Discovering Brain Mechanisms Using Network Analysis and Causal Modeling

Naftali Weinberger, Matteo Colombo

    Research output: Contribution to journalArticleScientificpeer-review

    5 Citations (Scopus)
    226 Downloads (Pure)


    Mechanist philosophers have examined several strategies scientists
    use for discovering causal mechanisms in neuroscience. Findings
    about the anatomical organization of the brain play a central role in
    several such strategies. Little attention has been paid, however, to the
    use of network analysis and causal modeling techniques for
    mechanism discovery. In particular, mechanist philosophers have not
    explored whether and how these strategies incorporate information
    about the anatomical organization of the brain. This paper clarifies
    these issues in the light of the distinction between structural,
    functional and effective connectivity. Specifically, we examine two
    quantitative strategies currently used for causal discovery from
    functional neuroimaging data: dynamic causal modeling and
    probabilistic graphical modeling. We show that dynamic causal
    modeling uses findings about the brain’s anatomical organization to
    improve the statistical estimation of parameters in an already
    specified causal model of the target brain mechanism. Probabilistic
    graphical modeling, in contrast, makes no appeal to the brain’s
    anatomical organization, but lays bare the conditions under which
    correlational data suffice to license reliable inferences about the
    causal organization of a target brain mechanism. The question of
    whether findings about the anatomical organization of the brain can
    and should constrain the inference of causal networks remains open,
    but we show how the tools supplied by graphical modeling methods
    help in addressing it.
    Original languageEnglish
    Pages (from-to)265–286
    JournalMinds and Machines
    Early online date2017
    Publication statusPublished - 2018


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