ArticleFrontiers in neuroscience2018
Paradigm Shift in Sensorimotor Control Research and Brain Machine Interface Control: The Influence of Context on Sensorimotor Representations.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Reward influences movement vigor through multiple motor cortical mechanisms.bioRxiv : the preprint server for biology · 2025Article
- Reward signals in the motor cortex: from biology to neurotechnology.Nature communications · 2025Review
- Protocol for state-based decoding of hand movement parameters using neural signals.STAR protocols · 2024Article
- Neural representation and modulation of volitional motivation in response to escalating efforts.The Journal of physiology · 2023Article
- Normalization by valence and motivational intensity in the sensorimotor cortices (PMd, M1, and S1).Scientific reports · 2021Article
- Mirror neurons are modulated by grip force and reward expectation in the sensorimotor cortices (S1, M1, PMd, PMv).Scientific reports · 2021Article
- Noise-Correlation Is Modulated by Reward Expectation in the Primary Motor Cortex Bilaterally During Manual and Observational Tasks in Primates.Frontiers in behavioral neuroscience · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
Neural activity in the primary motor cortex (M1) is known to correlate with movement related variables including kinematics and dynamics. Our recent work, which we believe is part of a paradigm shift in sensorimotor research, has shown that in addition to these movement related variables, activity in M1 and the primary somatosensory cortex (S1) are also modulated by context, such as value, during both active movement and movement observation. Here we expand on the investigation of reward modulation in M1, showing that reward level changes the neural tuning function of M1 units to both kinematic as well as dynamic related variables. In addition, we show that this reward-modulated activity is present during brain machine interface (BMI) control. We suggest that by taking into account these context dependencies of M1 modulation, we can produce more robust BMIs. Toward this goal, we demonstrate that we can classify reward expectation from M1 on a movement-by-movement basis under BMI control and use this to gate multiple linear BMI decoders toward improved offline performance. These findings demonstrate that it is possible and meaningful to design a more accurate BMI decoder that takes reward and context into consideration. Our next step in this development will be to incorporate this gating system, or a continuous variant of it, into online BMI performance.
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