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Nelson J, Woeste EM, Oba K, Bitterman K, Billings BK, Sacco J, Jacobs B, Sherwood CC, Manger PR, Spocter MA. Neuropil Variation in the Prefrontal, Motor, and Visual Cortex of Six Felids. Brain Behav Evol 2024; 99:25-44. [PMID: 38354714 DOI: 10.1159/000537843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 02/04/2024] [Indexed: 02/16/2024]
Abstract
INTRODUCTION Felids have evolved a specialized suite of morphological adaptations for obligate carnivory. Although the musculoskeletal anatomy of the Felidae has been studied extensively, the comparative neuroanatomy of felids is relatively unexplored. Little is known about how variation in the cerebral anatomy of felids relates to species-specific differences in sociality, hunting strategy, or activity patterns. METHODS We quantitatively analyzed neuropil variation in the prefrontal, primary motor, and primary visual cortices of six species of Felidae (Panthera leo, Panthera uncia, Panthera tigris, Panthera leopardus, Acinonyx jubatus, Felis sylvestris domesticus) to investigate relationships with brain size, neuronal cell parameters, and select behavioral and ecological factors. Neuropil is the dense, intricate network of axons, dendrites, and synapses in the brain, playing a critical role in information processing and communication between neurons. RESULTS There were significant species and regional differences in neuropil proportions, with African lion, cheetah, and tiger having more neuropil in all three cortical regions in comparison to the other species. Based on regression analyses, we find that the increased neuropil fraction in the prefrontal cortex supports social and behavioral flexibility, while in the primary motor cortex, this facilitates the neural activity needed for hunting movements. Greater neuropil fraction in the primary visual cortex may contribute to visual requirements associated with diel activity patterns. CONCLUSION These results provide a cross-species comparison of neuropil fraction variation in the Felidae, particularly the understudied Panthera, and provide evidence for convergence of the neuroanatomy of Panthera and cheetahs.
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Affiliation(s)
- Jacob Nelson
- Department of Anatomy, Des Moines University, West Des Moines, Iowa, USA
| | - Erin M Woeste
- Department of Anatomy, Des Moines University, West Des Moines, Iowa, USA
| | - Ken Oba
- Department of Anatomy, Des Moines University, West Des Moines, Iowa, USA
| | - Kathleen Bitterman
- Department of Anatomy, Des Moines University, West Des Moines, Iowa, USA
| | - Brendon K Billings
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - James Sacco
- Ellis Pharmacogenomics Laboratory, College of Pharmacy and Health Sciences, Drake University, Des Moines, Iowa, USA
| | - Bob Jacobs
- Department of Psychology, Laboratory of Quantitative Neuromorphology, Neuroscience Program, Colorado College, Colorado Springs, Colorado, USA
| | - Chet C Sherwood
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, District of Columbia, USA
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Muhammad A Spocter
- Department of Anatomy, Des Moines University, West Des Moines, Iowa, USA
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Department of Biomedical Sciences, College of Veterinary Medicine, Iowa State University, Ames, Iowa, USA
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2
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Gordon EM, Chauvin RJ, Van AN, Rajesh A, Nielsen A, Newbold DJ, Lynch CJ, Seider NA, Krimmel SR, Scheidter KM, Monk J, Miller RL, Metoki A, Montez DF, Zheng A, Elbau I, Madison T, Nishino T, Myers MJ, Kaplan S, Badke D'Andrea C, Demeter DV, Feigelis M, Ramirez JSB, Xu T, Barch DM, Smyser CD, Rogers CE, Zimmermann J, Botteron KN, Pruett JR, Willie JT, Brunner P, Shimony JS, Kay BP, Marek S, Norris SA, Gratton C, Sylvester CM, Power JD, Liston C, Greene DJ, Roland JL, Petersen SE, Raichle ME, Laumann TO, Fair DA, Dosenbach NUF. A somato-cognitive action network alternates with effector regions in motor cortex. Nature 2023; 617:351-359. [PMID: 37076628 PMCID: PMC10172144 DOI: 10.1038/s41586-023-05964-2] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/16/2023] [Indexed: 04/21/2023]
Abstract
Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations1,2, despite evidence for concentric functional zones3 and maps of complex actions4. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action5 and physiological control6, arousal7, errors8 and pain9. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions4 and connectivity to internal organs10 such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.
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Affiliation(s)
- Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA.
| | - Roselyne J Chauvin
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA
| | - Aishwarya Rajesh
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Ashley Nielsen
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, New York University Langone Medical Center, New York, NY, USA
| | - Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Nicole A Seider
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Samuel R Krimmel
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Kristen M Scheidter
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Julia Monk
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Ryland L Miller
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Immanuel Elbau
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Thomas Madison
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Tomoyuki Nishino
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Michael J Myers
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Sydney Kaplan
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Carolina Badke D'Andrea
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Damion V Demeter
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Matthew Feigelis
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Julian S B Ramirez
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Deanna M Barch
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, MO, USA
| | - Christopher D Smyser
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Kelly N Botteron
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - John R Pruett
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Jon T Willie
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
- Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA
| | - Peter Brunner
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA
- Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Scott Marek
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Scott A Norris
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Jonathan D Power
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Jarod L Roland
- Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA
| | - Steven E Petersen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Marcus E Raichle
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Damien A Fair
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, 55455, United States
| | - Nico U F Dosenbach
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA.
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA.
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA.
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, MO, USA.
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA.
- Program in Occupational Therapy, Washington University in St. Louis, St Louis, MO, USA.
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3
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Matuz-Budai T, Lábadi B, Kohn E, Matuz A, Zsidó AN, Inhóf O, Kállai J, Szolcsányi T, Perlaki G, Orsi G, Nagy SA, Janszky J, Darnai G. Individual differences in the experience of body ownership are related to cortical thickness. Sci Rep 2022; 12:808. [PMID: 35039541 PMCID: PMC8764083 DOI: 10.1038/s41598-021-04720-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 12/21/2021] [Indexed: 11/09/2022] Open
Abstract
The widely used rubber hand illusion (RHI) paradigm provides insight into how the brain manages conflicting multisensory information regarding bodily self-consciousness. Previous functional neuroimaging studies have revealed that the feeling of body ownership is linked to activity in the premotor cortex, the intraparietal areas, the occipitotemporal cortex, and the insula. The current study investigated whether the individual differences in the sensation of body ownership over a rubber hand, as measured by subjective report and the proprioceptive drift, are associated with structural brain differences in terms of cortical thickness in 67 healthy young adults. We found that individual differences measured by the subjective report of body ownership are associated with the cortical thickness in the somatosensory regions, the temporo-parietal junction, the intraparietal areas, and the occipitotemporal cortex, while the proprioceptive drift is linked to the premotor area and the anterior cingulate cortex. These results are in line with functional neuroimaging studies indicating that these areas are indeed involved in processes such as cognitive-affective perspective taking, visual processing of the body, and the experience of body ownership and bodily awareness. Consequently, these individual differences in the sensation of body ownership are pronounced in both functional and structural differences.
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Affiliation(s)
- Timea Matuz-Budai
- Institute of Psychology, University of Pécs, 6 Ifjúság str., Pécs, 7624, Hungary.
| | - Beatrix Lábadi
- Institute of Psychology, University of Pécs, 6 Ifjúság str., Pécs, 7624, Hungary
| | - Eszter Kohn
- Institute of Philosophy and Art Theory, University of Pécs, Pécs, Hungary
| | - András Matuz
- Department of Behavioural Sciences, Medical School, University of Pécs, Pécs, Hungary
| | - András Norbert Zsidó
- Institute of Psychology, University of Pécs, 6 Ifjúság str., Pécs, 7624, Hungary
| | - Orsolya Inhóf
- Institute of Psychology, University of Pécs, 6 Ifjúság str., Pécs, 7624, Hungary
| | - János Kállai
- Department of Behavioural Sciences, Medical School, University of Pécs, Pécs, Hungary
| | - Tibor Szolcsányi
- Department of Behavioural Sciences, Medical School, University of Pécs, Pécs, Hungary
| | - Gábor Perlaki
- Department of Neurology, Medical School, University of Pécs, Pécs, Hungary
- MTA-PTE, Clinical Neuroscience MR Research Group, Pécs, Hungary
- Pécs Diagnostic Centre, Pécs, Hungary
| | - Gergely Orsi
- MTA-PTE, Clinical Neuroscience MR Research Group, Pécs, Hungary
- Pécs Diagnostic Centre, Pécs, Hungary
- Department of Neurosurgery, Medical School, University of Pécs, Pécs, Hungary
| | - Szilvia Anett Nagy
- MTA-PTE, Clinical Neuroscience MR Research Group, Pécs, Hungary
- Pécs Diagnostic Centre, Pécs, Hungary
- Neurobiology of Stress Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
- Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary
| | - József Janszky
- Department of Neurology, Medical School, University of Pécs, Pécs, Hungary
- MTA-PTE, Clinical Neuroscience MR Research Group, Pécs, Hungary
| | - Gergely Darnai
- Department of Behavioural Sciences, Medical School, University of Pécs, Pécs, Hungary
- Department of Neurology, Medical School, University of Pécs, Pécs, Hungary
- MTA-PTE, Clinical Neuroscience MR Research Group, Pécs, Hungary
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4
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Bakken TE, Jorstad NL, Hu Q, Lake BB, Tian W, Kalmbach BE, Crow M, Hodge RD, Krienen FM, Sorensen SA, Eggermont J, Yao Z, Aevermann BD, Aldridge AI, Bartlett A, Bertagnolli D, Casper T, Castanon RG, Crichton K, Daigle TL, Dalley R, Dee N, Dembrow N, Diep D, Ding SL, Dong W, Fang R, Fischer S, Goldman M, Goldy J, Graybuck LT, Herb BR, Hou X, Kancherla J, Kroll M, Lathia K, van Lew B, Li YE, Liu CS, Liu H, Lucero JD, Mahurkar A, McMillen D, Miller JA, Moussa M, Nery JR, Nicovich PR, Niu SY, Orvis J, Osteen JK, Owen S, Palmer CR, Pham T, Plongthongkum N, Poirion O, Reed NM, Rimorin C, Rivkin A, Romanow WJ, Sedeño-Cortés AE, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Wang X, Xie F, Yanny AM, Zhang R, Ament SA, Behrens MM, Bravo HC, Chun J, Dobin A, Gillis J, Hertzano R, Hof PR, Höllt T, Horwitz GD, Keene CD, Kharchenko PV, Ko AL, Lelieveldt BP, Luo C, Mukamel EA, Pinto-Duarte A, Preissl S, Regev A, Ren B, Scheuermann RH, Smith K, Spain WJ, White OR, Koch C, Hawrylycz M, Tasic B, Macosko EZ, McCarroll SA, Ting JT, Zeng H, Zhang K, Feng G, Ecker JR, Linnarsson S, Lein ES. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 2021; 598:111-119. [PMID: 34616062 PMCID: PMC8494640 DOI: 10.1038/s41586-021-03465-8] [Citation(s) in RCA: 258] [Impact Index Per Article: 86.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 03/17/2021] [Indexed: 12/11/2022]
Abstract
The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.
