Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal regions

Domain: biology
Published: 2026-03-20
Editorial outcome: minor_revision

Abstract
Moving events on the skin can be perceived through vision and touch. How does the brain create a unified multisensory representation of motion directions initially acquired in different coordinate systems? Using functional magnetic resonance imaging, we demonstrate that individually and functionally defined hMT+/V5 shows univariate preference for both visual and tactile motion and encodes motion directions across hand postures. Unlike somatosensory regions, information about tactile directions is enhanced in right hMT+/V5 when mapped using an external rather than a somatotopic frame of reference. Crossmodal decoding reveals alignment between tactile and visual motion directions in the right hMT+/V5 (both in MT and MST) only when tactile motion is defined in external space. A whole-brain searchlight analysis extends this aligned representation to parietal and frontal regions. Our findings reveal a network involving right hMT+/V5 and fronto-parietal regions that encodes motion directions in vision and touch using a common external frame of reference.

Introduction
Imagine a mosquito moving on your arm-you instinctively want to swat it. Behind this seemingly simple action lies a remarkable computational challenge for the brain: it must integrate directional motion information from multiple sensory modalities, such as vision and touch. These computations are especially complex because the sensory signals are initially encoded in distinct reference frames - retinotopic for vision and somatotopic for touch, while the sensory organs themselves (eyes and limbs) are constantly moving through space1. To overcome this challenge, the brain must engage in a reformatting process - either remapping signals from one modality into the other’s reference frame or transforming both into a shared frame of reference that transcends their original topographic organization2,3. Behavioral evidence from bidirectional crossmodal motion aftereffects - where motion adaptation transfers between vision and touch - suggests that motion processing indeed relies on shared representations across modalities4. Yet a crucial question remains: where in the brain are these aligned motion representations instantiated?

In primates, the alignment of multisensory spatial information has traditionally been attributed to multisensory convergence zones such as the parietal and frontal cortices2,5–9. Within the parietal cortex, neurons in the Ventral Intraparietal (VIP) area exhibit preference for both visual10 and tactile motion signals9,11, with partially aligned receptive fields (RFs) across modalities3. Similarly, premotor areas contribute to transforming tactile inputs from a somatotopic to an external spatial representation12–14. Beyond these fronto-parietal regions, the human middle temporal complex (hMT+/V5) - long established as a critical node for visual motion processing15–18- also shows selective response to moving sounds19–23 and to tactile motion (refs. 24–26; but see ref. 27). Moreover, hMT+/V5 encodes visual, auditory and tactile motion directions22,28 and is causally involved in tactile motion direction discrimination29,30. However, whether hMT+/V5 transforms incoming moving signals across the senses into a unified frame of reference independent of body posture remains unknown. Previous studies have shown that hMT+/V5 responses to visual motion are modulated by gaze direction, suggesting a non-retinotopic (“spatiotopic”) coding format31–34- a mechanism that could also support multisensory integration of motion signals.

In this study, we investigated whether hMT+/V5 along with fronto-parietal regions encode visual and tactile motion directions using a shared neural code that is independent of body posture. In a first functional Magnetic Resonance Imaging (fMRI) experiment, we used independent visual and tactile motion localizers to individually and functionally define motion selective regions across both modalities. In a second fMRI experiment, we presented directional motion in both vision and touch while manipulating hand posture to systematically alter the perceived axis of tactile motion directions (Fig. 1). Using multivariate pattern analyses (MVPA) within individually defined functional regions of interest (fROIs), complemented by whole-brain searchlight analyses, we reveal an occipito-parieto-frontal network, including hMT+/V5, that represents visual and tactile motion using an aligned format independent of body posture.

Results
Behavioral responses
We assessed the behavioral performance of each participant in all the experiments, measuring their ability to detect targets in each condition. Table S1 presents the mean accuracy in each task. Participants performed consistently well across all conditions, with no significant differences between motion and static conditions in the localizer experiments or between conditions (Vhand Vv; HUpt and HUfw; HDpt and HDfw) in the main directional motion decoding experiments (Wilcoxon signed rank test; all p>0.05). This lack of difference in behavioral performance between conditions suggests that the observed motion selectivity in the localizers and the decoding results in the directional motion decoding experiments cannot be attributed to differences in performance or attention.

Eyepositionvariability
We examined whether variability in gaze position differed between the externally-defined tactile directions by performing a 2 (gaze axis: X, Y) × 2 (externally-defined tactile direction: horizontal, vertical) repeated-measures ANOVA on the estimated standard deviation. We observed a significant main effect of gaze axis, F (1, 19)=14.03, p=0.0014, partial η²=0.425, indicating greater overall variability along horizontal (X) gaze axis. Crucially, there was no main effect of tactile motion direction, F (1, 19)=0.00, p=0.96, partial η²<0.001, and no interaction, F (1, 19)=0.01, p=0.91, partial η²<0.001. These results indicate that tactile motion direction did not modulate gaze variability. We further compared eye-position variability between externally-defined horizontal and vertical tactile motion directions for each gaze axis using paired-samples t-tests. There was no significant difference between the two tactile motion directions in horizontal gaze variability (t(19)=−0.10, p=0.92, d=0.02) and vertical gaze variability (t(19)=0.06, p=0.95, d=0.01). Again, this analysis revealed that tactile motion directions did not modulate variability in either horizontal or vertical gaze axis.

Visual motion localizer
Table S2 lists the regions showing preferential responses to visual motion at a threshold p=0.05 FWE corrected, which are illustrated on Fig. 2A. We found clusters of voxels in the left and right middle temporal gyrus (hMT+/V5) extending bilaterally to lingual gyrus (V1) and left V3a. The hMT+/V5 clusters were observed at the intersection of the ascending limb of inferior temporal sulcus and lateral occipital sulcus35,36. We did not observe regions such as VIP and LIP in the parietal cortex to be visual motion selective using our localizer approach which is also consistent with previous studies17,19,20,22.

Tactile motion localizer
We adopted a similar approach to identify tactile motion selective areas from the whole brain univariate analyses and computation of the contrast tactile motion > tactile static (Fig. 2A, Table S3). Consistent with previous studies11,26,28, we observed preferential response to tactile motion in the left postcentral gyrus (left primary somatosensory cortex), bilateral rolandic operculum (secondary somatosensory cortex) and in the middle temporal gyrus. An overlay of the group-level contrasts (motion > static) revealed an overlap between the visual and tactile motion selective regions (Fig. 2A). More precisely, 32.21% of voxels of visually defined left hMT+/V5 and 42.55% of voxels in right hMT+/V5 showed tactile motion selectivity. Similar to the visual motion localizer, we did not observe regions in the parietal cortex to be tactile motion selective.

To further investigate tactile motion selectivity within the individually localized bilateral hMT+/V5, we compared the beta parameters for tactile motion and static conditions within each individual’s hMT+/V5 fROI (Fig. 2B). One-tailed paired t-tests revealed that responses to tactile motion conditions were significantly higher than the static conditions in both fROIs (left hMT+/V5 (t(17)=1.99, p=0.03, d=0.24) and right hMT+/V5 (t(17)=2.15, p=0.02, d=0.17). The fROI left primary somatosensory cortex being tactile motion selective did not show visual motion selectivity (t(17)=0.47, p=0.32, d=0.09).

MT/MST localizer
We identified subregions of hMT+/V5, which are presumed homologs to the macaque middle temporal (MT) area and the medial superior temporal (MST) area, in each individual36. In each subject, we computed the conjunction (AND)37 of the contrasts left-hemifield motion > 0 and right-hemifield motion > 0 at a threshold of p=0.001 uncorrected or p=0.05 FWE corrected, and constrained the activity to the individually localized clusters of hMT+/V5 (from the visual motion localizer) to identify MST area. Table S6 shows the number of voxels in the identified clusters. Following this, MT voxels were defined as part of individually localized clusters of hMT+/V5 which do not belong to MST. We were able to reliably localize these regions in 14 out of the 18 tested participants. Figure 5A shows the identified clusters of MST and MT in one exemplar subject.

To investigate tactile motion selectivity within the individually localized MST and MT, we extracted tactile motion and static mean beta parameters from the univariate analyses of tactile motion localizer and compared them within each individual’s MST and MT mask (Fig. 5A). We performed one-tailed paired t-tests (p-values subjected to Bonferroni correction for multiple comparisons) to compare the motion and static conditions within each fROI. We obtained significantly higher beta parameters for motion conditions as compared to the static conditions in all the fROIs (left MST (t(13)=3.37, p=0.005, d=0.39), left MT (t(13)=3.13, p=0.005, d=0.18), right MST (t(13)=4.76, p<0.001, d=0.37), right MT (t(13)=3.49, p<0.001, p=0.005, d=0.25)).

Functional regions of interest (fROIs). We performed whole-brain univariate analyses for independent visual and tactile motion localizers (contrast motion>static) to acquire the group-level coordinates of bilateral hMT+/V5 and left S1, selective to visual and tactile motion, respectively. We first used the MNI group coordinates: left S1 [−44, −2, 60mm]; left hMT+/V5: [−42, −65, 4mm]; right hMT+/V5: [48, −63, 1mm] to create group-level spherical fROI masks of radius 12mm. Following this, we identified individual peak coordinates, constrained by the group-level fROI masks, to create spherical fROI masks of radius 8mm. Table S5 shows the MNI coordinates of the individual spherical fROI.

Since we observed preferential response to tactile motion from the tactile motion localizer in the hMT+/V5 region (for overlap, see Fig. 2A), we performed separate MVPA in hMT+/V5 (defined using visual motion localizer) and MTt (defined using tactile motion localizer). Since the individually defined hMT+/V5 and MTt overlapped with each other (for dice coefficients see Table S7), results in hMT+/V5 are presented in the main manuscript and in MTt in supplemental; both fROIs yield similar results.

Foreshadowing our MVPA results, we observed significant cross-modal decoding only in the right hMT+/V5 (Fig. 3C). Therefore, we performed MVPA in the individually identified clusters of MST and MT to further investigate the visual and tactile motion representations across different frames of reference solely in the right hemisphere. However, due to the variability in the size of the fROIs masks across participants (as shown in Table S6), we chose a subset of 8 subjects which had at least 30 voxels for MVPA. This allowed us to keep the same number of features/voxels for MVPA across regions and subjects.

Within-modality decoding. When decoding visual motion axes of motion (vertical versus horizontal), we obtained above-chance (0.5) decoding accuracies in both left (p=0.046 FDR-corrected) and right hMT+/V5 (p=0.046; FDR-corrected), but not in the left S1 (p=0.145; FDR-corrected) (Fig. 3A). A separate analysis in MTt (Fig. S2A) also revealed significant decoding of visual motion direction (left MTt: p=0.031; FDR-corrected and right MTt: p=0.031; FDR-corrected). In addition, MT and MST (Fig. 5B) also suggest the presence of visual motion direction information.

To determine whether the tactile motion directions are represented within a somatotopic frame of reference or an external frame of reference, we performed decoding of tactile motion directions pooled across the hand postures. For somatotopic frame of reference, we classified pinky-thumb vs finger-wrist tactile directions across hand postures. For external frame of reference, we classified horizontal vs vertical tactile directions regardless of the hand postures (see Fig. 1A for the design). Decoding accuracies obtained for each frame of reference (somatotopic, external) within the 3 fROIs (left S1, left hMT+/V5, right hMT+/V5) were tested against the chance level (0.5).

The decoding of tactile motion directions in somatotopic frame of reference was significantly above chance in the left S1 (p<0.001; FDR-corrected) and left hMT+/V5 (p=0.024; FDR-corrected). The decoding of tactile motion directions in external frame of reference was above chance in all the three fROIs - left S1 (p=0.009; FDR-corrected), left hMT+/V5 (p=0.013; FDR-corrected), right hMT+/V5 (p=0.007; FDR-corrected) (Fig. 3A).

To further assess the recruitment of tactile frames of references within the fROIs, the decoding accuracies were entered into a Linear Mixed Model (LMM). The two frames of reference (somatotopic, external) and the three fROIs (left S1, left hMT+/V5, right hMT+/V5) were entered as fixed effects and participants as random effect. We observed a significant main effect of the fROIs (F (2,95)=28.01, ω2=0.35), showing that the primary somatosensory area showed highest decoding accuracy owing to its primary function of processing tactile inputs. There was no significant main effect of the frames of reference (F (1,95)=2.16, p=0.144, ω2=0.01). Crucially, there was a significant interaction of fROIs and frame of reference emerged (F (2,95)=11.25, p<0.001, ω2=0.17) suggesting that the influence of reference frame on decoding accuracies varied across the fROIs. Post hoc paired t-tests (FDR-corrected) were performed to compare decoding accuracies across the two frames of reference within each fROI. Decoding accuracies were significantly higher in the somatotopic frame of reference than the external frame of reference only in the left S1 (t (19)=4.21, p=0.0014, d=1.13) supporting a dominant somatotopic representation of tactile motion directions in the primary somatosensory cortex. While the decoding accuracy in the right hMT+/V5 appear descriptively higher for external frame of reference, the difference was not statistically significant (t(19)=−1.37, p=0.278, d=−0.43). Similarly, no significant difference was observed in left hMT+/V5 (t(19)=−0.28, p=0.78, d=−0.08).

Given the overlap between hMT+/V5 and MTt, we also examined decoding in MTt and observed similar results as that in hMT+/V5. The decoding of somatotopic frame of reference reliably above chance in left MTt (p=0.029) and the decoding of external frame of reference was above chance in both, left MTt (p=0.031) and right MTt (p=0.015) (Fig. S2). Paired t-tests, however, did not reveal significant differences between somatotopic and external coding of tactile motion direction in MTt (p<0.05). Descriptively, the decoding accuracy in right MTt, like right hMT/V5, appeared higher for external than the somatotopic frame of reference.

Finally, we performed decoding of tactile motion directions in different reference frames separately within MST and MT fROIs. Consistent with the results in right hMT+/V5, the individually defined right MT also showed a trend towards higher decoding accuracy of tactile motion direction in external, compared to the somatotopic frame of reference (Fig. 5B).

Multidimensional scaling. To further investigate the representational structure in left S1 and right hMT+/V5, we used Multidimensional Scaling (MDS) to visualize the relative distances between motion direction conditions in a two-dimensional abstract space (Fig. 3B). First, MDS revealed a clear separation between visual and tactile modalities in both fROIs. Second, we observed an interesting organization within the tactile modality. In the left S1, the somatotopically defined directions were more widely spaced than the externally defined directions supporting the trend in decoding accuracies across the tactile frames of reference. The layout in the left hMT+/V5 resembles the organizational pattern in the left S1. In contrast, in right hMT+/V5, externally defined directions were more widely spaced than the somatotopically defined directions, aligning with the trend of higher decoding accuracy for the external frame of reference.

