33 Modulating Fast Motor Memory Consolidation with Target Memory Reactivation

Faculty Mentor: Genevieve Albouy (Health and Kinesiology, University of Utah)

Abstract

Memory consolidation, the process by which newly learned information is transformed into robust memories, was historically thought to be a slow process that developed over time periods spanning hours to days following initial learning. However, more recent research has shown that consolidation may occur faster, i.e., during the seconds of rest interleaved with bouts of practice during initial learning. Previous research aiming at optimizing consolidation has never considered these early windows of plasticity. The goal of this study was to use Targeted Memory Reactivation (TMR), an intervention known to boost the slow consolidation process, to modulate fast motor memory consolidation occurring during the short rest periods interspersed with task practice. To do so, thirty young healthy adults learned a bimanual audio-motor sequential finger tapping task in which each key was associated to a different auditory tone such that the learned sequence of movements resulted in a unique melody. Participants were distributed in 3 groups according to the TMR condition, i.e., according to the stimulation administered during the rest periods interleaved with task practice. In the congruent TMR group, auditory stimuli that matched the melody of the learned sequence of movements was administered during rest. In the incongruent control group, the auditory stimulation consisted in random tones. In the no stimulation control group, no auditory stimuli were administered during rest. All groups were retested on the task 24 hours (hrs) – including a night of sleep spent at home – after training. Preliminary findings suggest a trend towards an increase in fast consolidation in the congruent group compared to the two control groups. In contrast, the congruent TMR intervention resulted in a decrease in performance gains at the 24hr retest. Altogether, these results suggest that TMR can boost fast motor memory consolidation and that these early plasticity processes may competitively interact with slower, more traditional, consolidation.

Introduction

Memory consolidation is the process by which newly learned information is transformed into robust memories (McGaugh, 2000). This process is crucial in our daily lives as it allows us to e.g. recall past experiences or utilize specific learned skills that make us who we are. Memories can broadly be divided into two categories depending on the type of information being stored – declarative memories (related to information about events or facts) and procedural memories (related to skills)(Squire et al., 2015). Motor memory is a form of procedural memory that is related to the retention of motor skills (goal-oriented actions with a motor component) and used to perform routine activities such as tying our shoes, buttoning a shirt, or typing on a keyboard. The processes underlying motor learning and motor memory consolidation are thought to happen over periods of time including active task practice during learning (“online”) as well as during periods of rest with no task practice (“offline”) (Robertson et al., 2004).

Traditionally, memory consolidation has been viewed as a process that occurs in the hours and days following initial learning (“macro-offline” timeframes) (King et al., 2017; McGaugh, 2000). More recently, research has begun to examine memory consolidation on shorter “micro-offline” timescales as short as the seconds of rest interleaved with bouts of practice within a learning session (Bönstrup et al., 2019). This research has revealed that not only are there distinct neural activations associated with memory consolidation that occur during the rest intervals interleaved within task practice (Buch et al., 2021), but these “micro-offline” periods contribute to the majority of gains in performance observed during early learning (Bönstrup et al., 2019). These initial findings have been replicated in multiple studies since initially being discovered (e.g., Gann et al., 2023; Jacobacci et al., 2020; Van Roy et al., 2024). While previous interventional research has successfully modulated slow consolidation on “macro-offline” timescales following learning (Hu et al., 2020; Nicolas et al., 2022), fast consolidation on the “micro-offline” timescale has yet to be targeted to enhance the fast consolidation process.

One behavioral intervention that has shown promise to enhance memory consolidation at the macro timescale is Targeted Memory Reactivation (TMR). In TMR paradigms, sensory stimuli are associated with the learned material during online practice and are subsequently presented during the offline consolidation window (Hu et al., 2020). TMR is thought to boost consolidation processes by strengthening ongoing neural reactivations associated with memory consolidation (Hu et al., 2020). The vast majority of prior TMR research has applied TMR during offline periods of sleep following learning and has shown TMR-induced boosts in memory consolidation in both the declarative and procedural domains (Hu et al., 2020). Given the evidence that fast consolidation within learning sessions plays a crucial role in early motor learning (Bönstrup et al., 2019) and the evidence that TMR can boost slow consolidation processes, the aim of the present study was to test whether TMR can be used to boost the fast motor memory consolidation process. We hypothesized that applying TMR during the rest periods interleaved with task practice during initial motor sequence learning would boost the behavioral correlates of consolidation on the micro timescale.

