Motor Imagery Learning Induced Changes in Functional Connectivity of the Default Mode Network

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.

Corticomotor Plasticity Underlying Priming Effects of Motor Imagery on Force Performance

The neurophysiological processes underlying the priming effects of motor imagery (MI) on force performance remain poorly understood. Here, we tested whether the priming effects of embedded MI practice involved short-term changes in corticomotor connectivity. In a within-subjects counterbalanced experimental design, participants (n = 20) underwent a series of experimental sessions consisting of successive maximal isometric contractions of elbow flexor muscles. During inter-trial rest periods, we administered MI, action observation (AO), and a control passive recovery condition. We collected electromyograms (EMG) from both agonists and antagonists of the force task, in addition to electroencephalographic (EEG) brain potentials during force trials. Force output was higher during MI compared to AO and control conditions (both p < 0.01), although fatigability was similar across experimental conditions. We also found a weaker relationship between triceps brachii activation and force output during MI and AO compared to the control condition. Imaginary coherence topographies of alpha (8−12 Hz) oscillations revealed increased connectivity between EEG sensors from central scalp regions and EMG signals from agonists during MI, compared to AO and control. Present results suggest that the priming effects of MI on force performance are mediated by a more efficient cortical drive to motor units yielding reduced agonist/antagonist coactivation.

Cerebellar Transcranial Direct Current Stimulation Reconfigures Brain Networks Involved in Motor Execution and Mental Imagery

Transcranial direct current stimulation (tDCS) is growingly applied to the cerebellum to modulate the activity of cerebellar circuitry, affecting both motor and cognitive performances in a polarity-specific manner. The remote effects of tDCS are mediated in particular via the dentato-thalamo-cortical pathway. We showed recently that tDCS of the cerebellum exerts dynamic effects on resting state networks. We tested the neural hypothesis that tDCS reconfigurates brain networks involved in motor execution (ME) and motor mental imagery (MMI). We combined tDCS applied over the right cerebellum and fMRI to investigate tDCS-induced reconfiguration of ME- and MMI-related networks using a randomized, sham-controlled design in 21 right-handed healthy volunteers. Subjects were instructed to draw circles at comfortable speed and to imagine drawing circles with their right hand. fMRI data were recorded after real anodal stimulation (1.5 mA, 20 min) or sham tDCS. Real tDCS compared with SHAM specifically reconfigurated the functional links between the main intrinsic connected networks, especially the central executive network, in relation with lobule VII, and the salience network. The right cerebellum mainly influenced prefrontal and anterior cingulate areas in both tasks, and improved the overt motor performance. During MMI, the cerebellum also modulated the default-mode network and associative visual areas. These results demonstrate that tDCS of the cerebellum represents a novel tool to modulate cognitive brain networks controlling motor execution and mental imagery, tuning the activity of remote cortical regions. This approach opens novel doors for the non-invasive neuromodulation of disorders involving cerebello-thalamo-cortical paths.

Modality of Practice Modulates Resting State Connectivity During Motor Learning

When bookending skilled motor practice, changes in resting state functional magnetic resonance imaging (rs-fMRI; used to characterise synchronized patterns of activity when the brain is at rest) reflect functional reorganization that supports motor memory consolidation and learning. Despite its use in practice in numerous domains, the neural mechanisms underlying motor memory consolidation and learning that result from motor imagery practice (MIP) relative to physical practice are not well understood. The current study examined how rs-fMRI is modulated by skilled motor practice that results through either MIP or physical practice. Two groups of participants engaged in five days of MIP or physical practice of a dart throwing task. Performance and rs-fMRI were captured before and after training. Relative to physical practice, where focal changes in rs-fMRI within a cerebellar-cortical network were observed, MIP stimulated widespread changes in rs-fMRI within a frontoparietal network encompassing bilateral regions. Findings indicate that functional reorganization that supports motor memory consolidation and learning is not equivalent across practice modality. Ultimately, this work provides new information regarding the unique neural underpinnings MIP relies on to drive motor memory consolidation and learning.

