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1. A comprehensive macaque fMRI pipeline and hierarchical atlas

Author

Benjamin Jung, Paul A. Taylor, Jakob Seidlitz, Caleb Sponheim, Pierce Perkins, Leslie G. Ungerleider, Daniel Glen, and Adam Messinger

Subjects
Stereotaxic, Template, Alignment, Monkey, Nonhuman primate, fMRI analysis, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

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.

Published
2021
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2. U-net model for brain extraction: Trained on humans for transfer to non-human primates

Author

Xindi Wang, Xin-Hui Li, Jae Wook Cho, Brian E. Russ, Nanditha Rajamani, Alisa Omelchenko, Lei Ai, Annachiara Korchmaros, Stephen Sawiak, R. Austin Benn, Pamela Garcia-Saldivar, Zheng Wang, Ned H. Kalin, Charles E. Schroeder, R. Cameron Craddock, Andrew S. Fox, Alan C. Evans, Adam Messinger, Michael P. Milham, and Ting Xu

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Published
2021
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3. The Subcortical Atlas of the Rhesus Macaque (SARM) for neuroimaging

Author

Renée Hartig, Daniel Glen, Benjamin Jung, Nikos K. Logothetis, George Paxinos, Eduardo A. Garza-Villarreal, Adam Messinger, and Henry C. Evrard

Subjects
Segmentation, Anatomy, Subcortex, Cerebellum, Thalamus, Brainstem, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Digitized neuroanatomical atlases that can be overlaid onto functional data are crucial for localizing brain structures and analyzing functional networks identified by neuroimaging techniques. To aid in functional and structural data analysis, we have created a comprehensive parcellation of the rhesus macaque subcortex using a high-resolution ex vivo structural imaging scan. This anatomical scan and its parcellation were warped to the updated NIMH Macaque Template (NMT v2), an in vivo population template, where the parcellation was refined to produce the Subcortical Atlas of the Rhesus Macaque (SARM) with 210 primary regions-of-interest (ROIs). The subcortical parcellation and nomenclature reflect those of the 4th edition of the Rhesus Monkey Brain in Stereotaxic Coordinates (Paxinos et al., in preparation), rather than proposing yet another novel atlas. The primary ROIs are organized across six spatial hierarchical scales from small, fine-grained ROIs to broader composites of multiple ROIs, making the SARM suitable for analysis at different resolutions and allowing broader labeling of functional signals when more accurate localization is not possible. As an example application of this atlas, we have included a functional localizer for the dorsal lateral geniculate (DLG) nucleus in three macaques using a visual flickering checkerboard stimulus, identifying and quantifying significant fMRI activation in this atlas region. The SARM has been made openly available to the neuroimaging community and can easily be used with common MRI data processing software, such as AFNI, where the atlas has been embedded into the software alongside cortical macaque atlases.

Published
2021
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4. Combining brain perturbation and neuroimaging in non-human primates

Author

P. Christiaan Klink, Jean-François Aubry, Vincent P. Ferrera, Andrew S. Fox, Sean Froudist-Walsh, Béchir Jarraya, Elisa E. Konofagou, Richard J. Krauzlis, Adam Messinger, Anna S. Mitchell, Michael Ortiz-Rios, Hiroyuki Oya, Angela C. Roberts, Anna Wang Roe, Matthew F.S. Rushworth, Jérôme Sallet, Michael Christoph Schmid, Charles E. Schroeder, Jordy Tasserie, Doris Y. Tsao, Lynn Uhrig, Wim Vanduffel, Melanie Wilke, Igor Kagan, and Christopher I. Petkov

Subjects
Microstimulation, Optogenetics, Chemogenetics, Ultrasound, Lesion, Infrared, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Brain perturbation studies allow detailed causal inferences of behavioral and neural processes. Because the combination of brain perturbation methods and neural measurement techniques is inherently challenging, research in humans has predominantly focused on non-invasive, indirect brain perturbations, or neurological lesion studies. Non-human primates have been indispensable as a neurobiological system that is highly similar to humans while simultaneously being more experimentally tractable, allowing visualization of the functional and structural impact of systematic brain perturbation. This review considers the state of the art in non-human primate brain perturbation with a focus on approaches that can be combined with neuroimaging. We consider both non-reversible (lesions) and reversible or temporary perturbations such as electrical, pharmacological, optical, optogenetic, chemogenetic, pathway-selective, and ultrasound based interference methods. Method-specific considerations from the research and development community are offered to facilitate research in this field and support further innovations. We conclude by identifying novel avenues for further research and innovation and by highlighting the clinical translational potential of the methods.

Published
2021
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5. A collaborative resource platform for non-human primate neuroimaging

Author

Adam Messinger, Nikoloz Sirmpilatze, Katja Heuer, Kep Kee Loh, Rogier B. Mars, Julien Sein, Ting Xu, Daniel Glen, Benjamin Jung, Jakob Seidlitz, Paul Taylor, Roberto Toro, Eduardo A. Garza-Villarreal, Caleb Sponheim, Xindi Wang, R. Austin Benn, Bastien Cagna, Rakshit Dadarwal, Henry C. Evrard, Pamela Garcia-Saldivar, Steven Giavasis, Renée Hartig, Claude Lepage, Cirong Liu, Piotr Majka, Hugo Merchant, Michael P. Milham, Marcello G.P. Rosa, Jordy Tasserie, Lynn Uhrig, Daniel S. Margulies, and P. Christiaan Klink

Subjects
Open science, Resource sharing, Toolbox, Pipeline, Structural, Functional, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field .

Published
2021
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6. CIVET-Macaque: An automated pipeline for MRI-based cortical surface generation and cortical thickness in macaques

Author

Claude Lepage, Konrad Wagstyl, Benjamin Jung, Jakob Seidlitz, Caleb Sponheim, Leslie Ungerleider, Xindi Wang, Alan C. Evans, and Adam Messinger

Subjects
Surface-based morphometry, Surface registration, Rhesus monkey, Template, Atlas, Primate, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The MNI CIVET pipeline for automated extraction of cortical surfaces and evaluation of cortical thickness from in-vivo human MRI has been extended for processing macaque brains. Processing is performed based on the NIMH Macaque Template (NMT), as the reference template, with the anatomical parcellation of the surface following the D99 and CHARM atlases. The modifications needed to adapt CIVET to the macaque brain are detailed. Results have been obtained using CIVET-macaque to process the anatomical scans of the 31 macaques used to generate the NMT and another 95 macaques from the PRIME-DE initiative. It is anticipated that the open usage of CIVET-macaque will promote collaborative efforts in data collection and processing, sharing, and automated analyses from which the non-human primate brain imaging field will advance.

Published
2021
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7. Representation of attended versus remembered locations in prefrontal cortex.

