Brain white matter correlates of learning ankle tracking using a wearable device: importance of the superior longitudinal fasciculus II

Abstract

Background: Wearable devices have been found effective in training ankle control in patients with neurological diseases. However, the neural mechanisms associated with using wearable devices for ankle training remain largely unexplored. This study aimed to investigate the ankle tracking performance and brain white matter changes associated with ankle tracking learning using a wearable-device system and the behavior-brain structure relationships in middle-aged and older adults.

Methods: Twenty-six middle-aged and older adults (48-75 years) participated in this study. Participants underwent 5-day ankle tracking learning with their non-dominant foot using a custom-built ankle tracking system equipped with a wearable sensor and a sensor-computer interface for real-time visual feedback and data acquisition. Repeated and random sequences of target tracking trajectories were both used for learning and testing. Ankle tracking performance, calculated as the root-mean-squared-error (RMSE) between the target and actual ankle trajectories, and brain diffusion spectrum MR images were acquired at baseline and retention tests. The general fractional anisotropy (GFA) values of eight brain white matter tracts of interest were calculated to indicate their integrity. Two-way (Sex × Time) mixed repeated measures ANOVA procedures were used to investigate Sex and Time effects on RMSE and GFA. Correlations between changes in RMSE and those in GFA were analyzed, controlling for age and sex.

Results: After learning, both male and female participants reduced the RMSE of tracking repeated and random sequences (both p < 0.001). Among the eight fiber tracts, the right superior longitudinal fasciculus II (R SLF II) was the only one which showed both increased GFA (p = 0.039) after learning and predictive power of reductions in RMSE for random sequence tracking with its changes in GFA [β = 0.514, R² change = 0.259, p = 0.008].

Conclusions: Our findings implied that interactive tracking movement learning using wearable sensors may place high demands on the attention, sensory feedback integration, and sensorimotor transformation functions of the brain. Therefore, the SLF II, which is known to perform these brain functions, showed corresponding neural plasticity after such learning, and its plasticity also predicted the behavioral gains. The SLF II appears to be a very important anatomical neural correlate involved in such learning paradigms.

Keywords: Diffusion-weighted spectrum imaging; Interactive ankle learning; Machine-computer interface; Magnetic resonance imaging; Middle-aged; Motor sequence learning; Older adults; Online visual feedback; Task-specific; Wearable device.

Fig. 1

Schematic diagram of the ankle tracking system and experimental setting during testing and learning. A The IMU sensor module used in testing and learning. B Examples of the 72-s-long repeated (six cycles of the same sequence) and random sequences generated by the interface software of the system and used in the baseline and retention tests. C An illustration of the target and ankle locations displaying on the screen to serve as real-time visual feedback to the participant. D A diagram of the experimental setting. The participant sat in front of a computer screen in a standard position, with the IMU sensor module worn on the non-dominant foot, and tracked the target cursor as accurately as possible with ankle dorsi- and plantar-flexion of the non-dominant foot. The x-axis of the IMU sensor was aligned to the mediolateral direction of the non-dominant foot, with the positive x, y, and z values on their corresponding axes pointing in the left, posterior, and superior directions, respectively. IMU: inertial measurement unit; MRI: Magnetic Resonance Imaging

Fig. 2

Average tracking performance of all participants at baseline test, during learning, and at retention test. The filled triangles and open circles indicate the average RMSE scores for random and repeated sequences, respectively. * and † indicate significant changes in RMSE scores from baseline to retention tests for repeated and random sequences, respectively. RMSE: root-mean-squared-error

Fig. 3

Partial correlations between changes in tracking errors and changes in white matter tract integrity. A, B Partial correlation plots showed positive correlations between increases in GFA values of the right SLF II and reductions in RMSE scores for A repeated (r = 0.469, p = 0.021) and B random (r = 0.526, p = 0.008) sequences, controlled for age and sex. C, D Partial correlation plots showed no significant correlations between increases in GFA values of the right CST-toe and reductions in RMSE scores for C repeated (r = 0.360, p = 0.084) and D random (r = 0.362, p = 0.082) sequences, controlled for age and sex. E Lateral view of the right SLF II extracted from the tract atlas developed by Chen et al. (2015). CST: corticospinal tract; GFA: generalized fractional anisotropy; R: right; SLF: superior longitudinal fasciculus