Connectomics and molecular imaging in neurodegeneration


Our understanding on human neurodegenerative disease was previously limited to clinical data and inferences about the underlying pathology based on histopathological examination. Animal models and in vitro experiments have provided evidence for a cell-autonomous and a non-cell-autonomous mechanism for the accumulation of neuropathology. Combining modern neuroimaging tools to identify distinct neural networks (connectomics) with target-specific positron emission tomography (PET) tracers is an emerging and vibrant field of research with the potential to examine the contributions of cell-autonomous and non-cell-autonomous mechanisms to the spread of pathology. The evidence provided here suggests that both cell-autonomous and non-cell-autonomous processes relate to the observed in vivo characteristics of protein pathology and neurodegeneration across the disease spectrum. We propose a synergistic model of cell-autonomous and non-cell-autonomous accounts that integrates the most critical factors (i.e., protein strain, susceptible cell feature and connectome) contributing to the development of neuronal dysfunction and in turn produces the observed clinical phenotypes. We believe that a timely and longitudinal pursuit of such research programs will greatly advance our understanding of the complex mechanisms driving human neurodegenerative diseases.

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  1. 1.

    Bischof GN, Endepols H, van Eimeren T, Drzezga A. Tau-imaging in neurodegeneration. Methods. 2017;130:114–23.

    CAS  Article  Google Scholar 

  2. 2.

    Hammes J, Bischof GN, Drzezga A. Molecular imaging in early diagnosis, differential diagnosis and follow-up of patients with neurodegenerative diseases. Clin Transl Imaging. 2017;5:465–71.

    Article  Google Scholar 

  3. 3.

    Teipel S, Drzezga A, Grothe MJ, Barthel H, Chételat G, Schuff N, et al. Multimodal imaging in Alzheimer’s disease: validity and usefulness for early detection. Lancet Neurol. 2015;14:1037–53.

    Article  Google Scholar 

  4. 4.

    Strafella AP, Bohnen NI, Perlmutter JS, Eidelberg D, Pavese N, Van Eimeren T, et al. Molecular imaging to track Parkinson’s disease and atypical parkinsonisms: new imaging frontiers. Mov Disord. 2017;32:181–92.

    Article  Google Scholar 

  5. 5.

    Barthel H, Sabri O. Clinical use and utility of amyloid imaging. J Nucl Med. 2017;58:1711–7.

    Article  Google Scholar 

  6. 6.

    Saint-Aubert L, Lemoine L, Chiotis K, Leuzy A, Rodriguez-Vieitez E, Nordberg A. Tau PET imaging: present and future directions. Mol Neurodegen [Internet] 2017 [cited 2017 Mar 31];12. Available from:

  7. 7.

    Passamonti L, Vázquez Rodríguez P, Hong YT, Allinson KSJ, Williamson D, Borchert RJ, et al. 18F-AV-1451 positron emission tomography in Alzheimer’s disease and progressive supranuclear palsy. Brain. 2017.

  8. 8.

    Boche D, Gerhard A, Rodriguez-Vieitez E. Prospects and challenges of imaging neuroinflammation beyond TSPO in Alzheimer’s disease. Eur. J. Nucl. Med. Mol. Imaging. 2019 (In press).

  9. 9.

    Grothe M, Heinsen H, Teipel SJ. Atrophy of the cholinergic basal forebrain over the adult age range and in early stages of Alzheimer’s disease. Biol Psychiatry. 2012;71:805–13.

    CAS  Article  Google Scholar 

  10. 10.

    Grothe MJ, Ewers M, Krause B, Heinsen H, Teipel SJ, Alzheimer’s Disease Neuroimaging Initiative. Basal forebrain atrophy and cortical amyloid deposition in nondemented elderly subjects. Alzheimers Dement. 2014;10:S344–53.

    Article  Google Scholar 

  11. 11.

    Richter N, Beckers N, Onur OA, Dietlein M, Tittgemeyer M, Kracht L, et al. Effect of cholinergic treatment depends on cholinergic integrity in early Alzheimer’s disease. Brain. 2018;141:903–15.

    Article  Google Scholar 

  12. 12.

    Sabri O, Meyer PM, Gräf S, Hesse S, Wilke S, Becker G-A, et al. Cognitive correlates of α4β2 nicotinic acetylcholine receptors in mild Alzheimer’s dementia. Brain. 2018;141:1840–54.

    Article  Google Scholar 

  13. 13.

