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Moser J, Nelson SM, Koirala S, Madison TJ, Labonte AK, Carrasco CM, Feczko E, Moore LA, Lundquist JT, Weldon KB, Grimsrud G, Hufnagle K, Ahmed W, Myers MJ, Adeyemo B, Snyder AZ, Gordon EM, Dosenbach NUF, Tervo-Clemmens B, Larsen B, Moeller S, Yacoub E, Vizioli L, Uğurbil K, Laumann TO, Sylvester CM, Fair DA. Multi-echo Acquisition and Thermal Denoising Advances Precision Functional Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.27.564416. [PMID: 37961636 PMCID: PMC10634909 DOI: 10.1101/2023.10.27.564416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development. Precise characterization of systems organization during periods of high plasticity is likely to be essential for discoveries promoting lifelong health. Obtaining precision fMRI data during development has unique challenges that highlight the importance of establishing new methods to improve data acquisition, processing, and analysis. Here, we investigate two methods that can facilitate attaining this goal: multi-echo (ME) data acquisition and thermal noise removal with Noise Reduction with Distribution Corrected (NORDIC) principal component analysis. We applied these methods to precision fMRI data from adults, children, and newborn infants. In adults, both ME acquisitions and NORDIC increased temporal signal to noise ratio (tSNR) as well as the split-half reliability of functional connectivity matrices, with the combination helping more than either technique alone. The benefits of NORDIC denoising replicated in both our developmental samples. ME acquisitions revealed longer and more variable T2* relaxation times across the brain in infants relative to older children and adults, leading to major differences in the echo weighting for optimally combining ME data. This result suggests ME acquisitions may be a promising tool for optimizing developmental fMRI, albeit application in infants needs further investigation. The present work showcases methodological advances that improve Precision Functional Mapping in adults and developmental populations and, at the same time, highlights the need for further improvements in infant specific fMRI.
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Affiliation(s)
- Julia Moser
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Thomas J Madison
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Jacob T Lundquist
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Kimberly B Weldon
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Gracie Grimsrud
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Kristina Hufnagle
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Weli Ahmed
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO, USA
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Bart Larsen
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Steen Moeller
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Kamil Uğurbil
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Timothy O Laumann
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Taylor Family Institute for Innovative Research, Washington University in St. Louis, St. Louis, MO, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
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2
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Dean DC, Tisdall MD, Wisnowski JL, Feczko E, Gagoski B, Alexander AL, Edden RAE, Gao W, Hendrickson TJ, Howell BR, Huang H, Humphreys KL, Riggins T, Sylvester CM, Weldon KB, Yacoub E, Ahtam B, Beck N, Banerjee S, Boroday S, Caprihan A, Caron B, Carpenter S, Chang Y, Chung AW, Cieslak M, Clarke WT, Dale A, Das S, Davies-Jenkins CW, Dufford AJ, Evans AC, Fesselier L, Ganji SK, Gilbert G, Graham AM, Gudmundson AT, Macgregor-Hannah M, Harms MP, Hilbert T, Hui SCN, Irfanoglu MO, Kecskemeti S, Kober T, Kuperman JM, Lamichhane B, Landman BA, Lecour-Bourcher X, Lee EG, Li X, MacIntyre L, Madjar C, Manhard MK, Mayer AR, Mehta K, Moore LA, Murali-Manohar S, Navarro C, Nebel MB, Newman SD, Newton AT, Noeske R, Norton ES, Oeltzschner G, Ongaro-Carcy R, Ou X, Ouyang M, Parrish TB, Pekar JJ, Pengo T, Pierpaoli C, Poldrack RA, Rajagopalan V, Rettmann DW, Rioux P, Rosenberg JT, Salo T, Satterthwaite TD, Scott LS, Shin E, Simegn G, Simmons WK, Song Y, Tikalsky BJ, Tkach J, van Zijl PCM, Vannest J, Versluis M, Zhao Y, Zöllner HJ, Fair DA, Smyser CD, Elison JT. Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol. Dev Cogn Neurosci 2024; 70:101452. [PMID: 39341120 PMCID: PMC11466640 DOI: 10.1016/j.dcn.2024.101452] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 08/29/2024] [Accepted: 09/13/2024] [Indexed: 09/30/2024] Open
Abstract
The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.
