Influence of genetic factors on neurophysiological mechanisms of neurodegenerative diseases

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Abstract

The review summarizes the main results of studies on the influence of genetic factors on neurophysiological changes in neurodegenerative age-related diseases – Alzheimer's (AD), Parkinson's (PD) and Huntington (HD) diseases. In some cases, neurophysiological methods make it possible to detect early changes already at the preclinical stage of neurodegenerative process. Such neurophysiological markers may be considered as endophenotypes and used for the early diagnosis of the diseases. The conducted studies are promising for clarifying which factors underlie the heterogeneity of diseases not only at the genetic level, but also at the neurophysiological level. At the same time, such an approach showed the presence of a number of neurophysiological alterations common to AD, PD, and HD. Disconnection of neural circuits, including interhemispheric disintegration, slowdown of information processes, disinhibition, hyperexcitability and epileptogenesis, as well as alterations in neurovascular coupling, are of great importance for the development of diseases. On the other hand, neurophysiological changes can directly affect the development of the disease, including the genetic level, as evidenced by experimental optogenetic studies, the results of deep brain stimulation and other neuromodulation methods. These data are valuable for a personalized approach to the prevention and treatment of age-dependent neurodegenerative diseases.

About the authors

Natalia V. Ponomareva

Research Center of Neurology

Author for correspondence.
Email: ponomare@yandex.ru
Russian Federation, Moscow

Vitaliy F. Fokin

Research Center of Neurology

Email: ponomare@yandex.ru
Russian Federation, Moscow

Evgeny I. Rogaev

Vavilov Institute of General Genetics, Russian Academy of Sciences; Lomonosov Moscow State University, Center of Genetic and Genetic Technologies; Department of Psychiatry, University of Massachusetts Medical School

