# Cortical processing for the vestibular and visual input of egomotion in macaque monkeys: Separate networks with targeted convergence

Sarah Marchand, Vanessa de Castro, Elisabeth Excoffier, Marie-Alphée

Laurent, Maxime Rosito, Nathalie Vayssière, Benoit R Cottereau, Alexandra

Séverac Cauquil, Jean-Baptiste Durand

► To cite this version:

Sarah Marchand, Vanessa de Castro, Elisabeth Excoffier, Marie-Alphée Laurent, Maxime Rosito, et al. Cortical processing for the vestibular and visual input of egomotion in macaque monkeys: Separate networks with targeted convergence. Imaging Neuroscience, 2026, 4, 10.1162/IMAG.a.1093. hal-05529212

HAL Id: hal-05529212

https://hal.science/hal-05529212v1

Submitted on 27 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
# Cortical processing for the vestibular and visual input of egomotion in macaque monkeys: Separate networks with targeted convergence

Sarah Marchand, Vanessa De Castro, Elisabeth Excoffier, Marie-Alphée Laurent, Maxime Rosito, Nathalie Vayssière, Benoit R. Cottereau, Alexandra Séverac Cauquil, Jean-Baptiste Durand

Centre de Recherche Cerveau et Cognition, Université de Toulouse, CNRS, Toulouse, France

Corresponding Authors: Sarah Marchand (sarah.marchand@univ-tlse3.fr), Jean-Baptiste Durand (jbdurand@cnrs.fr)

## ABSTRACT

The integration of visual and vestibular inputs during egomotion is fundamental for both postural and navigational control. In the present study, we used functional magnetic resonance imaging (fMRI) in four macaque monkeys to investigate cortical activation in response to galvanic vestibular stimulation (GVS), applied through transmastoid electrodes, and egomotion-compatible (EC) optic flow patterns. Visual and vestibular stimulations activate two largely independent cortical networks: the vestibular network encompasses the insular cortex, superior parietal lobule, frontal lobe, and cingulate cortex, while optic flow primarily activates regions in the superior temporal sulcus, temporoparietal junction, inferior parietal lobule, and restricted portions of the cingulate and frontal cortices. Despite this segregation, several areas exhibit visuo-vestibular convergence: ventral posterior suprasylvian (VPS) in the temporoparietal junction, area 7 in the inferior parietal lobule, ventral intraparietal area (VIP) and LIP in the intraparietal sulcus, MSTd in the superior temporal sulcus, CSv in the cingulate sulcus, and FEFsem in the frontal cortex. These findings demonstrate that visual and vestibular signals generated by egomotion are processed in extended and distinct cortical networks with several narrow convergence sites, consistent with the idea that multisensory integration during self-motion is achieved through selective convergence rather than general network overlap.

**Keywords:** optic flow, galvanic vestibular stimulation, multisensory integration, non-human primate, egomotion, fMRI

## 1. INTRODUCTION

Visuo-vestibular integration during egomotion represents a fundamental process for postural and navigational control in human and non-human primates. While both sensory modalities are activated during egomotion, we still have a fragmented view about how and where they converge at the cortical level. The vestibular system provides sensory foundation for detecting egomotion by its peripheral components: three orthogonally oriented semicircular canals to detect rotation of the head in space, and two otolith organs (utricle and saccule) to detect linear accelerations (lurato, 1967; Kingma & van de Berg, 2016).

Together, these components of the vestibular system form a robust tool for detecting gravitational forces and both angular and linear head motion necessary for spatial orientation and balance (Day & Fitzpatrick, 2005; Goldberg et al., 1984). Vestibular input provides immediate, unambiguous signal of head rotation and acceleration, with reaction times of the order of milliseconds following movement onset (Eatock et al., 2008; Fernandez & Goldberg, 1971). When vestibular function is impaired, individuals suffer spatial disorientations and navigation difficulties (Brandt & Dieterich, 1999; Seemungal et al., 2007), which provides indirect evidence for the essential role of the vestibular system in self-motion perception.

<figure><img src="image_2.png" /></figure>
Although various methods have been developed to study vestibular processing (Frank & Greenlee, 2014; Lopez et al., 2012), galvanic vestibular stimulation (GVS) is now the method of choice due to its ability to stimulate the whole vestibular system (Marchand, Langlade, et al., 2025 for a recent review). GVS consists of sending a low-intensity electric current through electrodes stuck on the skin at the mastoid process level to directly activate vestibular neuron afferents. On the anodal side, there is a decrease in neuronal activity, whereas cathode increases it, and this discrepancy generates ocular torsion (Séverac Cauquil et al., 2003) and body tilt (Nashner & Wolfson, 1974), directed toward the anode, and an illusion of egomotion can be reported (Fitzpatrick & Day, 2004). This non-invasive method opens a way to study vestibular processes and is particularly useful in neuroimaging research since it can be combined with functional magnetic resonance imaging (fMRI). Despite the fact that vestibular processing undoubtedly plays an essential role in the integration of egomotion, gaps in knowledge regarding cortical vestibular networks remain. Human neuroimaging studies consistently outlined a distributed vestibular network that included the dorsal portion of the medial superior temporal area (MSTd), ventral intraparietal area (VIP), cingulate sulcus visual area (CSv), and the parieto-insular vestibular cortex (PIVC) (Marchand, Langlade, et al., 2025; Smith et al., 2018). However, little is known about the organization of the cortical vestibular network in non-human primates (NHPs). Important insights into macaque visuo-vestibular integration have been gained from seminal work that examined regions such as the ventral posterior suprasclyvian (VPS) area, the medial superior temporal (MST) area, the frontal eye field (FEF) area, and the visual area V6. Nonetheless, these studies used focal electrophysiological approaches instead of whole-brain mapping techniques (Angelaki & Cullen, 2008; Chen et al., 2011b; Fan et al., 2015; Gu et al., 2008, 2010, 2012; B. Liu et al., 2021; Yang & Gu, 2017). Similarly, although some recent research has investigated the neural mechanisms of GVS in NHPs, explaining how GVS activates canal and otolith afferents (Forbes et al., 2023; Kwan et al., 2019), whole-brain cortical activations have not yet been investigated. Additionally, no research has combined GVS and fMRI in NHPs to date, making it difficult to compare the two species directly and to identify the large-scale cortical networks involved in vestibular stimulation.

The visual system's key role in egomotion perception has been studied in greater depth. Gibson's initial study defined optic flow and presented the process by which patterns of motion within the visual field create information about self-motion through the "focus of expansion" (Gibson, 1950). Following studies, including Lee's « swing-

ing room » experiments (Lee & Aronson, 1974; Lee & Lishman, 1975), demonstrated that postural response and perception of self-motion can be evoked by optic flow alone. Willing to examine optic flow in a strict paradigm, Smith and collaborators (Smith et al., 2006) developed the « 1P/9P » paradigm where egomotion-compatible (EC) single-patch stimuli—displaying random white dots on a dark background moving by rotational and expansion/contraction movements from a single focus of expansion in the center in the image, were compared with egomotion-incompatible (EI) nine-patch arrays—displaying a 3 × 3 grid of the EC stimulus, thus showing nine focuses of expansion, which is incompatible with self-motion. This technique has unveiled large-scale cortical networks for visual self-motion processing including MSTd, VIP, and other areas in humans but also in non-human primates (Cotterea et al., 2017).

The main resulting question is how visual and vestibular networks interact with each other. While visuo-vestibular convergence has been documented in human neuroimaging studies (Aedo-Jury et al., 2020; Marchand, Balcou, et al., 2025; Ruehl et al., 2022), the precise sites and mechanisms by which it occurs in non-human primates have been fruitfully studied with methods limited to focal explorations (Angelaki & Cullen, 2008; Chen et al., 2011b; Gu et al., 2008, 2010, 2012). Deepening our understanding of this process is, however, important for basic neuroscience as well as to ascertain the translational validity of NHPs model to understand human's postural and navigational control (Smith et al., 2017). The present study bridges these gaps in fundamental knowledge by combining GVS and fMRI for the first time in rhesus macaques, and by offering a comparison between vestibular activation patterns and visual egomotion-responsive areas. Our ultimate objective is to identify the cortical network activated by GVS and measure its overlap with visual egomotion-responsive areas to identify the sites of visuo-vestibular convergence. We hypothesize that visuo-vestibular integration is attained through selective convergence in some cortical regions rather than global networks overlap.

## 2. METHODS

### 2.1. Ethics statement

The authors declare no conflict of interest related to this work. All procedures including non-human primates were conducted in accordance with current European legislation (Directive 2010/63/EU) and French regulations (Décret 2013-118) governing the use of animals in scientific research. The project received approval from the local ethics committee (CNREEA code: C2EA-14) and
was authorized by the French Ministry of Research
(authorization number: MP/03/34/10/09).

## 2.2. Subjects

Four female rhesus macaques (Macaca mulatta) (M01, M02, M03, and M04) (5–15 years, 5–8 kg) were involved in this study. They were housed together in a large, enriched enclosure and could thus develop natural social and foraging behaviors. They returned to their individual cages to be fed twice a day, with standard primate biscuits supplemented with various types of fruits and vegetables. Health inspections were carried out quarterly. Details about the animals' surgical preparation are provided in Cottereau et al. (2017). These individuals were trained to stare at a screen in a room simulating the scanning environment, to accustom them to the task and the noise generated by the setup. They were also accustomed to the visual stimuli used in this study, to avoid any novelty effect during acquisition.

## 2.3. Experimental design

### 2.3.1. Galvanic vestibular stimulation (GVS)

We used GVS to stimulate the vestibular apparatus using a low-intensity current transmitted via electrodes placed on the skin surface (Marchand, Langlade, et al., 2025). The current generator (Digitimer, UK; model DS5) was piloted by a computer located in the MRI control room via a USB data acquisition card (NI USB-6009, DAQ 8AD 2 DA 14bit 48kS/s Labview). The use of a current-limited stimulator ensured safety and stability of the stimulation, preventing any excessive current that could cause any sort of damage. The electric signal went through the filter plate of the MRI acquisition room, which acts as a low-pass filter to prevent high-frequency noise from entering the room and interfering with image acquisition, sending the resulting current into the electrodes. The two gold electrodes (1 cm diameter) were attached to the skin of the monkey over each mastoid, and were covered with conductive jelly to increase impedance and improve signal delivery (Fig. 1A).