Author

Mikhail A Lebedev, Adam Messinger, Jerald D Kralik, and Steven P Wise

Subjects
Biology (General), QH301-705.5
Abstract

A great deal of research on the prefrontal cortex (PF), especially in nonhuman primates, has focused on the theory that it functions predominantly in the maintenance of short-term memories, and neurophysiologists have often interpreted PF's delay-period activity in the context of this theory. Neuroimaging results, however, suggest that PF's function extends beyond the maintenance of memories to include aspects of attention, such as the monitoring and selection of information. To explore alternative interpretations of PF's delay-period activity, we investigated the discharge rates of single PF neurons as monkeys attended to a stimulus marking one location while remembering a different, unmarked location. Both locations served as potential targets of a saccadic eye movement. Although the task made intensive demands on short-term memory, the largest proportion of PF neurons represented attended locations, not remembered ones. The present findings show that short-term memory functions cannot account for all, or even most, delay-period activity in the part of PF explored. Instead, PF's delay-period activity probably contributes more to the process of attentional selection.

Published
2004
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9. The cortical and subcortical correlates of face pareidolia in the macaque brain

Author

Jessica Taubert, Susan G Wardle, Clarissa T Tardiff, Elissa A Koele, Susheel Kumar, Adam Messinger, and Leslie G Ungerleider

Subjects
Brain Mapping, Pattern Recognition, Visual, Cognitive Neuroscience, Animals, Humans, Original Manuscript, Experimental and Cognitive Psychology, General Medicine, Macaca mulatta, Magnetic Resonance Imaging, Illusions, Temporal Lobe
Abstract

Face detection is a foundational social skill for primates. This vital function is thought to be supported by specialized neural mechanisms; however, although several face-selective regions have been identified in both humans and nonhuman primates, there is no consensus about which region(s) are involved in face detection. Here, we used naturally occurring errors of face detection (i.e. objects with illusory facial features referred to as examples of ‘face pareidolia’) to identify regions of the macaque brain implicated in face detection. Using whole-brain functional magnetic resonance imaging to test awake rhesus macaques, we discovered that a subset of face-selective patches in the inferior temporal cortex, on the lower lateral edge of the superior temporal sulcus, and the amygdala respond more to objects with illusory facial features than matched non-face objects. Multivariate analyses of the data revealed differences in the representation of illusory faces across the functionally defined regions of interest. These differences suggest that the cortical and subcortical face-selective regions contribute uniquely to the detection of facial features. We conclude that face detection is supported by a multiplexed system in the primate brain.

Published
2022
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11. Toward next-generation primate neuroscience: A collaboration-based strategic plan for integrative neuroimaging

Author

David C. Van Essen, Caspar M. Schwiedrzik, Takuro Ikeda, Shaomin Zhang, Marcello G. P. Rosa, Ning Liu, Aidan Murphy, Li Min Chen, Pinglei Bao, Julia Lehman, Yuki Hori, Pengcheng Li, Julien Vezoli, Peter H. Rudebeck, Yao Meng, Julian 'Bene' Ramirez, Pierre Pouget, Guillermo Gallardo, Rogier B. Mars, Charles E. Schroeder, Minqing Jiang, Steve Frey, Michael P. Milham, Mohammad Hadi Aarabi, Pascal Belin, Patrick Friedrich, Bichan Wu, Hector Figueroa, Ye He, Charles L. Wilson, Melanie Wilke, Eunha Baeg, Fadila Hadj-Bouziane, Danny Garside, Marco Pagani, Ting-Yat Wong, Igor Kagan, Abdelhadi Essamlali, Bharat B. Biswal, Wasana Ediri Arachchi, Julio Villalon, Zheng Wang, Kacie Dougherty, Neo Sunhang Shi, Luciano Simone, Roberto Toro, Benjamin Jung, Masaki Fukunaga, Zhanguang Zuo, Loïc Magrou, Xiaowei Song, Kadharbatcha S. Saleem, Michele A. Basso, Eduardo A. Garza-Villarreal, Chihiro Yokoyama, Aaron Tanenbaum, Brian E. Russ, Alexandre Rosa Franco, Alison R. Weiss, Isabel Restrepo, Alan C. Evans, Lixia Gao, Nobuyuki Kimura, Augix Guohua Xu, Piotr Majka, Colline Poirier, Justine Cléry, Bassem Hiba, Alessandro Gozzi, Xiaojie Wang, Nick Upright, Stan Colcombe, Yang Gao, Won Mok Shim, Eduardo Rojas Hortelano, Takuya Hayashi, Anna S. Mitchell, Andrew F. Rossi, Itamar Kahn, Jorge Jaramillo, Henry C. Evrard, Xin Yumeng, Gregory Kiar, Sean Froudist-Walsh, Elise Roger, Roberto A. Gulli, Yufan Wang, Damien A. Fair, Yuguang Zhao, Stephen J. Sawiak, Boris C. Bernhardt, Ulysse Klatzmann, Ashkan Alvand, Kep Kee Loh, David Schaeffer, Virginie Sivan, Daniel S. Margulies, Carly M. Drzewiecki, Tomoko Sakai, Ting Xu, Cirong Liu, Essa Yacoub, Theresa M. Desrochers, Seok-Jun Hong, Sethu Boopathy, Reza Azadi, Lu Yuheng, Aarit Ahuja, Zhifeng Liang, Elena Borra, Fernanda Ponce, Robert Dahnke, Julien Sein, Li Deying, Jitendra Sharma, A.J. Mitchell, Roger Little, Luqi Cheng, Du Xiao, Choong-Wan Woo, Xinhui Li, Chris Petkov, Ruiliang Bai, D Zaldivar, Sheyla Mejia, Haidong D. Lu, Nikoloz Sirmpilatze, Diego Emanuel Ortuzar Martinez, Suzanne N. Haber, Catherine Elorette, Yue Cui, Michael Hawrylycz, Jerome Sallet, Wim Vanduffel, Daniel R. Glen, Ralph Adolphs, Dongrong Xu, Simon Clavagnier, Rakshit Dadarwal, Marzio Gerbella, Hannah Doyle, Ningrong Ye, Xiaojin Liu, Xinyu Liu, Quansheng He, Christopher R. Madan, Vikas Pareek, James Cavanaugh, Sze Chai Kwok, Zhang Ying, Sam Vickery, Xiaoguang Tian, Zhou Xufeng, Bevil R. Conway, Mark Postans, Wei-an Sheng, Gianfranco Chavez, Rober Boshra, Yuki Kikuchi, Michael Ortiz-Rios, Céline Amiez, Felix Hoffstaedter, Elizabeth A. Buffalo, Amy Howard, Hsin-Yi Lai, Marianne Duyck, Samy Rima, Froesel Mathilda, Towela Mvula, Guilherme Freches, Alfonso Fajardo, Maria de la Iglesia-Vaya, Ana Rita Ribeiro Gomes, Xiongjie Yu, Afonso C. Silva, Andrew S. Fox, Long Cao, Anna W. Roe, Meizhen Qian, David Meunier, Erika Raven, Nicola Palomero-Gallagher, Jordy Tasserie, Joonas A. Autio, Francois Chouinard-Decorte, Hank P. Jedema, Shasha Yue, Xinjian Li, Xiaodong Chen, Kathleen Rockland, Satoka Hashimoto Fujimoto, Amiez Celine, Melina Cordeau, Olivier Coulon, Ravi S. Menon, Sandra Gonzalez Torrecilla, Bjørg Elisabeth Kilavik, Adam Messinger, Hecheng Jin, Steven Giavasis, Pierce Perkins, Conor Liston, Yujie Hou, Jakob Seidlitz, Kelly Shen, Yvonne Bennett, Franck Lamberton, Maxime Gaudet-Trafit, Suliann Ben Hamed, Chris Klink, Sabine Kastner, Lucas R. Trambaiolli, Lucija Jankovic-Rapan, Atsushi Fujimoto, Nadira Yusif Rodriguez, Maeva Gacoin, Amir Shmuel, Katja Heuer, Austin K. Behel, Susann Boretius, Paul A. Taylor, Child Mind Institute, Institute of Neurosciences and Psychology [Glasgow], University of Glasgow, Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (ISC-MJ), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), University of Tübingen, Oregon Health and Science University [Portland] (OHSU), New York University [New York] (NYU), NYU System (NYU), Princeton Neuroscience Institute [Princeton], Consortium, PRIMatE Data and Resource Exchange (PRIME-DRE) Global Collaboration Workshop and, Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (CNC), Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Nencki Institute of Experimental Biology, Polska Akademia Nauk = Polish Academy of Sciences (PAN), Monash University [Clayton], Wellcome Trust Centre for Integrative Neuroimaging (WIN - FMRIB), University of Oxford, Donders Institute for Brain, Cognition and Behaviour, Radboud University [Nijmegen], National Institute of Mental Health (NIMH), Newcastle University [Newcastle], McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Laboratorium voor Neuro- en Psychofysiologie, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), and Washington University in Saint Louis (WUSTL)

