How motor, cognitive and musical expertise shapes the brain: Focus on fMRI and EEG resting-state functional connectivity

Accepted Manuscript Title: How Motor, Cognitive and Musical Expertise Shapes the Brain: Focus on the fMRI and EEG Resting-State Functional Connectivity Authors: Pauline Cantou, Herv´e Platel, B´eatrice Desgranges, Mathilde Groussard PII: DOI: Reference:

S0891-0618(16)30251-4 http://dx.doi.org/10.1016/j.jchemneu.2017.08.003 CHENEU 1513

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18-11-2016 13-8-2017 16-8-2017

Please cite this article as: Cantou, Pauline, Platel, Herv´e, Desgranges, B´eatrice, Groussard, Mathilde, How Motor, Cognitive and Musical Expertise Shapes the Brain: Focus on the fMRI and EEG Resting-State Functional Connectivity.Journal of Chemical Neuroanatomy http://dx.doi.org/10.1016/j.jchemneu.2017.08.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

How Motor, Cognitive and Musical Expertise Shapes the Brain: Focus on the fMRI and EEG Resting-State Functional Connectivity Pauline Cantou1, Hervé Platel1, Béatrice Desgranges1, Mathilde Groussard*1 1

Normandie Univ, UNICAEN, EPHE, INSERM, U1077, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000 Caen, France *Correspondence and reprint requests: Mathilde Groussard, Inserm-EPHE-Unicaen U1077, Centre Cyceron, Campus Jules Horowitz, Boulevard Henri Becquerel, BP 5229F-14074 Caen Cedex 5 Phone: +33 (0)2 31 47 01 77; Fax: +33 (0)2 31 47 01 06, e-mail: [email protected]

Highlights

  

Motor, cognitive and musical expertise is associated with resting-state functional changes within various brain networks There is currently no clear pattern of results that would single out a specific neural signature of general expertise at rest The variability of findings might be explained by different settings of resting-state and various methods used to analyze the data.

ABSTRACT Brain activity and structure are shaped by life experiences. This plasticity has often been demonstrated with different types of expertise by using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Experts showed domain-specific functional neural changes during completion of a task when compared to non-experts. However, all of these results are task-dependent and even though they have proven useful for understanding neural interactions and their direct relation to individual skill, studying brain plasticity without any task might provide complementary information about functional cerebral reorganization due to expertise at the whole-brain level and might facilitate comparison across studies. Resting-state functional MRI and EEG makes it possible to explore the functional traces of expertise in the brain by measuring temporal correlations of blood oxygen level-dependent (BOLD) and spontaneous neural activity fluctuations at rest. Since these correlations are thought to reflect a prior history co-activation of brain regions, we propose reviewing studies that focused on the effects of expertise in the motor, cognitive and musical domains on brain plasticity at rest, to determine whether there is a domain-specific neural signature of expertise. After highlighting

expertise-related changes within resting-state networks for each domain, we discuss their specificity to the trained activity and the methodological considerations concerning different conditions and analyses used between studies.

Keywords: Expertise, neural plasticity, functional connectivity, resting-state networks 1

Introduction

Many studies using neuroimaging techniques have investigated the effects of regular and intensive training on neuroplasticity and revealed anatomical and functional changes related to experience. For example, an increased grey matter volume has been reported in the posterior hippocampus of London taxi drivers. The size was positively correlated with the time spent driving (Maguire et al., 2000). It has also been shown that learning to juggle induced grey matter changes in the brain areas related to visuospatial processing. Since these discoveries were made, the effects of expertise on structural and functional brain measures have been assessed for a large number of activities, especially using task-related functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) (See Debarnot et al. 2014 for a review). However, results from these researches strongly depend on the specific task demands and the type of paradigm used in the scanner, which makes the comparison of findings across studies difficult. A good way to overcome this issue is to explore the effects of expertise on whole brain plasticity without any task, which is possible thanks to resting-state functional connectivity (rsFC). Moreover, it has been proposed that connectivity strength reflects the history of coactivation of functionally connected regions (Guerra-Carillo et al., 2014). This hypothesis suggests that functional connectivity changes due to expertise might reflect the regular recruitment of specific regions during training and thus might reveal a neural signature of expertise detected during rest.

Resting-state fMRI is a technique measuring intrinsic spontaneous fluctuations in the blood oxygenation level-dependent (BOLD) signal between brain regions that are spatially separated and allows the exploration of cerebral network dynamics during a task-free state. Using this method, where participants are invited to relax in the scanner, it has been shown that restingstate activity was characterized by low-frequency oscillations (Fox and Raichle, 2007). When these oscillations are synchronized in several distant brain regions, they form resting-state functional networks (RSN). The motor network (MN) was the first RSN to be discovered after highlighting strong correlations at rest between the time course of left and right primary motor regions (Biswal et al., 1995). Subsequently, other studies replicated these results and reported other resting-state networks, including the visual network (VN), the auditory network (AN), the salience network (SN), the fronto-parietal network (FPN), and the default-mode network (DMN) (Van den Heuvel and Hulschoff Pol, 2010). Interestingly, these networks are related to specific behavioral functions such as perceptual processing for the AN and VN, detection of novel and salient information for the SN, attentional control for the FPN and an introspective role for the DMN (Van den Heuvel and Hulschoff Pol, 2010). The latter is mainly composed of the posterior cingulate cortex, the precuneus, the hippocampal formation including parahippocampus, the medial frontal and inferior parietal regions (Buckner et al., 2008) and is of particular interest in resting-state neuroimaging studies. Unlike other resting-state networks, the DMN is known to show greater neural activity during rest as compared to task conditions and the disengagement of this network is associated with enhanced cognitive performance (Gusnard and Raichle, 2001; Greicius and Menon, 2004). Moreover, improved cognitive abilities were linked to resting-state anti-correlations between the DMN and task-positive networks (i.e. involved when performing a specific task) (Keller et al., 2015).

