Discovering Brain Mechanisms Using Network Analysis and Causal Modeling

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.