1
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Schroeter CB, Nelke C, Schewe M, Spohler L, Herrmann AM, Müntefering T, Huntemann N, Kuzikov M, Gribbon P, Albrecht S, Bock S, Hundehege P, Neelsen LC, Baukrowitz T, Seebohm G, Wünsch B, Bittner S, Ruck T, Budde T, Meuth SG. Validation of TREK1 ion channel activators as an immunomodulatory and neuroprotective strategy in neuroinflammation. Biol Chem 2023; 404:355-375. [PMID: 36774650 DOI: 10.1515/hsz-2022-0266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/16/2023] [Indexed: 02/13/2023]
Abstract
Modulation of two-pore domain potassium (K2P) channels has emerged as a novel field of therapeutic strategies as they may regulate immune cell activation and metabolism, inflammatory signals, or barrier integrity. One of these ion channels is the TWIK-related potassium channel 1 (TREK1). In the current study, we report the identification and validation of new TREK1 activators. Firstly, we used a modified potassium ion channel assay to perform high-throughput-screening of new TREK1 activators. Dose-response studies helped to identify compounds with a high separation between effectiveness and toxicity. Inside-out patch-clamp measurements of Xenopus laevis oocytes expressing TREK1 were used for further validation of these activators regarding specificity and activity. These approaches yielded three substances, E1, B3 and A2 that robustly activate TREK1. Functionally, we demonstrated that these compounds reduce levels of adhesion molecules on primary human brain and muscle endothelial cells without affecting cell viability. Finally, we studied compound A2 via voltage-clamp recordings as this activator displayed the strongest effect on adhesion molecules. Interestingly, A2 lacked TREK1 activation in the tested neuronal cell type. Taken together, this study provides data on novel TREK1 activators that might be employed to pharmacologically modulate TREK1 activity.
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Affiliation(s)
- Christina B Schroeter
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany
| | - Christopher Nelke
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany
| | - Marcus Schewe
- Institute of Physiology, Christian-Albrechts University Kiel, Hermann-Rodewald-Straße 5, 24118 Kiel, Germany
| | - Lucas Spohler
- Department of Neurology with Institute for Translational Neurology, University Hospital Münster, Albert-Schweitzer-Campus 1, D-48149 Münster, Germany
| | - Alexander M Herrmann
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany
| | - Thomas Müntefering
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany
| | - Niklas Huntemann
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany
| | - Maria Kuzikov
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune mediated diseases (CIMD), Theodor-Stern-Kai 7, D-60596 Frankfurt, Germany
| | - Philip Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Schnackenburgallee 114, D-22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune mediated diseases (CIMD), Theodor-Stern-Kai 7, D-60596 Frankfurt, Germany
| | - Sarah Albrecht
- Department of Neurology with Institute for Translational Neurology, University Hospital Münster, Albert-Schweitzer-Campus 1, D-48149 Münster, Germany
| | - Stefanie Bock
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany
| | - Petra Hundehege
- Department of Neurology with Institute for Translational Neurology, University Hospital Münster, Albert-Schweitzer-Campus 1, D-48149 Münster, Germany
| | - Lea Christine Neelsen
- Institute of Physiology, Christian-Albrechts University Kiel, Hermann-Rodewald-Straße 5, 24118 Kiel, Germany
| | - Thomas Baukrowitz
- Institute of Physiology, Christian-Albrechts University Kiel, Hermann-Rodewald-Straße 5, 24118 Kiel, Germany
| | - Guiscard Seebohm
- IfGH-Cellular Electrophysiology, Department of Cardiology and Angiology, University Hospital of Münster, Albert-Schweitzer-Campus 1, D-48149 Münster, Germany
| | - Bernhard Wünsch
- Institute for Pharmaceutical and Medicinal Chemistry, Westfälische Wilhelms-Universität Münster, Corrensstraße 48, D-48149 Münster, Germany
| | - Stefan Bittner
- Department of Neurology, University Medical Center Mainz, Langenbeckstraße 1, D-55131 Mainz, Germany
| | - Tobias Ruck
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany
| | - Thomas Budde
- Institute of Physiology I, University of Münster, Robert-Koch-Straße 27A, D-48149 Münster, Germany
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany
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2
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Lemaitre H, Le Guen Y, Tilot AK, Stein JL, Philippe C, Mangin JF, Fisher SE, Frouin V. Genetic variations within human gained enhancer elements affect human brain sulcal morphology. Neuroimage 2023; 265:119773. [PMID: 36442731 DOI: 10.1016/j.neuroimage.2022.119773] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 10/07/2022] [Accepted: 11/24/2022] [Indexed: 11/26/2022] Open
Abstract
The expansion of the cerebral cortex is one of the most distinctive changes in the evolution of the human brain. Cortical expansion and related increases in cortical folding may have contributed to emergence of our capacities for high-order cognitive abilities. Molecular analysis of humans, archaic hominins, and non-human primates has allowed identification of chromosomal regions showing evolutionary changes at different points of our phylogenetic history. In this study, we assessed the contributions of genomic annotations spanning 30 million years to human sulcal morphology measured via MRI in more than 18,000 participants from the UK Biobank. We found that variation within brain-expressed human gained enhancers, regulatory genetic elements that emerged since our last common ancestor with Old World monkeys, explained more trait heritability than expected for the left and right calloso-marginal posterior fissures and the right central sulcus. Intriguingly, these are sulci that have been previously linked to the evolution of locomotion in primates and later on bipedalism in our hominin ancestors.