    Kocagoncu E, Quinn A, Firouzian A, Cooper E, Greve A, Gunn R, et al. Tau pathology in early Alzheimer’s disease disrupts selective neurophysiological networks dynamics. bioRxiv. 2019:524355.

  14. 14.

    Hoenig MC, Bischof GN, Seemiller J, Hammes J, Kukolja J, Onur ÖA, et al. Networks of tau distribution in Alzheimer’s disease. Brain. 2018.

  15. 15.

    Grothe MJ, Sepulcre J, Gonzalez-Escamilla G, Jelistratova I, Schöll M, Hansson O, et al. Molecular properties underlying regional vulnerability to Alzheimer’s disease pathology. Brain. 2018;141:2755–71.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Grothe MJ, Teipel SJ, Alzheimer’s Disease Neuroimaging Initiative. Spatial patterns of atrophy, hypometabolism, and amyloid deposition in Alzheimer’s disease correspond to dissociable functional brain networks. Hum Brain Mapp. 2016;37:35–53.

    Article  Google Scholar 

  17. 17.

    Drzezga A. The network degeneration hypothesis: spread of neurodegenerative patterns along neuronal brain networks. J Nucl Med. 2018;59:1645–8.

    CAS  Article  Google Scholar 

  18. 18.

    Chételat G, Villemagne VL, Bourgeat P, Pike KE, Jones G, Ames D, et al. Relationship between atrophy and beta-amyloid deposition in Alzheimer disease. Ann Neurol. 2010;67:317–24.

    PubMed  Google Scholar 

  19. 19.

    Myers N, Pasquini L, Göttler J, Grimmer T, Koch K, Ortner M, et al. Within-patient correspondence of amyloid-β and intrinsic network connectivity in Alzheimer’s disease. Brain. 2014;137:2052–64.

    Article  Google Scholar 

  20. 20.

    Drzezga A, Becker JA, Van Dijk KRA, Sreenivasan A, Talukdar T, Sullivan C, et al. Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden. Brain. 2011;134:1635–46.

    Article  Google Scholar 

  21. 21.

    Koch K, Myers NE, Göttler J, Pasquini L, Grimmer T, Förster S, et al. Disrupted intrinsic networks link amyloid-β pathology and impaired cognition in prodromal Alzheimer’s disease. Cereb Cortex. 2015;25:4678–88.

    Article  Google Scholar 

  22. 22.

    Jones DT, Graff-Radford J, Lowe VJ, Wiste HJ, Gunter JL, Senjem ML, et al. Tau, amyloid, and cascading network failure across the Alzheimer’s disease spectrum. Cortex. 2017;97:143–59.

    Article  Google Scholar 

  23. 23.

    Cope TE, Rittman T, Borchert RJ, Jones PS, Vatansever D, Allinson K, et al. Tau burden and the functional connectome in Alzheimer’s disease and progressive supranuclear palsy. Brain. 2018;141:550–67.

    Article  Google Scholar 

  24. 24.

    Franzmeier N, Rubinski A, Neitzel J, Kim Y, Damm A, Na DL, et al. Functional connectivity associated with tau levels in ageing, Alzheimer’s, and small vessel disease. Brain. 2019.

  25. 25.

    Neitzel J, Franzmeier N, Rubinski A, Ewers M. Left frontal connectivity attenuates the adverse effect of entorhinal tau pathology on memory. Neurology. 2019; in press.

  26. 26.

    Rabin JS, Perea RD, Buckley RF, Neal TE, Buckner RL, Johnson KA, et al. Global White matter diffusion characteristics predict longitudinal cognitive change independently of amyloid status in clinically Normal older adults. Cereb Cortex. 2019;29:1251–62.

    Article  Google Scholar 

  27. 27.

    Strain JF, Smith RX, Beaumont H, Roe CM, Gordon BA, Mishra S, et al. Loss of white matter integrity reflects tau accumulation in Alzheimer disease defined regions. Neurology. 2018;91:e313–8.

    CAS  Article  Google Scholar 

  28. 28.

    Jacobs HIL, Hedden T, Schultz AP, Sepulcre J, Perea RD, Amariglio RE, et al. Structural tract alterations predict downstream tau accumulation in amyloid-positive older individuals. Nat Neurosci. 2018;21:424–31.

    CAS  Article  Google Scholar 

  29. 29.

    Zempel H, Mandelkow E. Lost after translation: missorting of tau protein and consequences for Alzheimer disease. Trends Neurosci. 2014;37:721–32.

    CAS  Article  Google Scholar 

  30. 30.

