1
|
Suzuki Y, Clement P, Dai W, Dolui S, Fernández-Seara M, Lindner T, Mutsaerts HJMM, Petr J, Shao X, Taso M, Thomas DL. ASL lexicon and reporting recommendations: A consensus report from the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1743-1760. [PMID: 37876299 PMCID: PMC10950547 DOI: 10.1002/mrm.29815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/22/2023] [Accepted: 07/13/2023] [Indexed: 10/26/2023]
Abstract
The 2015 consensus statement published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group and the European Cooperation in Science and Technology ( COST) Action ASL in Dementia aimed to encourage the implementation of robust arterial spin labeling (ASL) perfusion MRI for clinical applications and promote consistency across scanner types, sites, and studies. Subsequently, the recommended 3D pseudo-continuous ASL sequence has been implemented by most major MRI manufacturers. However, ASL remains a rapidly and widely developing field, leading inevitably to further divergence of the technique and its associated terminology, which could cause confusion and hamper research reproducibility. On behalf of the ISMRM Perfusion Study Group, and as part of the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI), the ASL Lexicon Task Force has been working on the development of an ASL Lexicon and Reporting Recommendations for perfusion imaging and analysis, aiming to (1) develop standardized, consensus nomenclature and terminology for the broad range of ASL imaging techniques and parameters, as well as for the physiological constants required for quantitative analysis; and (2) provide a community-endorsed recommendation of the imaging parameters that we encourage authors to include when describing ASL methods in scientific reports/papers. In this paper, the sequences and parameters in (pseudo-)continuous ASL, pulsed ASL, velocity-selective ASL, and multi-timepoint ASL for brain perfusion imaging are included. However, the content of the lexicon is not intended to be limited to these techniques, and this paper provides the foundation for a growing online inventory that will be extended by the community as further methods and improvements are developed and established.
Collapse
Affiliation(s)
- Yuriko Suzuki
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Patricia Clement
- Department of Medical Imaging, Ghent University Hospital, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Weiying Dai
- State University of New York at Binghamton, Binghamton, NY, USA
| | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Maria Fernández-Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | | | - Henk JMM Mutsaerts
- Department of Radiology and Nuclear medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, the Netherlands, Amsterdam
| | - Jan Petr
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Manuel Taso
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | |
Collapse
|
2
|
Fan H, Mutsaerts HJ, Anazodo U, Arteaga D, Baas KP, Buchanan C, Camargo A, Keil VC, Lin Z, Lindner T, Hirschler L, Hu J, Padrela BE, Taghvaei M, Thomas DL, Dolui S, Petr J. ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): ASL pipeline inventory. Magn Reson Med 2024; 91:1787-1802. [PMID: 37811778 PMCID: PMC10950546 DOI: 10.1002/mrm.29869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 10/10/2023]
Abstract
PURPOSE To create an inventory of image processing pipelines of arterial spin labeling (ASL) and list their main features, and to evaluate the capability, flexibility, and ease of use of publicly available pipelines to guide novice ASL users in selecting their optimal pipeline. METHODS Developers self-assessed their pipelines using a questionnaire developed by the Task Force 1.1 of the ISMRM Open Science Initiative for Perfusion Imaging. Additionally, each publicly available pipeline was evaluated by two independent testers with basic ASL experience using a scoring system created for this purpose. RESULTS The developers of 21 pipelines filled the questionnaire. Most pipelines are free for noncommercial use (n = 18) and work with the standard NIfTI (Neuroimaging Informatics Technology Initiative) data format (n = 15). All pipelines can process standard 3D single postlabeling delay pseudo-continuous ASL images and primarily differ in their support of advanced sequences and features. The publicly available pipelines (n = 9) were included in the independent testing, all of them being free for noncommercial use. The pipelines, in general, provided a trade-off between ease of use and flexibility for configuring advanced processing options. CONCLUSION Although most ASL pipelines can process the common ASL data types, only some (namely, ASLPrep, ASLtbx, BASIL/Quantiphyse, ExploreASL, and MRICloud) are well-documented, publicly available, support multiple ASL types, have a user-friendly interface, and can provide a useful starting point for ASL processing. The choice of an optimal pipeline should be driven by specific data to be processed and user experience, and can be guided by the information provided in this ASL inventory.
