Patient-Tailored, Connectivity-Based Forecasts of Spreading Brain Atrophy

Jesse A. Brown, Jersey Deng, John Neuhaus, Isabel J. Sible, Ana C. Sias, Suzee E. Lee, John Kornak, Gabe A. Marx, Anna M. Karydas, Salvatore Spina, Lea T. Grinberg, Giovanni Coppola, Dan H. Geschwind, Joel H. Kramer, Maria Luisa Gorno-Tempini, Bruce L. Miller, Howard J. Rosen, William W. Seeley

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

Neurodegenerative diseases appear to progress by spreading via brain connections. Here we evaluated this transneuronal degeneration hypothesis by attempting to predict future atrophy in a longitudinal cohort of patients with behavioral variant frontotemporal dementia (bvFTD) and semantic variant primary progressive aphasia (svPPA). We determined patient-specific “epicenters” at baseline, located each patient's epicenters in the healthy functional connectome, and derived two region-wise graph theoretical metrics to predict future atrophy: (1) shortest path length to the epicenter and (2) nodal hazard, the cumulative atrophy of a region's first-degree neighbors. Using these predictors and baseline atrophy, we could accurately predict longitudinal atrophy in most patients. The regions most vulnerable to subsequent atrophy were functionally connected to the epicenter and had intermediate levels of baseline atrophy. These findings provide novel, longitudinal evidence that neurodegeneration progresses along connectional pathways and, further developed, could lead to network-based clinical tools for prognostication and disease monitoring.

Original languageEnglish
Pages (from-to)856-868.e5
JournalNeuron
Volume104
Issue number5
DOIs
StatePublished - 4 Dec 2019
Externally publishedYes

Keywords

  • brain networks
  • frontotemporal dementia
  • functional connectivity
  • graph theory
  • neurodegeneration
  • voxel-based morphometry

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