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Affiliation(s)
- Herve Lemaitre
- Institut des Maladies Neurodégénératives, CNRS UMR 5293, Université de bordeaux, Centre Broca Nouvelle-Aquitaine, Bordeaux, France.
| | - Yann Le Guen
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab UMR 9027, Gif-sur-Yvette, France
| | - Amanda K Tilot
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Jason L Stein
- Department of Genetics and the UNC Neuroscience Center, UNC-Chapel Hill, Chapel Hill, NC, United States of America
| | - Cathy Philippe
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab UMR 9027, Gif-sur-Yvette, France
| | - Jean-François Mangin
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab UMR 9027, Gif-sur-Yvette, France
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Vincent Frouin
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab UMR 9027, Gif-sur-Yvette, France
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3
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Sun BB, Loomis SJ, Pizzagalli F, Shatokhina N, Painter JN, Foley CN, Jensen ME, McLaren DG, Chintapalli SS, Zhu AH, Dixon D, Islam T, Ba Gari I, Runz H, Medland SE, Thompson PM, Jahanshad N, Whelan CD. Genetic map of regional sulcal morphology in the human brain from UK biobank data. Nat Commun 2022; 13:6071. [PMID: 36241887 PMCID: PMC9568560 DOI: 10.1038/s41467-022-33829-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 10/05/2022] [Indexed: 12/24/2022] Open
Abstract
Genetic associations with macroscopic brain structure can provide insights into brain function and disease. However, specific associations with measures of local brain folding are largely under-explored. Here, we conducted large-scale genome- and exome-wide associations of regional cortical sulcal measures derived from magnetic resonance imaging scans of 40,169 individuals in UK Biobank. We discovered 388 regional brain folding associations across 77 genetic loci, with genes in associated loci enriched for expression in the cerebral cortex, neuronal development processes, and differential regulation during early brain development. We integrated brain eQTLs to refine genes for various loci, implicated several genes involved in neurodevelopmental disorders, and highlighted global genetic correlations with neuropsychiatric phenotypes. We provide an interactive 3D visualisation of our summary associations, emphasising added resolution of regional analyses. Our results offer new insights into the genetic architecture of brain folding and provide a resource for future studies of sulcal morphology in health and disease.
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Affiliation(s)
- Benjamin B Sun
- Translational Biology, Research & Development, Biogen Inc., Cambridge, MA, US.
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Stephanie J Loomis
- Translational Biology, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Fabrizio Pizzagalli
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Natalia Shatokhina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Jodie N Painter
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Christopher N Foley
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Optima Partners, Edinburgh, UK
| | - Megan E Jensen
- Clinical Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Donald G McLaren
- Clinical Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | | | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Daniel Dixon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Tasfiya Islam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Heiko Runz
- Translational Biology, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US.
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US.
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4
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Rivière D, Leprince Y, Labra N, Vindas N, Foubet O, Cagna B, Loh KK, Hopkins W, Balzeau A, Mancip M, Lebenberg J, Cointepas Y, Coulon O, Mangin JF. Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy. Front Neuroinform 2022; 16:803934. [PMID: 35311005 PMCID: PMC8928460 DOI: 10.3389/fninf.2022.803934] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/17/2022] [Indexed: 11/14/2022] Open
Abstract
Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.
