Initiating a movement goal and maintaining that goal throughout the planning and execution of a goal-directed action is an essential element of all goal-directed behavior. In thecontext of Brain Machine Interfaces (BMIs), a direct communication pathway between thebrain and a man-made computing device, continuous access to movement goals is essential,so as to guide the control of neuroprosthetic limbs that provide neurologically impaired subjects with an alternative to their lost motor function. The Prefrontal cortex (PFC) has beensuggested as an executive control area of the brain that bridges the temporal gap betweenincoming sensory information and ensuing motor actions. The mechanisms underlying thedynamics of PFC neural activity, however, remain poorly understood. The main objectiveof this dissertation is to elucidate the role of PFC neurons in mediating goal initiation andmaintenance during goal-directed behavior.Using a combination of electrophysiological recordings, optogenetic and pharmacological manipulation of population activity and behavioral assays in awake behaving subjects,we demonstrate that the PFC plays a critical role in the planning and execution of a twoalternative forced choice task. In particular, PFC neurons were mostly goal selective duringthe choice epoch of the task when subjects had to select the action with the highest utility while suppressing all other unrewarded actions. Decoding PFC neural activity usingadvanced machine learning algorithms showed robust single trial prediction of motor goals,suggesting that PFC may be a candidate site for inferring volitional motor intent. In addition, results from inactivation experiments demonstrate a lateralized performance declinewith respect to the inactivation site, further confirming the critical role of the PFC in mediating the motor- but not the sensory- information during the execution of goal-directedbehavior. Taken together, our results suggest that the design of next generation BMIs couldbe further improved by incorporating goal information from cognitive control areas of thebrain, thereby augmenting the capability of current designs that only rely on decoding themoment-by-moment kinematics of intended limb movements from motor areas of the brain.
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