A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery based Brain-Computer Interface

The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph. We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies.

Motor imagery practice and feedback effects on functional connectivity

- Objective: the use of motor imagery (MI) in motor rehabilitation protocols has been increasingly investigated as a potential technique for enhancing traditional treatments, yielding better clinical outcomes. However, since MI performance can be challenging, practice is usually required. This demands appropriate training, actively engaging the MI-related brain areas, consequently enabling the user to properly benefit from it. The role of feedback is central for MI practice. Yet, assessing which underlying neural changes are feedback-specific or purely due to MI practice is still a challenging effort, mainly due to the difficulty in isolating their contributions. In this work, we aimed to assess functional connectivity (FC) changes following MI practice that are either extrinsic or specific to feedback.

APPROACH

to achieve this, we investigated FC, using graph theory, in electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, during MI performance and at resting-state (rs), respectively. Thirty healthy subjects were divided into three groups, receiving no feedback (control), "false" feedback (sham) or actual neurofeedback (active). Participants underwent 12 to 13 hands-MI EEG sessions and pre- and post-MI training fMRI exams.

MAIN RESULTS

following MI practice, control participants presented significant increases in degree and in eigenvector centrality for occipital nodes at rs-fMRI scans, whereas sham-feedback produced similar effects, but to a lesser extent. Therefore, MI practice, by itself, seems to stimulate visual information processing mechanisms that become apparent during basal brain activity. Additionally, only the active group displayed decreases in inter-subject FC patterns, both during MI performance and at rs-fMRI.

SIGNIFICANCE

hence, actual neurofeedback impacted FC by disrupting common inter-subject patterns, suggesting that subject-specific neural plasticity mechanisms become important. Future studies should consider this when designing experimental NFBT protocols and analyses.

BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks

Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the α band was paralleled by a decrease of the integration of visual processing and working memory areas in the β band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the α2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.

Synchrony Between Default-Mode and Sensorimotor Networks Facilitates Motor Function in Stroke Rehabilitation: A Pilot fMRI Study

Stroke is the most common cause of complex disability in Taiwan. After stroke onset, persistent physical practice or exercise in the rehabilitation procedure reorganizes neural assembly for reducing motor deficits, known as neuroplasticity. Neuroimaging literature showed rehabilitative effects specific to the brain networks of the sensorimotor network (SMN) and default-mode network (DMN). However, whether between-network interactions facilitate the neuroplasticity after stroke rehabilitation remains a mystery. Therefore, we conducted the longitudinal assessment protocol of stroke rehabilitation, including three types of clinical evaluations and two types of functional magnetic resonance imaging (fMRI) techniques (resting state and grasp task). Twelve chronic stroke patients completed the rehabilitation protocol for at least 24 h and finished the three-time assessments: before, after rehabilitation, and 1 month after the cessation of rehabilitation. For comparison, age-matched normal controls (NC) underwent the same fMRI evaluation once without repeated measure. Increasing scores of the Fugl–Meyer assessment (FMA) and upper extremity performance test reflected the enhanced motor performances after the stroke rehabilitation process. Analysis of covariance (ANCOVA) results showed that the connections between posterior cingulate cortex (PCC) and iM1 were persistently enhanced in contrast to the pre-rehabilitation condition. The interactions between PCC and SMN were positively associated with motor performances. The enhanced cross-network connectivity facilitates the motor recovery after stroke rehabilitation, but the cross-network interaction was low before the rehabilitation process, similar to the level of NCs. Our findings suggested that cross-network connectivity plays a facilitatory role following the stroke rehabilitation, which can serve as a neurorehabilitative biomarker for future intervention evaluations.

