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Benozzo D, Baron G, Coletta L, Chiuso A, Gozzi A, Bertoldo A. Macroscale coupling between structural and effective connectivity in the mouse brain. Sci Rep 2024; 14:3142. [PMID: 38326324 PMCID: PMC10850485 DOI: 10.1038/s41598-024-51613-7] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/07/2024] [Indexed: 02/09/2024] Open
Abstract
Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be considered: the directionality of the structural connectome and limitations in explaining networks functions through an undirected measure such as FC. Here, we employed an accurate directed SC of the mouse brain acquired through viral tracers and compared it with single-subject effective connectivity (EC) matrices derived from a dynamic causal model (DCM) applied to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their respective couplings by conditioning on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks; only within sensory motor networks did we observe connections that align in terms of both effective and structural strength.
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Affiliation(s)
- Danilo Benozzo
- Department of Information Engineering, University of Padova, Padua, Italy.
| | - Giorgia Baron
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandro Chiuso
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems @ UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padua, Italy.
- Padova Neuroscience Center (PNC), Padua, Italy.
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2
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López Lloreda C. Wi-Fi for neurons: first map of wireless nerve signals unveiled in worms. Nature 2023; 623:894-895. [PMID: 37990096 DOI: 10.1038/d41586-023-03619-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
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3
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Williams LZJ, Fitzgibbon SP, Bozek J, Winkler AM, Dimitrova R, Poppe T, Schuh A, Makropoulos A, Cupitt J, O'Muircheartaigh J, Duff EP, Cordero-Grande L, Price AN, Hajnal JV, Rueckert D, Smith SM, Edwards AD, Robinson EC. Structural and functional asymmetry of the neonatal cerebral cortex. Nat Hum Behav 2023; 7:942-955. [PMID: 36928781 DOI: 10.1038/s41562-023-01542-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 10/21/2021] [Accepted: 01/31/2023] [Indexed: 03/18/2023]
Abstract
Features of brain asymmetry have been implicated in a broad range of cognitive processes; however, their origins are still poorly understood. Here we investigated cortical asymmetries in 442 healthy term-born neonates using structural and functional magnetic resonance images from the Developing Human Connectome Project. Our results demonstrate that the neonatal cortex is markedly asymmetric in both structure and function. Cortical asymmetries observed in the term cohort were contextualized in two ways: by comparing them against cortical asymmetries observed in 103 preterm neonates scanned at term-equivalent age, and by comparing structural asymmetries against those observed in 1,110 healthy young adults from the Human Connectome Project. While associations with preterm birth and biological sex were minimal, significant differences exist between birth and adulthood.
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Affiliation(s)
- Logan Z J Williams
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK.
| | - Sean P Fitzgibbon
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Anderson M Winkler
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Tanya Poppe
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andreas Schuh
- Department of Computing, Imperial College London, London, UK
| | - Antonios Makropoulos
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - John Cupitt
- Department of Computing, Imperial College London, London, UK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Eugene P Duff
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
- UK Dementia Research Institute, Department of Brain Sciences, Imperial College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, ISCIII, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
- Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen M Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Neonatal Intensive Care Unit, Evelina London Children's Hospital, London, UK
| | - Emma C Robinson
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK.
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4
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [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: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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5
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Schiavi S, Lu PJ, Weigel M, Lutti A, Jones DK, Kappos L, Granziera C, Daducci A. Bundle myelin fraction (BMF) mapping of different white matter connections using microstructure informed tractography. Neuroimage 2022; 249:118922. [PMID: 35063648 PMCID: PMC7615247 DOI: 10.1016/j.neuroimage.2022.118922] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [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: 10/04/2021] [Revised: 01/13/2022] [Accepted: 01/17/2022] [Indexed: 12/13/2022] Open
Abstract
To date, we have scarce information about the relative myelination level of different fiber bundles in the human brain. Indirect evidence comes from postmortem histology data but histological stainings are unable to follow a specific bundle and determine its intrinsic myelination. In this context, quantitative MRI, and diffusion MRI tractography may offer a viable solution by providing, respectively, voxel-wise myelin sensitive maps and the pathways of the major tracts of the brain. Then, "tractometry" can be used to combine these two pieces of information by averaging tissue features (obtained from any voxel-wise map) along the streamlines recovered with diffusion tractography. Although this method has been widely used in the literature, in cases of voxels containing multiple fiber populations (each with different levels of myelination), tractometry provides biased results because the same value will be attributed to any bundle passing through the voxel. To overcome this bias, we propose a new method - named "myelin streamline decomposition" (MySD) - which extends convex optimization modeling for microstructure informed tractography (COMMIT) allowing the actual value measured by a microstructural map to be deconvolved on each individual streamline, thereby recovering unique bundle-specific myelin fractions (BMFs). We demonstrate the advantage of our method with respect to tractometry in well-studied bundles and compare the cortical projection of the obtained bundle-wise myelin values of both methods. We also prove the stability of our approach across different subjects and different MRI sensitive myelin mapping approaches. This work provides a proof-of-concept of in vivo investigations of entire neuronal pathways that, to date, are not possible.
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Affiliation(s)
- Simona Schiavi
- Department of Computer Science, University of Verona, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Italy.
| | - Po-Jui Lu
- Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Radiological Physics, Department of Radiology, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, United Kingdom
| | - Ludwig Kappos
- Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
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6
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Zekelman LR, Zhang F, Makris N, He J, Chen Y, Xue T, Liera D, Drane DL, Rathi Y, Golby AJ, O'Donnell LJ. White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project. Neuroimage 2021; 246:118739. [PMID: 34856375 PMCID: PMC8862285 DOI: 10.1016/j.neuroimage.2021.118739] [Citation(s) in RCA: 5] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 10/20/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
Language and theory of mind (ToM) are the cognitive capacities that allow for the successful interpretation and expression of meaning. While functional MRI investigations are able to consistently localize language and ToM to specific cortical regions, diffusion MRI investigations point to an inconsistent and sometimes overlapping set of white matter tracts associated with these two cognitive domains. To further examine the white matter tracts that may underlie these domains, we use a two-tensor tractography method to investigate the white matter microstructure of 809 participants from the Human Connectome Project. 20 association white matter tracts (10 in each hemisphere) are uniquely identified by leveraging a neuroanatomist-curated automated white matter tract atlas. The fractional anisotropy (FA), mean diffusivity (MD), and number of streamlines (NoS) are measured for each white matter tract. Performance on neuropsychological assessments of semantic memory (NIH Toolbox Picture Vocabulary Test, TPVT) and emotion perception (Penn Emotion Recognition Test, PERT) are used to measure critical subcomponents of the language and ToM networks, respectively. Regression models are constructed to examine how structural measurements of left and right white matter tracts influence performance across these two assessments. We find that semantic memory performance is influenced by the number of streamlines of the left superior longitudinal fasciculus III (SLF-III), and emotion perception performance is influenced by the number of streamlines of the right SLF-III. Additionally, we find that performance on both semantic memory & emotion perception is influenced by the FA of the left arcuate fasciculus (AF). The results point to multiple, overlapping white matter tracts that underlie the cognitive domains of language and ToM. Results are discussed in terms of hemispheric dominance and concordance with prior investigations.
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Affiliation(s)
- Leo R Zekelman
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, USA.
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, USA; Center for Morphometric Analysis, Department of Psychiatry and Neurology, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Psychiatric Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Jianzhong He
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Yuqian Chen
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, NSW, Australia
| | - Tengfei Xue
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, NSW, Australia
| | | | - Daniel L Drane
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA; Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, University of Washington School of Medicine, Seattle, WA, US
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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7
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Weinstein SM, Vandekar SN, Adebimpe A, Tapera TM, Robert‐Fitzgerald T, Gur RC, Gur RE, Raznahan A, Satterthwaite TD, Alexander‐Bloch AF, Shinohara RT. A simple permutation-based test of intermodal correspondence. Hum Brain Mapp 2021; 42:5175-5187. [PMID: 34519385 PMCID: PMC8519855 DOI: 10.1002/hbm.25577] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 12/15/2020] [Revised: 05/25/2021] [Accepted: 06/10/2021] [Indexed: 12/14/2022] Open
Abstract
Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state-of-the-art methods involve comparing observed group-level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group-level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this article we propose using subject-level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p-value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n-back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference.
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Affiliation(s)
- Sarah M. Weinstein
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | | | - Azeez Adebimpe
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Tinashe M. Tapera
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Timothy Robert‐Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Ruben C. Gur
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Raquel E. Gur
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Armin Raznahan
- Section on Developmental NeurogenomicsNational Institute of Mental Health Intramural Research ProgramBethesdaMaryland
| | - Theodore D. Satterthwaite
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Aaron F. Alexander‐Bloch
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
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8
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Sultana R, Brooks CB, Shrestha A, Ogundele OM, Lee CC. Perineuronal Nets in the Prefrontal Cortex of a Schizophrenia Mouse Model: Assessment of Neuroanatomical, Electrophysiological, and Behavioral Contributions. Int J Mol Sci 2021; 22:11140. [PMID: 34681799 PMCID: PMC8538055 DOI: 10.3390/ijms222011140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 06/29/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 01/01/2023] Open
Abstract
Schizophrenia is a neurodevelopmental disorder whose etiopathogenesis includes changes in cellular as well as extracellular structures. Perineuronal nets (PNNs) associated with parvalbumin-positive interneurons (PVs) in the prefrontal cortex (PFC) are dysregulated in schizophrenia. However, the postnatal development of these structures along with their associated neurons in the PFC is unexplored, as is their effects on behavior and neural activity. Therefore, in this study, we employed a DISC1 (Disruption in Schizophrenia) mutation mouse model of schizophrenia to assess these developmental changes and tested whether enzymatic digestion of PNNs in the PFC affected schizophrenia-like behaviors and neural activity. Developmentally, we found that the normal formation of PNNs, PVs, and colocalization of these two in the PFC, peaked around PND 22 (postnatal day 22). However, in DISC1, mutation animals from PND 0 to PND 60, both PNNs and PVs were significantly reduced. After enzymatic digestion of PNNs with chondroitinase in adult animals, the behavioral pattern of control animals mimicked that of DISC1 mutation animals, exhibiting reduced sociability, novelty and increased ultrasonic vocalizations, while there was very little change in other behaviors, such as working memory (Y-maze task involving medial temporal lobe) or depression-like behavior (tail-suspension test involving processing via the hypothalamic pituitary adrenal (HPA) axis). Moreover, following chondroitinase treatment, electrophysiological recordings from the PFC exhibited a reduced proportion of spontaneous, high-frequency firing neurons, and an increased proportion of irregularly firing neurons, with increased spike count and reduced inter-spike intervals in control animals. These results support the proposition that the aberrant development of PNNs and PVs affects normal neural operations in the PFC and contributes to the emergence of some of the behavioral phenotypes observed in the DISC1 mutation model of schizophrenia.
