Bimanual Coordination

This article was originally published in Brain Mapping: An Encyclopedic Reference, published by Elsevier, and the attached copy is provided by Elsevier for the author's benefit and for the benefit of the author's institution, for non-commercial research and educational use including without limitation use in instruction at your institution, sending it to specific colleagues who you know, and providing a copy to your institution’s administrator.

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Bimanual Coordination SP Swinnen and J Gooijers, KU Leuven, Leuven, Belgium ã 2015 Elsevier Inc. All rights reserved.

Glossary Catcher Bimanual coordination represents a unique example of collaboration between two interconnected yet functionally specialized hemispheres to accomplish goal-directed behavior by means of integration of the left and right limb movements into a functional control entity. Coordination effort The additional neural resources recruited for moving both limbs together as compared to the averaged neural activation associated with performing each limb movement separately. Corpus callosum (CC) The largest white matter structure in the brain connecting the left and right cerebral hemispheres to enable interhemispheric communication. Diffusion tensor imaging (DTI) A noninvasive magnetic resonance imaging technique providing information about microstructural properties of white matter bundles by measuring the directionality of water diffusion. Fractional anisotropy (FA) A scalar value representing microstructural organization of white matter ranging from 0 to 1, whereby 0 represents maximal isotropic diffusion of water molecules (i.e., equal diffusion across all directions) and 1 implies maximal anisotropic diffusion (i.e., diffusion

Introduction Many everyday movements require some degree of collaboration between the hands. It is even suggested that bimanual coordination is the default mode of the control system, such that unimanual actions require suppression of the contralateral limb. Primate evolution towards upright standing has freed the hands to engage into a highly sophisticated control scheme for manipulative activities such as tool use, preparing and eating food, and making gestures. At work, many manual handling jobs consist of actions requiring a strong synchrony between both hands, such as lifting and carrying heavy objects. In canoeing, intraperformer and interperformer bimanual synchronicity is critical for propulsion. Yet, other tasks require a more differentiated role or division of labor between hands in which one performs the focal action and the other serves as stabilizer/ background, such as opening a bottle and holding the tray while lifting a glass. From an artistic perspective, we are often astonished by the bewildering complexity of the bimanual actions that musical performers, such as keyboard players, exhibit. Not surprisingly, massive practice has shaped the brains of these experts to orchestrate their musical creations with the right order, tempo, and force via ingenious multifinger coordination. Understanding the control and learning of such complex skills is a critical objective for cognitive neuroscience. The aforementioned examples suggest that bimanual movement control has a high degree of modularity and flexibility.

Brain Mapping: An Encyclopedic Reference

occurs along the major axis of a white matter bundle and is restricted along all other directions). Functional connectivity (FC) A technique based on functional imaging data to identify network properties based on correlations between anatomically distinct time series, during the execution of a task (task-related functional connectivity) or during rest (resting-state functional connectivity). Functional magnetic resonance imaging (fMRI) A technique measuring brain activation by detecting small differences between oxygen-poor blood flow and oxygenrich blood flow, affecting magnetic susceptibility. Motor binding Using conceptual or visual transformation procedures to bind the separate movements of each limb together into an integrated movement plan or ‘gestalt’. Neural cross talk A special case of structural interference that refers to irradiation of neural activity from one brain locus or control site to another as a result of the highly interconnected cerebral cortex. Relative phase A measure of coordination between limbs during cyclic movement, obtained via subtraction of the phase angle of each limb at continuous or discrete time points.

Goal invariance is preserved by flexible covariation of the individual limb movements to secure successful performance (White & Diedrichsen, 2010; Wiesendanger & Serrien, 2001). During the past years, interest in bimanual movement has increased substantially for several reasons. First, complex bimanual skills form an entry point for the study of higher cognitive functions in perception and action, even including executive functions such as task switching, multitasking, and inhibition. Second, bimanual tasks are helpful tools to reveal motor developmental trajectories and deficits as a result of various brain disorders (Swinnen, 2002). Third, such tasks are valuable instruments to study associations between brain structure and behavior with a prominent role assigned to the corpus callosum (CC) (Gooijers & Swinnen, 2014). Principal theoretical frameworks that have dominated bimanual coordination research during the past decades are the information-processing and the dynamic-pattern perspective. Within the information-processing perspective, bimanual movement is essentially considered a special case of dual-task performance that is faced with interference between tasks performed simultaneously as a result of limitations in (neural) resources. Here, the focus has been on studying the limitations in bimanual performance when the subtasks assigned to each limb differ with respect to one or more parameters, such as timing, amplitude, force, and direction (Cattaert, Semjen, & Summers, 1999; Heuer, Kleinsorge, Spijkers, & Steglich, 2001; Marteniuk, MacKenzie, & Baba, 1984; Sherwood, 1994;