Various methods are used to analyze fMRI resting-state data. The three most frequently used are: seed-based, independent components analysis (ICA) and graph theory methods. The first needs a priori hypotheses because it involves extracting resting-state time series of a region of interest (ROI, also called the seed region) and correlating them with times-series of all other brain voxels. This seed-based method generates a functional connectivity map that provides information on regions that are functionally related to the seed region (Biswal et al., 1995). To select the regions of interest in a resting-state condition, it is possible either to rely on structural differences between the groups (i.e. regions with gray matter changes) or on functional brain changes that occurred in a task-related context. ROI-based analysis is a very similar method that compares regionally averaged signal from all voxels in a specific ROI with the average signal of another ROI whereas seed-based analysis compares regionally averaged signal from one ROI with that of all other voxels of the brain (Rosazza et al., 2012). ICA is an exploratory method and does not require a priori hypotheses. With this technique, sophisticated algorithms analyze all the BOLD data of the whole brain and separate them into components that are the most independent in statistical terms (Beckmann and Smith, 2004). The graph-theory approach aims to investigate the overall connectivity by describing the brain as interconnected networks (Bullmore and Sporns, 2009). These networks are divided into a set of nodes (brain regions) where edges represent the relationships between all possible node pairs (or connectivity). Two different parameters were calculated to describe graphs: the cluster coefficient C representing the likelihood that neighbors of a node will also be connected, and the path length L which is the average of the shortest distance between pairs of nodes, estimated by the number of edges (Watts and Strogatz, 1998). Regular networks have a high C and L whereas random networks have a low C and L. Interestingly, highly efficient networks called “small-world networks” have been described by a high C and a low L, allowing an efficient information segregation and integration (Rubinov and Sporns, 2009). Thus, graph theory

method provides information about the level of global organization of the network and about how efficiently information can be integrated between different systems (Van den Heuvel and Huschoff Pol, 2010). There are other more recent methods used in resting-state fMRI studies such as functional connectivity density (FCD) that measures the strength of intrinsic connectivity either between one voxel and others within the whole brain (global FCD) or in a local cluster surrounding a voxel (local FCD) (Tomasi et al., 2010). It is also possible to obtain local brain activity information at rest with for example amplitude of low-frequency fluctuations (ALFF) that measures the power of a given time course within typical low frequency range (e.g., 0.01-0.08 Hz), fractional ALFF (fALFF), which indicates the ratio of power spectrum of low-frequency to that of the entire frequency range (e.g., 0-0.25 Hz) thus reducing the sensitivity of ALFF to physiological noise, and ReHo (regional homogeneity), which calculates the degree of regional synchronization of fMRI time courses (Liang et al., 2013; Zhou et a. 2014).

The main advantage of fMRI concerns the high spatial resolution allowing to localize activated brain areas with millimeter resolution. However, this technique provides an indirect measure of neural activity and its temporal resolution is limited to the hemodynamic response which varies across individuals and brain regions. Due to its higher temporal resolution (milliseconds), EEG constitutes a complementary and optimal technique to measure the correspondence of neural signals over time. It is a non-invasive method used to measure changes in voltage at the scalp with electrodes, which occurs when a large population of neurons generates synchronous electrical activity. EEG recordings show oscillations in different frequency ranges categorized as 1-4 Hz (delta), 4-8 Hz (theta), 8-13 Hz (alpha), 13-30 Hz (beta), and <30 Hz (gamma) and associated with

different brain regions, functions and states (Niedermeyer, 2005). The alpha waves (posterior oscillations) are the most prominent EEG signal during the awake resting state with eyes closed. There are many different connectivity measures enabling to explore interactions between brain regions with EEG. They can be subdivided into functional connectivity measures that are based on statistical interdependencies (or temporal correlations) between signals of distant brain regions without providing information on causal interactions, and effective connectivity measures that determine the direct or indirect influence that one neural system exerts over another, more specifically the direction of the dynamic information flow in the brain (Sakkalis, 2011; Bonita et al. 2014). The most commonly used in the frequency domain are coherence (such as magnitude squared coherence) and phase synchronization (such as mean phase coherence) whereas in the time domain, correlation (such as cross correlation) and Granger causality are mainly applied. Coherence is defined as a linear correlation coefficient that

estimates the consistency of relative amplitude and phase between signals within a set frequency band (Bowyer et al., 2016). With this measure, a dependency value between 0-1 is obtained for each frequency component of the signals. Phase synchrony measures an index of neural synchrony and is defined by a phase locking value, ranging from zero (no synchronization) to one (perfect synchronization) (Hu et al., 2010). Correlation is a similar measure of coherence, which quantifies the linear relation in the time domain (Sakkalis, 2011). In contrast to these last three methods that measure undirected functional connectivity (or dependencies), Granger causality is a measure of effective connectivity based on the assumption that causes precede their effects in time (Sakkalis, 2011). Finally, spectral analysis is also widely used for quantification of the EEG. The power spectrum of time series describes the distribution of power into frequency components composing the signal (Dressler et al., 2004).

Several studies have investigated rs-FC modifications due to short-term and long-term trainings in various fields, assessing resting-state in a pre-task and post-task condition or without preceding or following any task performance. Although short-term training studies showed significant rs-FC changes (Albert et al., 2009; Keller and Just, 2016), longer-term trainings may result in more lasting and stable changes in RSN (Raichlen et al., 2016). Moreover, restingstate measured after a task completion might be influenced by a delayed recovery period (Tung et al., 2013) whereas pre-task resting-state might be altered by anticipating the upcoming task (Grigg and Grady., 2010). Instead, resting-state measured alone is more likely to reflect longterm effects of a training. In this review, we therefore focused on long-term rs-FC changes occuring after an intensive training, or expertise (i.e. with years of practice), in young adults for different activities that we classified into two categories based on the behavioral functions they involve: motor and cognitive activities. A third section was dedicated to musical practice, an activity that involves relatively equal contributions of both motor and cognitive abilities. We discuss the specificity of rs-FC changes for these different kinds of expertise as well as the comparability of studies.

