1. Анохин К.В. Молекулярная генетика памяти: когнитивная регуляция экспрессии генов в мозге при обучении // В сб.: Успехи функциональной нейрохимии. Санкт-Петербург: Издательство Санкт-Петербургского государственного университета, 2003. С. 33-45.
2. Малых С.Б. Генетически информативные исследования нейрофизиологических характеристик // В кн.: Геномика поведения: детское развитие и образование / Под ред. Малых С.Б., Ковас Ю.В., Гайсиной Д.А. - Томск: Издательский Дом Томского государственного университета, 2016. С. 332-381.
3. Петровский Е.Д., Савостьянов А.Н., Савелов А.А. и др. Влияние полиморфизма аллелей серотонинового транспортера на индивидуальные особенности мозговой гемодинамики у людей в условиях экспериментальной парадигмы "стоп-сигнал" // Вавиловский журнал генетики и селекции. 2014. Т. 18. № 4/3. С. 1281-1288.
4. Штарк М.Б., Коростышевская А.М., Резакова М.В., Савелов А.А. Функциональная магнитно-резонансная томография и нейронауки // Усп. физиол. наук. 2012. Т. 43. № 1. С. 3-29.
5. Alberini C.M., Kandel E.R. The regulation of transcription in memory consolidation // Cold Spring Harb. Perspect. Biol. 2015. V. 7. № 1. Article № a021741. https://doi.org/10.1101/cshperspect.a021741
6. Anderson K.M., Krienen F.M., Choi E.Y. et al. Gene expression links functional networks across cortex and striatum // Nat. Commun. 2018. V. 9. № 1. Article № 1428. https://doi.org/10.1038/s41467-018-03811-x
7. Arefin T.M., Mechling A.E., Meirsman A.C. et al. Remodeling of sensorimotor brain connectivity in Gpr88-deficient mice // Brain Connect. 2017. V. 7. № 8. P. 526-540. https://doi.org/10.1089/brain.2017.0486
8. Arnatkeviiūtė A., Fulcher B.D., Pocock R., Fornito A. Hub connectivity, neuronal diversity, and gene expression in the Caenorhabditis elegans connectome // PLoS Comput. Biol. 2018. V. 14. № 2. Article № e1005989. https://doi.org/10.1371/journal.pcbi.1005989
9. Averbeck B.B., Seo M. The statistical neuroanatomy of frontal networks in the macaque // PLoS Comput. Biol. 2008. V. 4. № 4. Article № e1000050. https://doi.org/10.1371/journal.pcbi.1000050
10. Baruch L., Itzkovitz S., Golan-Mashiach M. et al. Using expression profiles of Caenorhabditis elegans neurons to identify genes that mediate synaptic connectivity // PLoS Comput. Biol. 2008. V. 4. № 7. Article № e1000120. https://doi.org/10.1371/journal.pcbi.1000120
11. Bassett D.S., Sporns O. Network neuroscience // Nat. Neurosci. 2017. V. 20. № 3. P. 353-364. https://doi.org/10.1038/nn.4502
12. Bernard A., Lubbers L.S., Tanis K.Q. et al. Transcriptional architecture of the primate neocortex // Neuron. 2012. V. 73. № 6. P. 1083-1099. https://doi.org/10.1016/j.neuron.2012.03.002
13. Berry A.S., Blakely R.D., Sarter M., Lustig C. Cholinergic capacity mediates prefrontal engagement during challenges to attention: evidence from imaging genetics // Neuroimage. 2015. V. 108. P. 386-395. https://doi.org/10.1016/j.neuroimage.2014.12.036
14. Berry A.S., Demeter E., Sabhapathy S. et al. Disposed to distraction: genetic variation in the cholinergic system influences distractibility but not time-on-task effects // J. Cogn. Neurosci. 2014. V. 26. № 9. P. 1981-1991. https://doi.org/10.1162/jocn_a_00607
15. Berto S., Wang G.Z., Germi J. et al. Human genomic signatures of brain oscillations during memory encoding // Cereb. Cortex. 2018. V. 28. № 5. P. 1733-1748. https://doi.org/10.1093/cercor/bhx083
