The neural substrates of graphomotor sequence learning.

Title of Document: THE NEURAL SUBSTRATES OF GRAPHOMOTOR SEQUENCE LEARNING. Bruce A. Swett, PhD 2007 Directed By: Dr. Jose L. Contreras-Vidal, Department of Kinesiology; Neuroscience and Cognitive Science and Bioengineering Graduate Programs. Performing sequences of movements easily and automatically is an integral part of our everyday lives. This dissertation examines how a set of individual movements are assembled into a movement sequence, focusing on the neural regions involved, and the timing of their participation. A second, related question is whether the order of encoding of the individual movements can be detected with kinematic and neuroimaging methods. Understanding how sequences are learned is important for expanding our knowledge of how the brain performs neural computations within healthy persons, and of the alterations of these processes in persons with neurological disorders. To examine these questions, we combined behavioral, kinematic and neuroimaging methods to examine motor sequence learning in healthy adults. The behavioral task involved subjects learning to copy a novel sequence of line-pairs (a graphomotor trajectory sequence learning paradigm) while blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data were simultaneously acquired. This task required the subjects to assemble three sets of line-pairs into a sequence; real-time visual feedback for error correction was provided during production of each line-pair, and subjects were also provided with knowledge of results (their speed and accuracy) after each sequence of line-pairs. Normalized jerk (a kinematic measure of movement smoothness, computed off-line), movement speed and accuracy were used to assess ongoing changes in task performance during the scanning session. The learning curve derived from the normalized jerk scores were used to define five time-bins for analysis of the functional neuroimaging data. Differences in the patterns of BOLD activation during these times were used to characterize the relationship between neural activity and the phases of sequence learning (temporal dynamics). Furthermore, covariance of BOLD responses in selected neural regions, on a per-bin basis, was used to examine the dynamic evolution of functional connectivity between regions during graphomotor sequence learning. Finally, the neural correlates of serial line-pair encoding were evaluated by comparing BOLD responses in candidate brain areas and kinematic measures of learning (normalized jerk and movement time). Sequence learning, measured by normalized jerk, was best characterized by a curve with a double exponential fit, implying that an early, fast learning process (time-bins 1-2) merged with a slower learning process (bins 3-5). During early learning, the dorsal (superior parietal lobule) and ventral (fusiform gyrus) visual streams, and the dorsal lateral premotor areas were activated (relative to baseline). The inferior parietal lobule was also activated. An unexpected finding was that portions of the basal ganglia (head of caudate nucleus, anterior putamen) and cerebellum (anterior vermis; dentate nucleus) were deactivated during early learning, relative to baseline. Moreover, the medial prefrontal cortex was activated in bins 1 and 2. In the early portion of the second, slower learning process (bin 3), the dorsal visual stream areas became less active; dorsal lateral premotor cortex activation persisted; and the anterior putamen, head of caudate nucleus and posterior vermis became active for the first time. Ventral visual stream (fusiform gyrus) activation persisted. In bin 4, activation of the anterior vermis and dentate nucleus continued, along with persisting activation in the posterior vermis. The somatosensory cortex in the hand area became significantly activated in bin 4 only, relative to baseline. In the final time-bin (bin 5), when the subjects reached a plateau in performance, the spatial extent of the anterior putamen and head of the caudate nucleus was reduced, but the anterior vermis increased in activity. Dorsal visual stream activity was significantly lower than baseline, as was activity in the ventral visual stream regions, dorsal lateral premotor cortex, and inferior parietal lobule. During early learning (bins 1-2), the medial prefrontal cortex was activated; but by the late time-bins (bins 4-5), this region became deactivated, relative to baseline. Limbic regions showed the reverse pattern: they were deactivated in bins 1-2; but became active in bins 4-5. Covariance analysis (measuring associations between BOLD responses in a selected voxel and all other brain regions) showed positive correlations between activity in primary motor cortex and the anterior, middle, and posterior putamen, and the head of the caudate nucleus. These correlations were manifest during early learning (bin 2) and persisted throughout the late portion of the second learning process (bin 5). Activity in the superior parietal lobule was negatively correlated with activity in the anterior, middle, and posterior putamen, and the head of the caudate nucleus only during early sequence learning (bin 1). These regions were functionally uncoupled during bins 2-5. Kinematic analysis of the line-pairs showed that the third line-pair was produced more quickly and smoothly than line-pairs 1 and 2. In addition, a greater reduction in normalized jerk and movement time was evident in line-pair 3 than for line-pairs 1 and 2. Significant effects for line-pair, bin and the interaction of line-pair x bin were found for movement time; significant effects for line-pair and the interaction of line-pair x bin, but not bin were found for normalized jerk. The early portion of the sequence learning process was characterized by dorsal and ventral visual stream activation; dorsal lateral premotor cortical activity; and deactivation of the anterior putamen, head of the caudate nucleus and posterior vermis. This pattern of neural activity in early learning may indicate a relative predominance of visuomotor mapping of the novel sequences. The activation of regions within the ventral visual pathway may indicate either attempts to identify the ideographic characters, or more likely, the classification of the characters as visual objects. The dorsal visual stream areas are involved in visually-guided reaching, and the location of the visual targets and pen position in the work space. The deactivation of portions of the basal ganglia and cerebellum during the early phase of sequence learning may indicate that these regions were being reset, due to the high number of errors being produced. In the beginning of the second, slower learning process, several changes occurred: the dorsal visual stream areas became less active; the left dorsal premotor cortex activation persisted; and the anterior putamen, head of the caudate nucleus and posterior vermis became active. This pattern of neural activity in the beginning of the second, slower phase of sequence learning suggests that emphasis in the sequence learning process had shifted from visuomotor mapping to improving the kinematic and dynamic motor plans for the new sequences (sequence encoding). In the final time bin, when the subjects had reached a plateau in performance, the anterior putamen and head of caudate nucleus became less active, but the anterior vermis increased in activity. This dissociation between activity in specific cerebellar and basal ganglia regions may indicate two distinct learning processes within the latter portion of the second phase of sequence learning. The first may be the encoding of the characters (anterior putamen, head of caudate nucleus), using knowledge of results; the second may involve the improvement of the individual movements and encoding of the line-pairs (anterior vermis), using error feedback correction. Interestingly, the medial regions involved in the “default mode network” (which have been typically reported as being more active during rest, and deactivated during performance of cognitive or motor tasks) did not become deactivated until later in the task (bins 4-5), but –at least in the case of the medial premotor cortex – were unexpectedly activated during early learning, when task demands may be greatest. The functional connectivity analysis provides an important, alternate method of examining the fMRI data, complementary to the standard contrast methods. These analyses demonstrate, for example that some neural regions (such as the basal ganglia) participate in sequence learning prior to reaching significant levels of activity when compared to baseline. The significant effects of line-pair in both movement time and normalized jerk indicate that differential effects of sequence order can be detected in the kinematic measures. Taken together, a model of graphomotor sequence learning emerges, including patterns of neural activation and functional connectivity that correspond to changes in subject performance. This model adds to our current understanding of the neural substrates of graphomotor sequence learning, and may be important in explaining the alterations to these networks in persons with neurodegenerative disorders. THE NEURAL SUBSTRATES OF GRAPHOMOTOR SEQUENCE LEARNING