Cross-modal decoding. To further investigate whether the motion information is shared across sensory modalities within our fROIs, we performed cross modal decoding where the classifier was trained on one modality and tested on the other modality (training in vision and testing in tactile, and vice versa). Since tactile motion directions were defined in two different reference frames, cross-modal decoding was performed in two ways: (a) between visual motion directions and tactile motion directions in a somatotopic frame of reference, and (b) between visual motion directions and tactile motion directions in an external frame of reference (Fig. 3C and Fig. S1). When tactile motion directions were defined somatotopically, the cross modal decoding did not yield significant results in any of the fROIs (p>0.05). In contrast, when tactile motion directions were defined using an external frame of reference, significant cross modal decoding was observed in the right hMT+/V5 (p=0.013, FDR corrected). This suggests that the motion information is shared across vision and touch in right hMT+/V5, only when the tactile motion directions are represented in an external frame of reference.

Similar results were also found in right MTt where significant crossmodal decoding was obtained only when tactile motion was defined in an external frame of reference (p=0.032) (Fig. S2).

Interestingly, both right MST and right MT also exhibited the same trend: crossmodal decoding accuracies appeared to increase above chance when the tactile motion directions were expressed in an external reference frame (Fig. 5C).

Searchlight decoding analyses. To explore the broader network of regions sensitive to motion-directions across sensory modalities and frames of reference beyond our fROIs, we implemented whole-brain searchlight decoding analyses. Since our visual and tactile motion localizers did not reveal motion selective activity in parietal and frontal areas (Fig. 2A), the searchlight method allowed us to also further investigate whether these regions play a role in motion direction processing and using which frame of reference.

We performed both within-modal and cross-modal decoding and tested the statistical significance using Small Volume Correction (SVC) within a 10mm radius sphere. SVC (Table S4) was performed at hMT+/V5 identified independently from the visual motion localizer (left hMT+/V5: −47, −70, 5mm; right hMT+/V5: 47, −65, 3mm) and the parietal regions observed from a previous study11 assessing tactile motion selectivity (Talairach coordinates left Intraparietal Area: −30, −50, 45 and right Intraparietal Area: 42, −51, 49 were converted to MNI coordinates left Intraparietal Area: −30, −53, 49mm and right Intraparietal Area: 41, −55, 54mm, using Bioimage Suite Web: (www.bioimagesuite.org)38). We also tested frontal regions implicated in the external representation of touch12,13 in searchlight cross-modal decoding (left premotor cortex: −46, 8, 46mm; right premotor cortex: 24, 4, 58mm).

Group-level searchlight decoding of visual motion directions (horizontal versus vertical; Fig. 4B) revealed bilateral clusters in hMT+/V5 (peak coordinate at left: −41.6, −72.9, 5.9mm and right 43.5, −68.3, −1mm) and a wider network of clusters in the primary visual cortex (V1) (left Cuneus: −14, −75, 29mm; right Cuneus: 16, −82, 27mm) extending through the superior occipital gyrus to superior parietal lobule (left superior parietal gyrus: −28, −50, 50; right superior parietal gyrus: 27, −55, 59mm) and some additional clusters in the frontal lobe (left middle frontal gyrus: −28, −4, 52mm; right middle frontal gyrus: 34, −2, 52mm).

We decoded tactile motion directions represented in both somatotopic (Fig. 4A) and external frame (Fig. 4C) of reference. For the somatotopic frame of reference (Fig. 4A) we observed clusters in the left (peak coordinate: −44, −71, 8.2mm) and right middle temporal gyrus (peak coordinate: 46, −52, −3.3mm), as well as an extensive network of the primary (left postcentral gyrus) and secondary somatosensory (Rolandic Operculum and Insula) areas. We also observed parietal (superior parietal gyrus), motor (precentral gyrus; supplementary motor area) and frontal (middle superior frontal gyrus; superior frontal gyrus) areas. Decoding of tactile external frame of reference (Fig. 4C) revealed clusters bilaterally in the middle temporal area (middle temporal gyrus left: −48, −61, 15mm and right: 44, −66, 1mm) and a network of parietal areas (left superior parietal gyrus), primary (left postcentral gyrus) and secondary (left rolandic operculum, left supramarginal gyrus) somatosensory areas, motor areas (left supplementary motor area, left precentral gyrus) and frontal areas (medial and superior frontal gyrus).

Finally, we performed cross-modal searchlight decoding between visual and tactile motion directions in both frames of reference. No significant clusters were obtained when tactile motion directions were represented in somatotopic frame of reference (Fig. 4D). In contrast, when tactile motion directions were defined in the external reference frame (Fig. 4E) we observed a distinct network involving the right middle temporal gyrus (48, −59, 8mm), right superior frontal gyrus (25, −2, 52mm) and right superior parietal gyrus (18, −66, 52mm). The cluster in the middle temporal area closely overlapped with the right hMT+/V5 identified from the visual motion localizer (dice coefficient= 0.9091). The cluster in the frontal region encompassed the frontal eye field (FEF) area39 and the premotor cortex13. The cluster in the posterior parietal cortex included LIPv, LIPd, MIP and VIP based on the Glasser atlas40.

Modality decoding. Significant cross-modal decoding in the right hMT+/V5, only when tactile motion directions were externally-defined (Fig. 3C), indicated the presence of shared motion-direction information. However, this significant cross-modal decoding in the right hMT+/V5 does not imply that the underlying activity patterns were identical across the sensory modalities. This can also be seen in the multidimensional scaling results (Fig. 3B), where the main axis of data separation corresponds to the sensory modality (vision and touch). To statistically support this observation, we reasoned that if a region is purely abstracted from the input modality conveying the directional motion information, decoding the sensory modality (visual or tactile) of a specific motion direction would be difficult. In contrast, if the neural representations of visual and tactile motion remain largely distinct despite partial cross-modal alignment, decoding the sensory modality would be easy, even when both represent the same motion direction. To this end, we performed binary classification of visual horizontal versus tactile horizontal (externally-defined) and visual vertical versus tactile vertical (externally-defined) in the right hMT+/V5. The sensory modality decoding was significant in right hMT+/V5 (p<0.0001) for both horizontal and vertical directions, suggesting that the neural patterns associated with each direction are very dissimilar across the senses (Fig. S4).

Methods
Participants
We based our sample size on a previously published study22 that investigated visual and auditory motion direction representation in hMT+/V5. We performed a power analysis using G*Power version 3.185 based on the reported effect sizes for auditory axis-of-motion decoding accuracies (see Supplemental Table S4 in ref. 22. We computed the sample size using the average effect size across left and right hMT+/V5. With d=0.79, α=0.05, and 90% power, the estimated sample size was 19. Consequently, we recruited twenty healthy participants (11 females; mean age±SD=25.15±3.51 years; age range=19–33 years) for this study. All participants reported their sex/gender as part of routine demographic information. Sex/gender was not used as a factor in the study design because we had no specific hypotheses regarding sex- or gender-related effects, and our sample size was insufficient to perform meaningful sex- or gender-based analyses.

All participants were right-handed, as determined using the Edinburgh Handedness Inventory86 and had normal or corrected-to-normal vision. Two participants volunteered exclusively for the directional motion decoding experiments but did not participate in the localizer experiments. Consequently, the localizer experiments were conducted with only eighteen participants. All participants were naïve to the purpose of the study and reported no history of neurological or psychiatric disorders. The study protocol was approved by the Research Ethics Committee of Cliniques Universitaires Saint-Luc and Université Catholique de Louvain (UCLouvain). Written informed consent was obtained from all participants before the experiments, and they received monetary compensation for their participation. All the experiments were conducted over a minimum of two sessions, each lasting approximately 1 hour and 45 minutes.

Experimental setup
The experiments were conducted using a 3-Tesla GE MR scanner equipped with a 48-channel head coil (Signa™ Premier, General Electric Company, USA; serial number: 000000210036MR03; software version: 28-LX-MR, release: RX28.0_R04_2020.b). Participants lay comfortably on the scanner bed, viewing a screen positioned behind the scanner. In all the experiments, participants provided their responses by pressing the response buttons with the index finger of their left hand. For the tactile motion localizer, participants kept their right hand open (palm up) beside their body to facilitate tactile stimulation. In the directional motion decoding experiments, participants were instructed to adopt one of the two hand positions depending on the experimental condition. In the hand-up (HU) position, their forearm was positioned perpendicular to the body plane with the palm facing outward, while in the hand-down (HD) position, the forearm was kept parallel to the body plane, with the palm also facing outward (Fig. 1A).

Stimuli
Visual stimuli. Visual stimuli (Fig. 1A) were presented on a black screen with a frame rate of 60Hz and a resolution of 1920 × 1200 pixels, measuring 70 cm in width and 43.75 cm in height. Participants viewed the screen from a distance of 170 cm through a mirror mounted on the MRI head coil. The stimuli consisted of 300 white random dot kinematograms (RDKs) displayed within an invisible circular aperture, which was centered around a white fixation cross and occupied the full height of the screen (15° of visual angle). The dots could either remain static or move in one of four translational directions: left, right, up, or down, as well as radially receding or approaching. Each dot measured 0.25° in size, had a limited lifetime of 200ms87, and moved at a speed of 15°/s. The origin of each dot was randomized on each trial.

Tactile stimuli. Tactile stimuli (Fig. 1A) were delivered manually using a custom-built, 30cm wide MRI-compatible brush. The trained experimenter (author Iqra Shahzad), who was specifically trained to deliver tactile stimuli with consistent timing and frequency, brushed the right palm of the participants following computer-controlled auditory cues, which were delivered via MRI-compatible earphones (S14 insert earphones, SensiMetrics, USA; http://www.sens.com/). Each touch or swipe lasted approximately 1s. Participants were instructed to keep their eyes closed throughout all tactile runs, and compliance was monitored by the experimenter.

Experiments
Visual motion localizer. One run of visual motion localizer (Fig. 1B) was implemented to localize hMT+/V5 both at the group-level and in each participant individually22,36. The hMT+/V5 region, located in the medial temporal area respond preferentially to moving stimuli compared to static stimuli. Since the visual stimuli were presented at fixation in the center of the screen, the localizer defined the whole hMT+/V5 complex which includes both middle temporal area (MT) and medial superior temporal area (MST) (see below for a specific MT/MST localizer). Visual motion and static conditions were generated using white random dot kinematograms (RDK) on a black background. Each run lasted 4 minutes and 51 seconds, starting with an initial 5.25 seconds of a blank screen and ending with 14 seconds of blank screen. Within the run, there were 12 blocks of alternating motion and static conditions, with the starting block counterbalanced across participants. The blocks were separated by an inter-block interval (IBI) of 5 to 7 seconds, chosen randomly. Each block lasted 17.4 seconds and included 6 trials of 2.4 seconds each, separated by an inter-stimulus interval (ISI) of 0.5 seconds. In the motion blocks, the dots moved in one of four directions – left, right, up, and down. Within a motion block, a trial consisted of an axis of motion direction – horizontal (leftward movement of 1.2s followed by rightward movement of 1.2s) or vertical (upward movement of 1.2s followed by downward movement of 1.2s) axis. In the static blocks, each trial (2.4s) consisted of the sequential presentation of two static dots configurations, each lasting for 1.2s (following the cycle of motion direction change). Participants were asked to fixate on the central fixation cross. In the motion blocks, they were asked to detect a repeated direction within a trial, for example, leftward followed by a leftward motion stimulus. In the static blocks, they were asked to press when the dots did not change configuration in the two sequential presentations. The number of targets in each block varied between 0 and 2, randomly distributed and balanced across motion and static conditions.

MT/MST visual motion localizer. Using visual radial-motion stimulation, a separate localizer was implemented to divide the hMT+/V5 complex into subregions that may be identified as homologs to a pair of macaque motion-responsive visual areas: the middle temporal area (MT) and the medial superior temporal area (MST)36. MT, thought to have smaller retinotopically organized receptive fields should not respond to peripheral ipsilateral stimulation. MST, known to have larger receptive fields, is assumed to respond to peripheral visual motion stimulation in both ipsilateral and contralateral visual hemifields36. One run of MT/MST localizer (~7 min) (Fig. 1B) consisted of 20 blocks of stimulation: 10 blocks presented radial motion in the left hemifield, and the following 10 blocks presented the radial motion in the right hemifield, while the fixation was maintained in the opposite side of the screen i.e., right and left respectively. The order of presentation of the motion stimuli in the left or right hemifield was counterbalanced across participants. Within a block, there were 6 trials, where each trial consisted of receding (0.6s) followed by approaching (0.6s) RDKs. The dots were restricted to a circular aperture of radius 7° visual angle, with its edge 10° from fixation. The run started with a blank screen for 5s and ended with a blank screen for 14s. The inter-block interval (IBI) was 10s including trials during which participants had to change the fixation from one side to the other. Participants were asked to fixate their eyes on the fixation cross and detect changes in its color (white to red). The number of targets (range: 0-2 in each block) were randomized and balanced across conditions.

Tactile motion localizer. A tactile motion localizer (Fig. 1B) was implemented to identify regions that respond preferentially to tactile motion. Each participant performed two runs of the tactile motion localizer (10 min 49s each). Each run started with a with a 5s pause and ended with a 14s pause during which no tactile stimulation was delivered. It included 20 blocks of alternating motion and static conditions with the starting condition counterbalanced across participants. The blocks were separated with an inter-block interval (IBI) chosen randomly between 5-7s. Each block (19.5s) included 6 trials of 2s each and an inter-stimulus interval (ISI) of 0.5s. A motion trial consisted of a brush swipe on the right palm from pinky-to-thumb (1s) followed by thumb-to-pinky (1s) representing one somatotopic axis of motion (corresponding to the ulnar-radial axis of the hand), or from finger-to-wrist (1s) followed by wrist-to-finger (1s) representing the other somatotopic axis of motion. In the static trials, the brush was touched on the right palm in one orientation (e.g., pinky-thumb) for ~1s, lifted, and then touched again in the different orientation (e.g., finger-wrist) for ~1s. This ensured that the duration of tactile input corresponded across each static and motion conditions. So, in a block, the position of the static condition was changed 11 times, as for the number of changes in directions in the motion blocks. Participants kept their eyes closed throughout the run. In the motion blocks, they were asked to detect a repeated direction within a trial, for example, pinky-to-thumb followed by a pinky-to-thumb motion. In the static block, the participants were asked to detect the trials where the brush was touched in the same orientation consecutively. The number of targets within a block varied between 0 and 2 with the position of the target in the blocks randomized and balanced across conditions.

