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Overlapping representations for grip type and reach direction

Overlapping representations for grip type and reach direction Fabbri, S.; Strnad, L.; Caramazza, A.; Lingnau, A. 2014, Article / Letter to editor (NeuroImage, 94, (2014), pp. 138-146) Doi link to publisher: https://doi.org/10.1016/j.neuroimage.2014.03.017 Version of the following full text: Publisher’s version Published under the terms of article 25fa of the Dutch copyright act. Please follow this link for the Terms of Use: https://repository.ubn.ru.nl/page/termsofuse Downloaded from: https://hdl.handle.net/2066/129978 Download date: 2025-06-28 Note: To cite this publication please use the final published version (if applicable). NeuroImage 94 (2014) 138–146 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg a,1 a,b a,b a,c, Sara Fabbri ,Lukas Strnad , Alfonso Caramazza ,Angelika Lingnau ⁎ Center for Mind/Brain Sciences, University of Trento, 38100 Mattarello, Italy Department of Psychology, Harvard University, MA 02138, Cambridge, USA Department of Cognitive Sciences, University of Trento, 38068 Rovereto, Italy article i nfo abstract Article history: To grasp an object, we need to move the arm toward it and assume the appropriate hand configuration. While Accepted 8 March 2014 previous studies suggested dorsomedial and dorsolateral pathways in the brain specialized respectively for the Available online 17 March 2014 transport and grip components, more recent studies cast doubt on such a clear-cut distinction. It is unclear, how- ever, to which degree neuronal populations selective for the two components overlap, and if so, to which degree Keywords: they interact. Here, we used multivoxel pattern analysis (MVPA) of functional magnetic resonance imaging Directional tuning (fMRI) data to investigate the representation of three center-out movements (touch, pincer grip, whole-hand fMRI grip) performed in five reach directions. We found selectivity exclusively for reach direction in posterior and ros- Grasping tral superior parietal lobes (SPLp, SPLr), supplementary motor area (SMA), and the superior portion of dorsal Reaching Searchlight premotor cortex (PMDs). Instead, we found selectivity for both grip type and reach direction in the inferior por- MVPA tion of dorsal premotor cortex (PMDi), ventral premotor cortex (PMv), anterior intraparietal sulcus (aIPS), pri- mary motor (M1), somatosensory (S1) cortices and the anterior superior parietal lobe (SPLa). Within these regions, PMv, M1, aIPS and SPLa showed weak interactions between the transport and grip components. Our re- sults suggest that human PMDi and S1 contain both grip- and reach-direction selective neuronal populations that retain their functional independence, whereas this information might be combined at the level of PMv, M1, aIPS, and SPLa. © 2014 Elsevier Inc. All rights reserved. Introduction of separate streams for transport and grip, directionally tuned neurons have been found in monkey PMd (Caminiti et al., 1991) and parietal The ability to reach for and grasp objects is fundamental for our in- area V6A (Fattori et al., 2001, 2005). Likewise, the human SPOC and teraction with the environment. Reaching refers to the transport the rostral superior parietal lobe have been reported to show stronger phase of the hand toward the object, while grasping includes the activation during reaching to far locations in comparison to near preshaping of the hand in relation to the shape and size of the object. locations, indicating a general preference for the transport in com- It has been suggested that the transport component relies on a parison to the grip component (Cavina-Pratesi et al., 2010). How- dorsomedial pathway consisting of superior parieto-occipital cortex ever, directionally tuned neurons have also been reported outside (SPOC) in the medial wall of the parietal cortex, medial intraparietal dorsomedial areas, like monkey PMv (Kakei et al., 2001; Stark et al., area (MIP) and the dorsal premotor cortex (PMd); the grip component 2007), primary motor cortex (M1) (Georgopoulos et al., 1982), and is thought to rely on a dorsolateral pathway consisting of the anterior the cerebellum (Fortier et al., 1989). Using fMRI adaptation, directional intraparietal sulcus (aIPS) and the ventral premotor cortex (PMv) selectivity has been demonstrated both in regions of the human (Culham et al., 2006; Jeannerod et al., 1995; Tanné-Gariépy et al., dorsomedial and the dorsolateral pathway (Fabbri et al., 2010, 2012; 2002; Vesia and Crawford, 2012). Lingnau et al., 2014). Taken together, these studies indicate that the One of the best studied parameters of the transport component is di- representation of the transport component is not restricted to the rectional tuning, identified as maximal activity during reaching in the dorsomedial pathway. preferred direction and a gradual decrease of activity with increasing A number of studies support the view of a specialized role of the dor- angular difference from the preferred direction. In line with the view solateral stream for the representation of the grip component.Macaque area AIP contains neurons selective for the grip used to grasp a specific object (Murata et al., 2000; Taira et al., 1990). This area projects to area ⁎ Corresponding author at: Center for Mind/Brain Sciences, University of Trento, Via F5, which also shows grip selectivity (Fluet et al., 2010; Rizzolatti et al., delle Regole, 101, Mattarello, TN 38100, Italy. Fax: +39 0461 88 3066. 1988; Umilta et al., 2007). Inactivation of both areas causes impairment E-mail address: [email protected] (A. Lingnau). in appropriately grasping an object (Fogassi et al., 2001; Gallese et al., Present address: Brain and Mind Institute, University of Western Ontario, N6A 5B7 London, Canada. 1994). Human fMRI studies demonstrated that both aIPS and PMv http://dx.doi.org/10.1016/j.neuroimage.2014.03.017 1053-8119/© 2014 Elsevier Inc. All rights reserved. S. Fabbri et al. / NeuroImage 94 (2014) 138–146 139 respond more strongly during grasping in comparison to reaching vice versa (Fig. 1b), we aimed to identify areas that contain both grip- movements (Binkofski et al., 1999; Cavina-Pratesi et al., 2010; Culham type and reach-direction selective neuronal populations. Such areas et al., 2003; Frey et al., 2005). Moreover, permanent as well as tempo- might contain neuronal populations that are directionally tuned irre- rary lesions to both human aIPS and PMv lead to an impairment in shap- spective of the type of grip (Fig. 1c). Alternatively, they might consist ing the hand in relation to the shape and size of the object (Binkofski of neuronal populations that are both grip-type and reach-direction se- et al., 1998, 1999; Dafotakis et al., 2008; Davare et al., 2006, 2007; Rice lective, as it was reported in monkey PMd and PMv by Stark et al. et al., 2006). (2007); such areas should show an interaction between the two param- Whereas the studies reported above support the view of a relative eters (see Fig. 1d). specialization of the dorsolateral pathway for the grip component, it To test these predictions, we instructed participants to perform sim- has been shown that monkey PMd (Stark et al., 2007)and parietal ple non-visually guided center-out reach-to-grasp movements (touch, area V6A (Fattori et al., 2010) in the dorsomedial pathway also contain pincer grip, whole-hand grip) in five different reach directions (0, 45, grip selective neurons. Furthermore, it has been found, using multivoxel 90, 135, 180°, where 90° is straight ahead; see Figs. 2a–c). To measure pattern analysis (MVPA) to decode brain activity, that precision grasps selectivity for grip type, reach direction and their interaction, we per- of different object sizes can be distinguished both in regions of the dor- formed multivoxel pattern searchlight analysis. solateral and the dorsomedial pathway (Gallivan et al., 2011b). The fact that both the dorsomedial and the dorsolateral pathway Materials and methods seem to be sensitive to certain aspects of the transport and grasp com- ponents suggests that the functional distinction between these two Participants components is not as clear-cut as originally thought. Little is known, however, about the combined representation of transport and grip. Sixteen volunteers (9 males) took part in the experiment (age Stark et al. (2007) recorded from neurons in monkey PMd and PMv. range: 21–52 years). All but one were right handed. Participants had For each recording site, the authors determined whether intracortical normal or corrected-to-normal vision using MR-compatible glasses. microstimulation (ICMS) evoked movements of proximal (shoulder, Two of the authors (L.S., A.L.) took part in the experiment, while the elbow) or distal (finger) joints. In line with previous studies, ICMS in other participants were naïve to the purpose of the study; all gave writ- PMd and PMv led to activation of muscles involved in the transport ten informed consent for their participation. The experimental proce- and in the grip component, respectively. Surprisingly, the authors dures were approved by the ethics committee for research involving observed that roughly the same proportion of neurons modulated human subjects at the University of Trento. Data recorded from one par- by either reach direction or grip type were observed both within PMd ticipant were excluded from the analysis because it became clear and PMv. Moreover, in about 1/4 of all recorded neurons, the effect throughout the experiment that she did not properly understand the of reach direction and grip type interacted. They proposed that task. directionally tuned neurons in PMv and grip selective neurons in PMd might serve the purpose of relaying directional information through horizontal connections from proximal to distal sites, and information Procedure and visual stimulation about grip type from distal to proximal sites. Previous studies aiming to distinguish between the reach and grasp During each trial, participants were presented with an arrow at the components compared brain activity during reach-to-grasp movements center of the screen for 2 seconds (s), followed by an inter-trial- versus point-to-touch movements (Cavina-Pratesi et al., 2010; interval (ITI) of 1 s (see Fig. 2a). Using their right hand, participants Desmurget et al., 2001; Faillenot et al., 1997; Konen et al., 2013). Such had to execute a center-out reach-to-grasp task on a device attached paradigms, however, are limited by the fact that the spatial accuracy de- to their chest. Visual feedback was not provided so as to exclude con- mands of these two movements are clearly different. Here we used an founds such as systematic eye movements toward the target object innovative approach that does not rely on this assumption, varying and uncontrolled visual stimulation by the sight of the participant's both reach direction and grip type and measuring their selectivity across own hand (see also Fabbri et al., 2010, 2012; Lingnau et al., 2014). The the entire brain. This allowed us to ask the question whether selectivity device consisted of 5 half-spheres of polystyrene (3 cm diameter) for reach direction and grip type are present within the same region, glued on a black plastic surface. They were placed at five equidistant po- and if so, whether these two components interact. In addition to areas sitions on a virtual circle (8 cm radius) as well as at the center of that that are either grip selective but not directionally tuned (Fig. 1a) or circle. Fig. 1. Hypothetical Fisher-transformed correlations between odd and even runs as a function of angular difference in reach direction (x-axis) and combination of grip types (black circles: same grip type, white circles: different grip type). a–d: Hypothetical data from a ROI that contains neuronal populations that are grip-type selective, but not directionally tuned (a), directionally tuned, but not grip-type selective (b) selective for grip-type and reach-direction, but not for their interaction (c), selective for grip-type and reach-direction, as well as to their interaction (d). Note that the interaction depicted in (d) is only one out of several possible examples. 