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Affiliation(s)
- Douglas C Dean
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.
| | - M Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica L Wisnowski
- Department of Pediatrics, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA; Department of Radiology, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Wei Gao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA
| | - Brittany R Howell
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, USA
| | - Hao Huang
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kathryn L Humphreys
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
| | - Tracy Riggins
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA; Taylor Family Institute for Innovative Psychiatric Research, Washington University in St. Louis, St. Louis, MO, USA
| | - Kimberly B Weldon
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Banu Ahtam
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Natacha Beck
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | | | - Sergiy Boroday
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | | | - Bryan Caron
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Samuel Carpenter
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | | | - Ai Wern Chung
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Samir Das
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Christopher W Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Alexander J Dufford
- Department of Psychiatry and Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, USA
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Laetitia Fesselier
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Sandeep K Ganji
- MR Clinical Science, Philips Healthcare, Best, the Netherlands
| | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare, Mississauga, Ontario, Canada
| | - Alice M Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Maren Macgregor-Hannah
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland,; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,; LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Steve C N Hui
- Developing Brain Institute, Children's National Hospital, Washington, DC, USA; Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - M Okan Irfanoglu
- Quantitative Medical Imaging Laboratory, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | | | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland,; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,; LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Joshua M Kuperman
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Bidhan Lamichhane
- Center for Health Sciences, Oklahoma State University, Tulsa, OK, USA
| | - Bennett A Landman
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Xavier Lecour-Bourcher
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Erik G Lee
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Leigh MacIntyre
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; Lasso Informatics, Canada
| | - Cecile Madjar
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Mary Kate Manhard
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Cristian Navarro
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Sharlene D Newman
- Alabama Life Research Institute, University of Alabama, Tuscaloosa, AL, USA; Department of Psychology, University of Alabama, Tuscaloosa, AL, USA
| | - Allen T Newton
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Monroe Carell Jr. Children's Hospital at Vandebrilt, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Elizabeth S Norton
- Department of Communication Sciences and Disorders, School of Communication, Northwestern University, Evanston, IL, USA; Department of Medical Social Sciences, Feinberg School of Medicine, Chicago, IL, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Regis Ongaro-Carcy
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Xiawei Ou
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA; Arkansas Children's Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Minhui Ouyang
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Todd B Parrish
- Department of Radiology, Feinberg School of Medicine, Chicago, IL, USA; Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - James J Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Thomas Pengo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Carlo Pierpaoli
- Quantitative Medical Imaging Laboratory, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | | | - Vidya Rajagopalan
- Department of Pediatrics, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA; Department of Radiology, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | | | - Pierre Rioux
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Jens T Rosenberg
- Advanced Magnetic Resonance Imaging and Spectroscopy Facility, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa S Scott
- Department of Psychology, University of Florida, Gainesville, FL, USA
| | - Eunkyung Shin
- Department of Psychology, Pennsylvania State University, University Park, PA, USA
| | - Gizeaddis Simegn
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - W Kyle Simmons
- Department of Pharmacology and Physiology, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA; OSU Biomedical Imaging Center, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Barry J Tikalsky
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Jean Tkach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Peter C M van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jennifer Vannest
- Department of Communication Sciences and Disorders, University of Cincinnati, Cincinnati, OH, USA; Communication Sciences Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | - Yansong Zhao
- MR Clinical Science, Philips Healthcare, Cleveland, OH, USA
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Damien A Fair
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
| | - Christopher D Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
| | - Jed T Elison
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
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3
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Beeskow AB, Hirsch FW, Denecke T, Sorge I, Gräfe D. Large Numbers for Small Children-Up to What Age Do Infants Benefit from a Longer Echo Time in Cerebral T2 MRI Sequences? CHILDREN (BASEL, SWITZERLAND) 2024; 11:511. [PMID: 38790506 PMCID: PMC11119191 DOI: 10.3390/children11050511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024]
Abstract
In newborns, white matter shows a high T2-weighted (T2w) signal in MRI with poor grey-white matter contrast. To increase this contrast, an extremely long echo time (TE) is used in the examination of children. It is not known up to what age this long TE should be used. The purpose of this study was to find up to what age a long TE should be used in infants. In the prospective study, 101 infants (0-18 months) underwent cranial MRI at 3 Tesla. T2-weighted Fast Spin Echo sequences with long TE (200 ms) and medium TE (100 ms) were used. The signal intensities of the cortex and white matter were measured and the grey-white matter contrast (MC) was calculated. A cut-off age was determined. The T2w sequences with long TE had a statistically significantly higher MC until the age of six months (medium TE: 0.1 ± 0.05, Long TE: 0.19 ± 0.07; p < 0.001). After the tenth month, the T2w sequence with medium TE provided significantly better MC (Medium TE: 0.1 ± 0.05; long TE: 0.05 ± 0.4; p < 0.001). The use of a long TE is only helpful in the first six months of life. After the tenth month of life, a medium TE should be favored as is used in adult brain MRI.