Directional motion decoding experiments. The directional motion experiments (Fig. 1C) were conducted to characterize the pattern of neural activity underlying perception of visual and tactile motion directions across two hand positions – HU and HD using multivariate pattern analyses (MVPA). These experiments involved the presentation of directional motion stimuli across 30 runs, such that 10 runs were performed for each sensory modality and each hand position - i.e. visual, tactile HU, tactile HD. The order of the 3 types of runs was counterbalanced across participants. Note that the participants were allowed to rest their right hand in a comfortable position during the visual runs and the motion stimuli used in these experiments were identical to those used in the localizer tasks. Each run lasted 3 min 18s, beginning with a 5s blank screen ending with a 14s blank screen. Within each run, two directional motion stimuli were presented four times each in a randomized order. Thus, there were six motion conditions across 30 runs (visual: horizontal (Vh) and vertical (Vv); tactile: hand up pinky-thumb (HUpt) and finger-wrist (HUfw); tactile: hand down pinky-thumb (HDpt) and finger-wrist (HDfw)). Each block (17s) comprised 7 trials of a directional stimulus along one axis-of-motion (either pinky-thumb or finger-wrist axis for tactile runs; either horizontal or vertical axis for visual runs), with an ISI of 0.5s and an IBI chosen randomly between 5 and 7s.

MRI acquisition. A whole-brain T1-weighted anatomical scan was acquired (3D-MPRAGE; 1.0×1.0mm in-plane resolution; slice thickness 1mm; no gap; inversion time=900ms; repetition time (TR)= 2189.12ms; echo time (TE)=2.96ms; flip angle (FA)=8°; 156 slices; field of view (FOV)=256×256mm2; matrix size=256×256). Functional data for the localizers were acquired with T2*-weighted scans acquired with Echo Planar and gradient recalled (EP/GR) imaging (2.6×2.6mm in-plane resolution; slice thickness=2.6mm; no gap; Multiband factor=2; TR=1750ms, TE=30ms, 58 interleaved ascending slices; FA=75°; FOV=220×220mm2; matrix size=84×84). The number of volumes acquired in the visual motion localizer, tactile motion localizer and MT/MST visual localizer were 167, 372 and 282 volumes respectively. Functional data for the directional motion decoding experiments were acquired with T2*-weighted scans acquired with Echo Planar and gradient recalled (EP/GR) imaging (2.3×2.3mm in-plane resolution; slice thickness=2.3mm; no gap; Multiband factor=2; TR=1750ms, TE=35ms, 52 interleaved ascending slices; FA=75°; FOV=216×216mm2; matrix size=94×94). A total of 117 volumes were collected for each tactile HU and HD runs, while 114 volumes were collected for each visual runs.

Behavioral data analyses. The performance of participants in all the different experiments was analyzed and reported as mean accuracy of detecting targets with standard error. Wilcoxon signed rank tests were computed to check if there were differences in detection performance between two conditions within an experiment.

Eye position analyses. The eye positions of the participants in all the different experiments were computed using bidsMReye88, a BIDS app relying on deep MReye89 that decodes eye movement from fMRI time series data using the MR signal of the eyeballs. For each run, bidsMReye computes the variance for the X gaze position, variance for the Y gaze position, framewise gaze displacement, number of X gaze position outliers, number of Y gaze position outliers, and number of gaze displacement outliers. Outliers were robustly estimated using an implementation of the median rule90. We used the variance of the X and Y gaze positions corresponding to the different stimulus conditions within each run and computed their standard deviations. These standard deviations, used as indices of eye-position variability, were then averaged across runs to yield a single value per subject. We hypothesized that if externally-defined horizontal tactile motion elicited greater horizontal eye movements, this would be reflected as increased variability in the X gaze position; similarly, externally-defined vertical tactile motion would be expected to produce greater variability in the Y gaze position. To assess this, we pooled the tactile stimuli across hand postures to yield externally-defined tactile directions (horizontal, vertical) and performed a 2 (gaze axis: X, Y) × 2 (externally-defined tactile direction: horizontal, vertical) repeated-measures ANOVA. We also compare eye-position variability between externally-defined horizontal and vertical tactile motion directions for each gaze axis using paired-samples t-tests.

fMRI preprocessing. The structural and functional data were preprocessed with BidSpm (v2.2.0; https://github.com/cpp-lln-lab/bidspm91) using Statistical Parametric Mapping (SPM12−7771; Wellcome Center for Neuroimaging, London, UK; https://www.fil.ion.ucl.ac.uk/spm; RRID:SCR_007037) and MATLAB R2020b on a macOS (macOS Monterey Version 12.6) computer. The preprocessing of the functional images was performed in the following order: slice timing correction with reference to the middle slice; realignment and unwarping; segmentation and skull-stripping; coregistration of functional and anatomic data; normalization to a template in Montreal Neurologic Institute (MNI) space and smoothing with all default options implemented in BidSpm. Slice timing correction was performed taking the 29th (middle) slice as a reference (interpolation: sinc interpolation). Functional scans from each participant were realigned and unwarped using the mean image as a reference (SPM single pass; number of degrees of freedom: 6; cost function: least squares)92. The anatomical image was bias field corrected, segmented and normalized to MNI space (target space: IXI549Space; target resolution: 1mm; interpolation: 4th degree b-spline) using a unified segmentation. The tissue probability maps generated by the segmentation were used to skull-strip the bias corrected image removing any voxel with p(gray matter)+p(white matter)+p(CSF)>0.75. The mean functional image obtained from realignment was co-registered to the bias corrected anatomical image (number of degrees of freedom: 6; cost function: normalized mutual information)92. The transformation matrix from this coregistration was applied to all the functional images. The deformation field obtained from the segmentation was applied to all the functional images (target space: IXI549Space; target resolution: equal to that used at acquisition; interpolation: 4th degree b-spline). Preprocessed functional images were spatially smoothed using a 3D gaussian kernel (FWHM of 6mm for the localizers and 2mm for the directional motion decoding experiments). This section of preprocessing fMRI data was automatically generated using BidSpm91).

Univariate analyses. For both visual and tactile motion localizer, a general linear model (GLM) was fitted for every voxel with the motion and the static conditions as regressors of interest in each subject. Each regressor was convolved with SPM canonical hemodynamic response function (HRF). The 6 head motion parameters (3 translation and 3 rotation parameters) were included in the model as regressors of no interest. The contrast (motion > static) was computed for every subject individually to identify functional regions-of-interest (fROIs) responding preferentially to motion stimuli in vision and touch. In addition, these individual contrasts were further smoothed with a 6mm FWHM kernel and entered a random effects model for a standard second-level group analysis. Group-level statistical significance was assessed either using family-wise error (FWE) correction across the whole brain at a significance level of p<0.05 or using small volume correction (SVC) within small spherical volumes (10mm radius) around functional ROI coordinates (Tables S2 and S3). Regions were labelled using the Automated Anatomical Labelling 3 (AAL3) atlas93 available in SPM12.

A similar approach was adopted for the univariate analyses of MT/MST visual motion localizer. A GLM was fitted for every voxel with left-hemifield motion and right-hemifield motion conditions as regressors of interest in each subject. Each regressor was convolved with SPM canonical hemodynamic response function (HRF). The 6 head motion parameters (3 translation and 3 rotation parameters) were included in the model as regressors of no interest. The contrasts (left-hemifield motion > baseline and right-hemifield motion > baseline) were computed for every subject individually. The conjunction of these contrasts revealed the MST area in each individual.

Functional regions of interest (fROI) definition. The individually defined motion selective functional regions of interest (fROIs) were created following the Group-Constrained Subject-Specific (GSS) method, which algorithmically detects fROIs that are systematically activated across subjects. This method is similar to a random effects analysis, but is more tolerant of anatomical variability across subjects, adding sensitivity and selectivity to the results. The GSS method has been successfully used to define fROIs preferential for various visual categories94, language95 and visual and auditory motion19,20,22,80. The visual motion selective area, hMT+/V5 was identified from the visual motion localizer using the contrast motion > static. First, hMT+/V5 was identified bilaterally at the group-level as the cluster of voxels (p<0.05 FWE) in the middle-temporal gyrus. The group-level spherical fROI mask (radius 12mm) was drawn around the group-level peak coordinate in each participant. Within this group-constrained mask, individually identified peak of motion selectivity (motion > static) was identified and an individual spherical fROI masks was selected (radius 8mm; ~176 voxels).

Further, MT/MST visual motion localizer was used to identify and create fROI masks of the sub-divisions of the hMT+/V5: MT and MST. MST was defined as the part of hMT+/V5 that responds to ipsilateral and contralateral visual motion stimulation. Therefore, individually localized clusters of MST voxels were constrained by the individually localized clusters of hMT+/V5 and used as fROIs masks in further analyses. Individual MT masks were defined as the part of hMT+/V5 that prefers contralateral over ipsilateral motion and therefore excludes MST.

A similar approach was adopted to identify tactile motion selective areas. Group level analyses first revealed the presence of tactile motion selective response in the postcentral gyrus and middle temporal cortex. As in the visual motion localizer, a 12mm spherical fROI mask was drawn around the group-level peak coordinate to constrain individual subject activity. Ultimately, an 8mm-radius spherical fROI mask was drawn around the individually identified peak coordinates. Spherical masks of tactile motion selective areas in the middle temporal cortex were created in the same manner and were referred to as MTt (Middle Temporal tactile).

To foreshadow the univariate results, tactile motion selective areas overlapped with the visual motion selective hMT+/V5 in the middle temporal cortex, showing that a part of hMT+/V5 is engaged in tactile motion processing. Since the individually defined hMT+/V5 and MTt overlapped with each other (see Table S7 for dice coefficients), we reported hMT+/V5 results in the main findings while presenting MTt results as supplement (Fig. S2). The analyses in hMT+/V5 and MTt produced similar results (Fig. 3 and Fig. S2), which was expected given the substantial overlap between these regions (Table S7).

Multivariate pattern analyses (MVPA). Multivariate pattern analyses (MVPA) were carried out to investigate the representation of visual and tactile motion-directions across different hand postures in each subject. They were performed on the GLM output of directional motion decoding experiments. A GLM was fitted for each run and in each subject. Depending on the type of run, the regressors of interest were the two axis-of-motion stimuli (visual: horizontal (Vh) and vertical (Vv); tactile: hand up pinky-thumb (HUpt) and finger-wrist (HUfw); tactile: hand down pinky-thumb (HDpt) and finger-wrist (HDfw)) and the 6 head motion parameters were included as regressors of no interest. One beta map corresponding to each condition from each run was obtained. In total, we computed a total of 60 beta maps for each subject (10 for each Vh, Vv, HUpt, HUfw, HDpt, HDfw).

To improve the signal-to-noise ratio of each beta map used for decoding, the ten beta maps within each experimental condition (Vh, Vv, HUpt, HUfw, HDpt, HDfw) were paired and averaged to generate five pseudo-run beta maps per condition. Averaging across runs to create fewer but more reliable “samples” for the classifier has been shown to increase decoding reliability in fMRI data96,97. Because 10 real runs can form 45 unique two-run pairings (i.e., all possible combinations of two runs taken from ten), we employed a systematic pairing scheme that organized these 45 possible run pairs into 45 iterations. Each iteration consisted of 5 disjoint pseudo-runs (non-overlapping two-run pairs), ensuring that each real run contributed to only one pseudo-run within that iteration. This design ensured that all possible run pairs were systematically represented across the 45 iterations. The 5 pseudo-runs in each iteration served as independent samples for cross-validation and classification.

For each pseudo-run set, classification was performed using a linear Support Vector Machine (SVM; C=1) implemented in CoSMoMVPA98 and LIBSVM99. A five-fold leave-one-pseudo-run-out cross-validation scheme was used: in each fold, the classifier was trained on four pseudo-runs and tested on the remaining one. An ANOVA-based feature selection method100,101 was applied only to the training data in each fold to select the 100 most informative voxels within each region of interest. This method computes ANOVA F-statistic only on the training set and chooses features based on the variance. Voxels with the highest F-values were retained, and this subset was applied to the independent test set for decoding. This method not only ensures a similar number of voxels within a given fROI across participants but also avoids circularity (‘double dipping’102). Decoding accuracy for each pseudo-run set was computed as the mean accuracy across its five cross-validation folds. Finally, decoding accuracies were averaged across all 45 iterations to obtain one accuracy estimate per participant and fROI.

Statistical significance of the mean decoding accuracies within a fROI was assessed using non-parametric methods that combine permutations and sign flipping37,103,104. First, the observed mean decoding accuracy was calculated across the subjects. Then, 10,000 iterations of a sign-flipping procedure were performed to generate a group-level null distribution. In each iteration, the decoding accuracy of each subject was randomly assigned a reverse sign, and the mean decoding accuracy was recalculated for the group. This gave us 10,000 mean decoding accuracies which formed the group-level null distribution. Statistical significance was then determined by comparing the observed mean decoding accuracy to the null distribution, with the p-value defined as the proportion of null distribution values greater than or equal to the observed mean. To account for multiple comparisons across fROIs, all p values were corrected using false discovery rate (FDR) correction105.

Within-modality decoding. To test the presence of visual motion direction information in motion-selective areas, binary classification of visual horizontal and visual vertical directions was implemented in the fROIs defined from the localizers - left somatosensory cortex, left and right hMT+/V5. Since hMT+/V5 (defined from the visual motion localizer) and MTt (defined from the tactile motion localizer) overlapped with each other, the decoding of visual motion information in MTt is presented in supplemental material. To further investigate the visual motion selectivity in the sub-divisions of hMT+/V5, classification was performed in the MST and MT fROIs masks separately.

Since tactile motion directions can be coded either in a somatotopic or in an external frame of reference depending on the hand positions, the four experimental conditions were rearranged to perform two different classifications in the motion-selective fROIs. The somatotopic frame of reference was decoded by classifying pinky-thumb direction versus finger-wrist direction pooled across the hand position. The external frame of reference was decoded by classifying horizontal (pinky-thumb in HU position pooled with finger-wrist in HD position) versus vertical (finger-wrist in HU position pooled with pinky-thumb in HD position) directions. To further study the recruitment of tactile frame of references in the different ROIs, the decoding accuracies were entered into a Linear Mixed Model (LMM) using the lmer function implemented in R (R Core Team, 2023). The frame of references (somatotopic, external) and the fROIs (left S1, left hMT+/V5, right hMT+/V5) were entered as fixed effects and subjects as a random effect. Finally, the tactile motion representation was investigated in the same manner in the MST, MT and the MTt fROIs separately.

Multi-dimensional scaling. Multi-Dimensional Scaling (MDS) was used to visualize the underlying spatial structure of the different motion directions across the sensory modalities in each fROI. It allowed us to visualize the relative distance between the different pairs of motion directions across the sensory modalities. To do so, representational similarity analysis (RSA)106,107 was first used to assess the underlying dissimilarities across the 6 different experimental conditions (visual horizontal, visual vertical, tactile pinky-thumb in somatotopic frame of reference, tactile finger-wrist in somatotopic frame of reference, tactile horizontal in external frame of reference, tactile vertical in external frame of reference). The dissimilarity matrix was created using the pairwise distances (metric “correlation distance” in CoSMoMvpa) for each subject and then averaged across subjects for a single fROI. The averaged dissimilarity matrices were visualized on an abstract cartesian map of 2-dimensions using DISTASIS108.