Subjects
Strategic planning, 0303 health sciences, Open science, biology, Action, intention, and motor control, Resource exchange, [SCCO.NEUR]Cognitive science/Neuroscience, Neuroscience(all), General Neuroscience, Nonhuman primate, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, biology.animal, Primate, Beacon - Precision Imaging, ddc:610, Psychology, Neuroscience, ComputingMilieux_MISCELLANEOUS, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Contains fulltext : 239574.pdf (Publisher’s version ) (Closed access) Open science initiatives are creating opportunities to increase research coordination and impact in nonhuman primate (NHP) imaging. The PRIMatE Data and Resource Exchange community recently developed a collaboration-based strategic plan to advance NHP imaging as an integrative approach for multiscale neuroscience. 5 p.

Published
2022
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13. The Subcortical Atlas of the Rhesus Macaque (SARM) for Neuroimaging

Author

Benjamin Jung, Henry C. Evrard, Adam Messinger, Renée Hartig, Eduardo A. Garza-Villarreal, Nikos K. Logothetis, George Paxinos, and Daniel R. Glen

Subjects
Male, Data processing software, Computer science, Cognitive Neuroscience, Population, Neuroimaging, Neurosciences. Biological psychiatry. Neuropsychiatry, Macaque, 050105 experimental psychology, Functional networks, 03 medical and health sciences, Atlases as Topic, Segmentation, 0302 clinical medicine, Thalamus, Cerebellum, biology.animal, Animals, Humans, 0501 psychology and cognitive sciences, education, education.field_of_study, biology, Atlas (topology), 05 social sciences, Brain, Subcortex, biology.organism_classification, Macaca mulatta, Magnetic Resonance Imaging, Rhesus macaque, Neurology, Female, Anatomy, Brainstem, Neuroscience, 030217 neurology & neurosurgery, RC321-571
Abstract

Digitized neuroanatomical atlases that can be overlaid onto functional data are crucial for localizing brain structures and analyzing functional networks identified by neuroimaging techniques. To aid in functional and structural data analysis, we have created a comprehensive parcellation of the rhesus macaque subcortex using a high-resolution ex vivo structural imaging scan. This anatomical scan and its parcellation were warped to the updated NIMH Macaque Template (NMT v2), an in vivo population template, where the parcellation was refined to produce the Subcortical Atlas of the Rhesus Macaque (SARM) with 210 primary regions-of-interest (ROIs). The subcortical parcellation and nomenclature reflect those of the 4th edition of the Rhesus Monkey Brain in Stereotaxic Coordinates ( Paxinos et al., in preparation ), rather than proposing yet another novel atlas. The primary ROIs are organized across six spatial hierarchical scales from small, fine-grained ROIs to broader composites of multiple ROIs, making the SARM suitable for analysis at different resolutions and allowing broader labeling of functional signals when more accurate localization is not possible. As an example application of this atlas, we have included a functional localizer for the dorsal lateral geniculate (DLG) nucleus in three macaques using a visual flickering checkerboard stimulus, identifying and quantifying significant fMRI activation in this atlas region. The SARM has been made openly available to the neuroimaging community and can easily be used with common MRI data processing software, such as AFNI, where the atlas has been embedded into the software alongside cortical macaque atlases.

Published
2021

14. Separable neuronal contributions to covertly attended locations and movement goals in macaque frontal cortex

Author

Adam Messinger, Aldo Genovesio, Steven P. Wise, and Rossella Cirillo

Subjects
Frontal cortex, genetic structures, behavioral disciplines and activities, Macaque, Task (project management), 03 medical and health sciences, 0302 clinical medicine, biology.animal, Saccades, Animals, Prefrontal cortex, Research Articles, 030304 developmental biology, Neurons, 0303 health sciences, Multidisciplinary, biology, Movement (music), fungi, food and beverages, SciAdv r-articles, Cognition, Frontal Lobe, Psychological Science, nervous system, Covert, Saccade, Macaca, monkey, attention, motor, Psychology, Goals, Neuroscience, psychological phenomena and processes, 030217 neurology & neurosurgery, Research Article
Abstract

The monkey frontal cortex can flexibly direct covert spatial attention without tapping the circuitry for planning movements., We investigated the spatial representation of covert attention and movement planning in monkeys performing a task that used symbolic cues to decouple the locus of covert attention from the motor target. In the three frontal areas studied, most spatially tuned neurons reflected either where attention was allocated or the planned saccade. Neurons modulated by both covert attention and the motor plan were in the minority. Such dual-purpose neurons were especially rare in premotor and prefrontal cortex but were more common just rostral to the arcuate sulcus. The existence of neurons that indicate where the monkey was attending but not its movement goal runs counter to the idea that the control of spatial attention is entirely reliant on the neuronal circuits underlying motor planning. Rather, the presence of separate neuronal populations for each cognitive process suggests that endogenous attention is under flexible control and can be dissociated from motor intention.