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Affiliation(s)
- Denis Rivière
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Yann Leprince
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Nicole Labra
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
| | - Nabil Vindas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Ophélie Foubet
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Bastien Cagna
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Kep Kee Loh
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - William Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, United States
| | - Antoine Balzeau
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
- Department of African Zoology, Royal Museum for Central Africa, Tervuren, Belgium
| | - Martial Mancip
- Maison de la Simulation, CNRS, CEA Saclay, Gif-sur-Yvette, France
| | - Jessica Lebenberg
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- Université de Paris, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Yann Cointepas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Olivier Coulon
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - Jean-François Mangin
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
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5
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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6
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Bando Y, Ishibashi M, Yamagishi S, Fukuda A, Sato K. Orchestration of Ion Channels and Transporters in Neocortical Development and Neurological Disorders. Front Neurosci 2022; 16:827284. [PMID: 35237124 PMCID: PMC8884360 DOI: 10.3389/fnins.2022.827284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/24/2022] [Indexed: 12/17/2022] Open
Abstract
Electrical activity plays crucial roles in neural circuit formation and remodeling. During neocortical development, neurons are generated in the ventricular zone, migrate to their correct position, elongate dendrites and axons, and form synapses. In this review, we summarize the functions of ion channels and transporters in neocortical development. Next, we discuss links between neurological disorders caused by dysfunction of ion channels (channelopathies) and neocortical development. Finally, we introduce emerging optical techniques with potential applications in physiological studies of neocortical development and the pathophysiology of channelopathies.
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Affiliation(s)
- Yuki Bando
- Department of Organ and Tissue Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Japan
- *Correspondence: Yuki Bando,
| | - Masaru Ishibashi
- Department of Neurophysiology, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Satoru Yamagishi
- Department of Organ and Tissue Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Atsuo Fukuda
- Department of Neurophysiology, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Kohji Sato
- Department of Organ and Tissue Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Japan
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7
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Mortamais M, Laure-Anne G, Balem M, Bars EL, de Champfleur NM, Bouyahia A, Chupin M, Perus L, Fisher C, Vellas B, Andrieu S, Mangin JF, Berr C, Gabelle A. Sulcal morphology as cognitive decline predictor in older adults with memory complaints. Neurobiol Aging 2022; 113:84-94. [DOI: 10.1016/j.neurobiolaging.2022.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 12/08/2021] [Accepted: 02/08/2022] [Indexed: 11/16/2022]
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8
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Zheng X, Yang J, Zhu Z, Fang Y, Tian Y, Xie M, Wang W, Liu Y. The Two-Pore Domain Potassium Channel TREK-1 Promotes Blood-Brain Barrier Breakdown and Exacerbates Neuronal Death After Focal Cerebral Ischemia in Mice. Mol Neurobiol 2022; 59:2305-2327. [PMID: 35067892 DOI: 10.1007/s12035-021-02702-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/14/2021] [Indexed: 11/24/2022]
Abstract
Earlier studies have shown the neuroprotective role of TWIK-related K+ channel 1 (TREK-1) in global cerebral and spinal cord ischemia, while its function in focal cerebral ischemia has long been debated. This study used TREK-1-deficient mice to directly investigate the role of TREK-1 after focal cerebral ischemia. First, immunofluorescence assays in the mouse cerebral cortex indicated that TREK-1 expression was mostly abundant in astrocytes, neurons, and oligodendrocyte precursor cells but was low in myelinating oligodendrocytes, microglia, or endothelial cells. TREK-1 deficiency did not affect brain weight and morphology or the number of neurons, astrocytes, or microglia but did increase glial fibrillary acidic protein (GFAP) expression in astrocytes of the cerebral cortex. The anatomy of the major cerebral vasculature, number and structure of brain micro blood vessels, and blood-brain barrier integrity were unaltered. Next, mice underwent 60 min of focal cerebral ischemia and 72 h of reperfusion induced by the intraluminal suture method. TREK-1-deficient mice showed less neuronal death, smaller infarction size, milder blood-brain barrier (BBB) breakdown, reduced immune cell invasion, and better neurological function. Finally, the specific pharmacological inhibition of TREK-1 also decreased infarction size and improved neurological function. These results demonstrated that TREK-1 might play a detrimental rather than beneficial role in focal cerebral ischemia, and inhibition of TREK-1 would be a strategy to treat ischemic stroke in the clinic.
