The explanatory power of activity flow models of brain function
arxiv(2024)
摘要
Tremendous neuroscientific progress has recently been made by mapping brain
connectivity, complementing extensive knowledge of task-evoked brain activation
patterns. However, despite evidence that they are related, these connectivity
and activity lines of research have mostly progressed separately. Here I review
the notable productivity and future promise of combining connectivity and
task-evoked activity estimates into activity flow models. These data-driven
computational models simulate the generation of task-evoked activations
(including those linked to behavior), producing empirically-supported
explanations of the origin of neurocognitive functions based on the flow of
task-evoked activity over empirical brain connections. Critically, by
incorporating causal principles and extensive empirical constraints from brain
data, this approach can provide more mechanistic accounts of neurocognitive
phenomena than purely predictive (as opposed to explanatory) models or models
optimized primarily for task performance (e.g., standard artificial neural
networks). The variety of activity-flow-based explanations reported so far are
covered here along with important methodological and theoretical considerations
when discovering new activity-flow-based explanations. Together, these
considerations illustrate the promise of activity flow modeling for the future
of neuroscience and ultimately for the development of novel clinical treatments
(e.g., using brain stimulation) for brain disorders.
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