Published
2021

15. U-net model for brain extraction: Trained on humans for transfer to non-human primates

Author

Michael P. Milham, Zheng Wang, Jae Wook Cho, Xinhui Li, Charles E. Schroeder, R. Austin Benn, Andrew S. Fox, Alisa Omelchenko, Pamela Garcia-Saldivar, Brian E. Russ, Nanditha Rajamani, Alan C. Evans, Xindi Wang, R. Cameron Craddock, Ned H. Kalin, Stephen J. Sawiak, Annachiara Korchmaros, Ting Xu, Lei Ai, and Adam Messinger

Subjects
Male, Computer science, Image Processing, Datasets as Topic, computer.software_genre, Macaque, Convolutional neural network, Medical and Health Sciences, 0302 clinical medicine, Computer-Assisted, Theoretical, Models, Image Processing, Computer-Assisted, biology, 05 social sciences, Brain, Middle Aged, Magnetic Resonance Imaging, Neurology, Biomedical Imaging, Female, RC321-571, Adult, Neural Networks, Cognitive Neuroscience, Image registration, Neurosciences. Biological psychiatry. Neuropsychiatry, Sample (statistics), Neuroimaging, Machine learning, 050105 experimental psychology, Article, 03 medical and health sciences, Computer, Young Adult, biology.animal, Animals, Humans, 0501 psychology and cognitive sciences, Neurology & Neurosurgery, business.industry, Deep learning, Psychology and Cognitive Sciences, Neurosciences, Models, Theoretical, Sample size determination, Data quality, Feasibility Studies, Macaca, Artificial intelligence, Neural Networks, Computer, business, computer, 030217 neurology & neurosurgery
Abstract

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Published
2020

16. U-Net Model for Brain Extraction: Trained on Humans for Transfer to Non-human Primates

Author

Nanditha Rajamani, Michael P. Milham, Brian E. Russ, Zheng Wang, Jae Wook Cho, Alan C. Evans, Andrew S. Fox, Lei Ai, Adam Messinger, Stephen J. Sawiak, Ting Xu, Xinhui Li, Ned H. Kalin, Alisa Omelchenko, R. Austin Benn, Charles E. Schroeder, Annachiara Korchmaros, Xindi Wang, R. Cameron Craddock, and Pamela Garcia-Saldivar

Subjects
biology, Computer science, business.industry, Deep learning, Image registration, Sample (statistics), Machine learning, computer.software_genre, Convolutional neural network, Macaque, Neuroimaging, Sample size determination, biology.animal, Data quality, Artificial intelligence, business, computer
Abstract

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20s~10min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Published
2020
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17. Subcortical Atlas of the Rhesus Macaque (SARM) for Magnetic Resonance Imaging

Author

Adam Messinger, Nikos K. Logothetis, Renée Hartig, Benjamin Jung, George Paxinos, Daniel R. Glen, Eduardo A. Garza-Villarreal, and Henry C. Evrard

Subjects
education.field_of_study, biology, medicine.diagnostic_test, Data processing software, Population, Magnetic resonance imaging, biology.organism_classification, Macaque, Functional networks, Rhesus macaque, medicine.anatomical_structure, Neuroimaging, Atlas (anatomy), biology.animal, medicine, education, Neuroscience
Abstract

Digitized neuroanatomical atlases are crucial for localizing brain structures and analyzing functional networks identified by magnetic resonance imaging (MRI). To aid in MRI data analysis, we have created a comprehensive parcellation of the rhesus macaque subcortex using a high-resolution ex vivo structural imaging scan. The structural scan and its parcellation were warped to the updated NIMH Macaque Template (NMT v2), an in vivo population template, where the parcellation was refined to produce the Subcortical Atlas of the Rhesus Macaque (SARM). The subcortical parcellation and nomenclature reflect those of the 4th edition of the Rhesus Monkey Brain in Stereotaxic Coordinates (RMBSC4; Paxinos et al., in preparation). The SARM features six parcellation levels, arranged hierarchically from fine regions-of-interest (ROIs) to broader composite regions, suited for fMRI studies. As a test, we ran a functional localizer for the dorsal lateral geniculate (DLG) nucleus in three macaques and found significant fMRI activation in this atlas region. The SARM has been made openly available to the neuroimaging community and can easily be used with common MR data processing software, such as AFNI, where the atlas can be embedded into the software alongside cortical macaque atlases.HighlightsWe present the Subcortical Atlas of the Rhesus Macaque (SARM).SARM provides a neuroanatomical reference frame for neuroimaging analysis.The entire subcortex is mapped, including the thalamus, basal ganglia, and brainstem.ROIs are grouped hierarchically, making SARM useful at multiple spatial resolutions.SARM is in the NMT v2 template space and complements the CHARM atlas for the cortex.

Published
2020
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18. A comprehensive macaque fMRI pipeline and hierarchical atlas

Author

Pierce Perkins, Paul A. Taylor, Jakob Seidlitz, Leslie G. Ungerleider, Daniel R. Glen, Caleb Sponheim, Adam Messinger, and Benjamin Jung

Subjects
education.field_of_study, Resting state fMRI, biology, Orientation (computer vision), business.industry, Computer science, Population, Pattern recognition, biology.organism_classification, Macaque, Visualization, Cortex (botany), Rhesus macaque, medicine.anatomical_structure, Neuroimaging, Functional neuroimaging, Cerebral cortex, biology.animal, medicine, Segmentation, Artificial intelligence, business, education
Abstract

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 (Seidlitz, Sponheim et al., 2018). 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 (Cox, 1996) 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.HighlightsThe 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.

Published
2020
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19. A comprehensive macaque fMRI pipeline and hierarchical atlas

Author

Benjamin Jung, Jakob Seidlitz, Paul A. Taylor, Leslie G. Ungerleider, Caleb Sponheim, Pierce Perkins, Adam Messinger, and Daniel R. Glen

Subjects
Male, Computer science, Cognitive Neuroscience, Population, Neurosciences. Biological psychiatry. Neuropsychiatry, Macaque, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Atlases as Topic, Neuroimaging, Nonhuman primate, Functional neuroimaging, biology.animal, Animals, 0501 psychology and cognitive sciences, Segmentation, education, Alignment, education.field_of_study, biology, Resting state fMRI, business.industry, Orientation (computer vision), Template, Functional Neuroimaging, 05 social sciences, fMRI analysis, Brain, Pattern recognition, Macaca mulatta, Magnetic Resonance Imaging, Visualization, Monkey, Neurology, Female, Artificial intelligence, business, Stereotaxic, 030217 neurology & neurosurgery, RC321-571
Abstract

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.