1.1

Motor Activities and Resting-State Functional Connectivity

Expertise in the motor domain has been shown to induce rs-FC changes within and between brain regions that are typically involved in motor activities such as the cerebellar and frontoparietal networks, sensorimotor regions, and the MN (Di et al., 2012; Li et al., 2015; Wang et al., 2016; Raichlen et al., 2016). In this context, as a measure of neural activity during rest, Li et al. (2015) used FCD and seedbased analysis to explore the effects of dance expertise on rs-FC and reported functional changes in motor regions, especially within sensorimotor cortices. Indeed, expert dancers showed higher rs-FC in cortico-basal loops, such as between the middle cingulate cortex and the bilateral putamen and between the precentral and the postcentral gyri. These sensorimotor

areas are often reported to be active during dance practice (see Karpati et al., 2015 for a review) and are involved in motor execution, metric motion and in the learning of new sensori-motor association (Tanji, 1996). Moreover, these functional increases were positively correlated with the average training time per week, meaning that longer training was associated with greater functional integration in cortico-basal ganglia loops. These results support the idea that prolonged motor training is more likely to improve integration in brain regions directly involved in motor functions. Another study used ALFF intensity and seed-based method to explore resting-state modifications due to expertise in badminton among players with a mean of almost 10 years of practice (Di et al., 2012). They first highlighted an increased ALFF in the cerebellum, a brain region involved in motor timing (Penhune et al., 1998) anticipation performance and motor execution (Balser et al., 2014), which may reflect a better integration of these skills due to the regular and intensive recruitment of this brain region during badminton practice. Interestingly, their second result showed a decrease of ALFF and rs-FC respectively in the left parietal lobule and within the FPN. We can compare these results with a short-term training study (Ma et al., 2011) who reported an increased rs-FC between the right supramarginal gyrus and the right precentral gyrus after 2 weeks of training, but found a decreased rs-FC in the last two weeks when the behavioral performance started to improve and stabilize. Moreover, in other shortterm motor training studies, rs-FC increased in motor areas as well as in the FPN (Albert et al., 2009; Taubert et al., 2011), suggesting different resting-state connectivity variations depending on the training period. This could be explained by two processes occurring at different times. First, an increased activation may reflect the integration and consolidation mechanisms of new motor skills and second, a decreased activation due to an easier achievement of the motor task. Therefore, in the study of Di et al. (2012), the weaker involvement of parietal areas known to play a key role in motor control (Ma et al., 2011) and of the FPN supporting attentional control

as well as hand-eye coordination (Katsuki and Constantinidis, 2012) might reflect an automation of motor control processes. This finding suggests that learning new motor skills through prolonged intensive training produces a decreased rs-FC within the FPN that may be associated with a reduction of the attentive load and an automation of motor abilities (Bezzola et al., 2012). Nevertheless, further longitudinal studies combined with task-related findings are needed to better interpret the directionality of functional connectivity (increase or decrease) in different brain regions. This hypothesis is consistent with previous results. Graph theory analysis showed that worldclass gymnasts with over 10 years of practice exhibited lower rs-FC within the cerebellum, fronto-parietal, and cingulo-opercular networks compared to healthy controls (Wang et al., 2016). Given the crucial role of cerebellar network in motor functions and of both frontoparietal and cingulo-opercular networks in attentional control (Dosenbach et al., 2007), it appears that expert gymnasts show lower functional connectivity in regions related to their trained-activity at rest. We speculate that it would reflect a reduced utilization of resources with a diminished attentive load and a greater automaticity of their motor abilities. According to the authors, this decreased rs-FC might be due to the high intensity and amount of training and suggests that this type of experts possess a strong degree of automaticity leading to an increased neural efficiency. Moreover, rs-FC between the FPN and the sensori-motor network was negatively correlated with the number of years of training, indicating a decreased link between attention control and motor execution regions when the level of expertise increases. Once more, it supports the idea of motor skills automation in professional athletes. However, a recent study investigated neuroplasticity associated with expertise in endurance running and revealed enhanced rs-FC in networks related to executive functions and motor control compared with non-experts (Raichlen et al., 2016). Specifically, the authors used the seed-based method to highlight increased connectivity between the FPN and frontal regions

associated with working memory and executive functions such as the left and right superior/mid frontal regions (D’Esposito et al., 1998). Raichlen and collaborators attributed these connectivity changes to the implicit cognitive demands when running in a complex environment including self-awareness, planning, inhibition and attentional control. Here, the increased connectivity between the FPN and cognitive regions seems to be related to the enhanced involvement of cognitive functions in athletes compared with more sedentary individuals. In addition to changes in the FPN, they found anti-correlations between the DMN and the paracentral area, the post-central region and the occipital cortex that are associated with motor control, somatosensory needs and visual processing, respectively. Negative connectivity strength was also found between the MN and the posterior cingulate, a key region of the DMN. Since anti-correlations between the DMN and task-positive regions are associated with improved cognitive skills (Keller et al., 2015), these results suggest that DMN is deactivated to leave room for regions involved in these required functions during running, leading to enhanced performance. To summarize, it seems that motor expertise leads to rs-FC changes mainly in brain regions specific to the trained activity, particularly in those involved in motor execution and attentional control. These functional modifications occur either as an increase, which may reflect the ongoing learning and integration of motor abilities, or a decrease suggesting a stronger efficiency of functional networks and an automation process of new motor skills. Furthermore, athletes also recruit cognitive regions during their training that seem to leave a trace at rest, showing enhanced connectivity between cognitive regions and the FPN, as well as anticorrelations between the DMN and regions associated with cognitive functions involved during endurance running and suggesting improved cognitive skills.

