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Abstract
Cognition and behavior emerge from brain network interactions, such that investigating
causal interactions should be central to the study of brain function. Approaches that
characterize statistical associations among neural time series – functional connectivity
(FC) methods – are likely a good starting point for estimating brain network interactions.
Yet only a subset of FC methods (“effective connectivity”) are explicitly designed
to infer causal interactions from statistical associations. Here we incorporate best
practices from diverse areas of FC research to illustrate how FC methods can be refined
to improve inferences about neural mechanisms, with properties of causal neural interactions
as a common ontology to facilitate cumulative progress across FC approaches. We further
demonstrate how the most common FC measures (correlation and coherence) reduce the
set of likely causal models, facilitating causal inferences despite major limitations.
Alternative FC measures are suggested to immediately start improving causal inferences
beyond these common FC measures.
We investigated the relationship between individual subjects’ functional connectomes and 280 behavioral and demographic measures, in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation; subjects were predominantly spread along a single “positive-negative” axis, linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.
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