Decoding the Human Motor Cortex

A human being’s motor cortex becomes engaged when he or she performs specific motions. However, little is known about the relationship between the regions in the motor cortex. Neuroscience has not reached consensus over how motions for specific joints relate, or how engagement changes when a motion is performed more forcefully. We obtained fMRI BOLD scans of subjects’ motor cortices as they performed various movements. We found that we could distinguish between movements with considerable accuracy. Moreover, we confirmed that these techniques relied solely on data within the motor cortex, not skull-movement or other disruptions. BACKGROUND: BRANTNER EXPERIMENT A recent paper authored by members of the Stanford Artificial Intelligence Laboratory, SVM Based Classification of Motor Tasks on fMRI-BOLD [1], explored the technique of classifying motor tasks to gain insight into the structure of the motor cortex. We were inspired to first replicate their results, in order to verify our implementation of the machine learning techniques, and also to gain insight into where their model and techniques fell short. In their experiment, a subject performed one of ten tasks while being monitored by an fMRI scanner. During each task they were either holding a light (.05 kg) or heavy (.5 kg) weight. Table 1 lists these tasks. TABLE I: The list of tasks. For simplicity in later results, each task was given a number 1-10. Task # (light/heavy) Motion 1/6 Wrist-Flex 2/7 Wrist Rotate 3/8 Elbow-Flex 4/9 Shoulder-Flex 5/10 Shoulder-Rotate Each of three subjects performed a total of 116 motions. The pertinent data gathered from the experiment was a measurement of the activation in each voxel, a 2.5×2.5×2.5mm region in the brain; the scan area was a rectangular prism of 64 × 64 × 24 voxels, resulting in a scan volume with a total of 98,304 voxels. The fMRI volumes were taken every 1.57 seconds. The data was preprocessed with GLMdenoise, a software package by Kay et al [2]. GLMdenoise uses statistical methods to derive global noise regressors for use in a General Linear Model (GLM) analysis. GLMdenoise denoises the raw time-series data, and produces a “modelmd” volume for each task type, that represents the peak percent signal change in the BOLD (Blood Oxygen Level Dependant) signal when it is fitted to the Haemodynamic Response Function (HRF). Each of the “modelmd” volumes represent, intuitively, which voxels gave the clearest signals of engagement during a task. Figure 1 and Figure 2 are examples of such “modelmd” volumes, corresponding to the unweighted wrist-flex and unweighted elbowflex movements, respecitvely. Looking at the figures, it is visually evident that the activation regions are different for different motions. This is a strong indication that distinguishing between motions using fMRI data may be an appropriate machine learning task. FIG. 1: : Highlighted have voxels “modelmd” scores above a threshold for the unweighted wrist-flex. FIG. 2: : A different, though not disjoint, set of voxels exceeds this threshold for the unweighted elbow-flex. In Brantner, an SVM was trained with the following model: A subject performed tasks P and Q each p and q times respectively. Define a time interval around each task so that a total of T measurements were made during each time interval. Also, choose some set of voxels V = {v1, . , vj}. Now for each voxel vk we will measure its activation at some time t within the interval for the motion r. We can denote this ar(vj). We seek for each of our training examples to contain information about