Collapse
Affiliation(s)
- Hongli Fan
- The Johns Hopkins School of Medicine, Department of Biomedical Engineering, Baltimore, Maryland, USA
- MR Research and Development, Siemens Medical Solutions USA, Inc., Dallas, Texas, USA
| | - Henk J.M.M. Mutsaerts
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Udunna Anazodo
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Daniel Arteaga
- Ascension Saint Thomas Hospital, Nashville, Tennessee, USA
| | - Koen P.A. Baas
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Amsterdam UMC, Location AMC, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
| | - Charlotte Buchanan
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, United Kingdom
| | - Aldo Camargo
- School of Medicine, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland of Baltimore
| | - Vera C. Keil
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Zixuan Lin
- The Johns Hopkins School of Medicine, Department of Biomedical Engineering, Baltimore, Maryland, USA
| | - Thomas Lindner
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Lydiane Hirschler
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, the Netherlands
| | - Jian Hu
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, United Kingdom
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, United Kingdom
| | - Beatriz E. Padrela
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Mohammad Taghvaei
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - David L. Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jan Petr
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| |
Collapse
|
3
|
Padrela B, Mahroo A, Tee M, Sneve MH, Moyaert P, Geier O, Kuijer JPA, Beun S, Nordhøy W, Zhu YD, Buck MA, Hoinkiss DC, Konstandin S, Huber J, Wiersinga J, Rikken R, de Leeuw D, Grydeland H, Tippett L, Cawston EE, Ozturk-Isik E, Linn J, Brandt M, Tijms BM, van de Giessen EM, Muller M, Fjell A, Walhovd K, Bjørnerud A, Pålhaugen L, Selnes P, Clement P, Achten E, Anazodo U, Barkhof F, Hilal S, Fladby T, Eickel K, Morgan C, Thomas DL, Petr J, Günther M, Mutsaerts HJMM. Developing blood-brain barrier arterial spin labelling as a non-invasive early biomarker of Alzheimer's disease (DEBBIE-AD): a prospective observational multicohort study protocol. BMJ Open 2024; 14:e081635. [PMID: 38458785 DOI: 10.1136/bmjopen-2023-081635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/10/2024] Open
Abstract
INTRODUCTION Loss of blood-brain barrier (BBB) integrity is hypothesised to be one of the earliest microvascular signs of Alzheimer's disease (AD). Existing BBB integrity imaging methods involve contrast agents or ionising radiation, and pose limitations in terms of cost and logistics. Arterial spin labelling (ASL) perfusion MRI has been recently adapted to map the BBB permeability non-invasively. The DEveloping BBB-ASL as a non-Invasive Early biomarker (DEBBIE) consortium aims to develop this modified ASL-MRI technique for patient-specific and robust BBB permeability assessments. This article outlines the study design of the DEBBIE cohorts focused on investigating the potential of BBB-ASL as an early biomarker for AD (DEBBIE-AD). METHODS AND ANALYSIS DEBBIE-AD consists of a multicohort study enrolling participants with subjective cognitive decline, mild cognitive impairment and AD, as well as age-matched healthy controls, from 13 cohorts. The precision and accuracy of BBB-ASL will be evaluated in healthy participants. The clinical value of BBB-ASL will be evaluated by comparing results with both established and novel AD biomarkers. The DEBBIE-AD study aims to provide evidence of the ability of BBB-ASL to measure BBB permeability and demonstrate its utility in AD and AD-related pathologies. ETHICS AND DISSEMINATION Ethics approval was obtained for 10 cohorts, and is pending for 3 cohorts. The results of the main trial and each of the secondary endpoints will be submitted for publication in a peer-reviewed journal.