Cross-modal decoding. To test the presence of shared axes of motion information across the senses, we performed cross-modal binary decoding of visual and tactile motion axes of motion within each fROIs – left somatosensory cortex, left hMT+/V5 and right hMT+/V5 and separately for MT, MST and MTt. Here, the classification was implemented using a similar cross-validation scheme as described above, except that the training set was chosen from one modality and the testing set from another modality. Also, note that prior to the cross-modal classification, the patterns of each motion direction corresponding to each modality were demeaned to equate their mean activity. Since the tactile motion directions could be decoded in two different frames of reference, cross-modal decoding was performed between (a) visual motion directions and tactile motion directions in a somatotopic frame of reference, and (b) visual motion directions and tactile motion directions in an external frame of reference. Note that the analytical procedure was the same for both (a) and (b) except the way the tactile stimulation was assigned to different classes. Finally, statistical inference of the mean decoding accuracies across both the direction of training and testing sets (train on vision, test on touch and vice versa) within each fROI are reported. The cross-modal decoding for the different directions of train and test sets is reported in supplemental material (Fig. S1).

Searchlight analyses. In addition to the fROI-based MVPA, we implemented whole-brain searchlight decoding analyses109, following the approach used in ref. 22 to identify regions representing visual and tactile motion information outside of fROIs defined using the visual and tactile motion localizers. A sphere (3-voxel radius; ~113 voxels) was moved across the brain volume, where in each step, each voxel became the center of the searchlight sphere. The decoding accuracy computed within the sphere was assigned to the central voxel, generating a whole-brain map of decoding accuracies for each participant. Individual accuracy maps were smoothed using an 8mm FWHM gaussian kernel and entered in a second-level group analysis. Group-level maps were corrected using small-volume correction (SVC) within 10-mm radius spheres centered on hMT+/V5 locations, which were independently identified using the visual motion localizer (left hMT+/V5: −47, −70, 5mm; right hMT+/V5: 47, −65, 3mm). Since group-level univariate analyses did not reveal parietal areas as motion selective, we applied SVC at the coordinates of parietal regions previously shown to be selective for tactile motion11 (MNI coordinates left parietal area: −30, −53, 49mm; right parietal area: 41, −55, 54mm).

