Article Figures & Data
Figures
- Figure 1.
DBS and cortical lead localization. A, 3D reconstructions of all DBS lead locations in the STN (orange). Individual subject’s leads are shown in by different colors. B, 3D reconstructions of cortical electrode paddle location. The two most anterior contacts overlie the M1, while the two most posterior contacts overlie the S1.
- Figure 2.
Synchronized gait kinematic data with raw local field potential recordings during natural walking. A, Illustration of gait events and phases during a single gait cycle, aligned to left heel-strike (0% gait cycle). B, Heel-strike (squares) and toe-off (circles) gait events were detected from the left (black) and right (gray) force-sensitive resistor data. Heel-strikes were detected when the heel force (solid line) exceeded a threshold (dotted line), and toe-offs were detected when toe force (dashed line) fell below the threshold. C, Example local field potential recordings from both STN and M1 synchronized to a gait cycle. Left heel-strike (LHS), Right toe-off (RTO), Right heel-strike (RHS), Left toe-off (LTO).
- Figure 3.
STN local field potentials show spectral power modulations during the gait cycle. Grand average z score spectrograms from the dorsal and ventral STNs normalized to a gait cycle. A, B, Significant power increases are seen during weight acceptance of the left leg in the left hemisphere (∼0–10% gait cycle) and right leg in the right hemisphere (∼50–60% gait cycle). Power increases were observed in a wide frequency band (10–50 Hz) in the ventral STN and in the low-frequency band (5–15 Hz) in the dorsal STN. Significant beta (13–30 Hz) desynchronization was also seen during contralateral leg swing and heel-strikes. A, B, Gait cycle percentages and frequencies where power was significantly different compared with the average power during the entire walking task is outlined by the dashed white lines. A linear mixed-effect model was used to determine significance with p-value < 0.05. (Extended Data Fig. 3-1 shows grand average gait cycles from cortical recorded contacts. Extended Data Fig. 3-2 shows a single gait cycle from all recorded areas from all subjects in the study and shows alternating left–right power changes throughout the gait cycle.)
- Figure 4.
Low-frequency STN/cortical coherence increase during the initiation of contralateral leg swing. Grand average z score coherogram from STN–M1 and STN–S1 normalized to a gait cycle. Reciprocal coherence modulation was seen in both hemispheres. A, STN–M1 coherence showed significant increases in the theta band (5–8 Hz) during the initiation of contralateral leg swing through mid-swing. Additionally, the left hemisphere showed beta band coherence increases during initial ipsilateral weight acceptance. B, STN–S1 coherence modulation was seen theta/alpha band across both hemispheres during ipsilateral heel-strike. A, B, Gait cycle percentages and frequencies where coherence was significantly different from the average coherence during the entire walking task are outlined by the dashed white lines. A linear mixed-effect model was used to determine significance with a p-value < 0.05.
- Figure 5.
Unique frequency bands within each subject can differentiate gait events. A, Average heel-strike and toe-off PSDs from the STN and M1. Each subject had unique frequency bands where power during heel-strikes (green, left heel-strike (LHS); orange right heel-strike (RHS)) and toe-off (blue, left toe-off (LTO), pink right toe-off (RTO)) gait events were significantly different (p < 0.05). The unique frequency bands were mainly found within the canonical frequency ranges (color of shaded area), but rarely spanned the entire range (width of shaded area). Inset plots show power differences between gait events temporally distinct from each other in relation to the gait cycle. B, Average power and standard error ± 1 s around the gait event. Reciprocal power modulation, offset by half a gait cycle, is seen between temporally distinct gait events in all subjects. Furthermore, all left hemisphere data show higher power during left heel-strike/right toe-off, and most of the right hemisphere data show higher power during right heel-strike/left toe-off. C, Boxplot of gait event power within the frequency bands from B. Individual gait event powers are shown as transparent colored dots with outliers shown on the dotted line. Multiple-comparison tests were performed against each pair of gait event within the same hemisphere. Level of significance is indicated as follows: *p < 0.05, **p < 0.005. (Extended Data Fig. 5-1 shows a visualization of the arbitrary length frequency bands created an Kruskal-Wallis p-value heat-map.)