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Affiliation(s)
- Anne Bettina Beeskow
- Department for Diagnostic and Interventional Radiology, University Hospital Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany;
| | - Franz Wolfgang Hirsch
- Department for Pediatric Radiology, University Hospital Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany; (F.W.H.); (I.S.); (D.G.)
| | - Timm Denecke
- Department for Diagnostic and Interventional Radiology, University Hospital Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany;
| | - Ina Sorge
- Department for Pediatric Radiology, University Hospital Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany; (F.W.H.); (I.S.); (D.G.)
| | - Daniel Gräfe
- Department for Pediatric Radiology, University Hospital Leipzig, Liebigstrasse 20a, 04103 Leipzig, Germany; (F.W.H.); (I.S.); (D.G.)
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4
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Story L, Uus A, Hall M, Payette K, Bakalis S, Arichi T, Shennan A, Rutherford M, Hutter J. Functional assessment of brain development in fetuses that subsequently deliver very preterm: An MRI pilot study. Prenat Diagn 2024; 44:49-56. [PMID: 38126921 PMCID: PMC10952951 DOI: 10.1002/pd.6498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/14/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVES To evaluate changes occurring in the fetal brain prior to very preterm delivery using MRI T2* relaxometry, an indirect assessment of tissue perfusion. METHOD Fetuses that subsequently delivered spontaneously <32 weeks gestation and a control cohort were identified from pre-existing datasets. Participants had undergone a 3T MRI assessment including T2* relaxometry of the fetal brain using a 2D multi-slice gradient echo single shot echo planar imaging sequence. T2* maps were generated, supratentorial brain tissue was manually segmented and mean T2* values were generated. Groups were compared using quadratic regression. RESULTS Twenty five fetuses that subsequently delivered <32 weeks and 67 that delivered at term were included. Mean gestation at MRI was 24.5 weeks (SD 3.3) and 25.4 weeks (SD 3.1) and gestation at delivery 25.5 weeks (SD 3.4) and 39.7 weeks (SD 1.2) in the preterm and term cohorts respectively. Brain mean T2* values were significantly lower in fetuses that subsequently delivered before 32 weeks gestation (p < 0.001). CONCLUSION Alterations in brain maturation appear to occur prior to preterm delivery. Further work is required to explore these associations, but these findings suggest a potential window for therapeutic neuroprotective agents in fetuses at high risk of preterm delivery in the future.
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Affiliation(s)
- Lisa Story
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
- Fetal Medicine UnitSt Thomas' Hospital LondonLondonUK
| | - Alena Uus
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Megan Hall
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Kelly Payette
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
| | | | - Tomoki Arichi
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Andrew Shennan
- Department of Women's and Children's HealthKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Mary Rutherford
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
| | - Jana Hutter
- Centre for the Developing BrainSchool of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' Hospital LondonLondonUK
- Radiological InstituteUniversity Hospital ErlangenErlangenGermany
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5
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Pinto CR, Duarte JV, Dinis A, Duarte IC, Castelhano J, Pinto J, Oliveira G, Castelo-Branco M. Functional neuroimaging of responses to multiple sensory stimulations in newborns with perinatal asphyxia. Transl Pediatr 2023; 12:1646-1658. [PMID: 37814708 PMCID: PMC10560353 DOI: 10.21037/tp-23-135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/21/2023] [Indexed: 10/11/2023] Open
Abstract
Background Functional neuroimaging can provide pathophysiological information in perinatal asphyxia (PA). However, fundamental unresolved questions remain related to the influence of neurovascular coupling (NVC) maturation on functional responses in early development. We aimed to probe the feasibility and compare the responses to multiple sensory stimulations in newborns with PA using functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS). Methods Responses to visual, auditory, and sensorimotor passive stimulation were measured with fMRI and fNIRS and compared in 18 term newborns with PA and six controls. Results Most newborns exhibited a positive fMRI response during visual and sensorimotor stimulation, higher in the sensorimotor. An asymmetric pattern (negative in the left hemisphere) was observed in auditory stimulation. The fNIRS response most resembling the adult pattern (positive) in PA occurred during auditory stimulation, in which oxyhemoglobin (HbO) increased, and deoxyhemoglobin (HbR) decreased. Significative differences were found in the HbO and HbR profiles in newborns with PA compared to the controls, more evident in auditory stimulation. Positive correlations between the fMRI BOLD signal and at least one fNIRS channel (HbO) in all stimuli in newborns with PA were identified: the strongest was in the auditory (r=0.704) and the weakest in the sensorimotor (r=0.544); in more fNIRS channels, in the visual. Conclusions Both techniques are feasible physiological assessment tools, suggesting a distinctive level of maturation in sensory and motor areas. Differences in fNIRS profiles in newborns with PA and controls and the fMRI-fNIRS relationship observed can encourage the fNIRS as a clinically emergent valuable tool.
