Theta burst stimulation and ballistic motor learning
5.7 Control experiment: TBS on variability of TMS-evoked movements In order to assess the effect of iTBS on motor output variability in a manner
independent of task performance, we measured vectors of thumb movements evoked by a single TMS pulse (Experiment 2). This paradigm is similar to that employed by Classen et al 1998. A triaxial accelerometer (Entran Sensors & Electronics, Les Clayes-sous-bois, France) was placed on the right thumb proximal phalanx, allowing the derivation of a vector for each evoked thumb movement.
We used a stereotactic neuro-navigation system (Brainsight, Rogue Software,
Montreal Quebec, Canada) to identify a location in the hand area of the primary motor
cortex at which stimulation produces a TMS-evoked movement of a stable vector, defined by at least 8 out of 10 vectors lying within the same quadrant. After an initial baseline block (20 TMS-evoked movements), iTBS was then delivered to the same location of the motor cortex and a post-intervention block was recorded.
For experiment 3, the concentration parameter (κ) was derived from the TMS-evoked movement vectors using the circular statistics software Oriana (Oriana for Windows, Kovach Computing Services, Anglesey, Wales). κ is a measure of the directionality of the distribution (Fisher, 1993) for which a value of 0 would represent no vector directionality (a distribution resembling a perfect circle), and thus maximal motor output variability.
We first performed the Rayleigh test on the movement vectors in order to verify that they were not circularly uniform. This confirmed that the κ is a valid measure of non-uniformity for this data set. We derived this measure at baseline, after iTBS and after no stimulation. κ is a non-linear parameter and was thus transformed with log10 and a mean calculated for graphical representation. The non-parametric Wilcoxon paired signed ranks test was used to test for significant differences.
Here we tested the effect of iTBS on the directional variability of a TMS-evoked thumb movement, an outcome measure independent of movement magnitude. Figure 5.7a shows data from two representative subjects, in which the direction of movement was considerably dispersed following iTBS but remained stable after no intervention.
In Figure 5.7b the change in statistical concentration (κ) of TMS-evoked movement vectors following either iTBS or no intervention is shown for each subject. A lower value for κ denotes a greater degree of variability, so that a negative change in this parameter indicates an increase in movement dispersion. The baseline value for κ did not differ between the 2 session types (Wilcoxon paired signed rank test P=0.249). In
the sessions without stimulation κ was not significantly changed at the post-intervention time point (Pre 0.677 ± 0.159 (mean ± SE); Post 0.849 ± 0.219;
P=0.249). Following iTBS, by contrast, there was a significant reduction in κ (Pre 0.924 ± 0.183; Post 0.355 ± 0.183; P=0.046): iTBS was therefore associated here with an increase in directional variability of the TMS-evoked motor output.
Figure 5.7 The effect of iTBS on the variability of TMS-evoked thumb movements
(a) Directional vectors for 20 consecutive TMS-evoked thumb movements are shown for two representative subjects. At the start of each session, and after no intervention, the direction of thumb movement was stable. Following iTBS the direction of evoked
movements became more variable, such that the concentration parameter (κ) was reduced.
(b) The change in concentration parameter κ following either iTBS or no intervention is shown for each subject. A negative change in κ denotes an increase in the variability of TMS-evoked movement vectors. Baseline values did not differ between the 2 session types (Wilcoxon paired signed rank test P=0.249). κ was significantly reduced following iTBS (0.046) but not after no intervention (0.249), indicating that iTBS increased the variability of TMS-evoked thumb movements.
5.8 Conclusion
An analysis of performance variability in Experiment 1 revealed a pattern similar to that observed with learning outcome: iTBS increased variability but this effect was blocked by nicotine. Moreover, performance variability in a given session was correlated with the behavioural gain observed. One source of changes in performance variability may be due to increased variation in the voluntary drive to motor cortex from distant sites. However, the fact that iTBS increased directional variability in Experiment 2, in which a TMS pulse evoked motor cortical output directly, suggests that a major source of change lay within the motor cortex itself. We conclude that iTBS increases motor cortex output variability independent of voluntary drive.
Variability is known to increase with larger outputs (Jones et al 2002, Hamilton et al 2004). This scaling effect is recognised in sensory input from psychophysical literature and follows the Weber–Fechner law:
"In order that the intensity of a sensation may increase in arithmetical progression, the stimulus must increase in geometrical progression."
(translated from Fechner, 1860) If a Weber-type phenomenon is involved in the motor learning task, it is conceivable that the initial performance (or excitability) may produce a geometric increase in performance or performance variability. Although this is corrected for in the
calculation of coefficient of variation by normalisation with baseline, this correction for baseline may not fully account for the increase in performance variability. This could mean that any increased variability may be an epiphenomenon of increased baseline performance (or excitability). It is telling to note that in the TBS-placebo arm of the experiment, the mean performance in Block 1 appears to be larger than the mean performance in other experimental arms, although this is not significant
(p>0.05). Thus, small changes in baseline excitability or performance that this study is not powered to detect, may produce large changes in performance variability which is detected in our trial-by-trial analysis. Thus, a causal relationship between motor performance and variability is not established and further experiments will need to be done to clarify the statistical correlation.
Nicotine blocked the iTBS-related increase in performance variability, but did not alter variability on its own. A recent study in humans has suggested that cholinergic stimulation may increase the signal-to-noise ratio in the motor cortex (Kuo et al 2007). Similarly, nicotine increases the gain in thalamic inputs to the visual cortex (Disney et al 2007). The present results may be explained in these terms if nicotine were to reduce variability within the motor cortex, with a consequent negative effect on learning.
In the present study we demonstrate enhanced acquisition of a motor task following iTBS, and blockade of this effect by nicotine. This interaction was not explained by effects on synaptic plasticity, at least as judged by their influence on corticospinal excitability. We believe that iTBS may enhance task acquisition by increasing variability of motor cortical output and thereby driving performance change rather than by increasing synaptic strength although this causal relationship is not proven.
We used a simple mathematical model to demonstrate that a beneficial effect of variability on learning is theoretically feasible in a simple task and this model has similarities to differential stochastic motor learning (Frank et al., 2008; Schöllhorn et al., 2009) and schema motor learning models (Schmidt et al., 1975) in the movement science literature.
The potential to enhance the effects of rTMS pharmacologically has obvious clinical appeal. The positive effect of iTBS alone on subsequent learning may also provide encouragement in this regard, as it raises the possibility of enhancing the response to therapy in patients with motor impairment. However, the observed dissociation between the physiological and behavioural effects of such a combined approach introduces a note of caution. Incorporating concepts with computational or cognitive modelling into the study of motor learning may help to shed light on this relationship.