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Rolls ET, Deco G, Huang CC, Feng J. The connectivity of the human frontal pole cortex, and a theory of its involvement in exploit versus explore. Cereb Cortex 2024; 34:bhad416. [PMID: 37991264 DOI: 10.1093/cercor/bhad416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/23/2023] Open
Abstract
The frontal pole is implicated in humans in whether to exploit resources versus explore alternatives. Effective connectivity, functional connectivity, and tractography were measured between six human frontal pole regions and for comparison 13 dorsolateral and dorsal prefrontal cortex regions, and the 360 cortical regions in the Human Connectome Project Multi-modal-parcellation atlas in 171 HCP participants. The frontal pole regions have effective connectivity with Dorsolateral Prefrontal Cortex regions, the Dorsal Prefrontal Cortex, both implicated in working memory; and with the orbitofrontal and anterior cingulate cortex reward/non-reward system. There is also connectivity with temporal lobe, inferior parietal, and posterior cingulate regions. Given this new connectivity evidence, and evidence from activations and damage, it is proposed that the frontal pole cortex contains autoassociation attractor networks that are normally stable in a short-term memory state, and maintain stability in the other prefrontal networks during stable exploitation of goals and strategies. However, if an input from the orbitofrontal or anterior cingulate cortex that expected reward, non-reward, or punishment is received, this destabilizes the frontal pole and thereby other prefrontal networks to enable exploration of competing alternative goals and strategies. The frontal pole connectivity with reward systems may be key in exploit versus explore.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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2
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Rolls ET, Deco G, Huang CC, Feng J. Prefrontal and somatosensory-motor cortex effective connectivity in humans. Cereb Cortex 2022; 33:4939-4963. [PMID: 36227217 DOI: 10.1093/cercor/bhac391] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/07/2022] [Accepted: 09/08/2022] [Indexed: 11/12/2022] Open
Abstract
Effective connectivity, functional connectivity, and tractography were measured between 57 cortical frontal and somatosensory regions and the 360 cortical regions in the Human Connectome Project (HCP) multimodal parcellation atlas for 171 HCP participants. A ventral somatosensory stream connects from 3b and 3a via 1 and 2 and then via opercular and frontal opercular regions to the insula, which then connects to inferior parietal PF regions. This stream is implicated in "what"-related somatosensory processing of objects and of the body and in combining with visual inputs in PF. A dorsal "action" somatosensory stream connects from 3b and 3a via 1 and 2 to parietal area 5 and then 7. Inferior prefrontal regions have connectivity with the inferior temporal visual cortex and orbitofrontal cortex, are implicated in working memory for "what" processing streams, and provide connectivity to language systems, including 44, 45, 47l, TPOJ1, and superior temporal visual area. The dorsolateral prefrontal cortex regions that include area 46 have connectivity with parietal area 7 and somatosensory inferior parietal regions and are implicated in working memory for actions and planning. The dorsal prefrontal regions, including 8Ad and 8Av, have connectivity with visual regions of the inferior parietal cortex, including PGs and PGi, and are implicated in visual and auditory top-down attention.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.,Brain and Cognition, Pompeu Fabra University, Barcelona 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200602, China.,Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200602, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200403, China
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3
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Tanigawa H, Majima K, Takei R, Kawasaki K, Sawahata H, Nakahara K, Iijima A, Suzuki T, Kamitani Y, Hasegawa I. Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex. Cell Rep 2022; 39:110676. [PMID: 35417680 DOI: 10.1016/j.celrep.2022.110676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 02/08/2022] [Accepted: 03/22/2022] [Indexed: 11/19/2022] Open
Abstract
Sensory perception and memory recall generate different conscious experiences. Although externally and internally driven neural activities signifying the same perceptual content overlap in the sensory cortex, their distribution in the prefrontal cortex (PFC), an area implicated in both perception and memory, remains elusive. Here, we test whether the local spatial configurations and frequencies of neural oscillations driven by perception and memory recall overlap in the macaque PFC using high-density electrocorticography and multivariate pattern analysis. We find that dynamically changing oscillatory signals distributed across the PFC in the delta-, theta-, alpha-, and beta-band ranges carry significant, but mutually different, information predicting the same feature of memory-recalled internal targets and passively perceived external objects. These findings suggest that the frequency-specific distribution of oscillatory neural signals in the PFC serves cortical signatures responsible for distinguishing between different types of cognition driven by external perception and internal memory.
