Resting-state Functional Connectivity is an Age-dependent Predictor of Motor Learning Abilities

Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals

Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.

Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.

Alterations in learning-related cortical activation and functional connectivity by high-definition transcranial direct current stimulation after stroke: an fNIRS study

Introduction

Motor learning is a key component of stroke neurorehabilitation. High-definition transcranial direct current stimulation (HD-tDCS) was recently developed as a tDCS technique that increases the accuracy of current delivery to the brain using arrays of small electrodes. The purpose of this study was to investigate whether HD-tDCS alters learning-related cortical activation and functional connectivity in stroke patients using functional near-infrared spectroscopy (fNIRS).

Methods

Using a sham-controlled crossover study design, 16 chronic stroke patients were randomly assigned to one of two intervention conditions. Both groups performed the sequential finger tapping task (SFTT) on five consecutive days, either with (a) real HD-tDCS or (b) with sham HD-tDCS. HD-tDCS (1 mA for 20 min, 4 × 1) was administered to C3 or C4 (according to lesion side). fNIRS signals were measured during the SFTT with the affected hand before (baseline) and after each intervention using fNIRS measurement system. Cortical activation and functional connectivity of NIRS signals were analyzed using a statistical parametric mapping open-source software package (NIRS-SPM), OptoNet II^®.

Results

In the real HD-tDCS condition, oxyHb concentration increased significantly in the ipsilesional primary motor cortex (M1). Connectivity between the ipsilesional M1 and the premotor cortex (PM) was noticeably strengthened after real HD-tDCS compared with baseline. Motor performance also significantly improved, as shown in response time during the SFTT. In the sham HD-tDCS condition, functional connectivity between contralesional M1 and sensory cortex was enhanced compared with baseline. There was tendency toward improvement in SFTT response time, but without significance.

Discussion

The results of this study indicated that HD-tDCS could modulate learning-related cortical activity and functional connectivity within motor networks to enhance motor learning performance. HD-tDCS can be used as an additional tool for enhancing motor learning during hand rehabilitation for chronic stroke patients.

Resting-state fMRI functional connectivity of the left temporal parietal junction is associated with visual temporal order threshold

The study aimed to determine the relationship between the millisecond timing, measured by visual temporal order threshold (TOT), i.e. a minimum gap between two successive stimuli necessary to judge a before-after relation, and resting-state fMRI functional connectivity (rsFC). We assume that the TOT reflects a relatively stable feature of local internal state networks and is associated with rsFC of the temporal parietal junction (TPJ). Sixty five healthy young adults underwent the visual TOT, fluid intelligence (G_f) and an eyes-open resting-state fMRI examination. After controlling for the influence of gender, the higher the TOT, the stronger was the left TPJ’s rsFC with the left postcentral and the right precentral gyri, bilateral putamen and the right supplementary motor area. When the effects of G_f and TOT × G_f interaction were additionally controlled, the TOT—left TPJ’s rsFC relationship survived for almost all above regions with the exception of the left and right putamen. This is the first study demonstrating that visual TOT is associated with rsFC between the areas involved both in sub-second timing and motor control. Current outcomes indicate that the local neural networks are prepared to process brief, rapidly presented, consecutive events, even in the absence of such stimulation.

Connectivity in Large-Scale Resting-State Brain Networks Is Related to Motor Learning: A High-Density EEG Study

Previous research has shown that resting-state functional connectivity (rsFC) between different brain regions (seeds) is related to motor learning and motor memory consolidation. Using high-density electroencephalography (hdEEG), we addressed this question from a brain network perspective. Specifically, we examined frequency-dependent functional connectivity in resting-state networks from twenty-nine young healthy participants before and after they were trained on a motor sequence learning task. Consolidation was assessed with an overnight retest on the motor task. Our results showed training-related decreases in gamma-band connectivity within the motor network, and between the motor and functionally distinct resting-state networks including the attentional network. Brain-behavior correlation analyses revealed that baseline beta, delta, and theta rsFC were related to subsequent motor learning and memory consolidation such that lower connectivity within the motor network and between the motor and several distinct resting-state networks was correlated with better learning and overnight consolidation. Lastly, training-related increases in beta-band connectivity between the motor and the visual networks were related to greater consolidation. Altogether, our results indicate that connectivity in large-scale resting-state brain networks is related to—and modulated by—motor learning and memory consolidation processes. These finding corroborate previous seed-based connectivity research and provide evidence that frequency-dependent functional connectivity in resting-state networks is critically linked to motor learning and memory consolidation.

