Abstract

Grasping with 2 limbs in opposition to one another is older than the hand, yet the neural mechanisms for bimanual grasps remain unclear. Similar to unimanual grasping, bimanual grasping may require regions in the parietal cortex that use visual object-feature information to find matching stable grasp points on the object. The localization of matching points is computationally expensive, so it might make sense for the signals to converge in a single cortical area. To examine this, we use transcranial magnetic stimulation (TMS) to probe the contribution of cortical areas known to be associated with unimanual grasping, while participants performed bimanual grasps. We applied TMS to the anterior and caudal portion of the intra-parietal sulcus (aIPS and cIPS) in each hemisphere during a size-perturbation task using the index fingers of both hands to grasp an object whose orientation might or might not change. We found significant interaction effects between TMS and perturbation of the grasp-relevant object dimension that increased grip aperture only for the right aIPS. These results indicate that the aIPS is involved not only in unimanual, but also bimanual grasping, and the right aIPS is critically involved in bimanual grasps. This suggests that information from both hemispheres converges in the right hemisphere to achieve bimanual grasps.

Introduction

Grasping an object requires that forces be applied in opposition. In many cases, these oppositional forces may arise from the digits of one hand; for example, when we use the thumb in opposition to the index finger. However, we are also capable of picking up an object, either by using the palms or the digits of 2 hands in opposition. Such bimanual grasping is actually more ubiquitous in the animal kingdom than the hand (Whishaw and Coles 1996), but its neural correlates remain poorly understood. Nevertheless, it poses some important questions about how the brain extracts visual object information to guide and coordinate the control of 2 hands (Rochat 1989; Siddiqui 1995; Tresilian and Stelmach 1997; Smeets and Brenner 2001) compared to one (Jeannerod 1981; Blake 1992; Smeets and Brenner 1999; Castiello 2005; Grafton 2010).

For both unimanual and bimanual grasps, vision needs to guide the hand or hands to the specific sets of grasp points on opposite sides of the object to achieve stable grasps, especially when the minimum number of contact points is used (Lederman and Wing 2003), and of course, unimanual and bimanual grasps are governed by the same external mechanical forces (Tresilian and Stelmach 1997). Further, similar visual computations are required because stable grasp points can only be found “holistically,” in sets (e.g., Fig. 1A), so that the search for grasp points requires matching numerous points against many other possible points (Blake et al. 1993; Vahrenkamp et al. 2011). Given that this is computationally expensive (Blake 1992; Vahrenkamp et al. 2011), it makes sense for the visual signals required in such a search to converge in a single area or a small network of highly interconnected areas.

Figure 1.

(A) Algorithms for grasp point selection (regardless of the effectors) need to search the surface of an object for any combination of 2 points to identify matching pairs that together constitute a stable grasp. Points 1 and 3 (or 2 and 4, etc., but not 1 and 3 or 2 and 4) together form a stable grasp because they lie inside the “friction cone” (defined by local surface orientation and friction along with properties of the fingers; Blake et al. 1993) of the respective other point. (B) Experiment set-up. (C) Timeline of a single trial. (D) Location of stimulation sites aIPS and cIPS, as displayed in the left and right hemispheres of one subject (J.D.).

Figure 1.

(A) Algorithms for grasp point selection (regardless of the effectors) need to search the surface of an object for any combination of 2 points to identify matching pairs that together constitute a stable grasp. Points 1 and 3 (or 2 and 4, etc., but not 1 and 3 or 2 and 4) together form a stable grasp because they lie inside the “friction cone” (defined by local surface orientation and friction along with properties of the fingers; Blake et al. 1993) of the respective other point. (B) Experiment set-up. (C) Timeline of a single trial. (D) Location of stimulation sites aIPS and cIPS, as displayed in the left and right hemispheres of one subject (J.D.).

The network that controls unimanual grasps involves areas in both inferior frontal and intraparietal cortex (for reviews see Castiello and Begliomini 2008; Grafton 2010; Davare et al. 2011). Within that network, the anterior intraparietal sulcus (aIPS) has been implicated in visually guided grasping (Binkofski et al. 1998; Culham et al. 2003; Frey et al. 2005; Tunik et al. 2005, 2008; Culham and Valyear 2006; Davare et al. 2007; Castiello and Begliomini 2008; Cohen et al. 2009; Cavina-Pratesi et al. 2010; Davare et al. 2010; Grafton 2010; Koch et al. 2010). It is assumed to constitute the initial step of visual analysis of grasp computations (Castiello and Begliomini 2008; Rizzolatti and Luppino 2001) and appears to integrate visual information about the grasp-relevant dimension of an object as well as other aspects, such as object size (Monaco et al. 2013). Disrupting the aIPS with transcranial magnetic stimulation (TMS) primarily impairs unimanual grasp movements in a contralateral manner: TMS of the left aIPS impairs grip formation with the right hand and vice versa (Rice et al. 2007; note, though, that Davare et al. 2007 found a left brain dominance for grip force control).

Despite considerable evidence for a critical role of the aIPS in the control of unimanual grasping, it remains to be shown whether bimanual grasp movements also share similar neural correlates. In agreement with this possibility, some parietal functions appear to be effector independent with regard to different skeletomotor subsystems (Arbib 1981; Hoff and Arbib 1993; Tresilian and Stelmach 1997; Park and Shea 2002; Vangheluwe et al. 2006; Heed et al. 2011) perhaps to use neural resources efficiently (see Walsh et al. 2008). This suggests that the aIPS should be involved in unimanual grasping as well as in bimanual grasping. Furthermore, it might be computationally more efficient if the aIPS in one hemisphere was dominant to avoid costly 2-way information transfer across the corpus callosum (Braun 1992).

