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Meyer EE, Martynek M, Kastner S, Livingstone MS, Arcaro MJ. Expansion of a conserved architecture drives the evolution of the primate visual cortex. Proc Natl Acad Sci U S A 2025; 122:e2421585122. [PMID: 39805017 DOI: 10.1073/pnas.2421585122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2024] [Accepted: 12/09/2024] [Indexed: 01/16/2025] Open
Abstract
Human brain evolution is marked by a disproportionate expansion of cortical regions associated with advanced perceptual and cognitive functions. While this expansion is often attributed to the emergence of novel specialized brain areas, modifications to evolutionarily conserved cortical regions also have been linked to species-specific behaviors. Distinguishing between these two evolutionary outcomes has been limited by the ability to make direct comparisons between species. Here, we addressed this limitation by examining the expansion of the human visual cortex relative to macaques using a common functional architecture: retinotopy. Our findings revealed that human visual cortex expansion is primarily driven by increases in the surface area of a visual map architecture present in macaques rather than an increase in the number of individual areas. This expansion was not uniform, with higher-order areas, particularly in the parietal cortex, exhibiting the largest growth. Comparisons between neonate and adult humans revealed that these relative areal size differences were already established at birth. A meta-analysis of neuroimaging studies indicated that the most expanded areas are associated with advanced cognitive functions beyond visual processing. These results suggest that human perceptual and cognitive adaptations may be rooted in the expansion of evolutionarily conserved cortical architecture, with modifications even in the sensory cortex contributing to the broader cognitive functions characteristic of human behavior.
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Affiliation(s)
- Emily E Meyer
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
| | - Marcelina Martynek
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104
| | - Sabine Kastner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544
| | | | - Michael J Arcaro
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
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2
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Qian M, Wang J, Gao Y, Chen M, Liu Y, Zhou D, Lu HD, Zhang X, Hu JM, Roe AW. Multiple loci for foveolar vision in macaque monkey visual cortex. Nat Neurosci 2025; 28:137-149. [PMID: 39639181 PMCID: PMC11706779 DOI: 10.1038/s41593-024-01810-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/14/2024] [Indexed: 12/07/2024]
Abstract
In humans and nonhuman primates, the central 1° of vision is processed by the foveola, a retinal structure that comprises a high density of photoreceptors and is crucial for primate-specific high-acuity vision, color vision and gaze-directed visual attention. Here, we developed high-spatial-resolution ultrahigh-field 7T functional magnetic resonance imaging methods for functional mapping of the foveolar visual cortex in awake monkeys. In the ventral pathway (visual areas V1-V4 and the posterior inferior temporal cortex), viewing of a small foveolar spot elicits a ring of multiple (eight) foveolar representations per hemisphere. This ring surrounds an area called the 'foveolar core', which is populated by millimeter-scale functional domains sensitive to fine stimuli and high spatial frequencies, consistent with foveolar visual acuity, color and achromatic information and motion. Thus, this elaborate rerepresentation of central vision coupled with a previously unknown foveolar core area signifies a cortical specialization for primate foveation behaviors.
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Affiliation(s)
- Meizhen Qian
- Department of Neurosurgery of the Second Affiliated Hospital & Liangzhu Laboratory of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- Zhejiang Key Laboratory of Research and Transformation for Major Neurosurgical Diseases, Hangzhou, China
| | - Jianbao Wang
- Department of Neurosurgery of the Second Affiliated Hospital & Liangzhu Laboratory of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- Zhejiang Key Laboratory of Research and Transformation for Major Neurosurgical Diseases, Hangzhou, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yang Gao
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
- College of Electrical Engineering, Zhejiang University, Hangzhou, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Ming Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yin Liu
- Department of Neurosurgery of the Second Affiliated Hospital & Liangzhu Laboratory of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Dengfeng Zhou
- Department of Neurosurgery of the Second Affiliated Hospital & Liangzhu Laboratory of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Haidong D Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaotong Zhang
- Department of Neurosurgery of the Second Affiliated Hospital & Liangzhu Laboratory of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China.
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China.
- Zhejiang Key Laboratory of Research and Transformation for Major Neurosurgical Diseases, Hangzhou, China.
- College of Electrical Engineering, Zhejiang University, Hangzhou, China.
| | - Jia Ming Hu
- Department of Neurosurgery of the Second Affiliated Hospital & Liangzhu Laboratory of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China.
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China.
- Zhejiang Key Laboratory of Research and Transformation for Major Neurosurgical Diseases, Hangzhou, China.
| | - Anna Wang Roe
- Department of Neurosurgery of the Second Affiliated Hospital & Liangzhu Laboratory of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China.
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China.
- Zhejiang Key Laboratory of Research and Transformation for Major Neurosurgical Diseases, Hangzhou, China.
