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.

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.

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.

Sensorimotor adaptation benefits from learning in two parallel systems: one that has access to explicit knowledge, and another that relies on implicit, unconscious correction. However, it is unclear how these systems interact: does enhancing one system's contributions, for example through instruction, impair the other, or do they learn independently? Here we illustrate that certain contexts can lead to competition between implicit and explicit learning. In some cases, each system is responsive to a task-related visual error. This shared error appears to create competition between these systems, such that when the explicit system increases its response, errors are siphoned away from the implicit system, thus reducing its learning. This model suggests that explicit strategy can mask changes in implicit error sensitivity related to savings and interference. Other contexts suggest that the implicit system can respond to multiple error sources. When these error sources conflict, a second type of competition occurs. Thus, the data show that during sensorimotor adaptation, behavior is shaped by competition between parallel learning systems.

BackgroundFractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter 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.PurposeTo optimize normalization to improve registration in gray matter and reduce variability associated with multi-session registrations.Study TypeProspective longitudinal studySubjectsTwenty-one healthy subjects (18-31 years old) underwent 9 magnetic resonance imaging (MRI) scanning sessions each.Field Strength/Sequence3.0T, diffusion-weighted multiband-accelerated sequence, MP2RAGE sequence.AssessmentDiffusion-weighted images were registered to standard space using different pipelines that varied in the features used for normalization, namely the non-linear 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 from these normalization approaches for FA and MD in white and gray matters.Statistical TestsReproducibility errors were compared using a repeated-measures analysis of variance with pipeline as within-subject factor.ResultsThe 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 ConclusionA 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.

BackgroundFractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter 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. PurposeTo optimize normalization to improve registration in gray matter and reduce variability associated with multi-session registrations. Study Type Prospective longitudinal study SubjectsTwenty-one healthy subjects (18-31 years old) underwent 9 magnetic resonance imaging (MRI) scanning sessions each.

2Anterograde interference refers to the negative impact of prior learning on the propensity 3 for future learning. Previous work has shown that subsequent adaptation to two 4 perturbations of opposing sign, A and B, impairs performance in B. Here, we aimed to 5 unveil the mechanism at the basis of anterograde interference by tracking its impact as a 6 function of time through a 24h period. We found that the memory of A biased performance 7 in B for all time intervals. Conversely, learning from error was hindered up to 1h following 8 acquisition of A, with release from interference occurring at 6h. These findings suggest 9 that poor performance induced by prior learning is driven by two distinct mechanisms: a 10 long-lasting bias that acts as a prior and hinders the initial level of performance, and a 11 short-lasting learning impairment that originates from a reduction in error-sensitivity. Our 12 work provides insight into the timeline of memory stabilization in visuomotor adaptation. 13 14 15 16 We gain robustness through adaptation: in the face of environmental and/or internal 18 perturbations, adaptation maintains the precise control of elementary movements like 19 reaching and saccades. Like other types of learning, adaptation may lead to interference 20 or facilitation depending on the level of congruency of sequentially learned materials. 21Facilitation of learning is commonly referred to as savings, a process by which subsequent 22 exposure to the same perturbation results in faster learning (Krakauer, 2009). In contrast, 23 successive adaptation to opposing perturbations (e.g., rotation A followed by rotation B) 24 may lead to a deficit in the learning of B. This phenomenon, known as anterograde 25 interference, has been reported in visuomotor and force-field adaptation paradigms when 26 successively adapting to conflicting perturbations within the same reaching task (Brashers-

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