Email: ponomare@yandex.ru
Russian Federation, Moscow, Russia; Worcaster, USA

Sergey N. Illarioshkin

Research Center of Neurology

Email: ponomare@yandex.ru
Russian Federation, Moscow

References

  1. Hardy J., Selkoe D.J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 2002; 297 (5580): 353–356. doi: 10.1126/science.1072994. PMID: 12130773.
  2. Illarioshkin S.N. Konformatsionniye bolezni mozga [Conformational diseases of the brain]. M.: Janus-K, 2002. 246 p. (in Russ.)
  3. Illarioshkin S.N., Klyushnikov S.A., Seliverstov Yu.A. Bolezn’ Gentingtona [Huntington’s disease]. M.: ATMO, 2018. 472 p. (in Russ.)
  4. Piradov M.A., Suponeva N.A., Seliverstov Yu.A. et al. [The opportunities of modern imaging methods in the study of spontaneous activity of the brain at rest]. Neurological J 2016; 21: 4–12. doi: 10.18821/1560-9545-2016-21-1-4-12. (in Russ.)
  5. Fokin V.F., Ponomareva N.V. Energeticheskaya fiziologiya mozga [Neuroenergetics and brain physiology]. M. Antidor, 2003. 288 p. (in Russ.)
  6. Babiloni C., Del Percio C., Lizio R. et al. Abnormalities of cortical neural synchronization mechanisms in subjects with mild cognitive impairment due to Alzheimer’s and Parkinson’s diseases: an EEG study. J Alzheimers Dis 2017; 59:339–358. doi: 10.3233/JAD-160883. PMID: 28621693.
  7. Ponomareva N.V., Korovaitseva G.I., Rogaev E.I. EEG alterations in non-demented individuals related to apolipoprotein E genotype and to risk of Alzheimer disease. Neurobiol Aging 2008; 29: 819–827. doi: 10.1016/j.neurobiolaging. 2006.12.019. PMID: 17293007.
  8. Ponomareva N., Klyushnikov S., Abramycheva N., Malina D. at al. Alpha-theta border EEG abnormalities in preclinical Huntington’s disease. J Neurol Sci 2014; 344: 114–120. doi: 10.1016/j.jns.2014.06.035. PMID: 25015843.
  9. Yamamoto K., Tanei Z.I., Hashimoto T. et al. Chronic optogenetic activation augments aβ pathology in a mouse model of Alzheimer disease. Cell Rep 2015; 11: 859–865. doi: 10.1016/j.celrep.2015.04.017. PMID: 25937280.
  10. Cole S.R., van der Meij R., Peterson E.J. et al. Nonsinusoidal beta oscillations reflect cortical pathophysiology in Parkinson’s disease. J Neurosci 2017; 37: 4830–4840. doi: 10.1523/JNEUROSCI.2208-16.2017. PMID: 28416595.
  11. Gottesman I.I., Gould T.D. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 2003; 160: 636–645. doi: 10.1176/appi.ajp.160.4.636. PMID: 12668349.
  12. Ponomareva N.V., Andreeva T.V., Protasova M.A. et al. [Genetic association between Alzheimer’s disease risk variant of the PICALM gene and auditory event-related potentials in aging]. Biochemistry (Moscow) 2018; 83 (9): 1075–1082. (in Russ.)
  13. Yakhno N.N., Zakharov V.V., Lokshina A.B. et al. Dementia. A guide for physicians. M.: MEDPress-Inform 2013; 264p. (in Russ.)
  14. Rogaev E.I. Genetic factors and polygenic model of Alzheimer’s disease. Genetica 1999; 35 (11): 1558–1571. PMID: 10624576. (in Russ.)
  15. Saunders A.M., Strittmatter W.J., Schmechel D. et al. Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer’s disease. Neurology 1993; 43: 1467–1472. doi: 10.1212/WNL.43.8.1467. PMID: 8350998.
  16. Harold D., Abraham R., Hollingworth P. et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 2009; 41: 1088–1093. doi: 10.1038/ng.440. PMID: 19734902.
  17. Lambert J.C., Heath S., Even G. et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet 2009; 41: 1094–1099. doi: 10.1038/ng.439. PMID: 19734903.
  18. Golenkina S.A., Goltsov A.Yu., Kuznetsova I.L. et al. Clusterin gene polymorphism (CLU/APOJ) in Alzheimer’s disease and normal in Russian populations. Mol Biol 2010; 44: 620–626. PMID: 20873220. (in Russ.)
  19. Xu W., Tan L., Yu J.T. The role of PICALM in Alzheimer’s disease. Mol Neurobiol 2015; 52: 399–413. doi: 10.1007/s12035-014-8878-3. PMID: 25186232.
  20. Ishii R., Canuet L., Aoki Y. et al. Healthy and pathological brain aging: from the perspective of oscillations, functional connectivity, and signal complexity. Neuropsychobiology 2017; 75: 151–-161. doi: 10.1159/000486870. PMID: 29466802.
