Dynamic causal modeling of preclinical autosomal-dominant Alzheimer’s disease

Will Penny, Jorge Iglesias-Fuster, Yakeel T. Quiroz, Francisco Javier Lopera, Maria A. Bobes

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
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Dynamic causal modeling (DCM) is a framework for making inferences about changes in brain connectivity using neuroimaging data. We fitted DCMs to high-density EEG data from subjects performing a semantic picture matching task. The subjects are carriers of the PSEN1 mutation, which leads to early onset Alzheimer’s disease, but at the time of EEG acquisition in 1999, these subjects were cognitively unimpaired. We asked 1) what is the optimal model architecture for explaining the event-related potentials in this population, 2) which connections are different between this Presymptomatic Carrier (PreC) group and a Non-Carrier (NonC) group performing the same task, and 3) which network connections are predictive of subsequent Mini-Mental State Exam (MMSE) trajectories. We found 1) a model with hierarchical rather than lateral connections between hemispheres to be optimal, 2) that a pathway from right inferotemporal cortex (IT) to left medial temporal lobe (MTL) was preferentially activated by incongruent items for subjects in the PreC group but not the NonC group, and 3) that increased effective connectivity among left MTL, right IT, and right MTL was predictive of subsequent MMSE scores.
Original languageEnglish
Pages (from-to)697-711
Number of pages15
JournalJournal of Alzheimer's Disease
Issue number3
Early online date16 Mar 2018
Publication statusPublished - 11 Sep 2018


  • Autosomal dominant
  • dynamic causal modeling
  • EEG
  • effective connectivity
  • multivariate

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