140 S. Fabbri et al. / NeuroImage 94 (2014) 138–146 Fig. 2. a: Example sequence of three trials during which participants were instructed to execute reach-to-grasp movements in directions 90, 0, and 180°. The color of the arrow indicated the type of movement (yellow: touch, red: pincer, green: whole-hand grasp). b: Participants positioned their right index finger on the central location and executed reach-to-grasp move- ments toward the peripheral target indicated by the visual instruction. Superimposed arrows (not shown during the experiment) indicate the directions corresponding to the three ex- ample trials shown in panel a. c: The experimental design consisted of 3 (movement type) × 5 (reach direction) conditions. To reduce visual similarity between trials, we varied the visual appearance of the arrow that indicated the movement direction and type of grasp on each trial (see Materials and methods for details). Hand movements differed in the way the hand made contact with the target object (touch: using the index finger; pincer grip: index finger and thumb; whole-hand grip: all fingers). At the beginning of each trial, participants positioned their index fin- To reduce visual similarity between trials, we varied the visual ap- ger on the central half-sphere (Fig. 2b). They were instructed to execute pearance of the arrow that indicated the reach direction and type of center-out movements in one of the five possible directions using one of grasp on each trial (see Fabbri et al., 2010 for a similar approach). the three different movement types as soon as the arrow appeared on Arrow width and length were varied randomly from 0.41° to 1.22° in the screen, and to then move back to the start position. Reach direction steps of 0.405°. The x- and y-center coordinates of the arrow were was indicated by the orientation of the arrow presented on the screen, jittered in a range of ±0.07° in steps of 0.035°. Stimuli were back- while the type of movement was specified by its color (see Fig. 2c). projected onto a screen by a liquid-crystal projector at a frame rate of Note that both whole-hand and pincer grip differ from touching in 60 Hz and a screen resolution of 1280 × 1024 pixels. Participants that they require grasping a target. The two grasps differ in the configu- viewed the stimuli binocularly through a mirror above the head coil. ration of the hand: whole-hand grasp requires the use of all fingers The screen was visible as a rectangular aperture of 17.5 × 14.3°. Visual while pincer grip uses only thumb and index fingers (see Fig. 2c). stimulation was controlled by ASF (Schwarzbach, 2011) based on the Altogether there were 3 (movement type) × 5 (reach direction) con- MATLAB Psychtoolbox-3 for Windows (Brainard, 1997; Pelli, 1997). ditions (Fig. 2c). In addition, 12% of the trials were null trials in which participants had to maintain fixation while keeping the hand at the cen- Instructions and training ter position for 3 s (2 s trial duration + 1 s ITI). Instructions were counterbalanced between participants: for 8 out Before entering the scanner, participants learned to execute center- of 16 participants, a green arrow instructed them to reach the target out movements corresponding to the visual instructions, and they fa- using a whole-hand grip, a yellow arrow instructed to touch the target, miliarized themselves with the location of the half spheres on the device and a red arrow instructed to reach the target using a pincer grip. For the such that they were able to perform the movements accurately in the other half of the participants, the green arrow instructed to touch the absence of visual feedback (see also Fabbri et al., 2010, 2012; Lingnau target, the yellow arrow instructed to use a pincer grip, and the red et al., 2014). The experimenter instructed participants to execute each arrow instructed to use a whole-hand grip. movement within a constant time window of 2 s corresponding to the S. Fabbri et al. / NeuroImage 94 (2014) 138–146 141 presentation time of the arrow, rather than trying to move as fast as pos- data with respect to anatomical landmarks, we reconstructed the inflat- sible and thus risking head movements. Participants were asked to ed left and right hemisphere that represents the average curvature move their hand back to the center position before the arrow disap- maps of all 15 participants who took part in the study. Since we used peared, and to start each trial from the center position. right-hand movements that are known to preferentially recruit the left hemisphere, only data from the left hemisphere are presented. fMRI design Univariate analysis The entire experiment consisted of 12 event-related runs. Each At the first level, we estimated beta weights for each combination of run consisted of 75 experimental trials and 10 null trials for a total of type of movement (touch, pincer grip, whole-hand grip) and reach direc- 85 trials, and lasted 4.2 min. For each participant, each of the 15 condi- tion (0, 45, 90, 135, and 180°), time-locked to the onset of the arrow, tions (5 reach directions × 3 types of movement) was repeated 5 times separately for each participant. Altogether, we included 3 × 5 = 15 pre- in a run for a total of 60 repetitions per condition. dictors. Moreover, we added six parameters (x, y, z translation and rotation) resulting from 3D motion correction as predictors of no inter- Data acquisition est. The time course of each predictor of interest was convolved with a dual-gamma hemodynamic impulse response function (Friston et al., We acquired fMRI data using a 4T Bruker MedSpec MRI scanner and 1998), and the resulting reference time courses were used to fit the sig- an 8-channel birdcage head coil. Functional images were acquired with nal time course of each voxel. Beta estimates derived from the first-level a T2*-weighted gradient-recalled echo-planar imaging (EPI) sequence. analysis were projected on the surface and aligned to the group average Before each functional scan, we performed an additional scan to mea- inflated hemisphere of all participants using the correspondence map- sure the point-spread function (PSF) of the acquired sequence, which ping obtained during cortex-based alignment. The cortex-based aligned serves for correcting of the distortion expected with high-field imaging individual maps were entered into a second level random effects (RFX) (Zaitsev et al., 2004). We used 34 slices, acquired in ascending inter- general linear model (GLM) analysis carried out on the surface. To iden- leaved order, slightly tilted to run parallel to the calcarine sulcus tify areas recruited during movement execution, we computed the RFX (TR (time to repeat): 2000 ms; voxel resolution: 3 × 3 × 3 mm; TE GLM contrast “all conditions versus baseline”, where baseline refers (echo time): 33 ms; flip angle (FA): 73°; field of view (FOV): 192 × to all periods not explicitly modeled in the GLM. Statistical maps 192 mm; gap size: 0.45 mm). Each participant completed 12 scans of were corrected for multiple comparisons using a False Discovery Rate (FDR) b.01. 126 volumes each. To be able to co-register the low-resolution functional images to a high-resolution anatomical scan, we acquired a T1 weighted anatomical Searchlight-based multivoxel pattern analysis scan (MP-RAGE; voxel resolution: 1 × 1 × 1 mm; FOV: 256 × 224 mm; We performed correlation-based multivoxel pattern analysis GRAPPA acquisition with an acceleration factor of 2; TR: 2700 ms, inver- (MVPA, Haxby et al., 2001) using a searchlight approach (Kriegeskorte sion time (TI), 1020 ms; FA: 7°). et al., 2006). First of all, we created beta maps using a cortex mask that restricts the analysis to voxels falling within −3to+1 mm ofthe Data analysis gray–white matter boundary determined during segmentation. To do so, we extracted the time course for each combination of movement Data analysis was performed using BrainVoyager QX 4.1 (Brain type (touch, pincer grip, whole hand grip) and reach direction (0°, Innovation), the BVQX Toolbox (http://support.brainvoyager.com/ 45°, 90°, 135°, 180°), separately for each participant for each voxel in available-tools/52-matlab-tools-bvxqtools.html) and custom software the cortex mask. Next, we calculated z-transformed β-estimates of the written in MATLAB (MathWorks). BOLD response separately for each participant, condition and run. Then, we averaged β-estimates across odd and even runs, resulting in Preprocessing, segmentation, and cortex-based alignment a 15 (3 movement types × 5 reach directions) × N voxels correlation To correct for distortions in geometry and intensity in the matrix for each of the odd and even runs, separately for each participant. EPI images, we applied distortion correction on the basis of the Data were normalized by subtracting the grand mean response across PSF data acquired before each EPI scan (Zeng and Constable, 2002). conditions from each voxel, separately for odd and even runs (Haxby Before further analysis, we removed the first 4 volumes to avoid T1- et al., 2001). saturation. Next, we performed 3D motion correction with trilinear in- Second, in each participant, for each voxel in the cortex mask, we de- terpolation for estimation and sinc interpolation for resampling using termined a small region (the searchlight) containing