Collapse
Affiliation(s)
- Beatriz Padrela
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | - Amnah Mahroo
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Mervin Tee
- National University Health System, Singapore
| | - Markus H Sneve
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Paulien Moyaert
- Lawson Health Research Institute, London, Ontario, Canada
- Department of Diagnostic Sciences, University Hospital Ghent, Gent, Belgium
| | - Oliver Geier
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Joost P A Kuijer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | - Soetkin Beun
- Department of Diagnostic Sciences, University Hospital Ghent, Gent, Belgium
| | - Wibeke Nordhøy
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Yufei David Zhu
- Biomedical Engineering, University of California Davis, Davis, California, USA
| | - Mareike A Buck
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- University of Bremen, Bremen, Germany
| | | | - Simon Konstandin
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Jörn Huber
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Julia Wiersinga
- Department of Internal Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | - Roos Rikken
- Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | | | - Håkon Grydeland
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Lynette Tippett
- The University of Auckland School of Psychology, Auckland, New Zealand
| | - Erin E Cawston
- The University of Auckland Department of Pharmacology and Clinical Pharmacology, Auckland, New Zealand
| | - Esin Ozturk-Isik
- Bogazici University Institute of Biomedical Engineering, Istanbul, Turkey
| | - Jennifer Linn
- Department of Neurology, Faculty of Medicine, Babylon, Iraq
- Department of Neurology, Technische Universität Dresden, Dresden, Germany
| | - Moritz Brandt
- Department of Neurology, Faculty of Medicine, Babylon, Iraq
- Department of Neurology, Technische Universität Dresden, Dresden, Germany
| | - Betty M Tijms
- Neurology, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | | | - Majon Muller
- Department of Internal Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | - Anders Fjell
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Kristine Walhovd
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Atle Bjørnerud
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Lene Pålhaugen
- Department of Neurology, Akershus University Hospital, Lorenskog, Norway
- University of Oslo, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lorenskog, Norway
| | - Patricia Clement
- Department of Diagnostic Sciences, University Hospital Ghent, Gent, Belgium
| | - Eric Achten
- Department of Diagnostic Sciences, University Hospital Ghent, Gent, Belgium
| | - Udunna Anazodo
- Lawson Health Research Institute, London, Ontario, Canada
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
- University College London, London, UK
| | - Saima Hilal
- National University Health System, Singapore
- Department of Pharmacology, National University of Singapore, Singapore
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lorenskog, Norway
- University of Oslo, Oslo, Norway
| | - Klaus Eickel
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- University of Applied Sciences Bremerhaven, Bremerhaven, Germany
| | - Catherine Morgan
- The University of Auckland School of Psychology, Auckland, New Zealand
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, University College London, London, UK
| | - Jan Petr
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Matthias Günther
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- University of Bremen, Bremen, Germany
| | - Henk J M M Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| |
Collapse
|
4
|
Cerina V, Crivellaro C, Morzenti S, Pozzi FE, Bigiogera V, Jonghi-Lavarini L, Moresco RM, Basso G, De Bernardi E. A ROI-based quantitative pipeline for 18F-FDG PET metabolism and pCASL perfusion joint analysis: Validation of the 18F-FDG PET line. Heliyon 2024; 10:e23340. [PMID: 38163125 PMCID: PMC10755331 DOI: 10.1016/j.heliyon.2023.e23340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024] Open
Abstract
In Mild Cognitive Impairment (MCI), the study of brain metabolism, provided by 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) can be integrated with brain perfusion through pseudo-Continuous Arterial Spin Labeling Magnetic Resonance sequences (MR pCASL). Cortical hypometabolism identification generally relies on wide control group datasets; pCASL control groups are instead not publicly available yet, due to lack of standardization in the acquisition parameters. This study presents a quantitative pipeline to be applied to PET and pCASL data to coherently analyze metabolism and perfusion inside 16 matching cortical regions of interest (ROIs) derived from the AAL3 atlas. The PET line is tuned on 36 MCI patients and 107 healthy control subjects, to agree in identifying hypometabolic regions with clinical reference methods (visual analysis supported by a vendor tool and Statistical Parametric Mapping, SPM, with two parametrizations here identified as SPM-A and SPM-B). The analysis was conducted for each ROI separately. The proposed PET analysis pipeline obtained accuracy 78 % and Cohen's к 60 % vs visual analysis, accuracy 79 % and Cohen's к 58 % vs SPM-A, accuracy 77 % and Cohen's к 54 % vs SPM-B. Cohen's к resulted not significantly different from SPM-A and SPM-B Cohen's к when assuming visual analysis as reference method (p-value 0.61 and 0.31 respectively). Considering SPM-A as reference method, Cohen's к is not significantly different from SPM-B Cohen's к as well (p-value = 1.00). The complete PET-pCASL pipeline was then preliminarily applied on 5 MCI patients and metabolism-perfusion regional correlations were assessed. The proposed approach can be considered as a promising tool for PET-pCASL joint analyses in MCI, even in the absence of a pCASL control group, to perform metabolism-perfusion regional correlation studies, and to assess and compare perfusion in hypometabolic or normo-metabolic areas.
