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•We mapped patches in visual temporal cortex that are activated by dynamic bodies.•The body patch network is more extensive for dynamic than static bodies.•Response to dynamic bodies in upper and lower bank of the superior temporal sulcus.•Stronger effect of dynamics in body patches than in neighboring face patches. Previous functional imaging studies demonstrated body-selective patches in the primate visual temporal cortex, comparing activations to static bodies and static images of other categories. However, the use of static instead of dynamic displays of moving bodies may have underestimated the extent of the body patch network. Indeed, body dynamics provide information about action and emotion and may be processed in patches not activated by static images. Thus, to map with fMRI the full extent of the macaque body patch system in the visual temporal cortex, we employed dynamic displays of natural-acting monkey bodies, dynamic monkey faces, objects, and scrambled versions of these videos, all presented during fixation. We found nine body patches in the visual temporal cortex, starting posteriorly in the superior temporal sulcus (STS) and ending anteriorly in the temporal pole. Unlike for static images, body patches were present consistently in both the lower and upper banks of the STS. Overall, body patches showed a higher activation by dynamic displays than by matched static images, which, for identical stimulus displays, was less the case for the neighboring face patches. These data provide the groundwork for future single-unit recording studies to reveal the spatiotemporal features the neurons of these body patches encode. These fMRI findings suggest that dynamics have a stronger contribution to population responses in body than face patches.

In primates, faces and bodies activate distinct regions in the inferior temporal (IT) cortex and are typically studied separately. Yet, primates interact with whole agents and not with random concatenations of faces and bodies. Despite its social importance, it is still poorly understood how faces and bodies interact in IT. Here, we addressed this gap by measuring fMRI activations to whole agents and to unnatural face-body configurations in which the head was mislocated with respect to the body, and examined how these relate to the sum of the activations to their corresponding faces and bodies. First, we mapped patches in the IT of awake macaques that were activated more by images of whole monkeys compared to objects and found that these mostly overlapped with body and face patches. In a second fMRI experiment, we obtained no evidence for superadditive responses in these “monkey patches”, with the activation to the monkeys being less or equal to the summed face-body activations. However, monkey patches in the anterior IT were activated more by natural compared to unnatural configurations. The stronger activations to natural configurations could not be explained by the summed face-body activations. These univariate results were supported by regression analyses in which we modeled the activations to both configurations as a weighted linear combination of the activations to the faces and bodies, showing higher regression coefficients for the natural compared to the unnatural configurations. Deeper layers of trained convolutional neural networks also contained units that responded more to natural compared to unnatural monkey configurations. Unlike the monkey fMRI patches, these units showed substantial superadditive responses to the natural configurations. Our monkey fMRI data suggest configuration-sensitive face-body interactions in anterior IT, adding to the evidence for an integrated face-body processing in the primate ventral visual stream, and open the way for mechanistic studies using single unit recordings in these patches.

In humans, several neuroimaging studies have demonstrated that passive viewing of optic flow stimuli activates higher-level motion areas, like V6 and the cingulate sulcus visual area (CSv). In macaque, there are few studies on the sensitivity of V6 and CSv to egomotion compatible optic flow. The only fMRI study on this issue revealed selectivity to egomotion compatible optic flow in macaque CSv but not in V6 (Cotterau et al. Cereb Cortex 27(1):330–343, 2017, but see Fan et al. J Neurosci. 35:16303–16314, 2015). Yet, it is unknown whether monkey visual motion areas MT + and V6 display any distinctive fMRI functional profile relative to the optic flow stimulation, as it is the case for the homologous human areas (Pitzalis et al., Cereb Cortex 20(2):411–424, 2010). Here, we described the sensitivity of the monkey brain to two motion stimuli (radial rings and flow fields) originally used in humans to functionally map the motion middle temporal area MT + (Tootell et al. J Neurosci 15: 3215-3230, 1995a; Nature 375:139–141, 1995b) and the motion medial parietal area V6 (Pitzalis et al. 2010), respectively. In both animals, we found regions responding only to optic flow or radial rings stimulation, and regions responding to both stimuli. A region in the parieto-occipital sulcus (likely including V6) was one of the most highly selective area for coherently moving fields of dots, further demonstrating the power of this type of stimulation to activate V6 in both humans and monkeys. We did not find any evidence that putative macaque CSv responds to Flow Fields.