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Setup and stimulation protocols for monkey fMRI experiments. (A) Schematic representation of the monkey fMRI setup. The animal is seated in sphinx position within a primate chair, placed inside the MRI scanner bore, with the head restrained using a head post. An 8-channel phased-array coil is positioned over the head. During the visual stimulation protocol, the monkey performs a passive fixation task by maintaining gaze on a green fixation square, back-projected onto a screen via a video projector. Eye position is monitored using an infrared video-based eye tracker. Correct fixation triggers fluid rewards throughout the runs. Components specific to the optic flow stimulation protocol are marked with an asterisk (*). In the GVS protocol, the monkey is slightly anesthetized but remains in the same head-fixation setup. Low-intensity electrical currents are delivered through gold electrodes placed behind each mastoid. Elements specific to the GVS protocol are marked with a hash (#). (B) GVS protocol. During GVS runs, animals received alternating blocks of electrical stimulation (ON) and rest (OFF), each lasting 12 TRs. Each animal completed 12 runs, with 145 volumes acquired per run. The stimulation was delivered while the animal remained head fixed in the MRI setup under light anesthesia. (C) Illustration of the visual stimuli and block design used during optic flow stimulation. Egomotion-consistent (EC) stimuli consisted of a field of moving dots forming time-varying patterns of expansion, contraction, and rotation, consistent with self-motion along a spiral trajectory. Egomotion-inconsistent (EI) stimuli consisted of a 3 × 3 array of identical smaller EC stimuli, disrupting the global motion consistency. Runs followed a block design alternating EC and EI stimuli, interleaved with blank fixation periods. Each run comprised 7 blocks (total duration: 112 TRs). Half of the runs began with an EC block, and the other half with an EI block.</figcaption></figure>
Before positioning the electrodes, the subject's skin was prepared with alcohol. The electric stimulation was delivered during periods lasting 18 s. The waveform was sinusoidal, to avoid sharp ON/OFF transitions, with a frequency of 1 Hz and a peak-to-peak amplitude of ± 1.25 mA (Fig. 1B). This frequency was chosen since 1 Hz falls within the range of vestibular afferent modulation of physiological head motion stimuli (0.1–25 Hz) for physiological head motion stimuli (Goldberg et al., 2012; Kwan et al., 2019), and sinusoidal waveforms at this frequency are known to cause strong vestibular responses in human subjects with minimal discomfort. The ±1.25 mA amplitude was selected based on earlier research demonstrating that the general range of individual optimal current intensity is about 1–2.5 mA, seen across subjects (Caudron et al., 2018; for a recent review of GVS parameters, see Valter et al., 2025). In a similar range, Kwan et al. (2019) used a 2-mA peak-to-peak amplitude on awake macaque monkeys, obtaining strong and robust primary vestibular afferents and ocular responses. Although we applied low-intensity currents to anesthetized preparations, the absence of a sham condition precludes ruling out a priori that some of the cortical activations elicited by GVS may have been caused by tactile sensations at the electrode site during stimulation. Fortunately, this risk of contamination by somatosensory responses can be evaluated in *a posteriori*, by looking at whether activations are actually found around the head representation in the somaesthetic cortex.

### 2.3.2. Optic flow stimulation

The optic flow stimuli were similar to those used in earlier studies in humans (Cardin & Smith, 2010; Wall & Smith, 2008) and followed an identical protocol as the one described in Cottereau et al. (2017). Each stimulus was a 6-s video of 800 white dots moving on a black background, shown at 60 frames per second (fps). In the egomotion-consistent (EC) condition, dots moved according to a coherent flow field with expansion, contraction, and rotation, simulating self-motion along a spiral path. The egomotion-inconsistent (EI) condition consisted of a 3 × 3 grid of EC panels that disrupted the global optic flow needed to detect self-motion. Dot size, velocity, and number of dots were equalized across conditions to control for low-level visual information differences. For a full description of the motion equations, see Cottereau et al. (2017). The macaques were placed in the primate chair, in the sphinx position, with their heads fixed to the chair using a plastic implant attached to the surface of their skull, and with a tube dispensing water placed at the mouth entrance, connected to an automatic system, rewarding the monkey when it looked into a fixation area of 4° diameter centered around a green square

(visual size: 0.44° × 0.44°) acting as a fixation target in the center of the screen. Monkeys in the primate chair were introduced into the bore of the magnet, facing a translucent screen at a distance of 50 cm. Visual stimuli were rear projected on the screen by a video projector (Hitachi, CP_X809), at a spatial resolution of 1024 × 768 pixels and a refresh rate of 60 Hz. The position of one eye was monitored with an infrared video-based eye tracker at 60 Hz (Applied Science Laboratories, ASL Eye-Trac 6000, Bedford, MA, USA) at 60 Hz, and the system was calibrated for each subject prior to recording.

### 2.3.3. Data acquisition

#### 2.3.3.1. Anatomical and functional brain templates.
The MR images were acquired by a clinical 3T scanner (Philips Achieva), equipped with a custom 8-channel phased array coil (RapidBiomed) designed to fit the skull of our monkeys. For each individual, anatomical and functional brain templates were built from acquisitions made in a single session on slightly anesthetized animals (Zoletil 100: 10 mg/kg and Domitor: 0.04 mg/kg), during which we acquired four T1-weighted anatomical volumes (MPRAGE, Repetition time [TR] = 10.3 ms, echo time [TE] = 4.6 ms, flip angle = 8°, voxel size = 0.5 × 0.5 × 0.5 mm, 192 slices), and 300 functional volumes (GE-EPI, TR = 2000 ms, TE = 30 ms, flip angle = 90°, SENSE factor = 1.6, voxel size = 1.25 × 1.25 × 1.5 mm, 32 slices). Because of the monkeys' sphinx position inside the MRI, the direction of acquisition was FH (foot-head) and the direction of phase encoding was H (head). It corresponds to images in the axial direction (anteroposterior axis with a posterior encoding direction). Echo spacing of the GE-EPI has been obtained on the chronogramme of the sequence: ES (echo spacing) = 0.927 ms.

#### 2.3.3.2. GVS.
Each animal participated in one session under slight anesthesia (medetomidine: 0.04 mg/kg and ketamine: 10 mg/kg). Anesthesia prevented any discomfort and/or reflexive postural adjustments during stimulation. The animal was asleep, immobile, and with its eyes closed throughout the recording session. The scanner room was in total darkness and each session lasted about 45 min, during which 12 runs were recorded. Each run lasted 216 s (144 TRs) and consisted of alternating periods of galvanic stimulation (ON) and rest (OFF). There were two types of runs, with the same temporal structure but starting either with ON or OFF conditions (types 1 and 2, respectively). T2-weighted functional volumes were acquired by gradient echo-planar imaging (GE-EPI: TR = 1500 ms, TE = 30 ms, flip angle = 75°, SENSE factor = 1.6, voxel size = 1.25 × 1.25 × 1.5 mm, 46 axial slices with no inter-slice gap, FOV = 80 × 80 mm). These
T1 and EPI volumes were used to construct individual
anatomical and functional templates, respectively.

**2.3.3.3. Optic flow.** Functional images of the optic flow protocol were made on behaving awake macaques. By contrast with GVS, optic flow stimulation required to have the animals maintaining their gaze onto the visual stimuli. Therefore, no anesthesia or sedatives were administered. The scanner room was in total darkness except for the displayed optic flow stimuli. Sessions lasted a maximum of 1 h per day, and were repeated on successive days until 36 valid runs were obtained, that is, runs in which the monkey gazed for more than 85% of the acquisition time within the reward window centered on the fixation target (see above). Images were acquired with the same GE-EPI sequence used during the anesthetized sessions. EC and EI stimuli were presented using a block-design built with EventIDE software (Okazolab). Each run consisted of 224 s (112 TRs) divided into 7 similar cycles of 32 s (16 TRs). T2-weighted functional volumes were acquired by gradient echo-planar imaging (GE-EPI, TR = 2000 ms, TE = 30 ms, flip angle = 90°, SENSE factor = 1.6, voxel size = 1.25 × 1.25 × 1.5 mm, 32 slices). In half of the runs, a cycle started with a baseline of 10 s (5 TRs) where only the fixation point was present. It was followed by 6 s (3 TRs) of the EC condition, then by another 10 s of blank and finally by 6 s of the EI condition. In the other half of the runs, the EC and EI conditions were reversed within a cycle (i.e., a cycle had 10 s of blank, 6 s of the EI condition, 10 s of blank, and finally 6 s of the EC condition) (Fig. 1C).

**2.4. Data processing**

**2.4.1. Anatomical and functional templates**

To build the anatomical template, four T1-weighted
(MPRAGE) images were originally realigned and aver-
aged. The resulting volume was then registered onto the
NMT v2.0 template space (Jung et al., 2021; Seidlitz
et al., 2018). Functional templates were established
through the averaging of 300 GE-EPI volumes after
motion correction. After brain extraction of the anatom-
ical and functional volumes, we normalized the func-
tional volumes to the anatomical reference using SPM12
(http://fil.ion.ucl.ac.uk/spm/) non-linear normalization
tools, and the deformation parameters were saved for
application to the functional volumes acquired during
GVS and optic flow stimulations. Another non-linear
normalization was used to register the NMT v2.0 volume
to each of the individual structural templates. These
normalization parameters were used to warp the NMT
v2.0 cortical surfaces and CHARM atlas into the individ-
ual space of each animal.

**2.4.2. Preprocessing of the functional data**

**2.4.2.1. GVS.** Functional images were converted from DICOM to NIFTI format using dcm2niigui (MRICron) (Li et al., 2016). For each run, functional volumes were first motion corrected using rigid realignment. Although the animals were anesthetized and head restrained, rigid realignment was sometimes useful to correct for micro breathing-induced head movements (generally with a median absolute deviation between 0.01 mm and 0.05 mm). Slice timing correction was then applied to account for acquisition delays across slices. A mean image was computed from each run and used to co-register the data to the individual's functional template. This run-specific registration was combined with pre-computed, subject-specific normalization parameters that corrected the functional-to-anatomical distortions and aligned the functional template to the anatomical volume. Both sets of transformations were applied simultaneously during a single resampling step, producing volumes with 1 × 1 × 1 mm resolution and applying slight spatial smoothing using a Gaussian kernel (FWHM = 2 mm isotropic). Additionally, we applied band-pass filtering (0.01–0.25 Hz) on the GVS functional time series to alleviate the artifacts potentially caused by GVS. All these preprocessing steps were performed with SPM12.

**2.4.2.2. Optic flow.** Functional images acquired during optic flow stimulation were preprocessed in the same way as those acquired during GVS stimulation except that we did not apply band-pass but high-pass filtering (0.008 Hz) of the functional time series.