Collapse
Affiliation(s)
- Valeria Cerina
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Cinzia Crivellaro
- Nuclear Medicine, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
| | - Sabrina Morzenti
- Medical Physics, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
| | - Federico E. Pozzi
- PhD program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Italy
- Neurology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
- Milan center for Neuroscience (NeuroMI), University of Milano-Bicocca, Italy
| | | | | | - Rosa M. Moresco
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Gianpaolo Basso
- Milan center for Neuroscience (NeuroMI), University of Milano-Bicocca, Italy
- School of Medicine and Surgery, University of Milano-Bicocca, Italy
- Neuroradiology, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italia
| | | |
Collapse
|
5
|
Poldrack RA, Markiewicz CJ, Appelhoff S, Ashar YK, Auer T, Baillet S, Bansal S, Beltrachini L, Benar CG, Bertazzoli G, Bhogawar S, Blair RW, Bortoletto M, Boudreau M, Brooks TL, Calhoun VD, Castelli FM, Clement P, Cohen AL, Cohen-Adad J, D'Ambrosio S, de Hollander G, de la Iglesia-Vayá M, de la Vega A, Delorme A, Devinsky O, Draschkow D, Duff EP, DuPre E, Earl E, Esteban O, Feingold FW, Flandin G, Galassi A, Gallitto G, Ganz M, Gau R, Gholam J, Ghosh SS, Giacomel A, Gillman AG, Gleeson P, Gramfort A, Guay S, Guidali G, Halchenko YO, Handwerker DA, Hardcastle N, Herholz P, Hermes D, Honey CJ, Innis RB, Ioanas HI, Jahn A, Karakuzu A, Keator DB, Kiar G, Kincses B, Laird AR, Lau JC, Lazari A, Legarreta JH, Li A, Li X, Love BC, Lu H, Marcantoni E, Maumet C, Mazzamuto G, Meisler SL, Mikkelsen M, Mutsaerts H, Nichols TE, Nikolaidis A, Nilsonne G, Niso G, Norgaard M, Okell TW, Oostenveld R, Ort E, Park PJ, Pawlik M, Pernet CR, Pestilli F, Petr J, Phillips C, Poline JB, Pollonini L, Raamana PR, Ritter P, Rizzo G, Robbins KA, Rockhill AP, Rogers C, Rokem A, Rorden C, Routier A, Saborit-Torres JM, Salo T, Schirner M, Smith RE, Spisak T, Sprenger J, Swann NC, Szinte M, Takerkart S, Thirion B, Thomas AG, Torabian S, Varoquaux G, Voytek B, Welzel J, Wilson M, Yarkoni T, Gorgolewski KJ. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ARXIV 2024:arXiv:2309.05768v2. [PMID: 37744469 PMCID: PMC10516110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.