Consistent with such hemispheric dominance, we recently found that people are faster and more accurate at grasping objects bimanually in the left visual field compared to grasping in the right visual field, which suggests a right-hemispheric dominance for bimanual grasps (Le and Niemeier 2013). This is surprising given that unimanual grasps are assumed to be left-hemisphere dominant (e.g. Gonzalez et al. 2006; Davare et al. 2007; Gonzalez and Goodale 2009; Martin et al. 2011). However, behavioral visual-field effects provide only indirect evidence for hemisphere dominance and cannot specify the brain areas that are involved in bimanual grasps. Moreover, grasp movements to objects in the visual periphery might involve different mechanisms than actions in foveal vision (Prado et al. 2005; Vesia et al. 2010; Vesia and Crawford 2012).

So then, it is also possible that brain areas for bimanual grasps are different from those for unimanual grasping. For example, as for the control of gaze and skeletomotor systems, the human parietal cortex shows effector specificity (Vesia et al. 2010; Heed et al. 2011; Vesia and Crawford 2012). In fact, our finding of a right-hemispheric dominance for bimanual grasping suggests that there are anatomical differences, and the reason could be that on the motor side, bimanual grasping is very different from unimanual grasping. For example, it lacks the hand as a fulcrum of the movement, putting greater demand on interlimb coordination (Swinnen 1994) together to greater demands on interhemispheric coordination compared to coordinating thumb and index finger controlled by the same hemisphere (e.g. Dohle et al. 2000).

The objective of the present study was to test the hypotheses that (1) bimanual grasping involves the aIPS (just like unimanual grasping), but (2) that the aIPS in the right hemisphere is more specialized for the bimanual grasp (based on our visual-field work). To test this, we applied TMS to the aIPS and, as control site, the caudal intraparietal sulcus (cIPS) in either the left or right hemisphere during a bimanual grasping size-perturbation task that included online adjustments to the required grip aperture, similar to paradigms that have been used elsewhere with unimanual grasping (Paulignan et al. 1991; Glover et al. 2005; Tunik et al. 2005; Rice et al. 2006; Hesse and Franz 2009). We show that only TMS of the right aIPS, during online size perturbations along the grasp-relevant dimension of the object, resulted in disrupted grasp movements around the time of maximum grip aperture. This suggests that bimanual grasping shares common neural mechanisms with unimanual grasping, but also shows differences in hemispheric specialization.

Materials and Methods

Participants

Eleven healthy individuals participated in the experiment (3 females, mean age of 24 years). All participants had normal or corrected-to-normal vision, were right-handed, and had no known risk factors for TMS (Keel et al. 2001). After completing the experiment, all participants were required to complete a side effects questionnaire (see Machii et al. 2006). No side effects attributable to TMS were reported by any of the participants. All procedures were approved by the York University Human Participants Review Subcommittee and conformed to the ethical standards laid down in the Declaration of Helsinki.

Procedure and Apparatus

Participants performed a size-perturbation grasping task (Tunik et al. 2005; see also Glover et al. 2005; Paulignan et al. 1991) using the index and middle fingers of both hands to pick up an object at its left and right sides. To determine the causal relationship between disruption of cortical activity within the aIPS and a bimanual reach-to-grasp movement, we used magnetic resonance imaging-guided TMS (Magstim Rapid 2; see next section) to disrupt the putative processes in either hemisphere and measured kinematic data of both hands by recording from infrared light-emitting diodes taped to the tips of the 2 index fingers (Optotrak 3020, Waterloo, Canada; sampling rate 250 Hz, accuracy 0.2 mm).

During the experiment, the participants had their head stabilized by a bite bar. Participants wore ear plugs to reduce TMS noise, though auditory cues remained perceivable. Participants' eyes fixated a black fixation dot on a white rectangular block (43 g; front side: 87by 50 mm; thickness: 24 mm) that was mounted on the shaft of a motor (SureStep STP-MTR-17048, Atlanta, GA, USA) situated 55 cm in front of the participants and 30 cm below the eye level with the object's front side tilted backward, orthogonal to the line of sight (Fig. 1B).

Trials began with a low-pitched beep that instructed participants to depress 2 buttons on a box 14 cm in front of the body midline with their index fingers (40 cm below the eye level; distance between buttons: 2 cm), triggering the motor to rotate the object to its horizontal start orientation, that is, with the front narrow edge parallel to the ground. Two thousand and two hundred milliseconds later, a high-pitched beep sounded and cued participants to initiate movement (Go signal). Release of the start buttons (movement onset) triggered both the TMS (see below) and motor (Fig. 1C). In the Perturbation condition, the motor quickly rotated the object by ±90° or ±270° around the line of sight. In the No-perturbation condition, the object rotated by ±180°. This way, in both conditions, the object rotated 180° on average; however, in the Perturbation condition, the object's horizontal width increased, whereas in the No-perturbation condition its width remained the same. Consequently, as the fingers approached the object, participants either had to adjust or not to adjust their grasps. We adopted this specific size-perturbation task from small-to-large object width along the grasp-relevant object dimension, because previous research (e.g., Glover et al. 2005; Tunik et al. 2005) has found that small-to-large perturbations plus TMS are more effective in disrupting unimanual grasps and usually enlarge the grip aperture, which is consistent with reports that show maximum grip aperture (MGA) increases when grasping becomes more difficult (Schlicht and Schrater 2007; although note that Glover et al. 2005 also found this effect for large-to-small perturbations, but for a later TMS timing).