**2.5. Statistics and results presentation**

**2.5.1. GVS**

Voxel-wise statistics were computed for each individual
by fitting a general linear model (GLM) to the blood oxy-
gen level-dependent (BOLD) signal. The model contained
two main regressors representing the two experimental
conditions: ON and OFF. These regressors were con-
volved with the hemodynamic response function (HRF)
estimated from three of the four monkeys (see Cottereau
et al., 2017). In addition, we included six noise regressors
per run to capture acquisition and physiological noise.
They were computed by submitting the time series of all
the non-brain tissues (background, eye balls, masticatory
muscles, etc.) to a principal component analysis (PCA)
from which we retained the six regressors capturing the
most variance (Vanduffel, 2014). Both the preprocessing
steps and GLM analyses were implemented in MATLAB,
with the SPM12 software and custom scripts.
### 2.5.2. Optic flow

The GLM included three major regressors of the experimental conditions—egomotion consistent and inconsistent conditions (EC and EI), and blank periods—each convolved with individually estimated HRF. Noise regressors were computed with the PCA method described above. However, since the animals were awake, the noise sources were potentially more numerous and we thus included 12 noise regressors rather than 6 in the model.

For both GVS and optic flow, we performed statistical inference through cluster extent-based thresholding to correct for multiple comparisons. A voxel-wise threshold of $p < 0.001$ (uncorrected) was used to identify contiguous clusters and the resulting clusters were then tested against a cluster-extent threshold controlling for cluster family-wise error (FWEc) at $p < 0.05$ ($k = 32$) (Phillips, 2018). The number of voxels used in the statistical tests was calculated for each individual and ranged from 102,280 voxels to 123,046 voxels. The thresholded statistical parametric maps (SPM) were then projected on the individual cortical surfaces using Connectome Workbench (https://www.humanconnectome.org/software/connectome-workbench), with the “enclosing voxel” algorithm. Since all these cortical surfaces were derived from the NMTv2.0 symmetrical cortical surfaces, surface-based results from all four monkeys (eight hemispheres) were then projected on the right cortical surface of the NMT template. Results were considered significant at the group level for all surface nodes in which the individual cluster extent-based threshold was reached in at least three out of the eight hemispheres (i.e. at least two out of the four individuals).

## 2.6. Regions of interest (ROIs) analyses

We used the surface-based CHARM atlas to perform ROIs analyses. First, for each cortical region of the CHARM atlas ($n = 135$), we computed the percentage of surface nodes showing relevant group activation (>3 hemispheres) for GVS, for optic flow, and for both sensory modalities. We retained only the regions in which at least 10% of the nodes (10% coverage) were activated for at least one sensory modality. Then, for each CHARM's region, we computed the mean percentage signal change (PSC) across all nodes for the contrasts ON >OFF (GVS) and EC>EI (optic flow). These PSC were submitted to one-tailed t-tests for each of the eight hemispheres and the resulting individual p-values were combined across the eight hemispheres using Fisher's combined probability test. Since these statistical operations were repeated for each CHARM region, the resulting group-based p-values were then corrected for multiple comparisons using Bonferroni correction.

Parts of the data have been used in previous studies (optic flow data from three monkeys in Cottereau et al. (2017) and De Castro et al. (2021); and GVS data from one monkey in Sypré et al. (2023)).

# 3. RESULTS

## 3.1. Cortical network involved in processing galvanic vestibular stimulation

In the present study, we use functional MRI to investigate the cortical areas involved in GVS processing in non-human primates. To this end, four rhesus macaques were exposed to GVS, with stimulation (ON periods) interspersed with pauses without stimulation (OFF periods) (see Materials and methods). We then observed changes in the BOLD signal between the ON and OFF conditions. Figure 2 shows the statistical parametric maps (t-score) based on all 12 runs per animal. They are projected on inflated representations of their left and right cortical surfaces with cluster extent-based thresholding for multiple comparisons ($p < 0.05$ FWEc-corrected). Within these four individuals, robust activations were observed, notably in the superior temporal cortex, posterior parietal cortex, insular and opercular-insular regions, cingulate and medial cortical regions, and in at least three of the individuals ($M01, M02, and M03$), in the frontal cortex. We observe variable interhemispheric asymmetries on the activation maps of some of our individuals (e.g. $M01$ and $M04$), without showing consistent lateralization across our sample.

## 3.2. Cortical network involved in egomotion-coherent optic flow processing

We also used fMRI to study the cortical areas involved in egomotion-coherent optic flow processing in the non-human primate. To this end, the same four rhesus macaques were exposed to optic flow patterns that were either consistent (EC condition) or inconsistent (EI condition) with egomotion (see Materials and methods). We then observed changes in the BOLD signal between the EC and EI conditions. Figure 3 shows parametric statistical maps (t-values) based on all available data (36 runs/animal). Optic flow coherent with egomotion was found to elicit statistically robust increases in BOLD signal, with activation clusters having t-scores ranging from 3 to 8 (cluster range threshold, FWE-corrected $p < 0.05$). In all four individuals, robust activations were observed, notably in the superior temporal cortex, posterior parietal cortex, medial superior temporal areas, and frontal cortex, in agreement with previous studies (Cottereau et al., 2017; De Castro et al., 2021).
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Cortical activations evoked by GVS in the four macaque monkeys. For each monkey (M01, M02, M03, M04), statistically significant BOLD activations in the contrast “GVS ON” > “GVS OFF” (cluster-extent threshold, *p* < 0.05 FWE corrected) are shown on lateral (Lat.) and medial (Med.) views of both inflated and fiducial representations of the monkeys’ left and right hemispheres (LH and RH, respectively). The principal sulci are indicated in yellow in the upper left M01 cortical surfaces (as: arcuate sulcus; cs: central sulcus; cas: calcarine sulcus; cis: cingulate sulcus; ips: intraparietal sulcus; ls: lateral sulcus; sts: superior temporal sulcus).</figcaption></figure>

**3.3. Overlap of vestibular and visual activations**

The thresholded activation maps observed for our four animals (i.e. eight hemispheres) in response to GVS were then binarized (1: significant activation, 0: no significant activation) and summed up after node-to-node projection onto the right cortical surface of the NMT v2.0 template (Fig. 4A). This led to a new “group” map in which each node on the template surface has a score between 0 and 8, depending on the number of hemispheres in which that particular node exhibited statistically significant activation. Thresholding this group map at three hemispheres (i.e. at least two relevant individuals) provides a clean, extensive, and robust GVS-sensitive cortical network. The most consistent regions found in at least five of the eight hemispheres include the ventral posterior suprasylvian (VPS), the parieto-insular vestibular cortex (PIVC), the primary and secondary somatosensory cortices (S1/2), and the primary motor cortex (M1). Although we did not perform somatotopic mapping in our animals, a rough approximation is that the medio-dorsal third is dedicated to the feet and hindlimbs, the central third represents the trunk and forelimbs, and the latero-

ventral third is related to the face (Arcaro et al., 2019; Delhaye et al., 2018). Since our somatosensory and motor activations are located in the medio-dorsal third (as illustrated in Supplementary Fig. S1), they likely correspond to the lower limbs, maybe also to the trunk, but certainly not to the head representation. This excludes that the S1/2 and M1 activations are elicited by tactile sensation at the electrode sites during stimulation. In addition, a distinct activation cluster was consistently observed at the base of the central sulcus, corresponding to the somatosensory areas 3a and 3b. Extending to regions present in at least three of the eight hemispheres, that is, present in at least two of our four individuals, we also find the dorsal subdivision of the medial superior temporal area (MSTd), the parietal area 7 (7), the ventral intraparietal area (VIP), the granular insular area (lg), the belt portion of the auditory cortex (BELT), the superior parietal lobule (PE), the dorsal caudal premotor cortex (PMdc), the smooth eye movement subdivision of the frontal eye field (FEFsem), the cingulate sulcus visual area (CSv), the pre-supplementary motor area (PreSMA), and a portion of the precuneus gyrus (PGm).
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Cortical activations evoked by egomotion-consistent optic flow in the four macaque monkeys. For each monkey (M01, M02, M03, M04), statistically significant BOLD activations in the contrast “1P” > “9P” (cluster-extent threshold, p < 0.05 FWE corrected) are shown on lateral (Lat.) and medial (Med.) views of both inflated and fiducial representations of the monkeys' left and right hemispheres (LH and RH, respectively).</figcaption></figure>

By proceeding identically with acquisitions in the visual modality, Figure 4C reveals a well-known network for the processing of visual self-motion information (i.e., a constant presence in each of the eight hemispheres of MSTd and FEFsem), presence in the majority of hemispheres (at least four out of eight) of parietal area 7, and presence in at least two of the four individuals (at least three out of eight hemispheres), of VPS, the saccadic subdivision of the frontal eye field (FEFsac), V6/V6A, VIP, and CSv.

Consequently, an extensive and robust network involved in processing egomotion-related information is observed for each of these sensory modalities. However, as shown in Figure 4B, the vestibular network (in magenta) and the visual one (in blue) are predominantly distinct from each other. The vestibular network extends from the temporo-parietal junction (tpj) to the tip of the superior temporal sulcus (sts) along the length of the lateral sulcus, coupled with medial activations in the motor and premotor regions. By contrast, the visual network occupies a territory from the tpj to the more posterior portions up to the occipito-parietal junction, coupled with a large frontal patch located at the level of the frontal eye field (FEF). Despite the relatively large extent of the vestibular

and visual cortical networks, the sites of visuo-vestibular convergence (in yellow in Fig. 4B) are only found in a restricted number of areas, namely MSTd, VPS, area 7, FEFsem, VIP, LIP, and CSv. To facilitate the visualization of these regions, particularly VIP/LIP, and V6/V6A, we provide zoomed-in views in Supplementary Figure S3, showing the dorsal views of Figure 4B and 4C.