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INTRODUCTION TO SYSTEMS | Bimanual Coordination

Spijkers & Heuer, 1995; Swinnen, Young, Walter, & Serrien, 1991). The concept of neural cross talk is central here and refers to (mutual) interference between movements of each limb that can be overcome via practice and/or by integration of the subtasks into a meaningful global control structure (Swinnen, Walter, Lee, & Serrien, 1993). Learning to drive a car is an interesting example in this respect. Initially, the different movements of each limb are difficult to perform simultaneously and require maximal concentration, but practice allows the driver to coordinate these actions successfully and smoothly, while attention demands decrease. Within the dynamic-pattern perspective, biological systems are formally described in terms of their time-dependent changes. Such systems are composed of many subcomponents that organize themselves into coherent global patterns as a result of local interactions, such as the swirling behavior of a school of fish or the complex visual configurations formed by thousands of birds in the air. Accordingly, order is not prescribed by a hierarchical command structure, but emerges in a self-organized fashion as a consequence of cooperation among the subcomponents (Bressler & Kelso, 2001; Kelso, 1995; Scho¨ner & Kelso, 1988). Similarly, motor coordination emerges as orderly behavior in a system with many degrees of freedom at multiple levels (from neural to musculoskeletal). Here, the search for identifying the macroscopic features of emergent behavior in these complex systems and changes in dynamic states across time is prominent. Even though both aforementioned frameworks differ substantially, they share the search for the principal constraints that affect motor performance in general and bimanual performance in particular. Here, we will discuss the principles underlying bimanual control in which the movements of each limb can be combined in various ways to accomplish goal-directed behavior. First, we propose a general framework for the categorization of coordination constraints, leading to the identification of preferred and nonpreferred movements. Then, we will address the overruling of coordination constraints through cognitive intervention and/or training-induced plasticity. Finally, we will elaborate on recent progress in establishing the dynamic neural network underlying bimanual coordination and associations between brain structure and behavior.

Limitations in Bimanual Coordination: A Coalition of Constraints When moving more than one limb together, constraints or basic limitations in bimanual coordination emerge that cannot be inferred from the laws of unilateral movement. This has resulted in the identification of default coordination modes (preferred movements) as emergent properties of the system’s intrinsic dynamics on the one hand and those modes that defy principal coordination constraints (nonpreferred movements) on the other hand. For example, it is easy to clap your hands, but it requires extensive practice to move both hands across the keyboard during musical performance because we surpass the default coordination modes. Constraints are informative about neuromusculoskeletal limitations in dealing with complex (multiple) task organization and simultaneous generation of

