Human motor cortex encodes complex handwriting through a sequence of stable neural states
Apr 2, 2025·
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2 min read
Yu Qi
祝歆韵
Xinzhu Xiong
Xiaomeng Yang
Nai Ding
Hemmings Wu
Kedi Xu
Junming Zhu
Jianmin Zhang
Yueming Wang
Abstract
How the human motor cortex (MC) orchestrates sophisticated sequences of fine movements such as handwriting remains a puzzle. Here we investigate this question through Utah array recordings from human MC during attempted handwriting of Chinese characters (n = 306, each consisting of 6.3 +/- 2.0 strokes). We find that MC activity evolves through a sequence of states corresponding to the writing of stroke fragments during complicated handwriting. The directional tuning curve of MC neurons remains stable within states, but its gain or preferred direction strongly varies across states. By building models that can automatically infer the neural states and implement state-dependent directional tuning, we can significantly better explain the firing pattern of individual neurons and reconstruct recognizable handwriting trajectories with 69% improvement compared with baseline models. Our findings unveil that skilled and sophisticated movements are encoded through state-specific neural configurations.
Type
Publication
Nature Human Behaviour
🔶 Neural Encoding of Fine Motor Control: A Study on Handwriting 🔶
🧠 Human Fine Motor Control
Humans excel at controlling sophisticated fine movements, such as:
- ✍️ Writing
- ⌨️ Typing
- 🎼 Musical performance
🔁 Hierarchical Decomposition of Movements
These complex motor behaviors are often decomposed into simpler units:
- Word → sequence of letters
- Letter → sequence of strokes
- Stroke → complex movement trajectory
👉 This decomposition:
- Reduces complexity
- Shortens the time scale of each movement unit
- Aligns with neural characteristics of the motor cortex (MC):
- Neurons in MC show simple tuning to movement features
- Tuning varies over long time scales
❓ However, it remains unclear whether primitive units exist during fine movements, and how such units are encoded in the motor cortex.
✍️ Handwriting as a Model of Fine Motor Control
Handwriting is a learned fine motor skill, developed through years of practice.
🧪 Experimental Design
- Neural recording: Single-unit activity from human motor cortex (MC)
- Technology: Two 96-channel Utah microelectrode arrays
- Recording site: Left hand knob area of the precentral gyrus
- Task: Attempted writing of Chinese characters
- ➕ Highly complex characters: > 3500 common characters
- 🖋️ Composed of 32 stroke types
🔍 Key Findings
1. Stable Neural Tuning States
- During handwriting, neuronal directional tuning alternates between a few stable states
- Each state corresponds to the writing of multiple small fragments of a character
2. Dynamic Transitions Between States
- Neuronal tuning clearly changes across states
- Suggests state-based representation of motor components
- Challenges the assumption of static tuning during continuous movement
🧮 Computational Modeling
🔄 State-Based vs. Stable Tuning Models
| Model Type | Description | Performance Improvement |
|---|---|---|
| State-based model | Decomposes writing into a sequence of neural states | 🔼 +229% spiking activity explanation 🔼 +69% handwriting decoding accuracy |
| Stable tuning model | Assumes unchanging neural tuning | 🔽 Lower performance |
🧠 State-based models better capture the temporal dynamics of neuronal tuning during fine motor behaviors like handwriting.
✅ Conclusion
This study demonstrates that:
- Fine movements, like handwriting, involve alternating stable neural tuning states
- These states encode modular movement fragments
- Modeling the writing process as a sequence of tuning states:
- Enhances our understanding of motor cortex encoding
- Improves decoding accuracy in brain-computer interface applications