### 3.4. Quantification of the visuo-vestibular convergence

In order to situate the results shown in Figure 4B with respect to a well-established atlas space, we used the CHARM atlas (Jung et al., 2021; Reveley et al., 2017) to calculate, for each of the 135 atlas regions, the percentage of cortical nodes (% coverage) showing significance for the vestibular modality only, for the visual modality only and for both modalities. In order to focus on regions with substantial cortical involvement while excluding minor, potentially spurious activations, we only considered atlas regions presenting at least 10% of activated nodes for at least one modality. Results of this analysis are shown in Figure 5A as bar plots of percentage coverage for the 33 out of 135 atlas regions meeting the above-
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Group-level cortical responses to vestibular and visual egomotion stimuli. (A) Cortical activations evoked by GVS at the group level. Results for the left and right hemispheres of the four individual monkeys (cf. Fig. 2) are superimposed on lateral (Lat.), dorsal (Dor.), and medial (Med.) views of both inflated and fiducial representations of the right cortical surface of the NMT template. Only activations found in at least three hemispheres (i.e. at least two individuals) are shown. (B) Overlap of vestibular and visual activations. Activations evoked at the group level by GVS or egomotion-consistent optic flow are shown in magenta and blue, respectively, while the regions responding to both types of stimuli are indicated in yellow. Dotted lines in the medial view (lower panel) illustrate the borders of the cingulate areas 24c' and 23 in which CSv lies. (C) Cortical activations evoked by egomotion-consistent optic flow stimulation at the group level. Same conventions as (A).</figcaption></figure>

mentioned criteria. In response to GVS, 20 regions show significant coverage (magenta bars), including regions of the parietal cortex (LIPv, LIPd, VIP, 7op, 7b, 7a, PE, PEa), premotor cortex (PMdc, SMA), sensorimotor cortex (S1/2, S3a/b, M1), temporal cortex (MST, Tpt), as well as the caudo-lateral, medio-lateral, and caudo-medial portions of the auditory belt cortex (BCL, BML, BCM, respectively, and insular areas (AI, Ig)). Using the same approach for EC optic flow stimulation, 22 regions showing significant coverage (blue bars) are identified, covering the visual cortex (MT, MST, FST, V4d, V3a, V6Ad), parietal cortex (LIPv, LIPd, VIP, MIP, LOP, 7op, 7b, 7a, PGa), premotor and motor cortex (PMdc, F5), frontal cortex (44, 45b, 8Bs, 8Ad), and the temporoparietal junction (Tpt). Confirming the visual impression given by Figure 4B regarding the moderate overlap between the vestibular and visual networks, only 9 out of the 33 regions mentioned above exhibited both vestibular and visual activations (Tpt, MST, 7op, 7b, 7a, LIPv, VIP, PMdc, LIPd). Moreover, for three of these nine regions (VIP, PMdc, LIPd), we did not observe visuo-vestibular convergence

at the node level, and such convergence, in terms of nodes' coverage, is often moderate among the six other visuo-vestibular regions (Tpt: 21%; MSTd: 16%; 7op: 14%; 7a: 10%; 7b: 7% and LIPv: 3%). Thus, this atlas-based analysis confirms that visuo-vestibular convergence is located in very specific parietal and temporal regions, with in particular the most quantitatively extended overlap found at the temporo-parietal junction (MST, Tpt, 7a), followed by the inferior parietal lobule (7b, 7op), intraparietal sulcus (LIPv, VIP, LIPd), and finally the dorsal premotor cortex (PMdc).

By superimposing the vestibular and visual networks mapped in the same four monkeys (Fig. 4B), we identified several visuo-vestibular regions, albeit with varying degrees of convergence. The atlas-based analysis (Fig. 5) largely confirms and extends these findings. Among them, Tpt (which includes VPS) and MST (which includes MSTd) show the strongest voxel-level overlap, indicating robust convergence between visual and vestibular inputs. They are followed by parietal area 7 which, in the CHARM atlas, comprises three regions: 7a, 7b, and 7op (that lat-
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** ROI analyses of the vestibular and visual convergence with respect to the CHARM atlas. (A) For each area of the CHARM atlas (n = 135), we calculate the percentage of nodes exhibiting significant activations (% coverage) for vestibular stimulation (in magenta), visual stimulation (in blue), or both (in yellow). Only areas showing at least 10% of activated nodes for one sensory modality are shown as bar plots. (B) The location of these CHARM areas is shown in colors in lateral and medial views of the inflated right hemisphere of the NMT template. Vestibular and visual areas are shown in magenta and blue dots, respectively, while areas exhibiting activations for both modalities are reported in yellow dots. It should be noted that some functionally defined vestibular areas do not map well onto CHARM areas and so may be under-represented in the figure.</figcaption></figure>

ter includes PIVC as it is the case for its human homolog (Grüsser et al., 1990; Lopez et al., 2012). These three visuo-vestibular regions exhibit a functional gradient in which 7a, posteriorly, is dominated by visual activations and 7op, anteriorly, is dominated by vestibular activations, while 7b, in between, responds about equally well to both sensory modalities. The atlas-based analysis also shows that both the ventral and dorsal subdivisions of LIP (LIPv and LIPd, respectively) are activated by visual and vestibular inputs. However, as it is also the case for VIP and PMdc (which includes FEFsem), the within-area node-to-node correspondence between the vestibular and visual activations is weak or absent. In these regions, the activations may primarily reflect anatomical co-localization rather than true voxel-level convergence. This suggests that, in these regions, visual and vestibular responses could be spatially adjacent but largely distinct rather than truly bimodal at the voxel level, indicating a more limited form of convergence. Hence, throughout the manuscript, we use “convergence” in a graded sense, distinguishing between true voxel-level overlap and more general anatomical co-localization. The only missing area in the atlas-based analysis is the most recently discovered area CSv, which lies within the cingulate sulcus. CSv is not reported in the CHARM atlas, where it is diluted between the large cingulate areas 24c' and 23c, as illustrated in the lower panel of Figure 4B. Area 24c' contains

most of CSv in its posterior portion, but both the vestibular and visual coverages in this CHARM area do not reach our 10% coverage threshold (GVS coverage: 6.3%, FLOW coverage: 6.8%, GVS & FLOW coverage: 1.8%).

As a complement, we used the CHARM atlas to perform a more conventional ROI analysis, computing the mean BOLD PSC for GVS and optic flow for each of the atlas regions and in each individual (see Supplementary Fig. S2).

## 4. DISCUSSION

The present study aimed at characterizing the cortical networks involved in processing vestibular and visual signals generated by egomotion in non-human primates. Using fMRI in four macaque monkeys, we investigated cortical responses to GVS and optic flow patterns consistent with egomotion. Our results indicate that egomotion-related vestibular and visual stimuli are handled by widely dissociated cortical networks. GVS activates preferentially vestibular, associative, and motor/premotor areas located in the insular, superior parietal, temporal, frontal, and cingulate cortices. Our refined analysis around the central sulcus (Supplementary Fig. S1) also revealed consistent activation corresponding to areas 3a and 3b. These regions are known to contain neurons responsive to vestibular stimulation (Guldin
& Grüsser, 1998), suggesting that they may represent an additional somatosensory contribution to the vestibular cortical network. Vestibular-related activation extended slightly anterior to area 3b, reaching the posterior bank of the precentral gyrus and thus partially involving M1, mainly within the lower-limb representation. This localization is consistent with previous evidence of vestibular-responsive neurons in both the posterior and anterior banks of the central sulcus (Akbarian et al., 1992; Lewis & Van Essen, 2000), suggesting that vestibular inputs may reach primary sensorimotor territories and contribute to vestibular-somatic integration. Further studies are needed to disentangle vestibular and somatosensory contributions to cortical responses to GVS, in order to clarify to what extent GVS-evoked cortical activity can be considered purely vestibular, particularly through the addition of a sham condition controlling for the tactile aspect of the stimulation, as has been done in previous human studies (Aedo-Jury et al., 2020; Smith et al., 2012). In humans, somatosensory-vestibular interactions have been described at the behavioral and neurophysiological levels (e.g. Ferrè et al., 2012), and several human brain imaging studies (e.g. (Fasold et al., 2008; Lobel et al., 1998; Smith et al., 2012; zu Eulenburg et al., 2012) have reported activations in response to GVS localized around the central sulcus, encompassing somatosensory areas 1, 2, and 3 as well as motor area 1 (M1). These somatosensory and motor activations mirror our findings in montkeys. However, the accuracy of the locations reported for these activations in humans does not allow for a direct or more detailed comparison with our results. By contrast, optic flow elicits preferentially occipito-temporal, inferior parietal, and prefrontal oculomotor regions and the cortical network documented here has been described with more details in previous studies (Cottereau et al., 2017; De Castro et al., 2021). However, some areas of convergence are highlighted, where both modalities elicit strong activation. Statistical testing shows that seven regions in total show strong responses to both sensory modalities with widespread cortical coverage: MSTd in the superior temporal sulcus, VPS at the posterior tip of the lateral sulcus, CSv within the cingulate sulcus, FEFsem within the frontal eye field region, area 7 in the inferior parietal lobule, and both LIP and VIP within the intraparietal sulcus. Beyond these primary convergence sites, our analyses also reveal additional regions showing consistent responses to either modality and, in some cases, both, including subdivisions of area 7 in the inferior parietal lobule (i.e. 7op, 7b), subdivisions of the lateral intraparietal area (LIPv and LIPd), and premotor dorsal caudal cortex (PMdc). These results indicate that visuo-vestibular integration arises from selective convergence within a few specific cortical locations rather than from a general

overlap of vestibular and visual networks, providing new insight into the neural mechanisms underlying multisensory processing of self-motion information in primates. Figures 4 and 5 show a high degree of consistency, especially when it comes to overlapping regions such as 7a, VIP, and MSTd. Several other areas, such as FEFsem and PMdc, appear to represent adjacent or functionally related territories despite having different labels. This convergence across statistical approaches supports the robustness of the identified network involved in processing vestibular and visual cues for egomotion perception.

Our results confirm that visuo-vestibular convergence occurs within a restricted set of areas. Notably, our labeling of VPS, MSTd, area 7, LIP, VIP, FEFsem, and CSv as convergence regions aligns with earlier single-unit and fMRI findings that these regions are involved in integrating visual and vestibular information for egomotion (Chen et al., 2011b, 2011c, 2013; Gu et al., 2006, 2008, 2010; Schlack et al., 2002; Smith et al., 2012). Our findings are also in accordance with human fMRI studies that have characterized analogous regions as involved in visual and vestibular processing for self-motion integration, highlighting a salient conservation of cortical organization across primate species (Smith et al., 2017). Importantly, our results extend the existing macaque electrophysiology literature by providing whole-brain mapping that contextualizes previously reported single-unit properties. For example, MSTd neurons have been shown to integrate optic flow and vestibular signals with congruent or opposite directional tuning (Gu et al., 2007; Takahashi et al., 2007), and VIP neurons exhibit multisensory responses reflecting inputs from both MSTd and vestibular-related areas such as PIVC (Chen et al., 2011c; Lewis & Van Essen, 2000). Our fMRI data show how selective convergence occurs at the whole-brain level, highlighting additional regions, such as area 7 subdivisions and LIP, where electrophysiological studies (Avila et al., 2019; Zeng et al., 2023) have suggested multisensory contributions, but whole-area visuo-vestibular convergence had not been mapped.