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Affiliation(s)
- Hisashi Tanigawa
- Department of Neurosurgery of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310016, China; Department of Physiology, Niigata University School of Medicine, Niigata, Niigata 951-8501, Japan; Center for Transdisciplinary Research, Niigata University, Niigata, Niigata 951-8501, Japan
| | - Kei Majima
- Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan; ATR Computational Neuroscience Laboratories, Keihanna Science City, Kyoto 619-0288, Japan
| | - Ren Takei
- Department of Bio-cybernetics, Faculty of Engineering, Niigata University, Niigata, Niigata 950-2181, Japan
| | - Keisuke Kawasaki
- Department of Physiology, Niigata University School of Medicine, Niigata, Niigata 951-8501, Japan
| | - Hirohito Sawahata
- Department of Physiology, Niigata University School of Medicine, Niigata, Niigata 951-8501, Japan; Department of Industrial Engineering, Mechanical and Control Engineering Course, National Institute of Technology (KOSEN), Ibaraki College, Hitachinaka, Ibaraki 312-8508, Japan
| | - Kiyoshi Nakahara
- Center for Transdisciplinary Research, Niigata University, Niigata, Niigata 951-8501, Japan; Research Center for Brain Communication, Kochi University of Technology, Kami, Kochi 782-8502, Japan
| | - Atsuhiko Iijima
- Department of Bio-cybernetics, Faculty of Engineering, Niigata University, Niigata, Niigata 950-2181, Japan
| | - Takafumi Suzuki
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Osaka 565-0871, Japan; Osaka University, Suita, Osaka 565-0871, Japan
| | - Yukiyasu Kamitani
- Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan; ATR Computational Neuroscience Laboratories, Keihanna Science City, Kyoto 619-0288, Japan
| | - Isao Hasegawa
- Department of Physiology, Niigata University School of Medicine, Niigata, Niigata 951-8501, Japan; Center for Transdisciplinary Research, Niigata University, Niigata, Niigata 951-8501, Japan.
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4
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Ylinen A, Wikman P, Leminen M, Alho K. Task-dependent cortical activations during selective attention to audiovisual speech. Brain Res 2022; 1775:147739. [PMID: 34843702 DOI: 10.1016/j.brainres.2021.147739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 10/21/2021] [Accepted: 11/21/2021] [Indexed: 11/28/2022]
Abstract
Selective listening to speech depends on widespread networks of the brain, but how the involvement of different neural systems in speech processing is affected by factors such as the task performed by a listener and speech intelligibility remains poorly understood. We used functional magnetic resonance imaging to systematically examine the effects that performing different tasks has on neural activations during selective attention to continuous audiovisual speech in the presence of task-irrelevant speech. Participants viewed audiovisual dialogues and attended either to the semantic or the phonological content of speech, or ignored speech altogether and performed a visual control task. The tasks were factorially combined with good and poor auditory and visual speech qualities. Selective attention to speech engaged superior temporal regions and the left inferior frontal gyrus regardless of the task. Frontoparietal regions implicated in selective auditory attention to simple sounds (e.g., tones, syllables) were not engaged by the semantic task, suggesting that this network may not be not as crucial when attending to continuous speech. The medial orbitofrontal cortex, implicated in social cognition, was most activated by the semantic task. Activity levels during the phonological task in the left prefrontal, premotor, and secondary somatosensory regions had a distinct temporal profile as well as the highest overall activity, possibly relating to the role of the dorsal speech processing stream in sub-lexical processing. Our results demonstrate that the task type influences neural activations during selective attention to speech, and emphasize the importance of ecologically valid experimental designs.
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Affiliation(s)
- Artturi Ylinen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
| | - Patrik Wikman
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Department of Neuroscience, Georgetown University, Washington D.C., USA
| | - Miika Leminen
- Analytics and Data Services, HUS Helsinki University Hospital, Helsinki, Finland
| | - Kimmo Alho
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University, Espoo, Finland
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5
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Abstract
Working memory (WM) is the ability to maintain and manipulate information in the conscious mind over a timescale of seconds. This ability is thought to be maintained through the persistent discharges of neurons in a network of brain areas centered on the prefrontal cortex, as evidenced by neurophysiological recordings in nonhuman primates, though both the localization and the neural basis of WM has been a matter of debate in recent years. Neural correlates of WM are evident in species other than primates, including rodents and corvids. A specialized network of excitatory and inhibitory neurons, aided by neuromodulatory influences of dopamine, is critical for the maintenance of neuronal activity. Limitations in WM capacity and duration, as well as its enhancement during development, can be attributed to properties of neural activity and circuits. Changes in these factors can be observed through training-induced improvements and in pathological impairments. WM thus provides a prototypical cognitive function whose properties can be tied to the spiking activity of brain neurons. © 2021 American Physiological Society. Compr Physiol 11:1-41, 2021.
