Determining Reliable Neural Targets for enhancing Motor Sequence Learning: A preparatory study for focal tDCS

Abstract

BackgroundMotor sequence learning (MSL) underlies the acquisition of everyday skilled movements, and transcranial direct current stimulation (tDCS) has shown promise for enhancing it, although reported effects vary considerably. Before such interventions can be applied and interpreted reliably, the behavioural and neural signatures of the underlying task should be shown to be robust. This study therefore characterised an fMRI-based Serial Reaction Time Task (SRTT) protocol and evaluated the consistency of its behavioural and fMRI-based neural signatures in repeated measurements to identify potential network targets for tDCS. MethodsTwenty healthy young- to middle-aged adults (10 females; mean age 24, SD 4) performed the SRTT during 3T fMRI in two sessions about one week apart, each using one of two matched, non-overlapping task versions in counterbalanced order. We examined block-wise behaviour, task-related and sequence-specific brain activation (general task activation relative to baseline and the SEQ > RND contrast), and functional connectivity using two complementary approaches: the task and temporal dynamics of seed-based connectivity (SBC) within an a priori-defined motor network (M1, SMA, PMC, putamen, and cerebellum), and whole-brain generalised psychophysiological interaction (gPPI) for the SEQ > RND contrast. ResultsReaction time decreased steadily across sequence blocks and returned toward baseline during random blocks, confirming implicit sequence learning; this pattern was consistent across both sessions and both task versions. Test-retest reliability was excellent for random-block reaction time (ICC {approx} .80 - .85), and modest for sequence blocks (ICC {approx} .29 - .53). General task activation (sequence and random performance relative to baseline) engaged the expected sensorimotor network, including the primary motor cortex (M1), premotor cortex, and SMA, with M1 more strongly engaged during sequence than random blocks. The sequence-specific contrast (SEQ > RND) additionally revealed six clusters spanning the left superior parietal cortex, bilateral supplementary and cingulate motor areas, bilateral secondary somatosensory cortex, and left ventral visual cortex together with cerebellar Crus I. The a priori defined motor network was engaged across all task phases and was anchored throughout by strong M1-SMA coupling, with overall network coupling following a non-monotonic course across the session, consistent with successive stages of resource allocation, consolidation, and automatisation. The gPPI analysis identified four sequence-specific connectivity clusters, dominated by a right-lateralised visuomotor network and a somatosensory-visual integration channel. ConclusionsTogether, these findings indicate a two-component architecture for SRTT performance: a left hemispheric cortico-striato-cerebellar network that supports performance and acquisition of the sequence, and a right hemispheric network associated with monitoring its predictable structure. Within this protocol, the SRTT produced robust and stable group-level signatures of implicit motor learning, yielding well-defined network targets for future tDCS interventions..