Published
2020

20. CIVET-Macaque: an automated pipeline for MRI-based cortical surface generation and cortical thickness in macaques

Author

Claude Lepage, Alan C. Evans, Xindi Wang, Leslie G. Ungerleider, Konrad Wagstyl, Jakob Seidlitz, Caleb Sponheim, Adam Messinger, and Benjamin Jung

Subjects
biology, Neuroimaging, Computer science, biology.animal, Civet, Primate, Cortical surface, biology.organism_classification, Macaque, Neuroscience, Pipeline (software)
Abstract

The MNI CIVET pipeline for automated extraction of cortical surfaces and evaluation of cortical thickness fromin-vivohuman MRI has been extended for processing macaque brains. Processing is performed based on the NIMH Macaque Template (NMT), as the reference template, with the anatomical parcellation of the surface following the D99 and CHARM atlases. The modifications needed to adapt CIVET to the macaque brain are detailed. Results have been obtained using CIVET-macaque to process the anatomical scans of the 31 macaques used to generate the NMT and another 95 macaques from the PRIME-DE initiative. It is anticipated that the open usage of CIVET-macaque will promote collaborative efforts in data collection and processing, sharing, and automated analyses from which the non-human primate brain imaging field will advance.

Published
2020
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21. A collaborative resource platform for non-human primate neuroimaging

Author

Pamela Garcia-Saldivar, Marcello G. P. Rosa, Daniel R. Glen, Michael P. Milham, Lynn Uhrig, Rogier B. Mars, Rakshit Dadarwal, Xindi Wang, Hugo Merchant, Jakob Seidlitz, Eduardo A. Garza-Villarreal, Ting Xu, Kep Kee Loh, Cirong Liu, Bastien Cagna, Piotr Majka, Henry C. Evrard, Jordy Tasserie, Claude Lepage, Daniel S. Margulies, Adam Messinger, Nikoloz Sirmpilatze, P. Christiaan Klink, Steven Giavasis, Julien Sein, R. Austin Benn, Katja Heuer, Benjamin Jung, Caleb Sponheim, Renée Hartig, Roberto Toro, and Paul A. Taylor

Subjects
0303 health sciences, 03 medical and health sciences, Open science, 0302 clinical medicine, Non human primate, Resource (project management), Neuroimaging, Computer science, Data science, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field. HighlightsO_LIPRIME-RE is a bottom-up open science platform for non-human primate neuroimaging. C_LIO_LIThe website hosts a wiki and info about analytical tools and open data repositories. C_LIO_LISpecialized tools address structural, functional & diffusion-weighted analysis. C_LIO_LIMultiple templates & atlases for several non-human primate species are highlighted. C_LIO_LICommunication channels facilitate international conversation and collaboration C_LI

Published
2020
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22. Accelerating the Evolution of Nonhuman Primate Neuroimaging

Author

Olivier Coulon, Michael P. Milham, Patrik Lindenfors, Karl-Heinz Nenning, Xiaojin Liu, Ravi S. Menon, Stephanie J. Forkel, Adam Messinger, Zheng Wang, Alexander Thiele, Luciano Simone, Benjamin Jung, Chika Sato, Jamie Nagy, Sean Froudist-Walsh, Kelvin Mok, Renée Hartig, Julien Sein, Alessandro Gozzi, Julien Vezoli, Tomoko Sakai, Lynn Uhrig, Martine Meunier, Christienne G. Damatac, Bonhwang Koo, Roberto Toro, Rogier B. Mars, Henrietta Howells, Lea Roumazeilles, Ming Zhan, Ann-Marie Mallon, Román Rossi-Pool, Elinor L. Sullivan, Yannick Becker, Doris Y. Tsao, Antoine Grigis, Lei Ai, Céline Amiez, Sara Wells, Reza Rajimehr, Aki Nikolaidis, Anna S. Mitchell, Simon M. Reader, Michele A. Basso, Béchir Jarraya, Amir Raz, Wim Vanduffel, Charles R.E. Wilson, Brian E. Russ, Christopher R. Madan, Orlin S. Todorov, Wasana Madushanka, Carole Guedj, Mark A. Pinsk, Clémentine Bodin, Hugo Merchant, Jennifer Nacef, Damien A. Fair, Anna W. Roe, Sze Chai Kwok, Stephen J. Sawiak, Essa Yacoub, Bastien Cagna, Kevin N. Laland, Wilbert Zarco, Charles E. Schroeder, Ting Xu, P. Christiaan Klink, Stanislas Dehaene, Takuya Hayashi, Matthew F. S. Rushworth, Amir Shmuel, Fadila Hadj-Bouziane, Katja Heuer, Ioana-Sabina Rautu, Andrew S. Fox, Austin Benn, Sabine Kastner, Thomas Brochier, Emmanuel Procyk, Marco Pagani, David C. Van Essen, Frank Q. Ye, Dirk Jan Ardesch, Régis Trapeau, Jakob Seidlitz, Marike Schiffer, Bassem Hiba, John H. Morrison, David A. Rudko, Paula L. Croxson, Patrick Friedrich, Augix Guohua Xu, Lazar Fleysher, Piotr Majka, Jonathan Smallwood, Aihua Chen, Timothy D. Griffiths, Fabien Balezeau, Stefan Everling, Michael C. Schmid, Robert Leech, Leslie G. Ungerleider, Mark G. Baxter, Afonso C. Silva, Clare Kelly, Zhi-ming Shen, Daniel S. Margulies, Mark J. Prescott, Pascal Belin, Erwin L. A. Blezer, Igor Kagan, Suliann Ben Hamed, David A. Leopold, Adrien Meguerditchian, Wendy Jarrett, Michel Thiebaut de Schotten, Nikoloz Sirmpilatze, Julia Sliwa, Henry Kennedy, Vikas Pareek, Yong-di Zhou, Michael Ortiz-Rios, Sherif Hamdy El-Gohary, Susann Boretius, Christopher I. Petkov, Pamela Garcia-Saldivar, Bella Williams, Jordy