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Affiliation(s)
| | | | - Qiwen Hu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Blue B Lake
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Brian E Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Megan Crow
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Fenna M Krienen
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | - Jeroen Eggermont
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Andrew I Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | | | - Rosa G Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | | | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nikolai Dembrow
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Epilepsy Center of Excellence, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Dinh Diep
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Rongxin Fang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Stephan Fischer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Melissa Goldman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Brian R Herb
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaomeng Hou
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jayaram Kancherla
- Department of Computer Science, University of Maryland College Park, College Park, MD, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Baldur van Lew
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Yang Eric Li
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Christine S Liu
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Biomedical Sciences Program, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Anup Mahurkar
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Sheng-Yong Niu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Computer Science and Engineering Program, University of California, San Diego, La Jolla, CA, USA
| | - Joshua Orvis
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Julia K Osteen
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Scott Owen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Carter R Palmer
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Biomedical Sciences Program, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nongluk Plongthongkum
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Olivier Poirion
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Nora M Reed
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | - Angeline Rivkin
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - William J Romanow
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | | | - Kimberly Siletti
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Xinxin Wang
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Fangming Xie
- Department of Physics, University of California, San Diego, La Jolla, CA, USA
| | | | - Renee Zhang
- J. Craig Venter Institute, La Jolla, CA, USA
| | - Seth A Ament
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Hector Corrada Bravo
- Department of Computer Science, University of Maryland College Park, College Park, MD, USA
| | - Jerold Chun
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | | | - Jesse Gillis
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ronna Hertzano
- Departments of Otorhinolaryngology, Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Höllt
- Computer Graphics and Visualization Group, Delt University of Technology, Delft, The Netherlands
| | - Gregory D Horwitz
- Department of Physiology and Biophysics, Washington National Primate Research Center, University of Washington, Seattle, WA, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Regional Epilepsy Center, Harborview Medical Center, Seattle, WA, USA
| | - Boudewijn P Lelieveldt
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics group, Delft University of Technology, Delft, The Netherlands
| | - Chongyuan Luo
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eran A Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | | | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Richard H Scheuermann
- J. Craig Venter Institute, La Jolla, CA, USA
- Department of Pathology, University of California, San Diego, CA, USA
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, USA
| | | | - William J Spain
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Epilepsy Center of Excellence, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Owen R White
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Steven A McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Guoping Feng
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Sten Linnarsson
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA.
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5
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Muñoz-Castañeda R, Zingg B, Matho KS, Chen X, Wang Q, Foster NN, Li A, Narasimhan A, Hirokawa KE, Huo B, Bannerjee S, Korobkova L, Park CS, Park YG, Bienkowski MS, Chon U, Wheeler DW, Li X, Wang Y, Naeemi M, Xie P, Liu L, Kelly K, An X, Attili SM, Bowman I, Bludova A, Cetin A, Ding L, Drewes R, D'Orazi F, Elowsky C, Fischer S, Galbavy W, Gao L, Gillis J, Groblewski PA, Gou L, Hahn JD, Hatfield JT, Hintiryan H, Huang JJ, Kondo H, Kuang X, Lesnar P, Li X, Li Y, Lin M, Lo D, Mizrachi J, Mok S, Nicovich PR, Palaniswamy R, Palmer J, Qi X, Shen E, Sun YC, Tao HW, Wakemen W, Wang Y, Yao S, Yuan J, Zhan H, Zhu M, Ng L, Zhang LI, Lim BK, Hawrylycz M, Gong H, Gee JC, Kim Y, Chung K, Yang XW, Peng H, Luo Q, Mitra PP, Zador AM, Zeng H, Ascoli GA, Josh Huang Z, Osten P, Harris JA, Dong HW. Cellular anatomy of the mouse primary motor cortex. Nature 2021; 598:159-166. [PMID: 34616071 PMCID: PMC8494646 DOI: 10.1038/s41586-021-03970-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 08/27/2021] [Indexed: 12/24/2022]
Abstract
An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.
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Affiliation(s)
| | - Brian Zingg
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Xiaoyin Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nicholas N Foster
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | | | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Bingxing Huo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Laura Korobkova
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Chris Sin Park
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Young-Gyun Park
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Michael S Bienkowski
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Uree Chon
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Diek W Wheeler
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Peng Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Kathleen Kelly
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xu An
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Sarojini M Attili
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Ian Bowman
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Rhonda Drewes
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Corey Elowsky
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | - Lei Gao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Lin Gou
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Joshua T Hatfield
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Houri Hintiryan
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Junxiang Jason Huang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hideki Kondo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | - Xu Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Mengkuan Lin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Darrick Lo
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | | | - Philip R Nicovich
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | - Jason Palmer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiaoli Qi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Elise Shen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yu-Chi Sun
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Huizhong W Tao
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Yimin Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Muye Zhu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Li I Zhang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Byung Kook Lim
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
- Division of Biological Science, Neurobiology section, University of California San Diego, San Diego, CA, USA
| | | | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - X William Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Hanchuan Peng
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA.
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA.
- Cajal Neuroscience, Seattle, WA, USA.
| | - Hong-Wei Dong
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
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6
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Yao Z, Liu H, Xie F, Fischer S, Adkins RS, Aldridge AI, Ament SA, Bartlett A, Behrens MM, Van den Berge K, Bertagnolli D, de Bézieux HR, Biancalani T, Booeshaghi AS, Bravo HC, Casper T, Colantuoni C, Crabtree J, Creasy H, Crichton K, Crow M, Dee N, Dougherty EL, Doyle WI, Dudoit S, Fang R, Felix V, Fong O, Giglio M, Goldy J, Hawrylycz M, Herb BR, Hertzano R, Hou X, Hu Q, Kancherla J, Kroll M, Lathia K, Li YE, Lucero JD, Luo C, Mahurkar A, McMillen D, Nadaf NM, Nery JR, Nguyen TN, Niu SY, Ntranos V, Orvis J, Osteen JK, Pham T, Pinto-Duarte A, Poirion O, Preissl S, Purdom E, Rimorin C, Risso D, Rivkin AC, Smith K, Street K, Sulc J, Svensson V, Tieu M, Torkelson A, Tung H, Vaishnav ED, Vanderburg CR, van Velthoven C, Wang X, White OR, Huang ZJ, Kharchenko PV, Pachter L, Ngai J, Regev A, Tasic B, Welch JD, Gillis J, Macosko EZ, Ren B, Ecker JR, Zeng H, Mukamel EA. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 2021; 598:103-110. [PMID: 34616066 PMCID: PMC8494649 DOI: 10.1038/s41586-021-03500-8] [Citation(s) in RCA: 113] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 03/26/2021] [Indexed: 12/30/2022]
Abstract
Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.
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Affiliation(s)
- Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Fangming Xie
- Department of Physics, University of California, San Diego, La Jolla, CA, USA
| | - Stephan Fischer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ricky S Adkins
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Andrew I Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Seth A Ament
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Koen Van den Berge
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
| | | | - Hector Roux de Bézieux
- Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | | | | | - Héctor Corrada Bravo
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | | | - Carlo Colantuoni
- Johns Hopkins School of Medicine, Department of Neurology, Baltimore, MD, USA
- Johns Hopkins School of Medicine, Department of Neuroscience, Baltimore, MD, USA
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | - Jonathan Crabtree
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Heather Creasy
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Megan Crow
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Wayne I Doyle
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Sandrine Dudoit
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
| | - Rongxin Fang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, San Diego, CA, USA
| | - Victor Felix
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Olivia Fong
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michelle Giglio
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Brian R Herb
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ronna Hertzano
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Otorhinolaryngology, Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaomeng Hou
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Qiwen Hu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jayaram Kancherla
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yang Eric Li
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Jacinta D Lucero
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Chongyuan Luo
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Anup Mahurkar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Naeem M Nadaf
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Sheng-Yong Niu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Vasilis Ntranos
- University of California, San Francisco, San Francisco, CA, USA
| | - Joshua Orvis
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Julia K Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Antonio Pinto-Duarte
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Olivier Poirion
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Elizabeth Purdom
- Department of Statistics, University of California, Berkeley, Berkeley, CA, USA
| | | | - Davide Risso
- Department of Statistical Sciences, University of Padova, Padova, Italy
| | - Angeline C Rivkin
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Kelly Street
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Xinxin Wang
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Owen R White
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Lior Pachter
- California Institute of Technology, Pasadena, CA, USA
| | - John Ngai
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology, MIT, Cambridge, MA, USA
| | | | - Joshua D Welch
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jesse Gillis
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Eran A Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
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7
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Zhang M, Eichhorn SW, Zingg B, Yao Z, Cotter K, Zeng H, Dong H, Zhuang X. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 2021; 598:137-143. [PMID: 34616063 PMCID: PMC8494645 DOI: 10.1038/s41586-021-03705-x] [Citation(s) in RCA: 153] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 06/08/2021] [Indexed: 11/09/2022]
Abstract
A mammalian brain is composed of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing allows systematic identification of cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1,2. Single-cell epigenomic profiling3,4 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation. Here we used a single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)5, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. We profiled approximately 300,000 cells in the mouse primary motor cortex and its adjacent areas, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory but also most inhibitory neuronal clusters adopted laminar organizations. Intratelencephalic neurons formed a largely continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Furthermore, we integrated MERFISH with retrograde labelling to probe projection targets of neurons of the mouse primary motor cortex and found that their cortical projections formed a complex network in which individual neuronal clusters project to multiple target regions and individual target regions receive inputs from multiple neuronal clusters.
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Affiliation(s)
- Meng Zhang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Stephen W Eichhorn
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Brian Zingg
- Center for Integrative Connectomics, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kaelan Cotter
- Center for Integrative Connectomics, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongwei Dong
- Center for Integrative Connectomics, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Xiaowei Zhuang
- Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA.
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
- Department of Physics, Harvard University, Cambridge, MA, USA.
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8
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Scala F, Kobak D, Bernabucci M, Bernaerts Y, Cadwell CR, Castro JR, Hartmanis L, Jiang X, Laturnus S, Miranda E, Mulherkar S, Tan ZH, Yao Z, Zeng H, Sandberg R, Berens P, Tolias AS. Phenotypic variation of transcriptomic cell types in mouse motor cortex. Nature 2021; 598:144-150. [PMID: 33184512 PMCID: PMC8113357 DOI: 10.1038/s41586-020-2907-3] [Citation(s) in RCA: 140] [Impact Index Per Article: 46.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 10/16/2020] [Indexed: 12/13/2022]
Abstract
Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties1,2. Most existing neural taxonomies are based on either transcriptomic3,4 or morpho-electric5,6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells7. Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (those expressing Vip, Pvalb, Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neuronal types in the neocortex do not always form discrete entities. Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.
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Affiliation(s)
- Federico Scala
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Dmitry Kobak
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Matteo Bernabucci
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Yves Bernaerts
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- International Max Planck Research School for Intelligent Systems, Tübingen, Germany
| | - Cathryn René Cadwell
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | - Jesus Ramon Castro
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Leonard Hartmanis
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Xiaolong Jiang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Jan and Dan Duncan Neurological Research Institute, Houston, TX, USA
| | - Sophie Laturnus
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Elanine Miranda
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Shalaka Mulherkar
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Zheng Huan Tan
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Philipp Berens
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
- Center for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.
- Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany.
| | - Andreas S Tolias
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
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9
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BRAIN Initiative Cell Census Network (BICCN). A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 2021; 598:86-102. [PMID: 34616075 DOI: 10.1038/s41586-021-03950-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 08/25/2021] [Indexed: 05/26/2023]
Abstract
Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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10
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Callaway EM, Dong HW, Ecker JR, Hawrylycz MJ, Huang ZJ, Lein ES, Ngai J, Osten P, Ren B, Tolias AS, White O, Zeng H, Zhuang X, Ascoli GA, Behrens MM, Chun J, Feng G, Gee JC, Ghosh SS, Halchenko YO, Hertzano R, Lim BK, Martone ME, Ng L, Pachter L, Ropelewski AJ, Tickle TL, Yang XW, Zhang K, Bakken TE, Berens P, Daigle TL, Harris JA, Jorstad NL, Kalmbach BE, Kobak D, Li YE, Liu H, Matho KS, Mukamel EA, Naeemi M, Scala F, Tan P, Ting JT, Xie F, Zhang M, Zhang Z, Zhou J, Zingg B, Armand E, Yao Z, Bertagnolli D, Casper T, Crichton K, Dee N, Diep D, Ding SL, Dong W, Dougherty EL, Fong O, Goldman M, Goldy J, Hodge RD, Hu L, Keene CD, Krienen FM, Kroll M, Lake BB, Lathia K, Linnarsson S, Liu CS, Macosko EZ, McCarroll SA, McMillen D, Nadaf NM, Nguyen TN, Palmer CR, Pham T, Plongthongkum N, Reed NM, Regev A, Rimorin C, Romanow WJ, Savoia S, Siletti K, Smith K, Sulc J, Tasic B, Tieu M, Torkelson A, Tung H, van Velthoven CTJ, Vanderburg CR, Yanny AM, Fang R, Hou X, Lucero JD, Osteen JK, Pinto-Duarte A, Poirion O, Preissl S, Wang X, Aldridge AI, Bartlett A, Boggeman L, O’Connor C, Castanon RG, Chen H, Fitzpatrick C, Luo C, Nery JR, Nunn M, Rivkin AC, Tian W, Dominguez B, Ito-Cole T, Jacobs M, Jin X, Lee CT, Lee KF, Miyazaki PA, Pang Y, Rashid M, Smith JB, Vu M, Williams E, Biancalani T, Booeshaghi AS, Crow M, Dudoit S, Fischer S, Gillis J, Hu Q, Kharchenko PV, Niu SY, Ntranos V, Purdom E, Risso D, de Bézieux HR, Somasundaram S, Street K, Svensson V, Vaishnav ED, Van den Berge K, Welch JD, An X, Bateup HS, Bowman I, Chance RK, Foster NN, Galbavy W, Gong H, Gou L, Hatfield JT, Hintiryan H, Hirokawa KE, Kim G, Kramer DJ, Li A, Li X, Luo Q, Muñoz-Castañeda R, Stafford DA, Feng Z, Jia X, Jiang S, Jiang T, Kuang X, Larsen R, Lesnar P, Li Y, Li Y, Liu L, Peng H, Qu L, Ren M, Ruan Z, Shen E, Song Y, Wakeman W, Wang P, Wang Y, Wang Y, Yin L, Yuan J, Zhao S, Zhao X, Narasimhan A, Palaniswamy R, Banerjee S, Ding L, Huilgol D, Huo B, Kuo HC, Laturnus S, Li X, Mitra PP, Mizrachi J, Wang Q, Xie P, Xiong F, Yu Y, Eichhorn SW, Berg J, Bernabucci M, Bernaerts Y, Cadwell CR, Castro JR, Dalley R, Hartmanis L, Horwitz GD, Jiang X, Ko AL, Miranda E, Mulherkar S, Nicovich PR, Owen SF, Sandberg R, Sorensen SA, Tan ZH, Allen S, Hockemeyer D, Lee AY, Veldman MB, Adkins RS, Ament SA, Bravo HC, Carter R, Chatterjee A, Colantuoni C, Crabtree J, Creasy H, Felix V, Giglio M, Herb BR, Kancherla J, Mahurkar A, McCracken C, Nickel L, Olley D, Orvis J, Schor M, Hood G, Dichter B, Grauer M, Helba B, Bandrowski A, Barkas N, Carlin B, D’Orazi FD, Degatano K, Gillespie TH, Khajouei F, Konwar K, Thompson C, Kelly K, Mok S, Sunkin S. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 2021; 598:86-102. [PMID: 34616075 PMCID: PMC8494634 DOI: 10.1038/s41586-021-03950-0] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 08/25/2021] [Indexed: 12/14/2022]
Abstract
Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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11
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Booeshaghi AS, Yao Z, van Velthoven C, Smith K, Tasic B, Zeng H, Pachter L. Isoform cell-type specificity in the mouse primary motor cortex. Nature 2021; 598:195-199. [PMID: 34616073 PMCID: PMC8494650 DOI: 10.1038/s41586-021-03969-3] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 08/27/2021] [Indexed: 12/17/2022]
Abstract
Full-length SMART-seq1 single-cell RNA sequencing can be used to measure gene expression at isoform resolution, making possible the identification of specific isoform markers for different cell types. Used in conjunction with spatial RNA capture and gene-tagging methods, this enables the inference of spatially resolved isoform expression for different cell types. Here, in a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-seq, 280,327 cells assayed with MERFISH2 and 94,162 cells assayed with 10x Genomics sequencing3, we find examples of isoform specificity in cell types-including isoform shifts between cell types that are masked in gene-level analysis-as well as examples of transcriptional regulation. Additionally, we show that isoform specificity helps to refine cell types, and that a multi-platform analysis of single-cell transcriptomic data leveraging multiple measurements provides a comprehensive atlas of transcription in the mouse primary motor cortex that improves on the possibilities offered by any single technology.
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Affiliation(s)
- A Sina Booeshaghi
- Department of Mechanical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
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12
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Lefebvre S, Jann K, Schmiesing A, Ito K, Jog M, Schweighofer N, Wang DJJ, Liew SL. Differences in high-definition transcranial direct current stimulation over the motor hotspot versus the premotor cortex on motor network excitability. Sci Rep 2019; 9:17605. [PMID: 31772347 PMCID: PMC6879500 DOI: 10.1038/s41598-019-53985-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 11/06/2019] [Indexed: 01/07/2023] Open
Abstract
The effectiveness of transcranial direct current stimulation (tDCS) placed over the motor hotspot (thought to represent the primary motor cortex (M1)) to modulate motor network excitability is highly variable. The premotor cortex-particularly the dorsal premotor cortex (PMd)-may be a promising alternative target to reliably modulate motor excitability, as it influences motor control across multiple pathways, one independent of M1 and one with direct connections to M1. This double-blind, placebo-controlled preliminary study aimed to differentially excite motor and premotor regions using high-definition tDCS (HD-tDCS) with concurrent functional magnetic resonance imaging (fMRI). HD-tDCS applied over either the motor hotspot or the premotor cortex demonstrated high inter-individual variability in changes on cortical motor excitability. However, HD-tDCS over the premotor cortex led to a higher number of responders and greater changes in local fMRI-based complexity than HD-tDCS over the motor hotspot. Furthermore, an analysis of individual motor hotspot anatomical locations revealed that, in more than half of the participants, the motor hotspot is not located over anatomical M1 boundaries, despite using a canonical definition of the motor hotspot. This heterogeneity in stimulation site may contribute to the variability of tDCS results. Altogether, these preliminary findings provide new considerations to enhance tDCS reliability.
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Affiliation(s)
- Stephanie Lefebvre
- Neural Plasticity and Neurorehabilitation Laboratory, Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Kay Jann
- Laboratory of FMRI Technology (LOFT), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Allie Schmiesing
- Neural Plasticity and Neurorehabilitation Laboratory, Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Kaori Ito
- Neural Plasticity and Neurorehabilitation Laboratory, Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Mayank Jog
- Laboratory of FMRI Technology (LOFT), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Nicolas Schweighofer
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA
| | - Danny J J Wang
- Laboratory of FMRI Technology (LOFT), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Sook-Lei Liew
- Neural Plasticity and Neurorehabilitation Laboratory, Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States.
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
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13
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Cirillo R, Ferrucci L, Marcos E, Ferraina S, Genovesio A. Coding of Self and Other's Future Choices in Dorsal Premotor Cortex during Social Interaction. Cell Rep 2019; 24:1679-1686. [PMID: 30110624 DOI: 10.1016/j.celrep.2018.07.030] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 05/04/2018] [Accepted: 07/09/2018] [Indexed: 12/26/2022] Open
Abstract
Representing others' intentions is central to primate social life. We explored the role of dorsal premotor cortex (PMd) in discriminating between self and others' behavior while two male rhesus monkeys performed a non-match-to-goal task in a monkey-human paradigm. During each trial, two of four potential targets were randomly presented on the right and left parts of a screen, and the monkey or the human was required to choose the one that did not match the previously chosen target. Each agent had to monitor the other's action in order to select the correct target in that agent's own turn. We report neurons that selectively encoded the future choice of the monkey, the human agent, or both. Our findings suggest that PMd activity shows a high degree of self-other differentiation during face-to-face interactions, leading to an independent representation of what others will do instead of entailing self-centered mental rehearsal or mirror-like activities.
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Affiliation(s)
- Rossella Cirillo
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy; PhD program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy; PhD program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Encarni Marcos
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Stefano Ferraina
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy.
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14
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Mount CW, Yalçın B, Cunliffe-Koehler K, Sundaresh S, Monje M. Monosynaptic tracing maps brain-wide afferent oligodendrocyte precursor cell connectivity. eLife 2019; 8:e49291. [PMID: 31625910 PMCID: PMC6800000 DOI: 10.7554/elife.49291] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 10/06/2019] [Indexed: 12/20/2022] Open
Abstract
Neurons form bona fide synapses with oligodendrocyte precursor cells (OPCs), but the circuit context of these neuron to OPC synapses remains incompletely understood. Using monosynaptically-restricted rabies virus tracing of OPC afferents, we identified extensive afferent synaptic inputs to OPCs residing in secondary motor cortex, corpus callosum, and primary somatosensory cortex of adult mice. These inputs primarily arise from functionally-interconnecting cortical areas and thalamic nuclei, illustrating that OPCs have strikingly comprehensive synaptic access to brain-wide projection networks. Quantification of these inputs revealed excitatory and inhibitory components that are consistent in number across brain regions and stable in barrel cortex despite whisker trimming-induced sensory deprivation.
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Affiliation(s)
- Christopher W Mount
- Department of NeurologyStanford UniversityStanfordUnited States
- Medical Scientist Training ProgramStanford UniversityStanfordUnited States
- Neurosciences Graduate ProgramStanford UniversityStanfordUnited States
| | - Belgin Yalçın
- Department of NeurologyStanford UniversityStanfordUnited States
| | | | - Shree Sundaresh
- Department of NeurologyStanford UniversityStanfordUnited States
| | - Michelle Monje
- Department of NeurologyStanford UniversityStanfordUnited States
- Institute for Stem Cell Biology and Regenerative MedicineStanford UniversityStanfordUnited States
- Department of PathologyStanford UniversityStanfordUnited States
- Department of PediatricsStanford UniversityStanfordUnited States
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15
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Abstract
Newly learned motor skills are initially labile and then consolidated to permit retention. The circuits that enable the consolidation of motor memories remain uncertain. Most work to date has focused on primary motor cortex, and although there is ample evidence of learning-related plasticity in motor cortex, direct evidence for its involvement in memory consolidation is limited. Learning-related plasticity is also observed in somatosensory cortex, and accordingly, it may also be involved in memory consolidation. Here, by using transcranial magnetic stimulation (TMS) to block consolidation, we report the first direct evidence that plasticity in somatosensory cortex participates in the consolidation of motor memory. Participants made movements to targets while a robot applied forces to the hand to alter somatosensory feedback. Immediately following adaptation, continuous theta-burst transcranial magnetic stimulation (cTBS) was delivered to block retention; then, following a 24-hour delay, which would normally permit consolidation, we assessed whether there was an impairment. It was found that when mechanical loads were introduced gradually to engage implicit learning processes, suppression of somatosensory cortex following training almost entirely eliminated retention. In contrast, cTBS to motor cortex following learning had little effect on retention at all; retention following cTBS to motor cortex was not different than following sham TMS stimulation. We confirmed that cTBS to somatosensory cortex interfered with normal sensory function and that it blocked motor memory consolidation and not the ability to retrieve a consolidated motor memory. In conclusion, the findings are consistent with the hypothesis that in adaptation learning, somatosensory cortex rather than motor cortex is involved in the consolidation of motor memory.
