Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia

Abstract

Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.

Keywords: connectome; disease epicentre; frontotemporal dementia; gene expression; network spreading.

Figure 1

Atrophy patterns in intrinsic networks and cytoarchitectonic classes. Mean network atrophy (i.e. t-value) was calculated for Yeo intrinsic functional networks (left) and von Economo cytoarchitectonic classes (right). Higher t-values correspond to greater atrophy. The observed mean atrophy values are shown by filled circles for each intrinsic network and cytoarchitectonic class. Network labels are then randomly permuted using 10 000 rotations from spin tests, preserving the spatial autocorrelation in the data. The null distributions of means from spin tests are depicted using box plots for intrinsic networks and cytoarchitectonic classes for both (A) FTLDNI and (B) GENFI datasets (10 000 repetitions; two-tailed test). The bottom row displays the location of intrinsic networks (left) and cytoarchitectonic classes (right) on the cortex. List of Yeo networks: visual (vis), somatomotor (sm), dorsal attention (da), ventral attention (va), limbic (lim), frontoparietal (fp), default mode (dmn). List of von Economo classes: primary sensory cortex (ps), primary motor cortex (pm), primary/secondary sensory cortex (pss), limbic (lb), insular cortex (ic), association cortex (ac, ac2).

Figure 2

Network-dependent atrophy. (A) Atrophy of a node, estimated by t-values, was correlated with the mean atrophy of its connected neighbours to examine whether the distributed atrophy patterns in bvFTD reflect the underlying network organization. (B) If atrophy of a node is related to the atrophy of its connected neighbours (A), a node with high atrophy whose neighbours are also highly atrophied would be more likely to be a potential disease epicentre, compared with a high atrophy node with healthy neighbours. To quantify the epicentre likelihood across the cortex, the nodes were first ranked based on their atrophy values and their neighbours’ atrophy values. Epicentre likelihood ranking of each node was then defined as its mean ranking in the two lists. (C and D) Left: Node atrophy value was correlated with the mean atrophy value of its structurally- and functionally-weighted neighbours (SC and FC) for FTLDNI (C) and GENFI (D) datasets. Scatter plots show the correlation for high parcellation resolution. Middle: The observed correlation values (depicted by filled circles) were compared to a set of correlations obtained from 10 000 spin tests (depicted by box plots). Asterisks denote statistical significance (P_spin < 0.05, two-tailed). The association between node and neighbour atrophy was consistent across resolutions and significantly greater in empirical networks compared to null networks in both datasets. Right: Epicentre likelihood rankings are depicted across the cortex. The most likely epicentres with high significant rankings are regions that are mainly located at the bilateral anterior insular cortex and temporal lobes (10 000 spin tests).

Figure 3

Agent-based modelling. (A) The SIR model simulates the spread of pathology in the brain. Proteins propagate via the structural connections between brain regions and induce atrophy, both pre- and post-synaptically. (B) Left: The spreading process was initiated in every brain region and the correlation between the simulated and empirical patterns of atrophy was computed. The three largest correlations were obtained by seeding regions of the insula (r_max = 0.601), the superior-frontal cortex (r_max = 0.473) and lateral orbito-frontal cortex (r_max = 0.471). Right: To control for the potential effect of a brain region’s spatial embedding, r_max values were compared to r_max correlations obtained using rewired networks that preserve the wiring-cost of the empirical structural network. Asterisks denote statistical significance (P < 0.05, two-tailed). The r_max computed by seeding the insula of the empirical network (r_max = 0.60) was significantly larger than the r_max computed by seeding the insula of the rewired networks (P < 0.002). (C) The largest fit (r_max) obtained by seeding each brain region is shown on the surface of the brain. Larger values of r_max were generally obtained by seeding insular and prefrontal regions. (D) Left: Empirical pattern of atrophy (FTLDNI). Middle: Simulated pattern of atrophy producing the maximal fit. This pattern of atrophy was obtained with the insula as the seed, and at t = 4410 (see the arrow in B). Right: Scatter plot of the relationship between standardized empirical and simulated patterns of atrophy (r = 0.60, P = 0.0013).

Figure 4

Contribution of gene expression. (A) Vectors of regional gene expression were generated for four genes that have been associated with bvFTD: TARDBP, C9orf72, GRN and MAPT. These vectors of gene expression were incorporated into the SIR model. The correlations between empirical atrophy and simulated atrophy, with the insula selected as the seed of the simulated spreading process, were then computed for the FTLDNI dataset (B) and for the GENFI dataset (C). The maximal correlation scores (r_max) obtained for each gene were compared to the maximal correlation scores (r_max) obtained with spun distributions of gene expression vectors (left box plots). Asterisks denote statistical significance (P < 0.05, two-tailed). For both datasets, we find that the r_max scores obtained by incorporating information about the expression of C9orf72 and TARDBP were significantly larger than those obtained with permuted gene expression vectors. The maximal correlations were also compared to the maximal correlation scores obtained in distance-preserving rewired networks (right box plots).