This proposal is a collaborative effort by a team of five motor systems groups at five institutions seeking to probe the mechanisms that underlie the brain's capacity for learning a new motor skill. The common thread for all groups is to focus on changes that occur within the primary motor cortex as a new skill is acquired. Changes in motor cortex will be characterized in relationship to critical input areas including premotor and parietal cortex and the role of subcortical circuits in learning will also be modeled. Both immediate and long-lasting changes of motor cortex representation will be investigated using a synthesis of molecular, cellular, systems and computational level of analysis. Project 1: Dr. Peter Strick (at the University of Pittsburgh) will combine flavoprotein optical imaging and single unit recording in monkey to characterize changes of activity in premotor and motor cortex as animals learn sequential behavior. Flavoprotein imaging allows for long-term cortical mapping over at least two years time, making it possible to look at dynamic alterations of cortical neuronal activity throughout the training period. Project 2: Dr. Scott Grafton (at University of California, Santa Barbara) will use functional MRI and transcranial magnetic stimulation in humans to study the neural substrates for off-line consolidation of three types of newly acquired motor skills: sequencing, visuomotor and dynamic adaptation. The tasks are similar to those in the other projects allowing for translation between monkey and human studies. Project 3: Dr. Emilio Bizzi (at MIT) will collaborate with experts in nanotechnology and conducting polymers at MIT to develop a new type of electrode based on fine wires of conducting polymers. With this he will perform chronic recordings of primary motor neurons in primates learning to move in novel dynamics. Project 4: Dr. James Houk (at Northwestern University) and Dr. Andrew Barto (at the University of Massachusetts at Amherst) will develop computational models of learning that are integral to the other projects of this PPG. These models will be used to explore critical behavioral and representational issues. Core A: Support for program administration and videoconferencing to achieve PPG integration. In addition, the core will support 3 satellite conferences on motor learning and mechanisms of cortical reorganization that are relevant for translational research. These interrelated projects, focusing on single anatomical substrate and common set of learning behaviors, should provide an integration of methodologies across multiple levels of analysis that are far beyond those achievable if each project were pursued separately. The collaborative effort can be expected to significantly advance our knowledge about mechanisms that support motor cortex plasticity.
The proposed work is central to the problem of understanding the mechansims where practice leads to to reorganization of the hunnan motor system in the face of aging, neurodeneration, stroke or brain injury. Understanding these mechansims has an impact on the design of therapies directed at preserving function, developing compensator movements and ultimately, developing novel motor capacity.
|Soto, Fabian A; Vucovich, Lauren; Musgrave, Robert et al. (2015) General recognition theory with individual differences: a new method for examining perceptual and decisional interactions with an application to face perception. Psychon Bull Rev 22:88-111|
|Lawlor, Patrick Nathan; Kalisky, Tomer; Rosner, Robert et al. (2014) Conceptualizing cancer drugs as classifiers. PLoS One 9:e106444|
|Ashby, F Gregory (2014) Is state-trace analysis an appropriate tool for assessing the number of cognitive systems? Psychon Bull Rev 21:935-46|
|Devarajan, Karthik; Cheung, Vincent C K (2014) On nonnegative matrix factorization algorithms for signal-dependent noise with application to electromyography data. Neural Comput 26:1128-68|
|Smith, J David; Johnston, Jennifer J R; Musgrave, Robert D et al. (2014) Cross-modal information integration in category learning. Atten Percept Psychophys 76:1473-84|
|Acuna, Daniel E; Wymbs, Nicholas F; Reynolds, Chelsea A et al. (2014) Multifaceted aspects of chunking enable robust algorithms. J Neurophysiol 112:1849-56|
|Fernandes, Hugo L; Stevenson, Ian H; Vilares, Iris et al. (2014) The generalization of prior uncertainty during reaching. J Neurosci 34:11470-84|
|Klimm, Florian; Bassett, Danielle S; Carlson, Jean M et al. (2014) Resolving structural variability in network models and the brain. PLoS Comput Biol 10:e1003491|
|Smith, J David; Boomer, Joseph; Zakrzewski, Alexandria C et al. (2014) Deferred feedback sharply dissociates implicit and explicit category learning. Psychol Sci 25:447-57|
|Barany, Deborah A; Della-Maggiore, Valeria; Viswanathan, Shivakumar et al. (2014) Feature interactions enable decoding of sensorimotor transformations for goal-directed movement. J Neurosci 34:6860-73|
Showing the most recent 10 out of 72 publications