Modality decoding. To test if a region is purely abstracted from the input modality conveying the directional motion information, we performed binary classification of visual horizontal versus tactile horizontal (externally-defined) and visual vertical versus tactile vertical (externally-defined) in that fROI. Each motion direction pattern in each modality was demeaned separately to equate the mean activity of the visual and tactile modalities and the binary classification was implemented in the similar manner described above.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal regions\nShahzad et al\nIn the present manuscript authors investigated the neural bases subserving the representations of moving stimuli when such\ninformation comes from different sensory modalities (i.e., vision and touch) and has to be integrated to form a unified percept.\nAuthors asked a group of 20 participants to perform two functional magnetic resonance (fMRI) experiments. In the first\nexperiment, they used a visual and tactile localiser to functionally define the brain regions that are selective for motion\ndirection across the two modalities. In the second fMRI experiment, they presented to participants directional motion visual\nand tactile stimuli while changing their hand posture. Data were analysed using multivariate pattern analyses (MVPA) of the\nresponses in the pre-defined regions of interest (ROIs) and on the whole brain. Results showed that regions in the occipito-\nparietal-frontal network as well as area hMT +/V5 represented the visual and tactile motion independently of the hand\nposture. Authors interpreted these results as evidence that these brain network involving also hMT +/V5 area encodes\nmotion directions both in vision and touch with a common frame of reference.\nThis is a very interesting and elegantly designed study that provides novel insights regarding the brain areas involved in the\nintegration of directional motion across different sensory modalities (i.e., vision and touch). The introduction is well\npresented and straightforward with appropriate studies references to support the various statements that leads to the study\nmain question, which are the brain areas that integrates the directional motion for the visual and tactile modality? This is\ncertainly a very important question that applies to the everyday life perception and integration of the stimuli in the\nenvironment coming from different sensory modalities. The sample sounds appropriate; however, it would be beneficial to\nformalise their suitability by including a power analysis. The neuroimaging analyses sound and are appropriately reported\nwith good detail. I do have some comments that authors may want to consider in a revised version of the manuscript.\n- Line 202: The motion conditions of the motion localiser are clearly explained and intuitive, however, I am not sure I did\nunderstand the static conditions, was the brush posed on the hand then raised and posed down again in a different\norientation? During the 1s stimulation in the static conditions there were several up and down or just one touch? I am asking\nas if it was one touch the amount of tactile input in this case may be less compared to the motion condition. Such difference\ncould possibly explain the activity you found in the univariate analysis in the postcentral gyrus for the tactile localiser (lines\n316-318).\n- Line 209. At the beginning of this paragraph authors said that the screen was blank for 5s, however, it is not clear what\nhappened later. Overall, given that participants have their eyes close, I am not sure what was the rational of having changes\nin the screen presentation. Can you please clarify?\n- Line, 437: Table S1 seems to report the univariate analyses rather than the behavioural results. I do believe that the\nbehavioural results are reported in Table S4.\n- Line 211: Were participants’ responses given verbally and recorded?\n- Line 326: I believe that here you are referring to Table S7, not Table S4. I think there is some confusion in the cited tables.\n- Line 436 behavioural results paragraph: Looking at Table S4 with the behavioural results, as stated in the text by the\nauthors there are no differences between the static and motion conditions. However, at least numerically it seems that\nparticipants performed worst in the tactile compared to the visual condition. I was wondering if this is worth reporting, if they\nare significantly different.\n- Lines 456-459: Looking at Figure 2A there seems to be also right primary somatosensory activity for the tactile motion in\naddition to the left as for SII and the temporal gyrus. Can authors comment on this point, please?\n- Lines 560-568: When interpreting the MDS results authors commented on the separation between the somatotopic\ndirection widely spaced in left S1 and the externally defined direction more widely spaced in right hMT +/V5. However, there\nare no comments on the opposite pattern occurring in the left hMT +/V5 (i.e., same pattern as in Left S1). Can authors\ncomment on this point?\n- Lines 673-688, discussion: When discussing their results regarding the representation of tactile motion (and cross-modal\ndecoding) in the right hMT +/V5 and the specificity for this brain area (not left hMT +/V5) in revealing a well-defined\nseparation based on an external reference frame, there is no much discussion regarding the laterality of this brain response\nalso in relation with previous studies and/or literature of the possible reason. For instance, is there a hemispheric preference\nfor external compared to somatotopic coding? Sometime in the discussion the laterality is mentioned and sometime is not\nconsidered. Can authors comment and possibly expand on this aspect?", "reviewer_2": "The ms by Shahzad et al provides very strong evidence on the existence of spatiotopic processing in human MT+ of visual\nand tactile motion and of a shared cross-modal representation. Performing clever experiments where the authors changed\nthe orientation of the hand and stimulated with two orthogonal directions of tactile motion, they were able to reveal the\nexistence of several areas that were selective for the direction of tactile motion independent of the hand position (hence the\nreceptors orientation). Those areas overlap with several parietal (IPS, VIP) , frontal and occipital temporal areas (MT+),\nwhich previous experiments showed to be linked to the representation of space in external coordinates. However, only the\nRight hemisphere MT+ showed that decoding visual motion was generalizing to haptic motion in spatiotopic coordinate, but\nnot in somatic coordinates. The result is very important in helping to resolve a hot topic in the literature and highlighting an\noptimal brain strategy to handle cross-modality. The experimental design and data analysis are very thorough, and the\nmaterial presented very clearly.\nI have only minor comments that the authors may wish to address in the discussion.\n1) I understand that the major cross-modal effect for spatiotopicity is present both in MT and MST. This is very interesting\nand somewhat surprising given that the portion selected as MST was invariant for eye position and by definition higly likely\nto be spatiotopic. If I understood well, while Huk and at. presented visual stimuli only to the left and the right visual field (with\ncentral fixation) and selected MST by the portion with ipsilateral visual field response, in this paper the authors left the visual\nstimuli in the centre and shifted the gaze to the left or the right to stimulate the ipsi- and the contra-lateral visual response.\nThis localizer should boost the portion of visual MT+ with spatiotopic coding. Nevertheless, the cross-modal spatiotopic\ncoding was stronger in MT and not in MST. I found this interesting because it suggests that it is the selectivity for the type of\nmotion that confers cross-modal selectivity on an area. The authors used only planar tactile motion, probably a spiral motion\nfor which MST has high selectivity would have shown a stronger spatiotopicity.\n2) It may be possible that during tactile motion not only is attention toward external direction of motion activated, but also\nsmall eye movements. The fact that the spatiotopic haptic area includes also FEF and IPS (already demonstrated to have\nvisual map in external coordinates, see Zohary’s work) may implicate small drift eye-movement bias.\n3) MT+ also responds to auditory motion (Fine’s work). It would be nice to compare the present data localization with those\nof the auditory localization present in the literature.\n4) The ANOVA-based feature selection should be described. Selecting the most informative voxel is equivalent to selecting\nthe voxels with the higher decoding performance. So it may be circular.\n5) Cross-modal decoding: from the methods it is clear that the training was performed in one sensory response and decoded\nin the other. In the result it seems that the training is performed in the complete data set from both modalities. Please make it\nclear.\n6) I am sure there exists a more appropriate medical term than “pinky-thumb” direction!", "reviewer_3": "In the present study, Shahzad and colleagues use fMRI to investigate whether there is a common reference frame that aligns\nthe processing of visual and tactile motion. Participants see moving dots or felt moving touch on their hand. Importantly, the\nposture of the hand was manipulated to disentangle somatotopic from external reference frames. They find evidence that\nhMT+/V5 and fronto-parietal regions align visual and tactile processing in a common external reference frame. In contrast,\ntactile motion in somatotopic space was not aligned with visual motion in any brain region. They conclude that a common\nreference frame aligns processing of visual and tactile motion.\nThis is an interesting and well-written paper. The topic is timely and addresses and important gap in the literature. The\nmethods and analyses are appropriate for the most part. That being said, there are some shortcomings that need to be\naddressed. I detail these below.\n1) The theoretical links with the concept of ‘reference frames’ should be made stronger throughout the paper. Even though it\nis commonly used in the literature, the term “external reference frame” is rather ambiguous. This is especially the case in the\npresent study, where the visual and tactile information are not spatially aligned. Whereas a postural manipulation is often\nsufficient to distinguish reference frames within the somatosensory modalities, it is harder to make this argument when\nconsidering other senses. What is the nature of the reference frame in the current study? Does it have a concrete origin, such\nas the trunk, the eyes, the shoulder, etc.? Or is it more conceptual and higher-level, such as an abstract code for “left” and\n“right”? Given the spatial discrepancy between where the visual and tactile stimuli, it seems more likely to be an abstract\nreference frame—though perhaps the authors would disagree. I do not think the current study can adjudicate these\npossibilities, but I believe they warrant in-depth discussion. This would give the paper a stronger theoretical grounding.\n2) Relatedly, the authors claim that their results show that the representations of visual and tactile motion are “aligned”. Can\nthey expand more on what “aligned” means in the context of their findings? In the second paragraph of the Introduction,\n“alignment” during multisensory integration is discussed in reference to spatial maps. However, this feels unlikely in the\npresent study, given the lack of physical spatial alignment of the stimuli. Perhaps the findings instead reflect an alignment of\nhow left and right are represented, irrespective of physical alignment and the origin of the external reference frame? \nClarifying this would help make the “alignment” more concrete.\n3) As they are currently written, I find it hard to follow what the actual MVPA methods were. How does the leave-one-out\nprocedure of the pseudoruns (lines 340-347) relate to the 5-fold cross validation procedure described in the next paragraph?\nWas the 5-fold cross validation performed nine times, to cover all possible beta-map pairs? Or were the 45 pseudoruns\ncreated and then chosen from for a single cross-validation procedure? In this case, I would be very concerned about data\nleakage, which would artificially inflate classification accuracy. However, given that this information is written in two distinct\nparagraphs, it is hard to know exactly what was done. Please rewrite to make the MVPA procedure clearer.\n4) The cross-modal decoding analysis provides some intriguing evidence for an “alignment” of external representations for\nvisual and tactile motion. However, it does not say much about another important feature of these representations: Whether\nthey are mainly modality-independent (i.e., a fully aligned representation) or modality-specific (i.e., mostly distinct\nrepresentations, with some alignment). This question reflects an important feature of the representations that is necessary to\nfully understand the alignment between modalities. I believe it deserves attention by the authors and can be investigated\nwith the current data. One MVPA approach that could address this question is to train a classifier to decode the four\nclasses (visual-left, visual-right, tactile-left, tactile-right), and then analyze the types of the errors made by the classifier. If the\n“aligned” representations are largely modality-specific, then most errors should be within-modality (e.g., mistaking vision-left\nwith vision-right). If instead the “aligned” representations are largely modality-independent, then most errors should be for\nthe same motion direction in the other modality (e.g., mistaking vision-left with tactile-left) . However, I leave it up to the authors\nto determine what they believe to be an appropriate way to address this question."}, "author_response": "Response to Reviewers\nNature Communications manuscript NCOMMS-25-35604A\nTitle: Aligned representation of visual and tactile motion directions in hMT+/V5 and fronto-\nparietal regions\nWe would like to thank both reviewers and the editor for reading our manuscript thoroughly and for\nproviding insightful comments that helped us improve the quality of our work. In the following\nsection, the reviewers’ comments are presented in bold and are followed by our replies.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nAligned representation of visual and tactile motion directions in hMT+/V5 and fronto-parietal\nregions\nShahzad et al\nIn the present manuscript authors investigated the neural bases subserving the representations\nof moving stimuli when such information comes from different sensory modalities (i.e., vision\nand touch) and has to be integrated to form a unified percept. Authors asked a group of 20\nparticipants to perform two functional magnetic resonance (fMRI) experiments. In the first\nexperiment, they used a visual and tactile localiser to functionally define the brain regions that\nare selective for motion direction across the two modalities. In the second fMRI experiment,\nthey presented to participants directional motion visual and tactile stimuli while changing their\nhand posture. Data were analysed using multivariate pattern analyses (MVPA) of the responses\nin the pre-defined regions of interest (ROIs) and on the whole brain. Results showed that\nregions in the occipito-parietal-frontal network as well as area hMT +/V5 represented the visual\nand tactile motion independently of the hand posture. Authors interpreted these results as\nevidence that these brain network involving also hMT +/V5 area encodes motion directions\nboth in vision and touch with a common frame of reference.\nThis is a very interesting and elegantly designed study that provides novel insights regarding\nthe brain areas involved in the integration of directional motion across different sensory\nmodalities (i.e., vision and touch). The introduction is well presented and straightforward with\nappropriate studies references to support the various statements that leads to the study main\nquestion, which are the brain areas that integrates the directional motion for the visual and\ntactile modality? This is certainly a very important question that applies to the everyday life\nperception and integration of the stimuli in the environment coming from different sensory\nmodalities. The sample sounds appropriate; however, it would be beneficial to formalise their\nsuitability by including a power analysis. The neuroimaging analyses sound and are\nappropriately reported with good detail. I do have some comments that authors may want to\nconsider in a revised version of the manuscript.\nWe thank the reviewer for their positive assessment and thoughtful suggestions.\nWe based our sample size on a previous study investigating the representation of visual and auditory\nmotion directions in hMT+/V5 1. Since our main res earch question concerned decoding accuracies in\nhMT+/V5, we relied on the within-modality auditory axis-of-motion decoding values reported in Table\nS4 of 1. These were:\n Left hMT+/V5: mean = 58.93%, SD = 9.37 → Cohen’s d = 0.95 (large effect)\n1\n Right hMT+/V5: mean = 56.63%, SD = 10.64 → Cohen’s d = 0.62 (medium effect)\nThe average across hemispheres gave us an effect size of d = 0.79. We then performed a power\nanalysis in G*Power version 3.1 for a one-sample t-test against chance (50%), with a desired power\nof 90%, α = 0.05, and a two-tailed test. This analysis indicated that a minimum of 19 participants\nwould be required, which is consistent with our recruitment of 20 participants.\nAccordingly, we have added the following statement in the manuscript (section Methods:\nParticipants; page 6, lines 109-116) of the manuscript to report the sample size estimation:\n“We based our sample size on a previously published study 1 that investigated visual and\nauditory motion direction representation in hMT+/V5. We performed a power analysis using\nG*Power version 3.1 2 based on the reported effect sizes for auditory axis-of-motion decoding\naccuracies (see Supplemental Table S4 in 1. We computed the sample size using the average\neffect size across left and right hMT+/V5. With d = 0.79, α = 0.05, and 90% power, the\nestimated sample size was 19. Consequently, we recruited twenty healthy participants (11\nfemales; mean age ± SD = 25.15 ± 3.51 years; age range = 19–33 years) for this study.”\n2\n- Line 202: The motion conditions of the motion localiser are clearly explained and intuitive,\nhowever, I am not sure I did understand the static conditions, was the brush posed on the hand\nthen raised and posed down again in a different orientation? During the 1s stimulation in the\nstatic conditions there were several up and down or just one touch? I am asking as if it was one\ntouch the amount of tactile input in this case may be less compared to the motion condition.\nSuch difference could possibly explain the activity you found in the univariate analysis in the\npostcentral gyrus for the tactile localiser (lines 316-318).\nWe thank the reviewer for pointing out this potential source of confusion and allowing us to clarify\nthis point.\nIn the static trials, the brush was placed on the palm for ~1s in one orientation (e.g., thumb-to-pinky),\nlifted and then placed again for ~1s in a different orientation (e.g., finger-to-wrist), matching the\nduration of contact in motion trials. Thus, both motion and static blocks involved successive tactile\ntrials of equal duration, ensuring comparable tactile input while differing only in whether motion was\npresent or not. So, in a block, the position of the static condition was changed 11 times, as for the\nnumber of changes in directions in the motion blocks. This method of providing tactile motion and\nstatic stimuli is inspired from a previous study reporting tactile motion selectivity (tactile motion >\nstatic) in the left primary somatosensory cortex (post central gyrus), bilateral secondary\nsomatosensory cortex, Intra-parietal sulcus in the parietal cortex and hMT+/V5 3.\nWe have now clarified this procedure in the manuscript (section Methods: Tactile Motion Localizer;\npage 9, lines 219–224).\n“In the static trials, the brush was touched on the right palm in one orientation (e.g., pinky-\nthumb) for ~1s, lifted, and then touched again in the different orientation (e.g., finger-wrist)\nfor ~1s. This ensured that the duration of tactile input corresponded across each static and\nmotion conditions. So, in a block, the position of the static condition was changed 11 times,\nas for the number of changes in directions in the motion blocks.”\n3\n- Line 209. At the beginning of this paragraph authors said that the screen was blank for 5s,\nhowever, it is not clear what happened later. Overall, given that participants have their eyes\nclose, I am not sure what was the rational of having changes in the screen presentation. Can\nyou please clarify?