., Jia, X. & Li, K. Functional disconnection and compensation in mild cognitive impairment: evidence from DLPFC connectivity using resting-state fMRI. PLoS ONE 6, e22153 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Gardini, S. et al. Increased functional connectivity in the default mode network in mild cognitive impairment: a maladaptive compensatory mechanism associated with poor semantic memory performance. J. Alzheimers Dis. 45, 457–470 (2015).

    Article  PubMed  Google Scholar 

  129. Crossley, N. A. et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 2382–2395 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Buckner, R. L. et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29, 1860–1873 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. de Haan, W., Mott, K., van Straaten, E. C., Scheltens, P. & Stam, C. J. Activity dependent degeneration explains hub vulnerability in Alzheimer's disease. PLoS Comput. Biol. 8, e1002582 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Seo, E. H. et al. Whole-brain functional networks in cognitively normal, mild cognitive impairment, and Alzheimer's disease. PLoS ONE 8, e53922 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Zamora-López, G., Zhou, C. & Kurths, J. Cortical hubs form a module for multisensory integration on top of the hierarchy of cortical networks. Front. Neuroinform. 4, 1 (2010).

    PubMed  PubMed Central  Google Scholar 

  134. de Reus, M. A. & van den Heuvel, M. P. Simulated rich club lesioning in brain networks: a scaffold for communication and integration? Front. Hum. Neurosci. 8, 647 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Wang, J. et al. Apolipoprotein E ε4 modulates functional brain connectome in Alzheimer's disease. Hum. Brain Mapp. 36, 1828–1846 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Dai, Z. et al. Identifying and mapping connectivity patterns of brain network hubs in Alzheimer's disease. Cereb. Cortex 25, 3723–3742 (2015).

    Article  PubMed  Google Scholar 

  137. Daianu, M. et al. Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network. Hum. Brain Mapp. 36, 3087–3103 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  138. Daianu, M. et al. Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer's disease. Hum. Brain Mapp. 37, 868–883 (2016).

    Article  PubMed  Google Scholar 

  139. Filippini, N. et al. Distinct patterns of brain activity in young carriers of the APOE-ε4 allele. Proc. Natl Acad. Sci. USA 106, 7209–7214 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Sheline, Y. I. et al. APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Aβ42. J. Neurosci. 30, 17035–17040 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Patel, K. T. et al. Default mode network activity and white matter integrity in healthy middle-aged ApoE4 carriers. Brain Imaging Behav. 7, 60–67 (2013).

    Article  PubMed  Google Scholar 

  142. Goryawala, M. et al. Apolipoprotein-E4 (ApoE4) carriers show altered small-world properties in the default mode network of the brain. Biomed. Phys. Eng. Express 1, 015001 (2015).

    Article  Google Scholar 

  143. Trachtenberg, A. J. et al. The effects of APOE on the functional architecture of the resting brain. Neuroimage 59, 565–572 (2012).

    Article  CAS  PubMed  Google Scholar 

  144. Shaw, P. et al. Cortical morphology in children and adolescents with different apolipoprotein E gene polymorphisms: an observational study. Lancet Neurol. 6, 494–500 (2007).

    Article  CAS  PubMed  Google Scholar 

  145. Jones, D. et al. Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology 77, 1524–1531 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Greicius, M. D., Srivastava, G., Reiss, A. L. & Menon, V. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc. Natl Acad. Sci. USA 101, 4637–4642 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Langbaum, J. B. et al. Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Neuroimage 45, 1107–1116 (2009).

    Article  PubMed  Google Scholar 

  148. Chhatwal, J. P. et al. Impaired default network functional connectivity in autosomal dominant Alzheimer disease. Neurology 81, 736–744 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  149. Jahanshad, N. et al. in Multimodal Brain Image Analysis 29–40 (Springer, 2012).

    Book  Google Scholar 

  150. Brier, M. R. et al. Functional connectivity and graph theory in preclinical Alzheimer's disease. Neurobiol. Aging 35, 757–768 (2014).

    Article  PubMed  Google Scholar 

  151. Tijms, B. M. et al. Single-subject grey matter graphs in Alzheimer's disease. PLoS ONE 8, e58921 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Sanz-Arigita, E. J. et al. Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity. PLoS ONE 5, e13788 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Tijms, B. M. et al. Gray matter network disruptions and amyloid beta in cognitively normal adults. Neurobiol. Aging 37, 154–160 (2016).