Tasserie, Hank P. Jedema, Jerome Sallet, Pieter R. Roelfsema, Winrich A. Freiwald, Eduardo A. Garza-Villarreal, Noam Harel, Caspar M. Schwiedrzik, Kevin Marche, Colline Poirier, Yang Gao, Henry C. Evrard, Ashkan Alvand, ANS - Cellular & Molecular Mechanisms, Laboratoire des Sciences de l'Information et des Systèmes (LSIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Centre National de la Recherche Scientifique (CNRS), Institut cellule souche et cerveau (SBRI), Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Collège de France - Chaire Psychologie cognitive expérimentale, Collège de France (CdF (institution)), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (ISC-MJ), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Consortium, PRIMatE Data Exchange Global Collaboration Workshop and, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Newcastle University [Newcastle], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Medical Oncology, Department of Internal Medicine, Università Cattolica del Sacro Cuore [Roma] (Unicatt), Voice Neurocognition Laboratory, University of Glasgow, Oregon Health and Science University [Portland] (OHSU), Manchester Royal Infirmary, University of Manchester [Manchester], Princeton Neuroscience Institute [Princeton], University of Pennsylvania [Philadelphia], National Institute of Mental Health (NIMH), Harvard Medical School [Boston] (HMS), Washington University in St Louis, Laboratoire de psychologie cognitive (LPC), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (CNC), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institute of Psychiatry, Psychology & Neuroscience, King's College London, King‘s College London, New York University [New York] (NYU), NYU System (NYU), State Key Laboratory of Novel Software Technology, University of Nanjing, Center for Nanotechnology Innovation, @NEST (CNI), National Enterprise for nanoScience and nanoTechnology (NEST), Scuola Normale Superiore di Pisa (SNS)-Scuola Universitaria Superiore Sant'Anna [Pisa] (SSSUP)-Istituto Italiano di Tecnologia (IIT)-Consiglio Nazionale delle Ricerche [Pisa] (CNR PISA)-Scuola Normale Superiore di Pisa (SNS)-Scuola Universitaria Superiore Sant'Anna [Pisa] (SSSUP)-Istituto Italiano di Tecnologia (IIT)-Consiglio Nazionale delle Ricerche [Pisa] (CNR PISA), Unité Analyse et Traitement de l'Information (UNATI), Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Saclay (COmUE), Institut cellule souche et cerveau (U846 Inserm - UCBL1), Royal Netherlands Academy of Arts and Sciences (KNAW), East China Normal University [Shangaï] (ECNU), The Computational, Cognitive and Clinical Neuroimaging Lab, Imperial College London, Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Medical Research Counc, Station de primatologie (SP), Centre National de la Recherche Scientifique (CNRS), Institute of Language, Communication and the Brain (ILCB), The University of Western Ontario, Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Institute of integrative biology (Liverpool), University of Liverpool, McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), University of Cambridge [UK] (CAM), Rockefeller University [New York], McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], Laboratory for Neuro- and Psychofysiology, katho, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of York [York, UK], Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Génétique Humaine et Fonctions Cognitives, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Tianjin University of Science and Technology (TUST), Child Mind Institute, Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (U1208 Inserm - UCBL1 / SBRI), Icahn School of Medicine at Mount Sinai [New York] (MSSM), University Medical Center [Utrecht], Radboud university [Nijmegen], Chaire Psychologie cognitive expérimentale, Centre de recherche en neurosciences de Lyon (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of St Andrews [Scotland], Stockholm University, Hôpital du Bocage, Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Princeton University, Utrecht University [Utrecht], Netherlands Institute for Neuroscience, Wellcome Trust Centre for Integrative Neuroimaging (WIN - FMRIB), University of Oxford [Oxford], National Institute of Environmental Health Sciences [Durham] (NIEHS-NIH), National Institutes of Health [Bethesda] (NIH), Oregon National Primate Research Center (ONPRC), California Institute of Technology (CALTECH), Johns Hopkins University (JHU), The PRIMatE Data Exchange (PRIME-DE) Global Collaboration Workshop and Consortium, ANR-16-CONV-0002,ILCB,ILCB: Institute of Language Communication and the Brain(2016), Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Complex Trait Genetics, Centre National de la Recherche Scientifique (CNRS)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Université de Toulon (UTLN)-Aix Marseille Université (AMU), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA), and Vanduffel, Wim