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Affiliation(s)
- Razia Sultana
- Department of Comparative Biomedical Sciences, LSU School of Veterinary Medicine, Baton Rouge, LA 70803, USA; (C.B.B.); (A.S.); (O.M.O.)
| | | | | | | | - Charles Chulsoo Lee
- Department of Comparative Biomedical Sciences, LSU School of Veterinary Medicine, Baton Rouge, LA 70803, USA; (C.B.B.); (A.S.); (O.M.O.)
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McColgan P, Helbling S, Vaculčiaková L, Pine K, Wagstyl K, Attar FM, Edwards L, Papoutsi M, Wei Y, Van den Heuvel MP, Tabrizi SJ, Rees G, Weiskopf N. Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics. Hum Brain Mapp 2021; 42:4996-5009. [PMID: 34272784 PMCID: PMC8449108 DOI: 10.1002/hbm.25595] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 01/25/2021] [Revised: 06/26/2021] [Accepted: 07/06/2021] [Indexed: 12/24/2022] Open
Abstract
Ultra-high field MRI across the depth of the cortex has the potential to provide anatomically precise biomarkers and mechanistic insights into neurodegenerative disease like Huntington's disease that show layer-selective vulnerability. Here we compare multi-parametric mapping (MPM) measures across cortical depths for a 7T 500 μm whole brain acquisition to (a) layer-specific cell measures from the von Economo histology atlas, (b) layer-specific gene expression, using the Allen Human Brain atlas and (c) white matter connections using high-fidelity diffusion tractography, at a 1.3 mm isotropic voxel resolution, from a 300mT/m Connectom MRI system. We show that R2*, but not R1, across cortical depths is highly correlated with layer-specific cell number and layer-specific gene expression. R1- and R2*-weighted connectivity strength of cortico-striatal and intra-hemispheric cortical white matter connections was highly correlated with grey matter R1 and R2* across cortical depths. Limitations of the layer-specific relationships demonstrated are at least in part related to the high cross-correlations of von Economo atlas cell counts and layer-specific gene expression across cortical layers. These findings demonstrate the potential and limitations of combining 7T MPMs, gene expression and white matter connections to provide an anatomically precise framework for tracking neurodegenerative disease.
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Affiliation(s)
- Peter McColgan
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Saskia Helbling
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Lenka Vaculčiaková
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Kerrin Pine
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Konrad Wagstyl
- The Wellcome Centre for Human Neuroimaging, Institute of NeurologyUniversity College LondonLondonUK
| | | | - Luke Edwards
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Marina Papoutsi
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Yongbin Wei
- Vrije Universiteit AmsterdamComplex Traits Genetics LabAmsterdamNetherlands
| | | | - Sarah J Tabrizi
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Geraint Rees
- The Wellcome Centre for Human Neuroimaging, Institute of NeurologyUniversity College LondonLondonUK
| | - Nikolaus Weiskopf
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Felix Bloch Institute for Solid State PhysicsFaculty of Physics and Earth Sciences, Leipzig UniversityLeipzigGermany
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10
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Xiao D, Forys BJ, Vanni MP, Murphy TH. MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning. Nat Commun 2021; 12:5992. [PMID: 34645817 PMCID: PMC8514445 DOI: 10.1038/s41467-021-26255-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 11/16/2020] [Accepted: 09/23/2021] [Indexed: 01/17/2023] Open
Abstract
Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis.
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Affiliation(s)
- Dongsheng Xiao
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, V6T 1Z3, British Columbia, Canada
| | - Brandon J Forys
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, V6T 1Z3, British Columbia, Canada
- Department of Psychology, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matthieu P Vanni
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, V6T 1Z3, British Columbia, Canada
- Université de Montréal, École d'Optométrie, 3744 Jean Brillant H3T 1P1, Montréal, Québec, Canada
| | - Timothy H Murphy
- University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, 2255 Wesbrook Mall, Vancouver, V6T 1Z3, British Columbia, Canada.
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11
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Xia Z, Wang C, Hancock R, Vandermosten M, Hoeft F. Development of thalamus mediates paternal age effect on offspring reading: A preliminary investigation. Hum Brain Mapp 2021; 42:4580-4596. [PMID: 34219304 PMCID: PMC8410543 DOI: 10.1002/hbm.25567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 12/07/2020] [Revised: 05/31/2021] [Accepted: 06/13/2021] [Indexed: 12/20/2022] Open
Abstract
The importance of (inherited) genetic impact in reading development is well established. De novo mutation is another important contributor that is recently gathering interest as a major liability of neurodevelopmental disorders, but has been neglected in reading research to date. Paternal age at childbirth (PatAGE) is known as the most prominent risk factor for de novo mutation, which has been repeatedly shown by molecular genetic studies. As one of the first efforts, we performed a preliminary investigation of the relationship between PatAGE, offspring's reading, and brain structure in a longitudinal neuroimaging study following 51 children from kindergarten through third grade. The results showed that greater PatAGE was significantly associated with worse reading, explaining an additional 9.5% of the variance after controlling for a number of confounds-including familial factors and cognitive-linguistic reading precursors. Moreover, this effect was mediated by volumetric maturation of the left posterior thalamus from ages 5 to 8. Complementary analyses indicated the PatAGE-related thalamic region was most likely located in the pulvinar nuclei and related to the dorsal attention network by using brain atlases, public datasets, and offspring's diffusion imaging data. Altogether, these findings provide novel insights into neurocognitive mechanisms underlying the PatAGE effect on reading acquisition during its earliest phase and suggest promising areas of future research.
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Affiliation(s)
- Zhichao Xia
- Department of Psychiatry and Weill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- School of Systems ScienceBeijing Normal UniversityBeijingChina
| | - Cheng Wang
- Department of Psychiatry and Weill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Roeland Hancock
- Department of Psychological Sciences and Brain Imaging Research CenterUniversity of ConnecticutStorrsConnecticutUSA
| | - Maaike Vandermosten
- Department of Psychiatry and Weill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeuroscienceExperimental ORL, KU LeuvenLeuvenBelgium
| | - Fumiko Hoeft
- Department of Psychiatry and Weill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychological Sciences and Brain Imaging Research CenterUniversity of ConnecticutStorrsConnecticutUSA
- Haskins LaboratoriesNew HavenConnecticutUSA
- Department of NeuropsychiatryKeio University School of MedicineShinjuku‐kuTokyoJapan
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12
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Palesi F, Ferrante M, Gaviraghi M, Misiti A, Savini G, Lascialfari A, D'Angelo E, Gandini Wheeler‐Kingshott CAM. Motor and higher-order functions topography of the human dentate nuclei identified with tractography and clustering methods. Hum Brain Mapp 2021; 42:4348-4361. [PMID: 34087040 PMCID: PMC8356999 DOI: 10.1002/hbm.25551] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [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: 02/15/2021] [Revised: 05/11/2021] [Accepted: 05/17/2021] [Indexed: 01/29/2023] Open
Abstract
Deep gray matter nuclei are the synaptic relays, responsible to route signals between specific brain areas. Dentate nuclei (DNs) represent the main output channel of the cerebellum and yet are often unexplored especially in humans. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity as well as their microstructural features. Based on results, we defined DN parcellations deputed to motor and to higher-order functions in humans in vivo. Whole-brain probabilistic tractography was performed on 25 healthy subjects from the Human Connectome Project to infer DN parcellations based on their connectivity with either the cerebral or the cerebellar cortex, in turn. A third DN atlas was created inputting microstructural diffusion-derived metrics in an unsupervised fuzzy c-means classification algorithm. All analyses were performed in native space, with probability atlas maps generated in standard space. Cerebellar lobule-specific connectivity identified one motor parcellation, accounting for about 30% of the DN volume, and two non-motor parcellations, one cognitive and one sensory, which occupied the remaining volume. The other two approaches provided overlapping results in terms of geometrical distribution with those identified with cerebellar lobule-specific connectivity, although with some differences in volumes. A gender effect was observed with respect to motor areas and higher-order function representations. This is the first study that indicates that more than half of the DN volumes is involved in non-motor functions and that connectivity-based and microstructure-based atlases provide complementary information. These results represent a step-ahead for the interpretation of pathological conditions involving cerebro-cerebellar circuits.
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Affiliation(s)
- Fulvia Palesi
- Department of Brain and Behavioral SciencesUniversity of PaviaPavia
| | | | - Marta Gaviraghi
- Department of Electrical, Computer, and Biomedical EngineeringUniversity of PaviaPaviaItaly
| | - Anastasia Misiti
- Department of Electrical, Computer, and Biomedical EngineeringUniversity of PaviaPaviaItaly
| | - Giovanni Savini
- Department of NeuroradiologyIRCCS Humanitas Research HospitalMilanItaly
| | | | - Egidio D'Angelo
- Department of Brain and Behavioral SciencesUniversity of PaviaPavia
- Brain Connectivity CenterIRCCS Mondino FoundationPavia
| | - Claudia A. M. Gandini Wheeler‐Kingshott
- Department of Brain and Behavioral SciencesUniversity of PaviaPavia
- Brain Connectivity CenterIRCCS Mondino FoundationPavia
- NMR Research Unit, Queen Square MS Centre, Department of NeuroinflammationUCL Queen Square Institute of NeurologyLondon
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13
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Ferreira F, Akram H, Ashburner J, Zrinzo L, Zhang H, Lambert C. Ventralis intermedius nucleus anatomical variability assessment by MRI structural connectivity. Neuroimage 2021; 238:118231. [PMID: 34089871 PMCID: PMC8960999 DOI: 10.1016/j.neuroimage.2021.118231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 12/15/2020] [Revised: 05/14/2021] [Accepted: 06/01/2021] [Indexed: 12/11/2022] Open
Abstract
The ventralis intermedius nucleus (Vim) is centrally placed in the dentato-thalamo-cortical pathway (DTCp) and is a key surgical target in the treatment of severe medically refractory tremor. It is not visible on conventional MRI sequences; consequently, stereotactic targeting currently relies on atlas-based coordinates. This fails to capture individual anatomical variability, which may lead to poor long-term clinical efficacy. Probabilistic tractography, combined with known anatomical connectivity, enables localisation of thalamic nuclei at an individual subject level. There are, however, a number of confounds associated with this technique that may influence results. Here we focused on an established method, using probabilistic tractography to reconstruct the DTCp, to identify the connectivity-defined Vim (cd-Vim) in vivo. Using 100 healthy individuals from the Human Connectome Project, our aim was to quantify cd-Vim variability across this population, measure the discrepancy with atlas-defined Vim (ad-Vim), and assess the influence of potential methodological confounds. We found no significant effect of any of the confounds. The mean cd-Vim coordinate was located within 1.88 mm (left) and 2.12 mm (right) of the average midpoint and 3.98 mm (left) and 5.41 mm (right) from the ad-Vim coordinates. cd-Vim location was more variable on the right, which reflects hemispheric asymmetries in the probabilistic DTC reconstructed. The method was reproducible, with no significant cd-Vim location differences in a separate test-retest cohort. The superior cerebellar peduncle was identified as a potential source of artificial variance. This work demonstrates significant individual anatomical variability of the cd-Vim that atlas-based coordinate targeting fails to capture. This variability was not related to any methodological confound tested. Lateralisation of cerebellar functions, such as speech, may contribute to the observed asymmetry. Tractography-based methods seem sensitive to individual anatomical variability that is missed by conventional neurosurgical targeting; these findings may form the basis for translational tools to improve efficacy and reduce side-effects of thalamic surgery for tremor.