Methods

Participants

30 young healthy adults (ages 18-35 y.o, average age 22(3.8) y.o.) were recruited for this study via email and in-person recruitment efforts on the University of Utah campus. They all provided informed consent to participant in this study that was approved by the Institutional Review Board of the University of Utah. Exclusion criteria were any medical, neurological or psychological conditions, consumption of psychotropic or psychoactive medication, indication of abnormal sleep, previous extensive musical or typing training, mobility impairments in the fingers and hands, hearing impairments, and previous participation in a similar experiment. Participants were also excluded if they recently took a trans meridian trip or worked night shifts. One participant was excluded from the analyses due to accuracy issues attributed to equipment malfunction. The 24hr retest data of two participants were excluded from the analyses due to insufficient sleep on the night in between sessions. All demographic statistics by group are provided below in Table 1.

Experimental Design

Participants were first asked to complete online screening questionnaires to assess eligibility. Once eligibility was confirmed they were invited to schedule two experimental sessions in the lab that were 24hrs +/- 2hrs apart and between 8am-6pm. The first experimental session was scheduled for 1hr while the second experimental session was scheduled for 30 minutes (min). Three days prior to the first experimental session, participants were asked to respect a constant sleep schedule according to which they woke up and went to sleep within +/- 1hr around their habitual wake/sleep time, slept for at least 7hrs, and did not go to sleep after 1am. This was checked with a sleep diary. Participants were also asked to refrain from alcohol and recreational drugs for the 24hrs before the first experimental session and to refrain from caffeine before the session.

During this first session, participants completed a consent form and questionnaires to assess sleep quality and quantity of the preceding night (St. Mary’s Hospital Sleep Questionnaire) as well as subjective vigilance (Stanford Sleepiness Scale). Participants then completed a series of tasks on a laptop and specialized keyboard. The laptop was elevated with a tray, and the keyboard was placed beneath the tray to prevent participants from relying on visual checks of their fingers during the task.

The first task measured objective vigilance using a Psychomotor Vigilance Task (PVT) in which participants responded to a disappearing yellow cross by pressing a space bar with their dominant hand as quickly as possible. Participants’ auditory threshold was then determined with an Auditory Threshold Test (ATH). During the ATH test, participants were presented with auditory tones and were instructed to progressively lower the volume until they could no longer hear it and then increase the volume one last time to the lowest volume they could hear. This volume intensity is referred to as the auditory perception threshold. During the task, the tones were played at 1000% of the participants auditory threshold. Auditory stimulation was checked for each key before starting the motor tasks.

Participants then completed 4 blocks of a random serial reaction time test (SRTT) used to establish a measure of baseline task performance that could be compared between groups. During this task, 8 outlines of squares were presented on the screen which corresponded to the participants 8 fingers placed on 8 keys of a specialized keyboard (excluding thumbs). The squares alternated between green indicating task practice and red indicating rest. During the practice periods, the squares randomly filled one after the other and the participant was instructed to press the corresponding key as quickly as possible. Practice blocks included 64 key presses and the duration of the rest blocks was 30 seconds (s). Following the SRTT, participants were introduced to the Motor Sequence Learning (MSL) task via a power point.