16. Bota M., Dong H.W., Swanson L.W. Brain architecture management system // Neuroinformatics. 2005. V. 3. № 1. P. 15-48. https://doi.org/10.1385/NI:3:1:015
17. Braitenberg V., Schüz A. Cortex: Statistics and geometry of neuronal connectivity. Berlin: Springer, 1998. 249 p. https://doi.org/10.1007/978-3-662-03733-1
18. Bressler S.L. Large-scale cortical networks and cognition // Brain Res. Brain Res. Rev. 1995. V. 20. № 3. P. 288-304. https://doi.org/10.1016/0165-0173(94)00016-I
19. Buckholtz J.W., Meyer-Lindenberg A. Psychopathology and the human connectome: toward a transdiagnostic model of risk for mental illness // Neuron. 2012. V. 74. № 6. P. 990-1004. https://doi.org/10.1016/j.neuron.2012.06.002
20. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems // Nat. Rev. Neurosci. 2009. V. 10. № 3. P. 186-198. https://doi.org/10.1038/nrn2575
21. Chiesa P.A., Cavedo E., Lista S. et al. Revolution of resting-state functional neuroimaging genetics in Alzheimer's disease // Trends Neurosci. 2017. V. 40. № 8. P. 469-480. https://doi.org/10.1016/j.tins.2017.06.002
22. Colclough G.L., Smith S.M., Nichols T.E. et al. The heritability of multi-modal connectivity in human brain activity // Elife. 2017. V. 6. Article № e20178. https://doi.org/10.7554/eLife.20178
23. Constable R.T., Scheinost D., Finn E.S. et al. Potential use and challenges of functional connectivity mapping in intractable epilepsy // Front. Neurol. 2013. V. 4. Article № 39. https://doi.org/10.3389/fneur.2013.00039
24. Contreras J.A., Goñi J., Risacher S.L. et al. The structural and functional connectome and prediction of risk for cognitive impairment in older adults // Curr. Behav. Neurosci. Rep. 2015. V. 2. № 4. P. 234-245. https://doi.org/10.1007/s40473-015-0056-z
25. Crossley N.A., Mechelli A., Scott J. et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders // Brain. 2014. V. 137. № 8. P. 2382-2395. https://doi.org/10.1093/brain/awu132
26. Diez I., Sepulcre J. Neurogenetic profiles delineate large-scale connectivity dynamics of the human brain // Nat. Commun. 2018. V. 9. № 1. Article № 3876. https://doi.org/10.1038/s41467-018-06346-3
27. Dima D., Jogia J., Collier D. et al. Independent modulation of engagement and connectivity of the facial network during affect processing by CACNA1C and ANK3 risk genes for bipolar disorder // JAMA Psychiatry. 2013. V. 70. № 12. P. 1303-1311. https://doi.org/10.1001/jamapsychiatry.2013.2099
28. Dresler M., Shirer W.R., Konrad B.N. et al. Mnemonic training reshapes brain networks to support superior memory // Neuron. 2017. V. 93. № 5. P. 1227-1235. https://doi.org/10.1016/j.neuron.2017.02.003
29. Erk S., Meyer-Lindenberg A., Linden D.E.J. et al. Replication of brain function effects of a genome-wide supported psychiatric risk variant in the CACNA1C gene and new multi-locus effects // Neuroimage. 2014. V. 94. P. 147-154. https://doi.org/10.1016/j.neuroimage.2014.03.007
30. Erk S., Meyer-Lindenberg A., Schnell K. et al. Brain function in carriers of a genome-wide supported bipolar disorder variant // Arch. Gen. Psychiatry. 2010. V. 67. № 8. P. 803-811. https://doi.org/10.1001/archgenpsychiatry.2010.94
31. Esslinger C., Walter H., Kirsch P. et al. Neural mechanisms of a genome-wide supported psychosis variant // Science. 2009. V. 324. № 5927. Article № 605. https://doi.org/10.1126/science.1167768
32. Fakhry A., Ji S. High-resolution prediction of mouse brain connectivity using gene expression patterns // Methods. 2015. V. 73. P. 71-78. https://doi.org/10.1016/j.ymeth.2014.07.011