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Affiliation(s)
- Francisca Ferreira
- EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging in Healthcare (i4health), University College London, Gower Street, London WC1E 6BT, United Kingdom; Functional Neurosurgery Unit, Department of Clinical and Motor Neurosciences, UCL Institute of Neurology, Queen Square, WC1N 3BG London, United Kingdom; Wellcome Centre for Human Neuroimaging, 12 Queen Square, London WC1N 3AR, United Kingdom.
| | - Harith Akram
- Functional Neurosurgery Unit, Department of Clinical and Motor Neurosciences, UCL Institute of Neurology, Queen Square, WC1N 3BG London, United Kingdom
| | - John Ashburner
- Wellcome Centre for Human Neuroimaging, 12 Queen Square, London WC1N 3AR, United Kingdom
| | - Ludvic Zrinzo
- Functional Neurosurgery Unit, Department of Clinical and Motor Neurosciences, UCL Institute of Neurology, Queen Square, WC1N 3BG London, United Kingdom
| | - Hui Zhang
- EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging in Healthcare (i4health), University College London, Gower Street, London WC1E 6BT, United Kingdom; Department of Computer Science and Centre for Medical Image Computing, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Christian Lambert
- Wellcome Centre for Human Neuroimaging, 12 Queen Square, London WC1N 3AR, United Kingdom
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14
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Meier SK, Ray KL, Mastan JC, Salvage SR, Robin DA. Meta-analytic connectivity modelling of deception-related brain regions. PLoS One 2021; 16:e0248909. [PMID: 34432808 PMCID: PMC8386837 DOI: 10.1371/journal.pone.0248909] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 03/05/2021] [Accepted: 08/10/2021] [Indexed: 11/30/2022] Open
Abstract
Brain-based deception research began only two decades ago and has since included a wide variety of contexts and response modalities for deception paradigms. Investigations of this sort serve to better our neuroscientific and legal knowledge of the ways in which individuals deceive others. To this end, we conducted activation likelihood estimation (ALE) and meta-analytic connectivity modelling (MACM) using BrainMap software to examine 45 task-based fMRI brain activation studies on deception. An activation likelihood estimation comparing activations during deceptive versus honest behavior revealed 7 significant peak activation clusters (bilateral insula, left superior frontal gyrus, bilateral supramarginal gyrus, and bilateral medial frontal gyrus). Meta-analytic connectivity modelling revealed an interconnected network amongst the 7 regions comprising both unidirectional and bidirectional connections. Together with subsequent behavioral and paradigm decoding, these findings implicate the supramarginal gyrus as a key component for the sociocognitive process of deception.
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Affiliation(s)
- Sarah K. Meier
- Department of Communication Sciences and Disorders Research Laboratories, University of New Hampshire, Durham, New Hampshire, United States of America
- * E-mail: (SKM); (DAR)
| | - Kimberly L. Ray
- Department of Psychology, University of Texas, Austin, Texas, United States of America
| | - Juliana C. Mastan
- Department of Communication Sciences and Disorders Research Laboratories, University of New Hampshire, Durham, New Hampshire, United States of America
| | - Savannah R. Salvage
- Department of Communication Sciences and Disorders Research Laboratories, University of New Hampshire, Durham, New Hampshire, United States of America
| | - Donald A. Robin
- Department of Communication Sciences and Disorders Research Laboratories, University of New Hampshire, Durham, New Hampshire, United States of America
- Interdisciplinary Program in Neuroscience and Behavior, University of New Hampshire, Durham, New Hampshire, United States of America
- Department of Biological Sciences, University of New Hampshire, Durham, New Hampshire, United States of America
- * E-mail: (SKM); (DAR)
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15
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Chizhov AV, Graham LJ. A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex. PLoS Comput Biol 2021; 17:e1009007. [PMID: 34398895 PMCID: PMC8389851 DOI: 10.1371/journal.pcbi.1009007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 04/16/2021] [Revised: 08/26/2021] [Accepted: 07/27/2021] [Indexed: 11/18/2022] Open
Abstract
A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general operating principles. We present a strategy that successively maps a relatively detailed biophysical population model, comprising conductance-based Hodgkin-Huxley type neuron models with connectivity rules derived from anatomical data, to various representations with fewer parameters, finishing with a firing rate network model that permits analysis. We apply this methodology to primary visual cortex of higher mammals, focusing on the functional property of stimulus orientation selectivity of receptive fields of individual neurons. The mapping produces compact expressions for the parameters of the abstract model that clearly identify the impact of specific electrophysiological and anatomical parameters on the analytical results, in particular as manifested by specific functional signatures of visual cortex, including input-output sharpening, conductance invariance, virtual rotation and the tilt after effect. Importantly, qualitative differences between model behaviours point out consequences of various simplifications. The strategy may be applied to other neuronal systems with appropriate modifications. A hierarchy of theoretical approaches to study a neuronal network depends on a tradeoff between biological fidelity and mathematical tractibility. Biophysically-detailed models consider cellular mechanisms and anatomically defined synaptic circuits, but are often too complex to reveal insights into fundamental principles. In contrast, increasingly abstract reduced models facilitate analytical insights. To better ground the latter to the underlying biology, we describe a systematic procedure to move across the model hierarchy that allows understanding how changes in biological parameters—physiological, pathophysiological, or because of new data—impact the behaviour of the network. We apply this approach to mammalian primary visual cortex, and examine how the different models in the hierarchy reproduce functional signatures of this area, in particular the tuning of neurons to the orientation of a visual stimulus. Our work provides a navigation of the complex parameter space of neural network models faithful to biology, as well as highlighting how simplifications made for mathematical convenience can fundamentally change their behaviour.
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Affiliation(s)
- Anton V. Chizhov
- Computational Physics Laboratory, Ioffe Institute, Saint Petersburg, Russia
- Laboratory of Molecular Mechanisms of Neural Interactions, Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences, Saint Petersburg, Russia
- * E-mail:
| | - Lyle J. Graham
- Centre Giovanni Borelli - CNRS UMR9010, Université de Paris, France
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16
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Gu Z, Jamison KW, Sabuncu MR, Kuceyeski A. Heritability and interindividual variability of regional structure-function coupling. Nat Commun 2021; 12:4894. [PMID: 34385454 PMCID: PMC8361191 DOI: 10.1038/s41467-021-25184-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [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: 12/09/2020] [Accepted: 07/16/2021] [Indexed: 02/07/2023] Open
Abstract
White matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.
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Affiliation(s)
- Zijin Gu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | | | - Mert Rory Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
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17
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Wang Y, Metoki A, Xia Y, Zang Y, He Y, Olson IR. A large-scale structural and functional connectome of social mentalizing. Neuroimage 2021; 236:118115. [PMID: 33933599 DOI: 10.1016/j.neuroimage.2021.118115] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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: 12/17/2020] [Revised: 03/29/2021] [Accepted: 04/13/2021] [Indexed: 12/21/2022] Open
Abstract
Humans have a remarkable ability to infer the mind of others. This mentalizing skill relies on a distributed network of brain regions but how these regions connect and interact is not well understood. Here we leveraged large-scale multimodal neuroimaging data to elucidate the brain-wide organization and mechanisms of mentalizing processing. Key connectomic features of the mentalizing network (MTN) have been delineated in exquisite detail. We found the structural architecture of MTN is organized by two parallel subsystems and constructed redundantly by local and long-range white matter fibers. We uncovered an intrinsic functional architecture that is synchronized according to the degree of mentalizing, and its hierarchy reflects the inherent information integration order. We also examined the correspondence between the structural and functional connectivity in the network and revealed their differences in network topology, individual variance, spatial specificity, and functional specificity. Finally, we scrutinized the connectome resemblance between the default mode network and MTN and elaborated their inherent differences in dynamic patterns, laterality, and homogeneity. Overall, our study demonstrates that mentalizing processing unfolds across functionally heterogeneous regions with highly structured fiber tracts and unique hierarchical functional architecture, which make it distinguishable from the default mode network and other vicinity brain networks supporting autobiographical memory, semantic memory, self-referential, moral reasoning, and mental time travel.
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Affiliation(s)
- Yin Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Athanasia Metoki
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yinyin Zang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, PA, USA.
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18
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Cole M, Murray K, St‐Onge E, Risk B, Zhong J, Schifitto G, Descoteaux M, Zhang Z. Surface-Based Connectivity Integration: An atlas-free approach to jointly study functional and structural connectivity. Hum Brain Mapp 2021; 42:3481-3499. [PMID: 33956380 PMCID: PMC8249904 DOI: 10.1002/hbm.25447] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 08/13/2020] [Revised: 03/03/2021] [Accepted: 04/06/2021] [Indexed: 01/29/2023] Open
Abstract
There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose from and this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas-free approach, named Surface-Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra-cortical gray matter. SBCI represents both SC and FC in a continuous manner on the white surface, avoiding the need for prespecified atlases. The continuous SC is represented as a probability density function and is smoothed for better facilitation of its integration with FC. To infer the relationship between SC and FC, three novel sets of SC-FC coupling (SFC) measures are derived. Using data from the Human Connectome Project, we introduce the high-quality SFC measures produced by SBCI and demonstrate the use of these measures to study sex differences in a cohort of young adults. Compared with atlas-based methods, this atlas-free framework produces more reproducible SFC features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies.