all voxels falling the first volume as reference followed by slice timing correction with as- within a radius of 5 mm of the central voxel. Since the analysis was re- cending interleaved order. Functional data were temporally high-pass stricted to voxels along the gray–white matter boundary, the number of filtered using a cut-off frequency of 3 cycles per run. The time course voxels falling within each single searchlight varied between 5 and 19 of each voxel was normalized to reflect percent signal change. We (mean: 16, std: 3). Within each searchlight, we computed the correla- aligned the first volume of each run to the high resolution anatomy of tions between odd and even runs for all combinations of movement the respective participant. Both functional and anatomical data were types and angular differences between odd and even runs using the transformed into Talairach space using trilinear interpolation. values stored in the beta maps. Next, we collapsed correlations across To obtain a better spatial correspondence across participants, we same grip types (pincer/pincer; whole-hand/whole-hand), different segmented and inflated both hemispheres of each participant, and grip types (pincer/whole-hand; whole-hand/pincer), all combinations morphed them into a spherical representation for cortex-based of same reach directions (e.g. 0/0°, 45/45°) and different reach direc- alignment (Fischl et al., 1999). Using the curvature information of tions (e.g. 0/45°, 0/90°). We assigned these four correlation values to each individual hemisphere, we performed cortex-based alignment the center of each searchlight, and stored them in a volumetric (BrainVoyager 4.1) in an iterative procedure, starting with a strongly map, separately for each participant. Next, we generated surface maps smoothed curvature map and progressing towards less smoothed cur- from these volumetric maps and aligned them to the group average in- vature maps. Cortex-based alignment resulted in a correspondence flated hemisphere using the correspondence mapping derived during mapping relating each vertex in the individual sphere to the group- cortex-based alignment. aligned sphere. These correspondence mappings were used to trans- Third, for each vertex in the group average inflated hemisphere, we form the statistical map, computed in 3D and projected to each individ- read the four correlation values (grip type × reach direction) for each participant, resulting in a 15 (participants) × 4 (conditions) matrix for ual surface, to a group-aligned map. For better orientation of functional 142 S. Fabbri et al. / NeuroImage 94 (2014) 138–146 each vertex. For each of these matrices, we computed a repeated- Table 1 Talairach coordinates of regions of interests. PMv, ventral premotor cortex; SMA, supple- measures ANOVA with the factors grip type (same, different) and mentary motor area; PMDi, dorsal premotor cortex, inferior portion; PMDs, dorsal reach direction (same, different), separately for each vertex. The premotor cortex, superior portion; M1, primary motor cortex; S1, somatosensory cortex; resulting F-values for the two main effects and the interaction were SPLr, superior parietal lobe, rostral portion; aIPS, anterior intraparietal sulcus; SPLa, supe- saved into a new surface map. rior parietal lobe, anterior portion; SPLp, posterior parietal lobe, posterior portion. We used two different approaches to threshold the three maps con- xyz taining the F-values of the two main effects and their interaction PMv −44 −12 45 resulting from the repeated-measures ANOVAs as described above. SMA −7 −12 59 First, we applied family-wise error correction using a false-discovery PMDi −33 −22 56 rate (FDR) b.05 (Genovese et al., 2002). Second, we thresholded the PMDs −20 −25 63 M1 −33 −27 48 three maps retaining only those vertices with the top 1, 5 and 10% of S1 −41 −33 52 the highest F-values. SPLr −24 −36 62 aIPS −40 −37 35 SPLa −32 −49 51 Results SPLp −17 −56 55 Univariate analysis direction, grip type and their interaction, respectively. The results are As a first step, we identified areas involved in the execution of shown in Fig. 5. As can be seen, areas showing the strongest effect of reach direction are located along the dorsomedial pathway (blue) and center-out reach-to-grasp movements by running a RFX GLM contrast between all movement types versus baseline, collapsed across reach di- consist of PMDs, SPLr, SPLp/aPCu, and SMA. By contrast, areas showing the strongest effect of grip type (red) were located more laterally and rection. At a threshold of FDR b .01, this contrast revealed a recruitment of primary motor cortex (M1), the dorsal premotor cortex (PMd), ven- consist of PMDi, PMv, M1, S1, SPLa and aIPS. Beyond the main effect of grip type, SPLa, aIPS, PMv, and M1 also showed a weak interaction be- tral premotor cortex (PMv), primary somatosensory cortex (S1), the rostral part of the superior parietal lobe (SPLr), anterior SPL (SPLa) tween reach direction and grip type (yellow). To examine whether the results of the searchlight analysis change and posterior SPL (SPLp/anterior precuneus), anterior intraparietal sulcus (aIPS), and the supplementary motor area (SMA) in the left qualitatively depending on the threshold applied to each map, we re- peated the analysis described above, showing the top 1, 5 or 10% of all hemisphere known to be part of the prehension network (Fig. 3; see vertices for each of the three maps. The corresponding maps can be Table 1 for Talairach coordinates). seen in Supplementary Fig. 1. As it becomes clear, the qualitative pattern Rostral SPL most likely corresponds to area IPS4 (Mars et al., 2011) is invariant across the three different thresholds, with dorsomedial or area 5L (Scheperjans et al., 2008), whereas SPLa most likely resem- bles area VIP (Mars et al., 2011)orarea 7PC (Scheperjans et al., 2008). areas showing the strongest effect of reach direction and dorsolateral areas showing the strongest effect of grip type. Searchlight-based multivoxel pattern analysis Directional tuning and grip selectivity across regions Fig. 4 shows the results of the searchlight analysis, thresholded with an FDR b .05. This analysis revealed a widespread network of areas with- Fig. 6 shows correlations for same (black circles) and different in and beyond the dorsomedial pathway, in particular, SPLp, SPLr, SMA, (white circles) grip types as a function of the angular difference be- and the superior portion of the PMd (PMDs) that were sensitive to reach tween odd and even runs (0°, ±45°, ±90°, ±135°), separately for direction (blue). By contrast, the inferior portion of PMd (PMDi), PMv, each region revealed by the searchlight analysis. The boundaries of the M1, S1, SPLa and aIPS showed selectivity for both reach direction and regions used for this analysis are shown in Supplementary Fig. 2. Sup- grip type. At this statistical threshold, none of the areas showed an inter- plementary Fig. 3 shows the same as Fig. 6, but distinguishes between action between reach direction and grip type. Next, instead of FDR the two grip types instead of collapsing across them. Note that the re- correction, we thresholded all three maps, retaining only those sults shown in Fig. 6 and Supplementary Fig. 3 are biased by the search- vertices containing the top 5% F values, corresponding to uncorrected light analysis and thus just serve as an additional visualization of the min. p-values of .0012, .000023 and .041 for the main effect of reach results of the searchlight analysis shown in Figs. 4 and 5. Fig. 3. Statistical map resulting from the contrast “all conditions vs baseline”, superimposed on the averaged folded inflated brain of all N = 15 participants (FDR b .01). Major sulci and gyri are denoted by white lines. SMA, supplementary motor area; PMd, dorsal premotor cortex; M1, primary motor cortex; SPLr, superior parietal lobe, rostral portion; S1, somatosensory cor- tex; SPLp, posterior parietal lobe, posterior portion; SPLa, superior parietal lobe, anterior portion; aIPS, anterior intraparietal sulcus. S. Fabbri et al. / NeuroImage 94 (2014) 138–146 143 Fig. 4. Searchlight analysis showing the main effect of reach direction (blue), grip type (red), their overlap (purple) and the interaction (yellow; FDR b .05). Data are superimposed on averaged folded inflated brain of all N = 15 participants. Major sulci and gyri are denoted by white lines. SMA, supplementary motor area; PMDs, dorsal premotor cortex, superior portion; PMDi, dorsal premotor cortex, inferior portion; PMv, ventral premotor cortex; M1, primary motor cortex; SPLr, superior parietal lobe, rostral portion; S1, somatosensory cortex; SPLp, pos- terior parietal lobe, posterior portion; SPLa, superior parietal lobe, anterior portion; aIPS, anterior intraparietal sulcus. Compatible with the results obtained in Figs. 4 and 5, tuning curves been challenged by growing evidence that areas in both streams are in- for same grip type (black circles) and different grip type (white circles) volved in processing both components (Cavina-Pratesi et al., 2010; were very similar in SMA, PMDs, SPLr and SPLp/aPCu, suggesting that Fattori et al., 2010; Kakei et al., 2001). However, studies that examined neuronal populations in these regions are mainly selective for reach di- both reach and grip type selectivity within the same experimental par- rection, irrespective of grip type, similar to the predictions illustrated in adigm are scarce (Stark et al., 2007). Consequently, it is unclear to which Fig. 1b. By contrast, PMDi, M1, and S1 showed strong effects of grip type, degree neuronal populations sensitive to grip type are sensitive also to as evidenced by the differences between the curves for same and differ- the transport component and vice versa. Here, we used multivoxel pat- ent grip types. The effect of reach direction in these regions was weaker tern searchlight analysis of fMRI data in a paradigm that required partic- in comparison to those obtained along the dorsomedial stream, as evi- ipants to perform three different types of movements (touch, pincer denced by broader tuning curves, in line with the results of the search- grip, whole-hand grip) in five different reach directions. We found light analysis (Figs. 4 and 5), similar to the predictions illustrated in that regions of the dorsomedial pathway are selective for reach direc- Fig. 1c. Finally, the effect of grip type and reach direction tended to inter- tion, while areas of the dorsolateral pathway were selective for both act in SPLa, PMv, and aIPS, with the strongest effect in SPLa, similar to grip type and reach direction. Whereas using a family-wise error correc- the predictions shown in Fig. 1d. tion of FDR b .05 did not reveal any interaction, a more liberal selection of the top 1%, 5%, and 10% of all vertices suggested a weak interaction Discussion between the reach and grasp component in SPLa, aIPS, PMv and M1, with the strongest effect in SPLa. The hypothesized distinction between independent transport and grip components of the prehensile action has been challenged by behav- Areas specialized for reach direction ioral evidence showing that a perturbation of the transport component influences the kinematics of the grasp (Haggard and Wing, 1995) and Figs. 4, 5, and 6 show a main effect of reach direction, and no sensi- by a model proposing that grasping could be explained simply by tivity for grip type, in SMA, PMDs, SPLr and SPLp/aPCu, suggesting that pointing with two digits (Smeets and Brenner, 1999). At a neuronal these areas preferentially code the transport component of the action. level, the distinction between a dorsolateral and a dorsomedial stream Results in PMd and SMA are consistent with previous findings reporting specialized for the grip and transport component, respectively, has selectivity for reach direction in various regions of the monkey (Mahan Fig. 5. Searchlight analysis retaining only those vertices with the top 5% of the highest F-values for the main effect of reach direction (blue), grip type (red), their overlap (purple) and the interaction (yellow). Data are superimposed on averaged folded inflated brain of all N = 15 participants. Labels same as in Fig. 4. 