    Hammes J, Theis H, Giehl K, Hoenig MC, Greuel A, Tittgemeyer M, et al. Dopamine metabolism of the nucleus accumbens and fronto-striatal connectivity modulate impulse control. Brain. 2019;142:733–43.

    Article  Google Scholar 

  31. 31.

    Strafella AP. Mesolimbic dopamine and anterior cingulate cortex connectivity changes lead to impulsive behaviour in Parkinson’s disease. Brain. 2019;142:496–8.

    Article  Google Scholar 

  32. 32.

    Gargouri F, Gallea C, Mongin M, Pyatigorskaya N, Valabregue R, Ewenczyk C, et al. Multimodal magnetic resonance imaging investigation of basal forebrain damage and cognitive deficits in Parkinson’s disease. Mov Disord. 2019;34:516–25.

    PubMed  Google Scholar 

  33. 33.

    Tahmasian M, Eickhoff SB, Giehl K, Schwartz F, Herz DM, Drzezga A, et al. Resting-state functional reorganization in Parkinson’s disease: an activation likelihood estimation meta-analysis. Cortex. 2017;92:119–38.

    Article  Google Scholar 

  34. 34.

    Lang S, Hanganu A, Gan LS, Kibreab M, Auclair-Ouellet N, Alrazi T, et al. Network basis of the dysexecutive and posterior cortical cognitive profiles in Parkinson’s disease. Mov Disord. 2019.

  35. 35.

    Tessitore A, De Micco R, Giordano A, di Nardo F, Caiazzo G, Siciliano M, et al. Intrinsic brain connectivity predicts impulse control disorders in patients with Parkinson’s disease. Mov Disord. 2017;32:1710–9.

    CAS  Article  Google Scholar 

  36. 36.

    Horn A, Reich M, Vorwerk J, Li N, Wenzel G, Fang Q, et al. Connectivity predicts deep brain stimulation outcome in Parkinson disease. Ann Neurol. 2017;82:67–78.

    Article  Google Scholar 

  37. 37.

    Sepulcre J, Grothe MJ, d’Oleire Uquillas F, Ortiz-Terán L, Diez I, Yang H-S, et al. Neurogenetic contributions to amyloid beta and tau spreading in the human cortex. Nat Med. 2018;24:1910–8.

    CAS  Article  Google Scholar 

  38. 38.

    Rittman T, Rubinov M, Vértes PE, Patel AX, Ginestet CE, Ghosh BCP, 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;48:153–60.

    CAS  Article  Google Scholar 

  39. 39.

    Freeze B, Acosta D, Pandya S, Zhao Y, Raj A. Regional expression of genes mediating trans-synaptic alpha-synuclein transfer predicts regional atrophy in Parkinson disease. Neuroimage Clin. 2018;18:456–66.

    Article  Google Scholar 

  40. 40.

    Zeighami Y, Ulla M, Iturria-Medina Y, Dadar M, Zhang Y, KM-H L, et al. Network structure of brain atrophy in de novo Parkinson’s disease. Elife. 2015;4.

  41. 41.

    Yang H-S, Yu L, White CC, Chibnik LB, Chhatwal JP, Sperling RA, et al. Evaluation of TDP-43 proteinopathy and hippocampal sclerosis in relation to APOE ε4 haplotype status: a community-based cohort study. Lancet Neurol. 2018;17:773–81.

    CAS  Article  Google Scholar 

  42. 42.

    van Eimeren T, Antonini A, Berg D, Bohnen N, Ceravolo R, Drzezga A, et al. Neuroimaging biomarkers for clinical trials in atypical parkinsonian disorders: proposal for a neuroimaging biomarker utility system. Alzheimers Dement (Amst). 2019;11:301–9.

    Google Scholar 

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Faculty of the Multimodal Imaging in Neurodegeneration Cologne (MINC) symposium


The Molecular Imaging of Neurodegeneration Cologne (MINC) Symposium was partly funded by the Deutsche Forschungsgemeinschaft (DFG) awarded to Dr. Thilo van Eimeren (EI 892/5–1). The Deutsche Forschungsgemeinschaft (DFG) also awarded funding to Dr. Alexander Drzezga (DR 442/91).

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Correspondence to Gérard N. Bischof.

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Bischof, G.N., Ewers, M., Franzmeier, N. et al. Connectomics and molecular imaging in neurodegeneration. Eur J Nucl Med Mol Imaging 46, 2819–2830 (2019).

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  • Multimodal Imaging
  • Proteinpathology
  • Functional Connectivity
  • Pathophysiological Spreading
  • Selective Vulnerability