In our study, VPS (situated at the junction of Tpt, 7b, and 7op according to the CHARM atlas, as shown in Supplementary Fig. S4) is the area showing the greatest overlap between vestibular and visual activations, with a slight dominance of vestibular modality (cf. Fig. 5). VPS is located posterior to PIVC (which mostly correspond to 7op in the CHARM atlas) and, like that latter, it has been shown to receive short-latency vestibular input (Akbarian et al., 1992). However, it also receives input from a portion of thests likely encompassing MSTd (MST) (Guldin & Grüsser, 1998). The slight vestibular dominance documented here is consistent with previous electrophysiological studies showing that the responses of visuo-vestibular neurons in
VPS are often dominated by the vestibular modality, with generally opposite directional tuning between both sensory modalities (Chen et al., 2011b). This pattern of vestibular dominance in temporo-parietal areas aligns with human fMRI studies showing similar activation patterns in the posterior insular cortex (PIC), which was found in humans (Beer et al., 2023; Wirth et al., 2018) and presumably corresponds to VPS, suggesting conserved networks across primate species.

The second area of greatest visuo-vestibular overlap documented here is MSTd (MST). Previous studies showed with great rigor the distinction between the dorsal (MSTd) and ventral (MSTv) parts of the MST area, evidencing that MSTd mainly processes optic flow information for the perception of self-motion, while MSTv processes more object motion (Tanaka & Saito, 1989). MSTd is, therefore, a well-documented cortical area for egomotion processing of visual input (Duffy & Wurtz, 1991; Saito et al., 1986; Tanaka et al., 1986). This region is responsive to EC optic flow in our dataset, and its GVS response was less significant, which is in line with previous findings showing that visual responses are dominant in this area (Gu et al., 2006; Morgan et al., 2008; Takahashi et al., 2007). While it belongs to this bimodal network, MSTd cells typically have congruent or reverse visual/ vestibular tuning for translation but reverse preferences for rotation (Takahashi et al., 2007), which suggests specialization for solving self-translation signals (Gu et al., 2006). This is consistent with our results indicating that MSTd is a visuo-vestibular convergence point with visual bias (cf. Fig. 5). Moreover, human neuroimaging studies have consistently identified MT/MST complex as highly sensitive to EC visual stimuli (Cardin & Smith, 2010; Wall & Smith, 2008). The visual dominance we observe in macaque MSTd thus appears to be conserved in its human homolog, reinforcing the translational relevance of our results.

Our results indicate that the third region showing greatest visuo-vestibular overlap is the parietal area 7. The subdivision of area 7 is a matter of debate. However, regardless of the nomenclature system used, the properties of sensory specialization remain consistent. With regard to the initial subdivision, area 7a of the superior posterior parietal cortex is strongly activated by optical flow in both humans and macaques (Claeys et al., 2003; Deutschländer et al., 2002) and has been identified as essential for visuo-vestibular integration in both species (Andersen, 1997; Ruehl et al., 2022; Snyder et al., 1998). Traditionally, 7b has been considered a secondary somatosensory area involved in the processing of tactile and proprioceptive information (Andersen, 1997). However, visual and bimodal (visuo-tactile, visuo-cochlear) neurons have been described (Fogassi et al., 2005;

Hyvärinen, 1981). Some subregions of 7b are close to the vestibular areas identified in the parietal operculum, but 7b itself is not classically described as a pure vestibular area. As for 7op, it is a region close to or including areas identified in macaques and humans as being involved in vestibular processing, notably PIVC (Grüsser et al., 1990; Lopez et al., 2012). However, this traditional subdivision into portions 7a and 7b has been extended by further anatomical studies. Cytoarchitectonic and connectional data (Rozzi et al., 2006) suggest that area 7a actually comprises at least two distinct subregions, Opt and PG, with different connectivity profiles: Opt is more strongly linked to visual and oculomotor areas, while PG integrates both visual and somatosensory inputs. Similarly, the classic area 7b can be subdivided into PFG and PF, which have denser connections with premotor and somatosensory areas and are more involved in motor functions. There is, therefore, a clear rostrocaudal gradient, highlighted in the present study as well, along the inferior parietal convexity (IPL), with the most caudal portions (7a or Opt/PG) being more visual, the intermediate portions (7b or PG) being more equally driven by visual and vestibular input, and the rostral portions (7op or PFG/ PF) showing more vestibular responses and associated with premotor areas.

VIP is a functionally defined area in the intraparietal sulcus (Colby et al., 1993; Duhamel et al., 1998). This area also turning out to be an overlap region equally activated by both modalities in the present study aligns with past studies that showed that VIP integrates vestibular and optic flow information with equilibrated sensitivity (Chen et al., 2011c; Schlack et al., 2002), and that it receives input from both MSTd (visual information) and vestibular-related areas such as PIVC (Lewis & Van Essen, 2000). VIP neurons also exhibit congruent and opposite tuning for visual and vestibular stimuli, and their combined responses can be dominated by either modality (Chen et al., 2011c), suggesting flexibility according to task demands. Moreover, vestibular response latency of VIP is quicker than MSTd (Chen et al., 2011a), and microstimulation of VIP may elicit optic flow-based heading judgment bias (Zhang & Britten, 2011), validating its causal role in self-motion perception. VIP's multisensory integration properties, although they have long been questioned, have been reported in its human homologs (hVIP), which, in some studies, showed sensitivity to both visual and vestibular egomotion cues, with slightly less response to vestibular stimulation for the human counterpart of this region (Smith et al., 2017). Nevertheless, this partial functional correspondence between macaque and human VIP underscores the conserved role of this region across primate species in integrating multisensory self-motion signals.
LIP is functionally divided into LIPd—involved in ocu-
lomotor planning; and LIPv—implicated in attentional
and oculomotor processes (Liu et al., 2010). Even though
previous studies showed responsiveness to visual motion
signals as well as vestibular cues in LIP neurons, espe-
cially in heading discrimination tasks (Chen et al., 2013;
Gu et al., 2006), this area is not as specialized in multi-
sensory integration as areas such as MSTd or VIP are.
Further evidence also suggests that LIP could rather be a
critical interface between self-motion information and
eye movement control (Andersen & Cui, 2009).

Our finding of convergence in FEFsem, a region asso-
ciated with eye movement control and processing of
visual motion (Bremmer et al., 2002), is consistent with
the hypothesis that visuomotor areas are activated in a
modality-independent manner to regulate spatial orienta-
tion. FEFsem and PMdc are two separate but adjacent
areas in the arcuate sulcus; FEFsem is located in the fun-
dus of the arcuate sulcus, while PMdc is more caudal
(Leichnetz, 2001; Tanaka & Lisberger, 2001). Despite their
anatomical differences, both regions participate in com-
plementary but distinct oculomotor functions, indicating
a possible functional correspondence. While less high-
lighted in previous vestibular work, its presence as a
bimodal region in this study is consistent with recent
research in humans where FEF subdivisions are impli-
cated in the integration of egomotion signals (Cottereau
et al., 2017; Smith et al., 2018). This convergence with
human neuroimaging studies is particularly noteworthy,
as FEF regions in human subjects show similar activation
patterns during visual egomotion stimuli (Ruehl et al.,
2022), suggesting that the oculomotor interface with self-
motion processing represents a conserved characteristic
of primate cortical organization.

Finally, CSv, a visual motion- and motor control-related
area in both humans (Smith et al., 2012; Wall & Smith,
2008) and monkeys (Cottereau et al., 2017), also appears
on our overlap maps (cf. Fig. 4B). The fact that CSv does
not show up in the ROIs analyses is caused by the fact
that in the CHARM atlas, this small cortical region belongs
to the large cingulate sensorimotor areas 24c' and, to a
lesser extent, 23c, as illustrated in Figure 4B. While CSv
has been shown to have robust responses to optic flow
and artificial vestibular stimulation in humans (Billington &
Smith, 2015; Pitzalis et al., 2020), decoding analysis
reveals less integration in this area compared with hVIP.
Our demonstration of macaque CSv convergence pres-
ents a new view, suggesting that it may serve as a senso-
rimotor interface to process full-field self-motion
information (De Castro et al., 2021; Smith, 2021), though
its integrative function remains to be fully described. The
identification of CSv as a region of visuo-vestibular con-
vergence in our results provides crucial cross-species

validation of the importance of this component of the
human egomotion network (Ruehl et al., 2022). Moreover,
previous neuroimaging analyses of the human medial
cortex showed robust responses to both visual and ves-
tibular self-motion cues in CSv and vestibular responses
in the adjacent pericallosal sulcus (Beer et al., 2023),
making it an important component of the human egomo-
tion network (Ruehl et al., 2022) and supporting a broader
cingulate involvement in self-motion perception.

Our data show activations in the auditory belt cortex
during GVS. Given the anatomical proximity of the periph-
eral vestibular system to the cochlea, these activations
may reflect cochlear artifacts rather than genuine vestib-
ular processing. This interpretation is supported by neu-
roimaging meta-analyses showing that the superior
temporal gyrus (STG), which encompasses the auditory
belt, is primarily activated during sound-induced vestibu-
lar stimulation (e.g., clicks or short-tone bursts) but not
consistently during caloric vestibular stimulation (CVS) or
GVS (e.g. zu Eulenburg et al., 2012). The absence of
robust STG activation in CVS or GVS studies suggests
that the auditory responses observed during GVS in our
data could be related to cochlear co-activation rather
than vestibular processing per se. However, the auditory
belt is also known for its role in multisensory integration,
as demonstrated by anatomical and electrophysiological
studies in non-human primates. Tract-tracing studies
have shown direct connections between vestibular-
related regions—such as the parieto-insular vestibular
cortex and retroinsular cortex—and the caudal auditory
belt (Smiley & Falchier, 2009). Electrophysiological
recordings further confirm that neurons in the auditory
belt exhibit supra-additive responses to combined ves-
tibular, visual, and somatosensory stimuli (Kayser et al.,
2005), supporting its involvement in multisensory spatial
perception. While our GVS-related activations in the
auditory belt may include cochlear artifacts, they also
occur in a region with known multisensory properties.
Further research using control conditions or alternative
vestibular stimulation methods (e.g., CVS) is needed to
disentangle artifactual cochlear activation from genuine
vestibular-auditory integration. Nonetheless, the ana-
tomical and functional evidence suggests that the audi-
tory belt could contribute to spatial awareness and sound
localization during self-motion, particularly when vestibu-
lar and auditory cues are combined.