    Article  CAS  PubMed  Google Scholar 

  154. de Haan, W. et al. Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory. BMC Neurosci. 10, 101 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  155. Fischer, F. U., Wolf, D., Scheurich, A., Fellgiebel, A. & Alzheimer's Disease Neuroimaging Initiative. Altered whole-brain white matter networks in preclinical Alzheimer's disease. Neuroimage Clin. 8, 660–666 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  156. Zhao, X. et al. Disrupted small-world brain networks in moderate Alzheimer's disease: a resting-state FMRI study. PLoS ONE 7, e33540 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Li, Y., Qin, Y., Chen, X. & Li, W. Exploring the functional brain network of Alzheimer's disease: based on the computational experiment. PLoS ONE 8, e73186 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Supekar, K., Menon, V., Rubin, D., Musen, M. & Greicius, M. D. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS Comput. Biol. 4, e1000100 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Yao, Z. et al. Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Comput. Biol. 6, e1001006 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Honey, C. et al. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl Acad. Sci. USA 106, 2035–2040 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Hosseini, S. H. & Kesler, S. R. Comparing connectivity pattern and small-world organization between structural correlation and resting-state networks in healthy adults. Neuroimage 78, 402–414 (2013).

    Article  PubMed  Google Scholar 

  162. Tagliazucchi, E. & Laufs, H. Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron 82, 695–708 (2014).

    Article  CAS  PubMed  Google Scholar 

  163. Chang, C. et al. Association between heart rate variability and fluctuations in resting-state functional connectivity. Neuroimage 68, 93–104 (2013).

    Article  PubMed  Google Scholar 

  164. Airan, R. D. et al. Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI. Hum. Brain Mapp. 37, 1986–1997 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  165. Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L. & Breakspear, M. Time-resolved resting-state brain networks. Proc. Natl Acad. Sci. USA 111, 10341–10346 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Allen, E. A. et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24, 663–676 (2014).

    Article  PubMed  Google Scholar 

  167. Hutchison, R. M. et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013).

    Article  PubMed  Google Scholar 

  168. Rashid, B., Damaraju, E., Pearlson, G. D. & Calhoun, V. D. Dynamic connectivity states estimated from resting fMRI Identify differences among schizophrenia, bipolar disorder, and healthy control subjects. Front. Hum. Neurosci. 8, 897 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  169. Kaiser, R. H. et al. Dynamic resting-state functional connectivity in major depression. Neuropsychopharmacology 41, 1822–1830 (2016).

    Article  CAS  PubMed  Google Scholar 

  170. Jones, D. T. et al. Non-stationarity in the 'resting brain's' modular architecture. PLoS ONE 7, e39731 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Peraza, L. R., Taylor, J.-P. & Kaiser, M. Divergent brain functional network alterations in dementia with Lewy bodies and Alzheimer's disease. Neurobiol. Aging 36, 2458–2467 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  172. Sompolinsky, H. Computational neuroscience: beyond the local circuit. Curr. Opin. Neurobiol. http://dx.doi.org/10.1016/j.conb.2014.02.002 (2014).

  173. Bhalla, U. S. Molecular computation in neurons: a modeling perspective. Curr. Opin. Neurobiol. 25, 31–37 (2014).

    Article  CAS  PubMed  Google Scholar 

  174. Linderman, J. J., Cilfone, N. A., Pienaar, E., Gong, C. & Kirschner, D. E. A multi-scale approach to designing therapeutics for tuberculosis. Integr. Biol. (Camb.) 7, 591–609 (2015).

    Article  CAS  Google Scholar 

  175. Obiol-Pardo, C., Gomis-Tena, J., Sanz, F., Saiz, J. & Pastor, M. A multiscale simulation system for the prediction of drug-induced cardiotoxicity. J. Chem. Inf. Model. 51, 483–492 (2011).

    Article  CAS  PubMed  Google Scholar 

  176. Osborne, J. et al. A hybrid approach to multi-scale modelling of cancer. Phil. Trans. R. Soc. A Math. Phys. Eng. Sci. 368, 5013–5028 (2010).