Motor Imagery Training After Stroke Increases Slow-5 Oscillations and Functional Connectivity in the Ipsilesional Inferior Parietal Lobule

Background. Reorganization in motor areas have been suggested after motor imagery training (MIT). However, motor imagery involves a large-scale brain network, in which many regions, andnot only the motor areas, potentially constitute the neural substrate for MIT. Objective. This study aimed to identify the targets for MIT in stroke rehabilitation from a voxel-based whole brain analysis of resting-state functional magnetic resonance imaging (fMRI). Methods. Thirty-four chronic stroke patients were recruited and randomly assigned to either an MIT group or a control group. The MIT group received a 4-week treatment of MIT plus conventional rehabilitation therapy (CRT), whereas the control group only received CRT. Before and after intervention, the Fugl-Meyer Assessment Upper Limb subscale (FM-UL) and resting-state fMRI were collected. The fractional amplitude of low-frequency fluctuations (fALFF) in the slow-5 band (0.01-0.027 Hz) was calculated across the whole brain to identify brain areas with distinct changes between 2 groups. These brain areas were then targeted as seeds to perform seed-based functional connectivity (FC) analysis. Results. In comparison with the control group, the MIT group exhibited more improvements in FM-UL and increased slow-5 fALFF in the ipsilesional inferior parietal lobule (IPL). The change of the slow-5 oscillations in the ipsilesional IPL was positively correlated with the improvement of FM-UL. The MIT group also showed distinct alternations in FCs of the ipsilesional IPL, which were correlated with the improvement of FM-UL. Conclusions. The rehabilitation efficiency of MIT was associated with increased slow-5 oscillations and altered FC in the ipsilesional IPL. Clinical Trial Registration. http://www.chictr.org.cn . Unique Identifier. ChiCTR-TRC-08003005.

Effects of Different Ratios of Physical and Mental Practice on Postural Control Improvement

Mental practice (MP) is a reliable alternative or complement to physical practice (PP) for the training of postural control. We address how MP should ideally be combined with PP. Participants were assigned to four experimental groups where MP/PP ratios during training varied from 0 to 100%. Performance improved only for demanding postural adjustments, regardless of MP/PP ratio, and learning was partially consolidated after a night of sleep. Findings reinforce the relevance of MP for the training of weight shifting and further suggest that MP alone can be as efficient as PP for the learning of certain complex postural adjustments.

Structural network integrity of the central executive network is associated with the therapeutic effect of rTMS in treatment resistant depression

Repetitive transcranial magnetic stimulation (rTMS) is a first-line option for treatment-resistant depression (TRD), but prediction of treatment outcome remains a clinical challenge. The present study aimed to compare structural and functional covariance networks (SCNs and FCNs) between remitters and nonremitters. We determined the predictive capacities of SCNs and FCNs to discriminate the two groups. Fifty TRD patients underwent a course of rTMS to the left dorsolateral prefrontal cortex. They were categorized into remitters (n = 22) and nonremitters (n = 28) based on HDRS≤7 at the end of treatment. Baseline structural and functional magnetic imaging (sMRI and fMRI) of the patients and 42 healthy controls were collected. SCNs and FCNs were defined based on structural and functional covariance of gray mater volume (GMV) and fractional amplitude of low-frequency fluctuations (fALFF) from sMRI and fMRI, respectively. Structural/functional network integrity of these networks (default mode network [DMN], central executive network [CEN] and salience network [SN]) were compared between the three groups. In patients, associations between SCNs and FCNs with clinical improvements were studied using linear correlation analysis. Receiver-operating characteristic (ROC) analysis was conducted to confirm the utility of the SCNs and FCNs in classifying clinical sub-groups. Nonremitters exhibited lower structural integrity in CEN than remitters and controls. Higher structural integrity of CEN was related to clinical improvement (r = 0.423, p = .002), and structural integrity distinguished remitters and nonremitters with a fairly high accuracy (AUC = 0.71, p = .008). No group differences or correlation with clinical changes were found in FCNs. Results suggest the CEN may play a role mediating clinical improvement in rTMS for depression. Structural covariance networks may be features to consider in prediction of clinical improvement.