Some methodological limitations must be acknowl-
edged. First, our GVS paradigm lacked a sham or tactile
control condition, meaning that we cannot completely
rule out the possibility that some of the observed cortical
activations were driven by somatosensory rather than
vestibular inputs, especially given that GVS necessarily
engages vestibular afferents and cutaneous receptors at
the sites of stimulation (Ferrè et al., 2013; Stephan et al., 2005). However, it is important to note that the somatosensory activations reported here (Fig. 4A) correspond to the representation of the hindlimb rather than of the head, suggesting that they are not triggered by tactile sensation at the stimulation sites. Importantly, the absence of detectable somatosensory activity in head-related parts of S1/S2 (Supplementary Fig. S1) should not be taken to imply that somatosensory contributions are entirely absent in other regions, and subtle tactile influences cannot be fully excluded within the limits of our data, especially in areas such as VIP, known to integrate somatosensory inputs through tactile receptive fields mainly located on the face (Colby et al., 1993; Cooke et al., 2003). Moreover, vestibular and somatosensory responses have been confirmed in VIP by GVS studies including a tactile control condition (Smith et al., 2012). This observation extends to other regions such as CSv and PIVC. Subsequent research will need to have an appropriately matched somatosensory control in order to unmistakably disentangle these contributions. Second, the GVS sessions were performed under slight anesthesia, in contrast to the awake condition used in the optic flow part of the experiment. This difference in brain state must be considered when interpreting the convergence patterns, as anesthesia can cause dissociation between BOLD and neural activity (van Alst et al., 2019). Yet, despite this limitation, our GVS data still indicated robust and spatially coherent patterns of activation in animals, in keeping with known vestibular circuits. This suggests that the core organization of vestibular networks remains functionally preserved even under light anesthesia. Nevertheless, it is possible that anesthesia reduced the overall amplitude or extent of activations, or selectively suppressed higher-order integrative processes. The anesthesia was necessary in the present study because of technical and ethical constraints of GVS delivery in macaques. Indeed, to ensure a safe behavioral response in macaques upon receiving stimulation, we believe that many months of training and study of reliable behavioral cues would be required to ensure that the animal receives stimulation at a suitable threshold so that it does not experience any pain or discomfort. Developing such a habituation protocol represents an important future step, as conducting vestibular stimulation in awake, trained monkeys will be crucial to confirm the reliability of these convergence patterns and to assess potential state-dependent differences with improved spatial and temporal resolution. Finally, to directly link cortical activation with perceptual processing, it will be important to develop behavioral paradigms that assess the animals' perception of vestibular stimulation—such as by tracking eye movement responses (e.g. torsional or vergence shifts) or

through operant conditioning tasks. While such tools remain to be implemented, the convergence of our findings with previous electrophysiological and human fMRI studies supports the validity of the observed responses and reinforces the notion of a conserved cortical vestibular system across primates.

In summary, this research presents the first whole-brain fMRI mapping of cortical responses to both galvanic vestibular stimulation and egomotion-consistent optic flow in non-human primates. Through the localization of selective convergence zones in both modalities, we provide new insights into the multisensory organization of self-motion processing in the primate brain. These findings form the basis of follow-up experiments on visuo-vestibular integration dynamics in awake, behaving monkeys, enabling a better understanding of these networks' perceptual and functional contributions.

## DATA AND CODE AVAILABILITY

The data and codes used for data analysis are available to interested parties on request from one of the corresponding authors. The average activation surface maps corresponding to Figure 4 have been deposited in the BALSA repository (Van Essen et al., 2017) to promote transparency and reproducibility. The dataset is available at: https://balsa.wustl.edu/study/n6rPr

## AUTHOR CONTRIBUTIONS

Conceptualization: V.D.C., B.R.C., A.S.C., J.-B.D. Funding: A.S.C. Project supervision: A.S.C., J.-B.D. Programming: M.R. MRI acquisitions: V.D.C., E.E., M.-A.L., N.V., B.R.C., J.-B.D. MRI data analysis: S.M., J.-B.D. Figure design: S.M., J.-B.D. Writing (original draft): S.M., J.-B.D. Review and editing: S.M., E.E., V.D.C., M.-A.L., B.R.C., A.S.C., J.-B.D.

## FUNDING

Open access funding provided by Université de Toulouse. Funding was provided by Agence Nationale de la Recherche (Grant number: ANR-21-CE37-0023).

## DECLARATION OF COMPETING INTEREST

The authors of this manuscript acknowledge no conflicts of interest, financial or otherwise.

## ACKNOWLEDGMENTS

We thank Marie Mercier, Allison Rodriguez, Céline Martínez, and Marie Isnardon for animal care; and Frédéric
Brouillet, Jean-Pierre Desirat, and Sandrine Ferries for
assistance with MRI acquisitions.

SUPPLEMENTARY MATERIALS

Supplementary material for this article is available with
the online version here: https://doi.org/10.1162/IMAG.a
.1093

REFERENCES

Aedo-Jury, F., Cottereau, B. R., Celebrini, S., & Séverac
Cauquil, A. (2020). Antero-posterior vs. lateral vestibular
input processing in human visual cortex. *Frontiers in
Integrative Neuroscience, 14*, 43. https://doi.org/10.3389
/fint.2020.00043

Akbarian, S., Grüsser, O. J., & Guldin, W. O. (1992).
Thalamic connections of the vestibular cortical fields in
the squirrel monkey (Saimiri sciureus). *The Journal of
Comparative Neurology*, **326**(3), 423–441. https://doi.org
/10.1002/cne.903260308

Andersen, R. A. (1997). Multimodal integration for the representation of space in the posterior parietal cortex. *Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences*, **352**(1360), 1421–1428. https://doi.org/10.1098/rstb.1997.0128

Andersen, R. A., & Cui, H. (2009). Intention, action planning, and decision making in parietal-frontal circuits. *Neuron*, **63**(5), 568–583. https://doi.org/10.1016/j.neuron.2009.08.028

Angelaki, D. E., & Cullen, K. E. (2008). Vestibular system: The many facets of a multimodal sense. *Annual Review of Neuroscience*, **31**, 125–150. https://doi.org/10.1146/annurev.neuro.31.060407.125555

Arcaro, M. J., Schade, P. F., & Livingstone, M. S. (2019).
Body map proto-organization in newborn macaques.
*Proceedings of the National Academy of Sciences*, **116**(49),
24861–24871. https://doi.org/10.1073/pnas.1912636116

Avila, E., Lakshminarasimhan, K. J., DeAngelis, G. C., & Angelaki, D. E. (2019). Visual and vestibular selectivity for self-motion in macaque posterior parietal area 7a. *Cerebral Cortex* (New York, N.Y.: 1991), 29(9), 3932–3947. https://doi.org/10.1093/cercor/bhy272

Beer, A. L., Becker, M., Frank, S. M., & Greenlee, M. W. (2023). Vestibular and visual brain areas in the medial cortex of the human brain. *Journal of Neurophysiology*, **129**(4), 948–962. https://doi.org/10.1152/jn.00431.2022

Billington, J., & Smith, A. T. (2015). Neural mechanisms for discounting head-roll-induced retinal motion. *The Journal of Neuroscience*, **35**(12), 4851–4856. https://doi.org/10.1523/jneurosci.3640-14.2015

Brandt, T., & Dieterich, M. (1999). The vestibular cortex: Its locations, functions, and disorders. *Annals of the New York Academy of Sciences*, **871**(1), 293–312. https://doi.org/10.1111/j.1749-6632.1999.tb09193.x

Bremmer, F., Duhamel, J., Ben Hamed, S., & Graf, W. (2002). Heading encoding in the macaque ventral intraparietal area (VIP). *European Journal of Neuroscience*, **16**(8), 1554–1568. https://doi.org/10.1046/j.1460-9568.2002.02207.x

Cardin, V., & Smith, A. T. (2010). Sensitivity of human visual and vestibular cortical regions to egomotion-compatible visual stimulation. *Cerebral Cortex*, **20**(8), 1964–1973. https://doi.org/10.1093/cercor/bhp268

Caudron, S., Ceyte, H., Barraud, P.-A., Cian, C., & Guerraz, M. (2018). Perception of body movement when real and

simulated displacements are combined. *PLoS One*, **13**(3), e0193174. https://doi.org/10.1371/journal.pone.0193174

Chen, A., DeAngelis, G. C., & Angelaki, D. E. (2011a). A comparison of vestibular spatiotemporal tuning in macaque parietoinsular vestibular cortex, ventral intraparietal area, and medial superior temporal area. *The Journal of Neuroscience*, **31**(8), 3082–3094. https://doi.org/10.1523/JNEUROSCI.4476-10.2011

Chen, A., DeAngelis, G. C., & Angelaki, D. E. (2011b). Convergence of vestibular and visual self-motion signals in an area of the posterior sylvian fissure. *The Journal of Neuroscience: The Official Journal of the Society for Neuroscience*, **31**(32), 11617–11627. https://doi.org/10.1523/JNEUROSCI.1266-11.2011

Chen, A., DeAngelis, G. C., & Angelaki, D. E. (2011c). Representation of vestibular and visual cues to self-motion in ventral intraparietal cortex. *The Journal of Neuroscience: The Official Journal of the Society for Neuroscience*, **31**(33), 12036–12052. https://doi.org/10.1523/JNEUROSCI.0395-11.2011

Chen, A., DeAngelis, G. C., & Angelaki, D. E. (2013). Functional specializations of the ventral intraparietal area for multisensory heading discrimination. *The Journal of Neuroscience: The Official Journal of the Society for Neuroscience*, **33**(8), 3567–3581. https://doi.org/10.1523/JNEUROSCI.4522-12.2013

Claeys, K. G., Lindsey, D. T., Schutter, E. D., & Orban, G. A. (2003). A higher order motion region in human inferior parietal lobule: Evidence from fMRI. *Neuron*, **40**(3), 631–642. https://doi.org/10.1016/S0896-6273(03)00590-7

Colby, C. L., Duhamel, J. R., & Goldberg, M. E. (1993). Ventral intraparietal area of the macaque: Anatomic location and visual response properties. *Journal of Neurophysiology*, **69**(3), 902–914. https://doi.org/10.1152/jn.1993.69.3.902

Cooke, D. F., Taylor, C. S. R., Moore, T., & Graziano, M. S. A. (2003). Complex movements evoked by microstimulation of the ventral intraparietal area. *Proceedings of the National Academy of Sciences*, **100**(10), 6163–6168. https://doi.org/10.1073/pnas.1031751100

Cottereau, B. R., Smith, A. T., Rima, S., Fize, D., Héjja-Brichard, Y., Renaud, L., Lejards, C., Vayssière, N., Trotter, Y., & Durand, J.-B. (2017). Processing of egomotion-consistent optic flow in the rhesus macaque cortex. *Cerebral Cortex*, **27**(1), 330–343. https://doi.org/10.1093/cercor/bhw412

Day, B. L., & Fitzpatrick, R. C. (2005). The vestibular system. *Current Biology: CB*, **15**(15), R583–586. https://doi.org/10.1016/j.cub.2005.07.053

De Castro, V., Smith, A. T., Beer, A. L., Leguen, C., Vayssière, N., Héjja-Brichard, Y., Audurier, P., Cottereau, B. R., & Durand, J. B. (2021). Connectivity of the cingulate sulcus visual area (CSv) in macaque monkeys. *Cerebral Cortex* (New York, N.Y.: 1991), 31(2), 1347–1364. https://doi.org/10.1093/cercor/bhaa301

