Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes
arxiv(2024)
摘要
The human brain receives complex inputs when performing cognitive tasks,
which range from external inputs via the senses to internal inputs from other
brain regions. However, the explicit inputs to the brain during a cognitive
task remain unclear. Here, we present an input identification framework for
reverse engineering the control nodes and the corresponding inputs to the
brain. The framework is verified with synthetic data generated by a predefined
linear system, indicating it can robustly reconstruct data and recover the
inputs. Then we apply the framework to the real motor-task fMRI data from 200
human subjects. Our results show that the model with sparse inputs can
reconstruct neural dynamics in motor tasks (EV=0.779) and the identified 28
control nodes largely overlap with the motor system. Underpinned by network
control theory, our framework offers a general tool for understanding brain
inputs.
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