    Article  CAS  Google Scholar 

  177. Silva, J. R. et al. A multiscale model linking ion-channel molecular dynamics and electrostatics to the cardiac action potential. Proc. Natl Acad. Sci. USA 106, 11102–11106 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Barttfeld, P. et al. Signature of consciousness in the dynamics of resting-state brain activity. Proc. Natl Acad. Sci. USA 112, 887–892 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Spiros, A., Carr, R. & Geerts, H. Not all partial dopamine D2 receptor agonists are the same in treating schizophrenia. Exploring the effects of bifeprunox and aripiprazole using a computer model of a primate striatal dopaminergic synapse. Neuropsychiatr. Dis. Treat. 6, 589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  180. Anticevic, A. et al. NMDA receptor function in large-scale anticorrelated neural systems with implications for cognition and schizophrenia. Proc. Natl Acad. Sci. USA 109, 16720–16725 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Vattikuti, S. & Chow, C. C. A computational model for cerebral cortical dysfunction in autism spectrum disorders. Biol. Psychiatry 67, 672–678 (2010).

    Article  PubMed  Google Scholar 

  182. Foley, A. M., Ammar, Z. M., Lee, R. H. & Mitchell, C. S. Systematic review of the relationship between amyloid-β levels and measures of transgenic mouse cognitive deficit in Alzheimer's disease. J. Alzheimers Dis. 44, 787–795 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Franco, R. & Cedazo-Minguez, A. Successful therapies for Alzheimer's disease: why so many in animal models and none in humans. Front. Pharmacol. 5, 146 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Burns, T. C., Li, M. D., Mehta, S., Awad, A. J. & Morgan, A. A. Mouse models rarely mimic the transcriptome of human neurodegenerative diseases: a systematic bioinformatics-based critique of preclinical models. Eur. J. Pharmacol. 759, 101–117 (2015).

    Article  CAS  PubMed  Google Scholar 

  185. Przytycka, T. M., Singh, M. & Slonim, D. K. Toward the dynamic interactome: it's about time. Brief. Bioinform. 11, 15–29 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. Ohyama, T. et al. A multilevel multimodal circuit enhances action selection in Drosophila. Nature 520, 633–639 (2015).

    Article  CAS  PubMed  Google Scholar 

  187. Schadt, E. E., Buchanan, S., Brennand, K. J. & Merchant, K. M. Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders. Front. Pharmacol. 5, 252 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Southern, J. et al. Multi-scale computational modelling in biology and physiology. Prog. Biophys. Mol. Biol. 96, 60–89 (2008).

    Article  CAS  PubMed  Google Scholar 

  189. Neymotin, S. et al. Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex. Neuroscience 316, 344–366 (2016).

    Article  CAS  PubMed  Google Scholar 

  190. Rowan, M. S., Neymotin, S. A. & Lytton, W. W. Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front. Comput. Neurosci. 8, 39 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  191. DeWoskin, D. et al. Distinct roles for GABA across multiple timescales in mammalian circadian timekeeping. Proc. Natl Acad. Sci. USA 112, E3911–E3919 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Myung, J. et al. GABA-mediated repulsive coupling between circadian clock neurons in the SCN encodes seasonal time. Proc. Natl Acad. Sci. USA 112, E3920–E3929 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  193. Sanz Leon, P. et al. The Virtual Brain: a simulator of primate brain network dynamics. Front. Neuroinform. 7, 10 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  194. Izhikevich, E. M. & Edelman, G. M. Large-scale model of mammalian thalamocortical systems. Proc. Natl Acad. Sci. USA 105, 3593–3598 (2008). This work showed that a highly simplified neuronal model could be connected realistically with other models to reproduce large-scale patterns of brain activation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Eliasmith, C. & Trujillo, O. The use and abuse of large-scale brain models. Curr. Opin. Neurobiol. 25, 1–6 (2014).

    Article  CAS  PubMed  Google Scholar 

  196. Cabral, J. et al. Structural connectivity in schizophrenia and its impact on the dynamics of spontaneous functional networks. Chaos 23, 046111 (2013).

    Article  PubMed  Google Scholar 

  197. Chaudhuri, R., Knoblauch, K., Gariel, M.-A., Kennedy, H. & Wang, X.-J. A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex. Neuron 88, 419–431 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  198. Nageswaran, J. M., Dutt, N., Krichmar, J. L., Nicolau, A. & Veidenbaum, A. V. A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors. Neural Netw. 22, 791–800 (2009).