Neurofeedback learning for mental practice rather than repetitive practice improves neural pattern consistency and functional network efficiency in the subsequent mental motor execution

During brain modulation, repeated mental practice may not always result in efficient learning. Particularly, the effectiveness of mental motor practice depends on how well one induces neural activity in a desired state consistently across mental trials, which calls for feedbacks to adjust one's performance. We hypothesized that even a brief experience of neurofeedback learning enhances trial-by-trial neural pattern consistency during subsequent mental motor execution and that this experience would change recruitment of functional connectivity in the motor imagery and default mode networks. To test this hypothesis, we conducted an experiment with two sessions of mental motor practice before and after a neurofeedback training session, in which participants conducted four types of first-person mental motor execution tasks (walking forward, turning left, turning right, and touching a tree). During the neurofeedback training session, in which participants conducted a virtual navigation game, 10 experimental participants received real-time fMRI neuro-feedbacks, while 10 control participants simply repeated the same mental task according to given cues without feedbacks. The experimental group showed significantly higher effects of neuro-feedback training on trial-by-trial consistencies and classification accuracies of activated neural patterns than the control group. Task-performing global node strength and network efficiency were increased in the motor imagery network but decreased in the default mode network only in the experimental group. These results demonstrate that even a brief experience of feedback learning is more effective than simple practice repetitions without evaluation, which was reflected in increased neural pattern consistency and task-dependent functional connectivity during a mental motor execution task.

SCTICA: Sub-packet constrained temporal ICA method for fMRI data analysis

Independent component analysis (ICA) has become a widely used method for functional magnetic resonance imaging (fMRI) data analysis. However, spatial ICA usually performs better than temporal ICA with regard to the stability and accuracy of functional connectivity detection, and temporal ICA is often not feasible when it is applied to the analysis of real fMRI data of the whole brain because of the excessive spatial dimensions. In this paper, to overcome these problems, we propose a sub-packet constrained temporal ICA (SCTICA) method to take advantage of the a priori information using a multi-objective optimization framework with the Newton iterative algorithm. Moreover, a splitting strategy is presented to improve the feasibility of the temporal ICA for whole brain fMRI data analysis. The experimental results of real data show that the splitting strategy improved the ability of the temporal ICA to analyze whole brain fMRI data. Furthermore, the experimental results also demonstrated that the proposed SCTICA method can not only improve the stability of the temporal ICA, but can also improve the functional connectivity detection ability compared with the classical ICA and ICA with a priori information methods. In brief, the proposed SCTICA method overcomes the problem that prevents temporal ICA from being applied to fMRI data of the whole brain, and the functional connectivity detection performance is greatly improved compared with that of traditional methods.

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

This magnetoencephalography study investigates how ageing modulates the relationship between pre-learning resting-state functional connectivity (rsFC) and subsequent learning. Neuromagnetic resting-state activity was recorded 5 min before motor sequence learning in 14 young (19-30 years) and 14 old (66-70 years) participants. We used a seed-based beta-band power envelope correlation approach to estimate rsFC maps, with the seed located in the right primary sensorimotor cortex. In each age group, the relation between individual rsFC and learning performance was investigated using Pearson's correlation analyses. Our results show that rsFC is predictive of subsequent motor sequence learning but involves different cross-network interactions in the two age groups. In young adults, decreased coupling between the sensorimotor network and the cortico-striato-cerebellar network is associated with better motor learning, whereas a similar relation is found in old adults between the sensorimotor, the dorsal-attentional and the DMNs. Additionally, age-related correlational differences were found in the dorsolateral prefrontal cortex, known to subtend attentional and controlled processes. These findings suggest that motor skill learning depends-in an age-dependent manner-on subtle interactions between resting-state networks subtending motor activity on the one hand, and controlled and attentional processes on the other hand.