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Affiliation(s)
- Martin Cole
- Department of Biostatistics and Computational BiologyUniversity of RochesterRochesterNew YorkUSA
| | - Kyle Murray
- Department of Physics and AstronomyUniversity of RochesterRochesterNew YorkUSA
| | - Etienne St‐Onge
- Sherbrooke Connectivity Imaging Laboratory (SCIL)Université de SherbrookeQuébecCanada
| | - Benjamin Risk
- Department of Biostatistics and BioinformaticsEmory UniversityAtlantaGeorgiaUSA
| | - Jianhui Zhong
- Department of Physics and AstronomyUniversity of RochesterRochesterNew YorkUSA
- Department of Imaging SciencesUniversity of RochesterRochesterNew YorkUSA
| | - Giovanni Schifitto
- Department of Imaging SciencesUniversity of RochesterRochesterNew YorkUSA
- Department of NeurologyUniversity of RochesterRochesterNew YorkUSA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL)Université de SherbrookeQuébecCanada
| | - Zhengwu Zhang
- Department of Statistics and Operations ResearchUniversity of North Carolina at Chapel HillNorth CarolinaUSA
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19
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Guassi Moreira JF, McLaughlin KA, Silvers JA. Characterizing the Network Architecture of Emotion Regulation Neurodevelopment. Cereb Cortex 2021; 31:4140-4150. [PMID: 33949645 PMCID: PMC8521747 DOI: 10.1093/cercor/bhab074] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 01/26/2021] [Revised: 02/25/2021] [Accepted: 02/26/2021] [Indexed: 11/13/2022] Open
Abstract
The ability to regulate emotions is key to goal attainment and well-being. Although much has been discovered about neurodevelopment and the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework to parse the neural underpinnings of emotion regulation skill acquisition, while accounting for age, in a sample of children and adolescents (N = 70, 34 female, aged 8-17 years). Focusing on three key network metrics-network differentiation, modularity, and community number differences between active regulation and a passive emotional baseline-we found that the control network, the default mode network, and limbic network were each related to emotion regulation ability while controlling for age. Greater network differentiation in the control and limbic networks was related to better emotion regulation ability. With regards to network community structure (modularity and community number), more communities and more crosstalk between modules (i.e., less modularity) in the control network were associated with better regulatory ability. By contrast, less crosstalk (i.e., greater modularity) between modules in the default mode network was associated with better regulatory ability. Together, these findings highlight whole-brain connectome features that support the acquisition of emotion regulation in youth.
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Affiliation(s)
| | | | - Jennifer A Silvers
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
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20
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Edison P. Brain Connectivity: A Bidirectional Involvement of Structural Connectivity and Pathological Substrates in Neurodegeneration. Brain Connect 2021; 10:155-156. [PMID: 32407211 DOI: 10.1089/brain.2020.29009.ped] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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21
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Lee D, Son T. Structural connectivity differs between males and females in the brain object manipulation network. PLoS One 2021; 16:e0253273. [PMID: 34115811 PMCID: PMC8195422 DOI: 10.1371/journal.pone.0253273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 09/25/2020] [Accepted: 06/01/2021] [Indexed: 11/24/2022] Open
Abstract
Object control skills are one of the most important abilities in daily life. Knowledge of object manipulation is an essential factor in improving object control skills. Although males and females equally try to use object manipulation knowledge, their object control abilities often differ. To explain this difference, we investigated how structural brain networks in males and females are differentially organized in the tool-preferring areas of the object manipulation network. The structural connectivity between the primary motor and premotor regions and between the inferior parietal regions in males was significantly higher than that in females. However, females showed greater structural connectivity in various regions of the object manipulation network, including the paracentral lobule, inferior parietal regions, superior parietal cortices, MT+ complex and neighboring visual areas, and dorsal stream visual cortex. The global node strength found in the female parietal network was significantly higher than that in males but not for the entire object manipulation, ventral temporal, and motor networks. These findings indicated that the parietal network in females has greater inter-regional structural connectivity to retrieve manipulation knowledge than that in males. This study suggests that differential structural networks in males and females might influence object manipulation knowledge retrieval.
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Affiliation(s)
- Dongha Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, Republic of Korea
- * E-mail:
| | - Taekwon Son
- Korea Brain Bank, Korea Brain Research Institute, Daegu, Republic of Korea
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22
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Hiramoto A, Jonaitis J, Niki S, Kohsaka H, Fetter RD, Cardona A, Pulver SR, Nose A. Regulation of coordinated muscular relaxation in Drosophila larvae by a pattern-regulating intersegmental circuit. Nat Commun 2021; 12:2943. [PMID: 34011945 PMCID: PMC8134441 DOI: 10.1038/s41467-021-23273-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 06/19/2020] [Accepted: 04/22/2021] [Indexed: 02/03/2023] Open
Abstract
Typical patterned movements in animals are achieved through combinations of contraction and delayed relaxation of groups of muscles. However, how intersegmentally coordinated patterns of muscular relaxation are regulated by the neural circuits remains poorly understood. Here, we identify Canon, a class of higher-order premotor interneurons, that regulates muscular relaxation during backward locomotion of Drosophila larvae. Canon neurons are cholinergic interneurons present in each abdominal neuromere and show wave-like activity during fictive backward locomotion. Optogenetic activation of Canon neurons induces relaxation of body wall muscles, whereas inhibition of these neurons disrupts timely muscle relaxation. Canon neurons provide excitatory outputs to inhibitory premotor interneurons. Canon neurons also connect with each other to form an intersegmental circuit and regulate their own wave-like activities. Thus, our results demonstrate how coordinated muscle relaxation can be realized by an intersegmental circuit that regulates its own patterned activity and sequentially terminates motor activities along the anterior-posterior axis.
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Affiliation(s)
- Atsuki Hiramoto
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Julius Jonaitis
- School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
| | - Sawako Niki
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Hiroshi Kohsaka
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | | | - Albert Cardona
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
- MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Stefan R Pulver
- School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
| | - Akinao Nose
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
- Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo, Japan.
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23
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Li Y, Wang N, Wang H, Lv Y, Zou Q, Wang J. Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability. Neuroimage 2021; 235:118018. [PMID: 33794358 DOI: 10.1016/j.neuroimage.2021.118018] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [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: 08/24/2020] [Revised: 12/04/2020] [Accepted: 03/22/2021] [Indexed: 12/14/2022] Open
Abstract
Morphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded ~70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.
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Affiliation(s)
- Yinzhi Li
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Hao Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Yating Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education.
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24
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Takata N, Sato N, Komaki Y, Okano H, Tanaka KF. Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy. Sci Rep 2021; 11:6234. [PMID: 33737651 PMCID: PMC7973786 DOI: 10.1038/s41598-021-85807-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [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: 02/27/2020] [Accepted: 03/04/2021] [Indexed: 11/13/2022] Open
Abstract
A brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher's necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.
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Affiliation(s)
- Norio Takata
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan.
- Central Institute for Experimental Animals (CIEA), 3-25-12, Tonomachi, Kawasaki, Kanagawa, 210-0821, Japan.
| | - Nobuhiko Sato
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Yuji Komaki
- Central Institute for Experimental Animals (CIEA), 3-25-12, Tonomachi, Kawasaki, Kanagawa, 210-0821, Japan
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
| | - Kenji F Tanaka
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan
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25
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Brown APY, Cossell L, Strom M, Tyson AL, Vélez-Fort M, Margrie TW. Analysis of segmentation ontology reveals the similarities and differences in connectivity onto L2/3 neurons in mouse V1. Sci Rep 2021; 11:4983. [PMID: 33654118 PMCID: PMC7925549 DOI: 10.1038/s41598-021-82353-7] [Citation(s) in RCA: 5] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 01/15/2021] [Indexed: 12/02/2022] Open
Abstract
Quantitatively comparing brain-wide connectivity of different types of neuron is of vital importance in understanding the function of the mammalian cortex. Here we have designed an analytical approach to examine and compare datasets from hierarchical segmentation ontologies, and applied it to long-range presynaptic connectivity onto excitatory and inhibitory neurons, mainly located in layer 2/3 (L2/3), of mouse primary visual cortex (V1). We find that the origins of long-range connections onto these two general cell classes-as well as their proportions-are quite similar, in contrast to the inputs on to a cell type in L6. These anatomical data suggest that distal inputs received by the general excitatory and inhibitory classes of neuron in L2/3 overlap considerably.
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Affiliation(s)
- Alexander P Y Brown
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Lee Cossell
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Molly Strom
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Adam L Tyson
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Mateo Vélez-Fort
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK
| | - Troy W Margrie
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, 25 Howland Street, London, W1T 4JG, UK.
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26
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Grotheer M, Yeatman J, Grill-Spector K. White matter fascicles and cortical microstructure predict reading-related responses in human ventral temporal cortex. Neuroimage 2021; 227:117669. [PMID: 33359351 PMCID: PMC8416179 DOI: 10.1016/j.neuroimage.2020.117669] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 08/24/2020] [Revised: 12/10/2020] [Accepted: 12/12/2020] [Indexed: 01/30/2023] Open
Abstract
Reading-related responses in the lateral ventral temporal cortex (VTC) show a consistent spatial layout across individuals, which is puzzling, since reading skills are acquired during childhood. Here, we tested the hypothesis that white matter fascicles and gray matter microstructure predict the location of reading-related responses in lateral VTC. We obtained functional (fMRI), diffusion (dMRI), and quantitative (qMRI) magnetic resonance imaging data in 30 adults. fMRI was used to map reading-related responses by contrasting responses in a reading task with those in adding and color tasks; dMRI was used to identify the brain's fascicles and to map their endpoint densities in lateral VTC; qMRI was used to measure proton relaxation time (T1), which depends on cortical tissue microstructure. We fit linear models that predict reading-related responses in lateral VTC from endpoint density and T1 and used leave-one-subject-out cross-validation to assess prediction accuracy. Using a subset of our participants (N=10, feature selection set), we find that i) endpoint densities of the arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), and vertical occipital fasciculus (VOF) are significant predictors of reading-related responses, and ii) cortical T1 of lateral VTC further improves the predictions of the fascicle model. In the remaining participants (N=20, validation set), we show that a linear model that includes T1, AF, ILF and VOF significantly predicts i) the map of reading-related responses across lateral VTC and ii) the location of the visual word form area, a region critical for reading. Overall, our data-driven approach reveals that the AF, ILF, VOF and cortical microstructure have a consistent spatial relationship with an individual's reading-related responses in lateral VTC.