Motor sequences; separating the sequence from the motor. A longitudinal rsfMRI study

In motor learning, sequence specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI paradigms. However, offline changes in resting state functional connectivity after sequence-specific motor learning are less well understood. Previous research has established that plastic changes following motor learning can be divided into stages including fast learning, slow learning and retention. A description of how resting state functional connectivity after sequence-specific motor sequence learning (MSL) develops across these stages is missing. This study aimed to identify plastic alterations in whole-brain functional connectivity after learning a complex motor sequence by contrasting an active group who learned a complex sequence with a control group who performed a control task matched for motor execution. Resting state fMRI and behavioural performance were collected in both groups over the course of 5 consecutive training days and at follow-up after 12 days to encompass fast learning, slow learning, overall learning and retention. Between-group interaction analyses showed sequence-specific decreases in functional connectivity during overall learning in the right supplementary motor area (SMA). We found that connectivity changes in a key region of the motor network, the superior parietal cortex (SPC) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA that has previously been identified in online task-based learning studies, and extends it to resting state network changes after sequence-specific MSL.

Motor Learning Based on Oscillatory Brain Activity Using Transcranial Alternating Current Stimulation: A Review

Developing effective tools and strategies to promote motor learning is a high-priority scientific and clinical goal. In particular, motor-related areas have been investigated as potential targets to facilitate motor learning by noninvasive brain stimulation (NIBS). In addition to shedding light on the relationship between motor function and oscillatory brain activity, transcranial alternating current stimulation (tACS), which can noninvasively entrain oscillatory brain activity and modulate oscillatory brain communication, has attracted attention as a possible technique to promote motor learning. This review focuses on the use of tACS to enhance motor learning through the manipulation of oscillatory brain activity and its potential clinical applications. We discuss a potential tACS-based approach to ameliorate motor deficits by correcting abnormal oscillatory brain activity and promoting appropriate oscillatory communication in patients after stroke or with Parkinson's disease. Interpersonal tACS approaches to manipulate intra- and inter-brain communication may result in pro-social effects and could promote the teaching-learning process during rehabilitation sessions with a therapist. The approach of re-establishing oscillatory brain communication through tACS could be effective for motor recovery and might eventually drive the design of new neurorehabilitation approaches based on motor learning.

Frequency-Dependent Intrinsic Electrophysiological Functional Architecture of the Human Verbal Language Network

Functional magnetic resonance imaging (fMRI) allowed the spatial characterization of the resting-state verbal language network (vLN). While other resting-state networks (RSNs) were matched with their electrophysiological equivalents at rest and could be spectrally defined, such correspondence is lacking for the vLN. This magnetoencephalography (MEG) study aimed at defining the spatio-spectral characteristics of the neuromagnetic intrinsic functional architecture of the vLN. Neuromagnetic activity was recorded at rest in 100 right-handed healthy adults (age range: 18-41 years). Band-limited power envelope correlations were performed within and across frequency bands (θ, α, β, and low γ) from a seed region placed in the left Broca's area, using static orthogonalization as leakage correction. K-means clustering was used to segregate spatio-spectral clusters of resting-state functional connectivity (rsFC). Remarkably, unlike other RSNs, within-frequency long-range rsFC from the left Broca's area was not driven by one main carrying frequency but was characterized by a specific spatio-spectral pattern segregated along the ventral (predominantly θ and α) and dorsal (β and low-γ bands) vLN streams. In contrast, spatial patterns of cross-frequency vLN functional integration were spectrally more widespread and involved multiple frequency bands. Moreover, the static intrinsic functional architecture of the neuromagnetic human vLN involved clearly left-hemisphere-dominant vLN interactions as well as cross-network interactions with the executive control network and postero-medial nodes of the DMN. Overall, this study highlighted the involvement of multiple modes of within and cross-frequency power envelope couplings at the basis of long-range electrophysiological vLN functional integration. As such, it lays the foundation for future works aimed at understanding the pathophysiology of language-related disorders.

Age-related reduction in motor adaptation: brain structural correlates and the role of explicit memory

The adaption of movement to changes in the environment varies across life span. Recent evidence has linked motor adaptation and its reduction with age to differences in "explicit" learning processes. We examine differences in brain structure and cognition underlying motor adaptation in a population-based cohort (n = 322, aged 18-89 years) using a visuomotor learning task and structural magnetic resonance imaging. Reduced motor adaptation with age was associated with reduced volume in striatum, prefrontal, and sensorimotor cortical regions, but not cerebellum. Medial temporal lobe volume, including the hippocampus, became a stronger determinant of motor adaptation with age. Consistent with the role of the medial temporal lobes, declarative long-term memory showed a similar interaction, whereby memory was more positively correlated with motor adaptation with increasing age. By contrast, visual short-term memory was related to motor adaptation, independently of age. These results support the hypothesis that cerebellar learning is largely unaffected in old age, and the reduction in motor adaptation with age is driven by a decline in explicit memory systems.

Age-Related Declines in Motor Performance are Associated With Decreased Segregation of Large-Scale Resting State Brain Networks

Aging is typically associated with substantial declines in motor functioning as well as robust changes in the functional organization of brain networks. Previous research has investigated the link between these 2 age-varying factors but examinations were predominantly limited to the functional organization within motor-related brain networks. Little is known about the relationship between age-related behavioral impairments and changes in functional organization at the whole brain (i.e., multiple network) level. This knowledge gap is surprising given that the decreased segregation of brain networks (i.e., increased internetwork connectivity) can be considered a hallmark of the aging process. Accordingly, we investigated the association between declines in motor performance across the adult lifespan (20-75 years) and age-related modulations of functional connectivity within and between resting state networks. Results indicated that stronger internetwork resting state connectivity observed as a function of age was significantly related to worse motor performance. Moreover, performance had a significantly stronger association with the strength of internetwork as compared with intranetwork connectivity, including connectivity within motor networks. These findings suggest that age-related declines in motor performance may be attributed to a breakdown in the functional organization of large-scale brain networks rather than simply age-related connectivity changes within motor-related networks.