TMS and Localization of Brain Sites

To localize our stimulation sites and monitor the TMS coil position, we used frameless stereotaxic neuronavigation (BrainSight, Rogue Research, Montréal, Canada). Before testing in the behavioral sessions, 3-dimensional (3D) structural T1-weighted MRIs were obtained for each participant to identify the parietal stimulation sites (aIPS and cIPS in both hemispheres) and the left and right motor hand areas. We defined stimulation sites according to anatomical criteria. Specifically, we defined the aIPS as the most rostral part of the IPS at the intersection between the postcentral gyrus and the IPS (Rice et al. 2006). We defined the cIPS, which is associated with coding of 3D features of objects (Shikata et al. 2001, 2003), as the most caudal part of the IPS at the intersection with the transoccipital sulcus (Rice et al. 2006; Fig. 1D), and we defined the motor hand area as the segment of the precentral gyrus that had a knob-like structure (i.e., shaped like an omega or epsilon in the axial plane and like a hook in the sagittal plane; Yousry et al. 1997) and produced twitches of the hand when stimulated.

At the beginning of an experimental session, we determined the resting motor threshold (rMT) of each hand. An rMT was defined as the lowest intensity that evoked 5 visible hand muscle contractions in the contralateral index finger in a series of 10 stimuli when the participant kept the hand muscles relaxed in both hands (Rossini et al. 1994). The average rMT was 60% of machine output (SD = 4.61) for the left hemisphere and 61% (SD = 4.88) for the right hemisphere (no significant difference was found between hemispheres, t(10) = −1.27, P = 0.2). We then adjusted the intensity of the experimental stimulations to 110% of the individual rMT for each participant and hemisphere. We administered single-pulse TMS immediately after button release (<30 ms), using a Magstim Rapid 2 TMS system and an air-film cooled 70-mm figure-of-eight coil that was held tangentially to the scalp surface with the handle pointing downward. The minimum time interval between 2 TMS stimulations was 10 s.

Each stimulation site had its own block of 64 trials. Forty-eight trials were stimulation trials, and 16 were no-stimulation trials. Half of the trials were Perturbation trials, and the other half were No-perturbation trials. The order of the blocks was counterbalanced across participants.

Data Analysis

We visually inspected the finger movement data offline and excluded trials that had reaction times <50 ms or had artefacts/missing data in >10% of the total trajectory (total trial exclusion rate: 7.4%). For the remaining trials, we determined the onset of finger movements as well as movement end (the point at which the fingers reached the object) based on a 5% criterion of peak velocity. We then defined MGA as the largest distance between the 2 index fingers. Furthermore, to normalize trajectories, we converted time into percent of movement time—defined as movement onset to end, from movement onset to MGA, or from MGA to end of the grasp, and then resampled 3D (x, y, z coordinates) finger positions with a Gaussian filter. We then calculated the grip aperture at each normalized time point from movement onset to end and conducted repeated-measures analysis of variances (ANOVAs) for each time point. In addition, to better visualize interaction effects observed in the grip aperture, we defined 3 time periods of interest during, before, and after MGA [peri-MGA: 50–70% normalized movement time (NMT), i.e., the time roughly centered on average MGA at 61 ± 10%; pre-MGA: 30–50% NMT; and post-MGA: 70–90% NMT]. For each time period, we averaged across grip aperture values and then calculated interactions (i.e., [(TMS+ P+ minus TMS+ P−) minus (TMS− P+ minus TMS− P−)]) together with confidence intervals (CIs). Next, to determine the experimental effects on 3D curvature, we calculated 3D deviations of each index finger from a linear path. That is, for each normalized time point, we calculated the distance of the observed finger marker position from its virtual position had it traveled along a linear path (and at the same speed) based on its positions at the previous 2 time points [i.e., for virtual finger marker m′ at time ti: m(ti)′ = 2 × m(ti−1) − m(ti−2)]. In addition, to examine the variability of hand trajectories, we calculated the average deviation of individual trajectories from the mean trajectory at each time point (to ensure that this average deviation was not confounded with differences in numbers of trials, we bootstrapped the measure for the TMS conditions by calculating average deviations based on randomly selected subsamples of trials, repeated this 1000 times, and then averaged the results). As well, we calculated 3D velocities of unnormalized hand trajectories at each time point for the entire hand movement duration (from Go signal to the end of the recording, including object contact and return to the start position). Finally, we calculated several other measures: 3D finger endpoint on the object, time of MGA (tMGA) and of maximum and minimum curvature, as well as total movement time, which was calculated using the time of movement onset and offset (defined as the time at which the velocity of the index finger marker exceeded and then fell below 5% of peak velocity, respectively).

To examine these dependent variables, we compiled trials for individual participants and then performed group statistics using repeated-measures ANOVAs for each stimulation site separately due to differences in the number of subjects (aIPS left n = 11; aIPS right n = 10; cIPS left n = 10, cIPS right n = 11; stimulation sites had unequal n's due to corrupted data files in 2 participants). Specifically, for dependent variables that were sampled across normalized time, at each point we conducted 2- or 3-factorial repeated-measures ANOVAs, and we considered time periods as significant if P-values <5% for 12 or more consecutive time points (one time point = 16.89 ms; Guthrie and Buchwald 1991).

Results

Bimanual grasping trajectories attained an MGA during the second phase of the bimanual grasp movements (Fig. 2), much like unimanual grasping trajectories (e.g., Jeannerod 1981; Castiello 2005). To determine whether this behavioral similarity is governed by the same neural processes, for each stimulation site we tested the influence of factors Perturbation (P+ or P−) and TMS (TMS+ or TMS−). TMS is known to produce changes in response variability and/or biases, depending on the stimulation site and the task (e.g., Vesia et al. 2010), but based on the results of previous studies in a similar paradigm with unimanual grasping (Glover et al. 2005; Tunik et al. 2005; Rice et al. 2006), we expected the combination of TMS and perturbation together (TMS+ P+) to produce increased grip apertures around the time of maximum grip aperture.

Figure 2.

Trajectories for the left and right hands from movement onset to object contact. (A) Trajectories during the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. TMS+: stimulation, TMS−: no stimulation; P+: perturbation, P−: no perturbation.