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Affiliation(s)
- Mareike Grotheer
- Psychology Department, Stanford University, Stanford, CA 94305, USA..
| | - Jason Yeatman
- Psychology Department, Stanford University, Stanford, CA 94305, USA.; Graduate School of Education, Stanford University, Stanford, CA 94305, USA.; Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.; Wu Tsai Neurosciences Institute, Stanford University, CA 94305, USA
| | - Kalanit Grill-Spector
- Psychology Department, Stanford University, Stanford, CA 94305, USA.; Wu Tsai Neurosciences Institute, Stanford University, CA 94305, USA
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27
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Abstract
The past several years have brought revelations and paradigm shifts in research on the cerebellum. Historically viewed as a simple sensorimotor controller with homogeneous architecture, the cerebellum is increasingly implicated in cognitive functions. It possesses an impressive diversity of molecular, cellular and circuit mechanisms, embedded in a dynamic, recurrent circuit architecture. Recent insights about the diversity and dynamism of the cerebellum provide a roadmap for the next decade of cerebellar research, challenging some old concepts, reinvigorating others and defining major new research directions.
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Affiliation(s)
- Chris I De Zeeuw
- Department of Neuroscience, Erasmus MC, Rotterdam, The Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Sciences (KNAW), Amsterdam, The Netherlands
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28
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Abstract
Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using today's best supervised methods and that the mapping of these neural network models' hidden layers is neuroanatomically consistent across the ventral stream. Strikingly, we find that these methods produce brain-like representations even when trained solely with real human child developmental data collected from head-mounted cameras, despite the fact that these datasets are noisy and limited. We also find that semisupervised deep contrastive embeddings can leverage small numbers of labeled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results illustrate a use of unsupervised learning to provide a quantitative model of a multiarea cortical brain system and present a strong candidate for a biologically plausible computational theory of primate sensory learning.
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Affiliation(s)
- Chengxu Zhuang
- Department of Psychology, Stanford University, Stanford, CA 94305;
| | - Siming Yan
- Department of Computer Science, The University of Texas at Austin, Austin, TX 78712
| | - Aran Nayebi
- Neurosciences PhD Program, Stanford University, Stanford, CA 94305
| | - Martin Schrimpf
- Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Michael C Frank
- Department of Psychology, Stanford University, Stanford, CA 94305
| | - James J DiCarlo
- Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Daniel L K Yamins
- Department of Psychology, Stanford University, Stanford, CA 94305
- Department of Computer Science, Stanford University, Stanford, CA 94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305
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29
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Vezoli J, Magrou L, Goebel R, Wang XJ, Knoblauch K, Vinck M, Kennedy H. Cortical hierarchy, dual counterstream architecture and the importance of top-down generative networks. Neuroimage 2021; 225:117479. [PMID: 33099005 PMCID: PMC8244994 DOI: 10.1016/j.neuroimage.2020.117479] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [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: 04/03/2020] [Revised: 09/29/2020] [Accepted: 10/15/2020] [Indexed: 12/18/2022] Open
Abstract
Hierarchy is a major organizational principle of the cortex and underscores modern computational theories of cortical function. The local microcircuit amplifies long-distance inter-areal input, which show distance-dependent changes in their laminar profiles. Statistical modeling of these changes in laminar profiles demonstrates that inputs from multiple hierarchical levels to their target areas show remarkable consistency, allowing the construction of a cortical hierarchy based on a principle of hierarchical distance. The statistical modeling that is applied to structure can also be applied to laminar differences in the oscillatory coherence between areas thereby determining a functional hierarchy of the cortex. Close examination of the anatomy of inter-areal connectivity reveals a dual counterstream architecture with well-defined distance-dependent feedback and feedforward pathways in both the supra- and infragranular layers, suggesting a multiplicity of feedback pathways with well-defined functional properties. These findings are consistent with feedback connections providing a generative network involved in a wide range of cognitive functions. A dynamical model constrained by connectivity data sheds insight into the experimentally observed signatures of frequency-dependent Granger causality for feedforward versus feedback signaling. Concerted experiments capitalizing on recent technical advances and combining tract-tracing, high-resolution fMRI, optogenetics and mathematical modeling hold the promise of a much improved understanding of lamina-constrained mechanisms of neural computation and cognition. However, because inter-areal interactions involve cortical layers that have been the target of important evolutionary changes in the primate lineage, these investigations will need to include human and non-human primate comparisons.
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Affiliation(s)
- Julien Vezoli
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - Loïc Magrou
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Rainer Goebel
- Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, the Netherlands
| | - Xiao-Jing Wang
- Center for Neural Science, New York University (NYU), New York, NY 10003, USA
| | - Kenneth Knoblauch
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - Martin Vinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt, Germany.
| | - Henry Kennedy
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500 Bron, France; Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences (CAS) Key Laboratory of Primate Neurobiology, CAS, Shanghai 200031, China.
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30
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Wang P, Wang J, Tang Q, Alvarez TL, Wang Z, Kung YC, Lin CP, Chen H, Meng C, Biswal BB. Structural and functional connectivity mapping of the human corpus callosum organization with white-matter functional networks. Neuroimage 2020; 227:117642. [PMID: 33338619 DOI: 10.1016/j.neuroimage.2020.117642] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [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: 09/29/2020] [Revised: 11/28/2020] [Accepted: 12/03/2020] [Indexed: 11/13/2022] Open
Abstract
The corpus callosum serves as a crucial organization for understanding the information integration between the two hemispheres. Our previous study explored the functional connectivity between the corpus callosum and white-matter functional networks (WM-FNs), but the corresponding physical connectivity remains unknown. The current study uses the resting-state fMRI of Human Connectome Project data to identify ten WM-FNs in 108 healthy subjects, and then independently maps the structural and functional connectivity between the corpus callosum and above WM-FNs using the diffusion tensor images (DTI) tractography and resting-state functional connectivity (RSFC). Our results demonstrated that the structural and functional connectivity between the human corpus callosum and WM-FNs have the following high overall correspondence: orbitofrontal WM-FN, DTI map = 89% and RSFC map = 92%; sensorimotor middle WM-FN, DTI map = 47% and RSFC map = 77%; deep WM-FN, DTI map = 50% and RSFC map = 79%; posterior corona radiata WM-FN, DTI map = 82% and RSFC map = 73%. These findings reinforce the notion that the corpus callosum has unique spatial distribution patterns connecting to distinct WM-FNs. However, important differences between the structural and functional connectivity mapping results were also observed, which demonstrated a synergy between DTI tractography and RSFC toward better understanding the information integration of primary and higher-order functional systems in the human brain.
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Affiliation(s)
- Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianlin Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tara L Alvarez
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Zedong Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yi-Chia Kung
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
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31
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Abstract
Turbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of the brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1,003 healthy participants, we demonstrate turbulent-like human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed turbulent-like dynamics, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the exponential distance rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulent-like brain activity and optimal brain function. Overall, our results reveal a way of analyzing and modeling whole-brain dynamics that suggests a turbulent-like dynamic intrinsic backbone facilitating large-scale network communication.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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32
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Sarwar T, Tian Y, Yeo BTT, Ramamohanarao K, Zalesky A. Structure-function coupling in the human connectome: A machine learning approach. Neuroimage 2020; 226:117609. [PMID: 33271268 DOI: 10.1016/j.neuroimage.2020.117609] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [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: 07/04/2020] [Revised: 11/23/2020] [Accepted: 11/25/2020] [Indexed: 02/03/2023] Open
Abstract
While the function of most biological systems is tightly constrained by their structure, current evidence suggests that coupling between the structure and function of brain networks is relatively modest. We aimed to investigate whether the modest coupling between connectome structure and function is a fundamental property of nervous systems or a limitation of current brain network models. We developed a new deep learning framework to predict an individual's brain function from their structural connectome, achieving prediction accuracies that substantially exceeded state-of-the-art biophysical models (group: R=0.9±0.1, individual: R=0.55±0.1). Crucially, brain function predicted from an individual's structural connectome explained significant inter-individual variation in cognitive performance. Our results suggest that structure-function coupling in human brain networks is substantially tighter than previously suggested. We establish the margin by which current brain network models can be improved and demonstrate how deep learning can facilitate investigation of relations between brain function and behavior.
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Affiliation(s)
- T Sarwar
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - Y Tian
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
| | - B T T Yeo
- Department of Electrical and Computer Engineering, Center for Sleep & Cognition & N.1 Institute for Health, National University of Singapore, 15 119077, Singapore
| | - K Ramamohanarao
- Department of Computing and Information Systems, The University of Melbourne, Victoria, 3010, Australia
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia; Department of Biomedical Engineering, The University of Melbourne, Victoria, 3010, Australia.
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Paquola C, Seidlitz J, Benkarim O, Royer J, Klimes P, Bethlehem RAI, Larivière S, Vos de Wael R, Rodríguez-Cruces R, Hall JA, Frauscher B, Smallwood J, Bernhardt BC. A multi-scale cortical wiring space links cellular architecture and functional dynamics in the human brain. PLoS Biol 2020; 18:e3000979. [PMID: 33253185 PMCID: PMC7728398 DOI: 10.1371/journal.pbio.3000979] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [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: 03/26/2020] [Revised: 12/10/2020] [Accepted: 11/02/2020] [Indexed: 12/11/2022] Open
Abstract
The vast net of fibres within and underneath the cortex is optimised to support the convergence of different levels of brain organisation. Here, we propose a novel coordinate system of the human cortex based on an advanced model of its connectivity. Our approach is inspired by seminal, but so far largely neglected models of cortico-cortical wiring established by postmortem anatomical studies and capitalises on cutting-edge in vivo neuroimaging and machine learning. The new model expands the currently prevailing diffusion magnetic resonance imaging (MRI) tractography approach by incorporation of additional features of cortical microstructure and cortico-cortical proximity. Studying several datasets and different parcellation schemes, we could show that our coordinate system robustly recapitulates established sensory-limbic and anterior-posterior dimensions of brain organisation. A series of validation experiments showed that the new wiring space reflects cortical microcircuit features (including pyramidal neuron depth and glial expression) and allowed for competitive simulations of functional connectivity and dynamics based on resting-state functional magnetic resonance imaging (rs-fMRI) and human intracranial electroencephalography (EEG) coherence. Our results advance our understanding of how cell-specific neurobiological gradients produce a hierarchical cortical wiring scheme that is concordant with increasing functional sophistication of human brain organisation. Our evaluations demonstrate the cortical wiring space bridges across scales of neural organisation and can be easily translated to single individuals.
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Affiliation(s)
- Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, Maryland, United States of America
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Petr Klimes
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Raul Rodríguez-Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jeffery A. Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Abstract
Higher-order connectivity in complex systems described by simplexes of different orders provides a geometry for simplex-based dynamical variables and interactions. Simplicial complexes that constitute a functional geometry of the human connectome can be crucial for the brain complex dynamics. In this context, the best-connected brain areas, designated as hub nodes, play a central role in supporting integrated brain function. Here, we study the structure of simplicial complexes attached to eight global hubs in the female and male connectomes and identify the core networks among the affected brain regions. These eight hubs (Putamen, Caudate, Hippocampus and Thalamus-Proper in the left and right cerebral hemisphere) are the highest-ranking according to their topological dimension, defined as the number of simplexes of all orders in which the node participates. Furthermore, we analyse the weight-dependent heterogeneity of simplexes. We demonstrate changes in the structure of identified core networks and topological entropy when the threshold weight is gradually increased. These results highlight the role of higher-order interactions in human brain networks and provide additional evidence for (dis)similarity between the female and male connectomes.
