Sleep Consolidation Potentiates Sensorimotor Adaptation.

Contrary to its well-established role in declarative learning, the impact of sleep on motor memory consolidation remains a subject of debate. Current literature suggests that while motor skill learning benefits from sleep, consolidation of sensorimotor adaptation (SMA) depends solely on the passage of time. This has led to the proposal that SMA may be an exception to other types of memories. Here, we addressed this ongoing controversy in humans through three comprehensive experiments using the visuomotor adaptation paradigm (N = 290, 150 females). In Experiment 1, we investigated the impact of sleep on memory retention when the temporal gap between training and sleep was not controlled. In line with the previous literature, we found that memory consolidates with the passage of time. In Experiment 2, we used an anterograde interference protocol to determine the time window during which SMA memory is most fragile and, thus, potentially most sensitive to sleep intervention. Our results show that memory is most vulnerable during the initial hour post-training. Building on this insight, in Experiment 3, we investigated the impact of sleep when it coincided with the critical first hour of memory consolidation. This manipulation unveiled a benefit of sleep (30% memory enhancement) alongside an increase in spindle density and spindle-SO coupling during NREM sleep, two well-established neural markers of sleep consolidation. Our findings reconcile seemingly conflicting perspectives on the active role of sleep in motor learning and point to common mechanisms at the basis of memory formation.

Public support for and concerns regarding pediatric dose optimization for seizures in emergency medical services: An exception from informed consent (EFIC) trial.

BACKGROUND: Federal regulations allow exception from informed consent (EFIC) to study emergent conditions when obtaining prospective consent is not feasible. Little is known about public views on including children in EFIC studies. The Pediatric Dose Optimization for Seizures in EMS (PediDOSE) trial implements age-based, standardized midazolam dosing for pediatric seizures. The primary objective of this study was to determine public support for and concerns about the PediDOSE EFIC trial. The secondary objective was to assess how support for PediDOSE varied by demographics. METHODS: We conducted a mixed-methods study in 20 U.S. communities. Participants reviewed information about PediDOSE before completing an online survey. Descriptive data were generated. Univariable and multivariable logistic regression analysis identified factors associated with support for PediDOSE. Reviewers identified themes from free-text response data regarding participant concerns. RESULTS: Of 2450 respondents, 79% were parents/guardians, and 20% had a child with previous seizures. A total of 96% of respondents supported PediDOSE being conducted, and 70% approved of children being enrolled without prior consent. Non-Hispanic Black respondents were less likely than non-Hispanic White respondents to support PediDOSE with an adjusted odds ratio (aOR) of 0.57 (95% CI 0.42-0.75). Health care providers were more likely to support PediDOSE, with strongest support among prehospital emergency medicine clinicians (aOR 5.82, 95% CI 3.19-10.62). Age, gender, parental status, and level of education were not associated with support of PediDOSE. Common concerns about PediDOSE included adverse effects, legal and ethical concerns about enrolling without consent, and potential racial bias. CONCLUSIONS: In communities where this study will occur, most respondents supported PediDOSE being conducted with EFIC and most approved of children being enrolled without prior consent. Support was lowest among non-Hispanic Black respondents and highest among health care providers. Further research is needed to determine optimal ways to address the concerns of specific racial and ethnic groups when conducting EFIC trials.

Competition between parallel sensorimotor learning systems.

Sensorimotor learning is supported by at least two parallel systems: a strategic process that benefits from explicit knowledge and an implicit process that adapts subconsciously. How do these systems interact? Does one system's contributions suppress the other, or do they operate independently? Here, we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system's response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error.

Rapid hippocampal plasticity supports motor sequence learning.

Recent evidence suggests that gains in performance observed while humans learn a novel motor sequence occur during the quiet rest periods interleaved with practice (micro-offline gains, MOGs). This phenomenon is reminiscent of memory replay observed in the hippocampus during spatial learning in rodents. Whether the hippocampus is also involved in the production of MOGs remains currently unknown. Using a multimodal approach in humans, here we show that activity in the hippocampus and the precuneus increases during the quiet rest periods and predicts the level of MOGs before asymptotic performance is achieved. These functional changes were followed by rapid alterations in brain microstructure in the order of minutes, suggesting that the same network that reactivates during the quiet periods of training undergoes structural plasticity. Our work points to the involvement of the hippocampal system in the reactivation of procedural memories.

The Origins of Anterograde Interference in Visuomotor Adaptation.

Anterograde interference refers to the negative impact of prior learning on the propensity for future learning. There is currently no consensus on whether this phenomenon is transient or long lasting, with studies pointing to an effect in the time scale of hours to days. These inconsistencies might be caused by the method employed to quantify performance, which often confounds changes in learning rate and retention. Here, we aimed to unveil the time course of anterograde interference by tracking its impact on visuomotor adaptation at different intervals throughout a 24-h period. Our empirical and model-based approaches allowed us to measure the capacity for new learning separately from the influence of a previous memory. In agreement with previous reports, we found that prior learning persistently impaired the initial level of performance upon revisiting the task. However, despite this strong initial bias, learning capacity was impaired only when conflicting information was learned up to 1 h apart, recovering thereafter with passage of time. These findings suggest that when adapting to conflicting perturbations, impairments in performance are driven by two distinct mechanisms: a long-lasting bias that acts as a prior and hinders initial performance and a short-lasting anterograde interference that originates from a reduction in error sensitivity.

Improving Spatial Normalization of Brain Diffusion MRI to Measure Longitudinal Changes of Tissue Microstructure in the Cortex and White Matter.

BACKGROUND: Fractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter (WM) microstructure. Recently, there has been a growing interest in identifying experience-dependent plasticity in gray matter using MD. Improving registration has thus become a major goal to enhance the detection of subtle longitudinal changes in cortical microstructure. PURPOSE: To optimize normalization of diffusion tensor images (DTI) to improve registration in gray matter and reduce variability associated with multisession registrations. STUDY TYPE: Prospective longitudinal study. SUBJECTS: Twenty-one healthy subjects (18-31 years old) underwent nine MRI scanning sessions each. FIELD STRENGTH/SEQUENCE: 3.0T, diffusion-weighted multiband-accelerated sequence, MP2RAGE sequence. ASSESSMENT: Diffusion-weighted images were registered to standard space using different pipelines that varied in the features used for normalization, namely, the nonlinear registration algorithm (FSL vs. ANTs), the registration target (FA-based vs. T1 -based templates), and the use of intermediate individual (FA-based or T1 -based) targets. We compared the across-session test-retest reproducibility error of these normalization approaches for FA and MD in white and gray matter. STATISTICAL TESTS: Reproducibility errors were compared using a repeated-measures analysis of variance with pipeline as the within-subject factor. RESULTS: The registration of FA data to the FMRIB58 FA atlas using ANTs yielded lower reproducibility errors in white matter (P < 0.0001) with respect to FSL. Moreover, using the MNI152 T1 template as the target of registration resulted in lower reproducibility errors for MD (P < 0.0001), whereas the FMRIB58 FA template performed better for FA (P < 0.0001). Finally, the use of an intermediate individual template improved reproducibility when registration of the FA images to the MNI152 T1 was carried out within modality (FA-FA) (P < 0.05), but not via a T1 -based individual template. DATA CONCLUSION: A normalization approach using ANTs to register FA images to the MNI152 T1 template via an individual FA template minimized test-retest reproducibility errors both for gray and white matter. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:766-775.