Resting-state functional connectivity and motor imagery brain activation

Motor imagery (MI) relies on the mental simulation of an action without any overt motor execution (ME), and can facilitate motor learning and enhance the effect of rehabilitation in patients with neurological conditions. While functional magnetic resonance imaging (fMRI) during MI and ME reveals shared cortical representations, the role and functional relevance of the resting-state functional connectivity (RSFC) of brain regions involved in MI is yet unknown. Here, we performed resting-state fMRI followed by fMRI during ME and MI with the dominant hand. We used a behavioral chronometry test to measure ME and MI movement duration and compute an index of performance (IP). Then, we analyzed the voxel-matched correlation between the individual MI parameter estimates and seed-based RSFC maps in the MI network to measure the correspondence between RSFC and MI fMRI activation. We found that inter-individual differences in intrinsic connectivity in the MI network predicted several clusters of activation. Taken together, present findings provide first evidence that RSFC within the MI network is predictive of the activation of MI brain regions, including those associated with behavioral performance, thus suggesting a role for RSFC in obtaining a deeper understanding of neural substrates of MI and of MI ability. Hum Brain Mapp 37:3847-3857, 2016. © 2016 Wiley Periodicals, Inc.

The Role of Intrinsic Brain Functional Connectivity in Vulnerability and Resilience to Bipolar Disorder

OBJECTIVE

Bipolar disorder is a heritable disorder characterized by mood dysregulation associated with brain functional dysconnectivity. Previous research has focused on the detection of risk- and disease-associated dysconnectivity in individuals with bipolar disorder and their first-degree relatives. The present study seeks to identify adaptive brain connectivity features associated with resilience, defined here as avoidance of illness or delayed illness onset in unaffected siblings of patients with bipolar disorder.

METHOD

Graph theoretical methods were used to examine global and regional brain network topology in head-motion-corrected resting-state functional MRI data acquired from 78 patients with bipolar disorder, 64 unaffected siblings, and 41 healthy volunteers.

RESULTS

Global network properties were preserved in patients and their siblings while both groups showed reductions in the cohesiveness of the sensorimotor network. In the patient group, these sensorimotor network abnormalities were coupled with reduced integration of core default mode network regions in the ventromedial cortex and hippocampus. Conversely, integration of the default mode network was increased in the sibling group compared with both the patient group and the healthy volunteer group.

CONCLUSIONS

The authors found that trait-related vulnerability to bipolar disorder was associated with reduced resting-state cohesiveness of the sensorimotor network in patients with bipolar disorder. However, integration of the default mode network emerged as a key feature differentiating disease expression and resilience between the patients and their siblings. This is indicative of the presence of neural mechanisms that may promote resilience, or at least delay illness onset.

Abnormal functional connectivity within resting-state networks is related to rTMS-based therapy effects of treatment resistant depression: A pilot study

BACKGROUND

Treatment resistant depression (TRD) remains a clinical challenge, and finding biomarkers that predict treatment response are a long sought goal to precisely indicate treatments. This pilot study aims to characterize brain dysfunction in TRD patients who underwent rTMS to define neuroimaging biomarkers that discriminate non-responders (NR) from responders (R).

METHODS

20 TRD patients who underwent a course of rTMS to the left DLPFC were categorized into R and NR groups based on a >50% reduction in HRSD scores. Utilizing resting-state fMRI and ICA techniques, this study compared baseline RSNs of R vs. NR as well as TRD vs. healthy volunteer group. Regression analysis was conducted to link regions with clinical improvements. ROC analysis was further conducted to confirm the utility of the identified regions in classifying the patients.

RESULTS

Prior to treatment, non-responders displayed hyper-connectivity in ACC/VMPFC, PCC/pC, dACC and insula within RSNs that have been associated with MDD pathology. Regression results showed that regions associated with clinical improvements overlapped largely with regions that showed aberrant connectivity. ACC/VMPFC, dACC and left insula, which are hub regions of DMN and SN, exhibited excellent performance (highest sensitivity=100% and highest specificity=82%) in discriminating the response status of the patients.

LIMITATIONS

Relatively small sample size.

CONCLUSIONS

Our findings provide insight into fMRI predictive measures of treatment response to rTMS treatment, and demonstrate the potential of RSNs-based biomarkers in predicting response to rTMS treatment. Future studies are needed to validate the application of these measures to inform individual treatment indications.

Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior.