33. Fan C.C., Smeland O.B., Schork A.J. et al. Beyond heritability: improving discoverability in imaging genetics // Hum. Mol. Genet. 2018. V. 27. № R1. P. R22-R28. https://doi.org/10.1093/hmg/ddy082
34. Fazio L., Pergola G., Papalino M. et al. Transcriptomic context of DRD1 is associated with prefrontal activity and behavior during working memory // Proc. Natl. Acad. Sci. USA. 2018. V. 115. № 21. P. 5582-5587. https://doi.org/10.1073/pnas.1717135115
35. Fornito A., Zalesky A., Bassett D.S. et al. Genetic influences on cost-efficient organization of human cortical functional networks // J. Neurosci. 2011. V. 31. № 9. P. 3261-3270. https://doi.org/10.1523/JNEUROSCI.4858-10.2011
36. Fox A.S., Chang L.J., Gorgolewski K.J., Yarkoni T. Bridging psychology and genetics using large-scale spatial analysis of neuroimaging and neurogenetic data // bioRxiv. 2014. (https://www.biorxiv.org/content/early/2014/12/09/012310) https://doi.org/10.1101/012310
37. Franzmeier N., Rubinski A., Neitzel J. et al. Functional connectivity associated with tau levels in ageing, Alzheimer's, and small vessel disease // Brain. 2019. V. 142. № 4. P. 1093-1107. https://doi.org/10.1093/brain/awz026
38. French L., Pavlidis P. Relationships between gene expression and brain wiring in the adult rodent brain // PLoS Comput. Biol. 2011. V. 7. № 1. Article № e1001049. https://doi.org/10.1371/journal.pcbi.1001049
39. French L., Tan P.P., Pavlidis P. Large-scale analysis of gene expression and connectivity in the rodent brain: insights through data integration // Front. Neuroinf. 2011. V. 5. Article № 12. https://doi.org/10.3389/fninf.2011.00012
40. Fries P. Rhythms for cognition: Communication through coherence // Neuron. 2015. V. 88. № 1. P. 220-235. https://doi.org/10.1016/j.neuron.2015.09.034
41. Friston K.J. Functional and effective connectivity: a review // Brain Connect. 2011. V. 1. № 1. P. 13-36. https://doi.org/10.1089/brain.2011.0008
42. Fulcher B.D., Fornito A. A transcriptional signature of hub connectivity in the mouse connectome // Proc. Natl. Acad. Sci. USA. 2016. V. 113. № 5. P. 1435-1440. https://doi.org/10.1073/pnas.1513302113
43. Gamazon E.R., Wheeler H.E., Shah K.P. et al. A gene-based association method for mapping traits using reference transcriptome data // Nat. Genet. 2015. V. 47. № 9. P. 1091-1098. https://doi.org/10.1038/ng.3367
44. Ganglberger F., Kaczanowska J., Penninger J.M. et al. Predicting functional neuroanatomical maps from fusing brain networks with genetic information // NeuroImage. 2018. V. 170. P. 113-120. https://doi.org/10.1016/j.neuroimage.2017.08.070
45. Glahn D.C., Winkler A.M., Kochunov P. et al. Genetic control over the resting brain // Proc. Natl. Acad. Sci. USA. 2010. V. 107. № 3. P. 1223-1228. https://doi.org/10.1073/pnas.0909969107
46. Hawrylycz M., Miller J.A., Menon V. et al. Canonical genetic signatures of the adult human brain // Nat. Neurosci. 2015. V. 18. № 12. P. 1832-1844. https://doi.org/10.1038/nn.4171
47. Heck A., Fastenrath M., Ackermann S. et al. Converging genetic and functional brain imaging evidence links neuronal excitability to working memory, psychiatric disease, and brain activity // Neuron. 2014. V. 81. № 5. P. 1203-1213. https://doi.org/10.1016/j.neuron.2014.01.010
48. Hellwig B. A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex // Biol. Cybern. 2000. V. 82. № 2. P. 111-121. https://doi.org/10.1007/PL00007964