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Affiliation(s)
- Neeraj Kumar
- McGill University, Montreal, Canada
- Indian Institute of Technology Gandhinagar, Gandhinagar, India
| | | | - David J. Ostry
- McGill University, Montreal, Canada
- Haskins Laboratories, New Haven, Connecticut, United States of America
- * E-mail:
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16
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Tavor I, Botvinik‐Nezer R, Bernstein‐Eliav M, Tsarfaty G, Assaf Y. Short-term plasticity following motor sequence learning revealed by diffusion magnetic resonance imaging. Hum Brain Mapp 2019; 41:442-452. [PMID: 31596547 PMCID: PMC7267908 DOI: 10.1002/hbm.24814] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 09/12/2019] [Accepted: 09/22/2019] [Indexed: 01/06/2023] Open
Abstract
Current noninvasive methods to detect structural plasticity in humans are mainly used to study long-term changes. Diffusion magnetic resonance imaging (MRI) was recently proposed as a novel approach to reveal gray matter changes following spatial navigation learning and object-location memory tasks. In the present work, we used diffusion MRI to investigate the short-term neuroplasticity that accompanies motor sequence learning. Following a 45-min training session in which participants learned to accurately play a short sequence on a piano keyboard, changes in diffusion properties were revealed mainly in motor system regions such as the premotor cortex and cerebellum. In a second learning session taking place immediately afterward, feedback was given on the timing of key pressing instead of accuracy, while participants continued to learn. This second session induced a different plasticity pattern, demonstrating the dynamic nature of learning-induced plasticity, formerly thought to require months of training in order to be detectable. These results provide us with an important reminder that the brain is an extremely dynamic structure. Furthermore, diffusion MRI offers a novel measure to follow tissue plasticity particularly over short timescales, allowing new insights into the dynamics of structural brain plasticity.
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Affiliation(s)
- Ido Tavor
- Department of Anatomy and Anthropology, Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Affiliated to the Sackler School of MedicineTel Aviv UniversityTel AvivIsrael
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
| | - Rotem Botvinik‐Nezer
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
- Department of Neurobiology, Faculty of Life SciencesTel Aviv UniversityTel AvivIsrael
| | - Michal Bernstein‐Eliav
- Department of Anatomy and Anthropology, Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
| | - Galia Tsarfaty
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Affiliated to the Sackler School of MedicineTel Aviv UniversityTel AvivIsrael
| | - Yaniv Assaf
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
- Department of Neurobiology, Faculty of Life SciencesTel Aviv UniversityTel AvivIsrael
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17
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Little S, Bonaiuto J, Barnes G, Bestmann S. Human motor cortical beta bursts relate to movement planning and response errors. PLoS Biol 2019; 17:e3000479. [PMID: 31584933 PMCID: PMC6795457 DOI: 10.1371/journal.pbio.3000479] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 10/16/2019] [Accepted: 09/10/2019] [Indexed: 11/30/2022] Open
Abstract
Motor cortical beta activity (13-30 Hz) is a hallmark signature of healthy and pathological movement, but its behavioural relevance remains unclear. Using high-precision magnetoencephalography (MEG), we show that during the classical event-related desynchronisation (ERD) and event-related synchronisation (ERS) periods, motor cortical beta activity in individual trials (n > 12,000) is dominated by high amplitude, transient, and infrequent bursts. Beta burst probability closely matched the trial-averaged beta amplitude in both the pre- and post-movement periods, but individual bursts were spatially more focal than the classical ERS peak. Furthermore, prior to movement (ERD period), beta burst timing was related to the degree of motor preparation, with later bursts resulting in delayed response times. Following movement (ERS period), the first beta burst was delayed by approximately 100 milliseconds when an incorrect response was made. Overall, beta burst timing was a stronger predictor of single trial behaviour than beta burst rate or single trial beta amplitude. This transient nature of motor cortical beta provides new constraints for theories of its role in information processing within and across cortical circuits, and its functional relevance for behaviour in both healthy and pathological movement.
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Affiliation(s)
- Simon Little
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, United Kingdom
- Department of Neurology, University of San Francisco, California, United States of America
| | - James Bonaiuto
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, United Kingdom
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Bron, France
- Université Claude Bernard Lyon I, Lyon, France
| | - Gareth Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Sven Bestmann
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, United Kingdom
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
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18
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Aoki S, Smith JB, Li H, Yan X, Igarashi M, Coulon P, Wickens JR, Ruigrok TJH, Jin X. An open cortico-basal ganglia loop allows limbic control over motor output via the nigrothalamic pathway. eLife 2019; 8:e49995. [PMID: 31490123 PMCID: PMC6731092 DOI: 10.7554/elife.49995] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 08/26/2019] [Indexed: 01/08/2023] Open
Abstract
Cortico-basal ganglia-thalamocortical loops are largely conceived as parallel circuits that process limbic, associative, and sensorimotor information separately. Whether and how these functionally distinct loops interact remains unclear. Combining genetic and viral approaches, we systemically mapped the limbic and motor cortico-basal ganglia-thalamocortical loops in rodents. Despite largely closed loops within each functional domain, we discovered a unidirectional influence of the limbic over the motor loop via ventral striatum-substantia nigra (SNr)-motor thalamus circuitry. Slice electrophysiology verifies that the projection from ventral striatum functionally inhibits nigro-thalamic SNr neurons. In vivo optogenetic stimulation of ventral or dorsolateral striatum to SNr pathway modulates activity in medial prefrontal cortex (mPFC) and motor cortex (M1), respectively. However, whereas the dorsolateral striatum-SNr pathway exerts little impact on mPFC, activation of the ventral striatum-SNr pathway effectively alters M1 activity. These results demonstrate an open cortico-basal ganglia loop whereby limbic information could modulate motor output through ventral striatum control of M1.
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Affiliation(s)
- Sho Aoki
- Molecular Neurobiology LaboratorySalk Institute for Biological StudiesLa JollaUnited States
- Neurobiology Research UnitOkinawa Institute of Science and TechnologyOkinawaJapan
- Department of NeuroscienceErasmus Medical Center RotterdamRotterdamNetherlands
- Japan Society for the Promotion of SciencesTokyoJapan
| | - Jared B Smith
- Molecular Neurobiology LaboratorySalk Institute for Biological StudiesLa JollaUnited States
| | - Hao Li
- Molecular Neurobiology LaboratorySalk Institute for Biological StudiesLa JollaUnited States
| | - Xunyi Yan
- Molecular Neurobiology LaboratorySalk Institute for Biological StudiesLa JollaUnited States
| | - Masakazu Igarashi
- Neurobiology Research UnitOkinawa Institute of Science and TechnologyOkinawaJapan
- Japan Society for the Promotion of SciencesTokyoJapan
| | - Patrice Coulon
- Institut des Neurosciences de la TimoneCentre National de la Recherche Scientifique (CNRS), Aix-Marseille UniversitéMarseilleFrance
| | - Jeffery R Wickens
- Neurobiology Research UnitOkinawa Institute of Science and TechnologyOkinawaJapan
| | - Tom JH Ruigrok
- Department of NeuroscienceErasmus Medical Center RotterdamRotterdamNetherlands
| | - Xin Jin
- Molecular Neurobiology LaboratorySalk Institute for Biological StudiesLa JollaUnited States
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19
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Pilacinski A, Wallscheid M, Lindner A. Human posterior parietal and dorsal premotor cortex encode the visual properties of an upcoming action. PLoS One 2018; 13:e0198051. [PMID: 30300356 PMCID: PMC6177124 DOI: 10.1371/journal.pone.0198051] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 09/15/2018] [Indexed: 11/18/2022] Open
Abstract
Behavioral studies show that motor actions are planned by adapting motor programs to produce desired visual consequences. Does this mean that the brain plans these visual consequences independent of the motor actions required to obtain them? Here we addressed this question by investigating planning-related fMRI activity in human posterior parietal (PPC) and dorsal premotor (PMd) cortex. By manipulating visual movement of a virtual end-effector controlled via button presses we could dissociate motor actions from their sensory outcome. A clear representation of the visual consequences was visible in both PPC and PMd activity during early planning stages. Our findings suggest that in both PPC and PMd action plans are initially represented on the basis of the desired sensory outcomes while later activity shifts towards representing motor programs.
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Affiliation(s)
- Artur Pilacinski
- Hertie-Institute for Clinical Brain Research, Department of Cognitive Neurology, Tuebingen, Germany
- * E-mail:
| | - Melanie Wallscheid
- Hertie-Institute for Clinical Brain Research, Department of Cognitive Neurology, Tuebingen, Germany
| | - Axel Lindner
- Hertie-Institute for Clinical Brain Research, Department of Cognitive Neurology, Tuebingen, Germany
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20
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Hooks BM, Papale AE, Paletzki RF, Feroze MW, Eastwood BS, Couey JJ, Winnubst J, Chandrashekar J, Gerfen CR. Topographic precision in sensory and motor corticostriatal projections varies across cell type and cortical area. Nat Commun 2018; 9:3549. [PMID: 30177709 PMCID: PMC6120881 DOI: 10.1038/s41467-018-05780-7] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 07/25/2018] [Indexed: 12/17/2022] Open
Abstract
The striatum shows general topographic organization and regional differences in behavioral functions. How corticostriatal topography differs across cortical areas and cell types to support these distinct functions is unclear. This study contrasted corticostriatal projections from two layer 5 cell types, intratelencephalic (IT-type) and pyramidal tract (PT-type) neurons, using viral vectors expressing fluorescent reporters in Cre-driver mice. Corticostriatal projections from sensory and motor cortex are somatotopic, with a decreasing topographic specificity as injection sites move from sensory to motor and frontal areas. Topographic organization differs between IT-type and PT-type neurons, including injections in the same site, with IT-type neurons having higher topographic stereotypy than PT-type neurons. Furthermore, IT-type projections from interconnected cortical areas have stronger correlations in corticostriatal targeting than PT-type projections do. As predicted by a longstanding model, corticostriatal projections of interconnected cortical areas form parallel circuits in the basal ganglia.
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Affiliation(s)
- Bryan M Hooks
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
| | - Andrew E Papale
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Muhammad W Feroze
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Jonathan J Couey
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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21
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Abstract
Topographic regularity is a fundamental property in brain connectivity. In this work, we present a novel method for studying topographic regularity of functional connectivity based on resting-state fMRI (rfMRI), which is widely available and easy to acquire in large-scale studies. The main idea in our method is the incorporation of topographically regular structural connectivity for independent component analysis (ICA). This is enabled by the recent development of novel tractography and tract filtering algorithms that can generate highly organized fiber bundles connecting different brain regions. By leveraging these cutting-edge tractography algorithms, here we develop a kernel-regularized ICA method for the extraction of functional topography with rfMRI signals. In our experiments, we use rfMRI scans of 35 unrelated, right-handed subjects from the Human Connectome Project (HCP) to study the functional topography of the motor cortex. We first demonstrate that our method can generate functional connectivity maps with more regular topography than conventional group ICA. We also show that the components extracted by our algorithm are able to capture co-activation patterns that respect the organized topography of the motor cortex across the hemisphere. Finally, we show that our method achieves improved reproducibility as compared to conventional group ICA.