    Article  PubMed  Google Scholar 

  199. Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).

    Article  CAS  PubMed  Google Scholar 

  200. Potjans, T. C. & Diesmann, M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cereb. Cortex 24, 785–806 (2014).

    Article  PubMed  Google Scholar 

  201. Izhikevich, E. M. Simple model of spiking neurons. IEEE Trans. Neural Netw. 14, 1569–1572 (2003).

    Article  CAS  PubMed  Google Scholar 

  202. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  203. Oldoni, F. et al. Post-GWAS methodologies for localisation of functional non-coding variants: ANGPTL3. Atherosclerosis 246, 193–201 (2015).

    Article  CAS  PubMed  Google Scholar 

  204. Rajagopal, N. et al. High-throughput mapping of regulatory DNA. Nat. Biotechnol. 34, 167–174 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  205. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  206. Maurano, M. T. et al. Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo. Nat. Genet. 47, 1393–1401 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Akbarian, S. et al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  208. Gutenkunst, R. N. et al. Universally sloppy parameter sensitivities in systems biology models. PLoS Comput. Biol. 3, e189 (2007).

    Article  CAS  PubMed Central  Google Scholar 

  209. Karr, J. R. et al. A whole-cell computational model predicts phenotype from genotype. Cell 150, 389–401 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  210. Jolivet, R., Coggan, J. S., Allaman, I. & Magistretti, P. J. Multi-timescale modeling of activity-dependent metabolic coupling in the neuron-glia-vasculature ensemble. PLoS Comput. Biol. 11, e1004036–e1004036 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  211. Li, W.-K., Hausknecht, M. J., Stone, P. & Mauk, M. D. Using a million cell simulation of the cerebellum: network scaling and task generality. Neural Netw. 47, 95–102 (2013).

    Article  PubMed  Google Scholar 

  212. Insel, T. R., Landis, S. C. & Collins, F. S. The NIH brain initiative. Science 340, 687–688 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  213. Toga, A. W., Clark, K. A., Thompson, P. M., Shattuck, D. W. & Van Horn, J. D. Mapping the human connectome. Neurosurgery 71, 1 (2012).

    Article  PubMed  Google Scholar 

  214. Lichtman, J. W., Pfister, H. & Shavit, N. The big data challenges of connectomics. Nat. Neurosci. 17, 1448–1454 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  215. O'Leary, T., Sutton, A. C. & Marder, E. Computational models in the age of large datasets. Curr. Opin. Neurobiol. 32, 87–94 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  216. Rzhetsky, A., Foster, J. G., Foster, I. T. & Evans, J. A. Choosing experiments to accelerate collective discovery. Proc. Natl Acad. Sci. USA 112, 14569–14574 (2015). This work demonstrates that exploration of high risk, interdisciplinary research brings personal benefits and increases efficiency of scientific discovery.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  217. Uzzi, B., Mukherjee, S., Stringer, M. & Jones, B. Atypical combinations and scientific impact. Science 342, 468–472 (2013).

    Article  CAS  PubMed  Google Scholar 

  218. Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419–425 (2013).

    Article  CAS  PubMed  Google Scholar 

  219. Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C. & Morris, Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9, S4 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  220. Muldoon, S. F., Soltesz, I. & Cossart, R. Spatially clustered neuronal assemblies comprise the microstructure of synchrony in chronically epileptic networks. Proc. Natl Acad. Sci. USA 110, 3567–3572 (2013).

    Article  CAS  Google Scholar 

  221. Bonifazi, P. et al. GABAergic hub neurons orchestrate synchrony in developing hippocampal networks. Science 326, 1419–1424 (2009).

    Article  CAS  PubMed  Google Scholar 

  222. Takahashi, N., Sasaki, T., Matsumoto, W., Matsuki, N. & Ikegaya, Y. Circuit topology for synchronizing neurons in spontaneously active networks. Proc. Natl Acad. Sci. USA 107, 10244–10249 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  223. Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).