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Affiliation(s)
- Xiaolong Zheng
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Jun Yang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zhou Zhu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yongkang Fang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yeye Tian
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Minjie Xie
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Wei Wang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.,Key Laboratory of Neurological Diseases of Chinese Ministry of Education, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yang Liu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
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9
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Xiao Q, Bai X, Zhang C, He Y. Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review. J Adv Res 2022; 35:215-230. [PMID: 35003802 PMCID: PMC8721248 DOI: 10.1016/j.jare.2021.05.002] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 05/05/2021] [Accepted: 05/09/2021] [Indexed: 01/22/2023] Open
Abstract
Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods. Aim of Review: This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects. Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes.
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Affiliation(s)
- Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
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10
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Borne L, Rivière D, Cachia A, Roca P, Mellerio C, Oppenheim C, Mangin JF. Automatic recognition of specific local cortical folding patterns. Neuroimage 2021; 238:118208. [PMID: 34089872 DOI: 10.1016/j.neuroimage.2021.118208] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/30/2021] [Accepted: 05/25/2021] [Indexed: 11/15/2022] Open
Abstract
The study of local cortical folding patterns showed links with psychiatric illnesses as well as cognitive functions. Despite the tools now available to visualize cortical folds in 3D, manually classifying local sulcal patterns is a time-consuming and tedious task. In fact, 3D visualization of folds helps experts to identify different sulcal patterns but fold variability is so high that the distinction between these patterns sometimes requires the definition of complex criteria, making manual classification difficult and not reliable. However, the assessment of the impact of these patterns on the functional organization of the cortex could benefit from the study of large databases, especially when studying rare patterns. In this paper, several algorithms for the automatic classification of fold patterns are proposed to allow morphological studies to be extended and confirmed on such large databases. Three methods are proposed, the first based on a Support Vector Machine (SVM) classifier, the second on the Scoring by Non-local Image Patch Estimator (SNIPE) approach and the third based on a 3D Convolution Neural Network (CNN). These methods are generic enough to be applicable to a wide range of folding patterns. They are tested on two types of patterns for which there is currently no method to automatically identify them: the Anterior Cingulate Cortex (ACC) patterns and the Power Button Sign (PBS). The two ACC patterns are almost equally present whereas PBS is a particularly rare pattern in the general population. The three models proposed achieve balanced accuracies of approximately 80% for ACC patterns classification and 60% for PBS classification. The CNN-based model is more interesting for the classification of ACC patterns thanks to its rapid execution. However, SVM and SNIPE-based models are more effective in managing unbalanced problems such as PBS recognition.
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Affiliation(s)
- Léonie Borne
- Université Paris-Saclay, CEA, CNRS, Baobab, Neurospin, Gif-sur-Yvette, France; University of Newcastle, HMRI, Systems Neuroscience Group, NSW, Australia.
| | - Denis Rivière
- Université Paris-Saclay, CEA, CNRS, Baobab, Neurospin, Gif-sur-Yvette, France
| | - Arnaud Cachia
- Université de Paris, LaPsyDÉ, CNRS, Paris, France; Université de Paris, Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM, UMR S1266, Paris, France
| | - Pauline Roca
- Université de Paris, Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM, UMR S1266, Paris, France; Groupe Hospitalier Universitaire Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Imaging Department, Paris, France; Pixyl, Research and Development Laboratory, Grenoble, France
| | - Charles Mellerio
- Université de Paris, Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM, UMR S1266, Paris, France; Groupe Hospitalier Universitaire Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Imaging Department, Paris, France; Centre d'imagerie du Nord, Saint Denis, France
| | - Catherine Oppenheim
- Université de Paris, Institut de Psychiatrie et Neurosciences de Paris (IPNP), INSERM, UMR S1266, Paris, France; Groupe Hospitalier Universitaire Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Imaging Department, Paris, France
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11
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Genome-wide haplotype association study in imaging genetics using whole-brain sulcal openings of 16,304 UK Biobank subjects. Eur J Hum Genet 2021; 29:1424-1437. [PMID: 33664500 PMCID: PMC8440755 DOI: 10.1038/s41431-021-00827-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 12/18/2020] [Accepted: 02/04/2021] [Indexed: 11/29/2022] Open
Abstract
Neuroimaging-genetics cohorts gather two types of data: brain imaging and genetic data. They allow the discovery of associations between genetic variants and brain imaging features. They are invaluable resources to study the influence of genetics and environment in the brain features variance observed in normal and pathological populations. This study presents a genome-wide haplotype analysis for 123 brain sulcus opening value (a measure of sulcal width) across the whole brain that include 16,304 subjects from UK Biobank. Using genetic maps, we defined 119,548 blocks of low recombination rate distributed along the 22 autosomal chromosomes and analyzed 1,051,316 haplotypes. To test associations between haplotypes and complex traits, we designed three statistical approaches. Two of them use a model that includes all the haplotypes for a single block, while the last approach considers each haplotype independently. All the statistics produced were assessed as rigorously as possible. Thanks to the rich imaging dataset at hand, we used resampling techniques to assess False Positive Rate for each statistical approach in a genome-wide and brain-wide context. The results on real data show that genome-wide haplotype analyses are more sensitive than single-SNP approach and account for local complex Linkage Disequilibrium (LD) structure, which makes genome-wide haplotype analysis an interesting and statistically sound alternative to the single-SNP counterpart.