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Affiliation(s)
- Junyan Wang
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
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Barthas F, Kwan AC. Secondary Motor Cortex: Where 'Sensory' Meets 'Motor' in the Rodent Frontal Cortex. Trends Neurosci 2017; 40:181-193. [PMID: 28012708 PMCID: PMC5339050 DOI: 10.1016/j.tins.2016.11.006] [Citation(s) in RCA: 132] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Revised: 11/28/2016] [Accepted: 11/29/2016] [Indexed: 12/15/2022]
Abstract
In rodents, the medial aspect of the secondary motor cortex (M2) is known by other names, including medial agranular cortex (AGm), medial precentral cortex (PrCm), and frontal orienting field (FOF). As a subdivision of the medial prefrontal cortex (mPFC), M2 can be defined by a distinct set of afferent and efferent connections, microstimulation responses, and lesion outcomes. However, the behavioral role of M2 remains mysterious. Here, we focus on evidence from rodent studies, highlighting recent findings of early and context-dependent choice-related activity in M2 during voluntary behavior. Based on the current understanding, we suggest that a major function for M2 is to flexibly map antecedent signals such as sensory cues to motor actions, thereby enabling adaptive choice behavior.
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Affiliation(s)
- Florent Barthas
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Alex C Kwan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA.
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Mel'nikov MY, Savelov AA, Shtark MB, Pokrovskiy MA, Petrovskiy ED, Kozlova LI, Mazhirina KG, Bezmaternikh DD. [Experience of the Continuous Real Time fMRI Biofeedback of the Primary Motor Cortex Using a 1.5 T MR Scanner]. Zh Vyssh Nerv Deiat Im I P Pavlova 2017; 67:83-92. [PMID: 30695553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The neurofeedback based on the motor areas fMRI signal may be a promising treatment for improving motor impairment in post-stroke conditions and Parkinson's disease. In the majority of the studies has been conducted using the 3 T MR machines, and the region of interest has been placed to the secondary motor areas. The current study attempted to perform an fMRI neurofeeback based on response of the right hand projection locus within primary motor cortex utilizing the 1.5 T MR scanner and using the optimal parameters for the named magnetic field strength. The subjects were 16 healthy participants who underwent a 30-minute imaging session comprised 1) individual func- tional localization of the region of interest (using the hand clinging task) and attempts to control its activity with 2) motor imagery and 3) any cognitive strategy chosen by participant. In both self-regulation conditions subjects activated G. precentralis, G. cinguli anterior, G. frontalis superior, G. parietalis inferior, and 6-th Brodman area. Activation maps for these two tasks didn't differ one from another significantly, and the involved area had only a few overlays with the region of interest map which signifies that training was unsucessful. The limitations of the study and factors influenc- ing the biofeedback efficacy negatively are discussed.
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Harris R, van Kranenburg P, de Jong BM. Behavioral Quantification of Audiomotor Transformations in Improvising and Score-Dependent Musicians. PLoS One 2016; 11:e0166033. [PMID: 27835631 PMCID: PMC5105996 DOI: 10.1371/journal.pone.0166033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Accepted: 10/22/2016] [Indexed: 11/18/2022] Open
Abstract
The historically developed practice of learning to play a music instrument from notes instead of by imitation or improvisation makes it possible to contrast two types of skilled musicians characterized not only by dissimilar performance practices, but also disparate methods of audiomotor learning. In a recent fMRI study comparing these two groups of musicians while they either imagined playing along with a recording or covertly assessed the quality of the performance, we observed activation of a right-hemisphere network of posterior superior parietal and dorsal premotor cortices in improvising musicians, indicating more efficient audiomotor transformation. In the present study, we investigated the detailed performance characteristics underlying the ability of both groups of musicians to replicate music on the basis of aural perception alone. Twenty-two classically-trained improvising and score-dependent musicians listened to short, unfamiliar two-part excerpts presented with headphones. They played along or replicated the excerpts by ear on a digital piano, either with or without aural feedback. In addition, they were asked to harmonize or transpose some of the excerpts either to a different key or to the relative minor. MIDI recordings of their performances were compared with recordings of the aural model. Concordance was expressed in an audiomotor alignment score computed with the help of music information retrieval algorithms. Significantly higher alignment scores were found when contrasting groups, voices, and tasks. The present study demonstrates the superior ability of improvising musicians to replicate both the pitch and rhythm of aurally perceived music at the keyboard, not only in the original key, but also in other tonalities. Taken together with the enhanced activation of the right dorsal frontoparietal network found in our previous fMRI study, these results underscore the conclusion that the practice of improvising music can be associated with enhanced audiomotor transformation in response to aurally perceived music.
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Affiliation(s)
- Robert Harris
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- BCN Neuroimaging Center, University of Groningen, Groningen, The Netherlands
- Prince Claus Conservatoire, Hanze University of Applied Sciences, Groningen, The Netherlands
- * E-mail:
| | | | - Bauke M. de Jong
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- BCN Neuroimaging Center, University of Groningen, Groningen, The Netherlands
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25
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Abstract
One way to understand the topography of the cerebral cortex is that “like attracts like.” The cortex is organized to maximize nearest neighbor similarity. This principle can explain the separation of the cortex into discrete areas that emphasize different information domains. It can also explain the maps that form within cortical areas. However, because the cortex is two-dimensional, when a parameter space of much higher dimensionality is reduced onto the cortical sheet while optimizing nearest neighbor relationships, the result may lack an obvious global ordering into separate areas. Instead, the topography may consist of partial gradients, fractures, swirls, regions that resemble separate areas in some ways but not others, and in not a lack of topographic maps but an excess of maps overlaid on each other, no one of which seems to be entirely correct. Like a canvas in a gallery of modern art that no two observers interpret the same way, this lack of obvious ordering of high-dimensional spaces onto the cortex might then result in some scientific controversy over the true organization. In this review, the authors suggest that at least some sectors of the cortex do not have a simple global ordering and are better understood as a result of a reduction of a high-dimensional space onto the cortical sheet. The cortical motor system may be an example of this phenomenon. The authors discuss a model of the lateral motor cortex in which a reduction of many parameters onto a simulated cortical sheet results in a complex topographic pattern that matches the actual monkey motor cortex in surprising detail. Some of the ambiguities of topography and areal boundaries that have plagued the attempt to systematize the lateral motor cortex are explained by the model. NEUROSCIENTIST the attempt to syste 13(2):138—147, 2007.
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Abstract
Brodmann's cytoarchitectonic map of the human cortex designates area 4 as cortex in the anterior bank of the precentral sulcus and area 6 as cortex encompassing the precentral gyrus and the posterior portion of the superior frontal gyrus on both the lateral and medial surfaces of the brain. More than 70 years ago, Fulton proposed a functional distinction between these two areas, coining the terms primary motor areafor cortex in Brodmann area 4 and premotor areafor cortex in Brodmann area 6. The parcellation of the cortical motor system has subsequently become more complex. Several nonprimary motor areas have been identified in the brain of the macaque monkey, and associations between anatomy and function in the human brain are being tested continuously using brain mapping techniques. In the present review, the authors discuss the unique properties of the primary motor area (M1), the dorsal portion of the premotor cortex (PMd), and the ventral portion of the premotor cortex (PMv). They end this review by discussing how the premotor areas influence M1.
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Affiliation(s)
- Philippe A Chouinard
- Cognitive Neuroscience Unit, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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27
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Abstract
The corticospinal system is the principal motor system for controlling movements that require the greatest skill and flexibility. It is the last motor system to develop. The pattern of termination of corticospinal axons, as they grow into the spinal gray matter, bears little resemblance to the pattern later in development and in maturity. Refinement of corticospinal terminations occurs during a protracted postnatal period and includes both elimination of transient terminations and growth to new targets. This refinement is driven by neural activity in the motor cortical areas and by limb motor experience. Developing corticospinal terminals compete with each other for synaptic space on spinal neurons. More active terminals are more competitive and are able to secure more synaptic space than their less active counterparts. Corticospinal terminals can activate spinal neurons from very early in development. The importance of this early synaptic activity appears to be more for refining corticospinal connections than for transmitting signals to spinal motor circuits for movement control. The motor control functions of the corticospinal system are not expressed until development of connectional specificity with spinal cord neurons, a strong capacity for corticospinal synapses to facilitate spinal motor circuits, and the formation of the cortical motor map.
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Affiliation(s)
- John H Martin
- Center for Neurology and Behavior, Columbia University, 1051 Riverside Drive, New York, NY 10032, USA.
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28
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Houdayer E, Cursi M, Nuara A, Zanini S, Gatti R, Comi G, Leocani L. Cortical Motor Circuits after Piano Training in Adulthood: Neurophysiologic Evidence. PLoS One 2016; 11:e0157526. [PMID: 27309353 PMCID: PMC4911097 DOI: 10.1371/journal.pone.0157526] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 06/01/2016] [Indexed: 11/24/2022] Open
Abstract
The neuronal mechanisms involved in brain plasticity after skilled motor learning are not completely understood. We aimed to study the short-term effects of keyboard training in music-naive subjects on the motor/premotor cortex activity and interhemispheric interactions, using electroencephalography and transcranial magnetic stimulation (TMS). Twelve subjects (experimental group) underwent, before and after a two week-piano training: (1) hand-motor function tests: Jamar, grip and nine-hole peg tests; (2) electroencephalography, evaluating the mu rhythm task-related desynchronization (TRD) during keyboard performance; and (3) TMS, targeting bilateral abductor pollicis brevis (APB) and abductor digiti minimi (ADM), to obtain duration and area of ipsilateral silent period (ISP) during simultaneous tonic contraction of APB and ADM. Data were compared with 13 controls who underwent twice these measurements, in a two-week interval, without undergoing piano training. Every subject in the experimental group improved keyboard performance and left-hand nine-hole peg test scores. Pre-training, ISP durations were asymmetrical, left being longer than right. Post-training, right ISPAPB increased, leading to symmetrical ISPAPB. Mu TRD during motor performance became more focal and had a lesser amplitude than in pre-training, due to decreased activity over ventral premotor cortices. No such changes were evidenced in controls. We demonstrated that a 10-day piano-training was associated with balanced interhemispheric interactions both at rest and during motor activation. Piano training, in a short timeframe, may reshape local and inter-hemispheric motor cortical circuits.
