The application discloses a brain effect connection learning and
analysis method based on multi-source space-time collaborative modeling, and belongs to the technical field of brain science and medical
image processing. The method comprises the following steps: receiving multi-source brain
time sequence data, and constructing a unified space-time representation of brain regions; performing space correlation modeling and time
collaborative processing on the
brain region level
time sequence to obtain a unified space-time feature representation; dynamically modeling the unified space-time feature representation through a multi-source collaborative learning mechanism to obtain a
brain region fusion feature representation; constructing a connection
relationship learning model based on the
brain region fusion feature representation, determining the connection relationship between brain regions through a
structure generation and optimization process, and obtaining a brain effect connection network. Through unified space-time representation modeling, a multi-source collaborative learning mechanism and a structure feedback optimization process, the application effectively improves the accuracy, stability and
interpretability of brain effect connection modeling, and can be widely applied to the fields of
brain function analysis, auxiliary diagnosis of neurological diseases and
brain state recognition.