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Affiliation(s)
- Miroslav Andjelković
- Department of Theoretical Physics, Jožef Stefan Institute, 1000, Ljubljana, Slovenia
- Department of Thermal Engineering and Energy, Vinča Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade, 1100, Belgrade, Serbia
| | - Bosiljka Tadić
- Department of Theoretical Physics, Jožef Stefan Institute, 1000, Ljubljana, Slovenia.
- Complexity Science Hub, Josefstaedter Strasse 39, Vienna, Austria.
| | - Roderick Melnik
- MS2Discovery Interdisciplinary Research Institute, M2NeT Laboratory and Department of Mathematics, Wilfrid Laurier University, 75 University Ave. W, Waterloo, ON, N2L 3C5, Canada
- BCAM - Basque Center for Applied Mathematics, Alameda de Mazarredo 14, 48009, Bilbao, Spain
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Goodman AM, Allendorfer JB, Blum AS, Bolding MS, Correia S, Ver Hoef LW, Gaston TE, Grayson LE, Kraguljac NV, Lahti AC, Martin AN, Monroe WS, Philip NS, Tocco K, Vogel V, LaFrance WC, Szaflarski JP. White matter and neurite morphology differ in psychogenic nonepileptic seizures. Ann Clin Transl Neurol 2020; 7:1973-1984. [PMID: 32991786 PMCID: PMC7545605 DOI: 10.1002/acn3.51198] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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: 05/14/2020] [Revised: 08/10/2020] [Accepted: 08/24/2020] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE To further evaluate the relationship between the clinical profiles and limbic and motor brain regions and their connecting pathways in psychogenic nonepileptic seizures (PNES). Neurite Orientation Dispersion and Density Indices (NODDI) multicompartment modeling was used to test the relationships between tissue alterations in patients with traumatic brain injury (TBI) and multiple psychiatric symptoms. METHODS The sample included participants with prior TBI (TBI; N = 37) but no PNES, and with TBI and PNES (TBI + PNES; N = 34). Participants completed 3T Siemens Prisma MRI high angular resolution imaging diffusion protocol. Statistical maps, including fractional anisotropy (FA), mean diffusivity (MD), neurite dispersion [orientation dispersion index (ODI)] and density [intracellular volume fraction (ICVF), and free water (i.e., isotropic) volume fraction (V-ISO)] signal intensity, were generated for each participant. Linear mixed-effects models identified clusters of between-group differences in indices of white matter changes. Pearson's r correlation tests assessed any relationship between signal intensity and psychiatric symptoms. RESULTS Compared to TBI, TBI + PNES revealed decreases in FA, ICVF, and V-ISO and increases in MD for clusters within cingulum bundle, uncinate fasciculus, fornix/stria terminalis, and corticospinal tract pathways (cluster threshold α = 0.05). Indices of white matter changes for these clusters correlated with depressive, anxiety, PTSD, psychoticism, and somatization symptom severity (FDR threshold α = 0.05). A follow-up within-group analysis revealed that these correlations failed to reach the criteria for significance in the TBI + PNES group alone. INTERPRETATION The results expand support for the hypothesis that alterations in pathways comprising the specific PNES network correspond to patient profiles. These findings implicate myelin-specific changes as possible contributors to PNES, thus introducing novel potential treatment targets.
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Affiliation(s)
- Adam M. Goodman
- Department of Neurology and the UAB Epilepsy CenterUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Jane B. Allendorfer
- Department of Neurology and the UAB Epilepsy CenterUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Andrew S. Blum
- Department of NeurologyRhode Island HospitalProvidenceRhode IslandUSA
- Brown UniversityProvidenceRhode IslandUSA
| | - Mark S. Bolding
- Department of RadiologyUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Stephen Correia
- Brown UniversityProvidenceRhode IslandUSA
- Department of Psychiatry and Human BehaviorAlpert Medical SchoolBrown UniversityRhode Island HospitalProvidenceRhode IslandUSA
- Center for Neurorestoration and NeurotechnologyProvidence VA Medical CenterProvidenceRhode IslandUSA
| | - Lawrence W. Ver Hoef
- Department of Neurology and the UAB Epilepsy CenterUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Birmingham VA Medical CenterBirminghamAlabamaUSA
| | - Tyler E. Gaston
- Department of Neurology and the UAB Epilepsy CenterUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Birmingham VA Medical CenterBirminghamAlabamaUSA
| | - Leslie E. Grayson
- Department of Neurology and the UAB Epilepsy CenterUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Birmingham VA Medical CenterBirminghamAlabamaUSA
- Children’s of AlabamaUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Nina V. Kraguljac
- Department of Psychiatry and Behavioral NeurobiologyUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Adrienne C. Lahti
- Department of Psychiatry and Behavioral NeurobiologyUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Departments of Neurobiology and NeurosurgeryUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Amber N. Martin
- Department of Neurology and the UAB Epilepsy CenterUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - William S. Monroe
- Department of Research ComputingUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Noah S. Philip
- Brown UniversityProvidenceRhode IslandUSA
- Center for Neurorestoration and NeurotechnologyProvidence VA Medical CenterProvidenceRhode IslandUSA
| | - Krista Tocco
- Department of NeurologyRhode Island HospitalProvidenceRhode IslandUSA
- Brown UniversityProvidenceRhode IslandUSA
- Center for Neurorestoration and NeurotechnologyProvidence VA Medical CenterProvidenceRhode IslandUSA
| | - Valerie Vogel
- Department of NeurologyRhode Island HospitalProvidenceRhode IslandUSA
- Brown UniversityProvidenceRhode IslandUSA
- Center for Neurorestoration and NeurotechnologyProvidence VA Medical CenterProvidenceRhode IslandUSA
| | - W. Curt LaFrance
- Center for Neurorestoration and NeurotechnologyProvidence VA Medical CenterProvidenceRhode IslandUSA
- Departments of Psychiatry and NeurologyRhode Island Hospital and Brown UniversityProvidenceRhode IslandUSA
| | - Jerzy P. Szaflarski
- Department of Neurology and the UAB Epilepsy CenterUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Children’s of AlabamaUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Departments of Neurobiology and NeurosurgeryUniversity of Alabama at BirminghamBirminghamAlabamaUSA
- Comprehensive Neuroscience CenterUniversity of Alabama at BirminghamBirminghamAlabamaUSA
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Soutschek A, Moisa M, Ruff CC, Tobler PN. The right temporoparietal junction enables delay of gratification by allowing decision makers to focus on future events. PLoS Biol 2020; 18:e3000800. [PMID: 32776945 PMCID: PMC7447039 DOI: 10.1371/journal.pbio.3000800] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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: 02/04/2020] [Revised: 08/25/2020] [Accepted: 07/20/2020] [Indexed: 11/19/2022] Open
Abstract
Studies of neural processes underlying delay of gratification usually focus on prefrontal networks related to curbing affective impulses. Here, we provide evidence for an alternative mechanism that facilitates delaying gratification by mental orientation towards the future. Combining continuous theta-burst stimulation (cTBS) with functional neuroimaging, we tested how the right temporoparietal junction (rTPJ) facilitates processing of future events and thereby promotes delay of gratification. Participants performed an intertemporal decision task and a mental time-travel task in the MRI scanner before and after receiving cTBS over the rTPJ or the vertex (control site). rTPJ cTBS led to both stronger temporal discounting for longer delays and reduced processing of future relative to past events in the mental time-travel task. This finding suggests that the rTPJ contributes to the ability to delay gratification by facilitating mental representation of outcomes in the future. On the neural level, rTPJ cTBS led to a reduction in the extent to which connectivity of rTPJ with striatum reflected the value of delayed rewards, indicating a role of rTPJ–striatum connectivity in constructing neural representations of future rewards. Together, our findings provide evidence that the rTPJ is an integral part of a brain network that promotes delay of gratification by facilitating mental orientation to future rewards. Studies of neural processes underlying delay of gratification usually focus on prefrontal networks related to curbing affective impulses. This study reveals that the right temporo-parietal junction improves patience by shifting attention to future outcomes, strengthening the representations of future reward values in the brain.
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Affiliation(s)
- Alexander Soutschek
- Department of Psychology, Ludwig Maximilian University Munich, Munich, Germany
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
- * E-mail:
| | - Marius Moisa
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Christian C. Ruff
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Philippe N. Tobler
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
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Waymel A, Friedrich P, Bastian PA, Forkel SJ, Thiebaut de Schotten M. Anchoring the human olfactory system within a functional gradient. Neuroimage 2020; 216:116863. [PMID: 32325207 PMCID: PMC7116082 DOI: 10.1016/j.neuroimage.2020.116863] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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: 12/19/2019] [Revised: 04/10/2020] [Accepted: 04/15/2020] [Indexed: 02/07/2023] Open
Abstract
Margulies et al. (2016) demonstrated the existence of at least five independent functional connectivity gradients in the human brain. However, it is unclear how these functional gradients might link to anatomy. The dual origin theory proposes that differences in cortical cytoarchitecture originate from two trends of progressive differentiation between the different layers of the cortex, referred to as the hippocampocentric and olfactocentric systems. When conceptualising the functional connectivity gradients within the evolutionary framework of the Dual Origin theory, the first gradient likely represents the hippocampocentric system anatomically. Here we expand on this concept and demonstrate that the fifth gradient likely links to the olfactocentric system. We describe the anatomy of the latter as well as the evidence to support this hypothesis. Together, the first and fifth gradients might help to model the Dual Origin theory of the human brain and inform brain models and pathologies.
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Affiliation(s)
- Alice Waymel
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France; Hyperedge Instruments, France
| | - Patrick Friedrich
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Pierre-Antoine Bastian
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Hyperedge Instruments, France
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France; King's College London, Institute of Psychiatry Psychology and Neurosciences, Department of Neuroimaging, London, UK
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France.