49. Ji S., Fakhry A., Deng H. Integrative analysis of the connectivity and gene expression atlases in the mouse brain // Neuroimage. 2014. V. 84. P. 245-253. https://doi.org/10.1016/j.neuroimage.2013.08.049
50. Kaufman A., Dror G., Meilijson I., Ruppin E. Gene expression of Caenorhabditis elegans neurons carries information on their synaptic connectivity // PLoS Comput. Biol. 2006. V. 2. № 12. Article № e167. https://doi.org/10.1371/journal.pcbi.0020167
51. Kepa A., Martinez Medina L., Erk S. et al. Associations of the intellectual disability gene MYT1L with Helix-Loop-Helix gene expression, hippocampus volume and hippocampus activation during memory retrieval // Neuropsychopharmacology. 2017. V. 42. № 13. P. 2516-2526. https://doi.org/10.1038/npp.2017.91
52. Kong R., Li J., Orban C. et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion // Cereb. Cortex. 2019. V. 29. № 6. P. 2533-2551. https://doi.org/10.1093/cercor/bhy123
53. Kong X.Z., Song Y., Zhen Z., Liu J. Genetic variation in S100B modulates neural processing of visual scenes in Han Chinese // Cereb. Cortex. 2017. V. 27. № 2. P. 1326-1336. https://doi.org/10.1093/cercor/bhv322
54. Krienen F.M., Yeo B.T.T., Ge T. et al. Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain // Proc. Natl Acad. Sci. USA. 2016. V. 113. № 4. P. E469-E478. https://doi.org/10.1073/pnas.1510903113
55. Krug A., Witt S.H., Backes H. et al. A genome-wide supported variant in CACNA1C influences hippocampal activation during episodic memory encoding and retrieval // Eur. Arch. Psychiatry Clin. Neurosci. 2014. V. 264. № 2. P. 103-110. https://doi.org/10.1007/s00406-013-0428-x
56. Lein E.S., Hawrylycz M.J., Ao N. et al. Genome-wide atlas of gene expression in the adult mouse brain // Nature. 2007. V. 445. № 7124. P. 168-176. https://doi.org/10.1038/nature05453
57. Liégeois F., Baldeweg T., Connelly A. et al. Language fMRI abnormalities associated with FOXP2 gene mutation // Nat. Neurosci. 2003. V. 6. № 11. P. 1230-1237. https://doi.org/10.1038/nn1138
58. Liégeois F., Morgan A.T., Connelly A., Vargha-Khadem F. Endophenotypes of FOXP2: dysfunction within the human articulatory network // Eur. J. Paediatr. Neurol. 2011. V. 15. № 4. P. 283-288. https://doi.org/10.1016/j.ejpn.2011.04.006
59. Lisachev P.D., Shtark M.B. Long-term potentiation-associated gene expression: involvement of the tumour protein p53 // In: The Hippocampus - Plasticity and Functions / Ed. A. Stuchlik - London: IntechOpen, 2018. P. 49-64. https://doi.org/10.5772/intechopen.73219
60. Mattar M.G., Wymbs N.F., Bock A.S. et al. Predicting future learning from baseline network architecture // Neuroimage. 2018. V. 172. P. 107-117. https://doi.org/10.1016/j.neuroimage.2018.01.037
61. McClearn G.E., Johansson B., Berg S. et al. Substantial genetic influence on cognitive abilities in twins 80 or more years old // Science. 1997. V. 276. № 5318. P. 1560-1563. https://doi.org/10.1126/science.276.5318.1560
62. McFarland D.J. How neuroscience can inform the study of individual differences in cognitive abilities // Rev. Neurosci. 2017. V. 28. № 4. P. 343-362. https://doi.org/10.1515/revneuro-2016-0073
63. McIntosh A.R. Towards a network theory of cognition // Neural Netw. 2000. V. 13. № 8-9. P. 861-870. https://doi.org/10.1016/S0893-6080(00)00059-9
64. Mears D., Pollard H.B. Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease // J. Neurosci. Res. 2016. V. 94. № 6. P. 590-605. https://doi.org/10.1002/jnr.23705
