Work

Surrogate deep neural networks reveal hierarchical handwriting encoding in the human motor cortex

Year: 2025

Type: article

Abstract: Skilled fine movements are essential for daily life. Although prior work has identified motor cortical tuning to low-level kinematic features like velocity and position, these findings fall short of explaining the precision underlying complex motor behaviors. Critically, it remains unclear whether and how the motor cortex (MC) represents higher-level features of movement. Using single-unit recordings from the human MC during handwriting, we employed surrogate deep neural networks (DNNs) as a tool to investigate these mechanisms. We found that surrogate DNNs capture key aspects of neural activity at both single-unit and population levels. Through this approach, we demonstrate that the MC encodes hierarchical information of movement, including both low-level kinematics and high-level features related to the written content. These results uncover neural encoding behind dexterous motor execution and provide a framework for studying the neural basis of complex behavior.

Source: Cell Reports

DOI: 10.1016/j.celrep.2025.116837

Authors: Jiajun Chen, Tianqi Xu, Xinzhu Xiong, Xiaomeng Yang, Yueming Wang, Yu Qi

Institutions: Hangzhou Seventh Peoples Hospital, Zhejiang University

Language: English

Topic: Motor Control and Adaptation

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,620

Funders: National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, Key Research and Development Program of Zhejiang Province