    Article  CAS  PubMed  Google Scholar 

  224. Dobrin, R. et al. Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol. 10, R55 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  225. Gaiteri, C., Guilloux, J.-P., Lewis, D. A. & Sibille, E. Altered gene synchrony suggests a combined hormone-mediated dysregulated state in major depression. PLoS ONE 5, e9970 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  226. Ardlie, K. G. et al. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  CAS  Google Scholar 

  227. Croft, D. P., Krause, J. & James, R. Social networks in the guppy (Poecilia reticulata). Proc. Biol. Sci. 271, S516–S519 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  228. Kramer, A. D., Guillory, J. E. & Hancock, J. T. Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl Acad. Sci. USA 111, 8788–8790 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  229. Christakis, N. A. & Fowler, J. H. Social contagion theory: examining dynamic social networks and human behavior. Stat. Med. 32, 556–577 (2013).

    Article  PubMed  Google Scholar 

  230. Magger, O., Waldman, Y. Y., Ruppin, E. & Sharan, R. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. PLoS Comput. Biol. 8, e1002690 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  231. Das, J. & Yu, H. HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst. Biol. 6, 92 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  232. Rolland, T. et al. A proteome-scale map of the human interactome network. Cell 159, 1212–1226 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  233. Obayashi, T. et al. COXPRESdb: a database of coexpressed gene networks in mammals. Nucleic Acids Res. 36, D77–D82 (2008).

    Article  CAS  PubMed  Google Scholar 

  234. Debette, S. et al. Genome-wide studies of verbal declarative memory in nondemented older people: the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium. Biol. Psychiatry 77, 749–763 (2015).

    Article  PubMed  Google Scholar 

  235. De Jager, P. L. et al. A genome-wide scan for common variants affecting the rate of age-related cognitive decline. Neurobiol. Aging 33, 1017.e1–1017.e15 (2012).

    Article  CAS  Google Scholar 

  236. Bennett, D. A., Schneider, J. A., Tang, Y., Arnold, S. E. & Wilson, R. S. The effect of social networks on the relation between Alzheimer's disease pathology and level of cognitive function in old people: a longitudinal cohort study. Lancet Neurol. 5, 406–412 (2006).

    Article  PubMed  Google Scholar 

  237. Buchman, A. et al. Total daily physical activity and the risk of AD and cognitive decline in older adults. Neurology 78, 1323–1329 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  238. Wilson, R. S. et al. Life-span cognitive activity, neuropathologic burden, and cognitive aging. Neurology 81, 314–321 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  239. Wilson, R. S. et al. Participation in cognitively stimulating activities and risk of incident Alzheimer disease. JAMA 287, 742–748 (2002).

    Article  PubMed  Google Scholar 

  240. Alladi, S. et al. Bilingualism delays age at onset of dementia, independent of education and immigration status. Neurology 81, 1938–1944 (2013).

    Article  PubMed  Google Scholar 

  241. Stern, Y. et al. Influence of education and occupation on the incidence of Alzheimer's disease. JAMA 271, 1004–1010 (1994).

    Article  CAS  PubMed  Google Scholar 

  242. Hall, C. et al. Cognitive activities delay onset of memory decline in persons who develop dementia. Neurology 73, 356–361 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  243. Stern, Y. What is cognitive reserve? Theory and research application of the reserve concept. J. Int. Neuropsychol. Soc. 8, 448–460 (2002).

    Article  PubMed  Google Scholar 

  244. Landau, S. M. et al. Association of lifetime cognitive engagement and low β-amyloid deposition. Arch. Neurol. 69, 623–629 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  245. Bennett, D., Schneider, J., Wilson, R., Bienias, J. & Arnold, S. Education modifies the association of amyloid but not tangles with cognitive function. Neurology 65, 953–955 (2005).

    Article  CAS  PubMed  Google Scholar 

  246. Deary, I. J., Johnson, W. & Houlihan, L. M. Genetic foundations of human intelligence. Hum. Genet. 126, 215–232 (2009).

    Article  PubMed  Google Scholar 

  247. Davies, G. et al. Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53949). Mol. Psychiatry 20, 183–192 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  248. Chibnik, L. B. et al. CR1 is associated with amyloid plaque burden and age-related cognitive decline. Ann. Neurol. 69, 560–569 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  249. Barral, S. et al. Genotype patterns at PICALM, CR1, BIN1, CLU, and APOE genes are associated with episodic memory. Neurology 78, 1464–1471 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  250. Zhang, X. et al. Bridging integrator 1 (BIN1) genotype effects on working memory, hippocampal volume, and functional connectivity in young healthy individuals. Neuropsychopharmacology 40, 1794–1803 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  251. Carrasquillo, M. M. et al. Late-onset Alzheimer's risk variants in memory decline, incident mild cognitive impairment, and Alzheimer's disease. Neurobiol. Aging 36, 60–67 (2015).