65. Mills B.D., Grayson D.S., Shunmugavel A. et al. Correlated gene expression and anatomical communication support synchronized brain activity in the mouse functional connectome // J. Neurosci. 2018. V. 38. № 25. P. 5774-5787. https://doi.org/10.1523/JNEUROSCI.2910-17.2018
66. Miranda-Dominguez O., Feczko E., Grayson D.S. et al. Heritability of the human connectome: A connectotyping study // Netw. Neurosci. 2018. V. 2. № 2. P. 175-199. https://doi.org/10.1162/netn_a_00029
67. Moseley R.L., Ypma R.J., Holt R.J. et al. Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents // Neuroimage Clin. 2015. V. 9. P. 140-152. https://doi.org/10.1016/j.nicl.2015.07.015
68. Mufford M.S., Stein D.J., Dalvie S. et al. Neuroimaging genomics in psychiatry - a translational approach // Genome Med. 2017. V. 9. № 1. Article № 102. https://doi.org/10.1186/s13073-017-0496-z
69. Panizzon M.S., Neale M.C., Docherty A.R. et al. Genetic and environmental architecture of changes in episodic memory from middle to late middle age // Psychol. Aging. 2015. V. 30. № 2. P. 286-300. https://doi.org/10.1037/pag0000023
70. Pantazatos S.P., Li X. Commentary: BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241-4. // Front. Neurosci. 2017. V. 11. Article № 412. https://doi.org/10.3389/fnins.2017.00412
71. Park M.T.M., Raznahan A., Shaw P. et al. Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia // J. Psychiatry Neurosci. 2018. V. 43. № 3. P. 201-212. https://doi.org/10.1503/jpn.170094
72. Peng Q., Schork A., Bartsch H. et al. Conservation of distinct genetically-mediated human cortical pattern // PLoS Genet. 2016. V. 12. № 7. Article № e1006143. https://doi.org/10.1371/journal.pgen.1006143
73. Pergola G., Di Carlo P., Andriola I. et al. Combined effect of genetic variants in the GluN2B coding gene (GRIN2B) on prefrontal function during working memory performance // Psychol. Med. 2016. V. 46. № 6. P. 1135-1150. https://doi.org/10.1017/S0033291715002639
74. Pietzuch M., King A.E., Ward D.D., Vickers J.C. The influence of genetic factors and cognitive reserve on structural and functional resting-state brain networks in aging and Alzheimer's disease // Front. Aging Neurosci. 2019. V. 11. Article № 30. https://doi.org/10.3389/fnagi.2019.00030
75. Polimanti R., Meda S.A., Pearlson G.D. et al. S100A10 identified in a genome-wide gene × cannabis dependence interaction analysis of risky sexual behaviours // J. Psychiatry Neurosci. 2017. V. 42. № 4. P. 252-261. https://doi.org/10.1503/jpn.160189
76. Polimanti R., Yang C., Zhao H., Gelernter J. Dissecting ancestry genomic background in substance dependence genome-wide association studies // Pharmacogenomics. 2015. V. 16. № 13. P. 1487-1498. https://doi.org/10.2217/pgs.15.91
77. Quintana D.S., Rokicki J., van der Meer D. et al. Oxytocin pathway gene networks in the human brain // Nat. Commun. 2019. V. 10. № 1. Article № 668. https://doi.org/10.1038/s41467-019-08503-8
78. Raum H., Dietsche B., Nagels A. et al. A genome-wide supported psychiatric risk variant in NCAN influences brain function and cognitive performance in healthy subjects // Hum. Brain Mapp. 2015. V. 36. № 1. P. 378-390. https://doi.org/10.1002/hbm.22635
79. Richiardi J., Altmann A., Milazzo A.C. et al. Correlated gene expression supports synchronous activity in brain networks // Science. 2015. V. 348. № 6240. P. 1241-1244. https://doi.org/10.1126/science.1255905