Figure 2.

Trajectories for the left and right hands from movement onset to object contact. (A) Trajectories during the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. TMS+: stimulation, TMS−: no stimulation; P+: perturbation, P−: no perturbation.

To determine these interactions during bimanual grasping as a function of time, we first examined grip aperture at each normalized time point from movement onset to the end. As seen in Figure 3, we found that, for later time points that is ∼50–100% of NMT, grip aperture was significantly larger for the Perturbation condition (P+; M = 133.33 mm, SD = 10.57, averaged across 50–100% NMT) compared with the No-perturbation condition (P−; M = 116.20 mm, SD = 17.15), regardless of the stimulation site (Fig. 3A–D; also see Table 1 for details). This effect suggests that the Perturbation condition had the desired effect on the MGA, and that participants widened their grip apertures with increased object width. We also found a main effect of TMS for both the right aIPS (30–49% NMT, Fig. 3B and Table 1) and the right cIPS (28–40% NMT; Fig. 3D and Table 1); that is, grip aperture increased with TMS (TMS+: M = 125.94 mm, SD = 14.21; vs. TMS−: M = 124.57 mm, SD = 13.71, averaged across the right aIPS and right cIPS for ∼28–49% NMT). Crucially, we also observed the expected interaction (Perturbation × Stimulation): Increased object width together with TMS resulted in significantly greater grip apertures around the time of the MGA (51–79% NMT), but only when we stimulated the aIPS in the right hemisphere (TMS+ P+: M = 132.33 mm, SD = 10.77; vs. all other conditions: M = 121.60 mm, SD = 14.95, averaged across 51–79% NMT; Fig. 3B and Table 1). No other stimulation site showed significant interactions or trends (F ≤ 4.58, P ≥ 0.06; or <12 consecutive P-values <0.05).

Table 1

Significant time periods for grip aperture: F- and P-values

Stimulation site NMT (%) F-value P-value 
aIPS left 
 P 56–100 ≥5.21 ≤0.05 
aIPS right 
 P 48–100 ≥6.11 ≤0.03 
 TMS 30–49 ≥5.04 ≤0.05 
 TMS × P 51–79 ≥5.27 ≤0.05 
cIPS left 
 P 51–100 ≥5.19 ≤0.05 
cIPS right 
 P 50–100 ≥6.31 ≤0.03 
 TMS 28–40 ≥5.23 ≤0.05 
Stimulation site NMT (%) F-value P-value 
aIPS left 
 P 56–100 ≥5.21 ≤0.05 
aIPS right 
 P 48–100 ≥6.11 ≤0.03 
 TMS 30–49 ≥5.04 ≤0.05 
 TMS × P 51–79 ≥5.27 ≤0.05 
cIPS left 
 P 51–100 ≥5.19 ≤0.05 
cIPS right 
 P 50–100 ≥6.31 ≤0.03 
 TMS 28–40 ≥5.23 ≤0.05 

NMT: normalized movement time; P: perturbation condition; TMS: stimulation condition.

Figure 3.

Grip aperture across normalized time. (A) Grip apertures measured during the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. Plotted below each graph are the P-values of the repeated-measures ANOVAs for main effects of perturbation and TMS, and the interaction conducted at each normalized time point. Dark gray bars: significant P-values; light gray bars: nonsignificant trends (fewer than 12 P-values <0.05). Inserted in each graph are interaction effects quantified for 3 time periods. Pre: pre-MGA period; peri: peri-MGA period; post: post-MGA period. Error bars: CIs. For other acronyms see Figure 2.

Figure 3.

Grip aperture across normalized time. (A) Grip apertures measured during the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. Plotted below each graph are the P-values of the repeated-measures ANOVAs for main effects of perturbation and TMS, and the interaction conducted at each normalized time point. Dark gray bars: significant P-values; light gray bars: nonsignificant trends (fewer than 12 P-values <0.05). Inserted in each graph are interaction effects quantified for 3 time periods. Pre: pre-MGA period; peri: peri-MGA period; post: post-MGA period. Error bars: CIs. For other acronyms see Figure 2.

To illustrate the specificity of this interaction, we calculated interaction effects along with CIs for grip aperture during 3 time periods of interest (insets in Fig. 3A–D; see Materials and Methods). Consistent with the interaction effect mentioned above, only the peri-MGA period for the aIPS in the right hemisphere was significantly different from zero (t(10) = 3.24, P = 0.01, 1.74 ≤ CI ≤ 9.38, Fig. 3B). No other time periods for any other stimulation site were significant (t ≤ 2.02, P ≥ 0.07) with some trends opposite to the predicted ones.

Next, we ruled out that the interaction was due to the way we normalized individual movement trajectories from movement onset to the end. This approach could confound metrical effects of MGA with possible effects of MGA latencies or temporal variability, because MGAs derived from average trajectories are not necessarily identical with the average of MGAs found on a trial-by-trial basis. Therefore, we examined whether the tMGA was influenced by the experimental manipulations. For all stimulation sites, we submitted tMGA to repeated-measures ANOVAs with factors, such as “Perturbation” (+, −) and “TMS” (+, −). We found main effects of Perturbation for all stimulation sites (F ≥ 8.229, P ≤ 0.017), indicating that the tMGA was delayed when the object was perturbed in size (M = 410.02 ms, SD = 210.56; vs. no perturbation, M = 350.48 ms, SD = 162.76). However, there were no significant main effects of TMS or interaction effects (F ≤ 3.391, P ≥ 0.095), suggesting that our earlier grip aperture effects at the aIPS right were not merely due to differences in the tMGA. To further confirm this, we repeated our grip aperture statistical analyses by using 2 other normalization strategies: One strategy aligned individual trials such that movement onset once again was defined as 0%, but the MGA found on a trial-by-trial basis was set as 100% (phase 1); and the other set MGA to 0% and end of movement to 100% (phase 2). In addition, we found main effects of Perturbation at all stimulation sites from approximately 7–100% (F ≥ 7.49, P ≤ 0.042) and 0–100% (F ≥ 6.78, P ≤ 0.027) of movement time for phases 1 and 2, respectively. The main effect of TMS yielded the same trends as before though it did not reach significance (F ≤ 4.08, P ≥ 0.055, or <12 consecutive P-value of 0.05). However, we found significant interactions for the aIPS right around the time of the MGA from 86% to 100% (F ≥ 4.61, P ≤ 0.05) for phase 1 and from 0% to 12% (F ≥ 4.85, P ≤ 0.05) for phase 2. In summary, we confirmed that bimanual grasping was specifically influenced at the time of the MGA and specifically by a combination of Perturbation and TMS of the right aIPS.