1.2

Cognitive Activities and Resting-State Functional Connectivity

To examine how cognitive expertise impacts on rs-FC, we selected activities involving memory, executive functions and sustained attention such as meditation (Jang et al., 2011; Taylor et al., 2013), board games (Duan et al., 2012; Jung et al,. 2013; Duan et al., 2014) and video games (Gong et al., 2015). First, rs-FC changes were mostly studied in expert meditators within the DMN, given its’ involvement in self-referential processes and mind wandering (Buckner et al., 2008). For instance, Jang et al. (2011) showed stronger rs-FC within this network by using seed-based analysis and selecting the posterior cingulate cortex and the medial prefrontal cortex as seed regions. Specifically, this stronger coupling was found within the medial prefrontal areas that are known to be activated during self-related thoughts. The authors suggested that this result would reflect a greater internal attention allowing a detachment of the external world, even when experts do not practice. Interestingly, Brewer and collaborators (2011) explored functional activation in the DMN of experienced meditators and controls during a resting-state and a meditation period. To analyze the rs-FC data, they used the seed-based approach utilizing two seed regions of the DMN displaying reduced activation during a meditation state in experts compared to controls: the medial prefrontal cortex and the posterior cingulate cortex. They found a stronger co-activation between the posterior cingulate and regions involved in cognitive control such as the dorsal anterior cingulate and dorsolateral prefrontal cortices in experts compared to controls. This connectivity pattern was similar during rest and meditation in experts, suggesting that meditation expertise may orientate the resting-state toward a meditative-state. Moreover, since meditators reported reduced mind wandering during meditation compared to controls in this study, this similar pattern could reflect a persistent meditative state at rest with a greater awareness of the present moment. Analogous conclusions have been drawn from results showing stronger rs-FC between the dorsal prefrontal cortex involved in cognitive control and the right inferior parietal lobule that belongs to the DMN in

expert meditators compared to controls (Taylor et al., 2013). Here, the authors used independent component analysis (ICA) to identify a DMN map at the group level and to select regions of interests based on the peak-voxels in the DMN map. Then, they performed correlations between the time course of each seed regions within the two groups and compared them between expert and novice meditators. Using these analyses, they also revealed reduced rs-FC between different regions involved in self-referential processing (including between the medial prefrontal cortex and the left parietal lobule) and within regions involved in self-emotional evaluation (especially between the ventral and dorsal medial prefrontal cortex). More recently, Berkovich-Ohana (2016) reported a reduced rs-FC within the DMN using ROI correlations and selected the bilateral precuneus and the inferior parietal lobule as seed regions. A reduced rs-FC was mainly observed in the precuneus of meditators, suggesting increased sensory awareness and reduced mind wandering with a greater ability to focus attention on the present moment. This interpretation is in agreement with previous EEG findings from the same group who applied power spectral distribution measures (Berkovich-Ohana et al., 2012). They highlighted a frontal gamma power reduction at rest related to DMN activity as well as an increased temporal and parieto-occipital gamma power in meditators, plausibly reflecting less internal self-reference processing and increased environmental awareness. Interestingly, these results were found regardless the level of expertise suggesting that a low level of practice (from 894 hours) is sufficient to produce resting-state changes of gamma band activity in frontal and parieto-occipital areas. In the same cohort, they used MPC (mean phase coherence) to report a reduced functional connectivity in gamma, right theta and left alpha frequency bands, related to lower self-reference and mind-wandering as well as enhanced creativity (Berkovich-Ohana et al. 2013). All of these meditation-related changes were the trait (long-term) effects of the expertise measured in a resting-state that does not follow any meditation state. It appears important to

precise that there is also state (short-term) effects that can be measured immediately after meditation (Cahn and Polich, 2006). In this case, it was shown different results during post-task rest. Indeed, the previous meditation can have some persisting effects on resting-state delta frequency bands (Lehmann et al., 2012). Thus, meditation expertise principally leads to rs-FC modifications in the DMN, which seems specific to this activity, which requires strong attentional abilities. However, regions and interpretations differ across studies. As noted by Fingelkurts et al. (2016), findings might be biased when considering the DMN as a homogeneous unit involved in one function, especially since the behavioral role of this network remains unclear. Moreover, different methods were applied to analyze the data, leading to different results. When medial prefrontal and posterior cingulate cortices are selected as seed regions, results showed increased rs-FC within medial prefrontal areas and between the posterior cingulate and cognitive regions, whereas decreased rs-FC has been reported within the precuneus when the latter was chosen as a region of interest. Thus, directionality of connectivity changes appears to depend on the selected seeds. It appears to be irrelevant to make conclusions about rs-FC within the DMN as a single unit. Using the ICA method, both decreases and increases were highlighted between various brain regions, supporting the heterogeneity of the DMN. Other studies have examined connectivity at rest within the DMN after a cognitive training. In an fMRI study, Duan et al. (2012) investigated how chess expertise modulates activity of the DMN during a chess problem-solving task and a task-free resting-state using seed-based analysis and four seed regions belonging to the central executive network involved in attentional control, working memory and decision making, the dorsal attentional network which is part of the FPN (Farrant et al., 2015), the SN and the DMN. Not surprisingly, they highlighted stronger disengagement of the DMN in chess experts compared to novices, which is explained by higher involvement of cognitive networks, especially the dorsal attentional network and the

SN, leading to better abilities to focus on the task. Furthermore, they used the caudate nuclei as a seed region (which previously showed a smaller grey matter volume in chess experts), to show that rs-FC was enhanced between this region and the striatal-DMN (posterior cingulate cortex and bilateral angular gyrus). As pointed out by the authors, caudate nuclei are known to be involved in motivational, decision-making processes and stimulus-action association. Thus, increased rs-FC seems to reflect a better efficiency of this DMN-caudate network and a higher cognitive control in chess players. Interestingly, rs-FC changes have also been reported due to chess expertise within non-classical RSN. Indeed, Duan et al. (2014) used a similar population to their previous study and graph theory analysis to assess the organization of brain functional networks. They found that rs-FC was higher among expert chess players than in novices for the basal ganglia, thalamus, hippocampus, medial temporal regions and several parietal areas. These results suggest an effect of chess expertise on functional connectivity associated with learning and memory. Rs-FC was also assessed in Go game (a strategic spatially orientated board game) experts (Jung et al., 2013). Using graph theory analysis of resting-state fMRI, the authors reported an increased rs-FC between the amygdala and the orbitofrontal cortex, and a decreased rs-FC between nucleus accumbens and medial prefrontal cortex in professional players compared to controls. Due to the amygdala and nucleus accumbens being involved in affective and cognitive components of the limbic cortico-striatal loop, the authors suggested that these rs-FC changes may reflect better intuitive decision-making that is based on feelings rather than on reasoning. Finally, Gong et al. (2015) focused on rs-FC changes in the insula to examine the neural basis of attentional and sensorimotor benefits due to video games expertise. Indeed, playing video games requires strong attentional control and hand-eye coordination and has been shown to improve attentional and sensorimotor abilities (Green et al., 2003; Li et al., 2016). Moreover, the anterior and posterior parts of the insula are involved in attentional and sensorimotor

