Motor learning is modulated by dopamine availability in the sensorimotor putamen

Abstract

Successful motor skill acquisition requires the dynamic interaction of multiple brain regions, with the striatum playing a critical role in this network. Animal studies suggest that dopaminergic mechanisms are involved in the regulation of motor learning-associated striatal plasticity. In humans, however, the contribution of nigrostriatal dopaminergic transmission to motor learning remains elusive beyond its well-characterized role in initiation and fluent execution of movements. In this prospective observational study, we investigated motor sequence learning in individuals who had undergone ¹²³I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane single-photon emission computed tomography for the differential diagnosis of Parkinson's disease (n = 41) and age-matched healthy controls (n = 20). We found that striatal dopamine transporter depletion exhibited distinct spatial patterns that were associated with impairments in motor sequence learning and the manifestation of Parkinsonian motor symptoms, respectively. Specifically, significant associations between striatal dopamine transporter depletion and impairments in motor sequence learning were confined to posterior putaminal regions, whereas significant associations of striatal dopamine transporter depletion with Parkinsonian motor symptom severity showed a widespread spatial pattern across the entire striatal volume with an anterior maximum. Normative functional connectivity analysis revealed that both behavioural domains shared largely overlapping connectivity patterns with the basal ganglia and supplementary motor area. However, apart from connectivity with more posterior parts of the supplementary motor area, significant functional connectivity with primary motor cortical areas was only present for striatal dopamine transporter availability-related modulation of online motor learning. Our findings indicate that striatal dopaminergic signalling plays a specific role in motor sequence learning beyond its influence on mere motor execution, implicating learning-related sensorimotor striatum recruitment and cortico-striatal plasticity as dopamine-dependent mechanisms.

Keywords: Parkinson's disease; dopamine; dopamine transporter; motor learning; striatum.

Graphical Abstract

Figure 1

Experimental design. After assessment of Parkinsonian motor symptoms according to the MDS-UPDRS III (U1), all participants performed a motor sequence training session (training), which consisted of 18 blocks of task practice. The first two blocks of the training session were defined as the beginning of training baseline (BoT) and blocks 15 through 18 were defined as the end of training baseline (EoT). After an interval of 6 h, all participants completed a retest session (retest), which consisted of 14 blocks of the motor sequence learning task. The first two blocks of retest were defined as the beginning of retest (BoR). Participants who underwent dopamine transporter single-photon emission computed tomography (DaT-SPECT group) received an additional assessment of Parkinsonian motor symptoms before the retest session (U2), to exclude fluctuations of Parkinsonian motor symptoms as a potential source of task performance differences.

Figure 2

DaT-SPECT and brain computer tomography scan normalization. (A) Average normalized and standardized (occipital reference) and—if appropriate—mirrored DaT-SPECT scans after individual coregistration to CT scans (n = 38 participants). The volume of interest (standardized DaT-SPECT intensity > 2.0 relative to an occipital reference, termed ‘DaTStriatum’) is outlined by a grey line. Scans were mirrored right to left when participants trained with their left hand so that the DaTStriatum volume of interest was always on the contralateral left side for group level analysis. (B) Average CT scans of the participants after normalization to MNI space, same slices as in Fig. 2A. (C) Scatter plot of mean standardized DaT-SPECT intensity from the DaTStriatum volume (‘local SBR’) and mean (caudate nucleus and putamen) striatal SBR value according to the Hermes BRASS algorithm (n = 38; r = 0.934; P < 0.001).

Figure 3

Motor sequence learning task performance. (A) Training and retest performance of DaT-SPECT (n = 38) and control groups (n = 20) with important intervals marked (BoT, beginning-of-training performance; EoT, end of training; BoR, beginning of retest. (B) Online learning [left; unpaired, two-sided t-test, t(56) = 1.005; P = 0.319] and offline consolidation [right; unpaired, two-sided t-test, t(56) = 0.061; P = 0.952] of both groups (n.s., not significantly different).

Figure 4

Voxelwise regression and normative resting state connectivity: local SBR voxels within the DaTStriatum volume significantly associated with UES (A) and online learning (B). Significant voxels are coloured (n = 38, t-test FWE-corrected at cluster-level; P < 0.05). The significant cluster for online learning is highlighted with a dotted square (in slices corresponding to z-coordinate 8, 11 and 14) and the largest cluster (at z-coordinate 11) is depicted at the inset on the left side of the panel. (C) Voxels exhibiting significant resting state connectivity to the striatal ROI set around the peak voxel associated with UES (far left) and online learning (middle; in both panels computed from 100 healthy persons from the human connectome project as detailed in the ‘Materials and methods’ section, t-test FWE-corrected at cluster level P < 0.05). On the far right, the two maps (UES & Online learning) are overlaid (Overlap) with a binary contrast. Note the more posterior connectivity to the supplementary motor area and more M1 involvement for online learning compared to UES. Substantial overlap existed in particular at the level of the basal ganglia. Numbers indicate the MNI z-coordinate of the slices.

Figure 5

Online learning and offline consolidation in individuals with normal striatal dopamine transporter availability (DaT+) and impaired striatal dopamine transporter availability (DaT–). (A) Training and retest performance of DaT+ (n = 18) and DaT− (n = 20) sub-groups. (B) Online learning (left) and offline consolidation (right) of both sub-groups. We found a significant difference for online learning with significantly greater increase in performance in the DaT+ sub-group [unpaired one-sided t-test, t(36) = 1.785; P = 0.041].