Delhaye, B. P., Long, K. H., & Bensmaia, S. J. (2018). Neural basis of touch and proprioception in primate cortex. *Comprehensive Physiology*, **8**(4), 1575–1602. https://doi.org/10.1002/cphy.c170033

Deutschländer, A., Bense, S., Stephan, T., Schwaiger, M., Brandt, T., & Dieterich, M. (2002). Sensory system interactions during simultaneous vestibular and visual stimulation in PET. *Human Brain Mapping*, **16**(2), 92–103. https://doi.org/10.1002/hbm.10030

Duffy, C. J., & Wurtz, R. H. (1991). Sensitivity of MST neurons to optic flow stimuli. *II. Mechanisms of response
selectivity revealed by small-field stimuli. *Journal of Neurophysiology*, **65**(6), 1346-1359. https://doi.org/10.1152/jn.1991.65.6.1346

Duhamel, J. R., Colby, C. L., & Goldberg, M. E. (1998). Ventral intraparietal area of the macaque: Congruent visual and somatic response properties. *Journal of Neurophysiology*, *79*(1), 126-136. https://doi.org/10.1152/jn.1998.79.1.126

Eatock, R. A., Xue, J., & Kalluri, R. (2008). Ion channels in mammalian vestibular afferents may set regularity of firing. *Journal of Experimental Biology*, *211*(11), 1764-1774. https://doi.org/10.1242/jeb.017350

Fan, R. H., Liu, S., DeAngelis, G. C., & Angelaki, D. E. (2015). Heading tuning in macaque area V6. *The Journal of Neuroscience: The Official Journal of the Society for Neuroscience*, *35*(4), 661-675. https://doi.org/10.1152/jn.1971.34.4.661

Fasold, O., Heinau, J., Trenner, M. U., Villringer, A., & Wenzel, R. (2008). Proprioceptive head posture-related processing in human polysensory cortical areas. *NeuroImage*, *40*(3), 1232-1242. https://doi.org/10.1016/j.neuroimage.2007.12.060

Fernandez, C., & Goldberg, J. M. (1971). Physiology of peripheral neurons innervating semicircular canals of the squirrel monkey. II. Response to sinusoidal stimulation and dynamics of peripheral vestibular system. *Journal of Neurophysiology*, *34*(4), 661-675. https://doi.org/10.1152/jn.1971.34.4.661

Ferrè, E. R., Bottini, G., & Haggard, P. (2012). Vestibular inputs modulate somatosensory cortical processing. *Brain Structure & Function*, *217*(4), 859-864. https://doi.org/10.1007/s00429-012-0404-7

Ferrè, E. R., Longo, M., Fiori, F., & Haggard, P. (2013). Vestibular modulation of spatial perception. *Frontiers in Human Neuroscience*, *7*, 660. https://doi.org/10.3389/fnhum.2013.00660

Fitzpatrick, R. C., & Day, B. L. (2004). Probing the human vestibular system with galvanic stimulation. *Journal of Applied Physiology (Bethesda, Md.)*, *198*(5), 96(6), 2301-2316. https://doi.org/10.1152/japplphysiol.00008.2004

Fogassi, L., Ferrari, P. F., Gesierich, B., Rozzi, S., Chersi, F., & Rizzolatti, G. (2005). Parietal lobe: From action organization to intention understanding. *Science (New York, N.Y.)*, *308*(5722), 662-667. https://doi.org/10.1126/science.1106138

Forbes, P. A., Kwan, A., Mitchell, D. E., Blouin, J.-S., & Cullen, K. E. (2023). The neural basis for biased behavioral responses evoked by galvanic vestibular stimulation in primates. *The Journal of Neuroscience: The Official Journal of the Society for Neuroscience*, *43*(11), 1905-1919. https://doi.org/10.1523/JNEUROSCI.0987-22.2023

Frank, S. M., & Greenlee, M. W. (2014). An MRI-compatible caloric stimulation device for the investigation of human vestibular cortex. *Journal of Neuroscience Methods*, *235*, 208-218. https://doi.org/10.1016/j.jneumeth.2014.07.008

Gibson, J. J. (1950). *The perception of the visual world* (pp. xii, 242). Houghton Mifflin. https://doi.org/10.1126/science.113.2940.535

Goldberg, J. M., Smith, C. E., & Fernández, C. (1984). Relation between discharge regularity and responses to externally applied galvanic currents in vestibular nerve afferents of the squirrel monkey. *Journal of Neurophysiology*, *51*(6), 1236-1256. https://doi.org/10.1152/jn.1984.51.6.1236

Goldberg, J. M., Wilson, V. J., Cullen, K. E., Angelaki, D. E., Broussard, D. M., Buttner-Ennever, J., Fukushima, K., & Minor, L. B. (2012). *The vestibular system: A sixth*

sense. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195167085.001.0001

Grüsser, O. J., Pause, M., & Schreiter, U. (1990). Localization and responses of neurones in the parieto-insular vestibular cortex of awake monkeys (Macaca fascicularis). *The Journal of Physiology*, *430*(1), 537-557. https://doi.org/10.1113/jphysiol.1990.sp018306

Gu, Y., Angelaki, D. E., & Deangelis, G. C. (2008). Neural correlates of multisensory cue integration in macaque MSTd. *Nature Neuroscience*, *11*(10), 1201-1210. https://doi.org/10.1038/nn.2191

Gu, Y., DeAngelis, G. C., & Angelaki, D. E. (2007). A functional link between area MSTd and heading perception based on vestibular signals. *Nature Neuroscience*, *10*(8), 1038-1047. https://doi.org/10.1038/nn1935

Gu, Y., Deangelis, G. C., & Angelaki, D. E. (2012). Causal links between dorsal medial superior temporal area neurons and multisensory heading perception. *The Journal of Neuroscience: The Official Journal of the Society for Neuroscience*, *32*(7), 2299-2313. https://doi.org/10.1523/JNEUROSCI.5154-11.2012

Gu, Y., Fetsch, C. R., Adeyemo, B., Deangelis, G. C., & Angelaki, D. E. (2010). Decoding of MSTd population activity accounts for variations in the precision of heading perception. *Neuron*, *66*(4), 596-609. https://doi.org/10.1016/j.neuron.2010.04.026

Gu, Y., Watkins, P. V., Angelaki, D. E., & DeAngelis, G. C. (2006). Visual and nonvisual contributions to three-dimensional heading selectivity in the medial superior temporal area. *Journal of Neuroscience*, *26*(1), 73-85. https://doi.org/10.1523/JNEUROSCI.2356-05.2006

Guldin, W. O., & Grüsser, O. J. (1998). Is there a vestibular cortex? *Trends in Neurosciences*, *21*(6), 254-259. https://doi.org/10.1016/s0166-2236(97)01211-3

Hyvärinen, J. (1981). Regional distribution of functions in parietal association area 7 of the monkey. *Brain Research*, *206*(2), 287-303. https://doi.org/10.1016/0006-8993(81)90533-3

Iurato, S. (1967). *Submicroscopic structure of the inner ear*. Pergamon Press. https://doi.org/10.1016/b978-0-08-011878-9.50008-9

Jung, B., Taylor, P. A., Seidlitz, J., Sponheim, C., Perkins, P., Ungerleider, L. G., Glen, D., & Messinger, A. (2021). A comprehensive macaque fMRI pipeline and hierarchical atlas. *NeuroImage*, *235*, 117997. https://doi.org/10.1016/j.neuroimage.2021.117997

Kayser, C., Petkov, C. I., Augath, M., & Logothetis, N. K. (2005). Integration of touch and sound in auditory cortex. *Neuron*, *48*(2), 373-384. https://doi.org/10.1016/j.neuron.2005.09.018

Kingma, H., & van de Berg, R. (2016). Anatomy, physiology, and physics of the peripheral vestibular system. *Handbook of Clinical Neurology*, *137*, 1-16. https://doi.org/10.1016/B978-0-444-63437-5.00001-7

Kwan, A., Forbes, P. A., Mitchell, D. E., Blouin, J.-S., & Cullen, K. E. (2019). Neural substrates, dynamics and thresholds of galvanic vestibular stimulation in the behaving primate. *Nature Communications*, *10*(1), 1904. https://doi.org/10.1038/s41467-019-09738-1

Lee, D. N., & Aronson, E. (1974). Visual proprioceptive control of standing in human infants. *Perception & Psychophysics*, *15*(3), 529-532. https://doi.org/10.3758/BF03199297

Lee, D. N., & Lishman, J. R. (1975). Visual proprioceptive control of stance. *Journal of Human Movement Studies*, *1*(2), 87-95. https://doi.org/10.1016/j.humov.2021.102845
Leichnetz, G. R. (2001). Connections of the medial posterior parietal cortex (area 7m) in the monkey. *The Anatomical Record*, **263**(2), 215–236. https://doi.org/10.1002/ar.1082

Lewis, J. W., & Van Essen, D. C. (2000). Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey. *The Journal of Comparative Neurology*, **428**(1), 112–137. https://doi.org/10.1002/1096-9861(20001204)428:1<112::aid-cne8>3.0.co;2-9

Li, X., Morgan, P., Ashburner, J., Smith, J., & Rorden, C. (2016). The first step for neuroimaging data analysis: DICOM to NIfTI conversion. *Journal of Neuroscience Methods*, **264**, 47–56. https://doi.org/10.1016/j.jneumeth.2016.03.001

Liu, B., Tian, Q., & Gu, Y. (2021). Robust vestibular self-motion signals in macaque posterior cingulate region. *eLife*, **10**, e64569. https://doi.org/10.7554/eLife.64569

Liu, Y., Yttri, E. A., & Snyder, L. H. (2010). Intention and attention: Different functional roles for LIPd and LIPv. *Nature Neuroscience*, **13**(4), 495–500. https://doi.org/10.1038/nn.2496

Lobel, E., Kleine, J. F., Bihan, D. L., Leroy-Willig, A., & Berthoz, A. (1998). Functional MRI of galvanic vestibular stimulation. *Journal of Neurophysiology*, **80**(5), 2699–2709. https://doi.org/10.1152/jn.1998.80.5.2699

Lopez, C., Blanke, O., & Mast, F. W. (2012). The human vestibular cortex revealed by coordinate-based activation likelihood estimation meta-analysis. *Neuroscience*, **212**, 159–179. https://doi.org/10.1016/j.neuroscience.2012.03.028