  21. Imfeld P., Bodmer M., Schuerch M. et al. Seizures in patients with Alzheimer’s disease or vascular dementia: a population-based nested case-control analysis. Epilepsia 2013; 54: 700–707. doi: 10.1111/epi.12045. PMID: 23215680.
  22. Palop J.J., Mucke L. Epilepsy and cognitive impairments in Alzheimer disease. Arch Neurol 2009; 66: 435–440. doi: 10.1001/archneurol.2009.15. PMID: 19204149.
  23. Ochoa J.F., Alonso J.F., Duque J.E. et al. Precuneus failures in subjects of the PSEN1 E280A family at risk of developing Alzheimer’s disease detected using quantitative elecroencephalography. J Alzheimers Dis 2017; 58: 1229–1244. doi: 10.3233/JAD-161291. PMID: 28550254.
  24. Ponomareva N.V., Fokin V.F., Selezneva N.D. [Cerebral dysfunction in individuals genetically predisposed to Alzheimer’s disease]. Vest Ross Akad Med Nauk 1999; 1: 16–20. PMID: 10078057. (in Russ.)
  25. Ponomareva N.V., Andreeva T.V., Protasova M.S. et al. Quantitative EEG during normal aging: association with the Alzheimer’s disease genetic risk variant in PICALM gene. Neurobiol Aging 2017; 51: 177.e1–177.e8. DOI: 10.1016/j. neurobiolaging.2016.12.010. PMID: 28073586.
  26. Jin N., Lipponen A., Koivisto H. et al. Increased cortical beta power and spike-wave discharges in middle-aged APP/PS1 mice. Neurobiol Aging 2018; 71: 127–141. doi: 10.1016/j.neurobiolaging.2018.07.009. PMID: 30138766.
  27. Ponomareva N., Andreeva T., Protasova M. et al. Age-dependent effect of Alzheimer’s risk variant of CLU on EEG alpha rhythm in non-demented adults. Front Aging Neurosci 2013; 5: 86. doi: 10.3389/fnagi.2013.00086. PMID: 24379779.
  28. Moretti D.V., Prestia A., Fracassi C. et al. Specific EEG changes associated with atrophy of hippocampus in subjects with mild cognitive impairment and Alzheimer’s disease. Int J Alzheimers Dis 2012; 2012: 253153. doi: 10.1155/2012/253153. PMID: 22506130.
  29. Golob E.J., Ringman J.M., Irimajiri R. et al. Cortical event-related potentials in preclinical familial Alzheimer disease. Neurology 2009 17; 73: 1649–1655. doi: 10.1212/WNL.0b013e3181c1de77. PMID: 19917987.
  30. Braverman E.R., Blum K., Hussman K.L. et al. Evoked potentials and memory/ cognition tests validate brain atrophy as measured by 3T MRI (NeuroQuant) in cognitively impaired patients, PLoS One 2015; 10, e0133609. DOI: 10.1371/ journal.pone.0133609. PMID: 26244349.
  31. Ponomareva N.V., Andreeva Т.А., Protasova М.S. et al. [Asymmetric brain activation in cognitive load and its dependence on genotypes of apolopoprotein E and clusterin related with predisposition to Alzheimer’s disease]. In: [Functional interhemispheric asymmetry and plasticity]. М., 2012: 156–161. (in Russ.)
  32. Filippini N., Ebmeier K.P., MacIntosh B.J. et al. Differential effects of the APOE genotype on brain function across the lifespan. Neuroimage 2011; 54: 602–610. doi: 10.1016/j.neuroimage.2010.08.009. PMID: 20705142.
  33. Bakker A., Albert M.S., Krauss G. et al. Response of the medial temporal lobe network in amnestic mild cognitive impairment to therapeutic intervention assessed by fMRI and memory task performance. Neuroimage Clin 2015; 7: 688–698. doi: 10.1016/j.nicl.2015.02.009. PMID: 25844322.
  34. Wu J.W., Hussaini S.A., Bastille I.M. et al., Neuronal activity enhances tau propagation and tau pathology in vivo. Nat Neurosci 2016; 19: 1085–1092. doi: 10.1038/nn.4328. PMID: 27322420.
  35. Zeng X.-S., Geng W.-S., Jia J.-J. et al. Cellular and molecular basis of neurodegeneration in Parkinson’s disease. Front Aging Neurosci 2018; 10: 109. doi: 10.3389/fnagi.2018.00109. PMID: 29719505.
  36. Zhang X., Gao F., Wang D. et al. Tau pathology in Parkinson’s disease. Front Neurol 2018; 9: 809. doi: 10.3389/fneur.2018.00809. PMID: 30333786.
  37. Santos-Reboucas C.B., Goncalves A.P., Dos Santos J.M. et al. rs3851179 polymorphism at 5’ to the PICALM gene is associated with Alzheimer’s and Parkinson’s diseases in Brazilian population. Neuromolecular Med 2017; 19: 293–299. doi: 10.1007/s12017-017-8444-z. PMID: 28567584.
  38. Stoffers D., Bosboom J.L., Deijen J.B. et al. Slowing of oscillatory brain activity is a stable characteristic of Parkinson’s disease without dementia. Brain 2007; 130 (Pt 7): 1847–1860. doi: 10.1093/brain/awm034. PMID: 17412733.