144 S. Fabbri et al. / NeuroImage 94 (2014) 138–146 Fig. 6. Fisher-transformed correlations between odd and even runs as a function of angular difference between reach directions (x-axis) and grip type (black circles: same grip type, white circles: different grip type), separately for each region revealed by the searchlight analysis. Labels same as in Fig. 4. and Georgopoulos, 2013) and human fronto-parietal network including noted, however, that the rather weak grip selectivity we observed in PMd and SMA (Fabbri et al., 2010, 2012; Lingnau et al., 2014). Likewise, areas aIPS and PMv that are known to contain visually responsive neu- the results in SPLr and SPLp/aPCu are consistent with the preference rons (Murata et al., 2000; Raos et al., 2006) is likely to be due to the fact for the reaching component previously reported in these areas that we used non-visually guided movements in the current study. (Cavina-Pratesi et al., 2010; Filimon et al., 2009; Konen et al., 2013). In contrast to visually-guided actions where the required reach di- rection is typically given by a spatial cue indicating the target location Visually vs non-visually-guided reaching (see for example Filimon et al., 2009), here we used a centrally present- ed arrow to instruct reach direction. In comparison to spatially guided As one moves along the posterior–anterior axis of the posterior pari- actions, movements instructed by arbitrary stimulus–response associa- tions have been shown to lead to a stronger recruitment of ventral pre- etal cortex, the proportion of visually responsive neurons decreases whereas the proportion of movement-related neurons increases frontal cortex, the putamen/globus pallidus and dorsal premotor cortex/ BA6 (Toni et al., 2001). Whereas we obtained no recruitment of ventral (Battaglia-Mayer, 2001; Battaglia-Mayer et al., 2000; Burnod et al., 1999; Galletti et al., 1996, 1997; Johnson et al., 1996; Marconi et al., prefrontal cortex, one might argue that the results we obtained in dorsal premotor cortex to some degree reflect the association between the 2001). A similar visuo-motor gradient is present in frontal areas from dorso-rostral premotor cortex (F7) to dorso-caudal premotor cortex orientation of the arrow and the required reach direction. Whereas (F2) and M1 (Battaglia-Mayer, 2001; Marconi et al., 2001). Neuroimag- we cannot fully exclude this possibility, it is important to point out ing studies have suggested a similar functional organization in the that PMd has been consistently shown to be recruited in previous stud- human brain, where the superior parieto-occipital sulcus is more active ies using visually-guided reaching using spatially congruent cues (Cavina-Pratesi et al., 2010; Filimon et al., 2009; Gallivan et al., 2011a), during visually than non-visually guided actions and anterior precuneus is equally active in both conditions (Filimon et al., 2009). making a recruitment of this region on the basis of arbitrary stimulus– response mappings alone less likely. Here we focused on the neuronal basis of proprioceptively-guided actions, excluding visual information from the hand and the target. As one would expect on the basis of the literature reported above, we did Overlapping representations for reach direction and grip type not obtain any involvement of the superior parieto-occipital cortex that is known to show a preference for visually compared to non- Our results show that both reach direction and grip type are repre- visually guided actions (Filimon et al., 2009). Other than that, we sented in PMDi, PMv, M1, S1, SPLa and aIPS. The reported directional se- found the same set of parietal and frontal areas that are typically report- lectivity in these regions is in line with directional tuning measured in ed to be involved during visually-guided actions (e.g. Cavina-Pratesi monkey area M1 (Georgopoulos et al., 1982), area PMd (Caminiti et al., 2010; Filimon et al., 2009; Gallivan et al., 2011a,b). It should be et al., 1991), areas 2 and 5 (Kalaska et al., 1983), and in various fronto- S. Fabbri et al. / NeuroImage 94 (2014) 138–146 145 parietal areas in the human brain (Fabbri et al., 2010, 2012; Lingnau Appendix A. Supplementary data et al., 2014). Likewise, grip selectivity in these areas is consistent with similar findings in monkey area AIP (Murata et al., 2000; Taira et al., Supplementary data to this article can be found online at http://dx. 1990), area F5 (Fluet et al., 2010; Rizzolatti et al., 1988; Umilta et al., doi.org/10.1016/j.neuroimage.2014.03.017. 2007), PMd (Raos et al., 2004), and M1 (Muir and Lemon, 1983; Umilta et al., 2007) and in human aIPS, PMv (Binkofski et al., 1999; References Cavina-Pratesi et al., 2010; Culham et al., 2003; Frey et al., 2005), as well as PMd, M1, and S1 (Ehrsson et al., 2000). Selectivity for grip type Battaglia-Mayer, A., 2001. Eye–hand coordination during reaching. II. An analysis of the relationships between visuomanual signals in parietal cortex and parieto-frontal as- and reach direction in S1 is likely due to sensitivity to somatosensory sociation projections. Cereb. Cortex 11, 528–544. feedback associated with the specific movement. Somatosensory feed- Battaglia-Mayer, A., Ferraina, S., Mitsuda, T., Marconi, B., Genovesio, A., Onorati, P., back might also be reflected in the results in M1 and PMd, since both re- Lacquaniti, F., Caminiti, R., 2000. Early coding of reaching in the parietooccipital cor- tex. J. Neurophysiol. 83, 2374–2391. gions receive kinesthetic and proprioceptive information from S1 Binkofski, F., Dohle, C., Posse, S., Stephan, K.M., Hefter, H., Seitz, R.J., Freund, H.J., 1998. through short-loop and long-loop projections, respectively (Gardner Human anterior intraparietal area subserves prehension: a combined lesion and et al., 2007). functional MRI activation study. Neurology 50, 1253–1259. Binkofski, F., Buccino, G., Posse, S., Seitz, R.J., Rizzolatti, G., Freund, H., 1999. A fronto- Note that whereas previous studies investigated the reach and grip parietal circuit for object manipulation in man: evidence from an fMRI-study. components separately, the novelty of our study consists in the manip- European Journal of Neuroscience. Blackwell Science Ltd. ulation of grip type and reach direction within the same paradigm, Brainard, D.H., 1997. The psychophysics toolbox. Spat. Vis. 10, 433–436. allowing us to measure the relation between the selectivity for the Bremmer, F., Schlack, A., Shah, N.J., Zafiris, O., Kubischik, M., Hoffmann, K., Zilles, K., Fink, G.R., 2001. Polymodal motion processing in posterior parietal and premotor cortex: a two components. Within some of the overlapping regions selective for human fMRI study strongly implies equivalencies between humans and monkeys. grip type and reach direction (PMDi, S1), we observed independent se- Neuron 29, 287–296. lectivity for the two components. Using a more liberal statistical thresh- Burnod, Y., Baraduc, P., Battaglia-Mayer, A., Guigon, E., Koechlin, E., Ferraina, S., Lacquaniti, F., Caminiti, R., 1999. Parieto-frontal coding of reaching: an integrated framework. old, we obtained weak interactions in PMv, M1, aIPS and SPLa. One Exp. Brain Res. 129, 325–346. should note that Stark et al. (2007) reported an interaction between se- Caminiti, R., Johnson, P.B., Galli, C., Ferraina, S., Burnod, Y., 1991. Making arm movements lectivity for grip type and reach direction in only 1/4 of neurons in PMd within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets. J. Neurosci. 11, 1182–1197. and PMv. Cavina-Pratesi, C., Monaco, S., Fattori, P., Galletti, C., McAdam, T.D., Quinlan, D.J., Goodale, We observed the strongest trend for an interaction between reach M.A., Culham, J.C., 2010. Functional magnetic resonance imaging reveals the neural direction and grip type in SPLa, corresponding to monkey ventral substrates of arm transport and grip formation in reach-to-grasp actions in humans. J. Neurosci. 30, 10306–10323. intraparietal area (VIP) (Mars et al., 2011). This region has been report- Culham, J.C., Danckert, S.L., DeSouza, J.F.X., Gati, J.S., Menon, R.S., Goodale, M.A., 2003. ed to be sensitive to the direction of visual, tactile and auditory stimuli Visually guided grasping produces fMRI activation in dorsal but not ventral stream (Bremmer et al., 2001) and to the spatial congruency between visual brain areas. Exp. Brain Res. 153, 180–189. and tactile information (Duhamel et al., 1998) and thus might be a Culham, J.C., Cavina-Pratesi, C., Singhal, A., 2006. 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Overlapping representations for grip type and reach direction

