Advances are being made in the field of neural prosthetics for applications directed toward restoring function to those suffering from paralysis. A central question in this research is which areas to target for control signals. Initial efforts hve naturally focused on the primary motor cortex (M1) given its strong linkage to motor execution. These studies have extracted motor command signals for reconstructing the moment-by-moment kinematics of limb movement. Sensorimotor cortical areas one or two steps removed from motor cortex, including the posterior parietal cortex (PPC), have been examined in recent years for prosthetic control signals of a more cognitive nature related to the goals and context of movement. Despite the relatively high correlation between the M1 neural activity and kinematics of actual movements, controlling a virtual prosthetic device using this activity in brain-control experiments has produced much less accurate and slower movements than natural limb movements. Increasingly more studies report that accuracy and speed of the prosthetic movement can be significantly improved by incorporating more cognitive signals such as the intended goal when decoding the moment-by-moment kinematic information. We have found two regions of PPC, the parietal reach region (PRR) and the dorsal aspect of area 5 (area 5d) that, besides providing goal signals, also provide trajectory signals. The dynamics of the trajectory signal in PPC suggest that, rather than being a movement command signal similar to M1, it represents a state estimate of the limb movement. Although the decode performance for trajectories appears good in PPC, it is difficult to compare it to trajectory decodes from M1 from previous studies due to differences in the tasks, experimental conditions, and data analysis methods.
Aim 1 will directly compare the representation of trajectories in PPC (PRR and area 5d) with M1as a benchmark. These studies will be performed in the same animals performing the same tasks under the same experimental conditions. If PPC can provide trajectory signals of similar fidelity to M1 then it would be an ideal location for obtaining both trajectory and goal signals. If the number of implant sites in patients is limited, these experiments would provide insight into the best sites to extract a variety of control signals.
Aim 2 will compare neural adaptations in M1 and PPC to novel motor effector dynamics to infer the functional role of the trajectory signal of each area in motor skill learning. Understanding how the different brain areas adapt will be important for choosing sites for neural prosthetics that require learning the dynamics of mechanical devices.
This application has direct relevance to public health since its goal is to perform studies that will lead to the development neuroprosthetic medical devices for implantation in posterior parietal cortex. The goal is to help patients with severe paralysis, whic can result from spinal cord lesion and other traumatic accidents, peripheral neuropathies, amyotrophic lateral sclerosis, multiple sclerosis, and stroke.
|Graf, Arnulf B A; Andersen, Richard A (2015) Predicting oculomotor behaviour from correlated populations of posterior parietal neurons. Nat Commun 6:6024|
|Aflalo, Tyson; Kellis, Spencer; Klaes, Christian et al. (2015) Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348:906-10|
|Klaes, Christian; Kellis, Spencer; Aflalo, Tyson et al. (2015) Hand Shape Representations in the Human Posterior Parietal Cortex. J Neurosci 35:15466-76|
|Andersen, Richard A; Andersen, Kristen N; Hwang, Eun Jung et al. (2014) Optic ataxia: from Balint's syndrome to the parietal reach region. Neuron 81:967-83|
|Andersen, Richard A; Kellis, Spencer; Klaes, Christian et al. (2014) Toward more versatile and intuitive cortical brain-machine interfaces. Curr Biol 24:R885-97|
|Hwang, Eun Jung; Hauschild, Markus; Wilke, Melanie et al. (2014) Spatial and temporal eye-hand coordination relies on the parietal reach region. J Neurosci 34:12884-92|
|Graf, Arnulf Ba; Andersen, Richard A (2014) Inferring eye position from populations of lateral intraparietal neurons. Elife 3:e02813|
|Revechkis, Boris; Aflalo, Tyson N S; Kellis, Spencer et al. (2014) Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task. J Neural Eng 11:066014|
|Graf, Arnulf B A; Andersen, Richard A (2014) Brain-machine interface for eye movements. Proc Natl Acad Sci U S A 111:17630-5|
|Hwang, Eun Jung; Andersen, Richard A (2013) The utility of multichannel local field potentials for brain-machine interfaces. J Neural Eng 10:046005|
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