Collapse
Affiliation(s)
| | | | | | - Yoni K Ashar
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tibor Auer
- School of Psychology, University of Surrey, Guildford, UK
- Artificial Intelligence and Informatics group, Rosalind Franklin Institute, Harwell Campus, Didcot, UK
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Shashank Bansal
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Leandro Beltrachini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Wales, UK
| | - Christian G Benar
- Aix Marseille Université, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Giacomo Bertazzoli
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy
- Brigham and Women's Hospital, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Ross W Blair
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Marta Bortoletto
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Teon L Brooks
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Filippo Maria Castelli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Bioretics srl, Cesena, Italy
| | - Patricia Clement
- Department of Medical Imaging, Ghent University Hospital, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | | | - Sasha D'Ambrosio
- Dipartimento di Scienze della Salute dell'Università degli Studi di Milano, Milan, Italy
- Department of Clinical and Experimental Epilepsy, University College London, UK
| | - Gilles de Hollander
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
| | | | | | - Arnaud Delorme
- SCCN, University of California, San Diego, La Jolla CA USA
| | - Orrin Devinsky
- Department of Neurology, NYU Langone Medical Center, New York, NY, USA
| | - Dejan Draschkow
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Eugene Paul Duff
- UK Dementia Research Institute, Department of Brain Sciences, Imperial College London, London, UK
| | - Elizabeth DuPre
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Eric Earl
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, England, UK
| | - Anthony Galassi
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Giuseppe Gallitto
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Neurology, University Medicine Essen, Essen, Germany
| | - Melanie Ganz
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
- Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
| | - Rémi Gau
- Origamin Lab, The Neuro, McGill University, Montreal, Quebec, Canada
| | - James Gholam
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Wales, UK
| | | | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, England, UK
| | - Ashley G Gillman
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Queensland, Australia
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, England, UK
| | | | - Samuel Guay
- Université de Montréal, Montréal, QC, Canada
| | - Giacomo Guidali
- Department of Psychology & NeuroMI - Milan Centre for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
| | - Daniel A Handwerker
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Nell Hardcastle
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Peer Herholz
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Christopher J Honey
- Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Robert B Innis
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | - Horea-Ioan Ioanas
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, NH, USA
| | - Andrew Jahn
- Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, USA
| | - Agah Karakuzu
- NeuroPoly Lab, Polytechnique Montréal, Montréal, Quebec, Canada
| | - David B Keator
- Change Your Brain Change Your Life Foundation, Costa Mesa, CA, USA
- Amen Clinics, Costa Mesa, CA, USA
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA
| | - Gregory Kiar
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY USA
| | - Balint Kincses
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Neurology, University Medicine Essen, Essen, Germany
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Jonathan C Lau
- Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jon Haitz Legarreta
- Department of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, Boston, MA, USA
| | - Adam Li
- Columbia University, New York, NY, USA
| | - Xiangrui Li
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | | | - Hanzhang Lu
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Eleonora Marcantoni
- School for Psychology and Neuroscience and Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow
| | - Camille Maumet
- Inria, Univ Rennes, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, Rennes, France
| | - Giacomo Mazzamuto
- National Research Council - National Institute of Optics (CNR-INO), Florence, Italy
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
| | - Mark Mikkelsen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Henk Mutsaerts
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Gustav Nilsonne
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Swedish National Data Service, Gothenburg University, Gothenburg, Sweden
| | | | - Martin Norgaard
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Thomas W Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- NatMEG, Karolinska Institutet, Stockholm, Sweden
| | - Eduard Ort
- Heinrich Heine University, Department of Biological Psychology of Decision Making, Düsseldorf, Germany
| | | | - Mateusz Pawlik
- Paris-Lodron-University of Salzburg, Department of Psychology, Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Cyril R Pernet
- Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Jan Petr
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | | | - Jean-Baptiste Poline
- Neuro Data Science ORIGAMI Laboratory, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, Montréal, Canada
| | - Luca Pollonini
- Department of Engineering Technology, University of Houston, Houston, TX
- Basque Center on Cognition, Brain and Language, Donostia-San Sebastián, Spain
| | | | - Petra Ritter
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany
| | - Gaia Rizzo
- Invicro, London, UK
- Division of Brain Sciences, Imperial College London, London, UK
| | - Kay A Robbins
- Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA
| | - Alexander P Rockhill
- Department of Neurosurgery, Oregon Health & Science University, Portland, OR, USA
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience (MCIN), Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Ariel Rokem