regions, respectively (Nelson et al., 2010; Cauda et al., 2011). Here, the authors used the ROIbased method after selecting different areas of the anterior and posterior sub-regions of the insula as regions of interest. Compared to amateurs, video games experts exhibited enhanced rs-FC within these insular sub-regions, suggesting a better integration of attentional and sensorimotor skills. Thus, it appears that expertise involving specific cognitive functions modify rs-FC within various networks, especially those related to attentional abilities. We noticed that the DMN is often reported as modified, probably for two reasons: first, because compared to beginners, experts are characterized by less external focus on the activity resulting in a disengagement of the DMN and second, because regions belonging to the DMN are frequently chosen as seed regions to analyze functional changes within this network. 1.3

Musical Expertise and Resting-State Functional Connectivity

Musical practice is the most studied activity when examining neural changes due to expertise since this activity requires complex multimodal skills involving auditory, visual, somatosensory modalities, as well as memory processes and the motor system (Wan and Schlaug, 2010). Many neuroscience studies emphasize on the interaction between auditory and motor systems, because each action in a performance produces sound, which influences each subsequent action, leading to remarkable sensory–motor interplay. It is thus obviously expected that the hundreds of hours of training performed by the musicians increased significantly auditory–motor neural interactions (Zatorre, et al., 2007) In this context, Luo et al. (2012) studied effects of musical expertise on rs-FC using fMRI and ICA method. They found an increased rs-FC between the motor and multi-sensory cortices (such as visual, auditory and somatosensory cortices) in musicians compared to non-musician. This might reflect a higher functional integration among the lower-perceptual and motor networks as well as a better memory consolidation process. Moreover, this increased rs-FC was

positively correlated with both motor and auditory skills, supporting the idea that the more one trains a specific activity such as musical practice, the more rs-FC changes occur within brain networks involved in this trained activity. Following this same idea of task-specific rs-FC changes, it has been reported that modifications in various networks known to support memory, perceptual-motor and emotions are all required in long and intensive musical training (Fauvel et al., 2014). More specifically, the authors selected seed regions based on their previous anatomical results and extracted their time-series. They revealed greater rs-FC within an autobiographical network containing regions belonging to the DMN, a second network including SN areas, a third including auditory regions and finally an emotional-related network comprised of the orbitofrontal gyrus. Another fMRI study revealed enhanced rs-FC and increased functional integration within the SN of musicians by examining functional connectivity among ten ROI that first showed increased local FCD (Luo et al., 2014). Furthermore, the anterior insula, which belongs to the SN, appeared to undergo strong rs-FC modifications due to musical expertise. This structure plays a crucial role in deactivating the DMN in response to salient stimuli and its’ functional alteration after intensive musical practice suggests that this activity may improve efficiency of cognitive networks such as the central executive network (see Uddin, 2014 for a review). Finally, one last fMRI study conducted in female musicians and non-musicians used seed-based method to correlate the time course of the precuneus, a core region of the DMN known to be activated during mental imagery, and the courses of all other voxels. This area did not show any rs-FC modifications with other regions belonging to the DMN (Tanaka and Kirino, 2016). However, it showed enhanced rs-FC with insula and operculum in musicians, areas processing auditory, interoceptive, sensorimotor, and emotional information. According to the authors, this greater rs-FC would reflect the link between the scene construction during musical expression and the motor control system.

A recent resting-state EEG study assessed whole-brain functional connectivity as well as smallworld topologies using graph theory analysis in professional musicians and non-musicians (Klein et al., 2015). They showed higher rs-FC within brain networks related to musical practice such as auditory network, sensorimotor network, prefrontal regions and Broca’s area. Specifically, they found increased connectivity between the left and the right auditory cortices as well as between the left auditory cortex and the right sensorimotor cortex. As mentioned by the authors, these regions were shown to be modified in musicians, suggesting a higher functional coupling between them detectable even without any task. Moreover, musical experts exhibited increased connectivity patterns in theta and lower alpha-frequency ranges known to be involved in cognitive functions (Klimesch et al, 1999). Thus, it appears that musical expertise leads to increased rs-FC within many networks such as the DMN and the SN, but also in the sensorimotor and the auditory networks required during musical practice. In this respect, it appears worthwhile mentioning a recent study that examined gray matter (GM) differences at the whole-brain level between expert pianists and non-expert pianists (Vaquero et al., 2016). They revealed a reduction in the volume of GM in regions associated with sensorimotor control, auditory processing and score-reading as well as an increased GM volume in the right putamen positively correlated with the age of onset. These changes have been related to an improved efficiency of the multi-sensory-motor pathways involved in long-term music training. Furthermore, music is a complex and rich activity, which requires many functions that may vary according to the type of instrument, the musical genre or even the emotional involvement. For example, Bangert and Schlaug (2006) found anatomical differences between string and keyboard players in the motor cortex containing the representations of the hands. Different lateralization was observed with an enlargement in the left hemisphere in pianists and in the right hemisphere in violinists.

In the musical studies described throughout this review, analyses were performed in groups of musicians playing various instruments including: piano, flute, cello, violin, trumpet, clarinet, accordion and Chinese zither. However, the sample sizes were not large enough (average of 20 musicians) to draw conclusions about experience-dependent brain plasticity due to general musical expertise, since only a few participants represented each instrument. Furthermore, it makes the comparison across studies more difficult.

Table 1 Summary of Studies Reporting rs-FC Changes Due to Motor, Cognitive and Musical Expertise.

