Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface

By Mikhail A. Lebedev, Jose M. Carmena, Joseph E. O’Doherty, M. Zacksenhouse, Craig S. Henriquez, José Carlos Príncipe, Miguel A. L. Nicolelis. Journal of Neuroscience, 2005

303 citations recorded by the source corpus.

Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood. Here, we show that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI. Initially, monkeys controlled the actuator by moving a hand-held pole. During this period, the BMI was trained to predict the actuator velocity. As the monkeys started using their cortical activity to control the actuator, the activity of individual neurons and neuronal populations became less representative of the animal's hand movements while representing the movements of the actuator. As a result of this adaptation, the animals could eventually stop moving their hands yet continue to control the actuator. These results show that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animal's own limb.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/ca7218b7654329bc592f5052a58e82ea58da8a82

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Citations in the corpus

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “These findings further supported the claim, made in early BMI studies (114, 124, 463), that brain plasticity, in this case, corticostriatal plasticity, plays a major role in learning to control a BMI.”
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (2011, methodology): “For continuous control, cortical signals have been used to guide a computer cursor to targets on a screen [3–14].”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, unknown): “…Gao et al 2002, 2003a, Hatsopoulos et al 2004, Hu et al 2004, Karniel et al 2002, Kemere et al 2004, Kennedy et al 2000, Kim et al 2005a, 2005b, Lebedev et al 2005, Olson et al 2005, Patil et al 2004, Rao et al 2005, Roushe et al 2003, Sanchez et al 2002a, 2002b, 2003, Serruya et al 2002,…”
The Science of Neural Interface Systems (2009, unknown): “Although there are afferent projections from the muscles and spinal cord back to the cortex , they do not likely contribute to the observed behavioral changes because the animal is often not significantly moving its arm (Carmena et al. 2003, Lebedev et al. 2005, Velliste et al. 2008).”
Commentary: Cortical activity in the null space: permitting preparation without movement (2017, background): “Linear regressions that assign weights to neurons to generate a desired output are commonly utilized in brainmachine interfaces (BMIs) (Humphrey et al., 1970; Wessberg et al., 2000; Lebedev et al., 2005; Lebedev, 2014), but the goal is different there.”
Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals (2011, background): “…studies using potentials acquired from microelectrodes implanted within cortex (Taylor et al., 2002; Hatsopoulos et al., 2004; Carmena et al., 2005; Lebedev et al., 2005; Velliste et al., 2008), but also by ECoG (Wolpaw and McFarland, 2004; McFarland et al., 2008, 2010; Schwartz et al., 2001;…”
Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex (2007, methodology): “Similarly, in other studies that reported instability (Taylor et al., 2002; Lebedev et al., 2005), the animal switched between manual control and brain control of the on-screen cursor.”
Active tactile exploration enabled by a brain-machine-brain interface (2011, methodology): “During ‘brain control’, the joystick was disconnected and the actuator was controlled by the activity of right-hemisphere M1 neuron”
Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia (2008, methodology): “Hand position and velocity have both been used (together and separately) for closed-loop neural cursor control in able-bodied non-human primates using a variety of decoding algorithms including the population vector method [5], linear filtering [1-4, 23], and Kalman filtering [24-25].”