Marchand, S., Balcou, M., Picher, P., Rosito, M., Mateo, D., Vayssiere, N., Durand, J.-B., & Séverac Cauquil, A. (2025). Visuo-vestibular integration for self-motion: Human cortical area V6 prefers forward and congruent stimuli. *Experimental Brain Research*, **243**(6), 152. https://doi.org/10.1007/s00221-025-07106-8

Marchand, S., Langlade, A., Legois, Q., & Séverac Cauquil, A. (2025). A wide-ranging review of galvanic vestibular stimulation: From its genesis to basic science and clinical applications. *Experimental Brain Research*, **243**(5), 131. https://doi.org/10.1007/s00221-025-07079-8

Morgan, M. L., DeAngelis, G. C., & Angelaki, D. E. (2008). Multisensory integration in macaque visual cortex depends on cue reliability. *Neuron*, **59**(4), 662–673. https://doi.org/10.1016/j.neuron.2008.06.024

Nashner, L. M., & Wolfson, P. (1974). Influence of head position and proprioceptive cues on short latency postural reflexes evoked by galvanic stimulation of the human labyrinth. *Brain Research*, **67**(2), 255–268. https://doi.org/10.1016/0006-8993(74)90276-5

Phillips, C. (2018). *CyclotronResearchCentre, SPM cluster size threshold* [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.1689891

Pitzalis, S., Serra, C., Sulpizio, V., Committeri, G., de Pasquale, F., Fattori, P., Galletti, C., Sepe, R., & Galati, G. (2020). Neural bases of self- and object-motion in a naturalistic vision. *Human Brain Mapping*, **41**(4), 1084–1111. https://doi.org/10.1002/hbm.24862

Reveley, C., Gruslys, A., Ye, F. Q., Glen, D., Samaha, J., E Russ, B., Saad, Z., K Seth, A., Leopold, D. A., & Saleem, K. S. (2017). Three-dimensional digital template atlas of the macaque brain. *Cerebral Cortex (New York, N.Y.: 1991)*, **27**(9), 4463–4477. https://doi.org/10.1093/cercor/bhw248

Rozzi, S., Calzavara, R., Belmalih, A., Borra, E., Gregoriou, G. G., Matelli, M., & Luppino, G. (2006). Cortical connections of the inferior parietal cortical convexity of

the macaque monkey. *Cerebral Cortex*, **16**(10), 1389–1417. https://doi.org/10.1093/cercor/bhj076

Ruehl, R. M., Flanagin, V. L., Ophey, L., Raiser, T. M., Seiderer, K., Ertl, M., Conrad, J., & zu Eulenburg, P. (2022). The human egomotion network. *NeuroImage*, **264**, 119715. https://doi.org/10.1016/j.neuroimage.2022.119715

Saito, H., Yukie, M., Tanaka, K., Hikosaka, K., Fukada, Y., & Iwai, E. (1986). Integration of direction signals of image motion in the superior temporal sulcus of the macaque monkey. *The Journal of Neuroscience*, **6**(1), 145–157. https://doi.org/10.1523/jneurosci.06-01-00145.1986

Schlack, A., Hoffmann, K., & Bremmer, F. (2002). Interaction of linear vestibular and visual stimulation in the macaque ventral intraparietal area (VIP). *European Journal of Neuroscience*, **16**(10), 1877–1886. https://doi.org/10.1046/j.1460-9568.2002.02251.x

Seemungal, B. M., Glasauer, S., Gresty, M. A., & Bronstein, A. M. (2007). Vestibular perception and navigation in the congenitally blind. *Journal of Neurophysiology*, **97**(6), 4341–4356. https://doi.org/10.1152/jn.01321.2006

Seidlitz, J., Váša, F., Shinn, M., Romero-Garcia, R., Whitaker, K. J., Vértes, P. E., Wagstyl, K., Kirkpatrick Reardon, P., Clasen, L., Liu, S., Messinger, A., Leopold, D. A., Fonagy, P., Dolan, R. J., Jones, P. B., Goodyer, I. M., NSPN Consortium, Raznahan, A., & Bullmore, E. T. (2018). Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. *Neuron*, **97**(1), 231–247.e7. https://doi.org/10.1016/j.neuron.2017.11.039

Séverac Cauquil, A., Faldon, M., Popov, K., Day, B. L., & Bronstein, A. M. (2003). Short-latency eye movements evoked by near-threshold galvanic vestibular stimulation. *Experimental Brain Research*, **148**(3), 414–418. https://doi.org/10.1007/s00221-002-1326-z

Smiley, J. F., & Falchier, A. (2009). Multisensory connections of monkey auditory cerebral cortex. *Hearing Research*, **258**(1), 37–46. https://doi.org/10.1016/j.heares.2009.06.019

Smith, A. T. (2021). Cortical visual area CSv as a cingulate motor area: A sensorimotor interface for the control of locomotion. *Brain Structure & Function*, **226**(9), 2931–2950. https://doi.org/10.1007/s00429-021-02325-5

Smith, A. T., Beer, A. L., Furlan, M., & Mars, R. B. (2018). Connectivity of the cingulate sulcus visual area (CSv) in the human cerebral cortex. *Cerebral Cortex (New York, N.Y.: 1991)*, **28**(2), 713–725. https://doi.org/10.1093/cercor/bhx002

Smith, A. T., Greenlee, M. W., DeAngelis, G. C., & Angelaki, D. E. (2017). Distributed visual-vestibular processing in the cerebral cortex of man and macaque. *Multisensory Research*, **30**(2), 91–120. https://doi.org/10.1163/22134808-00002568

Smith, A. T., Wall, M. B., & Thilo, K. V. (2012). Vestibular inputs to human motion-sensitive visual cortex. *Cerebral Cortex (New York, N.Y.: 1991)*, **22**(5), 1068–1077. https://doi.org/10.1093/cercor/bhr179

Smith, A. T., Wall, M. B., Williams, A. L., & Singh, K. D. (2006). Sensitivity to optic flow in human cortical areas MT and MST. *The European Journal of Neuroscience*, **23**(2), 561–569. https://doi.org/10.1111/j.1460-9568.2005.04526.x

Snyder, L. H., Grieve, K. L., Brotchie, P., & Andersen, R. A. (1998). Separate body- and world-referenced representations of visual space in parietal cortex. *Nature*, **394**(6696), 887–891. https://doi.org/10.1038/29777

Stephan, T., Deutschländer, A., Nolte, A., Schneider, E., Wiesmann, M., Brandt, T., & Dieterich, M. (2005).
Functional MRI of galvanic vestibular stimulation with alternating currents at different frequencies. *NeurolImage*, 26(3), 721-732. https://doi.org/10.1016/j.neuroimage.2005.02.049

Sypré, L., Durand, J.-B., & Nelissen, K. (2023). Functional characterization of macaque insula using task-based and resting-state fMRI. *NeurolImage*, 276, 120217. https://doi.org/10.1016/j.neuroimage.2023.120217

Takahashi, K., Gu, Y., May, P. J., Newlands, S. D., DeAngelis, G. C., & Angelaki, D. E. (2007). Multimodal coding of three-dimensional rotation and translation in area MSTd: Comparison of visual and vestibular selectivity. *The Journal of Neuroscience*, 27(36), 9742-9756. https://doi.org/10.1523/jneurosci.0817-07.2007

Tanaka, K., Hikosaka, K., Saito, H., Yukie, M., Fukada, Y., & Iwai, E. (1986). Analysis of local and wide-field movements in the superior temporal visual areas of the macaque monkey. *The Journal of Neuroscience: The Official Journal of the Society for Neuroscience*, 6(1), 134-144. https://doi.org/10.1523/JNEUROSCI.06-01-00134.1986

Tanaka, K., & Saito, H. (1989). Analysis of motion of the visual field by direction, expansion/contraction, and rotation cells clustered in the dorsal part of the medial superior temporal area of the macaque monkey. *Journal of Neurophysiology*, 62(3), 626-641. https://doi.org/10.1152/jn.1989.62.3.626

Tanaka, M., & Lisberger, S. G. (2001). Regulation of the gain of visually guided smooth-pursuit eye movements by frontal cortex. *Nature*, 409(6817), 191-194. https://doi.org/10.1038/35051582

Valter, Y., Vataksi, L., Allred, A. R., Hebert, J. R., Brunyé, T. T., Clark, T. K., Serrador, J., & Datta, A. (2025). A review of parameter settings for galvanic vestibular stimulation in clinical applications. *Frontiers in Human Neuroscience*, 19. https://doi.org/10.3389/fnhum.2025.1518727

van Alst, T. M., Wachsmuth, L., Datunashvili, M., Albers, F., Just, N., Budde, T., & Faber, C. (2019). Anesthesia differentially modulates neuronal and vascular

contributions to the BOLD signal. *NeurolImage*, 195, 89-103. https://doi.org/10.1016/j.neuroimage.2019.03.057

Van Essen, D. C., Smith, J., Glasser, M. F., Elam, J., Donahue, C. J., Dierker, D. L., Reid, E. K., Coalson, T., & Harwell, J. (2017). The brain analysis library of spatial maps and Atlases (BALSA) database. *NeurolImage*, 144(Pt B), 270-274. https://doi.org/10.1016/j.neuroimage.2016.04.002

Vanduffel, W. (2014). *Functional MRI of awake behaving macaques using standard equipment*. IntechOpen. https://doi.org/10.5772/58281

Wall, M. B., & Smith, A. T. (2008). The representation of egomotion in the human brain. *Current Biology: CB*, 18(3), 191-194. https://doi.org/10.1016/j.cub.2007.12.053

Wirth, A. M., Frank, S. M., Greenlee, M. W., & Beer, A. L. (2018). White matter connectivity of the visual-vestibular cortex examined by diffusion-weighted imaging. *Brain Connectivity*, 8(4), 235-244. https://doi.org/10.1089/brain.2017.0544

Yang, L., & Gu, Y. (2017). Distinct spatial coordinate of visual and vestibular heading signals in macaque FEFsem and MSTd. *eLife*, 6, e29809. https://doi.org/10.7554/eLife.29809

Zeng, Z., Zhang, C., & Gu, Y. (2023). Visuo-vestibular heading perception: A model system to study multisensory decision making. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 378(1886), 20220334. https://doi.org/10.1098/rstb.2022.0334

Zhang, T., & Britten, K. H. (2011). Parietal area VIP causally influences heading perception during pursuit eye movements. *The Journal of Neuroscience*, 31(7), 2569-2575. https://doi.org/10.1523/jneurosci.5520-10.2011

zu Eulenburg, P., Caspers, S., Roski, C., & Eickhoff, S. B. (2012). Meta-analytical definition and functional connectivity of the human vestibular cortex. *NeurolImage*, 60(1), 162-169. https://doi.org/10.1016/j.neuroimage.2011.12.032