Networks with training-related resting-

DM

Motor areas Domain

Study

Technique Expertise

Duration (years)

Analysis / measures

Restingstate N SensoriFrontoSupplementary Cerebellar condition

motor network

Di et al. 2012

fMRI

Badminton

8.9

ALFF / SBA

Eyes open

20



Li et al. 2015

fMRI

Dance

10.7

SBA

Eyes closed

28



Rachlen et al. 2016

fMRI

Endurence running

Not specified

SBA

Eyes open

11



Wang et al. 2016

fMRI

Gymnastic

> 10

GTA

Eyes closed

13



fMRI

Meditation

3.3

SBA

Cross fixation

35

Motor activities

Cognitive Jang et al. 2011 activities

motor area



areas



Medial Posterior parietal prefrontal cingulate network areas cortex



between FPN and frontal regions





Brewer et al. 2011

BerkovichOhana et al. 2012

BerkovichOhana et al. 2013

fMRI

EEG

EEG

Meditation

> 10

SBA

Eyes closed

12

Meditation

> 894 (in hours)

PSD

Eyes closed

36

Meditation

> 894 (in hours)

MPC

Eyes closed

36

ICA

Cross fixation

13





frontal gamma power



Taylor et al. 2013

fMRI

Meditation

> 1000 (in hours)

BerkovichOhana et al. 2016

fMRI

Meditation

16

ROIs correlations

Eyes closed

18

Duan et al. 2012

fMRI

Chess

13.6

SBA

Cross fixation

15



Between VMPFC and DMPFC



with bilateral caudate nuclei

Duan et al. 2014

fMRI

Chess

11.8

GTA

Cross fixation

20

Jung et al. 2014

fMRI

Go

12.5

ROIs correlations

Eyes closed

17

Gong et al. 2015

fMRI

Video games

>6

ROIs Not correlations specified

Luo et al. 2012

Musical practice fMRI

Music Expertise

(Piano, Chinese zither or accordion)

6 to 20

SBA

Eyes closed

27

16

Musical practice Fauvel et al. 2014

fMRI

(violin, cello, guitar, flute, recorder, trumpet, clarinet, or piano)

16.1

SBA

Eyes closed

16



with the orbitofrontal gyrus

Luo et al. 2014

Klein et al. 2015

Musical practice fMRI

EEG

(Piano, Chinese zither or accordion)

6 to 20

ROIs correlations

Eyes closed

28

Musical practice

10 831 (in hours)

GTA

Eyes open

15

18

SBA

Eyes closed

26

(string)

Musical practice Tanaka et al. 2016

fMRI

(piano, violin, cello, contrabass, clarinet, or trumpet)

Networks with training-related resting-

DM

Motor areas Domain

Study

Technique Expertise

Duration (years)

Analysis / measures

Restingstate N SensoriFronto- Medial Posterior Supplementary Cerebellar condition motor parietal prefrontal cingulate

network

Motor activities

Di et al. 2012

fMRI

Badminton

8.9

ALFF / SBA

Eyes open

20

motor area



areas



network



areas



cortex



 Li et al. 2015

fMRI

Dance

10.7

SBA

Eyes closed

28



Rachlen et al. 2016

fMRI

Endurence running

Not specified

SBA

Eyes open

11



Wang et al. 2016

fMRI

Gymnastic

> 10

GTA

Eyes closed

13



Jang et al. 2011

fMRI

Meditation

3.3

SBA

Cross fixation

35

Brewer et al. 2011

fMRI

Meditation

> 10

SBA

Eyes closed

12

EEG

Meditation

> 894 (in hours)

PSD

Eyes closed

36

EEG

Meditation

between FPN and frontal regions

Cognitive activities BerkovichOhana et al. 2012

MPC

36

frontal gamma power

BerkovichOhana et al. 2013

> 894 (in hours)

Taylor et al. 2013

fMRI

Meditation

> 1000 (in hours)

ICA

Cross fixation

13



BerkovichOhana et al. 2016

fMRI

Meditation

16

ROIs correlations

Eyes closed

18



Duan et al. 2012

fMRI

Chess

13.6

SBA

Cross fixation

15

Duan et al. 2014

fMRI

Chess

11.8

GTA

Cross fixation

20

Jung et al. 2014

fMRI

Go

12.5

ROIs correlations

Eyes closed

17

fMRI

>6

Eyes closed



27



Between VMPFC and DMPFC



with bilateral caudate nuclei

Video games

Gong et al. 2015

Luo et al. 2012

ROIs Not correlations specified

Musical practice fMRI

(Piano, Chinese zither or accordion)

6 to 20

SBA

Eyes closed

16

Musical practice

Music Expertise

Fauvel et al. 2014

Luo et al. 2014

fMRI

(violin, cello, guitar, flute, recorder, trumpet, clarinet, or piano)

16.1

SBA

Eyes closed

16

6 to 20

ROIs correlations

Eyes closed

28

Musical practice fMRI

EEG

(Piano, Chinese zither or accordion)

GTA

15



with the orbitofrontal gyrus

Musical practice

Klein et al. 2015

(string)

10 831 (in hours)

Eyes open

Musical practice Tanaka et al. 2016

fMRI

(piano, violin, cello, contrabass, clarinet, or trumpet)

18

SBA

Eyes closed

26

ALFF = amplitude of low frequency fluctuation, SBA = seed-based analysis, ICA = independent component analysis, GTA = graph theory analysis, ROIs = regions of interest, MPC = mean phase coherence, PSD = power spectral distribution, DMPFC = Dorsal medial prefrontal cortex, VMPFC = Ventral medial prefrontal cortex, IPL = inferior parietal lobule

2

Discussion

In this review, we highlighted that motor, cognitive and musical expertise has various effects on rs-FC. In this paragraph we discuss the relationship between these changes and behavioral functions as well as specificity to the trained activity and the methodological considerations regarding the comparability across studies. 2.1