\nWe thank the reviewer for this observation and apologize for the lack of clarity in our initial\ndescription. Indeed, participants kept their eyes closed throughout the tactile runs and therefore did\nnot see the screen. The initial 5s blank screen at the beginning of each run served as a pause during\nwhich no tactile stimulation was applied (baseline). The cues displayed on the screen were intended\nsolely for the experimenter and were not visible to the participants.\nWe have now clarified this in the revised manuscript (section Methods: Tactile Motion Localizer, page\n8, lines 211-212).\n“Each run started with a with a 5s pause and ended with a 14s pause during which no tactile\nstimulation was delivered”.\n- Line, 437: Table S1 seems to report the univariate analyses rather than the behavioural results.\nI do believe that the behavioural results are reported in Table S4.\nWe apologize for the error in naming the tables. We have now reordered the tables and checked their\nreferencing in the text. The behavioral results are now in Table S1. We have corrected this reference\nin the revised manuscript supplementary information.\n- Line 211: Were participants’ responses given verbally and recorded?\nWe thank the reviewer for this question and apologize for the lack of clarity in the previous version of\nthe manuscript. The participants’ responses were recorded using a response button held in their left\nhand.\nWe have reworded this in the revised Manuscript (section Methods: Experimental Setup; page 6, lines\n132-134):\n“In all the experiments, participants provided their responses by pressing the response\nbuttons with the index finger of their left hand.”\n4\n- Line 326: I believe that here you are referring to Table S7, not Table S4. I think there is some\nconfusion in the cited tables.\nWe apologize for the confusion in our table references and thank the reviewer for catching this error.\nWe have carefully cross-checked all table references throughout the manuscript and corrected them\nto ensure consistency. The revised manuscript now cites the correct tables.\n- Line 436 behavioural results paragraph: Looking at Table S4 with the behavioural results, as\nstated in the text by the authors there are no differences between the static and motion\nconditions. However, at least numerically it seems that participants performed worst in the\ntactile compared to the visual condition. I was wondering if this is worth reporting, if they\nare significantly different.\nWe thank the reviewer for this helpful suggestion. To examine differences in performance between\nmodalities, we compared overall accuracy between visual and tactile conditions. Mean accuracy was\nslightly higher for visual (M = 0.99 ± 0.02 SD) than for tactile (M = 0.93 ± 0.08 SD) conditions. A non-\nparametric Wilcoxon signed-rank test confirmed that this difference was statistically significant (W =\n5.00, p = 0.021). It is therefore indeed true that accuracy in the one-back control task was slightly\nhigher in vision, mostly because performance was near perfect in this modality; however, accuracy\nwas still very high for touch, ensuring that participants paid attention to the stimuli (which was the\nprimary goal of the one-back task implemented here). Importantly, visual and tactile conditions were\nanalyzed independently and were never directly contrasted in either the univariate or decoding\nanalyses; therefore, this small behavioral difference cannot account for the neural findings. The only\nanalysis in which vision and touch were jointly examined was the crossmodal decoding analysis,\nwhich revealed partial alignment across modalities (when touch was coded in external coordinates).\nWe thus consider it very unlikely that the difference reported here could have influenced the outcome\nof this analysis.\n5\n- Lines 456-459: Looking at Figure 2A there seems to be also right primary somatosensory\nactivity for the tactile motion in addition to the left as for SII and the temporal gyrus. Can\nauthors comment on this point, please?\nWe thank the reviewer for this careful observation, and we apologize for the confusion. In Figure 2A,\nwe displayed the tactile motion > tactile static contrast at a liberal visualization threshold (p = 0.01\nuncorrected) to highlight motion-selective activity across modalities and across the whole brain (as\nmotion selectivity was overall weaker in touch compared to vision in our study), and to facilitate fROI\ndefinition at the single subject. At this liberal threshold, some clusters appear in right primary\nsomatosensory cortex (S1). However, our statistical inference was based on whole-brain univariate\nanalysis at a threshold of p < 0.05 FWE-corrected, under which only left S1 and bilateral S2 showed\nsignificant tactile motion selective responses (as reported in the text and Table S3). In addition, based\non our a priori hypothesis, tactile motion selective activity in middle-temporal region (bilateral\nhMT+/V5) is reported after small volume correction (Table S3).\nFurthermore, because participants received tactile stimulation to the right hand, robust activity was\nexpected in contralateral (left) S1, consistent with the well-established lateralization of\nsomatosensory pathways 4,5. The visualization of tactile motion selective activity in right S1 at the\nliberal threshold (p = 0.01 uncorrected) most likely reflects weak ipsilateral influences that can arise\nfrom transcallosal integration within SI (especially in BA1/BA2) and interactions with higher-level\nsomatosensory regions such as S2 and the parietal cortex 5.\nWe have revised the caption of Figure 2 A accordingly:\n“(A) Group-level univariate analyses of visual (orange) and tactile (green) motion > static\ncontrasts. Visual motion selective regions (orange) are displayed at p = 0.001 uncorrected.\nTactile motion selective regions (green) are visualized at p = 0.01 uncorrected. There is an\noverlap between the visual and tactile motion selectivity in the hMT+/V5 (yellow). The maps\nare for visualization purpose only and statistical inference is reported in Table S2 and S3.”\n6\n- Lines 560-568: When interpreting the MDS results authors commented on the separation\nbetween the somatotopic direction widely spaced in left S1 and the externally defined direction\nmore widely spaced in right hMT +/V5. However, there are no comments on the opposite\npattern occurring in the left hMT +/V5 (i.e., same pattern as in Left S1). Can authors comment\non this point?\nWe thank the reviewer for pointing this out. Indeed, we had not explicitly mentioned the layout in left\nhMT+/V5 in the Results section. We have now added the following sentence (section Results:\nMultidimensional Scaling; page 21, line 644):\n“The layout in the left hMT+/V5 resembles the organizational pattern in the left S1.”\nAs the reviewer correctly noted, the MDS plots reveal contrasting organizational layouts across\nhemispheres: left hMT+/V5 resembles left S1, showing greater separation between somatotopically\ndefined tactile directions, while right hMT+/V5 shows greater separation between externally defined\ndirections. These visualizations by MDS are informative as they summarize the data in two\ndimensions easy to visualize; but we rely on decoding analyses to reach statistical confirmation. The\nwithin-modality decoding analyses (Figure 3A) in left and right hMT+/V5 did not reveal significant\ndifferences between somatotopic and external representations. This may be because the decoding\nanalyses capture the full multidimensional feature space which makes it more sensitive to complex\nrepresentational differences that may not be visible in two-dimensional projections as in the MDS.\nThus, while the MDS provides a useful visualization of potential hemispheric differences, the absence\nof statistical support prevents us from drawing reliable inferential conclusions.\nWe also recognize that our description in the Discussion was not clear. We have therefore revised it\nfrom:\n“Multidimensional scaling results (Figure 3B) further illustrate these patterns: tactile motion\ndirections defined in external space were more distinctly separated in right hMT+/V5, whereas in\nleft S1 (and to a lesser extent in left hMT+/V5), somatotopically defined motion directions were\nmore distinctly represented.”\nTo (section Discussion; page 25, lines 787-791):\n“Multidimensional scaling plots (Figure 3B) further illustrate the relative organization of the\ndifferent conditions within each fROI: tactile motion directions defined in external space were\nmore distinctly separated in right hMT+/V5, whereas in left S1 and left hMT+/V5,\nsomatotopically-defined motion directions were more distinctly represented.\n- Lines 673-688, discussion: When discussing their results regarding the representation of\ntactile motion (and cross-modal decoding) in the right hMT +/V5 and the specificity for this\nbrain area (not left hMT +/V5) in revealing a well-defined separation based on an external\nreference frame, there is no much discussion regarding the laterality of this brain response\nalso in relation with previous studies and/or literature of the possible reason. For instance, is there a\nhemispheric preference for external compared to somatotopic coding? Sometime in the\ndiscussion the laterality is mentioned and sometime is not considered. Can authors comment\n7\nand possibly expand on this aspect?\nWe thank the reviewer for pointing out this inconsistency and we apologize for the oversight. To avoid\nambiguity, we have revised the Discussion to explicitly state the laterality of hMT+/V5, adding “right”\nbefore hMT+/V5 wherever it was previously missing. This ensures that references to the right\nhemisphere are consistent and unambiguous throughout the Discussion.\nWe also thank the reviewer for this insightful point about the hemispheric preference and giving us\nthe opportunity to expand on this aspect. In our study, we observed that both left and right hMT+/V5\nshowed within-modality decoding of externally defined tactile directions, but only the right hMT+/V5\nexhibited significant crossmodal alignment between visual and externally-defined tactile motion\ndirections. This suggests that both left and right hMT+/V5 contribute to encoding tactile motion in\nexternal coordinates but the right hMT+/V5 seems to be more specialized for crossmodal alignment\nbetween visual and tactile motion. For the posterior parietal cortex, it has been shown recently that\nspatial representation in posterior PPC is distributed and bilateral, but that lateralization exists for\nsome higher-order, rule-based processes (for details see 6). Our whole-brain crossmodal searchlight\nmap also shows that the hMT+/V5, along with the PPC, could use a distributed spatial code for tactile\nprocessing, but that the right hemisphere is more specialized for external spatial processing across\ndifferent sensory modalities. This specialization could reflect the broader right-hemisphere\ndominance for spatial attention and multisensory spatial integration observed in prior research 7–9. In\nparticular, the effect of hand crossing on tactile judgements generally activates the right hemisphere\nmore strongly 10–12 and the aligned directional motion signals across vision and audition were\npreviously found in the right hMT+/V5 only 1. Together, these results may suggest a lateralization in\nthe right hemisphere for the process of remapping and aligning the senses using an externally-\ndefined representation.\nWe have added the following paragraph to the manuscript (section Discussion, page 27 lines 872-\n889):\nAlthough both left and right hMT+/V5 showed within-modality decoding of externally\ndefined tactile directions, only the right hMT+/V5 exhibited significant crossmodal alignment\nbetween visual and externally-defined tactile motion directions. This suggests that while\nbilateral hMT+/V5 regions contribute to encoding tactile motion in external coordinates, the\nright hMT+/V5 may be more specialized for crossmodal alignment between visual and tactile\nmotion representations. Similar to a recent study showing that spatial representations in the\nPPC are widely distributed yet exhibit hemispheric asymmetries for higher-order spatial\ntransformations 6, the tactile spatial code is not restricted to one hemisphere but rather\ndistributed in bilateral hMT+/V5. Our whole-brain crossmodal searchlight map also shows\nthat the hMT+/V5, along with the PPC, could use a distributed spatial code for tactile\nprocessing, but that the right hemisphere is more specialized for external spatial processing\nacross different sensory modalities. This specialization could reflect the broader right-\nhemisphere dominance for spatial attention and multisensory spatial integration observed in\nprior research 7–9. For instance, the effect of hand crossing on tactile judgements generally\nactivates the right hemisphere more strongly 10–12. Also, the aligned directional motion\nsignals across vision and audition were previously found in the right hMT+/V5 only 1. Together,\nthis suggests a lateralization in the right hemisphere in the process of remapping and aligning\nthe senses using an externally-defined representation.\nThe ms by Shahzad et al provides very strong evidence on the existence of spatiotopic\nprocessing in human MT+ of visual and tactile motion and of a shared cross-modal\nrepresentation. Performing clever experiments where the authors changed the orientation of\nthe hand and stimulated with two orthogonal directions of tactile motion, they were able to\nreveal the existence of several areas that were selective for the direction of tactile motion\nindependent of the hand position (hence the receptors orientation). Those areas overlap with\nseveral parietal (IPS, VIP), frontal and occipital temporal areas (MT+), which previous\nexperiments showed to be linked to the representation of space in external coordinates.\nHowever, only the Right hemisphere MT+ showed that decoding visual motion was\ngeneralizing to haptic motion in spatiotopic coordinate, but not in somatic coordinates. The\nresult is very important in helping to resolve a hot topic in the literature and highlighting an\noptimal brain strategy to handle cross-modality. The experimental design and data analysis are\nvery thorough, and the material presented very clearly.\nWe thank the reviewer for the positive assessment\nI have only minor comments that the authors may wish to address in the discussion.\n1) I understand that the major cross-modal effect for spatiotopicity is present both in MT and\nMST. This is very interesting and somewhat surprising given that the portion selected as MST\nwas invariant for eye position and by definition higly likely to be spatiotopic. If I understood\nwell, while Huk and at. presented visual stimuli only to the left and the right visual field (with\ncentral fixation) and selected MST by the portion with ipsilateral visual field response, in this\npaper the authors left the visual stimuli in the centre and shifted the gaze to the left or the right\nto stimulate the ipsi- and the contra-lateral visual response. This localizer should boost the\nportion of visual MT+ with spatiotopic coding. Nevertheless, the cross-modal spatiotopic\ncoding was stronger in MT and not in MST. I found this interesting because it suggests that it is\nthe selectivity for the type of motion that confers cross-modal selectivity on an area. The\nauthors used only planar tactile motion, probably a spiral motion for which MST has high\nselectivity would have shown a stronger spatiotopicity.\nWe thank the reviewer for this insightful comment.\nWe apologize for the confusion regarding the description of our MT/MST localizer. Our localizer used\na procedure similar to the one employed in 13 (see Figure 1C in 13) and the Figure R1 below).\nSpecifically, the moving dot stimuli were presented in a peripheral circular aperture, with the closest\nedge positioned 10° of visual angle from fixation. Each run of our MT/MST localizer consisted of 10\nblocks of motion stimulation on one side of the screen while participants-maintained fixation on the\ncross, followed by another 10 blocks of motion stimulation presented on the opposite side of the\nscreen.\npanel shows the screenshot of the two conditions from a running MT/MST Localizer\nWe would also like to clarify that these localizer experiments were primarily used to define fROIs for\nMT and MST in each individual. The representation of tactile motion directions across reference\nframes was investigated using MVPA on a separate set of directional motion decoding experiments.\nSo, the effects due to the change in fixations within a run of MT/MST localizer could not contribute to\nthe spatiotopic coding observed in the separate decoding experiment.\nWe agree with the reviewer that, given the non-retinotopic nature of MST, one might expect MST to\nshow stronger external (or spatiotopic) representations than MT. However, this was not the case in\nour dataset. Interestingly, as the reviewer noted, the crossmodal decoding accuracies tended to be\neven numerically higher in right MT than right MST. This raises the possibility that motion cue\nselectivity could indeed modulate crossmodal decoding - for example, in MT, planar tactile motion\nmay align with planar visual motion representations, whereas MST might show stronger crossmodal\nalignment with more complex motion trajectories such as rotational or radial motion 14. This is a very\nstimulating suggestion, and we agree that future studies explicitly combining rotational or radial\nvisual motion stimulation with tactile posture manipulations could provide new insights into the\ndifferential contributions of MT and MST to crossmodal motion alignment. This might be reminiscent\nof the work of Angelaki’s group showing specific integration of visual and proprioceptive signals in\nMST 15–17. With the current dataset, we can conclude that shared crossmodal representations of\nmotion seem to be present in both right MT and right MST, suggesting that both subdivisions could\ncontribute to the alignment of multisensory motion information.\nWe have incorporated this valuable point raised by the reviewer in our Manuscript (Section\nDiscussion; page 26, lines 806-811):\n“Although MST is classically regarded as non-retinotopic and therefore a strong candidate\nfor external spatial coding, our data did not reveal stronger crossmodal decoding in right MST\ncompared to right MT. This may reflect the selectivity of the (right) MT for the planar motion\ncues used in our study, and suggests that the right MST could show stronger crossmodal\nalignment with more complex motion trajectories, such as rotational or radial motion 14–17, a\nhypothesis that remains to be tested in future work.”\n10\n2) It may be possible that during tactile motion not only is attention toward external direction\nof motion activated, but also small eye movements. The fact that the spatiotopic haptic area\nincludes also FEF and IPS (already demonstrated to have visual map in external coordinates,\nsee Zohary’s work) may implicate small drift eye-movement bias.\nWe thank the reviewer for raising this important point. We fully agree that the frontal eye field (FEF)\nand intraparietal sulcus (IPS) are regions implicated in eye movements and that activity in these\nregions could be influenced by subtle eye drifts.\nThe eye movements could confound interpretations of externally-defined tactile motion directions.\nTo directly assess this possibility, we computed standard deviation of eye positions using bidsMReye\n18, a BIDS app relying on deepMReye 19 that decodes eye movement from fMRI time series data using\nthe MR signal of the eyeballs. For each run, bidsMReye computes the variance for the X gaze position,\nvariance for the Y gaze position, framewise gaze displacement, number of X gaze position outliers,\nnumber of Y gaze position outliers, and number of gaze displacement outliers. Outliers were robustly\nestimated using an implementation of the median rule 20.\nWe used the variance of the X and Y gaze positions corresponding to the different stimulus conditions\nwithin each run and computed their standard deviations. These standard deviations, used as indices\nof eye-position variability, were then averaged across runs to yield a single value per subject. We\nhypothesized that if externally-defined horizontal tactile motion elicited greater horizontal eye\nmovements, this would be reflected as increased variability in the X gaze position; similarly,\nexternally-defined vertical tactile motion would be expected to produce greater variability in the Y\ngaze position.\nTo assess this, we pooled the tactile stimuli across hand postures to yield externally-defined tactile\ndirections (horizontal, vertical) and performed a 2 (gaze axis: X, Y) × 2 (externally-defined tactile\ndirection: horizontal, vertical) repeated-measures ANOVA. We observed a significant main effect of\ngaze axis, F (1, 19) = 14.03, p = 0.0014, partial η² = 0.425, indicating greater overall variability along\nhorizontal (X) gaze axis. Crucially, there was no main effect of tactile motion direction, F (1, 19) = 0.00,\np = 0.96, partial η² < 0.001, and no interaction, F (1, 19) = 0.01, p = 0.91, partial η² < 0.001. These results\nindicates that tactile motion direction did not modulate gaze variability.