Subjects
Primates, 0301 basic medicine, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, education, Neuroimaging, Article, [SPI]Engineering Sciences [physics], 03 medical and health sciences, 0302 clinical medicine, London, Psychology, Animals, Humans, Sociology, ComputingMilieux_MISCELLANEOUS, Cognitive science, Science & Technology, Human Connectome Project, [SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, Action, intention, and motor control, Information Dissemination, General Neuroscience, Neurosciences, Brain, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Congresses as Topic, Nonhuman primate, 030104 developmental biology, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurosciences & Neurology, Life Sciences & Biomedicine, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery
Abstract

Contains fulltext : 217200.pdf (Publisher’s version ) (Closed access) Nonhuman primate neuroimaging is on the cusp of a transformation, much in the same way its human counterpart was in 2010, when the Human Connectome Project was launched to accelerate progress. Inspired by an open data-sharing initiative, the global community recently met and, in this article, breaks through obstacles to define its ambitions. 4 p.

Published
2020
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23. An Open Resource for Non-human Primate Imaging

Author

Jennifer Nacef, Kevin N. Laland, Wilbert Zarco, Charles E. Schroeder, Jamie Nagy, Julien Sein, P. Christiaan Klink, Kelvin Mok, Michael P. Milham, Stanislas Dehaene, Charles R.E. Wilson, Orlin S. Todorov, Erwin L. A. Blezer, Winrich A. Freiwald, Béchir Jarraya, Fadila Hadj-Bouziane, John H. Morrison, David A. Rudko, Zheng Wang, Essa Yacoub, Leslie G. Ungerleider, Brian E. Russ, Amir Shmuel, Emmanuel Procyk, Stefan Everling, Elinor L. Sullivan, Doris Y. Tsao, Noam Harel, Bassem Hiba, Thomas Brochier, Jakob Seidlitz, Sabine Kastner, Michael Ortiz Rios, Martine Meunier, Alexander Thiele, Carole Guedj, Paula L. Croxson, Sze Chai Kwok, Yong-di Zhou, Christopher I. Petkov, Ting Xu, Caspar M. Schwiedrzik, Frank Q. Ye, Fabien Balezeau, Aihua Chen, Ravi S. Menon, Adam Messinger, Mark G. Baxter, Matthew F. S. Rushworth, Sean Froudist-Walsh, Rogier B. Mars, Reza Rajimehr, Christienne G. Damatac, Bonhwang Koo, Lei Ai, Colline Poirier, David A. Leopold, Simon M. Reader, Daniel S. Margulies, Mark A. Pinsk, Benjamin Jung, Lazar Fleysher, Timothy D. Griffiths, Michael C. Schmid, Céline Amiez, Suliann Ben Hamed, Jerome Sallet, Pieter R. Roelfsema, Damian A. Fair, Patrik Lindenfors, Nathan S. Kline Institute for Psychiatric Research (NKI), New York State Office of Mental Health, Child Mind Institute, Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (SBRI), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Newcastle University [Newcastle], Icahn School of Medicine at Mount Sinai [New York] (MSSM), University Medical Center [Utrecht], Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), East China Normal University [Shangaï] (ECNU), Radboud University [Nijmegen], Neuroimagerie cognitive - Psychologie cognitive expérimentale (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Collège de France - Chaire Psychologie cognitive expérimentale, Collège de France (CdF (institution)), The University of Western Ontario, Oregon Health and Science University [Portland] (OHSU), Rockefeller University [New York], New York University [New York] (NYU), NYU System (NYU), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (ISC-MJ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), University of Minnesota Medical School, University of Minnesota System, National Institute of Mental Health (NIMH), Princeton Neuroscience Institute [Princeton], Royal Netherlands Academy of Arts and Sciences (KNAW), University of St Andrews [Scotland], National Institute of Neurological Disorders and Stroke [Bethesda] (NINDS), National Institutes of Health [Bethesda] (NIH), Stockholm University, Wellcome Trust Centre for Integrative Neuroimaging (WIN - FMRIB), University of Oxford, McConnell Brain Imaging Centre (MNI), Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada]-McGill University = Université McGill [Montréal, Canada], University of California [Davis] (UC Davis), University of California (UC), Princeton University, McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), Utrecht University [Utrecht], McGill University = Université McGill [Montréal, Canada], Netherlands Institute for Neuroscience, National Institute of Environmental Health Sciences [Durham] (NIEHS-NIH), Oregon National Primate Research Center (ONPRC), California Institute of Technology (CALTECH), Tianjin University of Science and Technology (TUST), Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota System-University of Minnesota System, Johns Hopkins University (JHU), Centre de Recherche en Éthique [UdeM - Montréal] (CREUM), Université de Montréal (UdeM), Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Columbia University College of Physicians and Surgeons, University of St Andrews. School of Biology, University of St Andrews. Centre for Biological Diversity, University of St Andrews. Scottish Oceans Institute, University of St Andrews. Institute of Behavioural and Neural Sciences, University of St Andrews. Centre for Social Learning & Cognitive Evolution, Netherlands Institute for Neuroscience (NIN), Equipe Impact, Centre de Recherche en Neurosciences de Lyon, INSERM U1028, CNRS UMR5292, Academic Medical Center, Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (U1208 Inserm - UCBL1 / SBRI - USC 1361 INRAE), Radboud university [Nijmegen], Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Saclay (COmUE)-Institut National de la Santé et de la Recherche Médicale (INSERM), Chaire Psychologie cognitive expérimentale, Centre de recherche en neurosciences de Lyon (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut des sciences cognitives Marc Jeannerod - Centre de neuroscience cognitive - UMR5229 (CNC), University of Oxford [Oxford], University of California, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Seidlitz, Jakob [0000-0002-8164-7476], Apollo - University of Cambridge Repository, Integrative Neurophysiology, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France, Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Neuroimagerie cognitive (UNICOG-U992), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Sud - Paris 11 (UP11), The Rockefeller University, McGill University-McGill University, McGill University, Centre de Recherche en Éthique de l'Université de Montréal (CREUM), Université de Montréal [Montréal], Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Ben Hamed, Suliann

Subjects
0301 basic medicine, Primates, Computer science, Neuroscience(all), [SDV]Life Sciences [q-bio], QH301 Biology, Datasets as Topic, Neuroimaging, Article, 03 medical and health sciences, QH301, 0302 clinical medicine, Resource (project management), All institutes and research themes of the Radboud University Medical Center, SDG 17 - Partnerships for the Goals, Connectome, Animals, Psychology, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Independent data, Biological sciences, ComputingMilieux_MISCELLANEOUS, Non human primate, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Neurology & Neurosurgery, Quality assessment, Action, intention, and motor control, Information Dissemination, General Neuroscience, Neurosciences, Perception, Action and Control [DI-BCB_DCC_Theme 2], Brain, DAS, Data science, Magnetic Resonance Imaging, 030104 developmental biology, Data exchange, Biomedical Imaging, Cognitive Sciences, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 030217 neurology & neurosurgery
Abstract

Summary Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets., Highlights • Openly shared, large non-human primate neuroimaging data resource • Multiple imaging modalities contributed from investigators around the world • Quality assessments of the dataset • Discussed pitfalls and challenges in analyzing the non-human primate MRI data, The PRIMatE Data Exchange (PRIME-DE) consortium is an open science resource for the neuroimaging community aiming to facilitate efforts to map the non-human primate connectome. It aggregates and shares anatomical, functional, and diffusion MRI datasets from laboratories throughout the world.

Published
2018
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24. Morphometric Similarity Networks Detect Microscale Cortical Organisation and Predict Inter-Individual Cognitive Variation

Author

Rafael Romero-Garcia, Maxwell Shinn, Peter B. Jones, Edward T. Bullmore, Peter Fonagy, Liv S. Clasen, Adam Messinger, Jakob Seidlitz, Raymond J. Dolan, Paul K. Reardon, Petra E. Vértes, Armin Raznahan, Siyuan Liu, Konrad Wagstyl, František Váša, Kirstie Whitaker, Ian M. Goodyer, David A. Leopold, the NSPN Consortium, Seidlitz, Jakob [0000-0002-8164-7476], Whitaker, Kirstie [0000-0001-8498-4059], Vertes, Petra [0000-0002-0992-3210], Jones, Peter [0000-0002-0387-880X], Goodyer, Ian [0000-0001-9183-0373], Bullmore, Edward [0000-0002-8955-8283], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Male, cytoarchitecture, Intelligence, multi-modal, Biology, Macaque, Article, 050105 experimental psychology, cross-species, Young Adult, 03 medical and health sciences, Cognition, 0302 clinical medicine, Neuroimaging, Similarity (network science), biology.animal, morphology, Neural Pathways, Animals, Humans, 0501 psychology and cognitive sciences, Cerebral Cortex, General Neuroscience, connectome, macaque, 05 social sciences, Magnetic Resonance Imaging, 030104 developmental biology, Variation (linguistics), Cytoarchitecture, IQ, Cortical network, gene expression, Connectome, Macaca, Female, Neuroscience, 030217 neurology & neurosurgery, MRI
Abstract

Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organization comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs with tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust, and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.