The MSL task was a bimanual audio motor sequential finger tapping task that was taught by associating numbers to each finger and then presenting subjects with a number sequence that was associated with a sequence of key presses when executed (see Figure 2A, left panel). This sequence of finger movements resulted in a unique melody since each key was associated with a unique tone (see Figure 2A, right panel). Before beginning MSL training, participants completed a block of pre-training in which they were instructed to type the sequence presented to them slowly and accurately to become familiar with the sequence. After completing 3 consecutive correct sequences, the pre-training terminated. The MSL training then started and participants completed 16 blocks of practice in which they were instructed to continuously type the sequence as quickly and as accurately as possible when the sequence was presented in green on the screen. The practice blocks alternated with rest blocks where the screen changed to a red fixation square, and participants were instructed to rest, focus on the screen, and to not move their fingers. Practice blocks included 64 key presses (8 correct sequences if no mistakes were made) and rest blocks duration was of 30s. TMR was administered during the inter-practice rest periods of the training.

Participants were distributed in three groups according to the TMR condition they were assigned to. In the congruent TMR group (CONG, N = 10, see Figure 2B), TMR was administered during inter-practice rest using the melody corresponding to the learned sequence of movements. In the incongruent control group (RNDM group), random auditory stimuli were administered during rest. In the no stimulation control group (NO STIM), no stimulation was administered during rest. Auditory stimulation during rest included 24 tones per rest period (corresponding to 4 repetitions of the learned sequence in the CONG group and 8 tones presented 4 times each in a random order in the RNDM group). The tones were played at an individually tailored rate that corresponded to 0.8 times the fastest previous sequence. This acceleration factor was applied based on previous evidence that neural replays associated with memory consolidation during inter-practice rest intervals occur at an accelerated rate as compared to practice (Buch et al., 2021).

Following the training session, 4 blocks of motor sequence test were completed in which there was no inter-practice rest intervention applied (no stimulation during rest in the three groups, all groups continued to hear sounds during task practice). During test, practice blocks included 64 key presses and rest blocks duration was 10s. After completing the first experimental session, participants were given an ActiWatch to monitor sleep during the night between sessions. They were also asked to continue keeping their constant sleep schedule recorded via sleep diary, and to refrain from alcohol, recreational drugs, and caffeine before the next session. On the second experimental session, participants completed the questionnaires on sleep and vigilance and then performed the PVT task, auditory check, and 16 blocks of MSL retest which had an identical structure to the MSL test blocks described above (no stimulation during rest).

Variables

Outcome variables consisted in speed and accuracy measures. Speed was measured as the average inter-response interval (IRI) of correct transitions (correct IRI) in each block. Accuracy was measured as the percentage of correct transitions (percent correct transitions) in each block.

The behavioral marker of fast consolidation was micro-offline gains in performance across inter-practice rest intervals. Micro-offline gains in performance were calculated as the difference between the average correct IRI across the last 8 transitions of a block compared to the first 8 of the next blocks (see Figure 3). Micro-online gains in performance were calculated as the difference of the average correct IRI across the first 8 transitions of a block compared to the last 8 of the same block (see Figure 3).

The behavioral marker of slow consolidation was macro-offline gains in performance speed and accuracy that were computed as the difference between the average performance during the test at the end of session one and the first 4 blocks of the retest at the beginning of session two (see Figure 4). For all the gain values described above, a positive value represents improvement in performance while a negative value represents deterioration in performance.

Statistical Analysis

One-Way ANOVAs were performed to compare demographic variables between groups and to confirm that there were no demographic differences between groups. One-Way ANOVAs were conducted on median PVT from sessions 1 and 2 to ensure objective vigilance didn’t differ between groups on either day. Repeated-Measures ANOVAs were performed on accuracy and speed measures derived from the SRTT with group as the between subject factor and block (4) as the within subject factor to ensure baseline task performance didn’t differ between groups. Repeated-Measures ANOVAs were conducted on the outcome variables of speed and accuracy for the training, test, and retest sessions of the MSL task with group as the between subject factor and block (16, 4 and 16, respectively) as the within subject factor to assess the effect of the intervention on the outcome variables. One-Way ANOVAs were used to compare on micro and macro measures of performance gains between groups. The Greenhouse-Geisser correction was applied to all Repeated-Measures ANOVAs where the assumption of sphericity was not met.