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
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Shimaoka D, Wong YT, Rosa MGP, Price NSC. Naturalistic movies and encoding analysis define areal borders in marmoset third-tier visual cortex. Prog Neurobiol 2024; 240:102657. [PMID: 39103115 DOI: 10.1016/j.pneurobio.2024.102657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/24/2024] [Accepted: 07/30/2024] [Indexed: 08/07/2024]
Abstract
Accurate definition of the borders of cortical visual areas is essential for the study of neuronal processes leading to perception. However, data used for definition of areal boundaries have suffered from issues related to resolution, uniform coverage, or suitability for objective analysis, leading to ambiguity. Here, we present a novel approach that combines widefield optical imaging, presentation of naturalistic movies, and encoding model analysis, to objectively define borders in the primate extrastriate cortex. We applied this method to test conflicting hypotheses about the third-tier visual cortex, where areal boundaries have remained controversial. We demonstrate pronounced tuning preferences in the third-tier areas, and an organizational structure in which the dorsomedial area (DM) contains representations of both the upper and lower contralateral quadrants, and is located immediate anterior to V2. High-density electrophysiological recordings with a Neuropixels probe confirm these findings. Our encoding-model approach offers a powerful, objective way to disambiguate areal boundaries.
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Affiliation(s)
- Daisuke Shimaoka
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia.
| | - Yan Tat Wong
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia; Electrical and Computer Systems Engineering, Monash University, Clayton, Australia
| | - Marcello G P Rosa
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia
| | - Nicholas Seow Chiang Price
- Department of Physiology and Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia
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Abstract
The world is composed of objects, the ground, and the sky. Visual perception of objects requires solving two fundamental challenges: 1) segmenting visual input into discrete units and 2) tracking identities of these units despite appearance changes due to object deformation, changing perspective, and dynamic occlusion. Current computer vision approaches to segmentation and tracking that approach human performance all require learning, raising the question, Can objects be segmented and tracked without learning? Here, we show that the mathematical structure of light rays reflected from environment surfaces yields a natural representation of persistent surfaces, and this surface representation provides a solution to both the segmentation and tracking problems. We describe how to generate this surface representation from continuous visual input and demonstrate that our approach can segment and invariantly track objects in cluttered synthetic video despite severe appearance changes, without requiring learning.
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Klink PC, Chen X, Vanduffel V, Roelfsema P. Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex. eLife 2021; 10:67304. [PMID: 34730515 PMCID: PMC8641953 DOI: 10.7554/elife.67304] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 10/24/2021] [Indexed: 01/07/2023] Open
Abstract
Population receptive field (pRF) modeling is a popular fMRI method to map the retinotopic organization of the human brain. While fMRI-based pRF maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they represent. We addressed this question in awake nonhuman primates comparing whole-brain fMRI and large-scale neurophysiological recordings in areas V1 and V4 of the visual cortex. We examined the fits of several pRF models based on the fMRI blood-oxygen-level-dependent (BOLD) signal, multi-unit spiking activity (MUA), and local field potential (LFP) power in different frequency bands. We found that pRFs derived from BOLD-fMRI were most similar to MUA-pRFs in V1 and V4, while pRFs based on LFP gamma power also gave a good approximation. fMRI-based pRFs thus reliably reflect neuronal receptive field properties in the primate brain. In addition to our results in V1 and V4, the whole-brain fMRI measurements revealed retinotopic tuning in many other cortical and subcortical areas with a consistent increase in pRF size with increasing eccentricity, as well as a retinotopically specific deactivation of default mode network nodes similar to previous observations in humans.
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Affiliation(s)
| | - Xing Chen
- Vision and Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
| | | | - Pieter Roelfsema
- Vision and Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
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Audurier P, Héjja-Brichard Y, De Castro V, Kohler PJ, Norcia AM, Durand JB, Cottereau BR. Symmetry Processing in the Macaque Visual Cortex. Cereb Cortex 2021; 32:2277-2290. [PMID: 34617100 PMCID: PMC9113295 DOI: 10.1093/cercor/bhab358] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 11/30/2022] Open
Abstract
Symmetry is a highly salient feature of the natural world that is perceived by many species. In humans, the cerebral areas processing symmetry are now well identified from neuroimaging measurements. Macaque could constitute a good animal model to explore the underlying neural mechanisms, but a previous comparative study concluded that functional magnetic resonance imaging responses to mirror symmetry in this species were weaker than those observed in humans. Here, we re-examined symmetry processing in macaques from a broader perspective, using both rotation and reflection symmetry embedded in regular textures. Highly consistent responses to symmetry were found in a large network of areas (notably in areas V3 and V4), in line with what was reported in humans under identical experimental conditions. Our results suggest that the cortical networks that process symmetry in humans and macaques are potentially more similar than previously reported and point toward macaque as a relevant model for understanding symmetry processing.