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Fabbri, S.; Strnad, L.; Caramazza, A.; Lingnau, A. 2014, Article / Letter to editor (NeuroImage, 94, (2014), pp. 138-146) Doi link to publisher: https://doi.org/10.1016/j.neuroimage.2014.03.017 Version of the following full text: Publisher’s version Published under the terms of article 25fa of the Dutch copyright act. Please follow this link for the Terms of Use: https://repository.ubn.ru.nl/page/termsofuse Downloaded from: https://hdl.handle.net/2066/129978 Download date: 2025-06-28 Note: To cite this publication please use the final published version (if applicable). NeuroImage 94 (2014) 138–146 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg a,1 a,b a,b a,c, Sara Fabbri ,Lukas Strnad , Alfonso Caramazza ,Angelika Lingnau ⁎ Center for Mind/Brain Sciences, University of Trento, 38100 Mattarello, Italy Department of Psychology, Harvard University, MA 02138, Cambridge, USA Department of Cognitive Sciences, University of Trento, 38068 Rovereto, Italy article i nfo abstract Article history: To grasp an object, we need to move the arm toward it and assume the appropriate hand configuration. While Accepted 8 March 2014 previous studies suggested dorsomedial and dorsolateral pathways in the brain specialized respectively for the Available online 17 March 2014 transport and grip components, more recent studies cast doubt on such a clear-cut distinction. It is unclear, how- ever, to which degree neuronal populations selective for the two components overlap, and if so, to which degree Keywords: they interact. Here, we used multivoxel pattern analysis (MVPA) of functional magnetic resonance imaging Directional tuning (fMRI) data to investigate the representation of three center-out movements (touch, pincer grip, whole-hand fMRI grip) performed in five reach directions. We found selectivity exclusively for reach direction in posterior and ros- Grasping tral superior parietal lobes (SPLp, SPLr), supplementary motor area (SMA), and the superior portion of dorsal Reaching Searchlight premotor cortex (PMDs). Instead, we found selectivity for both grip type and reach direction in the inferior por- MVPA tion of dorsal premotor cortex (PMDi), ventral premotor cortex (PMv), anterior intraparietal sulcus (aIPS), pri- mary motor (M1), somatosensory (S1) cortices and the anterior superior parietal lobe (SPLa). Within these regions, PMv, M1, aIPS and SPLa showed weak interactions between the transport and grip components. Our re- sults suggest that human PMDi and S1 contain both grip- and reach-direction selective neuronal populations that retain their functional independence, whereas this information might be combined at the level of PMv, M1, aIPS, and SPLa. © 2014 Elsevier Inc. All rights reserved. Introduction of separate streams for transport and grip, directionally tuned neurons have been found in monkey PMd (Caminiti et al., 1991) and parietal The ability to reach for and grasp objects is fundamental for our in- area V6A (Fattori et al., 2001, 2005). Likewise, the human SPOC and teraction with the environment. Reaching refers to the transport the rostral superior parietal lobe have been reported to show stronger phase of the hand toward the object, while grasping includes the activation during reaching to far locations in comparison to near preshaping of the hand in relation to the shape and size of the object. locations, indicating a general preference for the transport in com- It has been suggested that the transport component relies on a parison to the grip component (Cavina-Pratesi et al., 2010). How- dorsomedial pathway consisting of superior parieto-occipital cortex ever, directionally tuned neurons have also been reported outside (SPOC) in the medial wall of the parietal cortex, medial intraparietal dorsomedial areas, like monkey PMv (Kakei et al., 2001; Stark et al., area (MIP) and the dorsal premotor cortex (PMd); the grip component 2007), primary motor cortex (M1) (Georgopoulos et al., 1982), and is thought to rely on a dorsolateral pathway consisting of the anterior the cerebellum (Fortier et al., 1989). Using fMRI adaptation, directional intraparietal sulcus (aIPS) and the ventral premotor cortex (PMv) selectivity has been demonstrated both in regions of the human (Culham et al., 2006; Jeannerod et al., 1995; Tanné-Gariépy et al., dorsomedial and the dorsolateral pathway (Fabbri et al., 2010, 2012; 2002; Vesia and Crawford, 2012). Lingnau et al., 2014). Taken together, these studies indicate that the One of the best studied parameters of the transport component is di- representation of the transport component is not restricted to the rectional tuning, identified as maximal activity during reaching in the dorsomedial pathway. preferred direction and a gradual decrease of activity with increasing A number of studies support the view of a specialized role of the dor- angular difference from the preferred direction. In line with the view solateral stream for the representation of the grip component.Macaque area AIP contains neurons selective for the grip used to grasp a specific object (Murata et al., 2000; Taira et al., 1990). This area projects to area ⁎ Corresponding author at: Center for Mind/Brain Sciences, University of Trento, Via F5, which also shows grip selectivity (Fluet et al., 2010; Rizzolatti et al., delle Regole, 101, Mattarello, TN 38100, Italy. Fax: +39 0461 88 3066. 1988; Umilta et al., 2007). Inactivation of both areas causes impairment E-mail address: [email protected] (A. Lingnau). in appropriately grasping an object (Fogassi et al., 2001; Gallese et al., Present address: Brain and Mind Institute, University of Western Ontario, N6A 5B7 London, Canada. 1994). Human fMRI studies demonstrated that both aIPS and PMv http://dx.doi.org/10.1016/j.neuroimage.2014.03.017 1053-8119/© 2014 Elsevier Inc. All rights reserved. S. Fabbri et al. / NeuroImage 94 (2014) 138–146 139 respond more strongly during grasping in comparison to reaching vice versa (Fig. 1b), we aimed to identify areas that contain both grip- movements (Binkofski et al., 1999; Cavina-Pratesi et al., 2010; Culham type and reach-direction selective neuronal populations. Such areas et al., 2003; Frey et al., 2005). Moreover, permanent as well as tempo- might contain neuronal populations that are directionally tuned irre- rary lesions to both human aIPS and PMv lead to an impairment in shap- spective of the type of grip (Fig. 1c). Alternatively, they might consist ing the hand in relation to the shape and size of the object (Binkofski of neuronal populations that are both grip-type and reach-direction se- et al., 1998, 1999; Dafotakis et al., 2008; Davare et al., 2006, 2007; Rice lective, as it was reported in monkey PMd and PMv by Stark et al. et al., 2006). (2007); such areas should show an interaction between the two param- Whereas the studies reported above support the view of a relative eters (see Fig. 1d). specialization of the dorsolateral pathway for the grip component, it To test these predictions, we instructed participants to perform sim- has been shown that monkey PMd (Stark et al., 2007)and parietal ple non-visually guided center-out reach-to-grasp movements (touch, area V6A (Fattori et al., 2010) in the dorsomedial pathway also contain pincer grip, whole-hand grip) in five different reach directions (0, 45, grip selective neurons. Furthermore, it has been found, using multivoxel 90, 135, 180°, where 90° is straight ahead; see Figs. 2a–c). To measure pattern analysis (MVPA) to decode brain activity, that precision grasps selectivity for grip type, reach direction and their interaction, we per- of different object sizes can be distinguished both in regions of the dor- formed multivoxel pattern searchlight analysis. solateral and the dorsomedial pathway (Gallivan et al., 2011b). The fact that both the dorsomedial and the dorsolateral pathway Materials and methods seem to be sensitive to certain aspects of the transport and grasp com- ponents suggests that the functional distinction between these two Participants components is not as clear-cut as originally thought. Little is known, however, about the combined representation of transport and grip. Sixteen volunteers (9 males) took part in the experiment (age Stark et al. (2007) recorded from neurons in monkey PMd and PMv. range: 21–52 years). All but one were right handed. Participants had For each recording site, the authors determined whether intracortical normal or corrected-to-normal vision using MR-compatible glasses. microstimulation (ICMS) evoked movements of proximal (shoulder, Two of the authors (L.S., A.L.) took part in the experiment, while the elbow) or distal (finger) joints. In line with previous studies, ICMS in other participants were naïve to the purpose of the study; all gave writ- PMd and PMv led to activation of muscles involved in the transport ten informed consent for their participation. The experimental proce- and in the grip component, respectively. Surprisingly, the authors dures were approved by the ethics committee for research involving observed that roughly the same proportion of neurons modulated human subjects at the University of Trento. Data recorded from one par- by either reach direction or grip type were observed both within PMd ticipant were excluded from the analysis because it became clear and PMv. Moreover, in about 1/4 of all recorded neurons, the effect throughout the experiment that she did not properly understand the of reach direction and grip type interacted. They proposed that task. directionally tuned neurons in PMv and grip selective neurons in PMd might serve the purpose of relaying directional information through horizontal connections from proximal to distal sites, and information Procedure and visual stimulation about grip type from distal to proximal sites. Previous studies aiming to distinguish between the reach and grasp During each trial, participants were presented with an arrow at the components compared brain activity during reach-to-grasp movements center of the screen for 2 seconds (s), followed by an inter-trial- versus point-to-touch movements (Cavina-Pratesi et al., 2010; interval (ITI) of 1 s (see Fig. 2a). Using their right hand, participants Desmurget et al., 2001; Faillenot et al., 1997; Konen et al., 2013). Such had to execute a center-out reach-to-grasp task on a device attached paradigms, however, are limited by the fact that the spatial accuracy de- to their chest. Visual feedback was not provided so as to exclude con- mands of these two movements are clearly different. Here we used an founds such as systematic eye movements toward the target object innovative approach that does not rely on this assumption, varying and uncontrolled visual stimulation by the sight of the participant's both reach direction and grip type and measuring their selectivity across own hand (see also Fabbri et al., 2010, 2012; Lingnau et al., 2014). The the entire brain. This allowed us to ask the question whether selectivity device consisted of 5 half-spheres of polystyrene (3 cm diameter) for reach direction and grip type are present within the same region, glued on a black plastic surface. They were placed at five equidistant po- and if so, whether these two components interact. In addition to areas sitions on a virtual circle (8 cm radius) as well as at the center of that that are either grip selective but not directionally tuned (Fig. 1a) or circle. Fig. 1. Hypothetical Fisher-transformed correlations between odd and even runs as a function of angular difference in reach direction (x-axis) and combination of grip types (black circles: same grip type, white circles: different grip type). a–d: Hypothetical data from a ROI that contains neuronal populations that are grip-type selective, but not directionally tuned (a), directionally tuned, but not grip-type selective (b) selective for grip-type and reach-direction, but not for their interaction (c), selective for grip-type and reach-direction, as well as to their interaction (d). Note that the interaction depicted in (d) is only one out of several possible examples. 