80. Rodríguez-Rojo I.C., Cuesta P., López M.E. et al. BDNF Val66Met polymorphism and gamma band disruption in resting state brain functional connectivity: A magnetoencephalography study in cognitively intact older females // Front. Neurosci. 2018. V. 12. Article № 684. https://doi.org/10.3389/fnins.2018.00684
81. Roostaei T., Sadaghiani S., Park M.T. et al. Channelopathy-related SCN10A gene variants predict cerebellar dysfunction in multiple sclerosis // Neurology. 2016. V. 86. № 5. P. 410-417. https://doi.org/10.1212/WNL.0000000000002326
82. Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations // Neuroimage. 2010. V. 52. № 3. P. 1059-1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
83. Rubinov M., Ypma R.J., Watson C., Bullmore E.T. Wiring cost and topological participation of the mouse brain connectome // Proc. Natl. Acad. Sci. USA. 2015. V. 112. № 32. P. 10032-10037. https://doi.org/10.1073/pnas.1420315112
84. Sadaghiani S., Ng B., Altmann A. et al. Overdominant effect of a CHRNA4 polymorphism on cingulo-opercular network activity and cognitive control // J. Neurosci. 2017. V. 37. № 40. P. 9657-9666. https://doi.org/10.1523/JNEUROSCI.0991-17.2017
85. Scannell J.W., Blakemore C., Young M.P. Analysis of connectivity in the cat cerebral cortex // J. Neurosci. 1995. V. 15. № 2. P. 1463-1483. https://doi.org/10.1523/JNEUROSCI.15-02-01463.1995
86. Schmitt J.E., Lenroot R.K., Wallace G.L. et al. Identification of genetically mediated cortical networks: a multivariate study of pediatric twins and siblings // Cereb. Cortex. 2008. V. 18. № 8. P. 1737-1747. https://doi.org/10.1093/cercor/bhm211
87. Scult M.A., Hariri A.R. A brief introduction to the neurogenetics of cognition-emotion interactions // Curr. Opin. Behav. Sci. 2018. V. 19. P. 50-54. https://doi.org/10.1016/j.cobeha.2017.09.014
88. Shtark M.B., Kozlova L.I., Bezmaternykh D.D. et al. Neuroimaging study of alpha and beta EEG biofeedback effects on neural networks // Appl. Psychophysiol. Biofeedback. 2018. V. 43. № 2. P. 169-178. https://doi.org/10.1007/s10484-018-9396-2
89. Sinclair B., Hansell N.K., Blokland G.A. et al. Heritability of the network architecture of intrinsic brain functional connectivity // Neuroimage. 2015. V. 121. P. 243-252. https://doi.org/10.1016/j.neuroimage.2015.07.048
90. Smit D.J., Stam C.J., Posthuma D. et al. Heritability of "small-world" networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity // Hum. Brain Mapp. 2008. V. 29. № 12. P. 1368-1378. https://doi.org/10.1002/hbm.20468
91. Stevens A.A., Tappon S.C., Garg A., Fair D.A. Functional brain network modularity captures inter- and intra-individual variation in working memory capacity // PLoS One. 2012. V. 7. № 1. Article № e30468. https://doi.org/10.1371/journal.pone.0030468
92. Suo X.S., Lei D.L., Li L.L. et al. Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders // J. Psychiatry Neurosci. 2018. V. 43. № 6. Article № 427. https://doi.org/10.1503/jpn.170214
93. Trost S., Diekhof E.K., Mohr H. et al. Investigating the impact of a genome-wide supported bipolar risk variant of MAD1L1 on the human reward system // Neuropsychopharmacology. 2016. V. 41. № 11. P. 2679-2687. https://doi.org/10.1038/npp.2016.70
94. van den Heuvel M.P., Hulshoff Pol H.E. Exploring the brain network: a review on resting-state fMRI functional connectivity // Eur. Neuropsychopharmacol. 2010. V. 20. № 8. P. 519-534. https://doi.org/10.1016/j.euroneuro.2010.03.008
