Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes

TL;DR

This paper introduces a network control theory-based framework to identify control nodes and inputs in the human brain during cognitive tasks, validated with synthetic and real fMRI data, revealing key motor system nodes.

Contribution

It presents a novel input identification framework rooted in network control theory for reverse engineering brain inputs during cognitive tasks.

Findings

Successfully reconstructs neural dynamics in motor tasks (EV=0.779)

Identifies 28 control nodes overlapping with the motor system

Framework is robust with synthetic data and applicable to real fMRI data

Abstract

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.