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Affiliation(s)
- Russell J Jaffe
- Department of Neurobiology & Anatomy, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Christos Constantinidis
- Department of Neurobiology & Anatomy, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Neuroscience Program, Vanderbilt University, Nashville, Tennessee, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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6
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Intrinsic functional clustering of ventral premotor F5 in the macaque brain. Neuroimage 2020; 227:117647. [PMID: 33338618 DOI: 10.1016/j.neuroimage.2020.117647] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 12/04/2020] [Indexed: 11/21/2022] Open
Abstract
Neurophysiological and anatomical data suggest the existence of several functionally distinct regions in the lower arcuate sulcus and adjacent postarcuate convexity of the macaque monkey. Ventral premotor F5c lies on the postarcuate convexity and consists of a dorsal hand-related and ventral mouth-related field. The posterior bank of the lower arcuate contains two additional premotor F5 subfields at different anterior-posterior levels, F5a and F5p. Anterior to F5a, area 44 has been described as a dysgranular zone occupying the deepest part of the fundus of the inferior arcuate. Finally, area GrFO occupies the most rostral portion of the fundus and posterior bank of inferior arcuate and extends ventrally onto the frontal operculum. Recently, data-driven exploratory approaches using resting-state fMRI data have been suggested as a promising non-invasive method for examining the functional organization of the primate brain. Here, we examined to what extent partitioning schemes derived from data-driven clustering analysis of resting-state fMRI data correspond with the proposed organization of the fundus and posterior bank of the macaque arcuate sulcus, as suggested by invasive architectonical, connectional and functional investigations. Using a hierarchical clustering analysis, we could retrieve clusters corresponding to the dorsal and ventral portions of F5c on the postarcuate convexity, F5a and F5p at different antero-posterior locations on the posterior bank of the lower arcuate, area 44 in the fundus, as well as part of area GrFO in the most anterior portion of the fundus. Additionally, each of these clusters displayed distinct whole-brain functional connectivity, in line with previous anatomical tracer and seed-based functional connectivity investigations of F5/44 subdivisions. Overall, our data suggests that hierarchical clustering analysis of resting-state fMRI data can retrieve a fine-grained level of cortical organization that resembles detailed parcellation schemes derived from invasive functional and anatomical investigations.
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7
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Yacoub E, Grier MD, Auerbach EJ, Lagore RL, Harel N, Adriany G, Zilverstand A, Hayden BY, Heilbronner SR, Uğurbil K, Zimmermann J. Ultra-high field (10.5 T) resting state fMRI in the macaque. Neuroimage 2020; 223:117349. [PMID: 32898683 PMCID: PMC7745777 DOI: 10.1016/j.neuroimage.2020.117349] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 08/20/2020] [Accepted: 08/31/2020] [Indexed: 01/02/2023] Open
Abstract
Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for contrast agents limiting translatability, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, imaging at magnetic fields above 7T has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed single-subject resting state analysis at high resolutions using a 10.5 Tesla scanner. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI 'macaque connectome' will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.
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Affiliation(s)
- Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States
| | - Edward J Auerbach
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Russell L Lagore
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Gregor Adriany
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Anna Zilverstand
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55455, United States
| | - Benjamin Y Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Kamil Uğurbil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States.
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8
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He B, Cao L, Xia X, Zhang B, Zhang D, You B, Fan L, Jiang T. Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles. Neurosci Bull 2020; 36:1454-1473. [PMID: 33108588 PMCID: PMC7719154 DOI: 10.1007/s12264-020-00589-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/30/2020] [Indexed: 11/25/2022] Open
Abstract
The frontal pole cortex (FPC) plays key roles in various higher-order functions and is highly developed in non-human primates. An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results. This is important for understanding the functional architecture of the cerebral cortex. Here, combining cross-validation and principal component analysis, we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC (2 males and 6 females) into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4T Bruker system, and then revealed their subregional connections. Furthermore, we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions, and found that the dorsolateral FPC, which contains an extension to the medial FPC, was mainly connected to regions of the default-mode network. The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network. These results enhance our understanding of the anatomy and circuitry of the macaque brain, and contribute to FPC-related clinical research.
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Affiliation(s)
- Bin He
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Long Cao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiaoluan Xia
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Dan Zhang
- Core Facility, Center of Biomedical Analysis, Tsinghua University, Beijing, 100084, China
| | - Bo You
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, China. .,University of CAS, Beijing, 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, China. .,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China. .,The Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia. .,University of CAS, Beijing, 100049, China. .,Chinese Institute for Brain Research, Beijing, 102206, China.