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38
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Raj A, Cai C, Xie X, Palacios E, Owen J, Mukherjee P, Nagarajan S. Spectral graph theory of brain oscillations. Hum Brain Mapp 2020; 41:2980-2998. [PMID: 32202027 PMCID: PMC7336150 DOI: 10.1002/hbm.24991] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [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: 07/26/2019] [Revised: 02/13/2020] [Accepted: 02/16/2020] [Indexed: 11/10/2022] Open
Abstract
The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Chang Cai
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Graduate School of Medical SciencesWeill Cornell MedicineNew YorkNew YorkUSA
| | - Eva Palacios
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
| | - Julia Owen
- Department of RadiologyUniversity of WashingtonSeattleWashingtonUSA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
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Kuśmierz Ł, Ogawa S, Toyoizumi T. Edge of Chaos and Avalanches in Neural Networks with Heavy-Tailed Synaptic Weight Distribution. Phys Rev Lett 2020; 125:028101. [PMID: 32701351 DOI: 10.1103/physrevlett.125.028101] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 03/03/2020] [Accepted: 05/26/2020] [Indexed: 06/11/2023]
Abstract
We propose an analytically tractable neural connectivity model with power-law distributed synaptic strengths. When threshold neurons with biologically plausible number of incoming connections are considered, our model features a continuous transition to chaos and can reproduce biologically relevant low activity levels and scale-free avalanches, i.e., bursts of activity with power-law distributions of sizes and lifetimes. In contrast, the Gaussian counterpart exhibits a discontinuous transition to chaos and thus cannot be poised near the edge of chaos. We validate our predictions in simulations of networks of binary as well as leaky integrate-and-fire neurons. Our results suggest that heavy-tailed synaptic distribution may form a weakly informative sparse-connectivity prior that can be useful in biological and artificial adaptive systems.
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Affiliation(s)
- Łukasz Kuśmierz
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Shun Ogawa
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Taro Toyoizumi
- Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
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40
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Haxby JV, Guntupalli JS, Nastase SA, Feilong M. Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies. eLife 2020; 9:e56601. [PMID: 32484439 PMCID: PMC7266639 DOI: 10.7554/elife.56601] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [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: 03/12/2020] [Accepted: 05/14/2020] [Indexed: 01/13/2023] Open
Abstract
Information that is shared across brains is encoded in idiosyncratic fine-scale functional topographies. Hyperalignment captures shared information by projecting pattern vectors for neural responses and connectivities into a common, high-dimensional information space, rather than by aligning topographies in a canonical anatomical space. Individual transformation matrices project information from individual anatomical spaces into the common model information space, preserving the geometry of pairwise dissimilarities between pattern vectors, and model cortical topography as mixtures of overlapping, individual-specific topographic basis functions, rather than as contiguous functional areas. The fundamental property of brain function that is preserved across brains is information content, rather than the functional properties of local features that support that content. In this Perspective, we present the conceptual framework that motivates hyperalignment, its computational underpinnings for joint modeling of a common information space and idiosyncratic cortical topographies, and discuss implications for understanding the structure of cortical functional architecture.
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Affiliation(s)
- James V Haxby
- Center for Cognitive Neuroscience, Dartmouth CollegeHanoverUnited States
| | | | | | - Ma Feilong
- Center for Cognitive Neuroscience, Dartmouth CollegeHanoverUnited States
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41
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Friedmann D, Pun A, Adams EL, Lui JH, Kebschull JM, Grutzner SM, Castagnola C, Tessier-Lavigne M, Luo L. Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network. Proc Natl Acad Sci U S A 2020; 117:11068-11075. [PMID: 32358193 PMCID: PMC7245124 DOI: 10.1073/pnas.1918465117] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [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] [Indexed: 12/22/2022] Open
Abstract
The projection targets of a neuronal population are a key feature of its anatomical characteristics. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a three-dimensional (3D) convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas. The identification of axonal structures as thin as one voxel benefits from data augmentation but also requires a loss function that tolerates errors in annotation. A network trained with volumes of serotonergic axons in all major brain regions can be generalized to map and quantify axons from thalamocortical, deep cerebellar, and cortical projection neurons, validating transfer learning as a tool to adapt the model to novel categories of axonal morphology. Speed of training, ease of use, and accuracy improve over existing tools without a need for specialized computing hardware. Given the recent emphasis on genetically and functionally defining cell types in neural circuit analysis, TrailMap will facilitate automated extraction and quantification of axons from these specific cell types at the scale of the entire mouse brain, an essential component of deciphering their connectivity.
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Affiliation(s)
- Drew Friedmann
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | - Albert Pun
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | - Eliza L Adams
- Department of Biology, Stanford University, Stanford, CA 94305
- Neurosciences Graduate Program, Stanford University, Stanford, CA 94305
| | - Jan H Lui
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | - Justus M Kebschull
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | - Sophie M Grutzner
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | | | | | - Liqun Luo
- Department of Biology, Stanford University, Stanford, CA 94305;
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
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Edison P. Brain Connectivity: Structural and Functional Neuronal Integrity and Its Relationship with Pathological Substrates. Brain Connect 2020; 10:106-107. [PMID: 32319829 DOI: 10.1089/brain.2020.29008.ped] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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43
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Cui Z, Li H, Xia CH, Larsen B, Adebimpe A, Baum GL, Cieslak M, Gur RE, Gur RC, Moore TM, Oathes DJ, Alexander-Bloch AF, Raznahan A, Roalf DR, Shinohara RT, Wolf DH, Davatzikos C, Bassett DS, Fair DA, Fan Y, Satterthwaite TD. Individual Variation in Functional Topography of Association Networks in Youth. Neuron 2020; 106:340-353.e8. [PMID: 32078800 PMCID: PMC7182484 DOI: 10.1016/j.neuron.2020.01.029] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [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: 07/08/2019] [Revised: 11/22/2019] [Accepted: 01/22/2020] [Indexed: 01/08/2023]
Abstract
The spatial distribution of large-scale functional networks on the cerebral cortex differs between individuals and is particularly variable in association networks that are responsible for higher-order cognition. However, it remains unknown how this functional topography evolves in development and supports cognition. Capitalizing on advances in machine learning and a large sample imaged with 27 min of high-quality functional MRI (fMRI) data (n = 693, ages 8-23 years), we delineate how functional topography evolves during youth. We found that the functional topography of association networks is refined with age, allowing accurate prediction of unseen individuals' brain maturity. The cortical representation of association networks predicts individual differences in executive function. Finally, variability of functional topography is associated with fundamental properties of brain organization, including evolutionary expansion, cortical myelination, and cerebral blood flow. Our results emphasize the importance of considering the plasticity and diversity of functional neuroanatomy during development and suggest advances in personalized therapeutics.
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Affiliation(s)
- Zaixu Cui
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hongming Li
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Cedric H Xia
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Azeez Adebimpe
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Graham L Baum
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matt Cieslak
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Desmond J Oathes
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuromodulation in Depression and Stress, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Yale University, New Haven, CT 06520, USA
| | - Armin Raznahan
- Developmental Neurogenomics Unit, Intramural Research Program, National Institutes of Mental Health, Bethesda, MD 20892, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Departments of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; Departments of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Damien A Fair
- Department of Behavioral Neuroscience, Department of Psychiatry, Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97239, USA
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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44
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Xu K, Duann JR. Brain connectivity in the left frontotemporal network dynamically modulated by processing difficulty: Evidence from Chinese relative clauses. PLoS One 2020; 15:e0230666. [PMID: 32271773 PMCID: PMC7144993 DOI: 10.1371/journal.pone.0230666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 07/14/2019] [Accepted: 03/05/2020] [Indexed: 11/18/2022] Open
Abstract
Although the connection between the left inferior frontal gyrus (LIFG) and the left superior temporal gyrus (LSTG) has been found to be essential for the comprehension of relative clause (RC) sentences, it remains unclear how the LIFG and the LSTG interact with each other, especially during the processing of Chinese RC sentences with different processing difficulty. This study thus conducted a 2 × 2 (modifying position × extraction position) factorial analyses to examine how these two factors influences regional brain activation. The results showed that, regardless of the modifying position, greater activation in the LIFG was consistently elicited in Chinese subject-extracted relative clauses (SRCs) with non-canonical word order than object-extracted relative clauses (ORCs) with canonical word order, implying that the LIFG subserving the ordering process primarily contributes to the processing of information with increased integration demands due to the non-canonical sequence. Moreover, the directional connection between the LIFG and the LSTG appeared to be modulated by different modifying positions. When the RC was at the subject-modifying position, the effective connectivity from the LIFG to the LSTG was dominantly activated for sentence comprehension; whereas when the RC was at the object-modifying position thus being more difficult, it might be the feedback mechanism from the LSTG back to the LIFG that took place in sentence processing. These findings reveal that brain activation in between the LIFG and the LSTG may be dynamically modulated by different processing difficulty and suggest the relative specialization but extensive collaboration involved in the LIFG and the LSTG for sentence comprehension.
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Affiliation(s)
- Kunyu Xu
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
| | - Jeng-Ren Duann
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Institute for Neural Computation, University of California San Diego, La Jolla, CA, United States of America
- * E-mail:
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45
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Wang Y, Metoki A, Smith DV, Medaglia JD, Zang Y, Benear S, Popal H, Lin Y, Olson IR. Multimodal mapping of the face connectome. Nat Hum Behav 2020; 4:397-411. [PMID: 31988441 PMCID: PMC7167350 DOI: 10.1038/s41562-019-0811-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [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: 01/29/2019] [Accepted: 12/09/2019] [Indexed: 01/13/2023]
Abstract
Face processing supports our ability to recognize friend from foe, form tribes and understand the emotional implications of changes in facial musculature. This skill relies on a distributed network of brain regions, but how these regions interact is poorly understood. Here we integrate anatomical and functional connectivity measurements with behavioural assays to create a global model of the face connectome. We dissect key features, such as the network topology and fibre composition. We propose a neurocognitive model with three core streams; face processing along these streams occurs in a parallel and reciprocal manner. Although long-range fibre paths are important, the face network is dominated by short-range fibres. Finally, we provide evidence that the well-known right lateralization of face processing arises from imbalanced intra- and interhemispheric connections. In summary, the face network relies on dynamic communication across highly structured fibre tracts, enabling coherent face processing that underpins behaviour and cognition.
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Affiliation(s)
- Yin Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Athanasia Metoki
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - David V Smith
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yinyin Zang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Susan Benear
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Haroon Popal
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Ying Lin
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, PA, USA.