These results are consistent with previous findings that unimanual grasping is disrupted by aIPS stimulations, suggesting that both forms of grasping use similar parietal correlates, except that bimanual grasping relied more on the right aIPS and not on the left aIPS as found for unimanual grasping (Davare et al. 2007). Such differences could be due to the fact that the motor aspects of both forms of grasping are quite different in that bimanual grasping puts greater demands on spatial aspects of motor control and interhemispheric coordination of movements (e.g. Serrien et al. 2006).

Therefore, we next investigated whether TMS, at any of the stimulation sites, had disrupted bimanual coordination or other aspects of the transport component of the grasps. As one possibility, TMS of the right aIPS might have affected the trajectory of the left hand thereby causing the aperture effects. To test these possibilities, we examined kinematic and kinetic variables of the 2 hand trajectories (curvature, variability, 3D endpoints on the object, velocity profiles, timing of maximum and minimum curvature, and total movement time). However, we found no TMS-by-Perturbation interactions. First, for the curvature measure (see Materials and Methods), as seen in Figure 4, the resulting curves were bimodal with an early peak around 20–30% NMT (M = 4461.90 mm, SD = 331.77), coinciding with the lift-off during the transport phase of the hands and a late peak around 80–90% NMT (M = 2626.00 mm, SD = 171.37), coinciding with the late stage of the grasp phase when the finger trajectories curved inward to lock onto the object. We examined these curves with 3-way repeated-measures ANOVAs with factors Hand, Perturbation, and TMS across all time points. For time points leading up to the MGA, we observed influences of TMS for the right hemisphere areas: We observed a main effect of TMS at the cIPS right (23– 40% NMT; Fig. 4D; see Table 2 for significant time periods, including F and P values, for all stimulation sites), suggesting that both hand trajectories curved more during TMS (TMS+: M = 4173.70 mm, SD = 637.95; vs. TMS−: M = 4062.50 mm, SD = 535.67, averaged across 23–40% NMT); and we observed a TMS × Hand interaction at the aIPS right (17–31% NMT, Fig. 4B and Table 2), again due to a greater degree of curvature during TMS specifically for the contralateral hand (F ≥ 4.81, P ≤ 0.05; TMS+: M = 4870.80 mm, SD = 434.60; vs. TMS−: M = 4568.00 mm, SD = 454.60, averaged across 17–31% NMT). All remaining significant effects were independent of TMS: A main effect of Hand suggested that the left hand (M = 2388.40 mm, SD = 611.88 for ∼40–100% NMT) curved more on average than the right hand (M = 2350.90 mm, SD = 481.44 for ∼40–100% NMT) for a longer stretch of the movement at all stimulation sites (Fig. 4A–D and Table 2). Perturbation showed influences at 3 of 4 sites—a main effect of Perturbation at the aIPS right and cIPS right (aIPS right: 47–64%, 83–100% NMT; cIPS right: 47–65%, 87–100% NMT; see Table 2) and a Perturbation effect for the right hand only (F ≥ 5.50, P ≤ 0.05) at the aIPS left (i.e., Perturbation × Hand interaction: 91–100% NMT; Fig. 4A and Table 2). All of the above influences of Perturbation resulted in a greater degree of curvature in the trajectories for P− (M = 2359.90 mm, SD = 505.88 for ∼47–100% NMT) than for P+ (M = 2221.00 mm, SD = 443.39 for ∼47–100% NMT), reflecting increased curvature near the end of the trajectory for the narrower object width. All other effects, including the TMS × Perturbation interaction, were not significant (F ≤ 4.04, P ≥ 0.07). It is possible, however, that normalizing trajectories mitigated the influence of TMS and Perturbation. Therefore, we realigned individual trials such that (1) movement onset was defined as 0%, but MGA was set as 100% (phase 1) and (2) set MGA to 0% and end of movement to 100% (phase 2). Trends and effects were similar to the first analysis (where movement onset = 0% and end of movement = 100%) such that TMS × Perturbation interactions for both phases were also not significant for the aIPS left, the aIPS right, and the cIPS left as well as phase 1 for the cIPS right (F ≤ 1.81, P ≥ 0.21). However, we found one difference for the cIPS right for phase 2, where there was a TMS by Perturbation by Hand effect (31–56% NMT, F ≥ 4.98, P ≤ 0.05). That is, the contralateral hand trajectory curved more for no TMS, contrary to what should be expected for a disrupted transport component, requiring future experimental confirmation (TMS−: M = 2512.70 mm, SD = 226.64 for ∼46–82% NMT) than TMS (TMS+: M = 2504.20 mm, SD = 178.05 for ∼46–82% MT; P−: t ≥ 2.28, P ≤ 0.05 for 46–65% MT; P+: 1.92 ≤ t ≤ 2.76, 0.02 ≤ P ≤ 0.08 marginally significant for 64–82% NMT). The difference in the degree of curvature for TMS− and TMS+ was not as pronounced for the ipsilateral hand (TMS−: M = 2623.60 mm, SD = 203.05; TMS+: M = 2613.80 mm, SD = 189.46 for ∼46–82% NMT; t ≤ 2.57, P ≥ 0.06 or <12 consecutive P-values <0.05).