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9
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Abstract
The asymmetry of the cerebral sulcal morphology is particularly obvious in higher primates. The sulcal asymmetry in macaque monkeys, a genus of the Old World monkeys, in our previous studies and others is summarized, and its evolutionary significance is speculated. Cynomolgus macaques displayed fetal sulcation and gyration symmetrically, and the sulcal asymmetry appeared after adolescence. Population-level rightward asymmetry was revealed in the length of arcuate sulcus (ars) and the surface area of superior temporal sulcus (sts) in adult macaques. When compared to other nonhuman primates, the superior postcentral sulcus (spcs) was left-lateralized in chimpanzees, opposite of the direction of asymmetry in the ars, anatomically-identical to the spcs, in macaques. This may be associated with handedness: either right-handedness in chimpanzees or left-handedness/ambidexterity in macaques. The rightward asymmetry in the sts surface area was seen in macaques, and it was similar to humans. However, no left/right side differences were identified in the sts morphology among great apes, which suggests the evolutionary discontinuity of the sts asymmetry. The diversity of the cortical lateralization among primate species suggests that the sulcal asymmetry reflects the species-related specialization of the cortical morphology and function, which is facilitated by evolutionary expansion in higher primates.
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10
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Archakov D, DeWitt I, Kuśmierek P, Ortiz-Rios M, Cameron D, Cui D, Morin EL, VanMeter JW, Sams M, Jääskeläinen IP, Rauschecker JP. Auditory representation of learned sound sequences in motor regions of the macaque brain. Proc Natl Acad Sci U S A 2020; 117:15242-15252. [PMID: 32541016 PMCID: PMC7334521 DOI: 10.1073/pnas.1915610117] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Human speech production requires the ability to couple motor actions with their auditory consequences. Nonhuman primates might not have speech because they lack this ability. To address this question, we trained macaques to perform an auditory-motor task producing sound sequences via hand presses on a newly designed device ("monkey piano"). Catch trials were interspersed to ascertain the monkeys were listening to the sounds they produced. Functional MRI was then used to map brain activity while the animals listened attentively to the sound sequences they had learned to produce and to two control sequences, which were either completely unfamiliar or familiar through passive exposure only. All sounds activated auditory midbrain and cortex, but listening to the sequences that were learned by self-production additionally activated the putamen and the hand and arm regions of motor cortex. These results indicate that, in principle, monkeys are capable of forming internal models linking sound perception and production in motor regions of the brain, so this ability is not special to speech in humans. However, the coupling of sounds and actions in nonhuman primates (and the availability of an internal model supporting it) seems not to extend to the upper vocal tract, that is, the supralaryngeal articulators, which are key for the production of speech sounds in humans. The origin of speech may have required the evolution of a "command apparatus" similar to the control of the hand, which was crucial for the evolution of tool use.
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Affiliation(s)
- Denis Archakov
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, FI-02150 Espoo, Finland
| | - Iain DeWitt
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Paweł Kuśmierek
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Michael Ortiz-Rios
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Daniel Cameron
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Ding Cui
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - Elyse L Morin
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057
| | - John W VanMeter
- Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington, DC 20057
| | - Mikko Sams
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, FI-02150 Espoo, Finland
| | - Iiro P Jääskeläinen
- Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, FI-02150 Espoo, Finland
| | - Josef P Rauschecker
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057;
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11
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Li S, Zhou X, Constantinidis C, Qi XL. Plasticity of Persistent Activity and Its Constraints. Front Neural Circuits 2020; 14:15. [PMID: 32528254 PMCID: PMC7247814 DOI: 10.3389/fncir.2020.00015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 03/26/2020] [Indexed: 11/13/2022] Open
Abstract
Stimulus information is maintained in working memory by action potentials that persist after the stimulus is no longer physically present. The prefrontal cortex is a critical brain area that maintains such persistent activity due to an intrinsic network with unique synaptic connectivity, NMDA receptors, and interneuron types. Persistent activity can be highly plastic depending on task demands but it also appears in naïve subjects, not trained or required to perform a task at all. Here, we review what aspects of persistent activity remain constant and what factors can modify it, focusing primarily on neurophysiological results from non-human primate studies. Changes in persistent activity are constrained by anatomical location, with more ventral and more anterior prefrontal areas exhibiting the greatest capacity for plasticity, as opposed to posterior and dorsal areas, which change relatively little with training. Learning to perform a cognitive task for the first time, further practicing the task, and switching between learned tasks can modify persistent activity. The ability of the prefrontal cortex to generate persistent activity also depends on age, with changes noted between adolescence, adulthood, and old age. Mean firing rates, variability and correlation of persistent discharges, but also time-varying firing rate dynamics are altered by these factors. Plastic changes in the strength of intrinsic network connections can be revealed by the analysis of synchronous spiking between neurons. These results are essential for understanding how the prefrontal cortex mediates working memory and intelligent behavior.