\nWe further compared eye-position variability between externally-defined horizontal and vertical\ntactile motion directions for each gaze axis using paired-samples t-tests. There was no significant\ndifference between the two tactile motion directions in horizontal gaze variability (t (19) = -0.10, p =\n0.92, d = 0.02) and vertical gaze variability (t (19) = 0.06, p = 0.95, d = 0.01). Again, this analysis\nrevealed that tactile motion directions did not modulate variability in either horizontal or vertical\ngaze axis.\nGiven that our eye-position analyses revealed no systematic differences across stimulus conditions,\nthe decoding results cannot be explained by eye-movement artifacts. As the reviewer noted, previous\nstudies have shown that FEF/IPS are not only involved in eye movement, but also are part of a\nnetwork capable of flexibly representing spatial information in multiple coordinate systems 21–26. The\nobserved activity in FEF/IPS appears indeed consistent with the engagement of a network that\nmultiplexes across spatial frames of reference to support flexible spatial transformations. This\ninterpretation is in line with converging evidence that FEF and IPS encode hybrid or spatiotopic\nrepresentations beyond their role in eye-movement execution 21.\nWe have added the following to the manuscript (section Methods; page 10, lines 269-287):\nThe eye positions of the participants in all the different experiments were computed using\nbidsMReye 18, a BIDS app relying on deepMReye 19 that decodes eye movement from fMRI\ntime series data using the MR signal of the eyeballs. For each run, bidsMReye computes the\nvariance for the X gaze position, variance for the Y gaze position, framewise gaze\ndisplacement, number of X gaze position outliers, number of Y gaze position outliers, and\nnumber of gaze displacement outliers. Outliers were robustly estimated using an\nimplementation of the median rule 20. We used the variance of the X and Y gaze positions\ncorresponding to the different stimulus conditions within each run and computed their\nstandard deviations. These standard deviations, used as indices of eye-position variability,\nwere then averaged across runs to yield a single value per subject. We hypothesized that if\nexternally-defined horizontal tactile motion elicited greater horizontal eye movements, this\nwould be reflected as increased variability in the X gaze position; similarly, externally-defined\nvertical tactile motion would be expected to produce greater variability in the Y gaze position.\nTo assess this, we pooled the tactile stimuli across hand postures to yield externally-defined\ntactile directions (horizontal, vertical) and performed a 2 (gaze axis: X, Y) × 2 (externally-\ndefined tactile direction: horizontal, vertical) repeated-measures ANOVA. We also compared\neye-position variability between externally-defined horizontal and vertical tactile motion\ndirections for each gaze axis using paired-samples t-tests.\nWe have added the following to the Manuscript (section Results; page 17, line 502-516):\nEye Position Variability\nWe examined whether variability in gaze position differed between the externally-defined\ntactile directions by performing a 2 (gaze axis: X, Y) × 2 (externally-defined tactile direction:\nhorizontal, vertical) repeated-measures ANOVA on the estimated standard deviation. We\nobserved a significant main effect of gaze axis, F (1, 19) = 14.03, p = 0.0014, partial η² = 0.425,\nindicating greater overall variability along horizontal (X) gaze axis. Crucially, there was no\nmain effect of tactile motion direction, F (1, 19) = 0.00, p = 0.96, partial η² < 0.001, and no\ninteraction, F (1, 19) = 0.01, p = 0.91, partial η² < 0.001. These results indicates that tactile\nmotion direction did not modulate gaze variability. We further compared eye-position\nvariability between externally-defined horizontal and vertical tactile motion directions for\neach gaze axis using paired-samples t-tests. There was no significant difference between the\ntwo tactile motion directions in horizontal gaze variability (t (19) = -0.10, p = 0.92, d = 0.02)\nand vertical gaze variability (t (19) = 0.06, p = 0.95, d = 0.01). Again, this analysis revealed that\ntactile motion directions did not modulate variability in either horizontal or vertical gaze axis.\nWe have added the following to the Manuscript (section Discussion; page 26, lines 827-834):\nIn addition to representing space across different sensory modalities, regions such as Frontal\nEye Fields (FEF) in the frontal cortex and Intra-parietal Sulcus (IPS) in the parietal cortex are\nalso implicated in eye-movement planning 21–26. However, our analyses of the variability in\nthe eye-positions during fMRI acquisition revealed no systematic differences across different\ntactile motion directions, suggesting that subtle eye movements are unlikely to explain the\nobserved decoding results. Therefore, the observed frontoparietal network might reflect a\nspatial coding network engaged during visual and tactile alignment in external coordinates.\n12\n3) MT+ also responds to auditory motion (Fine’s work). It would be nice to compare the present\ndata localization with those of the auditory localization present in the literature.\nWe thank the reviewer for this valuable suggestion. We now highlight this literature in the revised text\n(section Introduction; page 4, line 89) and situate our visual and tactile results within the broader\nmultisensory (including auditory) motion framework in the Discussion and integrated with the\nresponse to the comments of the Reviewer #1.\nWe have added the following in the Manuscript (section Discussion; page 28, lines 890-897):\nMoreover, considering previous reports of motion-selective responses in auditory modality\nwithin hMT+/V5 27–32 along with the evidence for shared audio-visual motion direction\nrepresentations in the right hMT+/V5 1, our visuo-tactile findings suggest that the right\nhMT+/V5 could constitute a multisensory motion hub. The group-level MNI coordinates of\nright hMT+/V5 reported in 1 and in the present study are in close spatial proximity\n(approximately 4 mm apart : 46, −62, 6 mm in 1 and 47, −65, 3 mm in this study) suggesting\naligned motion direction information from vision, touch, and audition within a shared\nrepresentation.\n13\n4) The ANOVA-based feature selection should be described. Selecting the most informative\nvoxel is equivalent to selecting the voxels with the higher decoding performance. So it may be\ncircular.\nWe thank the reviewer for raising this important point. We understand the risk of circularity (“double\ndipping”; 33) and took care to avoid it. The ANOVA-based feature selection was nested within the\ncross-validation procedure:\nWithin each fold, the training data were used to compute an ANOVA F-statistic for each voxel, which\ncompares the variance of voxel responses between conditions (e.g., vertical vs. horizontal motion) to\nthe variance within conditions (noise across trials) (F =\nvariance between conditions/variance within conditions). The voxels were then ranked by their F-\nvalues, and only the top subset was retained. In each fold, the ANOVA F-statistic was computed only\non the training data to identify the voxels showing the greatest condition-related variance; this\nsubset was then applied to the separate test data. The test set therefore played no role in voxel\nselection, ensuring that decoding performance remained unbiased.\nIn other words, the method first chooses informative voxels from the training set and only then\nevaluates decoding on unseen test data. This ensures that voxel selection was based on variance\nacross conditions rather than decoding accuracy, thereby preventing circularity. This procedure\nfollows standard MVPA practice as in 1,27.\nWe have revised manuscript accordingly to describe the ANOVA-based feature selection method\n(section Methods: Multivariate Pattern Analyses (MVPA); page 14, lines 397-403):\n“An ANOVA-based feature selection method 34,35 was applied only to the training data in each\nfold to select the 100 most informative voxels within each region of interest. This method\ncomputes ANOVA F - statistic only on the training set and chooses features based on the\nvariance. Voxels with the highest F-values were retained, and this subset was applied to the\nindependent test set for decoding. This method not only ensures a similar number of voxels\nwithin a given fROI across but also avoids circularity (‘double dipping’; 33)”\n14\n5) Cross-modal decoding: from the methods it is clear that the training was performed in one\nsensory response and decoded in the other. In the result it seems that the training is performed\nin the complete data set from both modalities. Please make it clear.\nWe thank the reviewer for this helpful comment. We confirm that in our cross-modal decoding\nanalyses, the classifier was trained exclusively on data from one modality (e.g., visual motion) and\nthen tested on data from the other modality (e.g., tactile motion), and vice versa.\nWe have now clarified this in the Manuscript (section Results; page 21, lines 650-657):\n“To further investigate whether the motion information is shared across sensory modalities\nwithin our fROIs, we performed crossmodal decoding where the classifier was trained on one\nmodality and tested on the other modality (training in vision and testing in tactile, and vice\nversa. Since tactile motion directions were defined in two different reference frames, cross-\nmodal decoding was performed in two ways: (a) between visual motion directions and tactile\nmotion directions in a somatotopic frame of reference, and (b) between visual motion\ndirections and tactile motion directions in an external frame of reference (Figure 3C and\nFigure S 2).”\n6) I am sure there exists a more appropriate medical term than “pinky-thumb” direction!\nWe thank the reviewer for this suggestion. We fully agree that terms such as “ulnar–radial axis” are\nanatomically more precise. Given that our manuscript is intended for an interdisciplinary audience,\nwe tried to ensure accessibility across disciplines, including readers who don’t have a strong\nbackground in the somatosensory systems and may not be familiar with anatomical terminology. The\nterm “pinky–thumb” conveys the stimulated axis in a way that is very intuitive. This terminology\nmakes the description more approachable for a broad readership. Therefore, we chose simple terms\nsuch as “pinky-thumb”, as in 4.\nWe agree with the reviewer to include a more scientific term. Therefore, we have added the following\nin the revised manuscript (section Methods: Tactile Motion Localizer; page 9 line 217 and caption of\nFigure 1A):\n“Corresponding to the ulnar-radial axis of the hand”\nReviewer #3 (Remarks to the Author):\nIn the present study, Shahzad and colleagues use fMRI to investigate whether there is a\ncommon reference frame that aligns the processing of visual and tactile motion. Participants\nsee moving dots or felt moving touch on their hand. Importantly, the posture of the hand was\nmanipulated to disentangle somatotopic from external reference frames. They find evidence\n15\nthat hMT+/V5 and fronto-parietal regions align visual and tactile processing in a common\nexternal reference frame. In contrast, tactile motion in somatotopic space was not aligned with\nvisual motion in any brain region. They conclude that a common reference frame aligns\nprocessing of visual and tactile motion.\nThis is an interesting and well-written paper. The topic is timely and addresses and important\ngap in the literature. The methods and analyses are appropriate for the most part. That being\nsaid, there are some shortcomings that need to be addressed. I detail these below.\nWe thank the Reviewer for their appreciation. Since comments 1, 2 and 4 are related, we have\naddressed 1 and 2 together, followed by our response to comment 4. The response to comment 3 is\nprovided in the end.\n1) The theoretical links with the concept of ‘reference frames’ should be made stronger\nthroughout the paper. Even though it is commonly used in the literature, the term “external\nreference frame” is rather ambiguous. This is especially the case in the present study, where the\nvisual and tactile information are not spatially aligned. Whereas a postural manipulation is often\nsufficient to distinguish reference frames within the somatosensory modalities, it is harder to\nmake this argument when considering other senses. What is the nature of the reference frame in\nthe current study? Does it have a concrete origin, such as the trunk, the eyes, the shoulder, etc.?\nOr is it more conceptual and higher-level, such as an abstract code for “left” and “right”? Given\nthe spatial discrepancy between where the visual and tactile stimuli, it seems more likely to be\nan abstract reference frame—though perhaps the authors would disagree. I do not think the\ncurrent study can adjudicate these possibilities, but I believe they warrant in-depth discussion.\nThis would give the paper a stronger theoretical grounding.\n2) Relatedly, the authors claim that their results show that the representations of visual and\ntactile motion are “aligned”. Can they expand more on what “aligned” means in the context of\ntheir findings? In the second paragraph of the Introduction, “alignment” during multisensory\nintegration is discussed in reference to spatial maps. However, this feels unlikely in the present\nstudy, given the lack of physical spatial alignment of the stimuli. Perhaps the findings instead\nreflect an alignment of how left and right are represented, irrespective of physical alignment\nand the origin of the external reference frame? Clarifying this would help make the “alignment”\nmore concrete.\nWe thank the reviewer for this very thoughtful and constructive comment. We agree that the notion\nof “external reference frame” can be ambiguous and merits further clarification.\nWe had defined the “external” frame of reference for tactile motion directions in the third paragraph\nof the Discussion as follows:\n“Representing tactile motion in external coordinates requires transforming skin-based inputs by taking\nhand posture into account 36–38. This transformation implies that tactile motion directions are abstracted\nfrom their original skin location and encoded relative to external space, independent of the body surface\non which they were initially sensed. In our study, we did not determine the specific anchor of the external\nreference frame: while changes in hand posture modified the perceived axis of tactile motion, the\npositions of potential anchors such as the eyes, head, and trunk remained constant. Therefore,\nthroughout this work, we use the term external frame of reference to refer broadly to representations\nthat are non-somatotopic”.\nAs the reviewer rightly notes, our experimental manipulation of hand posture does not allow us to\ndetermine whether the tactile external frame is anchored to a particular body part (e.g., trunk,\n16\nshoulders, head, or eyes). Thus, the “external” reference frame in our study is surely non-\nsomatotopic, but potentially encompassing several possible anchors such as the trunk, shoulders,\nhead, or eyes. In vision, previous research has shown that visual motion stimuli are encoded in a non-\nretinotopic (often referred to as spatiotopic) reference frame 39. Therefore, we can only conclude that\nthe aligned representation is non-retinotopic and non-somatotopic integrated with information\nabout the hand posture.\nWe fully agree with the reviewer that, given the physical separation between the visual and tactile\nstimuli in our experiment the observed “alignment” between these modalities likely reflects an\nalignment of the “abstract” mental representations of directional motion. The aligned representation\nthus refers to a higher-level representational correspondence of motion directions across senses - a\nmental representation that is partially shared for the direction of the moving stimuli independent of\nthe exact physical location of the stimuli or the sensory modality it originates from. Such an aligned\nrepresentation of motion direction is likely functionally advantageous supporting coherent spatial\nperception and multisensory interactions in the external world.\nTo further clarify the notion of the external frame of reference, and the way we envision it, we have\nrevised the Discussion (page 26, lines 838-844 and page 27, lines 855-861) as follows:\n“As noted above, the experimental manipulation of hand posture does not allow us to\ndetermine whether the tactile external frame is anchored to a particular body part (e.g.,\ntrunk, shoulders, head, or eyes). Thus, the “external” reference frame in our study is at least\nnon-somatotopic, but potentially encompassing several possible anchors such as the trunk,\nshoulders, head, or eyes. In vision, previous research has shown that visual motion stimuli are\nencoded in a non-retinotopic reference frame 39. Therefore, we can only conclude that the\naligned representation is non-retinotopic and non-somatotopic.”\n“It is also worth noting that given the physical separation between the visual and tactile\nstimuli in our experiment, the observed “alignment” between these modalities likely reflects\nan alignment of an “abstract” mental representations of directional motion, independent of\nsome specific position in space. The aligned representation thus refers to a higher-level\nrepresentational correspondence of motion directions across the senses, independent of the\nexact physical location of the stimuli. Future studies are needed to clarify onto which, if any,\nspecific reference frame such abstract directional code may anchor.”\n4) The cross-modal decoding analysis provides some intriguing evidence for an “alignment” of\nexternal representations for visual and tactile motion. However, it does not say much about\nanother important feature of these representations: Whether they are mainly modality-\nindependent (i.e., a fully aligned representation) or modality-specific (i.e., mostly distinct\nrepresentations, with some alignment). This question reflects an important feature of the\nrepresentations that is necessary to fully understand the alignment between modalities. I\nbelieve it deserves attention by the authors and can be investigated with the current data. One\nMVPA approach that could address this question is to train a classifier to decode the four\nclasses (visual-left, visual-right, tactile-left, tactile-right), and then analyze the types of the\nerrors made by the classifier. If the “aligned” representations are largely modality-specific, then\nmost errors should be within-modality (e.g., mistaking vision-left with vision-right). If instead\nthe “aligned” representations are largely modality-independent, then most errors should be for\nthe same motion direction in the other modality (e.g., mistaking vision-left with tactile-left) .\nHowever, I leave it up to the authors to determine what they believe to be an appropriate way\nto address this question.\nabout the aligned representations observed in the right hMT+/V5.\nWe acknowledge that the significant crossmodal decoding in right hMT+/V5 indicated the presence\nof shared aligned motion-direction representation across modalities. However, this significant\ncrossmodal decoding in the right hMT+/V5 does not imply that the underlying activity patterns were\nidentical across sensory modalities. This can be seen in the multidimensional scaling results (Figure\n3B), where the main axis of data separation corresponds to the sensory modality (vision vs. touch,\nwhile the secondary axis captures directional tuning.\nTo statistically support this observation, we reasoned that if a region is purely abstracted from the\ninput modality conveying the directional motion information, decoding the sensory modality (visual\nor tactile) of a specific motion direction would be difficult. In contrast, if the neural representations of\nvisual and tactile motion remain largely distinct despite partial crossmodal alignment, decoding the\nsensory modality would be easy, even when both represent the same motion direction. To this end,\nwe performed binary classification of visual horizontal versus tactile horizontal (externally-defined)\nand visual vertical versus tactile vertical (externally-defined) in the right hMT+/V5. Sensory modality\ndecoding was highly significant in right hMT+/V5 (p < 0.001) for both horizontal and vertical\ndirections, suggesting that the neural patterns associated with each direction are highly dissimilar\nacross the senses (Figure S4 A). This suggests that motion encoding in right hMT+/V5, while\nexhibiting shared crossmodal representations, is not fully abstracted from the input sensory\nmodality.\nWe present below two complementary analyses supporting this conclusion.