Patient‐tailored, connectivity‐based forecasts of spreading brain atrophy in frontotemporal dementia

Abstract

AbstractBackgroundNeurodegenerative diseases appear to progress by spreading via brain connections. Like Alzheimer’s disease, frontotemporal dementia (FTD) syndromes make compelling test cases for evaluating network‐based spreading models due to their focal and circumscribed atrophy patterns. In this talk, I will describe our recent work (Brown et al, 2019 Neuron) where we evaluated transneuronal degeneration by attempting to predict future atrophy in patients with behavioral variant frontotemporal dementia (bvFTD, n=42) and semantic variant primary progressive aphasia (svPPA, n=30). While evidence for this mode of progression has come from cross‐sectional studies, the critical test was whether network connectivity can predict longitudinal spread of atrophy in individual patients.MethodsWe used T1‐weighted MRI scans to identify each subject’s gray matter atrophy pattern. Using these maps, we identified each patient’s epicenter, the region whose healthy functional connectivity pattern most strongly resembled the patient’s atrophy. Next, we projected the patient’s epicenter and baseline atrophy map onto the healthy functional connectome. We used three measures to predict the amount future atrophy for 242 cortical and subcortical brain regions: 1) shortest path length to the epicenter (SPE); 2) nodal hazard, the amount of collective atrophy among a region’s first‐degree connected neighbors; and 3) regional baseline atrophy. A generalized additive model was used to generate predictions. Finally, we used a linear threshold model (LTM) to simulate disease spread from the identified epicenters.ResultbvFTD patients had epicenters in anterior cingulate and frontoinsula and svPPA patients in the anterior temporal lobe. The predictive model explained an average of 37% within‐scan variance in longitudinal atrophy. SPE, nodal hazard, and baseline atrophy were all significant predictors of change (all p < 2x10‐16). The regions most vulnerable to subsequent atrophy were functionally connected to the epicenter and had intermediate levels of baseline atrophy. LTM simulations revealed that epicenter‐neighboring nodes were network hubs and may drive amplification of disease spread.ConclusionA statistical model using connectivity‐based predictors could accurately predict the whole‐brain subsequent atrophy patterns in most FTD patients. This provides new human evidence that neurodegenerative disease spreads between functionally connected areas. The findings represent a next step toward clinical application of the network‐based neurodegeneration framework.