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Affiliation(s)
- Sihai Li
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Xin Zhou
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC, United States.,Department of Computer Science, Stanford University, Stanford, CA, United States
| | - Christos Constantinidis
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Xue-Lian Qi
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC, United States
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12
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Rolls ET. The orbitofrontal cortex and emotion in health and disease, including depression. Neuropsychologia 2019; 128:14-43. [DOI: 10.1016/j.neuropsychologia.2017.09.021] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 09/04/2017] [Accepted: 09/20/2017] [Indexed: 12/16/2022]
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13
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Sharma S, Mantini D, Vanduffel W, Nelissen K. Functional specialization of macaque premotor F5 subfields with respect to hand and mouth movements: A comparison of task and resting-state fMRI. Neuroimage 2019; 191:441-456. [PMID: 30802514 DOI: 10.1016/j.neuroimage.2019.02.045] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 02/05/2019] [Accepted: 02/18/2019] [Indexed: 10/27/2022] Open
Abstract
Based on architectonic, tract-tracing or functional criteria, the rostral portion of ventral premotor cortex in the macaque monkey, also termed area F5, has been divided into several subfields. Cytoarchitectonical investigations suggest the existence of three subfields, F5c (convexity), F5p (posterior) and F5a (anterior). Electrophysiological investigations have suggested a gradual dorso-ventral transition from hand- to mouth-dominated motor fields, with F5p and ventral F5c strictly related to hand movements and mouth movements, respectively. The involvement of F5a in this respect, however, has received much less attention. Recently, data-driven resting-state fMRI approaches have also been used to examine the presence of distinct functional fields in macaque ventral premotor cortex. Although these studies have suggested several functional clusters in/near macaque F5, so far the parcellation schemes derived from these clustering methods do not completely retrieve the same level of F5 specialization as suggested by aforementioned invasive techniques. Here, using seed-based resting-state fMRI analyses, we examined the functional connectivity of different F5 seeds with key regions of the hand and face/mouth parieto-frontal-insular motor networks. In addition, we trained monkeys to perform either hand grasping or ingestive mouth movements in the scanner in order to compare resting-state with task-derived functional hand and mouth motor networks. In line with previous single-cell investigations, task-fMRI suggests involvement of F5p, dorsal F5c and F5a in the execution of hand grasping movements, while non-communicative mouth movements yielded particularly pronounced responses in ventral F5c. Corroborating with anatomical tracing data of macaque F5 subfields, seed-based resting-state fMRI suggests a transition from predominant functional correlations with the hand-motor network in F5p to mostly mouth-motor network functional correlations in ventral F5c. Dorsal F5c yielded robust functional correlations with both hand- and mouth-motor networks. In addition, the deepest part of the fundus of the inferior arcuate, corresponding to area 44, displayed a strikingly different functional connectivity profile compared to neighboring F5a, suggesting a different functional specialization for these two neighboring regions.
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Affiliation(s)
- S Sharma
- Laboratory for Neuro- & Psychophysiology, Department of Neurosciences, KU Leuven, 3000, Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium
| | - D Mantini
- Movement Control & Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; Functional Neuroimaging Laboratory, Fondazione Ospedale San Camillo - IRCCS, Venezia, Italy
| | - W Vanduffel
- Laboratory for Neuro- & Psychophysiology, Department of Neurosciences, KU Leuven, 3000, Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA; Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - K Nelissen
- Laboratory for Neuro- & Psychophysiology, Department of Neurosciences, KU Leuven, 3000, Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium.
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14
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From ideas to action: The prefrontal–premotor connections that shape motor behavior. HANDBOOK OF CLINICAL NEUROLOGY 2019; 163:237-255. [DOI: 10.1016/b978-0-12-804281-6.00013-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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15
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Intrinsic Functional Boundaries of Lateral Frontal Cortex in the Common Marmoset Monkey. J Neurosci 2018; 39:1020-1029. [PMID: 30530862 DOI: 10.1523/jneurosci.2595-18.2018] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Revised: 11/29/2018] [Accepted: 12/01/2018] [Indexed: 12/28/2022] Open
Abstract
The common marmoset (Callithrix jacchus) is a small New World primate species that has been recently targeted as a potentially powerful preclinical model of human prefrontal cortex dysfunction. Although the structural boundaries of frontal cortex were described in marmosets at the start of the 20th century (Brodmann, 1909) and refined more recently (Paxinos et al., 2012), the broad functional boundaries of marmoset frontal cortex have yet to be established. In this study, we sought to functionally derive boundaries of the marmoset lateral frontal cortex (LFC) using ultra-high field (9.4 T) resting-state functional magnetic resonance imaging (RS-fMRI). We collected RS-fMRI data in seven (four females, three males) lightly anesthetized marmosets and used a data-driven hierarchical clustering approach to derive subdivisions of the LFC based on intrinsic functional connectivity. We then conducted seed-based analyses to assess the functional connectivity between these clusters and the rest of the brain. The results demonstrated seven distinct functional clusters within the LFC. The functional connectivity patterns of these clusters with the rest of the brain were also found to be distinct and organized along a rostrocaudal gradient, consonant with those found in humans and macaques. Overall, these results support the view that marmosets are a promising preclinical modeling species for studying LFC dysfunction related to neuropsychiatric or neurodegenerative human brain diseases.SIGNIFICANCE STATEMENT The common marmoset is a New World primate that has garnered recent attention as a powerful complement to canonical Old World primate (e.g., macaques) and rodent models (e.g., rats, mice) for preclinical modeling of the human brain in healthy and diseased states. A critical step in the development of marmosets for such models is to characterize functional network topologies of frontal cortex in healthy, normally functioning marmosets, that is, how these circuitries are functionally divided and how those topologies compare to human circuitry. To our knowledge, this is the first study to demonstrate functional boundaries of the lateral frontal cortex and the corresponding network topologies in marmoset monkeys.