\nFirst, using representational similarity analysis (RSA) 40, we investigated the differences in the\nrepresentational geometry of the motion directions within and across modalities: visual horizontal,\nvisual vertical, tactile horizontal (externally-defined), tactile vertical (externally-defined). We used\nPearson’s correlation as a measure of dissimilarity (1- Pearson’s correlation) to construct a neural\ndissimilarity matrix (DSM) in the right hMT+/V5 (as in 1) and tested the correlation between the neural\nDSM of right hMT+/V5 in each subject with different theoretical models. The theoretical models\nincluded a pure modality-invariant model, a multimodal model, a series of intermediate gradients\nbetween them, a unimodal visual model, and a unimodal tactile model 1. The analysis revealed a\nnegative correlation with the modality-invariant model that increased gradually as the models\nprogressed toward the multimodal model (see Table R1 for the correlation values). The model that\ncorrelated the most with the neural DSM in right hMT+/V5 was the multimodal model (mean r ± SD\n= 0.9992 ± 0.0026), and this correlation was significantly higher than all other models (Figure R2 A;\nTable R1). The gradual increase in correlation from the modality-invariant model to the multi-modal\nmodel added extra evidence that the representation of different motion directions in right hMT+/V5\nwas not abstracted from the input modality.\nFinally, as suggested by the Reviewer, we also performed a multi-class classification of the four\ndifferent conditions: visual horizontal, visual vertical, tactile horizontal (externally-defined), and\ntactile vertical (externally-defined), where the classifier was trained on all the 4 different conditions\nand then tested on a novel test data. We looked at the confusion matrices and the pattern of errors.\nAs suggested by the reviewer, we hypothesized that if the “aligned” representations are dependent\non the sensory modalities, then most errors should be within-modality (e.g., mistaking visual horizontal\nwith visual vertical). If instead the “aligned” representations are not dependent on the\nsensory modalities, then most errors should be for the same motion direction in the other modality\n(e.g., mistaking visual horizontal with tactile horizontal). We observed that the within-modality errors\nwere significantly higher than the cross-modal errors (wilcoxon signed rank test between within-\nmodality and cross-modal same directions and within-modality and cross-modal different directions:\np < 0.0001) in right hMT+/V5 (Figure R2 B). This also suggests that the aligned representations were\nnot abstracted from the input modality.\n(A) Representational Similarity Analysis. The top panel shows the theoretical models\nranging from a modality-invariant model to a multi-modal model, the intermediate\ngradients between them, and the two unimodal visual and auditory models. At the\nbottom is the average neural DSM obtained in right hMT+/V5 across participants, for\nthe four conditions: visual-horizontal, visual-vertical, tactile-horizontal (externally\ndefined), and tactile-vertical (externally defined). The neural DSM obtained in right\nhMT+/V5 is correlated with each theoretical model. The correlation between the neural\nDSM in the right hMT+/V5 and the multi-modal model is the highest.\n(B) Proportion of Errors. 4-way classification of the four conditions in the right hMT+/V5:\nvisual-horizontal, visual-vertical, tactile-horizontal (externally defined), and tactile-\nvertical (externally defined) and the proportion of errors made by the classifier (i) within-\nmodality errors (e.g., confusing tactile-horizontal with tactile-vertical, or visual-\nhorizontal with visual-vertical), (ii) cross-modality same-direction errors (e.g., confusing\ntactile-horizontal with visual-horizontal, or tactile-vertical with visual-vertical), and (iii)\ncross-modality different-direction errors (e.g., confusing tactile-horizontal with visual-\nvertical).\n19\nModality Invariant -0.4974 0.0059\nIM-0.1 0.0664 0.0072\nIM-0.3 0.2424 0.0072\nIM-0.5 0.5017 0.0067\nIM-0.7 0.8038 0.0053\nIM-0.9 0.9816 0.0032\nMultiModal 0.9992 0.0026\nUnimodal Visual 0.6393 0.0277\nUnimodal Tactile 0.6245 0.0300\nModel Comparisons z-score p-value\nModality Invariant - IM-0.1 -3.9199 0.0007\nIM-0.1 -IM-0.3 -3.9199 0.0007\nIM-0.3 -IM-0.5 -3.9199 0.0007\nIM-0.5 -IM-0.7 -3.9199 0.0007\nIM-0.7 -IM-0.9 -3.9199 0.0007\nIM-0.9 -Multimodal -3.9199 0.0007\nMultiModal - Unimodal Visual 3.9199 0.0007\nUnimodal Visual - Unimodal tactile 2.4640 0.1099\nTable R1. RSA analysis and the comparison of the RSA models’ correlation. The\ncorrelation of the neural DSM with each of the theoretical models from the right hMT+/V5\n(mean ± SD) (top). The statistical comparison between the correlation of the different\ntheoretical dissimilarity models and the neural DSM of right hMT+/V5 using a 2-sided Wilcoxon\nsigned-rank test (bottom). Results were corrected for multiple comparisons using FWE-\ncorrection.\nit is the most straightforward analyses demonstrating that even if a region shows significant\ncrossmodal decoding, it does not mean that the region is fully abstracted from sensory coding.\nIn the revised Manuscript (section Methods, page 16, lines 485-490):\nModality Decoding\nTo test if a region is purely abstracted from the input modality conveying the directional\nmotion information, we performed binary classification of visual horizontal versus tactile\nhorizontal (externally-defined) and visual vertical versus tactile vertical (externally-defined)\nin that fROI. Each motion direction pattern in each modality was demeaned separately to\nequate the mean activity of the visual and tactile modalities and the binary classification was\nimplemented in the similar manner described above.\nIn the revised Manuscript (section Results, page 23, lines 716-733):\nModality Decoding\nSignificant crossmodal decoding in the right hMT+/V5, only when tactile motion directions\nwere externally-defined (Figure 3C), indicated the presence of shared motion-direction\ninformation. However, this significant crossmodal decoding in the right hMT+/V5 does not\nimply that the underlying activity patterns were identical across the sensory modalities. This\ncan also be seen in the multidimensional scaling results (Figure 3B), where the main axis of\ndata separation corresponds to the sensory modality (vision and touch). To statistically\nsupport this observation, we reasoned that if a region is purely abstracted from the input\nmodality conveying the directional motion information, decoding the sensory modality\n(visual or tactile) of a specific motion direction would be difficult. In contrast, if the neural\nrepresentations of visual and tactile motion remain largely distinct despite partial crossmodal\nalignment, decoding the sensory modality would be easy, even when both represent the\nsame motion direction. To this end, we performed binary classification of visual horizontal\nversus tactile horizontal (externally-defined) and visual vertical versus tactile vertical\n(externally-defined) in the right hMT+/V5. The sensory modality decoding was significant in\nright hMT+/V5 (p < 0.0001) for both horizontal and vertical directions, suggesting that the\nneural patterns associated with each direction are very dissimilar across the senses (Figure\nS4).\nIn the revised Manuscript (section Discussion, page 26, lines 847-855):\nDecoding sensory modalities for each motion direction demonstrated that the neural\npatterns in right hMT+/V5, though partially shared (Figure 3C), were not identical across the\nsenses (Figure 3B and Figure S4) suggesting that the aligned representation is not fully\nabstracted from the sensory modalities. This shows that a successful cross-modal decoding\nindicates the presence of shared representations for axes of motion across modalities but\ndoes not, however, indicate that the region is immune to the input sensory modality. Our\nresults therefore serve as a timely reminder that cross-modal decoding is not a proxy for the\npresence of fully abstracted representations in a brain region (see also 1 for similar\nconclusion).\nIn the Supplementary information (Figure S4):\nModality Decoding. Binary classification of sensory modalities for each axis of motion direction\nin right hMT+/V5 i.e visual horizontal vs tactile horizontal and visual vertical vs tactile vertical.\nThe y-axis represents the decoding accuracy with chance level at 0.5. Results are FDR\ncorrected (*p<0.05) for the number of directions.\nMethods\nRepresentational Similarity Analysis\nWhile crossmodal decoding analysis highlights the presence of common/shared information across\nmodalities, it doesn’t inform us about how (dis)similar the patterns are across directions and across\nmodalities. Using representational similarity analysis (RSA) 40, we further investigated the differences\nin the representational geometry of the motion directions within and across modalities: visual\nhorizontal, visual vertical, tactile horizontal (externally-defined), tactile vertical (externally-defined).\nTo this end, we used Pearson’s correlation as a measure of dissimilarity (1- Pearson’s correlation) to\nconstruct a neural dissimilarity matrix (DSM) in the right hMT+/V5 in each subject (as in 1 ). To\ninvestigate if the representational structure of right hMT+/V5 reflects a modality-invariant, unimodal\nor a multimodal nature, we correlated the neural DSM in right hMT+/V5 of each subject with each of\nthe different theoretical models. We performed a 2-sided Wilcoxon signed rank test to investigate\nwhether there was a difference between the correlation values of each two consecutive theoretical\nmodels and the neural DSM. Results were corrected for multiple comparisons using FWE-correction.\nThe theoretical models (Figure R2 A) ranged from a pure modality-invariant model to a pure multi-\nmodal model with intermediate gradients in between them, and two unimodal (visual and tactile)\nmodels 42. In the modality-invariant model, the dissimilarity between the same motion direction\nacross the different modalities were set to zero (hence, motion direction processing is abstracted\nfrom the input modality), and the dissimilarity between different motion directions was set to 1\nregardless of the modality. Diagonal values were set to 0. On the other hand, the multimodal model\nsuggests that while motion directions within one modality are different from each other, they are\nmore dissimilar than motion directions from other modalities. In the multi-modal model, the\ndissimilarity between the different motion directions within the same modality was set to 0.5, while\nthe dissimilarity to motion directions from the different modality was set to 1. Diagonal values were\nset to 0. In the intermediate models, we created a gradient from the modality-invariant to the multi-\nmodal model. The dissimilarity between directions within the same modality was set to 0.5. The\ndissimilarity between the different directions across modalities was set to 1. While for the dissimilarity\nbetween the same directions across modalities were set to a gradient of values ranging from 0.1 to\n0.9 with steps of 0.2. In the unimodal visual and tactile models, the dissimilarity between the different\nmotion directions within the dominant unimodal modality was set to 0.5. The dissimilarity between\nthe different motion directions within the other non-dominant modality was set to 1 as this model\nassumes that there is no motion direction information in the non-dominant modality. The\ndissimilarity between the different directions across modalities was set to 1. We hypothesized that\nthe neural DSM in a fROI would correlate the most with a model that reflects the underlying\nrepresentational structure. If the right hMT+/V5 is a multi-modal region, we would expect to see a\nlow correlation with the modality-invariant model and a gradual increase in correlation as we\nprogress in the direction of the multi-modal model. Oppositely, if the right hMT+/V5 is a supramodal\nregion, we would expect to see a low correlation with the multi-modal model, and a gradual increase\nin correlation as we progress toward the modality-invariant model. If the right hMT+/ V5 is a unimodal\nregion, we would expect a higher correlation with one of the unimodal models than with the other\nmodels.\nTo further examine whether the aligned representations observed in hMT+/V5 were modality-\nindependent or modality-specific, we conducted a multiclass decoding analysis. We hypothesized\nthat if the “aligned” representations are dependent on the sensory modalities, most errors should\noccur within the same modality (e.g., mistaking visual horizontal with visual vertical). Alternatively, if\nthe “aligned” representations are not dependent on the sensory modalities, most errors should occur\nacross sensory modalities (e.g., mistaking visual horizontal with tactile horizontal). To this end, the\nclassifier was trained and tested to discriminate among four conditions: visual-horizontal, visual-\nvertical, tactile-horizontal (externally defined), and tactile-vertical (externally defined) in each\nindividual. Classifier performance was summarized as confusion matrices at the individual level. For\neach individual, we quantified the relative frequency of three error types: (i) within-modality errors\n(e.g., confusing tactile-horizontal with tactile-vertical, or visual-horizontal with visual-vertical),\n(ii) cross-modal same-direction errors (e.g., confusing tactile-horizontal with visual-horizontal, or\ntactile-vertical with visual-vertical), and (iii) cross-modal different-direction errors (e.g., confusing\ntactile-horizontal with visual-vertical). Average error proportions computed across subjects are\nreported with standard error mean. Statistical comparisons among within-modality, cross-modality\nsame-direction and cross-modality different-direction error proportions were performed using\nWilcoxon signed-rank tests.\nResults\nRepresentational Similarity Analysis\nUsing representational similarity analysis (RSA) 40, we investigated the differences in the\nrepresentational format of the motion directions within and across modalities: visual horizontal,\nvisual vertical, tactile horizontal (externally-defined), tactile vertical (externally-defined). We used\nPearson’s correlation as a measure of dissimilarity (1- Pearson’s correlation) to construct a neural\ndissimilarity matrix (DSM) in the right hMT+/V5 and tested the correlation between the neural DSM\nof right hMT+/V5 in each subject with different theoretical models that included a pure modality-\ninvariant model, a multimodal model, a series of intermediate gradients between them, a unimodal\nvisual model, and a unimodal tactile model (Rezk et. al., 2020). The analysis revealed a negative\ncorrelation with the modality-invariant model that increased gradually as the models progressed\ntoward the multi-modal model (Figure R2 A; Table R1). The model that correlated the most with our\ndata was the multi-modal model (mean r ± SD = 0.9992 ± 0.0026), and this correlation was\nsignificantly higher than all other models (Figure R2 A; Table R1). The gradual increase in correlation\nfrom the modality-invariant model to the multi-modal model added extra evidence that the\nrepresentation of different motion directions in right hMT+/V5 was not abstracted from the input\nmodality.\nMulticlass decoding and error pattern analysis\nWe performed a multiclass classification of the four different conditions: visual horizontal,\nvisual vertical, tactile horizontal (externally-defined), and tactile vertical (externally-defined),\nwhere the classifier was trained on all the four different conditions and tested on a test data. The\nconfusion matrices were used to evaluate the proportion of each error type. We observed significantly\nmore within-modality errors than the cross-modal errors (wilcoxon signed rank test between within-\nmodality and cross-modal same directions and within-modality and cross-modal different directions:\np < 0.0001) in right hMT+/V5 (Figure R2 B).\n25\n26\n3) As they are currently written, I find it hard to follow what the actual MVPA methods were.\nHow does the leave-one-out procedure of the pseudoruns (lines 340-347) relate to the 5-fold\ncross validation procedure described in the next paragraph? Was the 5-fold cross validation\nperformed nine times, to cover all possible beta-map pairs? Or were the 45 pseudoruns\ncreated and then chosen from for a single cross-validation procedure? In this case, I would be\nvery concerned about data leakage, which would artificially inflate classification accuracy. However,\ngiven that this information is written in two distinct\nparagraphs, it is hard to know exactly what was done. Please rewrite to make the MVPA procedure clearer.\nWe thank the reviewer for allowing us to clarify the description of the MVPA methods. To address\nthis, we have now revised the Methods section to describe the workflow more clearly and incorporate\nthe point 4 of reviewer#2 also.\nSpecifically, for each condition, the ten beta maps were first averaged in pairs to yield five pseudo-\nrun beta maps. This averaging step improves the signal-to-noise ratio by creating fewer but more\nreliable samples for decoding 43,44. Because ten runs can form 45 unique two-run combinations\n(pseudo-runs), we implemented 45 independent iterations. In each iteration, five non-overlapping\npseudo-runs (i.e., disjoint run pairs) were formed, ensuring that each real run contributed to only one\npseudo-run within that iteration. Thus, every iteration represented a unique sampling of the data,\nand across iterations, all possible run pairings were used at least once.\nEach iteration (containing 5 pseudo-run set) served as the basis for a 5-fold leave-one-pseudo-run-\nout cross-validation. In each fold, a linear SVM was trained on four pseudo-runs and tested on the\nremaining one. Feature selection (ANOVA-based, 100 most informative voxels) was applied strictly\nto the training data within each fold to prevent circularity. Decoding accuracy was computed as the\naverage across the five folds for that iteration, and the final accuracy per participant was obtained by\naveraging across all 45 iterations.\nThis procedure ensures that (i) the creation of pseudo-runs and the cross-validation steps are fully\nindependent, (ii) each real run contributes only once per iteration, and (iii) no data leakage occurs\nbetween training and testing datasets.\nWe hope the revised text in the manuscript (section Methods: Multivariate Pattern Analyses (MVPA);\npage 13, lines 383-406) now makes the procedure clearer:\nTo improve the signal-to-noise ratio of each beta map used for decoding, the ten beta maps\nwithin each experimental condition (Vh, Vv, HUpt, HUfw, HDpt, HDfw) were paired and\naveraged to generate five pseudo-run beta maps per condition. Averaging across runs to\ncreate fewer but more reliable “samples” for the classifier has been shown to increase\ndecoding reliability in fMRI data 43,44. Because 10 real runs can form 45 unique two-run\npairings (i.e., all possible combinations of two runs taken from ten), we employed a\nsystematic pairing scheme that organized these 45 possible run pairs into 45 iterations. Each\niteration consisted of 5 disjoint pseudo-runs (non-overlapping two-run pairs), ensuring that\neach real run contributed to only one pseudo-run within that iteration. This design ensured\nthat all possible run pairs were systematically represented across the 45 iterations. The 5\npseudo-runs in each iteration served as independent samples for cross-validation and\nclassification.\nFor each pseudo-run set, classification was performed using a linear Support Vector Machine\n(SVM; C = 1) implemented in CoSMoMVPA 45 and LIBSVM 46. A five-fold leave-one-pseudo-\nrun-out cross-validation scheme was used: in each fold, the classifier was trained on four\n27\npseudo-runs and tested on the remaining one. An ANOVA-based feature selection method\n34,35 was applied only to the training data in each fold to select the 100 most informative voxels\nwithin each region of interest. This method computes ANOVA F – statistic only on the training\nset and chooses features based on the variance. Voxels with the highest F-values were\nretained, and this subset was applied to the independent test set for decoding. This method\nnot only ensures a similar number of voxels within a given fROI across participants but also\navoids circularity (‘double dipping’; 33). Decoding accuracy for each pseudo-run set was\ncomputed as the mean accuracy across its five cross-validation folds. Finally, decoding\naccuracies were averaged across all 45 iterations to obtain one accuracy estimate per\nparticipant and fROI.\n1. Rezk, M. et al. Shared Representation of Visual and Auditory Motion Directions in the Human\nMiddle-Temporal Cortex. Current Biology 30, 2289-2299.e8 (2020).\n2. 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CoSMoMVPA: Multi-Modal Multivariate Pattern\nAnalysis of Neuroimaging Data in Matlab/GNU Octave. Front. Neuroinform. 10, (2016).\n46. Chang, C.-C. & Lin, C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst.\nTechnol. 2, 1–27 (2011).\n32"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all my concerns. I found the revision clear and informative, I am happy to endorse its\npublication.", "reviewer_2": "The revised manuscript addressed adequately all my criticisms, adding also important analysis like the eye movement\nposition control and the additional data processing that made stronger their claim. I would like to compliment the authors for\nthe thoroughly work performed for the revision. The manuscript can be accept for publication in the present form.", "reviewer_3": "The authors have adequately addressed my comments."}, "author_response": ""}]

Decision letter