Relationship Between rs-FC Changes and Behavioral Functions

Motor expertise such as dance, badminton, gymnastic or athletics is associated with rs-FC changes within regions known to be involved in motor task achievement including sensorimotor, cerebellar and fronto-parietal areas. These results are probably due to their regular and intensive recruitment and may underlie consolidation of new motor abilities, allowing trained people to acquire long-term skills. This statement can be supported by the behavioral transfer effects reported from motor learning to motor skills, including better visual anticipatory abilities required in many sports (Moore and Mueller, 2014). Furthermore, rs-FC changes were reported between the FPN and cognitive regions in athletes. Since it has been demonstrated that physical activity improves cognitive functions (see Ratey et al., 2011 for a review), the rs-FC changes in cognitive areas generated by motor expertise may reflect better cognitive skills. Cognitive expertise is accompanied by numerous rs-FC modifications especially within brain regions involved in attentional abilities such as the dorsal attentional network, the anterior insula and the DMN (whose deactivation is linked to better ability to focus on cognitive tasks), suggesting a crucial role for attention in individuals with expertise involving cognitive functions. Moreover, repeated training in a specific cognitive domain was shown to produce improved performance in training-related tasks (near-transfer effects) but also in untrained tasks (far-transfer effects) with improved general cognitive abilities. Specifically, meditation training is known to enhance attention (Tang et al., 2013) and executive functions (Zeidan et al., 2010), chess skills are significantly correlated with fluid reasoning, comprehension-knowledge, short-

term memory and processing speed (Burgoyne et al., 2016) and video game players outperform non-video game players in visuospatial attention (Green et al., 2006), cognitive control (Strobach et al., 2012; Glass et al., 2013) and processing speed (Dye et al., 2009). Interestingly, Moreno and Bidelman (2014) proposed a top-down control hypothesis suggesting attention processing as a mediator of transfer effects between cognitive functions. Therefore, we speculate that the rs-FC changes often reported in brain regions related to attentional abilities reflect transfer effects to general cognitive abilities. Finally, musical practice seems to produce functional changes at rest within various networks including motor, cognitive and emotional areas, which is not surprising given the requirement of numerous behavioral functions during this activity. This is consistent with behavioral results that showed numerous beneficial effects of musical practice on other abilities such as better spatial abilities (Jakobson et al., 2008), memory (Tierney et al., 2008), attention (Besson et al., 2011) and language skills (Forgeard et al., 2008). Interestingly, musical training seems to modify rs-FC within brain areas related to emotions such as the orbitofrontal gyrus and the insula, more than other activities. Neuroimaging studies have found these regions to be involved in musically-induced emotions (Blood and Zatorre, 2001; Koelsch et al., 2006; Trost et al., 2012). Moreover, musical expertise was shown to modulate functional connectivity of limbic regions constantly associated with affective processing of music (the amygdala, hippocampus and nucleus accumbens) when listening to musical pieces (Alluri et al., 2015). All of these findings suggest that musical expertise involves and modifies emotional regions, which appears to leave traces at rest. Nevertheless, these rs-FC changes have also been reported with other activities such as meditation and chess, and cannot be considered as a resting-state neural signature of musical expertise.

Taken together, these findings suggest that there is currently no clear pattern of results that would single out a general neural signature of expertise at rest. To better interpret results of resting-state changes due to training, it appears important to have precise and relevant hypotheses related to previous task-related analyses.

2.2

Methodological Considerations

It is likely that the observed varying pattern of findings is due to different settings applied for “resting-state” when performing fMRI and EEG experiments and to the variability of methods employed to analyze data. In fMRI studies, some instructions are usually given to participants before the imaging session. Although simple, their content differs across studies and may bias the comparison between results. For example, participants are instructed to close their eyes, keep them open or to fixate their gaze on a cross, which has been shown to affect rs-FC (Patriat et al., 2013). Indeed, the authors examined the test-retest reliability of rs-FC in the three conditions and revealed that connectivity was more reliable within default, attention, and auditory networks when the subjects were lying with their eyes fixated on a cross, whereas the primary visual network had most reliable connectivity when subjects kept their eyes open during the scan. Using EEG, it was shown that the eye-opening condition also affect neurophysiological recordings (Barry et al., 2007) and the eye closed condition demonstrated a higher inter-session stability (Corsi-Cabrera et al., 2007). Furthermore, this technique is very sensitive to eye movements such as eyes blinks in the eyes open condition and rolling of the eyes in the eye closed condition. Nevertheless, rolling the eyes is associated with drowsiness which affect alertness and wakefulness and is thus not recommended in a resting-state condition. This emphasizes the importance of giving clear and precise instructions and of controlling the vigilance state using a screen to eventually discard the portion influenced by an altered state of wakefulness from the analyses.

Moreover, it has been shown that different instructions concerning the noisy environment of the scanner alter rs-FC, especially with higher rs-FC within the dorsal medial prefrontal cortex (dmPFC) and between this region and the posterior cingulate cortex (PCC) when subjects were instructed to attend or ignore the background noise compared to when they just had to relax (Benjamin et al., 2010). Finally, with some exceptions, participants are asked not to think of anything in particular in order to reflect a “real” resting-state, although experimenters did not control for the participants thoughts about the scanner. It may provide an explanation for the rs-FC differences observed between controls and experts who may have mental images of their training, such as imagining playing a piece of music. Mental imagery and real-task achievement share brain activations as highlighted by Meister et al. (2004), who found that bilateral FPN were activated in both music performance and music imagery. Since the precise instructions affect rs-FC, it appears important to give consistent instructions across the studies to be able to compare the results.