- University of Washington, Department of Psychology and eScience Institute, Seattle, WA, USA
| | - Chris Rorden
- University of South Carolina, Department of Psychology, Columbia, SC, USA
| | | | | | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Schirner
- Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10117, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin 10117, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, Berlin 10117, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, Berlin 10117, Germany
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
- The Florey Department of Neuroscience and Mental Heath, The University of Melbourne, Parkville, Victoria, Australia
| | - Tamas Spisak
- Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Julia Sprenger
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | - Nicole C Swann
- University of Oregon, Department of Human Physiology, Eugene, OR, USA
| | - Martin Szinte
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | - Sylvain Takerkart
- Institut de Neurosciences de la Timone (INT), UMR7289, CNRS, Aix-Marseille Université, France
| | | | - Adam G Thomas
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA
| | | | | | - Bradley Voytek
- Department of Cognitive Science, Halıcıoğlu Data Science Institute, and Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | | | - Martin Wilson
- University of Birmingham, Centre for Human Brain Health and School of Psychology, Birmingham, UK
| | | | | |
Collapse
|
6
|
Kashyap S, Oliveira ÍAF, Uludağ K. Feasibility of high-resolution perfusion imaging using arterial spin labeling MRI at 3 Tesla. Front Physiol 2024; 14:1271254. [PMID: 38235379 PMCID: PMC10791866 DOI: 10.3389/fphys.2023.1271254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/27/2023] [Indexed: 01/19/2024] Open
Abstract
Cerebral blood flow (CBF) is a critical physiological parameter of brain health, and it can be non-invasively measured with arterial spin labeling (ASL) MRI. In this study, we evaluated and optimized whole-brain, high-resolution ASL as an alternative to the low-resolution ASL employed in the routine assessment of CBF in both healthy participants and patients. Two high-resolution protocols (i.e., pCASL and FAIR-Q2TIPS (PASL) with 2 mm isotropic voxels) were compared to a default clinical pCASL protocol (3.4 × 3.4 × 4 mm 3), all of whom had an acquisition time of ≈ 5 min. We assessed the impact of high-resolution acquisition on reducing partial voluming and improving sensitivity to the perfusion signal, and evaluated the effectiveness of z-deblurring on the ASL data. We compared the quality of whole-brain ASL acquired using three available head coils with differing number of receive channels (i.e., 20, 32, and 64ch). We found that using higher coil counts (32 and 64ch coils as compared to 20ch) offers improved signal-to-noise ratio (SNR) and acceleration capabilities that are beneficial for ASL imaging at 3 Tesla (3 T). The inherent reduction in partial voluming effects with higher resolution acquisitions improves the resolving power of perfusion without impacting the sensitivity. In conclusion, our results suggest that high-resolution ASL (2 to 2.5 mm isotropic voxels) has the potential to become a new standard for perfusion imaging at 3 T and increase its adoption into clinical research and cognitive neuroscience applications.
Collapse
Affiliation(s)
- Sriranga Kashyap
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
| | | | - Kâmil Uludağ
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| |
Collapse
|
7
|
Sollmann N, Hoffmann G, Schramm S, Reichert M, Hernandez Petzsche M, Strobel J, Nigris L, Kloth C, Rosskopf J, Börner C, Bonfert M, Berndt M, Grön G, Müller HP, Kassubek J, Kreiser K, Koerte IK, Liebl H, Beer A, Zimmer C, Beer M, Kaczmarz S. Arterial Spin Labeling (ASL) in Neuroradiological Diagnostics - Methodological Overview and Use Cases. ROFO-FORTSCHR RONTG 2024; 196:36-51. [PMID: 37467779 DOI: 10.1055/a-2119-5574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
BACKGROUND Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI)-based technique using labeled blood-water of the brain-feeding arteries as an endogenous tracer to derive information about brain perfusion. It enables the assessment of cerebral blood flow (CBF). METHOD This review aims to provide a methodological and technical overview of ASL techniques, and to give examples of clinical use cases for various diseases affecting the central nervous system (CNS). There is a special focus on recent developments including super-selective ASL (ssASL) and time-resolved ASL-based magnetic resonance angiography (MRA) and on diseases commonly not leading to characteristic alterations on conventional structural MRI (e. g., concussion or migraine). RESULTS ASL-derived CBF may represent a clinically relevant parameter in various pathologies such as cerebrovascular diseases, neoplasms, or neurodegenerative diseases. Furthermore, ASL has also been used to investigate CBF in mild traumatic brain injury or migraine, potentially leading to the establishment of imaging-based biomarkers. Recent advances made possible the acquisition of ssASL by selective labeling of single brain-feeding arteries, enabling spatial perfusion territory mapping dependent on blood flow of a specific preselected artery. Furthermore, ASL-based MRA has been introduced, providing time-resolved delineation of single intracranial vessels. CONCLUSION Perfusion imaging by ASL has shown promise in various diseases of the CNS. Given that ASL does not require intravenous administration of a gadolinium-based contrast agent, it may be of particular interest for investigations in pediatric cohorts, patients with impaired kidney function, patients with relevant allergies, or patients that undergo serial MRI for clinical indications such as disease monitoring. KEY POINTS · ASL is an MRI technique that uses labeled blood-water as an endogenous tracer for brain perfusion imaging.. · It allows the assessment of CBF without the need for administration of a gadolinium-based contrast agent.. · CBF quantification by ASL has been used in several pathologies including brain tumors or neurodegenerative diseases.. · Vessel-selective ASL methods can provide brain perfusion territory mapping in cerebrovascular diseases.. · ASL may be of particular interest in patient cohorts with caveats concerning gadolinium administration..