140 S. Fabbri et al. / NeuroImage 94 (2014) 138–146 Fig. 2. a: Example sequence of three trials during which participants were instructed to execute reach-to-grasp movements in directions 90, 0, and 180°. The color of the arrow indicated the type of movement (yellow: touch, red: pincer, green: whole-hand grasp). b: Participants positioned their right index finger on the central location and executed reach-to-grasp move- ments toward the peripheral target indicated by the visual instruction. Superimposed arrows (not shown during the experiment) indicate the directions corresponding to the three ex- ample trials shown in panel a. c: The experimental design consisted of 3 (movement type) × 5 (reach direction) conditions. To reduce visual similarity between trials, we varied the visual appearance of the arrow that indicated the movement direction and type of grasp on each trial (see Materials and methods for details). Hand movements differed in the way the hand made contact with the target object (touch: using the index finger; pincer grip: index finger and thumb; whole-hand grip: all fingers). At the beginning of each trial, participants positioned their index fin- To reduce visual similarity between trials, we varied the visual ap- ger on the central half-sphere (Fig. 2b). They were instructed to execute pearance of the arrow that indicated the reach direction and type of center-out movements in one of the five possible directions using one of grasp on each trial (see Fabbri et al., 2010 for a similar approach). the three different movement types as soon as the arrow appeared on Arrow width and length were varied randomly from 0.41° to 1.22° in the screen, and to then move back to the start position. Reach direction steps of 0.405°. The x- and y-center coordinates of the arrow were was indicated by the orientation of the arrow presented on the screen, jittered in a range of ±0.07° in steps of 0.035°. Stimuli were back- while the type of movement was specified by its color (see Fig. 2c). projected onto a screen by a liquid-crystal projector at a frame rate of Note that both whole-hand and pincer grip differ from touching in 60 Hz and a screen resolution of 1280 × 1024 pixels. Participants that they require grasping a target. The two grasps differ in the configu- viewed the stimuli binocularly through a mirror above the head coil. ration of the hand: whole-hand grasp requires the use of all fingers The screen was visible as a rectangular aperture of 17.5 × 14.3°. Visual while pincer grip uses only thumb and index fingers (see Fig. 2c). stimulation was controlled by ASF (Schwarzbach, 2011) based on the Altogether there were 3 (movement type) × 5 (reach direction) con- MATLAB Psychtoolbox-3 for Windows (Brainard, 1997; Pelli, 1997). ditions (Fig. 2c). In addition, 12% of the trials were null trials in which participants had to maintain fixation while keeping the hand at the cen- Instructions and training ter position for 3 s (2 s trial duration + 1 s ITI). Instructions were counterbalanced between participants: for 8 out Before entering the scanner, participants learned to execute center- of 16 participants, a green arrow instructed them to reach the target out movements corresponding to the visual instructions, and they fa- using a whole-hand grip, a yellow arrow instructed to touch the target, miliarized themselves with the location of the half spheres on the device and a red arrow instructed to reach the target using a pincer grip. For the such that they were able to perform the movements accurately in the other half of the participants, the green arrow instructed to touch the absence of visual feedback (see also Fabbri et al., 2010, 2012; Lingnau target, the yellow arrow instructed to use a pincer grip, and the red et al., 2014). The experimenter instructed participants to execute each arrow instructed to use a whole-hand grip. movement within a constant time window of 2 s corresponding to the S. Fabbri et al. / NeuroImage 94 (2014) 138–146 141 presentation time of the arrow, rather than trying to move as fast as pos- data with respect to anatomical landmarks, we reconstructed the inflat- sible and thus risking head movements. Participants were asked to ed left and right hemisphere that represents the average curvature move their hand back to the center position before the arrow disap- maps of all 15 participants who took part in the study. Since we used peared, and to start each trial from the center position. right-hand movements that are known to preferentially recruit the left hemisphere, only data from the left hemisphere are presented. fMRI design Univariate analysis The entire experiment consisted of 12 event-related runs. Each At the first level, we estimated beta weights for each combination of run consisted of 75 experimental trials and 10 null trials for a total of type of movement (touch, pincer grip, whole-hand grip) and reach direc- 85 trials, and lasted 4.2 min. For each participant, each of the 15 condi- tion (0, 45, 90, 135, and 180°), time-locked to the onset of the arrow, tions (5 reach directions × 3 types of movement) was repeated 5 times separately for each participant. Altogether, we included 3 × 5 = 15 pre- in a run for a total of 60 repetitions per condition. dictors. Moreover, we added six parameters (x, y, z translation and rotation) resulting from 3D motion correction as predictors of no inter- Data acquisition est. The time course of each predictor of interest was convolved with a dual-gamma hemodynamic impulse response function (Friston et al., We acquired fMRI data using a 4T Bruker MedSpec MRI scanner and 1998), and the resulting reference time courses were used to fit the sig- an 8-channel birdcage head coil. Functional images were acquired with nal time course of each voxel. Beta estimates derived from the first-level a T2*-weighted gradient-recalled echo-planar imaging (EPI) sequence. analysis were projected on the surface and aligned to the group average Before each functional scan, we performed an additional scan to mea- inflated hemisphere of all participants using the correspondence map- sure the point-spread function (PSF) of the acquired sequence, which ping obtained during cortex-based alignment. The cortex-based aligned serves for correcting of the distortion expected with high-field imaging individual maps were entered into a second level random effects (RFX) (Zaitsev et al., 2004). We used 34 slices, acquired in ascending inter- general linear model (GLM) analysis carried out on the surface. To iden- leaved order, slightly tilted to run parallel to the calcarine sulcus tify areas recruited during movement execution, we computed the RFX (TR (time to repeat): 2000 ms; voxel resolution: 3 × 3 × 3 mm; TE GLM contrast “all conditions versus baseline”, where baseline refers (echo time): 33 ms; flip angle (FA): 73°; field of view (FOV): 192 × to all periods not explicitly modeled in the GLM. Statistical maps 192 mm; gap size: 0.45 mm). Each participant completed 12 scans of were corrected for multiple comparisons using a False Discovery Rate (FDR) b.01. 126 volumes each. To be able to co-register the low-resolution functional images to a high-resolution anatomical scan, we acquired a T1 weighted anatomical Searchlight-based multivoxel pattern analysis scan (MP-RAGE; voxel resolution: 1 × 1 × 1 mm; FOV: 256 × 224 mm; We performed correlation-based multivoxel pattern analysis GRAPPA acquisition with an acceleration factor of 2; TR: 2700 ms, inver- (MVPA, Haxby et al., 2001) using a searchlight approach (Kriegeskorte sion time (TI), 1020 ms; FA: 7°). et al., 2006). First of all, we created beta maps using a cortex mask that restricts the analysis to voxels falling within −3to+1 mm ofthe Data analysis gray–white matter boundary determined during segmentation. To do so, we extracted the time course for each combination of movement Data analysis was performed using BrainVoyager QX 4.1 (Brain type (touch, pincer grip, whole hand grip) and reach direction (0°, Innovation), the BVQX Toolbox (http://support.brainvoyager.com/ 45°, 90°, 135°, 180°), separately for each participant for each voxel in available-tools/52-matlab-tools-bvxqtools.html) and custom software the cortex mask. Next, we calculated z-transformed β-estimates of the written in MATLAB (MathWorks). BOLD response separately for each participant, condition and run. Then, we averaged β-estimates across odd and even runs, resulting in Preprocessing, segmentation, and cortex-based alignment a 15 (3 movement types × 5 reach directions) × N voxels correlation To correct for distortions in geometry and intensity in the matrix for each of the odd and even runs, separately for each participant. EPI images, we applied distortion correction on the basis of the Data were normalized by subtracting the grand mean response across PSF data acquired before each EPI scan (Zeng and Constable, 2002). conditions from each voxel, separately for odd and even runs (Haxby Before further analysis, we removed the first 4 volumes to avoid T1- et al., 2001). saturation. Next, we performed 3D motion correction with trilinear in- Second, in each participant, for each voxel in the cortex mask, we de- terpolation for estimation and sinc interpolation for resampling using termined a small region (the searchlight) containing all voxels falling the first volume as reference followed by slice timing correction with as- within a radius of 5 mm of the central voxel. Since the analysis was re- cending interleaved order. Functional data were temporally high-pass stricted to voxels along the gray–white matter boundary, the number of filtered using a cut-off frequency of 3 cycles per run. The time course voxels falling within each single searchlight varied between 5 and 19 of each voxel was normalized to reflect percent signal change. We (mean: 16, std: 3). Within each searchlight, we computed the correla- aligned the first volume of each run to the high resolution anatomy of tions between odd and even runs for all combinations of movement the respective participant. Both functional and anatomical data were types and angular differences between odd and even