motor command streams to manage a multiple degree-offreedom control system. Various coordination constraints have been identified, ranging from high-level cognitive or perceptual to low-level neuromuscular (Swinnen & Wenderoth, 2004), ultimately reflecting the basic operational properties of the central nervous system (CNS) in perception–action interplays. Depending on task context, constraints operate across different levels of the control system. For example, patterns of in-phase and antiphase coordination are abundant in invertebrate and vertebrate species and reflect the basic operation of a central pattern generator that refers to an autonomous low-level network for the generation of rhythmic motor output in the limbs. It has been studied primarily in the context of locomotion in various species (Dietz, Fouad, & Bastiaanse, 2001). With respect to bimanual coordination, the neuromuscular constraint referring to the relative timing of homologous muscle activation has been studied most intensively. When moving the fingers, wrists, or forearms towards and away from the body midline, requiring the simultaneous activation of homologous muscle groups (in-phase, Ф ¼ 0 ), a more stable pattern of coordination emerges then when homologous muscle groups are activated in alternation (antiphase, Ф ¼ 180 ). The differential stability between both coordination modes becomes more prominent at higher movement speeds (Kelso, 1984). These coordination modes are generic as they are evident across different movement effectors. When registering and analyzing all types of bimanual actions that are typically produced during normal daily activities, in-phase and antiphase coordination modes are observed more frequently than any other types of phase relation, suggesting that these indeed constitute the preferred coordination repertoire (Howard, Ingram, Ko¨rding, & Wolpert, 2009). Other patterns may be less stable unless the performer has sufficiently practiced these nonpreferred coordination patterns. Constraints can also be identified in reference to the specification of different temporal or spatial movement parameters that are encoded by the CNS. The general preference to move our limbs within a common temporal framework reflects a basic tendency for neural synchronization according to a 1:1 frequency mode. During finger tapping, simple rhythms (e.g., 1:1 and 2:1), in which the frequency of one limb motion is an integer multiple of the other, are produced more successfully than polyrhythms (e.g., 3:2 and 5:3), in which noninteger combinations are realized (Summers, Rosenbaum, Burns, & Ford, 1993). When stressing the control system by increases in cycling frequency during polyrhythm production, loss of the pattern and a regress to simpler ratios can be observed in the unskilled performer (e.g., from 5:3 to 2:1) (Peper, Beek, & Van Wieringen, 1995). Within iso- or multifrequency patterns (Figure 1), different degrees of coordinative complexity can be further discerned, depending on the relative phasing between the limbs. More specifically, patterns that deviate from the common in-phase (f ¼ 0 ) and antiphase modes (f ¼ 180 ) are associated with lower degrees of stability (e.g., f ¼ 90 or 135 ) (Lee, Swinnen, & Verschueren, 1995; Zanone & Kelso, 1992). This does reflect not only motor but also perceptual constraints in view of a natural preference to perceive and produce simple temporal intervals, indicative of limitations in representing complex


Author's personal copy INTRODUCTION TO SYSTEMS | Bimanual Coordination

Neuromuscular

Bimanual coordination constraints

Relative timing of homologous muscles

f = 0⬚ in-phase

f = 180⬚ anti-phase

f = 90⬚ out-of-phase

Sensorimotor Temporal constraints Simple rhythms e.g., (isofrequency: 1:1), (multifrequency: 1:2)

Spatial constraints Amplitude

Direction

Polyrhythms e.g., 3:2, 5:3 Figure 1 Overview of bimanual coordination constraints.

temporal relationships (Semjen & Ivry, 2001). After all, this is a sensorimotor medium characterized by multimodal sensory integration. Whereas temporal constraints have been studied intensively, spatial constraints referring to the encoding of amplitude (Heuer et al., 2001; Marteniuk et al., 1984; Sherwood, 1994; Spijkers & Heuer, 1995) and/or direction (Eliassen, Baynes, & Gazzaniga, 2000; Franz, Eliassen, Ivry, & Gazzaniga, 1996; Lee, Almeida, & Chua, 2002; Swinnen, Dounskaia, Levin, & Duysens, 2001; Swinnen, Jardin, Verschueren, et al., 1998) also compromise complex bimanual movement. The default mode appears to be the production of bimanual movements with the same amplitudes and isotropic directional specifications. Accordingly, when performing short- and long-amplitude movements simultaneously with both arms/hands (Figure 1), assimilation effects occur whereby the amplitudes grow towards each other (Marteniuk et al., 1984; Sherwood, 1994). Similar evidence has been generated during the simultaneous production of movements in different directions where a tendency towards isodirectionality can be observed (i.e., the isodirectionality constraint) (Figure 1) (Franz et al., 1996; Swinnen, Jardin, Verschueren, et al., 1998; Swinnen et al., 2001). This can easily be experienced when drawing lines with different directions simultaneously or when combining line with circle drawing on a horizontal surface (Figure 1). In the unskilled performer, interference typically emerges as a failure to draw different directions simultaneously, and this is associated with increased activation in a parietofrontal pathway (Garbarini, D’Agata, Piedimonte, et al., 2013; Wenderoth, Debaere, Sunaert, van Hecke, & Swinnen, 2004). That such directional interference effects also take place when only imagining one of the component actions suggests that the interference emerges not only at the execution but also at the movement planning level (Garbarini et al., 2013). As such, behavioral interference is associated with increased brain activation as a function of degree of directional incompatibility. This increased brain activation represents the coordination effort.