Animals of several species, including primates, learn statistical regularities of their environment. In particular, they learn temporal regularities that occur in streams of visual images. Previous human neuroimaging studies reported discrepant effects of such statistical learning, ranging from stronger occipito-temporal activations for sequences in which image order was fixed, compared with sequences of randomly ordered images, to weaker activations for fixed order sequences compared with sequences that violated the learned order. Several single unit studies in macaque monkeys reported that after statistical learning of temporal regularities, inferior temporal neurons show reduced responses to learned fixed order sequences of visual images compared with random or mispredicted sequences. However, it is unknown how other macaque brain areas respond to such temporal statistical regularities. To address this gap, we exposed rhesus monkeys (Macaca mulatta) to two types of sequences of complex images. “Regular” sequences consisted of a continuous stream of quartets, and within each quartet, image order was fixed. The quartets themselves were displayed, uninterrupted, in random order. The same monkeys were exposed to sequences of other images, having a pseudorandomized order (“Random” sequence). After exposure, both monkeys were scanned with fMRI using a block design with 3 conditions: regular sequence, random sequence and fixation-only blocks. A whole brain analysis showed a reduced activation in mainly occipito-temporal cortex for the regular compared to the random sequences. Marked response reductions for the regular sequence were observed in early extrastriate visual cortical areas, including area V2, despite the use of rather complex images of animals. These data suggest that statistical learning signals are already present in early visual areas of monkeys, even for complex visual images. These monkey fMRI data are in line with recent human fMRI studies that showed a reduced activation in early visual areas for predicted compared with mispredicted complex images.

The proposal that sensory processing is achieved in segregated anatomical pathways has been profoundly revisited following the description of cross-modal anatomical connections both at higher and at lower processing levels. However, an understanding of the cortical extent of these long range cross-modal functional influences has been missing. Here, we use functional magnetic resonance imaging (fMRI) to map, in the non-human primate brain, the cortical regions which are activated by both visual and tactile stimulations. We describe an unprecedented pattern of functional visuo-tactile convergence, encompassing both low-level visual and somatosensory areas and multiple higher-order associative areas. We also show that the profile of this convergence depends on the physical properties of the mapping stimuli, indicating that visuo-tactile convergence is most probably even more prevailing than what we actually describe. Overall, these observations substantiate the view that the brain is massively multisensory. •fMRI mapping of widespread monkey visuo-tactile (VT) convergence cortical network•VT convergence encompasses low-level sensory and high-level associative areas.•VT convergence depends on the physical properties of the mapping stimuli.•Our observations substantiate the view that the brain is massively multisensory.

Primates use gaze cues to follow peer gaze to an object of joint attention. Gaze following of monkeys is largely determined by head or face orientation. We used fMRI in rhesus monkeys to identify brain regions underlying head gaze following and to assess their relationship to the ‘face patch’ system, the latter being the likely source of information on face orientation. We trained monkeys to locate targets by either following head gaze or using a learned association of face identity with the same targets. Head gaze following activated a distinct region in the posterior STS, close to-albeit not overlapping with-the medial face patch delineated by passive viewing of faces. This ‘gaze following patch’ may be the substrate of the geometrical calculations needed to translate information on head orientation from the face patches into precise shifts of attention, taking the spatial relationship of the two interacting agents into account. Gaze following—working out where someone else is looking, and then switching your attention to that position—is an important part of social behavior and learning. Additionally, it is thought to be an important step towards recognizing that others have a mind of their own. Humans mostly use eye position to work out the ‘gaze direction’ of someone else, whereas non-human primates rely instead on the orientation of the face. However, the neural circuits that control gaze following are thought to be similar in both. Gaze following is a complex process that requires the brain to process a lot of different information. A face must be recognized, and its orientation worked out. A series of complex geometrical calculations must then be performed to work out the direction of the gaze, and how this relates to the position of the observer. Finally, the object of interest needs to be recognized and the attention of the observer focused on it. In the monkey brain, there are six interconnected areas called face patch regions that respond when a monkey is shown a face. However, researchers do not understand how monkeys translate the information about face orientation gathered by these regions into information about where to look during gaze following. Marciniak et al. performed functional magnetic resonance imaging on monkeys to track the flow of blood to different regions of the brain—the higher the blood flow, the more that area of the brain is working. To identify the location of their face patch regions, the monkeys first looked at faces. When the monkeys then performed a gaze following task, a region of the brain close to—but not overlapping—the face patches was activated. Marciniak et al. suggest this is the ‘gaze following patch’ where the brain performs the demanding calculations to translate face orientation into a position to look at. As gaze following is important in social interactions, understanding the neural circuits behind it could help us understand social disorders.