- Figure 6.
Gait event decoding using oscillatory features achieves greater than chance accuracy. LDA ensemble classifiers were trained on left and right toe-off events for each contact and hemisphere across all subjects. All subjects had at least one contact where the classification accuracy of at least one model was ≥61.1%. Maximum accuracy achieved across all subjects were between 61.1% and 69.2%. Maximum discriminatory ability was calculated using the area under the receiver operator characteristic curve and ranged between 0.585 and 0.763. Each subject’s models are shown on each row. The recorded area the LDA model was built from is indicated in color and follows this order (left to right): red, ventral STN; green, dorsal STN; blue, S1; purple, M1. The bar pattern indicates the brain hemisphere the model was built from: solid, left hemisphere; striped, right hemisphere. *p < 0.05, **p < 0.005, ***p < 0.0005. (Extended Data Fig. 6-1 shows results from classifier models built using coherence values between the STN and M1.)
Tables
ID Age/sex Disease duration DBS target UPDRS III total off-meds UPDRS III total on-meds UPDRS III PIGD* on-meds Subject 1 42/M 06 STN 41 14 2 Subject 2 58/M 09 STN 34 09 1 Subject 3 61/M 05 STN 35 12 1 M, Male.
ID Median accuracy Maximum accuracy Median AUC Maximum AUC Subject 1 55.8% 69.2% 0.592 0.763 Subject 2 60.3% 68.0% 0.635 0.733 Subject 3 54.4% 61.1% 0.574 0.585
Figure 3-1
Cortical local field potentials show spectral power modulations during the gait cycle. A, Left, M1 shows alpha (8–10 Hz) and beta desynchronization during right leg heel strike and initial right leg swing, respectively. Right, M1 shows increased theta–alpha (5–12 Hz) synchronization during initial left leg swing and decreased beta around left heel-strike. B, Significantly decreased beta power is seen during left leg weight acceptance and initial right leg swing. Increases in theta-beta power (5–23 Hz) were seen during weight acceptance of the right leg and initial left leg swing. Download Figure 3-1, TIF file.
Figure 3-2
Individual gait cycle spectrograms. Spectrograms of a single gait cycle from the STN and sensorimotor cortices. All subjects show alternating left and right spectral power changes throughout the gait cycle. Download Figure 3-2, TIF file.
Figure 5-1
Example arbitrary frequency bands and Kruskal–Wallis testing (related to Fig. 5). Varying lengths of frequency bands were created between 0 and 50 Hz. Each frequency is referenced as a bin. Start and end bins refer to the varying lengths of frequency band for start and end frequencies. Power during left and right heel-strike and toe-off events were extracted from each frequency band and an Kruskal–Wallis test was performed. The p-value of the Kruskal–Wallis test was stored, and a heat map was created. Example of the resulting heat map is shown from subject 2 M1 recorded area. Significant Kruskal–Wallis test outcomes can be observed to fall within the low-gamma band (35–45 Hz) frequency. Download Figure 5-1, TIF file.
Figure 6-1
Toe-off gait event decoding using STN–M1 coherence. LDA ensemble classifiers were trained using coherence magnitude squared values between the ventral and dorsal STN to M1 and S1. The highest accuracy and discriminatory values achieved were similar to models built from individual recorded areas. The highest accuracy values achieved were between 58.9% and 68.3%, and the highest discriminatory values were between 0.602 and 0.786. Each subject’s models are shown in each row. The recorded area the LDA model was built from is indicated in color and follows this order (left to right): pink, ventral STN; yellow, dorsal STN; brown, S1; orange, M1. Bar patterns indicate the brain hemisphere the model was built from: solid, left hemisphere; striped, right hemisphere. *p < 0.05, **p < 0.005, ***p < 0.0005. Download Figure 6-1, TIF file.
In this issue