runs using the transformed into Talairach space using trilinear interpolation. values stored in the beta maps. Next, we collapsed correlations across To obtain a better spatial correspondence across participants, we same grip types (pincer/pincer; whole-hand/whole-hand), different segmented and inflated both hemispheres of each participant, and grip types (pincer/whole-hand; whole-hand/pincer), all combinations morphed them into a spherical representation for cortex-based of same reach directions (e.g. 0/0°, 45/45°) and different reach direc- alignment (Fischl et al., 1999). Using the curvature information of tions (e.g. 0/45°, 0/90°). We assigned these four correlation values to each individual hemisphere, we performed cortex-based alignment the center of each searchlight, and stored them in a volumetric (BrainVoyager 4.1) in an iterative procedure, starting with a strongly map, separately for each participant. Next, we generated surface maps smoothed curvature map and progressing towards less smoothed cur- from these volumetric maps and aligned them to the group average in- vature maps. Cortex-based alignment resulted in a correspondence flated hemisphere using the correspondence mapping derived during mapping relating each vertex in the individual sphere to the group- cortex-based alignment. aligned sphere. These correspondence mappings were used to trans- Third, for each vertex in the group average inflated hemisphere, we form the statistical map, computed in 3D and projected to each individ- read the four correlation values (grip type × reach direction) for each participant, resulting in a 15 (participants) × 4 (conditions) matrix for ual surface, to a group-aligned map. For better orientation of functional 142 S. Fabbri et al. / NeuroImage 94 (2014) 138–146 each vertex. For each of these matrices, we computed a repeated- Table 1 Talairach coordinates of regions of interests. PMv, ventral premotor cortex; SMA, supple- measures ANOVA with the factors grip type (same, different) and mentary motor area; PMDi, dorsal premotor cortex, inferior portion; PMDs, dorsal reach direction (same, different), separately for each vertex. The premotor cortex, superior portion; M1, primary motor cortex; S1, somatosensory cortex; resulting F-values for the two main effects and the interaction were SPLr, superior parietal lobe, rostral portion; aIPS, anterior intraparietal sulcus; SPLa, supe- saved into a new surface map. rior parietal lobe, anterior portion; SPLp, posterior parietal lobe, posterior portion. We used two different approaches to threshold the three maps con- xyz taining the F-values of the two main effects and their interaction PMv −44 −12 45 resulting from the repeated-measures ANOVAs as described above. SMA −7 −12 59 First, we applied family-wise error correction using a false-discovery PMDi −33 −22 56 rate (FDR) b.05 (Genovese et al., 2002). Second, we thresholded the PMDs −20 −25 63 M1 −33 −27 48 three maps retaining only those vertices with the top 1, 5 and 10% of S1 −41 −33 52 the highest F-values. SPLr −24 −36 62 aIPS −40 −37 35 SPLa −32 −49 51 Results SPLp −17 −56 55 Univariate analysis direction, grip type and their interaction, respectively. The results are As a first step, we identified areas involved in the execution of shown in Fig. 5. As can be seen, areas showing the strongest effect of reach direction are located along the dorsomedial pathway (blue) and center-out reach-to-grasp movements by running a RFX GLM contrast between all movement types versus baseline, collapsed across reach di- consist of PMDs, SPLr, SPLp/aPCu, and SMA. By contrast, areas showing the strongest effect of grip type (red) were located more laterally and rection. At a threshold of FDR b .01, this contrast revealed a recruitment of primary motor cortex (M1), the dorsal premotor cortex (PMd), ven- consist of PMDi, PMv, M1, S1, SPLa and aIPS. Beyond the main effect of grip type, SPLa, aIPS, PMv, and M1 also showed a weak interaction be- tral premotor cortex (PMv), primary somatosensory cortex (S1), the rostral part of the superior parietal lobe (SPLr), anterior SPL (SPLa) tween reach direction and grip type (yellow). To examine whether the results of the searchlight analysis change and posterior SPL (SPLp/anterior precuneus), anterior intraparietal sulcus (aIPS), and the supplementary motor area (SMA) in the left qualitatively depending on the threshold applied to each map, we re- peated the analysis described above, showing the top 1, 5 or 10% of all hemisphere known to be part of the prehension network (Fig. 3; see vertices for each of the three maps. The corresponding maps can be Table 1 for Talairach coordinates). seen in Supplementary Fig. 1. As it becomes clear, the qualitative pattern Rostral SPL most likely corresponds to area IPS4 (Mars et al., 2011) is invariant across the three different thresholds, with dorsomedial or area 5L (Scheperjans et al., 2008), whereas SPLa most likely resem- bles area VIP (Mars et al., 2011)orarea 7PC (Scheperjans et al., 2008). areas showing the strongest effect of reach direction and dorsolateral areas showing the strongest effect of grip type. Searchlight-based multivoxel pattern analysis Directional tuning and grip selectivity across regions Fig. 4 shows the results of the searchlight analysis, thresholded with an FDR b .05. This analysis revealed a widespread network of areas with- Fig. 6 shows correlations for same (black circles) and different in and beyond the dorsomedial pathway, in particular, SPLp, SPLr, SMA, (white circles) grip types as a function of the angular difference be- and the superior portion of the PMd (PMDs) that were sensitive to reach tween odd and even runs (0°, ±45°, ±90°, ±135°), separately for direction (blue). By contrast, the inferior portion of PMd (PMDi), PMv, each region revealed by the searchlight analysis. The boundaries of the M1, S1, SPLa and aIPS showed selectivity for both reach direction and regions used for this analysis are shown in Supplementary Fig. 2. Sup- grip type. At this statistical threshold, none of the areas showed an inter- plementary Fig. 3 shows the same as Fig. 6, but distinguishes between action between reach direction and grip type. Next, instead of FDR the two grip types instead of collapsing across them. Note that the re- correction, we thresholded all three maps, retaining only those sults shown in Fig. 6 and Supplementary Fig. 3 are biased by the search- vertices containing the top 5% F values, corresponding to uncorrected light analysis and thus just serve as an additional visualization of the min. p-values of .0012, .000023 and .041 for the main effect of reach results of the searchlight analysis shown in Figs. 4 and 5. Fig. 3. Statistical map resulting from the contrast “all conditions vs baseline”, superimposed on the averaged folded inflated brain of all N = 15 participants (FDR b .01). Major sulci and gyri are denoted by white lines. SMA, supplementary motor area; PMd, dorsal premotor cortex; M1, primary motor cortex; SPLr, superior parietal lobe, rostral portion; S1, somatosensory cor- tex; SPLp, posterior parietal lobe, posterior portion; SPLa, superior parietal lobe, anterior portion; aIPS, anterior intraparietal sulcus. S. Fabbri et al. / NeuroImage 94 (2014) 138–146 143 Fig. 4. Searchlight analysis showing the main effect of reach direction (blue), grip type (red), their overlap (purple) and the interaction (yellow; FDR b .05). Data are superimposed on averaged folded inflated brain of all N = 15 participants. Major sulci and gyri are denoted by white lines. SMA, supplementary motor area; PMDs, dorsal premotor cortex, superior portion; PMDi, dorsal premotor cortex, inferior portion; PMv, ventral premotor cortex; M1, primary motor cortex; SPLr, superior parietal lobe, rostral portion; S1, somatosensory cortex; SPLp, pos- terior parietal lobe, posterior portion; SPLa, superior parietal lobe, anterior portion; aIPS, anterior intraparietal sulcus. Compatible with the results obtained in Figs. 4 and 5, tuning curves been challenged by growing evidence that areas in both streams are in- for same grip type (black circles) and different grip type (white circles) volved in processing both components (Cavina-Pratesi et al., 2010; were very similar in SMA, PMDs, SPLr and SPLp/aPCu, suggesting that Fattori et al., 2010; Kakei et al., 2001). However, studies that examined neuronal populations in these regions are mainly selective for reach di- both reach and grip type selectivity within the same experimental par- rection, irrespective of grip type, similar to the predictions illustrated in adigm are scarce (Stark et al., 2007). Consequently, it is unclear to which Fig. 1b. By contrast, PMDi, M1, and S1 showed strong effects of grip type, degree neuronal populations sensitive to grip type are sensitive also to as evidenced by the differences between the curves for same and differ- the transport component and vice versa. Here, we used multivoxel pat- ent grip types. The effect of reach direction in these regions was weaker tern searchlight analysis of fMRI data in a paradigm that required partic- in comparison to those obtained along the dorsomedial stream, as evi- ipants to perform three different types of movements (touch, pincer denced by broader tuning curves, in line with the results of the search- grip, whole-hand grip) in five different reach directions. We found light analysis (Figs. 4 and 5), similar to the predictions illustrated in that regions of the dorsomedial pathway are selective for reach direc- Fig. 1c. Finally, the effect of grip type and reach direction tended to inter- tion, while areas of the dorsolateral pathway were selective for both act in SPLa, PMv, and aIPS, with the strongest effect in SPLa, similar to grip type and reach direction. Whereas using a family-wise error correc- the predictions shown in Fig. 1d. tion of FDR b .05 did not reveal any interaction, a more liberal selection of the top 1%, 5%, and 10% of all vertices suggested a weak interaction Discussion between the reach and grasp component in SPLa, aIPS, PMv and M1, with the strongest effect in SPLa. The hypothesized distinction between independent transport and grip components of the prehensile action has been challenged by behav- Areas specialized for reach direction ioral evidence showing that a perturbation of the transport component influences the kinematics of the grasp (Haggard and Wing, 1995) and Figs. 4, 5, and 6 show a main effect of reach direction, and no sensi- by a model proposing that grasping could be explained simply by tivity for grip type, in SMA, PMDs, SPLr and SPLp/aPCu, suggesting that pointing with two digits (Smeets and Brenner, 1999). At a neuronal these areas preferentially code the transport component of the action. level, the distinction between a dorsolateral and a dorsomedial stream Results in PMd and SMA are consistent with previous findings reporting specialized for the grip and transport component, respectively, has selectivity for reach direction in various regions of the monkey (Mahan Fig. 5. Searchlight analysis retaining only those vertices with the top 5% of the highest F-values for the main effect of reach direction (blue), grip type (red), their overlap (purple) and the interaction (yellow). Data are superimposed on averaged folded inflated brain of all N = 15 participants. Labels same as in Fig. 4. 