Evaluation of tremor interference with control of voluntary reaching movements in patients with Parkinson’s disease

Abstract Background A large population of patients with Parkinson’s disease (PD) displays the symptom of resting tremor. However, the extent that resting tremor may affect the performance of movement control has not been evaluated specifically. This study aims at establishing methods to quantitatively evaluate motor performance in PD patients with tremor, and at analyzing the interfering effects of tremor on control of reaching movements. Methods Ten PD patients with tremor and Ten healthy control subjects were recruited to participate in this study. All patients and healthy control subjects performed point-to-point reaching movements with their tremor affected arm or preferred arm. We verified that a smoothing model of minimum-jerk trajectory (MJT) can be used to extract voluntary movement trajectory from tremor-corrupted movement trajectory in the reaching tasks by the patients. Performance indices of reaction time (RT) and movement time (MT) of reaching movements by the PD subjects with tremor were evaluated using MJT trajectories. Differences of RT and MT between the recorded trajectories and MJT in PD and control subjects were calculated to investigate the extent that tremor may affect their motor performance. Linear mixed-effects model was used to identify the contributions of tremor, bradykinesia and rigidity to the performance indices of RT and MT based on UPDRS scores. The power spectrum densities (PSD) of tremor were also evaluated using hand velocities to represent tremor intensity and to analyze their correlations with RT and MT. Results The MJT model demonstrated good fit to recorded trajectory with a more consistent estimation of motor performance for both PD and control subjects. The RT and MT of patients were found to be 43.4 and 79.5% longer respectively than those of healthy control subjects. Analysis of the linear mixed-effects model was not able to reveal that tremor, bradykinesia and rigidity each had a significant contribution to RT or MT in PD patients with tremor. However, the PSD of tremor was found to correlate significantly to RT, but not to MT, in both linear regression and linear mixed-effects model. Conclusions The minimum-jerk trajectory and power spectrum densities are effective quantitative tools for evaluating motor performance for PD patients with tremor. Resting tremor is one of the factors prolonging the initiation of voluntary reaching movement in these patients

Parameter Settings of CS-VA Model Simulations.

1<p>Propriospinal Neurons.</p>2<p>gains of <i>Ia</i> afferent to PN.</p>3<p>gains of descending gamma command.</p>4<p>gains of PN to reciprocal inhibition.</p>5<p>Reciprocal inhibition gains.</p>6<p>Stretch reflex gains.</p>7<p>Golgi Tendon Organ reflex gains.</p>8<p>Recurrent inhibition gains.</p>9<p>Pectoralis major Clavicle portion.</p>10<p>Deltoid Posterior.</p>11<p>Triceps long head.</p>12<p>Brachialis.</p>13<p>Triceps lateral head.</p

Results of frequency dependent damping effects on tremor by neuromuscular dynamics were obtained with cortical inputs at a high level ( = 0.9 And = 0.3) across the range of tremor frequency (3∼8 <i>Hz</i>).

<p>Different colors represent variables of different muscles. (A) The amplitude of muscle inputs (U) was presented, which showed a frequency dependent attenuation. This damping effect was mainly due to the excitation dynamics of motoneuron pools. This built-in excitation dynamics was embedded into the spinal reflexes of the VA model. (B) The overall frequency dependent damping effects on shoulder and elbow tremors were shown. The total damping effect of neuromuscular dynamics included the contributions of excitation of motoneuron pools, the muscle activation-contraction dynamics, and the inertia and viscosity of the limb. It was shown that the damping effect was greater with higher tremor frequency. In particular, at the tremor frequency of 8 <i>Hz</i>, the amplitude of tremor at both shoulder and elbow joints diminished to almost zero.</p

Results of the oscillatory amplitude of shoulder and elbow with varying and obtained with PN network are shown in 3-D maps as a function of cortical oscillation amplitudes of and .

<p>Oscillatory amplitudes of shoulder and elbow joints are both increased with amplitude of central oscillations. The tremor amplitude of elbow is varied more significantly than that of shoulder. It appears that has a stronger influence on peripheral tremor because at low , no peripheral tremor can be initiated by . And at high , the amplitudes of peripheral tremor climbed rapidly. This suggests that is the direct driving command for muscles.</p

The cortical commands of alpha dynamic and gamma dynamic during 1 <i>sec</i>.

<p> denotes the amplitude of alpha command and denotes the amplitude of gamma command with respect to a constant bias of 0.5.</p

Results of the tremor amplitude of shoulder and elbow with varying and obtained without PN network are shown in 3-D maps as a function of cortical oscillation amplitudes of and .

<p>It is clear that for large ranges of cortical oscillations, no peripheral tremor at either 5 <i>Hz</i> or 10 <i>Hz</i> were evident, except for high values above 0.7, where the small oscillation elicited was of double tremor frequency of 10 <i>Hz</i>. This may be due to direct pass of command onto motoneuron pools that cause co-contraction of antagonistic muscles.</p

Calculated Phase Shifts Of Agonists To Antagonists From Simulated And Recorded Pd Tremor.

*<p>Simulated Muscle Activation Frequency 5.00 Hz.</p>**<p>Recorded Surface EMG Frequency 4.11 Hz.</p>1<p>Propriospinal Neurons.</p>2<p>Pectoralis major Clavicle portion.</p>3<p>Deltoid Posterior.</p>4<p>Biceps short head.</p>5<p>Triceps long head.</p>6<p>Brachialis.</p>7<p>Triceps lateral head.</p>8<p>Flexor Digitorum Superficialis.</p>9<p>Extensor Digitorum.</p

Result of a simulation without PN network is presented with 5/10 <i>Hz</i> as the representative pair of single/double tremor frequencies.

<p>(<b>A</b>) The joint movements of the virtual arm are presented. There are small fluctuations of 10 <i>Hz</i> both in shoulder and elbow joints. The spectrums of joint movements were calculated with a data window between 20 s∼30 s of simulation. There was no peak at 5 <i>Hz</i> in the amplitude of spectrum of both shoulder and elbow joints. In (<b>B</b>), the direct excitations (U) of all pairs of antagonistic muscles show a co-contraction pattern, and no alternating activations of antagonist muscles are evident. The neural inputs of muscles (U) show a peak at 10 <i>Hz</i> and its harmonic components in frequency spectrum.</p