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Affiliation(s)
- Carla R. Pinto
- Pediatric Intensive Care Unit, Pediatric Hospital, Coimbra Hospital and University Centre, Coimbra, Portugal
- University Clinic of Pediatrics, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT) and Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - João V. Duarte
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT) and Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Alexandra Dinis
- Pediatric Intensive Care Unit, Pediatric Hospital, Coimbra Hospital and University Centre, Coimbra, Portugal
| | - Isabel C. Duarte
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT) and Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - João Castelhano
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT) and Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Joana Pinto
- Neuroradiology Unit, Medical Imaging Department, Coimbra Hospital and University Centre, Coimbra, Portugal
| | - Guiomar Oliveira
- University Clinic of Pediatrics, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT) and Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Child Developmental Center, Research and Clinical Training Center, Pediatric Hospital, Coimbra Hospital and University Centre, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT) and Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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6
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Karolis VR, Fitzgibbon SP, Cordero-Grande L, Farahibozorg SR, Price AN, Hughes EJ, Fetit AE, Kyriakopoulou V, Pietsch M, Rutherford MA, Rueckert D, Hajnal JV, Edwards AD, O'Muircheartaigh J, Duff EP, Arichi T. Maturational networks of human fetal brain activity reveal emerging connectivity patterns prior to ex-utero exposure. Commun Biol 2023; 6:661. [PMID: 37349403 PMCID: PMC10287667 DOI: 10.1038/s42003-023-04969-x] [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: 10/05/2022] [Accepted: 05/23/2023] [Indexed: 06/24/2023] Open
Abstract
A key feature of the fetal period is the rapid emergence of organised patterns of spontaneous brain activity. However, characterising this process in utero using functional MRI is inherently challenging and requires analytical methods which can capture the constituent developmental transformations. Here, we introduce a novel analytical framework, termed "maturational networks" (matnets), that achieves this by modelling functional networks as an emerging property of the developing brain. Compared to standard network analysis methods that assume consistent patterns of connectivity across development, our method incorporates age-related changes in connectivity directly into network estimation. We test its performance in a large neonatal sample, finding that the matnets approach characterises adult-like features of functional network architecture with a greater specificity than a standard group-ICA approach; for example, our approach is able to identify a nearly complete default mode network. In the in-utero brain, matnets enables us to reveal the richness of emerging functional connections and the hierarchy of their maturational relationships with remarkable anatomical specificity. We show that the associative areas play a central role within prenatal functional architecture, therefore indicating that functional connections of high-level associative areas start emerging prior to exposure to the extra-utero environment.