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Affiliation(s)
- Pauline Audurier
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, 31052 Toulouse, France.,Centre National de la Recherche Scientifique, 31055 Toulouse, France
| | - Yseult Héjja-Brichard
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, 31052 Toulouse, France.,Centre National de la Recherche Scientifique, 31055 Toulouse, France
| | - Vanessa De Castro
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, 31052 Toulouse, France.,Centre National de la Recherche Scientifique, 31055 Toulouse, France
| | - Peter J Kohler
- Department of Psychology, York University, Toronto, ON M3J 1P3, Canada.,Centre for Vision Research, York University, Toronto, ON, M3J 1P3, Canada
| | - Anthony M Norcia
- Department of Psychology, Stanford University, Stanford, CA 94305, USA.,Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA
| | - Jean-Baptiste Durand
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, 31052 Toulouse, France.,Centre National de la Recherche Scientifique, 31055 Toulouse, France
| | - Benoit R Cottereau
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, 31052 Toulouse, France.,Centre National de la Recherche Scientifique, 31055 Toulouse, France
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Myelin densities in retinotopically defined dorsal visual areas of the macaque. Brain Struct Funct 2021; 226:2869-2880. [PMID: 34417886 PMCID: PMC8541961 DOI: 10.1007/s00429-021-02363-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 08/09/2021] [Indexed: 11/30/2022]
Abstract
The visuotopic organization of dorsal visual cortex rostral to area V2 in primates has been a longstanding source of controversy. Using sub-millimeter phase-encoded retinotopic fMRI mapping, we recently provided evidence for a surprisingly similar visuotopic organization in dorsal visual cortex of macaques compared to previously published maps in New world monkeys (Zhu and Vanduffel, Proc Natl Acad Sci USA 116:2306–2311, 2019). Although individual quadrant representations could be robustly delineated in that study, their grouping into hemifield representations remains a major challenge. Here, we combined in-vivo high-resolution myelin density mapping based on MR imaging (400 µm isotropic resolution) with fine-grained retinotopic fMRI to quantitatively compare myelin densities across retinotopically defined visual areas in macaques. Complementing previously documented differences in populational receptive-field (pRF) size and visual field signs, myelin densities of both quadrants of the dorsolateral posterior area (DLP) and area V3A are significantly different compared to dorsal and ventral area V3. Moreover, no differences in myelin density were observed between the two matching quadrants belonging to areas DLP, V3A, V1, V2 and V4, respectively. This was not the case, however, for the dorsal and ventral quadrants of area V3, which showed significant differences in MR-defined myelin densities, corroborating evidence of previous myelin staining studies. Interestingly, the pRF sizes and visual field signs of both quadrant representations in V3 are not different. Although myelin density correlates with curvature and anticorrelates with cortical thickness when measured across the entire cortex, exactly as in humans, the myelin density results in the visual areas cannot be explained by variability in cortical thickness and curvature between these areas. The present myelin density results largely support our previous model to group the two quadrants of DLP and V3A, rather than grouping DLP- with V3v into a single area VLP, or V3d with V3A+ into DM.
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Russ BE, Petkov CI, Kwok SC, Zhu Q, Belin P, Vanduffel W, Hamed SB. Common functional localizers to enhance NHP & cross-species neuroscience imaging research. Neuroimage 2021; 237:118203. [PMID: 34048898 PMCID: PMC8529529 DOI: 10.1016/j.neuroimage.2021.118203] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 05/15/2021] [Accepted: 05/24/2021] [Indexed: 11/25/2022] Open
Abstract
Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies and laboratories. To achieve these goals within the non-human primate (NHP) imaging community, there is a pressing need for the use of standardized and validated localizers that can be readily implemented across different groups. The goal of this paper is to provide an overview of the value of localizer protocols to imaging research and we describe a number of commonly used or novel localizers within NHPs, and keys to implement them across studies. As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers in NHP imaging. By coming together to collect large datasets across research group, implementing the same functional localizers, and sharing the localizers and data via PRIME-DE, it is now possible to fully test their robustness, selectivity and specificity. To do this, we reviewed a number of common localizers and we created a repository of well-established localizer that are easily accessible and implemented through the PRIME-RE platform.
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Affiliation(s)
- Brian E Russ
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, United States; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York City, NY, United States; Department of Psychiatry, New York University at Langone, New York City, NY, United States.
| | - Christopher I Petkov
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, United Kingdom
| | - Sze Chai Kwok
- Shanghai Key Laboratory of Brain Functional Genomics, Key Laboratory of Brain Functional Genomics Ministry of Education, Shanghai Key Laboratory of Magnetic Resonance, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu, China; NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai, China
| | - Qi Zhu
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France; Laboratory for Neuro-and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven, 3000, Belgium
| | - Pascal Belin
- Institut de Neurosciences de La Timone, Aix-Marseille Université et CNRS, Marseille, 13005, France
| | - Wim Vanduffel
- Laboratory for Neuro-and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven, 3000, Belgium; Leuven Brain Institute, KU Leuven, Leuven, 3000, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA 02144, United States.
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229, Université de Lyon - CNRS, France.
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