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Sleep benefits motor memory consolidation. This mnemonic process is thought to be mediated by thalamo-cortical spindle activity during NREM-stage2 sleep episodes as well as changes in striatal and hippocampal activity. However, direct experimental evidence supporting the contribution of such sleep-dependent physiological mechanisms to motor memory consolidation in humans is lacking. In the present study, we combined EEG and fMRI sleep recordings following practice of a motor sequence learning (MSL) task to determine whether spindle oscillations support sleep-dependent motor memory consolidation by transiently synchronizing and coordinating specialized cortical and subcortical networks. To that end, we conducted EEG source reconstruction on spindle epochs in both cortical and subcortical regions using novel deep-source localization techniques. Coherence-based metrics were adopted to estimate functional connectivity between cortical and subcortical structures over specific frequency bands. Our findings not only confirm the critical and functional role of NREM-stage2 sleep spindles in motor skill consolidation, but provide first-time evidence that spindle oscillations [11–17 Hz] may be involved in sleep-dependent motor memory consolidation by locally reactivating and functionally binding specific task-relevant cortical and subcortical regions within networks including the hippocampus, putamen, thalamus and motor-related cortical regions.

•Brain decoding using a dense individual fMRI dataset (∼7 h fMRI data per subject).•Challenging benchmark: decoding 21 conditions from single fMRI brain volumes.•The best individual model reaches 58–67 % accuracy, close to state-of-the-art group decoding (76 %).•Individual decoding models generalized poorly to other subjects. Brain decoding aims to infer cognitive states from patterns of brain activity. Substantial inter-individual variations in functional brain organization challenge accurate decoding performed at the group level. In this paper, we tested whether accurate brain decoding models can be trained entirely at the individual level. We trained several classifiers on a dense individual functional magnetic resonance imaging (fMRI) dataset for which six participants completed the entire Human Connectome Project (HCP) task battery >13 times over ten separate fMRI sessions. We evaluated nine decoding methods, from Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) to Graph Convolutional Neural Networks (GCN). All decoders were trained to classify single fMRI volumes into 21 experimental conditions simultaneously, using ∼7 h of fMRI data per participant. The best prediction accuracies were achieved with GCN and MLP models, whose performance (57–67 % accuracy) approached state-of-the-art accuracy (76 %) with models trained at the group level on >1 K hours of data from the original HCP sample. Our SVM model also performed very well (54–62 % accuracy). Feature importance maps derived from MLP —our best-performing model— revealed informative features in regions relevant to particular cognitive domains, notably in the motor cortex. We also observed that inter-subject classification achieved substantially lower accuracy than subject-specific models, indicating that our decoders learned individual-specific features. This work demonstrates that densely-sampled neuroimaging datasets can be used to train accurate brain decoding models at the individual level. We expect this work to become a useful benchmark for techniques that improve model generalization across multiple subjects and acquisition conditions. [Display omitted]

Functional magnetic resonance imaging (fMRI) studies investigating the acquisition of sequential motor skills in humans have revealed learning-related functional reorganizations of the cortico-striatal and cortico-cerebellar motor systems accompanied with an initial hippocampal contribution. Yet, the functional significance of these activity-level changes remains ambiguous as they convey the evolution of both sequence-specific knowledge and unspecific task ability. Moreover, these changes do not specifically assess the occurrence of learning-related plasticity. To address these issues, we investigated local circuits tuning to sequence-specific information using multivariate distances between patterns evoked by consolidated or newly acquired motor sequences production. The results reveal that representations in dorsolateral striatum, prefrontal and secondary motor cortices are greater when executing consolidated sequences than untrained ones. By contrast, sequence representations in the hippocampus and dorsomedial striatum becomes less engaged. Our findings show, for the first time in humans, that complementary sequence-specific motor representations evolve distinctively during critical phases of skill acquisition and consolidation.