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Affiliation(s)
- Vyacheslav R Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Lucilio Cordero-Grande
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Ahmed E Fetit
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
- UKRI CDT in Artificial Intelligence for Healthcare, Department of Computing, Imperial College London, London, UK
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
- Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Paediatric Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK
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7
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Sylvester CM, Kaplan S, Myers MJ, Gordon EM, Schwarzlose RF, Alexopoulos D, Nielsen AN, Kenley JK, Meyer D, Yu Q, Graham AM, Fair DA, Warner BB, Barch DM, Rogers CE, Luby JL, Petersen SE, Smyser CD. Network-specific selectivity of functional connections in the neonatal brain. Cereb Cortex 2023; 33:2200-2214. [PMID: 35595540 PMCID: PMC9977389 DOI: 10.1093/cercor/bhac202] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
The adult human brain is organized into functional brain networks, groups of functionally connected segregated brain regions. A key feature of adult functional networks is long-range selectivity, the property that spatially distant regions from the same network have higher functional connectivity than spatially distant regions from different networks. Although it is critical to establish the status of functional networks and long-range selectivity during the neonatal period as a foundation for typical and atypical brain development, prior work in this area has been mixed. Although some studies report distributed adult-like networks, other studies suggest that neonatal networks are immature and consist primarily of spatially isolated regions. Using a large sample of neonates (n = 262), we demonstrate that neonates have long-range selective functional connections for the default mode, fronto-parietal, and dorsal attention networks. An adult-like pattern of functional brain networks is evident in neonates when network-detection algorithms are tuned to these long-range connections, when using surface-based registration (versus volume-based registration), and as per-subject data quantity increases. These results help clarify factors that have led to prior mixed results, establish that key adult-like functional network features are evident in neonates, and provide a foundation for studies of typical and atypical brain development.
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Affiliation(s)
- Chad M Sylvester
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Sydney Kaplan
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Michael J Myers
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Evan M Gordon
- Department of Radiology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Rebecca F Schwarzlose
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Jeanette K Kenley
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Dominique Meyer
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Qiongru Yu
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, 6363 Alvarado Court, Suite 103, San Diego, CA 92120, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Department of Pediatrics, and Institute of Child Development, University of Minnesota, 2025 E. River Parkway, Minneapolis, MN 55414, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Radiology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Psychological and Brain Sciences, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Steven E Petersen
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Christopher D Smyser
- Department of Neurology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Radiology, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
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8
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Boursianis T, Kalaitzakis G, Nikiforaki K, Kosteletou E, Antypa D, Gourzoulidis GA, Karantanas A, Papadaki E, Simos P, Maris TG, Marias K. The Significance of Echo Time in fMRI BOLD Contrast: A Clinical Study during Motor and Visual Activation Tasks at 1.5 T. Tomography 2021; 7:333-343. [PMID: 34449739 PMCID: PMC8396192 DOI: 10.3390/tomography7030030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/28/2021] [Accepted: 07/31/2021] [Indexed: 11/16/2022] Open
Abstract
Blood Oxygen Level Dependent (BOLD) is a commonly-used MR imaging technique in studying brain function. The BOLD signal can be strongly affected by specific sequence parameters, especially in small field strengths. Previous small-scale studies have investigated the effect of TE on BOLD contrast. This study evaluates the dependence of fMRI results on echo time (TE) during concurrent activation of the visual and motor cortex at 1.5 T in a larger sample of 21 healthy volunteers. The experiment was repeated using two different TE values (50 and 70 ms) in counterbalanced order. Furthermore, T2* measurements of the gray matter were performed. Results indicated that both peak beta value and number of voxels were significantly higher using TE = 70 than TE = 50 ms in primary motor, primary somatosensory and supplementary motor cortices (p < 0.007). In addition, the amplitude of activation in visual cortices and the dorsal premotor area was also higher using TE = 70 ms (p < 0.001). Gray matter T2* of the corresponding areas did not vary significantly. In conclusion, the optimal TE value (among the two studied) for visual and motor activity is 70 ms affecting both the amplitude and extent of regional hemodynamic activation.
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Affiliation(s)
- Themistoklis Boursianis
- Department of Medical Physics, School of Medicine, University of Crete, 71003 Heraklion, Greece; (G.K.); (T.G.M.)
- Correspondence:
| | - Georgios Kalaitzakis
- Department of Medical Physics, School of Medicine, University of Crete, 71003 Heraklion, Greece; (G.K.); (T.G.M.)
| | - Katerina Nikiforaki
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (K.N.); (A.K.); (E.P.); (P.S.); (K.M.)
| | | | - Despina Antypa
- Department of Psychiatry, School of Medicine, University of Crete, 71003 Heraklion, Greece;
| | - George A. Gourzoulidis
- Research & Measurements Center of OHS Hazardous Agents, OHS Directorate, Hellenic Ministry of Labor, 10110 Athens, Greece;
- Lighting Lab, National Technical University of Athens, 15780 Athens, Greece
| | - Apostolos Karantanas
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (K.N.); (A.K.); (E.P.); (P.S.); (K.M.)
- Department of Radiology, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Efrosini Papadaki
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (K.N.); (A.K.); (E.P.); (P.S.); (K.M.)
