An integrated system for quantitatively characterizing different handgrips and identifying their cortical substrates
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Motor recovery of hand function in stroke patients requires months of regular rehabilitation therapy, and is often not measured in a quantitative manner. The first goal of this project was to design a system that can quantitatively track hand movements and, in practice, related changes in hand movements over time. The second goal of this project was to acquire hand and finger movement data during functional imaging (in our case we used magnetoencephalography (MEG)) to be used for characterizing cortical plasticity associated with training. To achieve these goals, for each hand, finger flexion and extension were measured with a data glove and wrist rotation was calculated using an accelerometer. To accomplish the first goal of the project, we designed and implemented Matlab algorithms for the acquisition of behavioral data on different handgrips, specifically power and precision grips. We compiled a set of 52 objects (26 man-made and 26 natural), displayed one at the time on a computer screen, and the subject was asked to form the appropriate handgrip for picking up the object image presented. To accomplish the second goal, we used the setup described above during an MEG scanning session. The timescales for the signals from the glove, accelerometer, and MEG were synchronized and the data analyzed using Brainstorm. We validated proper functionality of the system by demonstrating that the glove and accelerometer data during handgrip formation correspond to the appropriate neural responses.