Motor memory consolidation is thought to depend on sleep-dependent reactivation of brain areas recruited during learning. However, up to this point, there has been no direct evidence to support this assertion in humans, and the physiological processes supporting such reactivation are unknown. Here, simultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were conducted during post-learning sleep to directly investigate the spindle-related reactivation of a memory trace formed during motor sequence learning (MSL), and its relationship to overnight enhancement in performance (reflecting consolidation). We show that brain regions within the striato-cerebello-cortical network recruited during training on the MSL task, and in particular the striatum, were also activated during sleep, time-locked to spindles. Interestingly, the consolidated trace in the striatum was not simply strengthened, but was transformed/reorganized from rostrodorsal (associative) to caudoventral (sensorimotor) subregions. Moreover, the degree of the reactivation was correlated with overnight improvements in performance. Altogether, the present findings demonstrate that striatal reactivation linked to sleep spindles in the post-learning night, is related to motor memory consolidation.

There is now ample evidence that sleep spindles play a critical role in the consolidation of newly acquired motor sequences. Previous studies have also revealed that the interplay between different types of sleep oscillations (e.g. spindles, slow waves, sharp-wave ripples) promotes the consolidation process of declarative memories. Yet the functional contribution of this type of frequency-specific interactions to motor memory consolidation remains unknown. Thus, this study sought to investigate whether spindle oscillations are associated with low- or high-frequency activity at the regional (local) and interregional (connectivity) levels. Using an olfactory-targeted memory reactivation paradigm paired to a motor sequence learning task, we compared the effect of cuing (Cond) to no-cuing (NoCond) on frequency interactions during sleep spindles. Time-frequency decomposition analyses revealed that cuing induced significant differential and localized changes in delta (1-4 Hz) and theta (4-8 Hz) frequencies before, during, and after spindles, as well as changes in high-beta (20-30 Hz) during the spindle oscillation. Finally, coherence analyses yielded significant increases in connectivity during sleep spindles in both theta and sigma (11-17 Hz) bands in the cued group only. These results support the notion that the synchrony between spindle and associated low- or high-frequency rhythmic activity is an integral part of the memory reactivation process. Furthermore, they highlight the importance of not only measuring spindles' characteristics, but to investigate such oscillations in both time and frequency domains when assessing memory consolidation-related changes.

Videogames are emerging as a promising experimental paradigm in neuroimaging. Acquiring gameplay in a scanner remains challenging due to the lack of a scanner-compatible videogame controller that provides a similar experience to standard, commercial devices. In this paper, we introduce a videogame controller designed for use in the functional magnetic resonance imaging as well as magnetoencephalography. The controller is made exclusively of 3D-printed and commercially available parts. We evaluated the quality of our controller by comparing it to a non-MRI compatible controller that was kept outside the scanner. The comparison of response latencies showed reliable button press accuracies of adequate precision. Comparison of the subjects’ motion during fMRI recordings of various tasks showed that the use of our controller did not increase the amount of motion produced compared to a regular MR compatible button press box. Motion levels during an ecological videogame task were of moderate amplitude. In addition, we found that the controller only had marginal effect on temporal SNR in fMRI, as well as on covariance between sensors in MEG, as expected due to the use of non-magnetic building materials. Finally, the reproducibility of the controller was demonstrated by having team members who were not involved in the design build a reproduction using only the documentation. This new videogame controller opens new avenues for ecological tasks in fMRI, including challenging videogames and more generally tasks with complex responses. The detailed controller documentation and build instructions are released under an Open Source Hardware license to increase accessibility, and reproducibility and enable the neuroimaging research community to improve or modify the controller for future experiments.

Animal models suggest that consolidated memories return to their labile state when reactivated and need to be restabilized through reconsolidation processes to persist. Consistent with this notion, post-reactivation pharmacological protein synthesis blockage results in mnemonic failure in hippocampus-dependent memories. It has been proposed that, in humans, post-reactivation experience with a competitive task can also interfere with memory restabilization. However, several studies failed to induce performance deficit implementing this approach. Moreover, even upon effective post-reactivation interference, hindered performance may rapidly recover, raising the possibility of a retrieval rather than a storage deficit. Here, to address these issues in procedural memory domain, we used new learning to interfere with restabilization of motor memory acquired through training on a sequence of finger movements. Only immediate post-reactivation interference was associated with the loss of post-training delayed gains in performance, a hallmark of motor sequence memory consolidation. We also demonstrate that such performance deficit more likely indicates a genuine memory impairment rather than a retrieval failure. However, the reconsolidation view on a reactivation-induced plasticity is not supported. Instead, our results are in line with the integration model according to which new knowledge acquired during the interfering experience, is integrated through its consolidation creating memory competition.