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16
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Vijayakumar S, Sallet J, Verhagen L, Folloni D, Medendorp WP, Mars RB. Mapping multiple principles of parietal-frontal cortical organization using functional connectivity. Brain Struct Funct 2018; 224:681-697. [PMID: 30470895 PMCID: PMC6420483 DOI: 10.1007/s00429-018-1791-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 11/08/2018] [Indexed: 12/02/2022]
Abstract
Resting state functional connectivity has been promoted as a promising tool for creating cortical maps that show remarkable similarity to those established by invasive histological methods. While this tool has been largely used to identify and map cortical areas, its true potential in the context of studying connectional architecture and in conducting comparative neuroscience has remained unexplored. Here, we employ widely used resting state connectivity and data-driven clustering methods to extend this approach for the study of the organizational principles of the macaque parietal–frontal system. We show multiple, overlapping principles of organization, including a dissociation between dorsomedial and dorsolateral pathways and separate parietal–premotor and parietal–frontal pathways. These results demonstrate the suitability of this approach for understanding the complex organizational principles of the brain and for large-scale comparative neuroscience.
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Affiliation(s)
- Suhas Vijayakumar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525HR, Nijmegen, The Netherlands.
| | - Jerome Sallet
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, 9 South Parks Road, Oxford, OX1 3UD, United Kingdom
| | - Lennart Verhagen
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, 9 South Parks Road, Oxford, OX1 3UD, United Kingdom
| | - Davide Folloni
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, 9 South Parks Road, Oxford, OX1 3UD, United Kingdom
| | - W Pieter Medendorp
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525HR, Nijmegen, The Netherlands
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525HR, Nijmegen, The Netherlands.,Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX3 9DU, United Kingdom
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17
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Functional MRI in Macaque Monkeys during Task Switching. J Neurosci 2018; 38:10619-10630. [PMID: 30355629 DOI: 10.1523/jneurosci.1539-18.2018] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 08/27/2018] [Accepted: 10/15/2018] [Indexed: 11/21/2022] Open
Abstract
Nonhuman primates have proven to be a valuable animal model for exploring neuronal mechanisms of cognitive control. One important aspect of executive control is the ability to switch from one task to another, and task-switching paradigms have often been used in human volunteers to uncover the underlying neuronal processes. To date, however, no study has investigated task-switching paradigms in nonhuman primates during functional magnetic resonance imaging (fMRI). We trained two rhesus macaques to switch between arm movement, eye movement, and passive fixation tasks during fMRI. Similar to results obtained in human volunteers, task switching elicits increased fMRI activations in prefrontal cortex, anterior cingulate cortex, orbitofrontal cortex, and caudate nucleus. Our results indicate that the macaque monkey is a reliable model with which to investigate higher-order cognitive functioning such as task switching. As such, these results can pave the way for a detailed investigation of the neural basis of complex human behavior.SIGNIFICANCE STATEMENT Task switching is an important aspect of cognitive control, and task-switching paradigms have often been used to investigate higher-order executive functioning in human volunteers. We used a task-switching paradigm in the nonhuman primate during fMRI and found increased activation mainly in prefrontal areas (46, 45, frontal eye field, and anterior cingulate), in orbitofrontal area 12, and in the caudate nucleus. These data fit surprisingly well with previous human imaging data, proving that the monkey is an excellent model to study task switching with high spatiotemporal resolution tools that are currently not applicable in humans. As such, our results pave the way for a detailed interrogation of regions performing similar executive functions in humans and monkeys.
