Level of M1 GABAB predicts micro offline consolidation of motor learning during wakefulness

Abstract

The consolidation process stabilizes a new initially labile memory. This consolidation could operate on a shorter timescale during wakefulness after initial motor learning. Within micro-offline learning states, sequences of simple individual actions learned through interleaved practice are condensed into a unified skill through a time-dependent consolidation process occurring during wakeful periods. While emerging evidence links Glutamate and GABA modulations in the primary motor cortex (M1) to motor learning, its relationship with micro-offline consolidation processes in brief resting states during motor learning is unclear. To investigate this issue, we employed Transcranial magnetic stimulation (TMS) to evaluate whether interindividual variation of different neurotransmitters at rest influences motor learning consolidation in humans. Our results point to the role of GABAB in micro-offline motor consolidation processes during motor learning in M1. This finding could have an important impact on planning neuropharmacology or non-invasive brain stimulation approaches in clinical domains, such as post-stroke rehabilitation.

Fig. 1. Skill learning task and behavioral performance.

Panel a Participants learned the motor-skill task consisting of alternating practice and rest periods of 10 s duration for a total of 36 blocks. In each practice task block, participants were asked to repeatedly tap the sequence 4-1-3-2-4 as quickly and accurately as possible using their left, non-dominant hand. Practice task blocks were interleaved with 10-s waking rest intervals (inter-practice rest). Note that motor learning performance increases occur during the intervening periods of waking rest (micro-offline gains), but not during the active practice task (micro-online learning). Panel b Mean performance of all participants. Skill was measured as the correct sequence typing speed (sequences [seq]/s). Mean average performance (mean ± SEM) increased rapidly during early learning (the set of trials within which 95% of total learning occurred, trials 1–11 (left panel) extracted by means of the curve fitting performance (right panel). The vertical black line in the right panel corresponds to the block T where 95% of total learning occurred. Micro-online changes were calculated as the difference in tapping speed (keypresses/s) between the first and last correct sequence within a practice period (black in inset) and micro-offline changes as the difference between the last correct sequence within a practice period and the first of the next practice period (red in inset).

Fig. 2. Motor learning t-test.

Data points in the violin plot depict the sum of changes in performance measured as the average inter-tap interval within correct sequences (tapping speed measured in keypresses/s) in the first block (T₀) and in the block (T) where 95% of total learning occurred in each participant.

Fig. 3. Scatter plot of correlations.

Relationship between baseline GABA concentrations and micro-offline learning consolidation when 95% of total learning occurred. In the left panel, the short intracortical inhibition corresponding to GABA_A modulation is reported; in the right panel, the long intracortical inhibition corresponds to GABA_B neurotransmitter modulation.

Fig. 4. Inclusion probabilities.

Bar graph of posterior inclusion probabilities for the Bayesian linear regression of the LICI data. The dashed line represents the prior inclusion probabilities.