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Bimanual coordination can also be mediated by a coalescence of constraints. For example, performing in-phase movements (homologous muscle grouping) in the same direction in extrinsic space (isodirectionality) leads to higher consistency than when only one of these constraints applies. This also prompts questions about the reference frames in which movements are encoded, that is, relative to the midline of the body (an intrinsic or egocentric reference) or by reference to the external world (an extrinsic or allocentric reference) (Swinnen, 2002; Swinnen, Dounskaia, & Duysens, 2002; Swinnen, Jardin, Meulenbroek, Dounskaia, & Den Brandt, 1997). Such reference frames may coexist and task context may determine the relative prevalence of one over the other (Swinnen et al., 2002). In summary, it appears that the default state of bimanual control refers to the specification of congruent or compatible movement parameters to each of both limbs. More importantly, coordination arises from the mutual interplay of constraints at multiple levels of the motor system. Constraints can be more or less compelling, depending on task context and individual characteristics (such as age, pathology, and medication) (Swinnen, 2002). Furthermore, constraints arise at different levels of the motor system: whereas some are closely associated with the neuromuscular output system, others refer more to the abstract neural codes during movement planning.

Overcoming Coordination Constraints to Enrich the Action Repertoire Even though coordination constraints not only reflect economy in motor control but also burden the performer with limitations, they can be overcome as many everyday tasks and artistic and athletic performances attest. Sometimes, the overruling of constraints is easily accomplished. At other times, it requires considerable practice. A primary goal of motor learning is to overcome the persistent errors induced by basic coordination constraints in order to explore the less preferred coordination workspace. There are various ways to do so, such as the use of conceptualization strategies, instructional means to promote task integration, and binding rules or visual transformation procedures, as will be discussed later. The trick is to ensure that the upper limb motions are not in competition with each other but become part of an integrated action plan. As different as these limb motions can be, they are never performed in total independence of each other in intact brains. When tapping complex polyrhythms with the fingers (e.g., a 3:2 or 5:3 frequency ratio), binding rules can support integration of the subtasks into a common temporal structure in which the taps of each limb are interleaved, such that attention is focused on the ‘gestalt’ rather than on the isolated motions of each limb. Contextual means to alleviate task interference can also refer to how task goals or movement targets are represented. Amplitude interference between limbs can be reduced or even eliminated when direct visual cues (target light illuminates) instead of symbolic cues (e.g., a verbal representation of the required movement amplitudes) are used to trigger bimanual reaching (Diedrichsen, Hazeltine, Kennerley, & Ivry, 2001; Ivry, Diedrichsen, Spencer, Hazeltine, & Semjen, 2004). The direct visual representation of targets appears to drive each limb to the




correct location in space with minimal interference. Alternative cognitive strategies can also be evoked that serve as binding rules to represent the task as a meaningful gestalt (Swinnen & Wenderoth, 2004). Seemingly difficult tasks then become easy when familiar events or symbols are called upon (Franz, Zelaznik, Swinnen, & Walter, 2001). For example, it is generally difficult to produce a 90 out-of-phase finger tapping pattern (that sits in between in-phase and antiphase), but performers may catch the trick when referring to a sound made by a galloping horse. Alternatively, bimanual rotational movements in the horizontal plane with different (even noninteger) frequency ratios become easy when transformed into a simplified visual representation of a 1:1 frequency ratio whereby the actual hand motions are hidden from vision (Mechsner, Kerzel, Knoblich, & Prinz, 2001) or when simply showing the left versus right hand rotation kinematics as a single integrated cursor moving on the computer screen (Sisti, Geurts, Clerckx, et al., 2011). Such binding procedures can also be helpful as instructional aids to support the acquisition of new coordination skills via a simplified visual representation of the task, that is, a single moving stimulus is shown on the screen to represent the movement of both limbs instead of two separate signals for each limb. More specifically, when orthogonally plotting the ongoing displacement signals of each limb to each other (i.e., a Lissajous figure), direct visual information about the resulting interlimb coordination emerges such that difficult coordination patterns suddenly become ‘graspable’ (Kovacs & Shea, 2010, 2011; Lee et al., 1995; Swinnen, Dounskaia, Walter, & Serrien, 1997; Swinnen, Verschueren, Bogaerts, et al., 1998). This source of visual augmented feedback activates the motionsensitive MT/V5 þ region and promotes ‘motor binding.’ It has been used successfully in the context of learning bimanual coordination patterns with a 90 phase offset under 1:1 and 2:1 frequency ratio regimes. Thus, in spite of the fact that coordination constraints may in principle reduce our action repertoire, the human motor system is sufficiently plastic to overrule these constraints. In this respect, abstract binding or visual transformation rules can enslave the sensorimotor networks, consistent with the notion of hierarchical control, ranging from high-level abstract representations to low-level muscle- or effector-specific codes.