- Department of Radiology, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Panagiotis Simos
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (K.N.); (A.K.); (E.P.); (P.S.); (K.M.)
- Department of Psychiatry, School of Medicine, University of Crete, 71003 Heraklion, Greece;
| | - Thomas G. Maris
- Department of Medical Physics, School of Medicine, University of Crete, 71003 Heraklion, Greece; (G.K.); (T.G.M.)
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (K.N.); (A.K.); (E.P.); (P.S.); (K.M.)
| | - Kostas Marias
- Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology—Hellas (FORTH), 70013 Heraklion, Greece; (K.N.); (A.K.); (E.P.); (P.S.); (K.M.)
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
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9
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Baxter L, Moultrie F, Fitzgibbon S, Aspbury M, Mansfield R, Bastiani M, Rogers R, Jbabdi S, Duff E, Slater R. Functional and diffusion MRI reveal the neurophysiological basis of neonates' noxious-stimulus evoked brain activity. Nat Commun 2021; 12:2744. [PMID: 33980860 PMCID: PMC8115252 DOI: 10.1038/s41467-021-22960-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 04/05/2021] [Indexed: 11/20/2022] Open
Abstract
Understanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.
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Affiliation(s)
- Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, UK
| | - Fiona Moultrie
- Department of Paediatrics, University of Oxford, Oxford, UK
| | - Sean Fitzgibbon
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | | | - Matteo Bastiani
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Richard Rogers
- Nuffield Department of Anaesthetics, John Radcliffe Hospital, Oxford, UK
| | - Saad Jbabdi
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Eugene Duff
- Department of Paediatrics, University of Oxford, Oxford, UK
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, UK.
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10
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Dubois J, Alison M, Counsell SJ, Hertz‐Pannier L, Hüppi PS, Benders MJ. MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances. J Magn Reson Imaging 2021; 53:1318-1343. [PMID: 32420684 PMCID: PMC8247362 DOI: 10.1002/jmri.27192] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Jessica Dubois
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Marianne Alison
- University of ParisNeuroDiderot, INSERM,ParisFrance
- Department of Pediatric RadiologyAPHP, Robert‐Debré HospitalParisFrance
| | - Serena J. Counsell
- Centre for the Developing BrainSchool of Biomedical Engineering & Imaging Sciences, King's College LondonLondonUK
| | - Lucie Hertz‐Pannier
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Petra S. Hüppi
- Division of Development and Growth, Department of Woman, Child and AdolescentUniversity Hospitals of GenevaGenevaSwitzerland
| | - Manon J.N.L. Benders
- Department of NeonatologyUniversity Medical Center Utrecht, Utrecht UniversityUtrechtthe Netherlands
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11
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Neurodevelopmental effects of childhood malnutrition: A neuroimaging perspective. Neuroimage 2021; 231:117828. [PMID: 33549754 DOI: 10.1016/j.neuroimage.2021.117828] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/28/2021] [Accepted: 01/31/2021] [Indexed: 02/08/2023] Open
Abstract
Approximately one in five children worldwide suffers from childhood malnutrition and its complications, including increased susceptibility to inflammation and infectious diseases. Due to improved early interventions, most of these children now survive early malnutrition, even in low-resource settings (LRS). However, many continue to exhibit neurodevelopmental deficits, including low IQ, poor school performance, and behavioral problems over their lifetimes. Most studies have relied on neuropsychological tests, school performance, and mental health and behavioral measures. Few studies, in contrast, have assessed brain structure and function, and to date, these have mainly relied on low-cost techniques, including electroencephalography (EEG) and evoked potentials (ERP). The use of more advanced methods of neuroimaging, including magnetic resonance imaging (MRI) and functional near-infrared spectroscopy (fNIRS), has been limited by cost factors and lack of availability of these technologies in developing countries, where malnutrition is nearly ubiquitous. This report summarizes the current state of knowledge and evidence gaps regarding childhood malnutrition and the study of its impact on neurodevelopment. It may help to inform the development of new strategies to improve the identification, classification, and treatment of neurodevelopmental disabilities in underserved populations at the highest risk for childhood malnutrition.
