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.
KeywordsMultimodal Imaging Proteinpathology Functional Connectivity Pathophysiological Spreading Selective Vulnerability
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).
Compliance with ethical standards
Conflict of interest
The authors declare no conflict of interest in relation to this article.
This article does not contain any studies with human participants performed by any of the authors.
- 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: http://molecularneurodegeneration.biomedcentral.com/articles/10.1186/s13024-017-0162-3
- 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.Google Scholar
- 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).Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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