Bimanual Coordination: Functional and Structural Brain Correlates Brain Function and Bimanual Coordination It is a long-standing tradition in neuroscience to assign particular functions to distinct brain areas, and this has also been a dominant theme in early bimanual coordination research. Even though the supplementary motor area (SMA) has traditionally been uniquely associated with bimanual movement, functional magnetic resonance imaging (fMRI) studies on action and action observation have not been able to confirm the role of SMA or any other brain area as an exclusive control center for bimanual coordination (Heitger, Mace´, Jastorff, Swinnen, & Orban, 2012; Swinnen, 2002). Instead, it is more fruitful to assign this complex function to a network of brain areas rather than a single locus (Swinnen, 2002; Wiesendanger & Serrien, 2001). In support of this viewpoint, imaging studies

using either bilateral finger (Goerres, Samuel, Jenkins, & Brooks, 1998; Immisch, Waldvogel, Van Gelderen, & Hallett, 2001; Ja¨ncke, Peters, Himmelbach, et al., 2000; Nair, Purcott, Fuchs, Steinberg, & Kelso, 2003; Sadato, Yonekura, Waki, Yamada, & Ishii, 1997; Stephan, Binkofski, Halsband, et al., 1999; Stephan, Binkofski, Posse, Seitz, & Freund, 1999; Toyokura, Muro, Komiya, & Obara, 2002; Ullen, Forssberg, & Ehrsson, 2003) or forearm movements (Toyokura, Muro, Komiya, & Obara, 1999; Tracy, Faro, Mohammed, et al., 2001) have revealed activation of a basic sensorimotor network that does essentially not differ from that observed during performance of unilateral tasks (Swinnen, 2002). Nevertheless, some support has been obtained for increased recruitment of neural resources within this basic motor network (and beyond) as a function of required coordination effort as compared to activations associated with single limb motions, more specifically in a parietofrontal and subcortical network (Toyokura et al., 1999, 2002; Tracy et al., 2001; Wenderoth et al., 2004). Additionally, single-cell recording techniques in nonhuman primates have revealed that a subpool of neurons in motor areas (SMA and primary motor cortex (M1)) are uniquely activated during the production of bimanual movements (Donchin, Gribova, Steinberg, Bergman, & Vaadia, 1998). This argues for some degree of specificity in neural recruitment associated with bimanual movements in the absence of a specialized brain area. The aforementioned brain activation network for bimanual coordination should not be considered as a rigid set of brain areas, but rather as a contextually dependent dynamic scaffold. Depending on internal (expertise level, age, and pathology) and external factors (environmental information and task difficulty and complexity), activations may expand into (pre)frontal, parietooccipital, and temporal areas and the insular cortex. With respect to external factors, task difficulty can be raised by increasing the speed or cycling frequency of motion, whereas task complexity grows from preferred to nonpreferred coordination patterns. Increasing cycling frequency primarily induces modulation of activation in (pre)motor areas (SMA, premotor (PM), and M1) in addition to subcortical structures (Debaere, Wenderoth, Sunaert, Van Hecke, & Swinnen, 2004a; Goble, Coxon, Van Impe, et al., 2010). Conversely, manipulations of task complexity extend activations beyond the basic motor network, including parietotemporal areas when performing nonpreferred coordination modes (Debaere et al., 2004a). For example, comparisons of in-phase and antiphase bimanual patterns have revealed brain activation increases in the SMA, extending into the cingulate motor cortex at lower cycling frequencies (up to 1 Hz), but additional activations show up in the PM, M1, and cerebellum – occasionally also activations in Broca’s area (BA 44/6) as well as the secondary auditory/somatosensory cortices – at higher frequencies (1 Hz) (Immisch et al., 2001; Meyer-Lindenberg, Ziemann, Hajak, Cohen, & Berman, 2002; Stephan, Binkofski, Halsband, et al., 1999; Stephan, Binkofski, Posse, Seitz, & Freund, 1999; Toyokura et al., 1999; Ullen et al., 2003). Similar observations have been made when comparing more complex 3:2 with basic 1:1 (in-phase) coordination patterns (Ullen et al., 2003). The caudal part of Broca’s area (BA 44/6) and the insular cortex also become activated during rhythm perception (Platel, Price, Baron, et al., 1997). The secondary somatosensory cortex