Table 2

Significant time periods for curvature: F- and P-values

Stimulation site NMT (%) F-value P-value 
aIPS left 
 Hand 36–94 ≥4.65 ≤0.05 
 Hand × P 91–100 ≥7.83 ≤0.02 
aIPS right 
 P 47–64, 83–100 ≥4.61 ≤0.05 
 Hand 36–92 ≥4.76 ≤0.05 
 TMS × Hand 17–31 ≥6.44 ≤0.03 
cIPS left 
 Hand 36–100 ≥4.93 ≤0.05 
cIPS right 
 P 47–65, 87–100 ≥5.58 ≤0.04 
 TMS 23– 40 ≥6.20 ≤0.03 
 Hand 40–93 ≥5.37 ≤0.04 
Stimulation site NMT (%) F-value P-value 
aIPS left 
 Hand 36–94 ≥4.65 ≤0.05 
 Hand × P 91–100 ≥7.83 ≤0.02 
aIPS right 
 P 47–64, 83–100 ≥4.61 ≤0.05 
 Hand 36–92 ≥4.76 ≤0.05 
 TMS × Hand 17–31 ≥6.44 ≤0.03 
cIPS left 
 Hand 36–100 ≥4.93 ≤0.05 
cIPS right 
 P 47–65, 87–100 ≥5.58 ≤0.04 
 TMS 23– 40 ≥6.20 ≤0.03 
 Hand 40–93 ≥5.37 ≤0.04 

NMT: normalized movement time; P: perturbation condition; TMS: stimulation condition.

Figure 4.

Curvature of index finger trajectories of the left and right hands across normalized time. Plotted below each graph are the P-values of the repeated-measures ANOVAs for main effects of perturbation, TMS and hand, and all interactions conducted at each normalized time point. (A) Curvature profiles obtained during the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. Same conventions as previous figures.

Figure 4.

Curvature of index finger trajectories of the left and right hands across normalized time. Plotted below each graph are the P-values of the repeated-measures ANOVAs for main effects of perturbation, TMS and hand, and all interactions conducted at each normalized time point. (A) Curvature profiles obtained during the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. Same conventions as previous figures.

Next, we examined the variability of the trajectories (see Materials and Methods). As shown in Figure 5, the variability curves peaked before the MGA at around 40–50% NMT (M = 26.07 mm, SD = 0.90), coinciding with the transport phase of the movement, and reached a local minimum after MGA at around 70–80% NMT (M = 15.64 mm, SD = 5.32), coinciding with the final prehension phase of the movement. Next, we submitted the variability measure to 3-way repeated-measures ANOVAs with factors Hand, Perturbation, and TMS at all normalized time points. We found various influences of Hand. The main effects of Hand at the aIPS right, cIPS right, and cIPS left (aIPS right: 52–100% NMT; cIPS right: 29–100% NMT; cIPS left: 48–92% NMT; Fig. 5B–D and Table 3 for significant time periods, including F and P values) were due to more variability in the trajectories for the left hand (M = 20.52 mm, SD = 5.69 for ∼30–100% NMT) than the right hand (M = 19.60 mm, SD = 5.32 for ∼30–100% NMT). Similarly, a Hand by Perturbation interaction at the aIPS left (58–81% NMT; Fig. 5A and Table 3) suggested a greater effect for P+ for the left hand. Furthermore, a TMS × Hand interaction (cIPS left: 63–75% NMT; cIPS right: 46–100% MT; Fig. 5C,D and Table 3) was due to greater variability of the left than the right-hand trajectory during no TMS (cIPS left: 31–62% NMT; t ≥ 2.27, P ≤ 0.05; cIPS right: 27–72% NMT; t ≥ 2.24, P ≤ 0.05) but not during TMS and, thus, arguably an artifact of stimulation. Moreover, we found no main effect of TMS (TMS+; F ≤ 3.20, P ≥ 0.10), indicating that the variability of the hand trajectories was not generally affected by TMS at any of the stimulation sites. In fact, when we realigned individual trials according to phases 1 and 2 (same method as analyses above), the trends and effects were similar to the first analysis (movement onset = 0% and end of movement = 100%) in that the variability of the hands was not affected by TMS (F ≤ 3.84, P ≥ 0.08). Consistent with this, when we examined finger locations at the endpoint of the grasp, there were no Perturbation × TMS effects or any TMS effects in general at any of the stimulation sites (F ≤ 7.70, P ≥ 0.23; although note that there was a main effect of Perturbation, F ≥ 22.14, P ≤ 0.02, where the hands were higher and farther to the back when the object was not perturbed; M = 2002.60 mm, SD = 10.31, vs. for perturbed: M = 1991.50 mm, SD = 10.71). Furthermore, although the hands differed in that the left hand touched the object higher and farther to the back for all stimulation sites (M = 1998.60 mm, SD = 11.52, vs. for right hand: M = 1995.50 mm, SD = 12.01; F ≥ 22.14, P ≤ 0.0008), we found no evidence for any TMS-induced hand-specific effects (F ≤ 0.97, P ≥ 0.35).

