Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911852898811904 |
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| author | Liang, Zhichao Zhang, Yinuo Wu, Jushen Liu, Quanying |
| author_facet | Liang, Zhichao Zhang, Yinuo Wu, Jushen Liu, Quanying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16357 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes Liang, Zhichao Zhang, Yinuo Wu, Jushen Liu, Quanying Neurons and Cognition Systems and Control 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. |
| title | Reverse engineering the brain input: Network control theory to identify cognitive task-related control nodes |
| topic | Neurons and Cognition Systems and Control |
| url | https://arxiv.org/abs/2404.16357 |