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Affiliation(s)
- Elise Houdayer
- Experimental Neurophysiology Unit, Institute of Experimental Neurology (INSPE), San Raffaele Scientific Institute, Milan, Italy
- * E-mail:
| | - Marco Cursi
- Experimental Neurophysiology Unit, Institute of Experimental Neurology (INSPE), San Raffaele Scientific Institute, Milan, Italy
| | - Arturo Nuara
- Experimental Neurophysiology Unit, Institute of Experimental Neurology (INSPE), San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, San Raffaele Scientific Institute, Milan, Italy
| | - Sonia Zanini
- Experimental Neurophysiology Unit, Institute of Experimental Neurology (INSPE), San Raffaele Scientific Institute, Milan, Italy
- Laboratory of Movement Analysis, San Raffaele Scientific Institute, Milan, Italy
| | - Roberto Gatti
- Laboratory of Movement Analysis, San Raffaele Scientific Institute, Milan, Italy
| | - Giancarlo Comi
- Experimental Neurophysiology Unit, Institute of Experimental Neurology (INSPE), San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, San Raffaele Scientific Institute, Milan, Italy
| | - Letizia Leocani
- Experimental Neurophysiology Unit, Institute of Experimental Neurology (INSPE), San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, San Raffaele Scientific Institute, Milan, Italy
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Ipekchyan NM, Badalyan SA. [THE DISTRIBUTION OF CORTICO-THALAMIC PROJECTIONS OF DIFFERENT OF DIFFERENT SOMATOTOPIC REPRESENTATIONS OF PRIMARY MOTOR AND SENSORY CORTEX]. Morfologiia 2016; 149:15-21. [PMID: 27487657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The peculiarities of localization and distribution of cortico-thalamic efferents of different somatotopical representations of primary motor (MI) and sensory (SI) cortex were studied in cat brain. MI efferent fibers (4y, 6ab areas) preferentially projected to ventral posterolateral and medial (VPL, VPM), ventrolateral (VL), and reticular (R) nuclei, localized in rostral part of the thalamus (T), as opposed to SI (areas 1, 2, 3a, 3b), which projected preferentially to caudal part of T, VPL, VPM and R nuclei. Latero-medial organization of cortico-thalamic connections was demonstrated, with predominant localization of cortical representation of hindlimbs in the lateral part of VPL, of forelimbs--in the medial part of VPL, of face and head--also in VM and VPM. Quantitative analysis of the distribution of corticothalamic efferents of different somatotopical representations of MI has demonstrated the most extensive, massive connections with T nuclei (VPL, VL, R) of the motor representation of forelimb, followed by the representation of hindlimb, trunk and, finally, the minimal projection of the representation of face and head. As opposed to motor representation of the forelimb and also of the face and head, with uniform distribution of fibers in VPL, VL and R, the number of efferents of motor representation of hindlimb, passing in VL, was almost 2.5 time lower than in VPL and R, whereas the representation of trunk had the predominant projection to VL. Dominant cortico-thalamic connection suggests greater involvement of T nuclei studied in the realization of functional specialization of certain somatotopical representations of MI.
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Lee HJ, Preda A, Ford JM, Mathalon DH, Keator DB, van Erp TG, Turner JA, Potkin SG. Functional magnetic resonance imaging of motor cortex activation in schizophrenia. J Korean Med Sci 2015; 30:625-31. [PMID: 25931795 PMCID: PMC4414648 DOI: 10.3346/jkms.2015.30.5.625] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Accepted: 01/20/2015] [Indexed: 11/22/2022] Open
Abstract
Previous fMRI studies of sensorimotor activation in schizophrenia have found in some cases hypoactivity, no difference, or hyperactivity when comparing patients with controls; similar disagreement exists in studies of motor laterality. In this multi-site fMRI study of a sensorimotor task in individuals with chronic schizophrenia and matched healthy controls, subjects responded with a right-handed finger press to an irregularly flashing visual checker board. The analysis includes eighty-five subjects with schizophrenia diagnosed according to the DSM-IV criteria and eighty-six healthy volunteer subjects. Voxel-wise statistical parametric maps were generated for each subject and analyzed for group differences; the percent Blood Oxygenation Level Dependent (BOLD) signal changes were also calculated over predefined anatomical regions of the primary sensory, motor, and visual cortex. Both healthy controls and subjects with schizophrenia showed strongly lateralized activation in the precentral gyrus, inferior frontal gyrus, and inferior parietal lobule, and strong activations in the visual cortex. There were no significant differences between subjects with schizophrenia and controls in this multi-site fMRI study. Furthermore, there was no significant difference in laterality found between healthy controls and schizophrenic subjects. This study can serve as a baseline measurement of schizophrenic dysfunction in other cognitive processes.
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Affiliation(s)
- Hyo Jong Lee
- Division of Computer Science and Engineering, CAIIT, Chonbuk National University, Jeonju, Korea
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Judith M. Ford
- Department of Psychiatry, San Francisco VAMC and University of California, San Francisco, CA, USA
| | - Daniel H. Mathalon
- Department of Psychiatry, San Francisco VAMC and University of California, San Francisco, CA, USA
| | - David B. Keator
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Theo G.M. van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Jessica A. Turner
- Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, USA
| | - Steven G. Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
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Vedder A, Smigielski L, Gutyrchik E, Bao Y, Blautzik J, Pöppel E, Zaytseva Y, Russell E. Neurofunctional correlates of environmental cognition: an FMRI study with images from episodic memory. PLoS One 2015; 10:e0122470. [PMID: 25875000 PMCID: PMC4397013 DOI: 10.1371/journal.pone.0122470] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Accepted: 02/11/2015] [Indexed: 11/18/2022] Open
Abstract
This study capitalizes on individual episodic memories to investigate the question, how dif-ferent environments affect us on a neural level. Instead of using predefined environmental stimuli, this study relied on individual representations of beauty and pleasure. Drawing upon episodic memories we conducted two experiments. Healthy subjects imagined pleasant and non-pleasant environments, as well as beautiful and non-beautiful environments while neural activity was measured by using functional Magnetic Resonance Imaging. Although subjects found the different conditions equally simple to visualize, our results revealed more distribut-ed brain activations for non-pleasant and non-beautiful environments than for pleasant and beautiful environments. The additional regions activated in non-pleasant (left lateral prefrontal cortex) and non-beautiful environments (supplementary motor area, anterior cortical midline structures) are involved in self-regulation and top-down cognitive control. Taken together, the results show that perceptual experiences and emotional evaluations of environments within a positive and a negative frame of reference are based on distinct patterns of neural activity. We interpret the data in terms of a different cognitive and processing load placed by exposure to different environments. The results hint at the efficiency of subject-generated representations as stimulus material.
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Affiliation(s)
- Aline Vedder
- Human Science Center, Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Institute of Sociology, (LMU), Munich, Germany
- Institute of Medical Psychology, (LMU), Munich, Germany
- Parmenides Center for Art and Science, Pullach, Germany
| | - Lukasz Smigielski
- Human Science Center, Ludwig-Maximilians-Universität (LMU), Munich, Germany
| | - Evgeny Gutyrchik
- Human Science Center, Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Institute of Medical Psychology, (LMU), Munich, Germany
- Parmenides Center for Art and Science, Pullach, Germany
- * E-mail:
| | - Yan Bao
- Human Science Center, Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Parmenides Center for Art and Science, Pullach, Germany
- Department of Psychology, Peking University, Beijing, People’s Republic of China
- Key Laboratory of Machine Perception, Peking University, Beijing, People’s Republic of China
| | | | - Ernst Pöppel
- Human Science Center, Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Institute of Medical Psychology, (LMU), Munich, Germany
- Parmenides Center for Art and Science, Pullach, Germany
- Department of Psychology, Peking University, Beijing, People’s Republic of China
| | - Yuliya Zaytseva
- Human Science Center, Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Institute of Medical Psychology, (LMU), Munich, Germany
- Parmenides Center for Art and Science, Pullach, Germany
- Moscow Research Institute of Psychiatry, Moscow, Russia
| | - Edmund Russell
- Human Science Center, Ludwig-Maximilians-Universität (LMU), Munich, Germany
- Department of History, University of Kansas, Lawrence, United States of America
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Abstract
The stratified motor cortex is variously thought to either lack or contain layer 4. Yamawaki et al. described a functional layer 4 in mouse motor cortex with properties and connections similar to layer 4 in sensory areas. Their results bolster a theoretical framework suggesting all primary cortical areas are equivalent.
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Affiliation(s)
- Helen Barbas
- Boston University, Neural Systems Lab, 635 Commonwealth Ave., Boston, MA 02215, USA.
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Sun W, May PJ. Central pupillary light reflex circuits in the cat: I. The olivary pretectal nucleus. J Comp Neurol 2014; 522:3960-77. [PMID: 24706328 PMCID: PMC4185307 DOI: 10.1002/cne.23602] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2014] [Revised: 04/01/2014] [Accepted: 04/03/2014] [Indexed: 12/18/2022]
Abstract
The central pathways subserving the feline pupillary light reflex were examined by defining retinal input to the olivary pretectal nucleus (OPt), the midbrain projections of this nucleus, and the premotor neurons within it. Unilateral intravitreal wheat germ agglutinin-conjugated horseradish peroxidase (WGA-HRP) injections revealed differences in the pattern of retinal OPt termination on the two sides. Injections of WGA-HRP into OPt labeled terminals bilaterally in the anteromedian nucleus, and to a lesser extent in the supraoculomotor area, centrally projecting Edinger-Westphal nucleus, and nucleus of the posterior commissure. Labeled terminals, as well as retrogradely labeled multipolar cells, were present in the contralateral OPt, indicating a commissural pathway. Injections of WGA-HRP into the anteromedian nucleus labeled fusiform premotor neurons within the OPt, as well as multipolar cells in the nucleus of the posterior commissure. Connections between retinal terminals and the pretectal premotor neurons were characterized by combining vitreous chamber and anteromedian nucleus injections of WGA-HRP in the same animal. Fusiform-shaped, retrogradely labeled cells fell within the anterogradely labeled retinal terminal field in the OPt. Ultrastructural analysis revealed labeled retinal terminals containing clear spherical vesicles. They contacted labeled pretectal premotor neurons via asymmetric synaptic densities. These results provide an anatomical substrate for the pupillary light reflex in the cat. Pretectal premotor neurons receive direct retinal input via synapses suggestive of an excitatory drive, and project directly to nuclei containing preganglionic motoneurons. These projections are concentrated in the anteromedian nucleus, indicating its involvement in the pupillary light reflex.
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Affiliation(s)
- Wensi Sun
- Department of Neurobiology & Anatomical Sciences, University of Mississippi Medical Center, Jackson, MS 39216 U.S.A
| | - Paul J. May
- Department of Neurobiology & Anatomical Sciences, University of Mississippi Medical Center, Jackson, MS 39216 U.S.A
- Department of Ophthalmology, University of Mississippi Medical Center, Jackson, MS 39216 U.S.A
- Department of Neurology, University of Mississippi Medical Center, Jackson, MS 39216 U.S.A
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Pavlova OG. [Primary motor cortex as one of the levels of the construction of movements]. Zh Vyssh Nerv Deiat Im I P Pavlova 2014; 64:600-614. [PMID: 25975137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The data obtained during the recent decades led to revision of the dominant in neurophysiology view of primary motor cortex as "the cord area" which transfers the motor commands to the spinal cord. Contrary to this point of view, it was shown that MI primates neurons participate in all stages of organization of motor behavior and that the final postures of complex coordinated movements are represented in the MI map. Characteristics of movements controlled by MI revealed by currently available methods were predicted and explained by N.A. Bernstein about 70 years ago. According to his concept, there are some levels of the construction of movements that exist in the central nervous system. Area 4 (i.e. MI), which is one of them, appeared on the definite stage of evolution for resolving the particular movement tasks. In support of this conception we are showing that: 1) MI controls the movements that differ from the movements of other levels by their characteristics (the mode of operating and the sense content); 2) some voluntary movements can be executed without participation of MI; 3) different motor areas of the cortex are coupled with different aspects of movement behavior.