Table 3

Significant time periods for variability: F- and P-values

Stimulation site NMT (%) F-value P-value 
aIPS left 
 Hand × P 58–81 ≥4.93 ≤0.05 
aIPS right 
 Hand 52–100 ≥4.93 ≤0.05 
cIPS left 
 Hand 48–92 ≥4.95 ≤0.05 
 TMS × Hand 63–75 ≥4.99 ≤0.05 
cIPS right 
 Hand 29–100 ≥4.96 ≤0.05 
 TMS × Hand 46–100 ≥5.35 ≤0.04 
Stimulation site NMT (%) F-value P-value 
aIPS left 
 Hand × P 58–81 ≥4.93 ≤0.05 
aIPS right 
 Hand 52–100 ≥4.93 ≤0.05 
cIPS left 
 Hand 48–92 ≥4.95 ≤0.05 
 TMS × Hand 63–75 ≥4.99 ≤0.05 
cIPS right 
 Hand 29–100 ≥4.96 ≤0.05 
 TMS × Hand 46–100 ≥5.35 ≤0.04 

NMT: normalized movement time; P: perturbation condition; TMS: stimulation condition.

Figure 5.

Variability of index finger trajectories of the left and right hands across normalized time. (A) Variability profiles for the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. Same conventions as previous figures.

Figure 5.

Variability of index finger trajectories of the left and right hands across normalized time. (A) Variability profiles for the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. Same conventions as previous figures.

The analyses of curvature and variability yielded evidence for a greater hand and arm proficiency for the right arm and hand than the left as expected for right-handed participants (less curvature, smaller variability). In contrast, the right-hemispheric dominance for bimanual grasping could still be reflected in a leading role of the left hand for bimanual coordination (e.g., Buckingham et al. 2010). Therefore, we further investigated timing in terms of velocity profiles, curvature features, as well as total movement times. When we calculated the 3D velocity for each hand separately (Fig. 6), none of the stimulation sites showed significant differences between the hands (F ≤ 1.04, P ≥ 0.33), nor influences of perturbation or TMS (F ≤ 2.72, P ≥ 0.13), nor interaction effects (F ≤ 2.42, P ≥ 0.15). Thus, velocity profiles did not reveal a leading role of the left hand in bimanual grasping. This also implies that the increased aperture observed for TMS stimulation of the aIPS right was not caused by the left hand, reaching MGA earlier than the right hand. This was further confirmed with our analysis on the timing of maximum curvature and subsequent minimum curvature; neither aIPS right nor any of the other sites showed differences between the 2 hands (F ≤ 4.60, P ≥ 0.06). Consistent with this, our analysis of the total movement time revealed no differences between the 2 hands (nor any influence of TMS on the hands) for any of the stimulation sites (F ≤ 5.32, P ≥ 0.06; the only significant effect was a perturbation effect for cIPS left, where movement time was slower when the object was not perturbed, F1,10= 7.58, P = 0.02, as can be seen in the velocity profiles, Fig. 6, although this effect was not significant for velocity).

Figure 6.

Velocity profiles of index finger trajectories of the left and right hands across nonnormalized time, from Go signal to object contact and back to the start position. (A) Velocity for the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. Same conventions as previous figures.

Figure 6.

Velocity profiles of index finger trajectories of the left and right hands across nonnormalized time, from Go signal to object contact and back to the start position. (A) Velocity for the left aIPS experiment block. (B) Right aIPS block. (C) Left cIPS block. (D) Right cIPS block. Same conventions as previous figures.

Discussion

The posterior parietal cortex, particularly the aIPS, plays a key role in unimanual grasp control (Tunik et al. 2005; Rice et al. 2006; Davare et al. 2011). Here, we tested 2 hypotheses: (1) The aIPS controls not only unimanual, but also bimanual grasps, and (2) the aIPS in the right hemisphere is more specialized. Consistent with our predictions, we found TMS-induced grasp deficits that occurred only when we stimulated the right aIPS at movement onset and at object-size perturbation. Specifically, stimulation and perturbation combined resulted in enlarged grip apertures, but only around the time of MGA, whereas we observed no comparable effects for the left aIPS or the cIPS in either hemisphere. Furthermore, for none of our stimulation sites did we find evidence that TMS had any other effect on the transport component of the grasp or the coordination of the 2 hands.

The observed increase in grip aperture could be due to a strategy developed by the brain to cope with increased noise or uncertainty in the grasp network caused by TMS (see, e.g., Schlicht and Schrater 2007 for equivalent effects induced by perceptual uncertainty). However, it is not presently clear that the brain is aware of its own internal noise, especially when artificially induced by TMS. Therefore, alternative explanations might be viable too, such as TMS causing the normal opening to maximum grip aperture to overshoot.

Regardless of the underlying mechanisms, our results show that the aIPS is causally involved in bimanual grasp movements, but not the transport or the bimanual coordination of the 2 hands, consistent with reports from unimanual grasping studies that found that aIPS stimulation increases the MGA during object-size perturbation (Tunik et al. 2005; Rice et al. 2006), but does not affect hand transport (Vesia et al. 2013). Beyond this, unimanual grasp research suggests that aIPS is involved in specifying grasp goals regardless of whether perturbation is used or not (Rice et al. 2006) and whether object size or grasp type is perturbed (Tunik et al. 2005). This suggests that aIPS may play a critical role in the dynamic control of the intended action goal of unimanual grasps (Tunik et al. 2007). It matches recent evidence that the grasp system is able to incorporate sensory information in a nonballistic, online manner (e.g., Karok and Newport 2010), and it is in agreement with neurophysiological and neuroimaging evidence that the aIPS is crucially involved in grasp computations (Rizzolatti and Luppino 2001; Tunik et al. 2005, 2008; Castiello and Begliomini 2008; Monaco et al. 2013), parallel with computations in other areas that control the transport component of the grasps (Cavina-Pratesi et al. 2010; Vesia et al. 2013).