Recognition is strongly impaired when the normal contrast polarity of faces is reversed. For instance, otherwise-familiar faces become very difficult to recognize when viewed as photographic negatives. Here, we used fMRI to demonstrate related properties in visual cortex: 1) fMRI responses in the human Fusiform Face Area (FFA) decreased strongly (26%) to contrast-reversed faces across a wide range of contrast levels (5.3–100% RMS contrast), in all subjects tested. In a whole brain analysis, this contrast polarity bias was largely confined to the Fusiform Face Area (FFA; p<0.0001), with possible involvement of a left occipital face-selective region. 2) It is known that reversing facial contrast affects three image properties in parallel (absorbance, shading, and specular reflection). Here, comparison of FFA responses to those in V1 suggests that the contrast polarity bias is produced in FFA only when all three component properties were reversed simultaneously, which suggests a prominent non-linearity in FFA processing. 3) Across a wide range (180°) of illumination source angles, 3D face shapes without texture produced response constancy in FFA, without a contrast polarity bias. 4) Consistent with psychophysics, analogous fMRI biases for normal contrast polarity were not produced by non-face objects, with image statistics similar to the face stimuli. 5) Using fMRI, we also demonstrated a contrast polarity bias in awake behaving macaque monkeys, in the cortical region considered homologous to human FFA. Thus common cortical mechanisms may underlie facial contrast processing across ~25million years of primate evolution.

Declarative knowledge and experiences are represented in the association cortex and are recalled by reactivation of the neural representation. Electrophysiological experiments have revealed that associations between semantically linked visual objects are formed in neural representations in the temporal and limbic cortices. Memory traces are created by the reorganization of neural circuits. These regions are reactivated during retrieval and contribute to the contents of a memory. Two different types of retrieval signals are suggested as follows: automatic and active. One flows backward from the medial temporal lobe during the automatic retrieval process, whereas the other is conveyed as a top-down signal from the prefrontal cortex to the temporal cortex during the active retrieval process. By sending the top-down signal, the prefrontal cortex manipulates and organizes to-be-remembered information, devises strategies for retrieval and monitors the outcome. To further understand the neural mechanism of memory, the following two complementary views are needed: how the multiple cortical areas in the brain-wide network interact to orchestrate cognitive functions and how the properties of single neurons and their synaptic connections with neighbouring neurons combine to form local circuits and to exhibit the function of each cortical area. We will discuss some new methodological innovations that tackle these challenges.

•The NMT v2, a stereotaxically aligned symmetric macaque template, is introduced.•A new atlas (CHARM), defined on NMT v2, parcellates the cortex at six spatial scales.•AFNI's @animal_warper aligns and maps data between monkey anatomicals and templates.•AFNI's afni_proc.py facilitates monkey fMRI analysis with automated scripting and QC.•Demos of macaque task and resting state fMRI analysis with these tools are provided. Functional neuroimaging research in the non-human primate (NHP) has been advancing at a remarkable rate. The increase in available data establishes a need for robust analysis pipelines designed for NHP neuroimaging and accompanying template spaces to standardize the localization of neuroimaging results. Our group recently developed the NIMH Macaque Template (NMT), a high-resolution population average anatomical template and associated neuroimaging resources, providing researchers with a standard space for macaque neuroimaging . Here, we release NMT v2, which includes both symmetric and asymmetric templates in stereotaxic orientation, with improvements in spatial contrast, processing efficiency, and segmentation. We also introduce the Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM), a hierarchical parcellation of the macaque cerebral cortex with varying degrees of detail. These tools have been integrated into the neuroimaging analysis software AFNI to provide a comprehensive and robust pipeline for fMRI processing, visualization and analysis of NHP data. AFNI's new @animal_warper program can be used to efficiently align anatomical scans to the NMT v2 space, and afni_proc.py integrates these results with full fMRI processing using macaque-specific parameters: from motion correction through regression modeling. Taken together, the NMT v2 and AFNI represent an all-in-one package for macaque functional neuroimaging analysis, as demonstrated with available demos for both task and resting state fMRI.

A modified and extended version, HCPex, is provided of the surface-based Human Connectome Project-MultiModal Parcellation atlas of human cortical areas (HCP-MMP v1.0, Glasser et al. 2016). The original atlas with 360 cortical areas has been modified in HCPex for ease of use with volumetric neuroimaging software, such as SPM, FSL, and MRIcroGL. HCPex is also an extended version of the original atlas in which 66 subcortical areas (33 in each hemisphere) have been added, including the amygdala, thalamus, putamen, caudate nucleus, nucleus accumbens, globus pallidus, mammillary bodies, septal nuclei and nucleus basalis. HCPex makes available the excellent parcellation of cortical areas in HCP-MMP v1.0 to users of volumetric software, such as SPM and FSL, as well as adding some subcortical regions, and providing labelled coronal views of the human brain.