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Cárdenas-Morales L, Grön G, Sim EJ, Stingl JC, Kammer T. Neural activation in humans during a simple motor task differs between BDNF polymorphisms. PLoS One 2014; 9:e96722. [PMID: 24828051 PMCID: PMC4020821 DOI: 10.1371/journal.pone.0096722] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2013] [Accepted: 04/10/2014] [Indexed: 12/04/2022] Open
Abstract
The BDNF Val66Met polymorphism has been linked to decreased synaptic plasticity involved in motor learning tasks. We investigated whether individual differences in this polymorphism may promote differences in neural activity during a two-alternative forced-choice motor performance. In two separate sessions, the BOLD signal from 22 right-handed healthy men was measured during button presses with the left and right index finger upon visual presentation of an arrow. 11 men were Val66Val carriers (ValVal group), the other 11 men carried either the Val66Met or the Met66Met polymorphism (Non-ValVal group). Reaction times, resting and active motor thresholds did not differ between ValVal and Non-ValVal groups. Compared to the ValVal group the Non-ValVal group showed significantly higher BOLD signals in the right SMA and motor cingulate cortex during motor performance. This difference was highly consistent for both hands and across all four sessions. Our finding suggests that this BDNF polymorphism may not only influence complex performance during motor learning but is already associated with activation differences during rather simple motor tasks. The higher BOLD signal observed in Non-ValVal subjects suggests the presence of cumulative effects of the polymorphism on the motor system, and may reflect compensatory functional activation mediating equal behavioral performance between groups.
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Affiliation(s)
- Lizbeth Cárdenas-Morales
- Department of Psychiatry, University of Ulm, Ulm, Germany
- Max Planck Institute for Neurological Research, Cologne, Germany
- Neurological Department, University of Cologne, Cologne, Germany
| | - Georg Grön
- Department of Psychiatry, University of Ulm, Ulm, Germany
| | - Eun-Jin Sim
- Department of Psychiatry, University of Ulm, Ulm, Germany
| | - Julia C. Stingl
- Institute of Pharmacology of Natural Products and Clinical Pharmacology, University of Ulm, Ulm, Germany
- Research Department, Federal Institute for Drugs and Medical Devices, Bonn, Germany
| | - Thomas Kammer
- Department of Psychiatry, University of Ulm, Ulm, Germany
- * E-mail:
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Aslanian EV, Kiroĭ VN, Lazurenko DM, Bakhtin OM, Miniaeva NR. [EEG spectral characteristics in the dynamics of planned movements]. Zh Vyssh Nerv Deiat Im I P Pavlova 2014; 64:147-158. [PMID: 25713865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The spectral characteristics of the EEG in 11 healthy right-handed volunteers with no neurological disorders in dynamics of the unusual finger movements of both hands in random rhythm have been investigated. In particular, it is shown that the preparation and execution of voluntary finger movements compared to the rest were accompanied by a reduction level of activation within almost all the surface of cerebral cortex, except for right frontal lobe. It has been experimentally demonstrated that a significant increasing EEG activity of the right frontal area in high-frequency domain power spectrum was independent of the working limb. This might testify the direct participation of this cortical area in the processes of voluntary muscular activity planning, initiation and control.
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38
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Pron'ko PK, Prokof'ev AO, Osadchiĭ AE, Chernyshev BV, Stroganova TA. [Functional dissociation of parts of the "sensorimotor complex" in the human cortex with the method of magnetoencephalography]. Zh Vyssh Nerv Deiat Im I P Pavlova 2014; 64:218-230. [PMID: 25713872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
A method of non-invasive human primary motor cortex mapping has been developed. In 18 healthy right-handed subjects magnetic brain responses caused by repeated voluntary left or right index finger movements were studied. Movement onsets were derived from the accelerometer signal. Recordings of magnetic activity throughout whole experimental sessions in all subjects were concatenated into a single sequence, which was separated into independent components using independent component analysis and ranked according to the amount of mutual information with the modified accelerometer signal. Independent components that demonstrated maximum relation to the finger movement were averaged relative to the movement onset. The results of the distributed brain source modeling of the two independent components that manifested maximum amount of mutual information has demonstrated that their sources localize in the cortical areas corresponding to anatomical markers of hand representation in the primary motor and the primary sensory cortices contralateral to the movement. The method developed has demonstrated the fundamental possibility of localizing the M1 area in healthy subjects.
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Krieg TD, Salinas FS, Narayana S, Fox PT, Mogul DJ. PET-based confirmation of orientation sensitivity of TMS-induced cortical activation in humans. Brain Stimul 2013; 6:898-904. [PMID: 23827648 PMCID: PMC5293002 DOI: 10.1016/j.brs.2013.05.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2012] [Revised: 05/16/2013] [Accepted: 05/25/2013] [Indexed: 10/26/2022] Open
Abstract
BACKGROUND Currently, it is difficult to predict precise regions of cortical activation in response to transcranial magnetic stimulation (TMS). Most analytical approaches focus on applied magnetic field strength in the target region as the primary factor, placing activation on the gyral crowns. However, imaging studies support M1 targets being typically located in the sulcal banks. OBJECTIVE/HYPOTHESIS To more thoroughly investigate this inconsistency, we sought to determine whether neocortical surface orientation was a critical determinant of regional activation. METHODS MR images were used to construct cortical and scalp surfaces for 18 subjects. The angle (θ) between the cortical surface normal and its nearest scalp normal for ~50,000 cortical points per subject was used to quantify cortical location (i.e., gyral vs. sulcal). TMS-induced activations of primary motor cortex (M1) were compared to brain activations recorded during a finger-tapping task using concurrent positron emission tomographic (PET) imaging. RESULTS Brain activations were primarily sulcal for both the TMS and task activations (P < 0.001 for both) compared to the overall cortical surface orientation. Also, the location of maximal blood flow in response to either TMS or finger-tapping correlated well using the cortical surface orientation angle or distance to scalp (P < 0.001 for both) as criteria for comparison between different neocortical activation modalities. CONCLUSION This study provides further evidence that a major factor in cortical activation using TMS is the orientation of the cortical surface with respect to the induced electric field. The results show that, despite the gyral crown of the cortex being subjected to a larger magnetic field magnitude, the sulcal bank of M1 had larger cerebral blood flow (CBF) responses during TMS.
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Affiliation(s)
- Todd D. Krieg
- Department of Biomedical Engineering, Illinois Institute of Technology, Wishnick Hall 314, 3255 S. Dearborn St., Chicago, IL 60616, USA
| | - Felipe S. Salinas
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Shalini Narayana
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Peter T. Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- South Texas Veterans Health Care System, San Antonio, TX, USA
| | - David J. Mogul
- Department of Biomedical Engineering, Illinois Institute of Technology, Wishnick Hall 314, 3255 S. Dearborn St., Chicago, IL 60616, USA
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Abstract
The brain's cortical maps serve as a macroscopic framework upon which additional levels of detail can be overlaid. Unlike sensory maps generated by measuring the brain's responses to incoming stimuli, motor maps are made by directly stimulating the brain itself. To understand the significance of motor maps and the functions they represent, it is necessary to consider the relationship between the natural operation of the motor system and the pattern of activity evoked in it by artificial stimulation. We review recent findings from the study of the cortical motor system and new insights into the control of movement based on its mapping within cortical space.
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Affiliation(s)
- Thomas C Harrison
- Department of Psychiatry and Brain Research Centre, University of British Columbia, 2255 Wesbrook Mall, Vancouver BC Canada V6T1Z3
| | - Timothy H Murphy
- Department of Psychiatry and Brain Research Centre, University of British Columbia, 2255 Wesbrook Mall, Vancouver BC Canada V6T1Z3.
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41
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Conant D, Bouchard KE, Chang EF. Speech map in the human ventral sensory-motor cortex. Curr Opin Neurobiol 2013; 24:63-7. [PMID: 24492080 DOI: 10.1016/j.conb.2013.08.015] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2013] [Revised: 08/15/2013] [Accepted: 08/18/2013] [Indexed: 11/17/2022]
Abstract
The study of spatial maps of the ventral sensory-motor cortex (vSMC) dates back to the earliest cortical stimulation studies. This review surveys a number of recent and historical reports of the features and function of spatial maps within vSMC towards the human behavior of speaking. Representations of the vocal tract, like other body parts, are arranged in a somatotopic fashion within ventral SMC. This region has unique features and connectivity that may give insight into its specialized function in speech production. New methods allow us to probe further into the functional role of this organization by studying the spatial dynamics of vSMC during natural speaking in humans.
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Affiliation(s)
- David Conant
- Department of Neurological Surgery, University of California, San Francisco, United States; Department Physiology, University of California, San Francisco, United States
| | - Kristofer E Bouchard
- Department of Neurological Surgery, University of California, San Francisco, United States; Department Physiology, University of California, San Francisco, United States
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, United States; Department Physiology, University of California, San Francisco, United States.
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Abstract
Motivations of arterial spin labeling (ASL) at ultrahigh magnetic fields include prolonged blood T1 and greater signal-to-noise ratio (SNR). However, increased B0 and B1 inhomogeneities and increased specific absorption ratio (SAR) challenge practical ASL implementations. In this study, Turbo-FLASH (Fast Low Angle Shot) based pulsed and pseudo-continuous ASL sequences were performed at 7T, by taking advantage of the relatively low SAR and short TE of Turbo-FLASH that minimizes susceptibility artifacts. Consistent with theoretical predictions, the experimental data showed that Turbo-FLASH based ASL yielded approximately 4 times SNR gain at 7T compared to 3T. High quality perfusion images were obtained with an in-plane spatial resolution of 0.85×1.7 mm2. A further functional MRI study of motor cortex activation precisely located the primary motor cortex to the precentral gyrus, with the same high spatial resolution. Finally, functional connectivity between left and right motor cortices as well as supplemental motor area were demonstrated using resting state perfusion images. Turbo-FLASH based ASL is a promising approach for perfusion imaging at 7T, which could provide novel approaches to high spatiotemporal resolution fMRI and to investigate the functional connectivity of brain networks at ultrahigh field.
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Affiliation(s)
- Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
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43
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Smaers JB, Soligo C. Brain reorganization, not relative brain size, primarily characterizes anthropoid brain evolution. Proc Biol Sci 2013; 280:20130269. [PMID: 23536600 PMCID: PMC3619515 DOI: 10.1098/rspb.2013.0269] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 03/04/2013] [Indexed: 12/27/2022] Open
Abstract
Comparative analyses of primate brain evolution have highlighted changes in size and internal organization as key factors underlying species diversity. It remains, however, unclear (i) how much variation in mosaic brain reorganization versus variation in relative brain size contributes to explaining the structural neural diversity observed across species, (ii) which mosaic changes contribute most to explaining diversity, and (iii) what the temporal origin, rates and processes are that underlie evolutionary shifts in mosaic reorganization for individual branches of the primate tree of life. We address these questions by combining novel comparative methods that allow assessing the temporal origin, rate and process of evolutionary changes on individual branches of the tree of life, with newly available data on volumes of key brain structures (prefrontal cortex, frontal motor areas and cerebrocerebellum) for a sample of 17 species (including humans). We identify patterns of mosaic change in brain evolution that mirror brain systems previously identified by electrophysiological and anatomical tract-tracing studies in non-human primates and functional connectivity MRI studies in humans. Across more than 40 Myr of anthropoid primate evolution, mosaic changes contribute more to explaining neural diversity than changes in relative brain size, and different mosaic patterns are differentially selected for when brains increase or decrease in size. We identify lineage-specific evolutionary specializations for all branches of the tree of life covered by our sample and demonstrate deep evolutionary roots for mosaic patterns associated with motor control and learning.
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Affiliation(s)
- J B Smaers
- Department of Anthropology, University College London, 14 Taviton Street, London WC1H 0BW, UK.