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46
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Galdi P, Blesa M, Stoye DQ, Sullivan G, Lamb GJ, Quigley AJ, Thrippleton MJ, Bastin ME, Boardman JP. Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth. Neuroimage Clin 2020; 25:102195. [PMID: 32044713 PMCID: PMC7016043 DOI: 10.1016/j.nicl.2020.102195] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [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: 10/23/2019] [Revised: 01/14/2020] [Accepted: 01/21/2020] [Indexed: 01/01/2023]
Abstract
Multi-contrast MRI captures information about brain macro- and micro-structure which can be combined in an integrated model to obtain a detailed "fingerprint" of the anatomical properties of an individual's brain. Inter-regional similarities between features derived from structural and diffusion MRI, including regional volumes, diffusion tensor metrics, neurite orientation dispersion and density imaging measures, can be modelled as morphometric similarity networks (MSNs). Here, individual MSNs were derived from 105 neonates (59 preterm and 46 term) who were scanned between 38 and 45 weeks postmenstrual age (PMA). Inter-regional similarities were used as predictors in a regression model of age at the time of scanning and in a classification model to discriminate between preterm and term infant brains. When tested on unseen data, the regression model predicted PMA at scan with a mean absolute error of 0.70 ± 0.56 weeks, and the classification model achieved 92% accuracy. We conclude that MSNs predict chronological brain age accurately; and they provide a data-driven approach to identify networks that characterise typical maturation and those that contribute most to neuroanatomic variation associated with preterm birth.
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Affiliation(s)
- Paola Galdi
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK.
| | - Manuel Blesa
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - David Q Stoye
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Gillian J Lamb
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Alan J Quigley
- Department of Radiology, Royal Hospital for Sick Children, Edinburgh EH9 1LF, UK
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK; Edinburgh Imaging, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - James P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
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47
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Barnes-Davis ME, Williamson BJ, Merhar SL, Holland SK, Kadis DS. Rewiring the extremely preterm brain: Altered structural connectivity relates to language function. Neuroimage Clin 2020; 25:102194. [PMID: 32032818 PMCID: PMC7005506 DOI: 10.1016/j.nicl.2020.102194] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [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: 02/20/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 11/26/2022]
Abstract
Children born preterm are at increased risk for cognitive impairment, with higher-order functions such as language being especially vulnerable. Previously, we and others have reported increased interhemispheric functional connectivity in children born extremely preterm; the finding appears at odds with literature showing decreased integrity of the corpus callosum, the primary commissural bundle, in preterm children. We address the apparent discrepancy by obtaining advanced measures of structural connectivity in twelve school-aged children born extremely preterm (<28 weeks) and ten term controls. We hypothesize increased extracallosal structural connectivity might support the functional hyperconnectivity we had previously observed. Participants were aged four to six years at time of study and groups did not differ in age, sex, race, ethnicity, or socioeconomic status. Whole-brain and language-network-specific (functionally-constrained) connectometry analyses were performed. At the whole-brain level, preterm children had decreased connectivity in the corpus callosum and increased connectivity in the cerebellum versus controls. Functionally-constrained analyses revealed significantly increased extracallosal connectivity between bilateral temporal regions in preterm children (FDRq <0.05). Connectivity within these extracallosal pathways was positively correlated with performance on standardized language assessments in children born preterm (FDRq <0.001), but unrelated to performance in controls. This is the first study to identify anatomical substrates for increased interhemispheric functional connectivity in children born preterm; increased reliance on an extracallosal pathway may represent a biomarker for resiliency following extremely preterm birth.
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Affiliation(s)
- Maria E Barnes-Davis
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, United States; Department of Pediatrics, University of Cincinnati College of Medicine, United States.
| | - Brady J Williamson
- Department of Psychology, University of Cincinnati, United States; Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, United States
| | - Stephanie L Merhar
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, United States; Department of Pediatrics, University of Cincinnati College of Medicine, United States
| | - Scott K Holland
- Department of Physics, University of Cincinnati, United States; Medpace Imaging Core Laboratory, Medpace Inc., United States
| | - Darren S Kadis
- Neurosciences and Mental Health Research Institute, Hospital for Sick Children, Canada; Department of Physiology, Faculty of Medicine, University of Toronto, Canada
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48
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Wang ZM, Wei PH, Shan Y, Han M, Zhang M, Liu H, Gao JH, Lu J. Identifying and characterizing projections from the subthalamic nucleus to the cerebellum in humans. Neuroimage 2020; 210:116573. [PMID: 31968232 DOI: 10.1016/j.neuroimage.2020.116573] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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: 08/20/2019] [Revised: 01/17/2020] [Accepted: 01/17/2020] [Indexed: 12/31/2022] Open
Abstract
A connection between the subthalamic nucleus (STN) and the cerebellum which has been shown to exist in non-human primates, was recently identified in humans. However, its anatomical features, network properties and function have yet to be elucidated in humans. In the present study, we quantified the STN-cerebellum pathway in humans and explored its function based on structural observations. Anatomical features and asymmetry index (AI) were explored using high definition fiber tractography data of 30 individuals from the Massachusetts General Hospital - Human Connectome Project adult diffusion database. Pearson's correlation analysis was performed to determine the interrelationship between the subdivisions of the STN-cerebellum and the global cortical-STN connections. The pathway was visualized bilaterally in all the subjects. Typically, after setting out from the STN, the STN-cerebellum projections incorporated into the nearby corticopontine tracts, passing through the cerebral peduncle, mediated by the pontine nucleus and then connecting in two opposite directions to join the bilateral middle cerebellar peduncle. On the group averaged level, 78.03% and 62.54% of fibers from the right and left STN respectively, distributed to Crus I in the cerebellum, part of the remaining fibers projected to Crus II, with most of the fibers crossing contralaterally. According to the AI evaluation, 60% of the participants were right STN dominant, 23% were left STN dominant, and 17% were relatively symmetric. Pearson's correlation analysis further indicated that the number of pathways from mesial Brodmann area 8 to the STN (hyperdirect pathway associated with decision making) was positively correlated with the number of fibers from the right STN to Crus I. The insertion and termination, the right-side dominance, and the positive correlation with the hyperdirect pathway all suggest that the STN-cerebellum pathway might be involved in decision-making processes.
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Affiliation(s)
- Zhen-Ming Wang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Peng-Hu Wei
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yi Shan
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Meizhen Han
- Center for MRI Research, Peking University, Beijing, China
| | - Miao Zhang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jia-Hong Gao
- Center for MRI Research, Peking University, Beijing, China.
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, 100053, China; Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
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49
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Haslbeck FB, Jakab A, Held U, Bassler D, Bucher HU, Hagmann C. Creative music therapy to promote brain function and brain structure in preterm infants: A randomized controlled pilot study. Neuroimage Clin 2020; 25:102171. [PMID: 31972397 PMCID: PMC6974781 DOI: 10.1016/j.nicl.2020.102171] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [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: 10/28/2019] [Revised: 12/18/2019] [Accepted: 01/10/2020] [Indexed: 01/17/2023]
Abstract
Cognitive and neurobehavioral problems are among the most severe adverse outcomes in very preterm infants. Such neurodevelopmental impairments may be mitigated through nonpharmacological interventions such as creative music therapy (CMT), an interactive, resource- and needs-oriented approach that provides individual social contact and musical stimulation. The aim was to test the feasibility of a study investigating the role of CMT and to measure the short- and medium-term effects of CMT on structural and functional brain connectivity with MRI. In this randomized, controlled clinical pilot feasibility trial, 82 infants were randomized to either CMT or standard care. A specially trained music therapist provided CMT via infant-directed humming and singing in lullaby style. To test the short-term effects of CMT on brain structure and function, diffusion tensor imaging data and resting-state functional imaging data were acquired. Clinical feasibility was achieved despite moderate parental refusal mainly in the control group after randomization. 40 infants remained as final cohort for the MRI analysis. Structural brain connectivity appears to be moderately affected by CMT, structural connectomic analysis revealed increased integration in the posterior cingulate cortex only. Lagged resting-state MRI analysis showed lower thalamocortical processing delay, stronger functional networks, and higher functional integration in predominantly left prefrontal, supplementary motor, and inferior temporal brain regions in infants treated with CMT. This trial provides unique evidence that CMT has beneficial effects on functional brain activity and connectivity in networks underlying higher-order cognitive, socio-emotional, and motor functions in preterm infants. Our results indicate the potential of CMT to improve long-term neurodevelopmental outcomes in children born very preterm.
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Affiliation(s)
- Friederike Barbara Haslbeck
- Department of Neonatology, University Hospital Zurich and University Zurich, Frauenklinikstrasse 10, 8091 Zürich, Switzerland.
| | - Andras Jakab
- MR Research Center, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032 Zürich, Switzerland
| | - Ulrike Held
- Department of Biostatistics Epidemiology, Biostatistics and Prevention Institute UZH, Hirschengraben 84, 8001 Zürich, Switzerland
| | - Dirk Bassler
- Department of Neonatology, University Hospital Zurich and University Zurich, Frauenklinikstrasse 10, 8091 Zürich, Switzerland
| | - Hans-Ulrich Bucher
- Department of Neonatology, University Hospital Zurich and University Zurich, Frauenklinikstrasse 10, 8091 Zürich, Switzerland
| | - Cornelia Hagmann
- Department of Neonatology and Pediatric Intensive Care, Children's University Hospital of Zurich, Steinwiesstrasse 75, 8032 Zürich, Switzerland; Children's Research Center, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032 Zürich, Switzerland
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50
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Maggioni E, Squarcina L, Dusi N, Diwadkar VA, Brambilla P. Twin MRI studies on genetic and environmental determinants of brain morphology and function in the early lifespan. Neurosci Biobehav Rev 2020; 109:139-149. [PMID: 31911159 DOI: 10.1016/j.neubiorev.2020.01.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [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: 07/31/2019] [Revised: 12/19/2019] [Accepted: 01/02/2020] [Indexed: 02/04/2023]
Abstract
Neurodevelopment represents a period of increased opportunity and vulnerability, during which a complex confluence of genetic and environmental factors influences brain growth trajectories, cognitive and mental health outcomes. Recently, magnetic resonance imaging (MRI) studies on twins have increased our knowledge of the extent to which genes, the environment and their interactions shape inter-individual brain variability. The present review draws from highly salient MRI studies in young twin samples to provide a robust assessment of the heritability of structural and functional brain changes during development. The available studies suggest that (as with many other traits), global brain morphology and network organization are highly heritable from early childhood to young adulthood. Conversely, genetic correlations among brain regions exhibit heterogeneous trajectories, and this heterogeneity reflects the progressive, experience-related increase in brain network complexity. Studies also support the key role of environment in mediating brain network differentiation via changes of genetic expression and hormonal levels. Thus, rest- and task-related functional brain circuits seem to result from a contextual and dynamic expression of heritability.
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Affiliation(s)
- Eleonora Maggioni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via F. Sforza 28, Milano, Italy
| | - Letizia Squarcina
- Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, via Don Luigi Monza 20, Bosisio Parini, LC, Italy
| | - Nicola Dusi
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via F. Sforza 28, Milano, Italy
| | - Vaibhav A Diwadkar
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University, 42 W Warren Ave, Detroit, MI, United States
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via F. Sforza 28, Milano, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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