Article Figures & Data
Figures
- Figure 1.
ECoG array placement, experimental task, and behavioral data. A, In S1 through S5 and S7, we targeted the primary motor and premotor cortices. Array placement for S6 was determined by clinical need. For S1 and S2, we recorded ECoG from the right hemisphere; the other subjects’ ECoG were recorded from the left hemisphere. B, One trial (∼2.5 s) of the kinematic-kinetic task. At the beginning of the trial, the subjects held their index finger in a neutral position (upper left photograph) until visually cued on a screen. Cyan trace, finger kinematics (amount of flexion; arbitrary units) during the trial. Cyan triangle, time of flexion movement onset. Upon contact with the force sensor (lower inset photograph), the subjects exerted isometric force until matching a force target on the screen with a cursor (data not shown). Blue trace, recorded force. Blue circle, time of force onset. At bottom is a schematic representation of behavioral mode segmentation, premovement (from target presentation until the start of flexion), movement (start of flexion until start of force), and force (from force onset lasting 500 ms). C, We measured index finger flexion using a CyberGlove; movement onset was identified using the first PC calculated on the data from the highlighted sensors.
- Figure 2.
Spectral power modulation during the movement-force grasp task. Each panel shows data from a high-frequency or low-frequency spectral feature taken from an individual ECoG electrode. The single-trial frequency band power (grayscale in each plot) was z-scored and aligned either to movement onset (cyan dashed lines; A–C, F) or to force onset (blue dashed lines; D, E). Blue circles show force onset times when trials were aligned to movement onset. Cyan triangles show movement onset times when trials were aligned to force onset. High-frequency features (A–C) exhibited power increases, which could be time locked to both movement and force (A) or force only (B, C). Low-frequency features (D–F) exhibited power decreases just preceding, and aligned to, the onset of movement (D, E), or aligned to the start of force (F).
- Figure 3.
Decoding maps reveal changes in the cortical representations of movement and force. A, Example decoding maps for S4; 4 folds of data are shown, the actual analysis used 10 folds per recording. Square recording arrays are shown in a rotated perspective for compact visualization. We compared single-electrode decoding maps for movement (top) and force (bottom) using a distance metric
for every possible combination of fold pairs. As a control, we calculated
between all possible fold pairs, for within-movement and within-force decoding. B, Boxplot of distance measures for all subjects. The central horizontal line in each box shows the median, while the notches show 95% confidence intervals. Overall, the median
was significantly greater than the median
in six of seven subjects (red stars). Note that the maps in A show 64 channels; for the distance measures in B, only the PM/M1 electrodes were included. - Figure 4.
Modeling ECoG features as an underlying dynamical system using LFADS uncovers repeatable trajectories through a low-dimensional state space during the kinematic-kinetic task. Shown are LFADS-PCs (labeled as PC for simplicity) derived from high-frequency (A, B) and low-frequency (C, D) ECoG features. Single-trial trajectories are shown for subjects S5 (78 trials; A, C) and S6 (73 trials; B, D). Inset boxes in each panel show the trajectories resulting from PCA performed directly on the ECoG features (without LFADS). The color code at bottom defines the portion of each trial corresponding to each behavioral mode.
- Figure 5.
The NVA summarizes the cortical state change associated with the behavioral mode change from movement to force. A, B, Electrodes selected for S5, using k-means clustering. CS; central sulcus. Anterior-posterior and superior-inferior are indicated on the rosette; compare to Figure 1A. A, B, Two of the three resulting clusters; the unsupervised cluster analysis natively divided the responses into low-frequency and high-frequency responses. C, The NVA, θ(t) for the low-frequency features selected in A. The dark red dashed line shows the average time of target appearance, relative to force onset (time = 0). The vertical cyan lines show the mean (solid line) and standard deviation (dashed lines) of movement onset, relative to force onset. The vertical black lines show the time of maximum force for each trial (equivalent to the reference period m ref). D, The NVA for the high-frequency features shown in B. E, F, NVAs calculated across all trials, all subjects in the study. Labeling conventions are the same as in C, D.
- Figure 6.
Decoding behavioral mode from ECoG features before and after LFADS denoising. The median classification accuracy was greater than chance for all subjects. Tree; boosted aggregate decision tree classifier.
Tables
- Table 1
Displacement of peak location (in mm) for movement decoding performance relative to force decoding performance in each subject
Mean ± SD S1 16.1 ± 4.1 S2 16.5 ± 8.8 S3 3.2 ± 5.4 S4 10.2 ± 8.4 S5 4.2 ± 6.6 S6 8.8 ± 5.4 S7 10.7 ± 8.0 - Table 2
Number of PCs required to account for 90% of the variance in the ECoG features (PCA-only) or the latent factors (LFADS PCs)
PCA-only LFADS PCs S1 43/66 2/66 S2 32/48 2/48 S3.1 26/44 2/44 S3.2 24/32 3/32 S4.1 40/74 3/74 S4.2 35/72 2/72 S5.1 19/36 2/36 S5.2 24/40 2/40 S6.1 28/38 4/38 S6.2 27/36 3/36 S6.3 27/36 3/36 S7 32/78 2/78
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