95. Varadan V., Miller D.M. 3rd, Anastassiou D. Computational inference of the molecular logic for synaptic connectivity in C. elegans // Bioinformatics. 2006. V. 22. № 14. P. e497-e506. https://doi.org/10.1093/bioinformatics/btl224
96. Vértes P.E., Rittman T., Whitaker K.J. et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks // Philos. Trans. R. Soc. Lond. B Biol. Sci. 2016. V. 371. № 1705. Article № 20150362. https://doi.org/10.1098/rstb.2015.0362
97. Vecchio F., Miraglia F., Rossini P.M. Connectome: Graph theory application in functional brain network architecture // Clinical Neurophysiology Practice. 2017. V. 2. P. 206-213. doi.org/https://doi.org/10.1016/j.cnp.2017.09.003
98. Volk H.E., McDermott K.B., Roediger H.L.3rd, Todd R.D. Genetic influences on free and cued recall in long-term memory tasks // Twin Res. Hum. Genet. 2006. V. 9. № 5. P. 623-631. https://doi.org/10.1375/183242706778553462
99. Wang C., Zhang Y., Liu B. et al. Dosage effects of BDNF Val66Met polymorphism on cortical surface area and functional connectivity // J. Neurosci. 2014. V. 34. P. 2645-2651. https://doi.org/10.1523/JNEUROSCI.3501-13.2014
100. Wang G.Z., Belgard T.G., Mao D. et al. Correspondence between resting-state activity and brain gene expression // Neuron. 2015. V. 88. № 4. P. 659-666. https://doi.org/10.1016/j.neuron.2015.10.022
101. Wang J., Zuo X., He Y. Graph-based network analysis of resting-state functional MRI // Front. Syst. Neurosci. 2010. V. 4. Article № 16. https://doi.org/10.3389/fnsys.2010.00016
102. Wen J., Goyal M.S., Astafiev S.V. et al. Genetically defined cellular correlates of the baseline brain MRI signal // Proc. Natl. Acad. Sci. USA. 2018. V. 115. № 41. P. E9727-E9736. https://doi.org/10.1073/pnas.1808121115
103. White J.G., Southgate E., Thomson J.N., Brenner S. The structure of the nervous system of the nematode Caenorhabditis elegans // Philos. Trans. R. Soc. Lond. B Biol. Sci. 1986. V. 314. № 1165. P. 1-340. https://doi.org/10.1098/rstb.1986.0056
104. Wolf L., Goldberg C., Manor N. et al. Gene expression in the rodent brain is associated with its regional connectivity // PLoS Comput Biol. 2011. V. 7. № 5. Article № e1002040. https://doi.org/10.1371/journal.pcbi.1002040
105. Zapala M.A., Hovatta I., Ellison J.A. et al. Adult mouse brain gene expression patterns bear an embryologic imprint // Proc. Natl. Acad. Sci. USA. 2005. V. 102. № 29. P. 10357-10362. https://doi.org/10.1073/pnas.0503357102
106. Zhang N., Liu H., Qin W. et al. APOE and KIBRA interactions on brain functional connectivity in healthy young adults // Cereb. Cortex. 2017. V. 27. № 10. P. 4797-4805. https://doi.org/10.1093/cercor/bhw276
107. Yang Z., Zuo X.-N., McMahon K.L. et al. Genetic and environmental contributions to functional connectivity architecture of the human brain // Cereb. Cortex. 2016. V. 26. № 5. P. 2341-2352. https://doi.org/10.1093/cercor/bhw027
108. Yarkoni T., Poldrack R.A., Nichols T.E. et al. Large-scale automated synthesis of human functional neuroimaging data // Nat. Methods. 2011. V. 8. № 8. P. 665-670. https://doi.org/10.1038/nmeth.1635
109. Yin Y., Hou Z., Wang X. et al. The BDNF Val66Met polymorphism, resting-state hippocampal functional connectivity and cognitive deficits in acute late-onset depression // J. Affect. Disord. 2015. V. 183. P. 22-30. https://doi.org/10.1016/j.jad.2015.04.050