Collapse
Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- cBrain, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Gabriel Hoffmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Severin Schramm
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Miriam Reichert
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Moritz Hernandez Petzsche
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Joachim Strobel
- Department of Nuclear Medicine, University Hospital Ulm, Ulm, Germany
| | - Lorenzo Nigris
- cBrain, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Johannes Rosskopf
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Section of Neuroradiology, Bezirkskrankenhaus Günzburg, Günzburg, Germany
| | - Corinna Börner
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- LMU Hospital, Department of Pediatrics - Dr. von Hauner Children's Hospital, Division of Pediatric Neurology and Developmental Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
- LMU Center for Children with Medical Complexity - iSPZ Hauner, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Michaela Bonfert
- LMU Hospital, Department of Pediatrics - Dr. von Hauner Children's Hospital, Division of Pediatric Neurology and Developmental Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
- LMU Center for Children with Medical Complexity - iSPZ Hauner, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Maria Berndt
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Georg Grön
- Department of Psychiatry and Psychotherapy III, University Hospital Ulm, Ulm, Germany
| | | | - Jan Kassubek
- Department of Neurology, University Hospital Ulm, Ulm, Germany
- German Center for Neurodegenerative Diseases (DZNE), Ulm University, Ulm, Germany
| | - Kornelia Kreiser
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Radiology and Neuroradiology, Universitäts- und Rehabilitationskliniken Ulm, Ulm, Germany
| | - Inga K Koerte
- cBrain, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, United States
- Department of Psychiatry, Harvard Medical School, Massachusetts General Hospital, Boston, United States
| | - Hans Liebl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Radiology, Berufsgenossenschaftliche Unfallklinik Murnau, Murnau, Germany
| | - Ambros Beer
- Department of Nuclear Medicine, University Hospital Ulm, Ulm, Germany
- MoMan - Center for Translational Imaging, University Hospital Ulm, Ulm, Germany
- i2SouI - Innovative Imaging in Surgical Oncology, University Hospital Ulm, Ulm, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- MoMan - Center for Translational Imaging, University Hospital Ulm, Ulm, Germany
- i2SouI - Innovative Imaging in Surgical Oncology, University Hospital Ulm, Ulm, Germany
| | - Stephan Kaczmarz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Market DACH, Philips GmbH, Hamburg, Germany
| |
Collapse
|
8
|
Yoo HJ, Nashiro K, Min J, Cho C, Mercer N, Bachman SL, Nasseri P, Dutt S, Porat S, Choi P, Zhang Y, Grigoryan V, Feng T, Thayer JF, Lehrer P, Chang C, Stanley JA, Head E, Rouanet J, Marmarelis VZ, Narayanan S, Wisnowski J, Nation DA, Mather M. Multimodal neuroimaging data from a 5-week heart rate variability biofeedback randomized clinical trial. Sci Data 2023; 10:503. [PMID: 37516756 PMCID: PMC10387077 DOI: 10.1038/s41597-023-02396-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 07/17/2023] [Indexed: 07/31/2023] Open
Abstract
We present data from the Heart Rate Variability and Emotion Regulation (HRV-ER) randomized clinical trial testing effects of HRV biofeedback. Younger (N = 121) and older (N = 72) participants completed baseline magnetic resonance imaging (MRI) including T1-weighted, resting and emotion regulation task functional MRI (fMRI), pulsed continuous arterial spin labeling (PCASL), and proton magnetic resonance spectroscopy (1H MRS). During fMRI scans, physiological measures (blood pressure, pulse, respiration, and end-tidal CO2) were continuously acquired. Participants were randomized to either increase heart rate oscillations or decrease heart rate oscillations during daily sessions. After 5 weeks of HRV biofeedback, they repeated the baseline measurements in addition to new measures (ultimatum game fMRI, training mimicking during blood oxygen level dependent (BOLD) and PCASL fMRI). Participants also wore a wristband sensor to estimate sleep time. Psychological assessment comprised three cognitive tests and ten questionnaires related to emotional well-being. A subset (N = 104) provided plasma samples pre- and post-intervention that were assayed for amyloid and tau. Data is publicly available via the OpenNeuro data sharing platform.