144 S. Fabbri et al. / NeuroImage 94 (2014) 138–146 Fig. 6. Fisher-transformed correlations between odd and even runs as a function of angular difference between reach directions (x-axis) and grip type (black circles: same grip type, white circles: different grip type), separately for each region revealed by the searchlight analysis. Labels same as in Fig. 4. and Georgopoulos, 2013) and human fronto-parietal network including noted, however, that the rather weak grip selectivity we observed in PMd and SMA (Fabbri et al., 2010, 2012; Lingnau et al., 2014). Likewise, areas aIPS and PMv that are known to contain visually responsive neu- the results in SPLr and SPLp/aPCu are consistent with the preference rons (Murata et al., 2000; Raos et al., 2006) is likely to be due to the fact for the reaching component previously reported in these areas that we used non-visually guided movements in the current study. (Cavina-Pratesi et al., 2010; Filimon et al., 2009; Konen et al., 2013). In contrast to visually-guided actions where the required reach di- rection is typically given by a spatial cue indicating the target location Visually vs non-visually-guided reaching (see for example Filimon et al., 2009), here we used a centrally present- ed arrow to instruct reach direction. In comparison to spatially guided As one moves along the posterior–anterior axis of the posterior pari- actions, movements instructed by arbitrary stimulus–response associa- tions have been shown to lead to a stronger recruitment of ventral pre- etal cortex, the proportion of visually responsive neurons decreases whereas the proportion of movement-related neurons increases frontal cortex, the putamen/globus pallidus and dorsal premotor cortex/ BA6 (Toni et al., 2001). Whereas we obtained no recruitment of ventral (Battaglia-Mayer, 2001; Battaglia-Mayer et al., 2000; Burnod et al., 1999; Galletti et al., 1996, 1997; Johnson et al., 1996; Marconi et al., prefrontal cortex, one might argue that the results we obtained in dorsal premotor cortex to some degree reflect the association between the 2001). A similar visuo-motor gradient is present in frontal areas from dorso-rostral premotor cortex (F7) to dorso-caudal premotor cortex orientation of the arrow and the required reach direction. Whereas (F2) and M1 (Battaglia-Mayer, 2001; Marconi et al., 2001). Neuroimag- we cannot fully exclude this possibility, it is important to point out ing studies have suggested a similar functional organization in the that PMd has been consistently shown to be recruited in previous stud- human brain, where the superior parieto-occipital sulcus is more active ies using visually-guided reaching using spatially congruent cues (Cavina-Pratesi et al., 2010; Filimon et al., 2009; Gallivan et al., 2011a), during visually than non-visually guided actions and anterior precuneus is equally active in both conditions (Filimon et al., 2009). making a recruitment of this region on the basis of arbitrary stimulus– response mappings alone less likely. Here we focused on the neuronal basis of proprioceptively-guided actions, excluding visual information from the hand and the target. As one would expect on the basis of the literature reported above, we did Overlapping representations for reach direction and grip type not obtain any involvement of the superior parieto-occipital cortex that is known to show a preference for visually compared to non- Our results show that both reach direction and grip type are repre- visually guided actions (Filimon et al., 2009). Other than that, we sented in PMDi, PMv, M1, S1, SPLa and aIPS. The reported directional se- found the same set of parietal and frontal areas that are typically report- lectivity in these regions is in line with directional tuning measured in ed to be involved during visually-guided actions (e.g. Cavina-Pratesi monkey area M1 (Georgopoulos et al., 1982), area PMd (Caminiti et al., 2010; Filimon et al., 2009; Gallivan et al., 2011a,b). It should be et al., 1991), areas 2 and 5 (Kalaska et al., 1983), and in various fronto- S. Fabbri et al. / NeuroImage 94 (2014) 138–146 145 parietal areas in the human brain (Fabbri et al., 2010, 2012; Lingnau Appendix A. Supplementary data et al., 2014). Likewise, grip selectivity in these areas is consistent with similar findings in monkey area AIP (Murata et al., 2000; Taira et al., Supplementary data to this article can be found online at http://dx. 1990), area F5 (Fluet et al., 2010; Rizzolatti et al., 1988; Umilta et al., doi.org/10.1016/j.neuroimage.2014.03.017. 2007), PMd (Raos et al., 2004), and M1 (Muir and Lemon, 1983; Umilta et al., 2007) and in human aIPS, PMv (Binkofski et al., 1999; References Cavina-Pratesi et al., 2010; Culham et al., 2003; Frey et al., 2005), as well as PMd, M1, and S1 (Ehrsson et al., 2000). Selectivity for grip type Battaglia-Mayer, A., 2001. Eye–hand coordination during reaching. II. An analysis of the relationships between visuomanual signals in parietal cortex and parieto-frontal as- and reach direction in S1 is likely due to sensitivity to somatosensory sociation projections. Cereb. Cortex 11, 528–544. feedback associated with the specific movement. Somatosensory feed- Battaglia-Mayer, A., Ferraina, S., Mitsuda, T., Marconi, B., Genovesio, A., Onorati, P., back might also be reflected in the results in M1 and PMd, since both re- Lacquaniti, F., Caminiti, R., 2000. Early coding of reaching in the parietooccipital cor- tex. J. Neurophysiol. 83, 2374–2391. gions receive kinesthetic and proprioceptive information from S1 Binkofski, F., Dohle, C., Posse, S., Stephan, K.M., Hefter, H., Seitz, R.J., Freund, H.J., 1998. through short-loop and long-loop projections, respectively (Gardner Human anterior intraparietal area subserves prehension: a combined lesion and et al., 2007). functional MRI activation study. Neurology 50, 1253–1259. Binkofski, F., Buccino, G., Posse, S., Seitz, R.J., Rizzolatti, G., Freund, H., 1999. A fronto- Note that whereas previous studies investigated the reach and grip parietal circuit for object manipulation in man: evidence from an fMRI-study. components separately, the novelty of our study consists in the manip- European Journal of Neuroscience. Blackwell Science Ltd. ulation of grip type and reach direction within the same paradigm, Brainard, D.H., 1997. The psychophysics toolbox. Spat. Vis. 10, 433–436. allowing us to measure the relation between the selectivity for the Bremmer, F., Schlack, A., Shah, N.J., Zafiris, O., Kubischik, M., Hoffmann, K., Zilles, K., Fink, G.R., 2001. Polymodal motion processing in posterior parietal and premotor cortex: a two components. Within some of the overlapping regions selective for human fMRI study strongly implies equivalencies between humans and monkeys. grip type and reach direction (PMDi, S1), we observed independent se- Neuron 29, 287–296. lectivity for the two components. Using a more liberal statistical thresh- Burnod, Y., Baraduc, P., Battaglia-Mayer, A., Guigon, E., Koechlin, E., Ferraina, S., Lacquaniti, F., Caminiti, R., 1999. Parieto-frontal coding of reaching: an integrated framework. old, we obtained weak interactions in PMv, M1, aIPS and SPLa. One Exp. Brain Res. 129, 325–346. should note that Stark et al. (2007) reported an interaction between se- Caminiti, R., Johnson, P.B., Galli, C., Ferraina, S., Burnod, Y., 1991. Making arm movements lectivity for grip type and reach direction in only 1/4 of neurons in PMd within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets. J. Neurosci. 11, 1182–1197. and PMv. Cavina-Pratesi, C., Monaco, S., Fattori, P., Galletti, C., McAdam, T.D., Quinlan, D.J., Goodale, We observed the strongest trend for an interaction between reach M.A., Culham, J.C., 2010. Functional magnetic resonance imaging reveals the neural direction and grip type in SPLa, corresponding to monkey ventral substrates of arm transport and grip formation in reach-to-grasp actions in humans. J. Neurosci. 30, 10306–10323. intraparietal area (VIP) (Mars et al., 2011). This region has been report- Culham, J.C., Danckert, S.L., DeSouza, J.F.X., Gati, J.S., Menon, R.S., Goodale, M.A., 2003. ed to be sensitive to the direction of visual, tactile and auditory stimuli Visually guided grasping produces fMRI activation in dorsal but not ventral stream (Bremmer et al., 2001) and to the spatial congruency between visual brain areas. Exp. Brain Res. 153, 180–189. and tactile information (Duhamel et al., 1998) and thus might be a Culham, J.C., Cavina-Pratesi, C., Singhal, A., 2006. The role of parietal cortex in visuomotor control: what have we learned from neuroimaging? Neuropsychologia 44, suitable candidate to combine information from the reach and grasp 2668–2684. component. Dafotakis, M., Sparing, R., Eickhoff, S.B., Fink, G.R., Nowak, D.A., 2008. On the role of the ventral premotor cortex and anterior intraparietal area for predictive and reactive scaling of grip force. Brain Res. 1228, 73–80. Conclusions Davare, M., Andres, M., Cosnard, G., Thonnard, J.-L., Olivier, E., 2006. Dissociating the role of ventral and dorsal premotor cortex in precision grasping. J. Neurosci. 26, 2260–2268. We found overlapping representations for both the reach and grasp Davare, M., Andres, M., Clerget, E., Thonnard, J.-L., Olivier, E., 2007. Temporal dissociation components in PMDi, PMv, M1, S1, SPLa, and aIPS. These results provide between hand shaping and grip force scaling in the anterior intraparietal area. further evidence against the view of a clear-cut distinction between a J. Neurosci. 27, 3974–3980. Desmurget, M., Gréa, H., Grethe, J.S., Prablanc, C., Alexander, G.E., Grafton, S.T., 2001. dorsomedial and a dorsolateral pathway specialized for the two compo- Functional anatomy of nonvisual feedback loops during reaching: a positron emission nents (Cavina-Pratesi et al., 2010; Fattori et al., 2010; Stark et al., 2007), tomography study. J. Neurosci. 21, 2919–2928. leaving open the possibility of alternative accounts like a different tem- Duhamel, J.R., Colby, C.L., Goldberg, M.E., 1998. Ventral intraparietal area of the macaque: congruent visual and somatic response properties. J. 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