may be involved in integrating sensory information from multiple modalities (Calvert, 2001), such that these areas may play a specific role in monitoring whether the generated movements match with the temporal requirements. Besides interpreting brain activation contrasts between bimanual tasks with differential difficulty/complexity levels, it is also meaningful to identify changes at the network level as a function of task context. In other words, what happens at the global network level when a less stable as compared with stable coordination task is performed at higher cycling frequency? The production of antiphase as compared with in-phase coordination patterns is associated with increased task-related functional connectivity, implying that the slowwave brain activations across the different brain areas entertain a tighter communication mode when task demands increase (Heitger, Goble, Dhollander, et al., 2013). With respect to internal factors, older as compared with younger adults exhibit increased activations in a parietofrontal pathway with a notable increase in prefrontal brain areas during performance of in-phase and antiphase tasks, suggesting age-related penetration of cognition into action (Goble et al., 2010). Relative to a young group, older adults also show increased functional connectivity within the task-related brain network during bimanual performance, which suggests that interactions among brain areas are more in sync with each other to support behavior (Heitger et al., 2013). Shifting to the expert–novice comparison to provide insights into long-term plasticity, professional musicians show lower degrees of neural recruitment during performance of various bimanual coordination tasks than novice performers (Haslinger, Erhard, Altenmu¨ller, et al., 2004; Ja¨ncke, Shah, & Peters, 2000). Moreover, training of bimanual skills in novices gives rise to alterations in brain activation with prominent activation decreases in temperoparietofrontal areas, signifying increased economy associated with skillful behavior (Andres, Mima, Schulman, et al., 1999; Beets, Gooijers, Boisgontier, et al., 2014; Debaere, Wenderoth, Sunaert, Van Hecke, & Swinnen, 2004b; Puttemans, Wenderoth, & Swinnen, 2005; Remy, Wenderoth, Lipkens, & Swinnen, 2008; Ronsse, Puttemans, Coxon, et al., 2011; Serrien & Brown, 2003). Training-induced plasticity does give rise not only to brain activation changes but also to increases in task-related functional network connectivity, pointing to a stronger interaction among brain regions (Heitger, Ronsse, Dhollander, et al., 2012). To summarize, the bimanual coordination network is a highly dynamic entity that changes as a function of various internal and external factors, exemplifying the high degree of adaptability of the brain over short and larger timescales.

Brain Structure and Bimanual Coordination Considering all the white matter tracts that interconnect the cortical and subcortical brain areas, the CC has obtained a privileged status in relation to bimanual coordination. Early studies in split-brain patients as well as those with agenesis of the CC have provided support for the critical role of this interhemispheric pathway in bimanual coordination (Eliassen et al., 2000; Franz et al., 1996; Kennerley, Diedrichsen, Hazeltine, Semjen, & Ivry, 2002). Split-brain patients show deficits in tasks that rely heavily on continuous bimanual

