jackie-fmri-sim: Digital twin of dog 'Jackie' reproduced with real fMRI data

Simulates the real-time brain state of the dog 'Jackie' based on real functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. It calculates activation levels across 25 brain regions, including the olfactory bulb, amygdala, and septal nucleus, along with a 6-dimensional emotion vector, to predict behavioral patterns in response to specific stimuli.

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BOLD signals and biometric indicators extracted from a total of 6 scan sessions are input directly. It visualizes how activation patterns in the amygdala and septal nucleus change under social conditions such as unfamiliar dogs, familiar dogs, and neighbor dogs, and returns corresponding emotional states and behavioral phenotypes when text stimuli are provided.

Neuroscientific reliability is ensured by using only numerical values from actual recording logs, without synthetic data. Implemented using only the Python standard library, it can be run without complex dependencies and is suitable for use in canine social neuroscience research or as learning material for brain-behavior correlations.