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18
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Milham MP, Ai L, Koo B, Xu T, Amiez C, Balezeau F, Baxter MG, Blezer ELA, Brochier T, Chen A, Croxson PL, Damatac CG, Dehaene S, Everling S, Fair DA, Fleysher L, Freiwald W, Froudist-Walsh S, Griffiths TD, Guedj C, Hadj-Bouziane F, Ben Hamed S, Harel N, Hiba B, Jarraya B, Jung B, Kastner S, Klink PC, Kwok SC, Laland KN, Leopold DA, Lindenfors P, Mars RB, Menon RS, Messinger A, Meunier M, Mok K, Morrison JH, Nacef J, Nagy J, Rios MO, Petkov CI, Pinsk M, Poirier C, Procyk E, Rajimehr R, Reader SM, Roelfsema PR, Rudko DA, Rushworth MFS, Russ BE, Sallet J, Schmid MC, Schwiedrzik CM, Seidlitz J, Sein J, Shmuel A, Sullivan EL, Ungerleider L, Thiele A, Todorov OS, Tsao D, Wang Z, Wilson CRE, Yacoub E, Ye FQ, Zarco W, Zhou YD, Margulies DS, Schroeder CE. An Open Resource for Non-human Primate Imaging. Neuron 2018; 100:61-74.e2. [PMID: 30269990 PMCID: PMC6231397 DOI: 10.1016/j.neuron.2018.08.039] [Citation(s) in RCA: 126] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 03/02/2018] [Accepted: 08/30/2018] [Indexed: 01/11/2023]
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 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.
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Affiliation(s)
- Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA.
| | - Lei Ai
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Bonhwang Koo
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Céline Amiez
- University of Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute U1208, Lyon, France
| | - Fabien Balezeau
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Mark G Baxter
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Erwin L A Blezer
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Thomas Brochier
- Institut de Neurosciences de la Timone, CNRS & Aix-Marseille Université, UMR 7289, Marseille, France
| | - Aihua Chen
- Key Laboratory of Brain Functional Genomics (Ministry of Education & Science and Technology Commission of Shanghai Municipality), School of Life Sciences, East China Normal University, Shanghai 200062, China
| | - Paula L Croxson
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christienne G Damatac
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6525 EN Nijmegen, Netherlands
| | - Stanislas Dehaene
- NeuroSpin, CEA, INSERM U992, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Stefan Everling
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, ON N6A 3K7, Canada
| | - Damian A Fair
- Department of Behavior Neuroscience, Department of Psychiatry, Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, USA
| | - Lazar Fleysher
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Winrich Freiwald
- Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA
| | | | - Timothy D Griffiths
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Carole Guedj
- INSERM, U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
| | | | - Suliann Ben Hamed
- Institut des Sciences Cognitives - Marc Jeannerod, UMR5229, CNRS-Université de Lyon, Lyon, France
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Bassem Hiba
- Institut des Sciences Cognitives - Marc Jeannerod, UMR5229, CNRS-Université de Lyon, Lyon, France
| | - Bechir Jarraya
- NeuroSpin, CEA, INSERM U992, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Benjamin Jung
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Sabine Kastner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - P Christiaan Klink
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Sze Chai Kwok
- Shanghai Key Laboratory of Brain Functional Genomics, School of Psychology and Cognitive Science, Key Laboratory of Brain Functional Genomics (Ministry of Education), East China Normal University, Shanghai 200062, China; Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai 200062, China; NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai 200062, China
| | - Kevin N Laland
- Centre for Social Learning and Cognitive Evolution, School of Biology, University of St. Andrews, St. Andrews, UK
| | - David A Leopold
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, MD 20892, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Patrik Lindenfors
- Institute for Future Studies, Stockholm, Sweden; Centre for Cultural Evolution & Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6525 EN Nijmegen, Netherlands; Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, ON N6A 3K7, Canada
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Martine Meunier
- INSERM, U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
| | - Kelvin Mok
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Departments of Neurology, Neurosurgery, and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - John H Morrison
- California National Primate Research Center, Davis, CA 95616, USA; Department of Neurology, School of Medicine, University of California, Davis, CA 95616, USA
| | - Jennifer Nacef
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Jamie Nagy
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Ortiz Rios
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Christopher I Petkov
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Mark Pinsk
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Colline Poirier
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Emmanuel Procyk
- University of Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute U1208, Lyon, France
| | - Reza Rajimehr
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Simon M Reader
- Department of Biology and Helmholtz Institute, Utrecht University, 35 84 CH Utrecht, The Netherlands; Department of Biology, McGill University, Montreal, QC H3A 1BA, Canada
| | - Pieter R Roelfsema
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands; Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit, 1081 HV Amsterdam, the Netherlands
| | - David A Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Departments of Neurology, Neurosurgery, and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - Matthew F S Rushworth
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3AQ, UK
| | - Brian E Russ
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3AQ, UK
| | | | | | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Julien Sein