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Ciarrusta J, Dimitrova R, McAlonan G. Early maturation of the social brain: How brain development provides a platform for the acquisition of social-cognitive competence. PROGRESS IN BRAIN RESEARCH 2020; 254:49-70. [PMID: 32859293 DOI: 10.1016/bs.pbr.2020.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Across the last century psychology has provided a lot of insight about social-cognitive competence. Recognizing facial expressions, joint attention, discrimination of cues and experiencing empathy are just a few examples of the social skills humans acquire from birth to adolescence. However, how very early brain maturation provides a platform to support the attainment of highly complex social behavior later in development remains poorly understood. Magnetic Resonance Imaging provides a safe means to investigate the typical and atypical maturation of regions of the brain responsible for social cognition in as early as the perinatal period. Here, we first review some technical challenges and advances of using functional magnetic resonance imaging on developing infants to then describe current knowledge on the development of diverse systems associated with social function. We will then explain how these characteristics might differ in infants with genetic or environmental risk factors, who are vulnerable to atypical neurodevelopment. Finally, given the rapid early development of systems necessary for social skills, we propose a new framework to investigate sensitive time windows of development when neural substrates might be more vulnerable to impairment due to a genetic or environmental insult.
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Affiliation(s)
- Judit Ciarrusta
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom; Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom; Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Grainne McAlonan
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom; South London and Maudsley NHS Foundation Trust, London, United Kingdom.
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13
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Fitzgibbon SP, Harrison SJ, Jenkinson M, Baxter L, Robinson EC, Bastiani M, Bozek J, Karolis V, Cordero Grande L, Price AN, Hughes E, Makropoulos A, Passerat-Palmbach J, Schuh A, Gao J, Farahibozorg SR, O'Muircheartaigh J, Ciarrusta J, O'Keeffe C, Brandon J, Arichi T, Rueckert D, Hajnal JV, Edwards AD, Smith SM, Duff E, Andersson J. The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants. Neuroimage 2020; 223:117303. [PMID: 32866666 PMCID: PMC7762845 DOI: 10.1016/j.neuroimage.2020.117303] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 08/12/2020] [Accepted: 08/17/2020] [Indexed: 02/08/2023] Open
Abstract
An automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data. Includes integrated dynamic distortion and slice-to-volume motion correction. A robust multimodal registration approach which includes custom neonatal templates. Incorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection. Data analysis of 538 infants imaged at 26–45 weeks post-menstrual age.
The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20–45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.
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Affiliation(s)
- Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
| | - Samuel J Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Switzerland
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Luke Baxter
- Paediatric Neuroimaging Group, Department of Paediatrics, University of Oxford, UK
| | - Emma C Robinson
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; NIHR Biomedical Research Centre, University of Nottingham, UK
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Vyacheslav Karolis
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Lucilio Cordero Grande
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Anthony N Price
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emer Hughes
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | | | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Jianliang Gao
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK; Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Judit Ciarrusta
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Camilla O'Keeffe
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Jakki Brandon
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK; Department of Bioengineering, Imperial College London, UK; Children's Neurosciences, Evelina London Children's Hospital, King's Health Partners, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - A David Edwards
- Centre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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Rajasilta O, Tuulari JJ, Björnsdotter M, Scheinin NM, Lehtola SJ, Saunavaara J, Häkkinen S, Merisaari H, Parkkola R, Lähdesmäki T, Karlsson L, Karlsson H. Resting-state networks of the neonate brain identified using independent component analysis. Dev Neurobiol 2020; 80:111-125. [PMID: 32267069 DOI: 10.1002/dneu.22742] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 03/10/2020] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been successfully used to probe the intrinsic functional organization of the brain and to study brain development. Here, we implemented a combination of individual and group independent component analysis (ICA) of FSL on a 6-min resting-state data set acquired from 21 naturally sleeping term-born (age 26 ± 6.7 d), healthy neonates to investigate the emerging functional resting-state networks (RSNs). In line with the previous literature, we found evidence of sensorimotor, auditory/language, visual, cerebellar, thalmic, parietal, prefrontal, anterior cingulate as well as dorsal and ventral aspects of the default-mode-network. Additionally, we identified RSNs in frontal, parietal, and temporal regions that have not been previously described in this age group and correspond to the canonical RSNs established in adults. Importantly, we found that careful ICA-based denoising of fMRI data increased the number of networks identified with group-ICA, whereas the degree of spatial smoothing did not change the number of identified networks. Our results show that the infant brain has an established set of RSNs soon after birth.
