Connectomics and molecular imaging in neurodegeneration

Abstract

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.

Keywords: Functional Connectivity; Multimodal Imaging; Pathophysiological Spreading; Proteinpathology; Selective Vulnerability.

Figure 1

1) Overlap of Tau Networks and known functional Networks in Alzheimer’s Disease. Tau pathology networks correspond with functional connectivity networks quantified with a dice-coefficient of d=.57, d=.56 (adapted from Hönig et al.,). 2) Regions with high functional connectivity show covarying tau levels across normal aging, Alzheimer’s disease and vascular cognitive impairment adopted from Franzmeier et al., 2018

Figure 2

3) Correlations between network measures and tau deposition. Plots display correlations of participation coefficient (functional involvement in multiple modules) and eigenvector centrality (functional influence) with the local Tau burden across functional lobes in the delta and theta bands. All - whole brain; Fro - frontal; Lim - limbic; Occ - occipital; Par - parietal; Tem - temporal. Adapted from Kocagoncu et al., 2018

Figure 4

Regional expression levels of APP and MAPT genes (x-axes) correlate differentially with the regional severities of amyloid deposition (top) and neurodegeneration (bottom) in AD patients as compared to healthy controls (y-axes). Each point corresponds to one of 34 different cortical areas; r indicates Spearman correlation across these areas. Adopted from Grothe et al., 2018

Figure 5

Triad of Vulnerability: A unification of the cell-autonomous and cell-non-autonomous accounts of protein aggregation, via a triad-interaction of protein strain, cell feature and Connectome may instigate the neural vulnerability that leads to the observed pattern of neuronal dysfunction, that produces different clinical phenotypes.

Figure 6

Example of how the synergistic triad of vulnerability may translate into the clinical phenotypes of AD. Investigations of protein accumulation and examination of the critical contribution of the connectome and cell feature such as metabolic demand or gene expression profiles may lead to the observed spatial pattern of neuronal dysfunction that in turn produces distinct clinical phenotype in AD. AD = Alzheimer’s Disease, bvAD = behavioral variant of AD, lvPPA = logopenic variant of primary progressive aphasia, PCA= Posterior Cortical Atrophy.