  39. Moll C.K., Buhmann C., Gulberti A. et al. Synchronized cortico-subthalamic beta oscillations in Parkin-associated Parkinson’s disease. Clin Neurophysiol 2015; 126: 2241–2243. doi: 10.1016/j.clinph.2015.02.008. PMID: 25891422.
  40. Caviness J.N., Lue L.F., Hentz J.G. et al. Cortical phosphorylated α-Synuclein levels correlate with brain wave spectra in Parkinson’s disease. Mov Disord. 2016: 1012–1019. doi: 10.1002/mds.26621. PMID: 27062301.
  41. Tahmasian M., Eickhoff S.B., Giehl K. et al. Resting-state functional reorganization in Parkinson’s disease: An activation likelihood estimation meta-analysis. Cortex 2017; 92: 119–138. doi: 10.1016/j.cortex.2017.03.016. PMID: 28467917.
  42. Polito C., Berti V., Ramat S. et al.. Interaction of caudate dopamine depletion and brain metabolic changes with cognitive dysfunction in early Parkinson’s disease Neurobiol Aging 2012; 33: 206.e29–39. doi: 10.1016/j.neurobiolaging. 2010.09.004. PMID: 20961661.
  43. Bekar L.K., Wei H.S., Nedergaard M. The locus coeruleus-norepinephrine network optimizes coupling of cerebral blood volume with oxygen demand. J Cereb Blood Flow Metab 2012; 32: 2135–2145. doi: 10.1038/jcbfm.2012.115. PMID: 22872230.
  44. Klassen B.T., Hentz J,G., Shill H.A. et al. Quantitative EEG as a predictive biomarker for Parkinson disease dementia. Neurology 2011; 77: 118–124. doi: 10.1212/WNL.0b013e318224af8d. PMID: 21633128.
  45. Malina D.D., Dikevich E.A., Fedotova E.Yu., Ponomareva N.V. [Alpha EEG activity and cognitive function in patients with Parkinson’s disease]. In: [Modern areas of research of functional interhemispheric asymmetry and plasticity of the brain] M., 2010: 584–587. (in Russ.)
  46. Polich J. Updating P300: an integrative theory of P3a and P3b, Clin. Neurophysiol 2007; 118: 2128–2148. PMID: 17573239.
  47. Morley J.F., Xie S.X., Hurtig H.I. et al., Genetic influences on cognitive decline in Parkinson’s disease. Mov Disord 2012; 27: 512–518. DOI: 10.1002/ mds.24946. PMID: 22344634.
  48. Rittman T., Rubinov M., Vértes P.E. et al. Regional expression of the MAPT gene is associated with loss of hubs in brain networks and cognitive impairment in Parkinson disease and progressive supranuclear palsy. Neurobiol Aging 2016; 48:153–160. doi: 10.1016/j.neurobiolaging.2016.09.001. PMID: 27697694.
  49. Dickson D.W., Heckman M.G., Murray M.E. et al. APOE ε4 is associated with severity of Lewy body pathology independent of Alzheimer pathology. Neurology 2018; 91: e1182–e1195. doi: 10.1212/WNL.0000000000006212. PMID: 30143564.
  50. Videnovic A., Golombek D. Circadian dysregulation in Parkinson’s disease. Neurobiol Sleep Circadian Rhythms 2017; 2: 53–58. DOI: 10.1016/j. nbscr.2016.11.001. PMID: 28713867.
  51. The Huntington’s disease Collaborative Research Group. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 1993; 72: 971–983. doi: 10.1016/0092- 8674(93)90585-E. PMID: 8458085.
  52. Illarioshkin S.N., Igarashi S., Onodera O. et al. Trinucleotide repeat length and rate of progression of Huntington’s disease. Ann Neurol 1994; 36: 630–635. PMID: 7944295.
  53. Nguyen L., Bradshaw J.L., Julie C. et al. Electrophysiological measures as potential biomarkers in Huntington’s disease: review and future directions. Brain Res Rev 2010; 64: 177–194. doi: 10.1016/j.brainresrev.2010.03.004. PMID: 20381528.
  54. Ponomareva N.V., Klyushnikov S.A., Abramycheva N.Yu. et al. [Changes in the neurophysiological patterns of brain activation during cognitive load at the preclinical stage of Huntington’s disease]. In: [Fundamental problems of neuroscience: functional hemispheric asymmetry, plasticity, neurodegeneration] M.: Scientific world, 2014: 983–989. (in Russ.)
  55. Beste C., Stock A.K., Ness V. et al. A novel cognitive-neurophysiological state biomarker in premanifest Huntington’s disease validated on longitudinal data. Sci Rep 2013; 3: 1797. doi: 10.1038/srep01797. PMID: 23652721.

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