- Institut de Neurosciences de la Timone, CNRS & Aix-Marseille Université, UMR 7289, Marseille, France
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Departments of Neurology, Neurosurgery, and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - Elinor L Sullivan
- Divisions of Neuroscience and Cardiometabolic Health, Oregon National Primate Research Center, Beaverton, OR, USA; Department of Human Physiology, University of Oregon, Eugene, OR, USA
| | - Leslie Ungerleider
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Alexander Thiele
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Orlin S Todorov
- Department of Biology and Helmholtz Institute, Utrecht University, 35 84 CH Utrecht, The Netherlands
| | - Doris Tsao
- Department of Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA
| | - Zheng Wang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Charles R E Wilson
- University of Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute U1208, Lyon, France
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Wilbert Zarco
- Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA
| | - Yong-di Zhou
- Krieger Mind/Brain Institute, Department of Neurosurgery, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Daniel S Margulies
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; Centre national de la recherche scientifique, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, 75013 Paris, France
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; Department of Neurological Surgery, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA; Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA
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19
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Constantinidis C, Qi XL. Representation of Spatial and Feature Information in the Monkey Dorsal and Ventral Prefrontal Cortex. Front Integr Neurosci 2018; 12:31. [PMID: 30131679 PMCID: PMC6090048 DOI: 10.3389/fnint.2018.00031] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 07/17/2018] [Indexed: 01/04/2023] Open
Abstract
The primate prefrontal cortex (PFC) is critical for executive functions including working memory, task switching and response selection. The functional organization of this area has been a matter of debate over a period of decades. Early models proposed segregation of spatial and object information represented in working memory in the dorsal and ventral PFC, respectively. Other models emphasized the integrative ability of the entire PFC depending on task demands, not necessarily tied to working memory. An anterior-posterior hierarchy of specialization has also been speculated, in which progressively more abstract information is represented more anteriorly. Here we revisit this debate, updating these arguments in light of recent evidence in non-human primate neurophysiology studies. We show that spatial selectivity is predominantly represented in the posterior aspect of the dorsal PFC, regardless of training history and task performed. Objects of different features excite both dorsal and ventral prefrontal neurons, however neurons highly specialized for feature information are located predominantly in the posterior aspect of the ventral PFC. In accordance with neuronal selectivity, spatial working memory is primarily impaired by inactivation or lesion of the dorsal PFC and object working memory by ventral inactivation or lesion. Neuronal responses are plastic depending on task training but training too has dissociable effects on ventral and dorsal PFC, with the latter appearing to be more plastic. Despite the absence of an overall topography, evidence exists for the orderly localization of stimulus information at a sub-millimeter scale, within the dimensions of a cortical column. Unresolved questions remain, regarding the existence or not of a functional map at the areal and columnar scale, and the link between behavior and neuronal activity for different prefrontal subdivisions.
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Affiliation(s)
- Christos Constantinidis
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Xue-Lian Qi
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC, United States
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20
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Stiers P, Goulas A. Functional connectivity of task context representations in prefrontal nodes of the multiple demand network. Brain Struct Funct 2018; 223:2455-2473. [PMID: 29502145 PMCID: PMC5968070 DOI: 10.1007/s00429-018-1638-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 02/21/2018] [Indexed: 11/29/2022]
Abstract
A subset of regions in the lateral and medial prefrontal cortex and the anterior insula increase their activity level whenever a cognitive task becomes more demanding, regardless of the specific nature of this demand. During execution of a task, these areas and the surrounding cortex temporally encode aspects of the task context in spatially distributed patterns of activity. It is not clear whether these patterns reflect underlying anatomical subnetworks that still exist when task execution has finished. We use fMRI in 12 participants performing alternating blocks of three cognitive tasks to address this question. A first data set is used to define multiple demand regions in each participant. A second dataset from the same participants is used to determine multiple demand voxel assemblies with a preference for one task over the others. We then show that these voxels remain functionally coupled during execution of non-preferred tasks and that they exhibit stronger functional connectivity during rest. This indicates that the assemblies of task preference sharing voxels reflect patterns of underlying anatomical connections. Moreover, we show that voxels preferring the same task have more similar whole brain functional connectivity profiles that are consistent across participants. This suggests that voxel assemblies differ in patterns of input-output connections, most likely reflecting task demand-specific information exchange.
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Affiliation(s)
- Peter Stiers
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Universiteitssingel 40 (East), 6229 ER, Maastricht, The Netherlands.
| | - Alexandros Goulas
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany
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