Results

Performance Speed

During training, a Repeated-Measures ANOVA revealed a significant effect of block (F(15,390) = 54.385, p = <0.001) but no significant effect of group (F(2,26)=0.014, p=0.986) or group x block interaction (F(6.483, 84.283)=0.0340, p = 0.924) suggesting that while speed increased as a function of practice across groups, there was no effect of the TMR intervention. During test, no significant effects were found for block (F(3,78)=1.373, p=0.257), group (F(2,26)=0.068, p=0.934), or block x group interaction (F(6,78)=0.370, p=0.896) suggesting that there was no effect of the TMR intervention on immediate retention. During retest, a significant effect of block (F(5.383, 129.181) = 5.792, p = 0.001) was observed but there was no significant effect of group (F(2,24)=0.011, p=0.989) or block x group interaction (F(10.442,125.307)=1.437, p=0.168). Exploratory analyses collapsing the two control groups together revealed a trend towards a significant group x block interaction during retest (F(5.83,129.181)=2.171, p=0.057). Data inspection suggests that there was a relearning effect during the retest which was steeper in the congruent TMR group driven by slower initial performance speed at the beginning of retest (Figure 5).

Performance Accuracy

During training, a Repeated- Measures ANOVA revealed no significant differences between groups (F(2,26) = 2.13, p = 0.13) with a trending effect of block (F(6.94,180.439)=2.002, p = 0.058) and no group x block interaction (F(13.88,180.439)=0.997, p = 0.458). Collapsing the two control groups together revealed a significant effect of group (F(1,27) = 4.247, p = 0.049) indicating lower accuracy during training in the congruent TMR group. During test, no significant differences were observed for group (F(2,26)=0.796, p=0.462), block (F(3,78)=2.362, p=0.078) or group x block interaction (F(6,78)=0.968, p=0.453) suggesting there was stable accuracy throughout the test which was not impacted by the intervention. During retest, a significant effect of group (F(2,24) = 6.335, p = 0.006) was found but there was no significant effect of block (F(5.261,126.273) = 1.665, p=0.0144) or block x group interaction (F(10.523,126.273) = 1.177, p = 0.311) suggesting accuracy remained stable but was lower in the congruent TMR group compared to the other two controls (Figure 6).

Gains in performance at the micro timescale

One-Way ANOVAs revealed no significant effect of group on micro-online (F(2,26) = 1.028, p = 0.372) or micro-offline (F(2,26) = 1.05, p = 0.364) gains in performance. Exploratory analyses collapsing the two control groups revealed a non-significant trend for a group effect on micro-offline (F(1,27) = 2.076, p = 0.161) and micro-online (online: F(1,27) = 1.80, p = 0.186) gains in performance. This suggests that there is a trend towards increases in micro-offline gains in performance in the congruent TMR group.

Gains in performance at the macro timescale

One-Way ANOVAs revealed a non-significant trend for an effect of group on macro-offline gains in speed (F(2,24) = 2.079, p = 0.147) and no significant differences in macro-offline gains in accuracy between groups (F(2,24) = 1.155, p = 0.332). Collapsing the control groups revealed a nearly significant effect of group on macro-offline gains in speed (F(1,25) = 4.192, p = 0.051) indicating that there is a trend towards decreased macro-offline gains in speed in the congruent TMR group as compared to the two control groups.

Conclusions

Preliminary results suggest that the TMR intervention did not influence consolidation at the micro timescale, however exploratory analyses collapsing the two control groups together revealed a trend towards a boost of the fast consolidation process in the congruent TMR group. This result suggests that TMR is a promising intervention to modulate motor memory consolidation at the micro-timescale as it appears to enhance the fast consolidation processes. Preliminary results also suggest that the congruent TMR intervention disrupted the slow consolidation process as evidenced by lower macro-offline gains in performance after a night of sleep. This might reflect a potential competitive interaction between the fast and slow motor memory consolidation processes. A larger sample size is needed to increase power and confirm these preliminary results.

Acknowledgments

This work was supported by SPUR from the Office of Undergraduate Research at the University of Utah awarded to Sophia Terrill. The project was funded by internal grants from the University of Utah awarded to Genevieve Albouy and Bradley R. King.

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