Connectivity patterns during music listening: Evidence for action-based processing in musicians

Musical expertise is visible both in the morphology and functionality of the brain. Recent research indicates that functional integration between multi-sensory, somato-motor, default-mode (DMN), and salience (SN) networks of the brain differentiates musicians from non-musicians during resting state. Here, we aimed at determining whether brain networks differentially exchange information in musicians as opposed to non-musicians during naturalistic music listening. Whole-brain graph-theory analyses were performed on participants' fMRI responses. Group-level differences revealed that musicians' primary hubs comprised cerebral and cerebellar sensorimotor regions whereas non-musicians' dominant hubs encompassed DMN-related regions. Community structure analyses of the key hubs revealed greater integration of motor and somatosensory homunculi representing the upper limbs and torso in musicians. Furthermore, musicians who started training at an earlier age exhibited greater centrality in the auditory cortex, and areas related to top-down processes, attention, emotion, somatosensory processing, and non-verbal processing of speech. We here reveal how brain networks organize themselves in a naturalistic music listening situation wherein musicians automatically engage neural networks that are action-based while non-musicians use those that are perception-based to process an incoming auditory stream. Hum Brain Mapp 38:2955-2970, 2017. © 2017 Wiley Periodicals, Inc.

Online and Offline Performance Gains Following Motor Imagery Practice: A Comprehensive Review of Behavioral and Neuroimaging Studies

There is now compelling evidence that motor imagery (MI) promotes motor learning. While MI has been shown to influence the early stages of the learning process, recent data revealed that sleep also contributes to the consolidation of the memory trace. How such "online" and "offline" processes take place and how they interact to impact the neural underpinnings of movements has received little attention. The aim of the present review is twofold: (i) providing an overview of recent applied and fundamental studies investigating the effects of MI practice (MIP) on motor learning; and (ii) detangling applied and fundamental findings in support of a sleep contribution to motor consolidation after MIP. We conclude with an integrative approach of online and offline learning resulting from intense MIP in healthy participants, and underline research avenues in the motor learning/clinical domains.

Enhancing transcranial direct current stimulation via motor imagery and kinesthetic illusion: crossing internal and external tools

Background

Transcranial direct current stimulation is a safe technique which is now part of the therapeutic armamentarium for the neuromodulation of motor functions and cognitive operations. It is currently considered that tDCS is an intervention that might promote functional recovery after a lesion in the central nervous system, thus reducing long-term disability and associated socio-economic burden.

Discussion

A recent study shows that kinesthetic illusion and motor imagery prolong the effects of tDCS on corticospinal excitability, overcoming one of the limitations of this intervention.

Conclusion

Because changes in excitability anticipate changes in structural plasticity in the CNS, this interesting multi-modal approach might very soon find applications in neurorehabilitation.

Processing of Self versus Non-Self in Alzheimer's Disease

Despite considerable evidence for abnormalities of self-awareness in Alzheimer's disease (AD), the cognitive mechanisms of altered self-processing in AD have not been fully defined. Here we addressed this issue in a detailed analysis of self/non-self-processing in three patients with AD. We designed a novel neuropsychological battery comprising tests of tactile body schema coding, attribution of tactile events to self versus external agents, and memory for self- versus non-self-generated vocal information, administered in conjunction with a daily life measure of self/non-self-processing (the Interpersonal Reactivity Index). Three male AD patients (aged 54-68 years; one with a pathogenic mutation in the Presenilin 1 gene, one with a pathogenic mutation in the Amyloid Precursor Protein gene, and one with a CSF protein profile supporting underlying AD pathology) were studied in relation to a group of eight healthy older male individuals (aged 58-74 years). Compared to healthy controls, all patients had relatively intact tactile body schema processing. In contrast, all patients showed impaired memory for words previously presented using the patient's own voice whereas memory for words presented in other voices was less consistently affected. Two patients showed increased levels of emotional contagion and reduced perspective taking on the Interpersonal Reactivity Index. Our findings suggest that AD may be associated with deficient self/non-self differentiation over time despite a relatively intact body image: this profile of altered self-processing contrasts with the deficit of tactile body schema previously described in frontotemporal dementia associated with C9orf72 mutations. We present these findings as a preliminary rationale to direct future systematic study in larger patient cohorts.