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12
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Ahtam B, Turesky TK, Zöllei L, Standish J, Grant PE, Gaab N, Im K. Intergenerational Transmission of Cortical Sulcal Patterns from Mothers to their Children. Cereb Cortex 2021; 31:1888-1897. [PMID: 33230560 PMCID: PMC7945013 DOI: 10.1093/cercor/bhaa328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 10/09/2020] [Accepted: 10/10/2020] [Indexed: 12/23/2022] Open
Abstract
Intergenerational effects are described as the genetic, epigenetic, as well as pre- and postnatal environmental influence parents have on their offspring's behavior, cognition, and brain. During fetal brain development, the primary cortical sulci emerge with a distinctive folding pattern that are under strong genetic influence and show little change of this pattern throughout postnatal brain development. We examined intergenerational transmission of cortical sulcal patterns by comparing primary sulcal patterns between children (N = 16, age 5.5 ± 0.81 years, 8 males) and their biological mothers (N = 15, age 39.72 ± 4.68 years) as well as between children and unrelated adult females. Our graph-based sulcal pattern comparison method detected stronger sulcal pattern similarity for child-mother pairs than child-unrelated pairs, where higher similarity between child-mother pairs was observed mostly for the right lobar regions. Our results also show that child-mother versus child-unrelated pairs differ for daughters and sons with a trend toward significance, particularly for the left hemisphere lobar regions. This is the first study to reveal significant intergenerational transmission of cortical sulcal patterns, and our results have important implications for the study of the heritability of complex behaviors, brain-based disorders, the identification of biomarkers, and targets for interventions.
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Affiliation(s)
- Banu Ahtam
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
| | - Ted K Turesky
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Julianna Standish
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
| | - Nadine Gaab
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Kiho Im
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Department of Pediatrics, Boston, MA 02115, USA
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13
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Tang H, Liu T, Liu H, Jiang J, Cheng J, Niu H, Li S, Brodaty H, Sachdev P, Wen W. A slower rate of sulcal widening in the brains of the nondemented oldest old. Neuroimage 2021; 229:117740. [PMID: 33460796 DOI: 10.1016/j.neuroimage.2021.117740] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 01/09/2021] [Indexed: 11/15/2022] Open
Abstract
The relationships between aging and brain morphology have been reported in many previous structural brain studies. However, the trajectories of successful brain aging in the extremely old remain underexplored. In the limited research on the oldest old, covering individuals aged 85 years and older, there are very few studies that have focused on the cortical morphology, especially cortical sulcal features. In this paper, we measured sulcal width and depth as well as cortical thickness from T1-weighted scans of 290 nondemented community-dwelling participants aged between 76 and 103 years. We divided the participants into young old (between 76 and 84; mean = 80.35±2.44; male/female = 76/88) and oldest old (between 85 and 103; mean = 91.74±5.11; male/female = 60/66) groups. The results showed that most of the examined sulci significantly widened with increased age and that the rates of sulcal widening were lower in the oldest old. The spatial pattern of the cortical thinning partly corresponded with that of sulcal widening. Compared to females, males had significantly wider sulci, especially in the oldest old. This study builds a foundation for future investigations of neurocognitive disorders and neurodegenerative diseases in the oldest old, including centenarians.
