  Effective connectivity (EC), indicative of the causal interactions between
brain regions, is fundamental to understanding information processing in the
brain. Traditional approaches, which infer EC from neural responses to
stimulations, are not suited for mapping whole-brain EC in human due to being
invasive and limited spatial coverage of stimulations. To address this gap, we
present Neural Perturbational Inference (NPI), a data-driven framework designed
to map EC across the entire brain. NPI employs an artificial neural network
trained to learn large-scale neural dynamics as a computational surrogate of
the brain. NPI maps EC by perturbing each region of the surrogate brain and
observing the resulting responses in the rest of regions. NPI captures the
directionality, strength, and excitatory/inhibitory properties of EC on a
brain-wide scale. Our validation of NPI, using models with established EC,
shows its superiority over Granger Causality and Dynamic Causal Modeling.
Applying NPI to resting-state fMRI data from diverse datasets reveals
consistent and structurally supported EC. Applications on a disease-specific
dataset highlight the potential of using personalized EC as biomarkers for
neurological diseases. By transitioning from correlational to causal
understandings of brain functionality, NPI marks a stride in decoding the
brain's functional architecture and can facilitate neuroscience research and
clinical applications.