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44
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Stock AK, Wascher E, Beste C. Differential effects of motor efference copies and proprioceptive information on response evaluation processes. PLoS One 2013; 8:e62335. [PMID: 23658624 PMCID: PMC3637248 DOI: 10.1371/journal.pone.0062335] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Accepted: 03/20/2013] [Indexed: 11/19/2022] Open
Abstract
It is well-kown that sensory information influences the way we execute motor responses. However, less is known about if and how sensory and motor information are integrated in the subsequent process of response evaluation. We used a modified Simon Task to investigate how these streams of information are integrated in response evaluation processes, applying an in-depth neurophysiological analysis of event-related potentials (ERPs), time-frequency decomposition and sLORETA. The results show that response evaluation processes are differentially modulated by afferent proprioceptive information and efference copies. While the influence of proprioceptive information is mediated via oscillations in different frequency bands, efference copy based information about the motor execution is specifically mediated via oscillations in the theta frequency band. Stages of visual perception and attention were not modulated by the interaction of proprioception and motor efference copies. Brain areas modulated by the interactive effects of proprioceptive and efference copy based information included the middle frontal gyrus and the supplementary motor area (SMA), suggesting that these areas integrate sensory information for the purpose of response evaluation. The results show how motor response evaluation processes are modulated by information about both the execution and the location of a response.
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Affiliation(s)
- Ann-Kathrin Stock
- Institute for Cognitive Neuroscience, Department of Biopsychology, Ruhr-UniversityBochum, Bochum, Germany.
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45
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Yu R, Liu B, Wang L, Chen J, Liu X. Enhanced functional connectivity between putamen and supplementary motor area in Parkinson's disease patients. PLoS One 2013; 8:e59717. [PMID: 23555758 PMCID: PMC3608571 DOI: 10.1371/journal.pone.0059717] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Accepted: 02/17/2013] [Indexed: 11/18/2022] Open
Abstract
Parkinson's disease (PD) is a surprisingly heterogeneous disorder with symptoms including resting tremor, bradykinesia and rigidity. PD has been associated with abnormal task related brain activation in sensory and motor regions as well as reward related network. Although corticostriatal skeletomotor circuit dysfunction is implicated in the neurobiology of Parkinson's disease, the functional connectivity within this circuit at the resting state is still unclear for PD. Here we utilized resting state functional magnetic resonance imaging to measure the functional connectivity of striatum and motor cortex in 19 patients with PD and 20 healthy controls. We found that the putamen, but not the caudate, exhibited enhanced connectivity with supplementary motor area (SMA), using either the putamen or the SMA as the "seed region". Enhanced SMA-amygdala functional connectivity was also found in the PD group, compared with normal controls. Our findings highlight the key role of hyper-connected putamen-SMC circuit in the pathophysiology of PD.
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Affiliation(s)
- Rongjun Yu
- School of Psychology and Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
- * E-mail: (RY); (BL)
| | - Bo Liu
- Department of Image, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, China
- * E-mail: (RY); (BL)
| | - Lingling Wang
- School of Psychology and Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Jun Chen
- Department of Image, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, China
| | - Xian Liu
- Department of Image, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, China
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Huang Y, Zhen Z, Song Y, Zhu Q, Wang S, Liu J. Motor training increases the stability of activation patterns in the primary motor cortex. PLoS One 2013; 8:e53555. [PMID: 23308252 PMCID: PMC3538534 DOI: 10.1371/journal.pone.0053555] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2012] [Accepted: 11/29/2012] [Indexed: 11/18/2022] Open
Abstract
Learning to be skillful is an endowed talent of humans, but neural mechanisms underlying behavioral improvement remain largely unknown. Some studies have reported that the mean magnitude of neural activation is increased after learning, whereas others have instead shown decreased activation. In this study, we used functional magnetic resonance imaging (fMRI) to investigate learning-induced changes in the neural activation in the human brain with a classic motor training task. Specifically, instead of comparing the mean magnitudes of activation before and after training, we analyzed the learning-induced changes in multi-voxel spatial patterns of neural activation. We observed that the stability of the activation patterns, or the similarity of the activation patterns between the even and odd runs of the fMRI scans, was significantly increased in the primary motor cortex (M1) after training. By contrast, the mean magnitude of neural activation remained unchanged. Therefore, our study suggests that learning shapes the brain by increasing the stability of the activation patterns, therefore providing a new perspective in understanding the neural mechanisms underlying learning.
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Affiliation(s)
- Yi Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zonglei Zhen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yiying Song
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Song Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jia Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- * E-mail:
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Tian F, Kozel FA, Yennu A, Croarkin PE, McClintock SM, Mapes KS, Husain MM, Liu H. Test-retest assessment of cortical activation induced by repetitive transcranial magnetic stimulation with brain atlas-guided optical topography. J Biomed Opt 2012; 17:116020. [PMID: 23139044 DOI: 10.1117/1.jbo.17.11.116020] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a technology that stimulates neurons with rapidly changing magnetic pulses with demonstrated therapeutic applications for various neuropsychiatric disorders. Functional near-infrared spectroscopy (fNIRS) is a suitable tool to assess rTMS-evoked brain responses without interference from the magnetic or electric fields generated by the TMS coil. We have previously reported a channel-wise study of combined rTMS/fNIRS on the motor and prefrontal cortices, showing a robust decrease of oxygenated hemoglobin concentration (Δ[HbO2]) at the sites of 1-Hz rTMS and the contralateral brain regions. However, the reliability of this putative clinical tool is unknown. In this study, we develop a rapid optical topography approach to spatially characterize the rTMS-evoked hemodynamic responses on a standard brain atlas. A hemispherical approximation of the brain is employed to convert the three-dimensional topography on the complex brain surface to a two-dimensional topography in the spherical coordinate system. The test-retest reliability of the combined rTMS/fNIRS is assessed using repeated measurements performed two to three days apart. The results demonstrate that the Δ[HbO2] amplitudes have moderate-to-high reliability at the group level; and the spatial patterns of the topographic images have high reproducibility in size and a moderate degree of overlap at the individual level.
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Affiliation(s)
- Fenghua Tian
- University of Texas at Arlington, Department of Bioengineering, 500 UTA Building, Arlington, Texas, USA
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48
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Abstract
Observing someone perform an action engages brain regions involved in motor planning, such as the inferior frontal, premotor, and inferior parietal cortices. Recent research suggests that during action observation, activity in these neural regions can be modulated by membership in an ethnic group defined by physical differences. In this study we expanded upon previous research by matching physical similarity of two different social groups and investigating whether likability of an outgroup member modulates activity in neural regions involved in action observation. Seventeen Jewish subjects were familiarized with biographies of eight individuals, half of the individuals belonged to Neo-Nazi groups (dislikable) and half of which did not (likable). All subjects and actors in the stimuli were Caucasian and physically similar. The subjects then viewed videos of actors portraying the characters performing simple motor actions (e.g. grasping a water bottle and raising it to the lips), while undergoing fMRI. Using multivariate pattern analysis (MVPA), we found that a classifier trained on brain activation patterns successfully discriminated between the likable and dislikable action observation conditions within the right ventral premotor cortex. These data indicate that the spatial pattern of activity in action observation related neural regions is modulated by likability even when watching a simple action such as reaching for a cup. These findings lend further support for the notion that social factors such as interpersonal liking modulate perceptual processing in motor-related cortices.
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Affiliation(s)
- Mona Sobhani
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
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Harmer J, Sanchez-Panchuelo RM, Bowtell R, Francis ST. Spatial location and strength of BOLD activation in high-spatial-resolution fMRI of the motor cortex: a comparison of spin echo and gradient echo fMRI at 7 T. NMR Biomed 2012; 25:717-725. [PMID: 21948326 DOI: 10.1002/nbm.1783] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2011] [Revised: 07/19/2011] [Accepted: 07/23/2011] [Indexed: 05/31/2023]
Abstract
The increased blood oxygenation level-dependent contrast-to-noise ratio at ultrahigh field (7 T) has been exploited in a comparison of the spatial location and strength of activation in high-resolution (1.5 mm isotropic) gradient echo (GE) and spin echo (SE), echo planar imaging data acquired during the execution of a simple motor task in five subjects. SE data were acquired at six echo times from 30 to 55 ms. Excellent fat suppression was achieved in the SE echo planar images using slice-selective gradient reversal. Threshold-free cluster enhancement was used to define regions of interest (ROIs) containing voxels showing significant stimulus-locked signal changes from the GE and average SE data. These were used to compare the signal changes and spatial locations of activated regions in SE and GE data. T(2) and T(2)* values were measured, with means of 48.3 ± 1.1 ms and 36.5 ± 3.4 ms in the SE ROI. In addition, we identified a dark band in SE images of the motor cortex corresponding to a region in which T(2) and T(2)* were significantly lower than in the surrounding grey matter. The fractional SE signal change in the ROI was found to vary linearly as a function of TE, with a slope that was dependent on the particular ROI assessed: the mean ΔR(2) value was found to be 0.85 ± 0.11 s(-1) for the SE ROI and -0.37 ± 0.05 s(-1) for the GE ROI. The fractional signal change relative to the shortest TE revealed that the largest signal change occurred at a TE of 45 ms outside of the dark band. At this TE, the ratio of the fractional signal change in GE and SE data was found to be 0.48 ± 0.05. Phase maps produced from high-resolution GE images spanning the right motor cortex were used to identify veins. The GE ROI was found to contain 18% more voxels overlying the venous mask than the SE ROI.
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Affiliation(s)
- J Harmer
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
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Besseling RMH, Jansen JFA, Overvliet GM, Vaessen MJ, Braakman HMH, Hofman PAM, Aldenkamp AP, Backes WH. Tract specific reproducibility of tractography based morphology and diffusion metrics. PLoS One 2012; 7:e34125. [PMID: 22485157 PMCID: PMC3317780 DOI: 10.1371/journal.pone.0034125] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2011] [Accepted: 02/22/2012] [Indexed: 12/16/2022] Open
Abstract
Introduction The reproducibility of tractography is important to determine its sensitivity to pathological abnormalities. The reproducibility of tract morphology has not yet been systematically studied and the recently developed tractography contrast Tract Density Imaging (TDI) has not yet been assessed at the tract specific level. Materials and Methods Diffusion tensor imaging (DTI) and probabilistic constrained spherical deconvolution (CSD) tractography are performed twice in 9 healthy subjects. Tractography is based on common space seed and target regions and performed for several major white matter tracts. Tractograms are converted to tract segmentations and inter-session reproducibility of tract morphology is assessed using Dice similarity coefficient (DSC). The coefficient of variation (COV) and intraclass correlation coefficient (ICC) are calculated of the following tract metrics: fractional anisotropy (FA), apparent diffusion coefficient (ADC), volume, and TDI. Analyses are performed both for proximal (deep white matter) and extended (including subcortical white matter) tract segmentations. Results Proximal DSC values were 0.70–0.92. DSC values were 5–10% lower in extended compared to proximal segmentations. COV/ICC values of FA, ADC, volume and TDI were 1–4%/0.65–0.94, 2–4%/0.62–0.94, 3–22%/0.53–0.96 and 8–31%/0.48–0.70, respectively, with the lower COV and higher ICC values found in the proximal segmentations. Conclusion For all investigated metrics, reproducibility depended on the segmented tract. FA and ADC had relatively low COV and relatively high ICC, indicating clinical potential. Volume had higher COV but its moderate to high ICC values in most tracts still suggest subject-differentiating power. Tract TDI had high COV and relatively low ICC, which reflects unfavorable reproducibility.
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Affiliation(s)
- René M. H. Besseling
- Department of Radiology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Jacobus F. A. Jansen
- Department of Radiology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
| | - Geke M. Overvliet
- Department of Neurology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Maarten J. Vaessen
- Department of Radiology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Hilde M. H. Braakman
- Department of Neurology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Paul A. M. Hofman
- Department of Radiology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Albert P. Aldenkamp
- Department of Neurology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Walter H. Backes
- Department of Radiology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
- * E-mail:
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