Reconsolidation theory posits that upon retrieval, consolidated memories are destabilized and need to be restabilized in order to persist. It has been suggested that experience with a competitive task immediately after memory retrieval may interrupt these restabilization processes leading to memory loss. Indeed, using a motor sequence learning paradigm, we have recently shown that, in humans, interference training immediately after active task-based retrieval of the consolidated motor sequence knowledge may negatively affect its performance levels. Assessing changes in tapping pattern before and after interference training, we also demonstrated that this performance deficit more likely indicates a genuine memory loss rather than an initial failure of memory retrieval. Here, applying a similar approach, we tested the necessity of the hypothetical retrieval-induced destabilization of motor memory to allow its impairment. The impact of memory retrieval on performance of a new motor sequence knowledge acquired during the interference training was also evaluated. Similar to the immediate post-retrieval interference, interference training alone without the preceding active task-based memory retrieval was also associated with impairment of the pre-established motor sequence memory. Performance levels of the sequence trained during the interference training, on the other hand, were impaired only if this training was given immediately after memory retrieval. Noteworthy, an 8-hour interval between memory retrieval and interference allowed to express intact performance levels for both sequences. The current results suggest that susceptibility of the consolidated motor memory to behavioral interference is independent of its active task-based retrieval. Differential effects of memory retrieval on performance levels of the new motor sequence encoded during the interference training further suggests that memory retrieval may influence the way new information is stored by facilitating its integration within the retrieved memory trace. Thus, impairment of the pre-established motor memory may reflect interference from a competing memory trace rather than involve interruption of reconsolidation.

Functional MRI acquisition is sensitive to subjects' motion that cannot be fully constrained. Therefore, signal corrections have to be applied a posteriori in order to mitigate the complex interactions between changing tissue localization and magnetic fields, gradients and readouts. To circumvent current preprocessing strategies limitations, we developed an integrated method that correct motion and spatial low-frequency intensity fluctuations at the level of each slice in order to better fit the acquisition processes. The registration of single or multiple simultaneously acquired slices is achieved online by an Iterated Extended Kalman Filter, favoring the robust estimation of continuous motion, while an intensity bias field is non-parametrically fitted. The proposed extraction of gray-matter BOLD activity from the acquisition space to an anatomical group template space, taking into account distortions, better preserves fine-scale patterns of activity. Importantly, the proposed unified framework generalizes to high-resolution multi-slice techniques. When tested on simulated and real data the latter shows a reduction of motion explained variance and signal variability when compared to the conventional preprocessing approach. These improvements provide more stable patterns of activity, facilitating investigation of cerebral information representation in healthy and/or clinical populations where motion is known to impact fine-scale data.

How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called \"the dual intertwined rings architecture\") that relates the functional specialization of cortical networks to their spatial distribution over the cerebral cortex (or \"corticotopy\"). Recent results suggest that the resting state networks (RSNs) are organized into two large families: 1) a sensorimotor family that includes visual, somatic, and auditory areas and 2) a large association family that comprises parietal, temporal, and frontal regions and also includes the default mode network. We used two large databases of resting state fMRI data, from which we extracted 32 robust RSNs. We estimated: (1) the RSN functional roles by using a projection of the results on task based networks (TBNs) as referenced in large databases of fMRI activation studies; and (2) relationship of the RSNs with the Brodmann Areas. In both classifications, the 32 RSNs are organized into a remarkable architecture of two intertwined rings per hemisphere and so four rings linked by homotopic connections. The first ring forms a continuous ensemble and includes visual, somatic, and auditory cortices, with interspersed bimodal cortices (auditory-visual, visual-somatic and auditory-somatic, abbreviated as VSA ring). The second ring integrates distant parietal, temporal and frontal regions (PTF ring) through a network of association fiber tracts which closes the ring anatomically and ensures a functional continuity within the ring. The PTF ring relates association cortices specialized in attention, language and working memory, to the networks involved in motivation and biological regulation and rhythms. This \"dual intertwined architecture\" suggests a dual integrative process: the VSA ring performs fast real-time multimodal integration of sensorimotor information whereas the PTF ring performs multi-temporal integration (i.e., relates past, present, and future representations at different temporal scales).