Published
2018
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25. An open resource for nonhuman primate imaging

Author

Matthew F. S. Rushworth, Lei Ai, Jennifer Nacef, Doris Y. Tsao, Michael P. Milham, John H. Morrison, David A. Leopold, Wilbert Zarco, Charles E. Schroeder, Frank Q. Ye, Michael Ortiz Rios, Jakob Seidlitz, Alexander Thiele, Winrich A. Freiwald, Reza Rajimehr, Mark G. Baxter, Sabine Kastner, Jerome Sallet, Ting Xu, Christopher I. Petkov, Colline Poirier, Timothy D. Griffiths, Rogier B. Mars, Bonhwang Koo, Michael C. Schmid, Paula L. Croxson, Leslie G. Ungerleider, Jamie Nagy, Noam Harel, Caspar M. Schwiedrzik, Mark A. Pinsk, Ravi S. Menon, Adam Messinger, Stefan Everling, Brian E. Russ, Christienne G. Damatac, Benjamin Jung, Essa Yacoub, Fabien Balezeau, and Daniel S. Margulies

Subjects
medicine.diagnostic_test, biology, Computer science, 05 social sciences, Magnetic resonance imaging, Macaque, Data science, 050105 experimental psychology, Nonhuman primate, 03 medical and health sciences, 0302 clinical medicine, Resource (project management), Neuroimaging, biology.animal, medicine, 0501 psychology and cognitive sciences, 030217 neurology & neurosurgery
Abstract

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience.Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMate Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 13 independent data collections aggregated across 11 sites (total = 98 macaque monkeys). We also outline the unique pitfalls and challenges that should be considered in the analysis of the non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

Published
2017
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26. Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation

Author

Edward T. Bullmore, Rafael Romero-Garcia, Peter B. Jones, Peter Fonagy, David A. Leopold, Liv S. Clasen, Petra E. Vértes, Ian M. Goodyer, František Váša, Raymond J. Dolan, Kirstie Whitaker, Jakob Seidlitz, Paul K. Reardon, Adam Messinger, Maxwell Shinn, and Armin Raznahan

Subjects
0303 health sciences, biology, business.industry, Cognition, Macaque, 03 medical and health sciences, 0302 clinical medicine, Variation (linguistics), Similarity (network science), Neuroimaging, Cortical network, biology.animal, Connectome, Artificial intelligence, business, Neuroscience, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

SummaryMacroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping, based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organisation comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs to tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.

Published
2017
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28. Comparing population receptive fields in human and macaque visual cortex

Author

Chris I. Baker, Elissa Koele, Clarissa James, Edward H. Silson, Benjamin Jung, Adam Messinger, Susheel Kumar, and Jessica Taubert

Subjects
Ophthalmology, education.field_of_study, Visual cortex, medicine.anatomical_structure, biology, Receptive field, biology.animal, Population, medicine, education, Neuroscience, Macaque, Sensory Systems
Published
2019
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31. Multitasking of attention and memory functions in the primate prefrontal cortex

Author

Steven P. Wise, Adam Messinger, Jerald D. Kralik, and Mikhail A. Lebedev

Subjects
Male, education.field_of_study, Working memory, General Neuroscience, Population, Prefrontal Cortex, Sensory system, Articles, Stimulus (physiology), Macaca mulatta, Receptive field, Memory, Human multitasking, Premovement neuronal activity, Animals, Attention, Prefrontal cortex, education, Psychology, Neuroscience, Photic Stimulation, Psychomotor Performance, Cognitive psychology
Abstract

In motor and sensory areas of cortex, neuronal activity often depends on the location of a movement target or a sensory stimulus, with each neuron tuned to a single part of space called a preferred direction (when motor) or a receptive field (when sensory). As we previously reported, some neurons in the monkey prefrontal cortex are tuned to two parts of space, which we interpreted as reflecting attention and working memory, respectively. Monkeys performed a behavioral task in which they attended to a visual stimulus at one location while remembering a second place, and these locations were varied from trial to trial to assess spatial tuning. Most spatially tuned neurons specialized in either attentional or mnemonic processing, but about one-third of the cells showed tuning for both. Here, we show that the latter population, called multitasking neurons, improves the encoding of both the attended and remembered locations. These neurons do so for three reasons: (1) the preferred directions for attention and for working memory usually differ (and often diametrically oppose one another), (2) they have stronger tuning than specialized cells, and (3) pairs of multitasking neurons represent these cognitive parameters more efficiently than pairs that include even a single specialized cell. These findings suggest that multitasking neurons provide a computational advantage for behaviors that place simultaneous demands on two or more cognitive processes.

Published
2009

32. Neural Correlates of Knowledge: Stable Representation of Stimulus Associations across Variations in Behavioral Performance

Author

Adam Messinger, Larry R. Squire, Thomas D. Albright, and Stuart M. Zola

Subjects
Male, Visual perception, Time Factors, Photic Stimulation, Neuroscience(all), Conditioning, Classical, Action Potentials, Cell Count, Stimulus (physiology), Choice Behavior, Article, Memory, medicine, Animals, Attention, Second-order stimulus, Visual Cortex, Temporal cortex, Neurons, Communication, Neural correlates of consciousness, Analysis of Variance, Behavior, Animal, business.industry, General Neuroscience, Association Learning, Content-addressable memory, Macaca mulatta, Visual cortex, medicine.anatomical_structure, business, Psychology, Neuroscience
Abstract

SummaryBehavioral responses to a sensory stimulus are often guided by associative memories. These associations remain intact even when other factors determine behavior. The substrates of associative memory should therefore be identifiable by neuronal responses that are independent of behavioral choices. We tested this hypothesis using a paired-associates task in which monkeys learned arbitrary associations between pairs of visual stimuli. We examined the activity of neurons in inferior temporal cortex as the animals prepared to choose a remembered stimulus from a visual display. The activity of some neurons (22%) depended on the monkey's behavioral choice; but for a novel class of neurons (54%), activity reflected the stimulus that the monkey was instructed to choose, regardless of the behavioral response. These neurons appear to represent memorized stimulus associations that are stable across variations in behavioral performance. In addition, many neurons (74%) were modulated by the spatial arrangement of the stimuli in the display.

Published
2005

33. Neuronal representations of stimulus associations develop in the temporal lobe during learning

Author

Thomas D. Albright, Larry R. Squire, Adam Messinger, and Stuart M. Zola

Subjects
Temporal cortex, Male, Neurons, Multidisciplinary, Visual perception, genetic structures, Sensory system, Stimulus (physiology), Biological Sciences, Macaca mulatta, Temporal Lobe, Temporal lobe, medicine.anatomical_structure, Cerebral cortex, Perirhinal cortex, Task Performance and Analysis, medicine, Animals, Learning, Psychology, Neuroscience, Photic Stimulation, Recognition memory
Abstract

Visual stimuli that are frequently seen together become associated in long-term memory, such that the sight of one stimulus readily brings to mind the thought or image of the other. It has been hypothesized that acquisition of such long-term associative memories proceeds via the strengthening of connections between neurons representing the associated stimuli, such that a neuron initially responding only to one stimulus of an associated pair eventually comes to respond to both. Consistent with this hypothesis, studies have demonstrated that individual neurons in the primate inferior temporal cortex tend to exhibit similar responses to pairs of visual stimuli that have become behaviorally associated. In the present study, we investigated the role of these areas in the formation of conditional visual associations by monitoring the responses of individual neurons during the learning of new stimulus pairs. We found that many neurons in both area TE and perirhinal cortex came to elicit more similar neuronal responses to paired stimuli as learning proceeded. Moreover, these neuronal response changes were learning-dependent and proceeded with an average time course that paralleled learning. This experience-dependent plasticity of sensory representations in the cerebral cortex may underlie the learning of associations between objects.

Published
2001

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