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Affiliation(s)
- Hui Tang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China.
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Sydney, NSW 2052, Australia
| | - Jian Cheng
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing, China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Shuyu Li
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Sydney, NSW 2052, Australia; Dementia Centre for Research Collaboration, School of Psychiatry, UNSW Sydney, NSW 2052, Australia
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Sydney, NSW 2052, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, UNSW Sydney, NSW 2052, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
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14
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Pizzagalli F, Auzias G, Yang Q, Mathias SR, Faskowitz J, Boyd JD, Amini A, Rivière D, McMahon KL, de Zubicaray GI, Martin NG, Mangin JF, Glahn DC, Blangero J, Wright MJ, Thompson PM, Kochunov P, Jahanshad N. The reliability and heritability of cortical folds and their genetic correlations across hemispheres. Commun Biol 2020; 3:510. [PMID: 32934300 PMCID: PMC7493906 DOI: 10.1038/s42003-020-01163-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 07/24/2020] [Indexed: 12/22/2022] Open
Abstract
Cortical folds help drive the parcellation of the human cortex into functionally specific regions. Variations in the length, depth, width, and surface area of these sulcal landmarks have been associated with disease, and may be genetically mediated. Before estimating the heritability of sulcal variation, the extent to which these metrics can be reliably extracted from in-vivo MRI must be established. Using four independent test-retest datasets, we found high reliability across the brain (intraclass correlation interquartile range: 0.65-0.85). Heritability estimates were derived for three family-based cohorts using variance components analysis and pooled (total N > 3000); the overall sulcal heritability pattern was correlated to that derived for a large population cohort (N > 9000) calculated using genomic complex trait analysis. Overall, sulcal width was the most heritable metric, and earlier forming sulci showed higher heritability. The inter-hemispheric genetic correlations were high, yet select sulci showed incomplete pleiotropy, suggesting hemisphere-specific genetic influences.
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Grants
- P41 EB015922 NIBIB NIH HHS
- R01 EB015611 NIBIB NIH HHS
- P01 AG026276 NIA NIH HHS
- R21 NS064534 NINDS NIH HHS
- R01 MH078111 NIMH NIH HHS
- R01 HD050735 NICHD NIH HHS
- R01 NS056307 NINDS NIH HHS
- R01 MH121246 NIMH NIH HHS
- P50 MH071616 NIMH NIH HHS
- R03 EB012461 NIBIB NIH HHS
- R01 AG059874 NIA NIH HHS
- U24 RR021382 NCRR NIH HHS
- P30 AG066444 NIA NIH HHS
- P01 AG003991 NIA NIH HHS
- P50 AG005681 NIA NIH HHS
- U54 EB020403 NIBIB NIH HHS
- R01 MH117601 NIMH NIH HHS
- U54 MH091657 NIMH NIH HHS
- R01 AG021910 NIA NIH HHS
- R01 MH078143 NIMH NIH HHS
- P41 RR015241 NCRR NIH HHS
- S10 OD023696 NIH HHS
- R01 MH083824 NIMH NIH HHS
- This research was funded in part by NIH ENIGMA Center grant U54 EB020403, supported by the Big Data to Knowledge (BD2K) Centers of Excellence program funded by a cross-NIH initiative. Additional grant support was provided by: R01 AG059874, R01 MH117601, R01 MH121246, and P41 EB015922. QTIM was supported by NIH R01 HD050735, and the NHMRC 486682, Australia; GOBS: Financial support for this study was provided by the National Institute of Mental Health grants MH078143 (PI: DC Glahn), MH078111 (PI: J Blangero), and MH083824 (PI: DC Glahn & J Blangero); HCP data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University; UK Biobank: This research was conducted using the UK Biobank Resource under Application Number ‘11559’; BrainVISA’s Morphologist software development received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No 720270 & 785907 (Human Brain ProjectSGA1 & SGA2), and by the FRM DIC20161236445. OASIS: Cross-Sectional: Principal Investigators: D. Marcus, R. Buckner, J. Csernansky J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382. KKI was supported by NIH grants NCRR P41 RR015241 (Peter C.M. van Zijl), 1R01NS056307 (Jerry Prince), 1R21NS064534-01A109 (Bennett A. Landman/Jerry L. Prince), 1R03EB012461-01 (Bennett A. Landman). Neda Jahanshad and Paul Thompson are MPIs of a research project grant from Biogen, Inc. (PO 969323).
