Introduction/Background: The function and organization of the brain primary motor cortex (M1) circuits -- crucial for motor control -- has not yet been resolved. Most brain-machine interfaces used by spinal cord injury patients decode their motor information from M1, predominantly from large layer 5 corticospinal cells. As part of our NIH-funded project we have developed the most realistic and detailed computational model of M1 circuits and corticospinal neurons up to date, based on novel data provided by experimentalist collaborators. The model will help decipher the neural code underlying the brain circuits responsible for producing movement, and help understand motor disorders, including spinal cord injury. We will complement this computational work by analysing data collected from experiments where nonhuman primates performed reaching and grasping movements, many of which involved controlling a full anthropomorphic robotic arm using a brain-machine interface. The proposed computational modeling and data analysis can drastically accelerate the development of robust autonomous bidirectional brain-machine interfaces that can be trained and adapted using the patient's own brain signals, and can provide the user with natural touch sensation.
Summary of Goals and Objectives: We will employ our detailed M1 model to study how information arriving from thalamus, somatosensory cortex and other areas modulates corticospinal output through transformation within M1. This will permit us to interpret and isolate the different dynamical components encoded in M1, including motor, sensory and reward-related information. We will analyse the wealth of data accumulated during past brain-machine interface experiments to elucidate the relationship between neural firing times and force related variables, kinematic related variables and the influence of reward and motivation. The funds we are requesting will support these computational and analysis studies, will enable us to decode motor cortex signals more accurately and extract previously unattainable sensory and reward information, bringing this technology closer to clinical trials on spinal cord injury patients.