Another important issue to consider is the different methods used to analyze rs-FC data. We first noticed three different methods in fMRI studies reported in this review: seed-based, ICA and graph-theory methods. Seed-based analysis are hypothesis driven since they highlight the brain regions that are most strongly functionally connected with the region of interest. The main advantage is that this provides a direct answer to a specific question. This was demonstrated by meditation studies that restricted their analyses to the DMN, allowing the exploration of its’ specific modifications due to the training in a resting-state. This method facilitates the interpretation of results and might explain the frequent use of this approach in studies reported in our review. Moreover, test-retest reliability has been assessed and revealed that connectivity within RSN can be identified by the seed-based approach with moderate to high reliability (Shehzad et al., 2009). However, this method requires the selection of a specific region of

interest based on brain atlases or previous structural and functional analyses. The same region is used for all subjects. Since individual differences in functional connectivity have been shown (Mueller et al., 2013), this approach ignores the between-subject variability. The pre-definition of a seed region is also likely to influence rs-FC, as even small differences in the position of the seed can result in significant variation in connectivity patterns (Cole et al., 2010). Therefore, consensus mapping is required to compare results found with the seed-based method. Finally, another limitation is presented by the resulting correlation map where connectivity is restrained to regions linked to the seed region and does not provide information on resting-state network reorganization at the whole brain level. Explorative approaches such as ICA are complementary to hypothesis-driven methods since they enable the identification of spatially distinct RSN without predefining a region of interest. Indeed, ICA separates the BOLD signal into non-overlapping spatial and temporal components and takes into account relationships between multiple data points. ICA has been shown to extract DMN and many other networks with high reliability (Beckmann et al., 2005) and results between different subjects were found to be highly consistent (Van den Heuvel and Hulshoff Pol, 2010). ICA also has the advantage of improved signal noise and physiological artifact reduction compared to the seed-based method. This is due to the ability of ICA to account for structured noise effects within additional components that are not RSN (Birn et al., 2008). However, the main issue concerns the selection of components, since one has to decide how many components to estimate and the most relevant number to use remains unclear. Indeed, extracting multiple-component data can result in the spatial and temporal segregation of RSN into multiple sub-networks (Smith et al., 2009), whereas choosing a few components can lead to a failure to reveal the networks of interest.

Despite the differences between the seed-based and ICA methods, these two approaches have been shown to lead to significantly similar results in a group of healthy subjects (Rosazza et al., 2012). Finally, with graph theory analysis, several studies demonstrated that the brain exhibits smallworld topology, which is characterized by a short path length where signals can reach other regions faster and reflect the level of global integration and efficiency in RSN (Bassett and Bullmore, 2006). Thus, it allows the network to support higher-level cognitive functions requiring the integration of information from specialized and divergent brain regions (Uehara et al., 2014). Therefore, graph theory analysis might be of particular interest for exploring the neural mechanisms underlying cognitive training (Taya et al., 2015). For example, Van den Heuvel et al. (2009) highlighted a negative correlation between the normalized characteristic path length of RSN and intelligent quotient, suggesting that intellectual performance is related to how efficiently the brain integrates information between regions. However, it is worth mentioning that measures of theoretical graph connectivity showed moderate test-retest reliability, since some factors affect rs-FC results, such as the choice of preprocessing steps (Andellini et al., 2015). When rest is studied unimodally with fMRI, the interpretation of RSN functional significance remains difficult, especially due to the indirect measure of neural activity and to the low temporal resolution of this technique hardly compatible with momentary mental thoughts. Therefore, EEG can be used as a complementary method to clarify the neurophysiological basis of RSN. In this context, the question whether and how low BOLD fluctuations (< 0.1 Hz) can be related to higher neuronal fluctuations (1-80 Hz) observed with EEG arises. Interestingly, simultaneous EEG-fMRI studies addressed this issue by correlating the time course of EEG frequency power with the BOLD signal (Laufs et al., 2003; Moosmann et al., 2003; Mantini et al., 2007). They reported that spontaneous oscillations in alpha and beta bands are related to

RSN sharing many commonalities with default mode and attentional networks. Still, little is known about the EEG correlates of RSN and it has been proposed that the neural activity at a specific frequency band is unlikely to constitute the electrophysiological correlate of one specific RSN (Mantini et al., 2007). Over the last decade, microstates of the EEG signal have been assessed to identify EEG correlates of spontaneous BOLD activity. Instead of measuring activity specific to the different frequency bands, microstates reflect the summation of concomitant neuronal activity across brain regions (Yuan et al., 2012). It was shown that microstate-associated networks correspond to previously described RSN such as the visual, sensorimotor, auditory and attentional networks as well as the DMN (Musso et al., 2010; Yuan et al., 2012). In this context of association between RSN and electrophysiological signal, the EEG studies on meditation reported in this review related the reduced gamma activity in expert meditators to DMN lower connectivity at rest (Berkovich-Ohana et al., 2012; Berkovich-Ohana et al., 2013). It is consistent with the positive correlation between resting-state gamma power and DMN BOLD signal highlighted by Mantini and collaborators (2007). We noticed that the authors used different methods to measure rs-FC including mean phase coherence and spectral power distribution. Indeed, it is important to mention that many different connectivity measures are applied in EEG studies which may influence rs-FC results. To choose the appropriate EEG connectivity measures, different issues have to be accounted, especially considering the linear or non-linear relations, using amplitude or phase-based measures, analyzing data in the time-domain or frequency-domain and acquiring directed or undirected information (see Bastos and Schoffelen 2016 for a review). However, the current literature is too diverse to conclude on the best method to measure EEG rs-FC and to our knowledge, no research is conducted to directly compare them. Therefore, we suggest that different methodological approaches should be compared in further resting-state EEG studies.

Conclusion In this review, we reported rs-FC changes in the motor, cognitive and musical domains due to expertise. We observed that all domains of expertise lead to various modifications of RSN and resting-state neurophysiological activity, regardless of the activity type. We thus failed to provide strong evidence concerning specificity, which suggests that expertise might produce a more general reorganization of large-scale networks than specific changes in certain RSN and frequency bands, depending on the trained-domain. This finding supports the idea that the brain should be considered a whole network in which many sub-networks interact with each other. Thus, it appears important to better understand which networks are involved in processing domain-related stimuli to improve interpretation of resting-state fMRI and EEG results. However, the inconsistency of findings might also be explained by different settings of restingstate and methods of analysis. All of these methodological considerations emphasize the need to conduct further studies examining the reliability of rs-FC results across different conditions and methods of analysis, to facilitate comparison between resting-state studies.

Acknowledgements: This work was supported by a FEDER (Fonds Européen de Développement Régional) grant of Basse-Normandie (n°2889/33527).

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