Collapse
Affiliation(s)
- Hyun Joo Yoo
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Kaoru Nashiro
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Jungwon Min
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Christine Cho
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Noah Mercer
- University of Southern California, Los Angeles, CA, 90007, USA
| | | | - Padideh Nasseri
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Shubir Dutt
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Shai Porat
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Paul Choi
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Yong Zhang
- University of Southern California, Los Angeles, CA, 90007, USA
| | | | - Tiantian Feng
- University of Southern California, Los Angeles, CA, 90007, USA
| | | | - Paul Lehrer
- Rutgers University, New Brunswick-Piscataway, USA
| | | | | | | | | | | | | | | | | | - Mara Mather
- University of Southern California, Los Angeles, CA, 90007, USA.
| |
Collapse
|
9
|
Tang S, Liu X, Nie L, Qian F, Chen W, He L. Three-dimensional pseudocontinuous arterial spin labeling perfusion imaging shows cerebral blood flow perfusion decline in attention-deficit/hyperactivity disorder children. Front Psychiatry 2023; 14:1064647. [PMID: 36741108 PMCID: PMC9889924 DOI: 10.3389/fpsyt.2023.1064647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 01/04/2023] [Indexed: 01/19/2023] Open
Abstract
PURPOSE To investigate the feasibility of three-dimensional pseudocontinuous arterial spin labeling (3D-pcASL) perfusion imaging in the brain of children with Attention-deficit/hyperactivity disorder (ADHD). METHODS A total of 78 ADHD children aged 5-13 years were prospectively selected as the study group, and 89 healthy children matched in age and sex were selected as the control group. All children underwent MRI conventional sequence, 3D-pcASL, and 3D-T1 sequence scans. The brain gray and white matter volume and cerebral blood flow (CBF) perfusion values were obtained by software post-processing, and were compared and analyzed in the two groups to find out their characteristics in the brain of ADHD children. RESULTS The total brain volume and total CBF values were lower in ADHD children than in healthy children (P < 0.05); the gray and white matter volumes in the frontal lobe, temporal lobe, hippocampus, caudate nucleus, putamen, globus pallidus and other brain regions were lower in ADHD children than in healthy children (P < 0.05); the gray matter CBF values in the frontal lobe, temporal lobe, hippocampus, caudate nucleus, putamen, globus pallidus and other brain regions were lower in ADHD children than in healthy children (P < 0.05); the differences between the white matter CBF values of white matter in the said brain regions of ADHD children and healthy children were not statistically significant (P > 0.05); and the CBF values in frontal lobe and caudate nuclei could distinguish ADHD children (AUC > 0.05, P < 0.05). CONCLUSION The 3D-pcASL technique showed reduced cerebral perfusion in some brain regions of ADHD children.
Collapse
Affiliation(s)
- Shilong Tang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xianfan Liu
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Lisha Nie
- GE Healthcare, MR Research China, Beijing, China
| | - Fangfang Qian
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Wushang Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| |
Collapse
|