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interaction (Preilowski, 1972). Conversely, when seemingly incompatible movements with different spatial trajectories have to be produced simultaneously, split-brain patients, whose posterior CC has been sectioned, do not exhibit the interference or neural cross talk that is typically observed in controls (Swinnen, Jardin, Verschueren, et al., 1998; Swinnen et al., 2001, 2002). Instead, they generate the spatial trajectories in each limb seemingly independent of each other. This suggests that information about the spatial codes of each limb movement is exchanged between the hemispheres within a normally connected brain (Eliassen et al., 2000; Franz et al., 1996). This is consistent with previously discussed fMRI research in normal participants, showing modulation of brain activation in a parietal–PM pathway as a function of degree of directional (in)compatibility between bimanual movements. More specifically, this activation pattern is distributed across PM and superior parietal and intraparietal areas with right hemisphere prominence (Wenderoth et al., 2004). With the advent of diffusion tensor imaging (DTI) techniques and associated technologies for tracking white matter connections, new and more fine-grained evidence for the role of the CC substructures in bimanual coordination has been provided (Gooijers & Swinnen, 2014). DTI enables a quantification of the microstructural organization of the interhemispheric tracts. Fractional anisotropy (FA), one of the most common metrics of microstructural organization based on water diffusion properties in the brain, shows correlations with performance on bimanual coordination tasks in young and older adults and in pathological conditions, such as traumatic brain injury and multiple sclerosis (Bonzano, Tacchino, Roccatagliata, et al., 2008; Caeyenberghs, Leemans, Coxon, et al., 2011; Gooijers, Caeyenberghs, Sisti, et al., 2013; Gooijers, Leemans, Van Cauter, et al., 2014; Johansen-Berg, Della-Maggiore, Behrens, Smith, & Paus, 2007; Muetzel, Collins, Mueller, et al., 2008; Serbruyns, Gooijers, Caeyenberghs, et al., 2013; Sisti, Geurts, Gooijers, et al., 2012; Sullivan, Adalsteinsson, Hedehus, et al., 2001). A higher value of FA in different subareas of the CC (interconnecting the bilateral sensorimotor, parietal, and occipital areas) is usually associated with better performance on clinical and computerized bimanual tasks, depending on task context. Interestingly, when learning new bimanual coordination modes, the microstructural organization of the most anterior part of the CC (interconnecting the prefrontal areas) is predictive of learning capability, consistent with the important role of prefrontal cortex (e.g., dorsolateral prefrontal cortex) during initial skill learning (Sisti et al., 2012). Not surprisingly, the CC has also been a focus of interest in the study of skilled musicians, such as keyboard players. In view of the multimodal information processing associated with musical expression, higher FA values in different CC substructures (anterior and posterior) have been established in pianists versus controls (Schmithorst & Wilke, 2002; Steele, Bailey, Zatorre, & Penhune, 2013), and even as a function of training intensity and time of musical initiation (Bengtsson, Nagy, Skare, et al., 2005; Imfeld, Oechslin, Meyer, Loenneker, & Jancke, 2009; Steele et al., 2013). Complementary evidence has been provided by midsagittal measures of anterior and posterior CC size or volume (Lee, Chen, & Schlaug, 2003; Ozturk, Tascioglu, Aktekin, Kurtoglu, & Erden, 2002; Schlaug, Jancke, Huang, Staiger, &




Steinmetz, 1995; for a review see Wan & Schlaug, 2010). Even though such correlational approaches do not constitute causal associations between brain structure and behavior, keyboard training across 15 months has been shown to induce structural changes in the CC midbody of young participants over time (Hyde, Lerch, Norton, et al., 2009).

Summary Decades of behavioral research into the principles governing bimanual coordination have culminated into a neuroscientific approach that focuses on brain structure and function in relation to bimanual coordination. This has resulted in the identification of a dynamic motor coordination network of functional activations whose distribution across the brain is dependent on various internal and external factors. Nevertheless, differences in brain activation as a result of external (task difficulty/complexity) and internal (expertise, age, and pathology) factors should be addressed not only at the level of individual activation blobs but also at the network level in terms of functional connectivity changes among brain areas. With respect to brain structure, the early seminal work on split-brain patients has more recently been complemented by noninvasive structural MRI research that allows the characterization of white matter microstructural organization of the CC. This has fueled associations between the status of CC substructures and bimanual coordination performance, amplifying the important role of interhemispheric pathways in bimanual movement control. This brain–behavior association reflects a mutual interaction in which the bihemispheric brain enables (bi)manual coordination and bimanual movement shapes the interhemispheric white matter connections that support the highly sophisticated control we witness during everyday and professional contexts.

Acknowledgment This work was supported by the Interuniversity Attraction Poles Programme initiated by the Belgian Federal Science Policy Office (P7/11), the Research Fund KU Leuven (OT/11/ 071), and the Fonds Wetenschappelijk Onderzoek (FWO) Vlaanderen (G.A114.11, G.0721.12, and G.0708.14).

See also: INTRODUCTION TO ANATOMY AND PHYSIOLOGY: Functional Connectivity; Insular Cortex; Motor Cortex; Somatosensory Cortex; INTRODUCTION TO COGNITIVE NEUROSCIENCE: Music; Neural Correlates of Motor Skill Acquisition and Consolidation; INTRODUCTION TO METHODS AND MODELING: Diffusion Tensor Imaging; Resting-State Functional Connectivity; INTRODUCTION TO SYSTEMS: Motion Perception.

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Bimanual Coordination

Bimanual Coordination

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