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Affiliation(s)
- Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
| | - Guillaume Auzias
- Institut de Neurosciences de la Timone, UMR7289, Aix-Marseille Université & CNRS, Marseille, France
| | - Qifan Yang
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Samuel R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Joshua Faskowitz
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Joshua D Boyd
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Armand Amini
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Denis Rivière
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, France
- CATI, Multicenter Neuroimaging Platform, Paris, France
| | - Katie L McMahon
- School of Clinical Sciences and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Greig I de Zubicaray
- Faculty of Health, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia
| | | | - Jean-François Mangin
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, France
- CATI, Multicenter Neuroimaging Platform, Paris, France
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
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15
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Automatic labeling of cortical sulci using patch- or CNN-based segmentation techniques combined with bottom-up geometric constraints. Med Image Anal 2020; 62:101651. [PMID: 32163879 DOI: 10.1016/j.media.2020.101651] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 01/14/2020] [Accepted: 01/16/2020] [Indexed: 11/23/2022]
Abstract
The extreme variability of the folding pattern of the human cortex makes the recognition of cortical sulci, both automatic and manual, particularly challenging. Reliable identification of the human cortical sulci in its entirety, is extremely difficult and is practiced by only a few experts. Moreover, these sulci correspond to more than a hundred different structures, which makes manual labeling long and fastidious and therefore limits access to large labeled databases to train machine learning. Here, we seek to improve the current model proposed in the Morphologist toolbox, a widely used sulcus recognition toolbox included in the BrainVISA package. Two novel approaches are proposed: patch-based multi-atlas segmentation (MAS) techniques and convolutional neural network (CNN)-based approaches. Both are currently applied for anatomical segmentations because they embed much better representations of inter-subject variability than approaches based on a single template atlas. However, these methods typically focus on voxel-wise labeling, disregarding certain geometrical and topological properties of interest for sulcus morphometry. Therefore, we propose to refine these approaches with domain specific bottom-up geometric constraints provided by the Morphologist toolbox. These constraints are utilized to provide a single sulcus label to each topologically elementary fold, the building blocks of the pattern recognition problem. To eliminate the shortcomings associated with the Morphologist's pre-segmentation into elementary folds, we complement this regularization scheme using a top-down perspective which triggers an additional cleavage of the elementary folds when required. All the newly proposed models outperform the current Morphologist model, the most efficient being a CNN U-Net-based approach which carries out sulcus recognition within a few seconds.
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16
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Jonsson BA, Bjornsdottir G, Thorgeirsson TE, Ellingsen LM, Walters GB, Gudbjartsson DF, Stefansson H, Stefansson K, Ulfarsson MO. Brain age prediction using deep learning uncovers associated sequence variants. Nat Commun 2019; 10:5409. [PMID: 31776335 PMCID: PMC6881321 DOI: 10.1038/s41467-019-13163-9] [Citation(s) in RCA: 165] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 10/21/2019] [Indexed: 02/08/2023] Open
Abstract
Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: \documentclass[12pt]{minimal}
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\begin{document}$$N=4456$$\end{document}N=4456) yielded two sequence variants, rs1452628-T (\documentclass[12pt]{minimal}
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\begin{document}$$P=1.15\times{10}^{-9}$$\end{document}P=1.15×10−9) and rs2435204-G (\documentclass[12pt]{minimal}
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\begin{document}$$P=9.73\times 1{0}^{-12}$$\end{document}P=9.73×10−12). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2). Machine learning algorithms can be trained to estimate age from brain structural MRI. Here, the authors introduce a new deep-learning-based age prediction approach, and then carry out a GWAS of the difference between predicted and chronological age, revealing two associated variants.
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Affiliation(s)
- B A Jonsson
- deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland.,University of Iceland, 101, Reykjavik, Iceland
| | | | | | | | - G Bragi Walters
- deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland.,University of Iceland, 101, Reykjavik, Iceland
| | - D F Gudbjartsson
- deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland.,University of Iceland, 101, Reykjavik, Iceland
| | - H Stefansson
- deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland
| | - K Stefansson
- deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland. .,University of Iceland, 101, Reykjavik, Iceland.
| | - M O Ulfarsson
- deCODE Genetics/Amgen, Inc., 101, Reykjavik, Iceland. .,University of Iceland, 101, Reykjavik, Iceland.
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