Towards understanding neural network signatures of motor skill learning in Parkinson's disease and healthy aging

In the past decade, neurorehabilitation has been shown to be an effective therapeutic supplement for patients with Parkinson's disease (PD). However, patients still experience severe problems with the consolidation of learned motor skills. Knowledge on the neural correlates underlying this process is thus essential to optimize rehabilitation for PD. This review investigates the existing studies on neural network connectivity changes in relation to motor learning in healthy aging and PD and critically evaluates the imaging methods used from a methodological point of view. The results indicate that despite neurodegeneration there is still potential to modify connectivity within and between motor and cognitive networks in response to motor training, although these alterations largely bypass the most affected regions in PD. However, so far training-related changes are inferred and possible relationships are not substantiated by brain-behavior correlations. Furthermore, the studies included suffer from many methodological drawbacks. This review also highlights the potential for using neural network measures as predictors for the response to rehabilitation, mainly based on work in young healthy adults. We speculate that future approaches, including graph theory and multimodal neuroimaging, may be more sensitive than brain activation patterns and model-based connectivity maps to capture the effects of motor learning. Overall, this review suggests that methodological developments in neuroimaging will eventually provide more detailed knowledge on how neural networks are modified by training, thereby paving the way for optimized neurorehabilitation for patients.

Resting-state brain network features associated with short-term skill learning ability in humans and the influence of N-methyl-d-aspartate receptor antagonism

Graph theoretical functional magnetic resonance imaging (fMRI) studies have demonstrated that brain networks reorganize significantly during motor skill acquisition, yet the associations between motor learning ability, brain network features, and the underlying biological mechanisms remain unclear. In the current study, we applied a visually guided sequential pinch force learning task and graph theoretical analyses to investigate the associations between short-term motor learning ability and resting-state brain network metrics in 60 healthy subjects. We further probed the test-retest reliability (n = 26) and potential effects of the N-methyl-d-aspartate (NMDA) antagonist ketamine (n = 19) in independent healthy volunteers. Our results show that the improvement of motor performance after short-term training was positively correlated with small-worldness (p = 0.032) and global efficiency (p = 0.025), whereas negatively correlated with characteristic path length (p = 0.014) and transitivity (p = 0.025). In addition, using network-based statistics (NBS), we identified a learning ability–associated (p = 0.037) and ketamine-susceptible (p = 0.027) cerebellar-cortical network with fair to good reliability (intraclass correlation coefficient [ICC] > 0.7) and higher functional connectivity in better learners. Our results provide new evidence for the association of intrinsic brain network features with motor learning and suggest a role of NMDA-related glutamatergic processes in learning-associated subnetworks.

Learning a new motor skill prompts immediate reconfigurations of distributed brain networks followed by adaptive changes in intrinsic brain circuits related to synaptic plasticity. Here, we identify global brain network properties and a cerebellar-cortical functional subnetwork that are both significantly associated with motor learning ability in a previously trained visuomotor task in humans. We further show that the associated functional subnetwork connectivity but not the global brain network properties are susceptible to ketamine. Our findings suggest a distinct functional role for learning-related global versus local network metrics and support the idea of a preferential susceptibility of learning-associated subnetworks to N-methyl-d-aspartate antagonist and plasticity-related consolidation effects.

The electrophysiological connectome is maintained in healthy elders: a power envelope correlation MEG study

Functional magnetic resonance imaging (fMRI) studies report age-related changes in resting-state functional connectivity (rsFC), suggesting altered or reorganized connectivity patterns with age. However, age-related changes in neurovascular coupling might also partially account for altered connectivity patterns. Here, we used resting-state magnetoencephalography (MEG) and a connectome approach in carefully selected healthy young adults and elders. The MEG connectome was estimated as rsFC matrices involving forty nodes from six major  resting-state networks. Source-level rsFC maps were computed in relevant frequency bands using leakage-corrected envelope correlations. Group differences were statistically assessed using non-parametric permutation tests. Our results failed to evidence significant age-related differences after correction for multiple comparisons in the α and the β bands both for static and dynamic rsFC, suggesting that the electrophysiological connectome is maintained in healthy ageing. Further studies should compare the evolution of the human brain connectome as estimated using fMRI and MEG in same healthy young and elder adults, as well as in ageing conditions associated with cognitive decline. At present, our results are in agreement with the brain maintenance theory for successful aging as they suggest that preserved intrinsic functional brain integration contributes to preserved cognitive functioning in healthy elders.