Atrophy patterns in sporadic and genetic behavioral variant frontotemporal dementia reflect brain network architecture
Authors: Shafiei Golia , Bazinet Vincent , Dadar Mahsa , Manera Ana Laura , Collins Louis , Dagher Alain , Bocchetta Martina , Todd Emily G. , Peakman Georgia , Cash David M. , Convery Rhian S. , Russell Lucy L. , Thomas David L. , Iglesias Juan Eugenio , van Swieten John C. , Jiskoot Lize C. , Seelaar Harro , Borroni Barbara , Galimberti Daniela , Sanchez‐Valle Raquel , Laforce Robert , Moreno Fermin , Synofzik Matthis , Graff Caroline , Masellis Mario , Tartaglia Maria Carmela , Rowe James B. , Vandenberghe Rik , Finger Elizabeth , Tagliavini Fabrizio , Mendonca Alexandre , Santana Isabel , Butler Christopher , Gerhard Alexander , Danek Adrian , Levin Johannes , Otto Markus , Sorbi Sandro , Ber Isabelle Le , Pasquier Florence , Rohrer Jonathan D. , Bratislav Misic , Ducharme Simon 
Published: February 2022

Background

Connections among brain regions allow pathological perturbations to spread from a single source node to multiple nodes. Patterns of neurodegeneration in multiple diseases, including behavioral variant of frontotemporal dementia (bvFTD), resemble the network architecture (Seeley et al., 2009, Neuron), but how bvFTD‐related atrophy patterns relate to the network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture, such that connected regions display similar atrophy patterns.

Method

Deformation‐based morphometry (DBM) was used to estimate regional changes in tissue volume density from T1‐weighted magnetic resonance images of 75 genetic bvFTD patients and 247 healthy controls (GENFI, http://genfi.org.uk/). We used linear mixed effects model to obtain a bvFTD‐related atrophy map, controlling for age, sex and aquision site. Structural and functional connectivity (SC and FC), derived from an independent sample of 70 healthy participants (Griffa et al., 2019, Zenodo), were used to estimate mean neighbor atrophy values of each region. Relationship between node and neighbor atrophy was examined by correlating neighbor atrophy with nodal atrophy. Statistical significance of the analyses was assessed using a spatial autocorrelation‐preserving null model. Analyses were replicated in an independent dataset (FTLDNI, AG032306) with 70 sporadic bvFTD patients and 123 healthy controls.

Result

Distributed atrophy patterns were observed in bvFTD, mainly targeting areas associated with limbic intrinsic network and insular cytoarchitectonic class (Fig 1a). A node’s atrophy was significantly correlated with atrophy of its connected neighbors (e.g. high resolution: r=0.58, p=0.006 and r=0.54, p=0.0006, for SC‐ and FC‐ defined neighbors respectively) (Fig 2c). Relationship between node and neighbor atrophy was consistent across resolutions and greater in empirical networks compared to null networks. While a number of frontotemporal regions were identified as potential disease epicenters, anterior insula was the most likely one. Results were consistent in the sporadic cohort (Fig 1b&2d).

Conclusion

Using connectivity models and rigorous statistical analyses that account for spatial autocorrelation, we demonstrate that bvFTD‐related neurodegeneration is conditioned by connectome architecture, accounting for 30‐40% of variance in atrophy. Atrophy is most profound in regions associated with insular cortex.