While bimanual grasp mechanisms have yet to be confirmed to show similar properties, it is conceivable that similar effects will be found. Importantly, because (1) grasp computations in the aIPS are governed by the same principles of visual object analysis and grasp point selection largely independent of the effectors that later perform the grasp (Blake 1992), and because (2) they may be necessary to calculate the action for both unimanual and bimanual grasps, it makes sense that bimanual and unimanual grasping share neural structures, consistent with the idea of effector independent control for grasping (Bernstein 1967; Raibert 1977; Saltzman 1979; Schmidt 1980; Flanagan and Tresilian 1994; Tresilian and Stelmach 1997; Park and Shea 2002; Vangheluwe et al. 2006; Walsh et al. 2008). What is more, the fact that we found aIPS stimulation to affect grasps as a whole further supports the idea that the aIPS represents grasp goals.

Nevertheless, the overlap in neural mechanisms for controlling bimanual and unimanual grasps is not complete in that hemisphere specialization differs: Unimanual precision grasping is associated with the hemisphere contralateral to the respective grasping hand (Rice et al. 2007; Martin et al. 2011) with a dominance of the left hemisphere for some aspects of grasps, such as grip force (Davare et al. 2007). In contrast, we found that bimanual grasping is associated with the aIPS in the right hemisphere, but not with the left aIPS. This difference in lateralization could be due to the effector-dependent context in which motor control executes grasp movements. More specifically, unimanual grasping is often used for tasks associated with structures in the left hemisphere, including skilled fine motor movements (Serrien et al. 2006) and handwriting (Pujol et al. 1999; Knecht et al. 2000; Serrien et al. 2006). In contrast, bimanual grasping involves synchronized motor control processes in both hemispheres for both arms and hands. These processes require interhemispheric coordination, which is associated with areas in the right hemisphere such as the right temporo-parietal junction (TPJ), the right supplementary motor area, and the right primary and premotor cortices (Blanke et al. 2002; Wenderoth et al. 2004; Serrien et al. 2006; Duque et al. 2009; van den Berg et al. 2010).

A second, biomechanical reason for the difference in lateralization might be that bimanual grasps are more demanding than unimanual grasps, because they involve more joints and, thus, more degrees of freedom. For example, bimanual grasps use the shoulders as a fulcrum, whereas unimanual grasps revolve around the hand. Therefore, control of bimanual grasping should depend more on a comprehensive representation of the 3D structure of the body, which is predominantly associated with the right hemisphere, such as the right TPJ or the right angular gyrus (Sadato et al. 1997; Blanke et al. 2002; Wenderoth et al. 2004; Aramaki et al. 2006; Serrien et al. 2006; Duque et al. 2009; van den Berg et al. 2010; Baas et al. 2011).

Both these possible reasons for right-dominant bimanual grasping could be reflected in a leading role of the left hand in bimanual actions (e.g., Buckingham et al. 2010 for bilateral reaching). For the size-perturbation grasping paradigm, we found no evidence for such an asymmetry in the velocity profiles (Fig. 6), peak curvature timing, and movement time. Instead, left-hand trajectories exhibited more pronounced curvature (Fig. 4) and greater trajectory variability (Fig. 5) with and without TMS reflecting the greater proficiency of right-arm motor control of right-handed participants. These hand effects occurred across all stimulation sites and thus cannot explain the grip aperture effects that we found exclusively during aIPS right stimulation. Instead, stimulations to the right aIPS appeared to have affected both hands together, consistent with the idea of a “holistic” representation of the grasp goal in the right aIPS and surrounding network. Nevertheless, we cannot exclude the possibility that bimanual grasping encompasses leading left-hand effects in other paradigms or at other stimulation sites that are more suitable to challenge transport and coordination aspects of bimanual grasps.

One could potentially regard the right-hemispheric dominance that we observed for bimanual grasping as surprising in light of Smeets and Brenner's (2001) “double-pointing” model of grasping. The model considers motor control of hand and finger trajectories for grasping as equal to pointing movements to the same locations in space. Specifically, pointing as well as grasping movements are planned so as to avoid obstacles, such as the edges of the object's front surface, when the finger trajectories aim for the object's lateral sides (Smeets and Brenner 2001). Thus, it could be expected that bimanual grasp control is represented bilaterally. However, in our view, there is not necessarily a contradiction because the double-pointing model considers processes downstream from those that are associated with the aIPS. Crucially, aIPS processes involve mechanisms of visual object analysis necessary to select surface points on the object at which it can be grasped (Rizzolatti and Luppino 2001; Castiello and Begliomini 2008) and grasp goal representation (e.g., Tunik et al. 2005). In contrast, the double-pointing model assumes that grasp points are already selected and focuses on the subsequent geometrical requirements of the overt grasp trajectories (Smeets and Brenner 2001). This said, the model does not consider the mechanisms underlying the trajectories that are necessary to synchronize them in time. Therefore, whether double pointing and bimanual grasping do indeed yield the same trajectories will have to be tested in the future.

Additional investigation is also necessary to revisit the contribution of the left aIPS to bimanual grasping. Here, we found no evidence for an involvement, even though the left aIPS is larger than the right aIPS, so that TMS effects could have been more prominent for the left aIPS based on unimanual grasping studies (Falk et al. 1991; Volkmann et al. 1998; Davare et al. 2007; Rice et al. 2007). However, for now it remains unclear whether the left aIPS assumes a minor role in bimanual grasp control, or whether the right aIPS is solely involved.

To conclude, we found here that the aIPS is involved not only in unimanual grasping, but also bimanual grasping, with a right-hemispheric specialization, consistent with our earlier observation of a left visual-field dominance for bimanual grasping (Le and Niemeier 2013). Our data provide novel insights into the mechanisms that give rise to bimanual grasp control based on visual grasp point selection and dynamic grasp goal representation, potentially associated with downstream processes of temporo-spatial coordination of limbs and representations of one's own body in the right hemisphere.

Funding

This work was supported by a Canadian Institutes of Health Research grant held by J.D. Crawford. J.D. Crawford is supported by the Canada Research Chair program.

Notes

Conflict of Interest: None declared.

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