    Article  PubMed  Google Scholar 

  252. Hayden, K. M., Lutz, M. W., Kuchibhatla, M., Germain, C. & Plassman, B. L. Effect of APOE and CD33 on cognitive decline. PLoS ONE 10, e0130419 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by NIH grants U01AG46152, R01AG36042, P30AG10161, RF1AG15819, R01AG17917, R01AG36836.

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Authors and Affiliations

  1. Rush Alzheimer's Disease Center, Rush University Medical Center, 600 S Paulina Street, Chicago, 60612, Illinois, USA

    Chris Gaiteri & David A. Bennett

  2. Department of Statistics, and Medical Genetics; Centre for Molecular and Medicine and Therapeutics, University of British Columbia,

    Sara Mostafavi

  3. Centre for Molecular and Medicine and Therapeutics, University of British Columbia, 950 West 28th Avenue, Vancouver, V5Z 4H4, British Columbia, Canada

    Sara Mostafavi

  4. Department of Psychology, University of Toronto, 100 St. George Street, 4th Floor Sidney Smith Hall, Toronto, M5S 3G3, Ontario, Canada

    Christopher J. Honey

  5. Departments of Neurology and Psychiatry, Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Brigham and Women's Hospital, 75 Francis Street, Boston, 02115, MA, USA

    Philip L. De Jager

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  1. Chris Gaiteri
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All authors researched data for the article, made substantial contributions to discussions of the content, wrote the article, and reviewed and/or edited the manuscript before submission.

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Glossary

Genetic variants

Insertion, deletion or alternative coding of DNA

LOAD

Late-onset Alzheimer disease (LOAD) is the most common form of the neurodegenerative disease, typically diagnosed clinically after the age of 65 years and definitively diagnosed postmortem. LOAD is associated with functionally diverse, weak genetic variants

Multiscale models

Mathematical or conceptual models that couple processes that occur on a varying range of physical or temporal scales, which are typically studied in isolation from each other

Amyloid hypothesis

Proposal according to which the root cause of Alzheimer disease is the accumulation of amyloid-β (Aβ), with nuances around sufficiency and form of amyloid-β responsible for pathogenesis

Amyloid precursor protein

Amyloid precursor protein (APP) is cleaved to form amyloid-β peptides. Presenilin (PSEN1 and PSEN2) mutations promote the cleavage of APP into plaque-forming peptides

Apolipoprotein E

Apolipoprotein E is a protein that physically interacts with amyloid-β (Aβ) and tau; its interaction with Aβ influences plaque aggregation. Dosage of the ε4 allele of the APOE gene is the strongest genetic risk factor for late-onset Alzheimer disease

PPAR-γ

Peroxisome proliferator- activated receptor γ (PPAR-γ) is a component of a nuclear receptor complex that includes the retinoid X receptor. This complex is activated endogenously by fatty acids and leads to transcription of the apolipoprotein E gene, among other genes

Epistasis

When a combination of two or more genetic variants have a greater effect on a phenotype than their linear combination would predict

Pleiotropic effects

The contribution of a single gene, or variants of that gene, to two or more 'non-related' phenotypes

Directed networks

Networks where elements are connected by links with a specific direction that represents an asymmetric temporal or transfer function

Cognitive reserve

Tolerance and adaptation to neuropathology, in part attributed to genetic and lifestyle-associated factors such as education and social activity, and their neural correlates

Small-world organization

Network structure in which most nodes are connected by a small number of hops, yet form relatively isolated (modular) clusters

Clustering coefficient

Measure of modularity around a network node. The coefficient represents the number of connections among neighbours of a node divided by the maximum possible number of connections among those neighbours

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Gaiteri, C., Mostafavi, S., Honey, C. et al. Genetic variants in Alzheimer disease — molecular and brain network approaches. Nat Rev Neurol 12, 413–427 (2016). https://doi.org/10.1038/nrneurol.2016.84

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