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Affiliation(s)
- Olli Rajasilta
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Jetro J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland.,Department of Psychiatry, University of Oxford, Oxford, UK.,Turku Collegium for Science and Medicine, University of Turku, Turku, Finland
| | - Malin Björnsdotter
- The Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Noora M Scheinin
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Satu J Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Suvi Häkkinen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Tuire Lähdesmäki
- Department of Pediatric Neurology, University of Turku and Turku University Hospital, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Child Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
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Abstract
Measuring brain activity in infants provides an objective surrogate approach with which to infer pain perception following noxious events. Here we discuss different approaches which can be used to measure noxious-evoked brain activity, and discuss how these measures can be used to assess the analgesic efficacy of pharmacological and non-pharmacological interventions. We review factors that can modulate noxious-evoked brain activity, which may impact infant pain experience, including gestational age, sex, prior pain, stress, and illness.
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Affiliation(s)
- Deniz Gursul
- Department of Paediatrics, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, United Kingdom
| | - Caroline Hartley
- Department of Paediatrics, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, United Kingdom
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, United Kingdom.
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16
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Baxter L, Fitzgibbon S, Moultrie F, Goksan S, Jenkinson M, Smith S, Andersson J, Duff E, Slater R. Optimising neonatal fMRI data analysis: Design and validation of an extended dHCP preprocessing pipeline to characterise noxious-evoked brain activity in infants. Neuroimage 2019; 186:286-300. [PMID: 30414984 PMCID: PMC6347570 DOI: 10.1016/j.neuroimage.2018.11.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 10/16/2018] [Accepted: 11/06/2018] [Indexed: 11/21/2022] Open
Abstract
The infant brain is unlike the adult brain, with considerable differences in morphological, neurodynamic, and haemodynamic features. As the majority of current MRI analysis tools were designed for use in adults, a primary objective of the Developing Human Connectome Project (dHCP) is to develop optimised methodological pipelines for the analysis of neonatal structural, resting state, and diffusion MRI data. Here, in an independent neonatal dataset we have extended and optimised the dHCP fMRI preprocessing pipeline for the analysis of stimulus-response fMRI data. We describe and validate this extended dHCP fMRI preprocessing pipeline to analyse changes in brain activity evoked following an acute noxious stimulus applied to the infant's foot. We compare the results obtained from this extended dHCP pipeline to results obtained from a typical FSL FEAT-based analysis pipeline, evaluating the pipelines' outputs using a wide range of tests. We demonstrate that a substantial increase in spatial specificity and sensitivity to signal can be attained with a bespoke neonatal preprocessing pipeline through optimised motion and distortion correction, ICA-based denoising, and haemodynamic modelling. The improved sensitivity and specificity, made possible with this extended dHCP pipeline, will be paramount in making further progress in our understanding of the development of sensory processing in the infant brain.
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Affiliation(s)
- Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Sean Fitzgibbon
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Fiona Moultrie
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Sezgi Goksan
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Stephen Smith
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Jesper Andersson
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Eugene Duff
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom.
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17
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Counsell SJ, Arichi T, Arulkumaran S, Rutherford MA. Fetal and neonatal neuroimaging. HANDBOOK OF CLINICAL NEUROLOGY 2019; 162:67-103. [PMID: 31324329 DOI: 10.1016/b978-0-444-64029-1.00004-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Magnetic resonance imaging (MRI) can provide detail of the soft tissues of the fetal and neonatal brain that cannot be obtained by any other imaging modality. Conventional T1 and T2 weighted sequences provide anatomic detail of the normally developing brain and can demonstrate lesions, including those associated with preterm birth, hypoxic ischemic encephalopathy, perinatal arterial stroke, infections, and congenital malformations. Specialized imaging techniques can be used to assess cerebral vasculature (magnetic resonance angiography and venography), cerebral metabolism (magnetic resonance spectroscopy), cerebral perfusion (arterial spin labeling), and function (functional MRI). A wealth of quantitative tools, most of which were originally developed for the adult brain, can be applied to study the developing brain in utero and postnatally including measures of tissue microstructure obtained from diffusion MRI, morphometric studies to measure whole brain and regional tissue volumes, and automated approaches to study cortical folding. In this chapter, we aim to describe different imaging approaches for the fetal and neonatal brain, and to discuss their use in a range of clinical applications.
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Affiliation(s)
- Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sophie Arulkumaran
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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18
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Tuulari JJ, Scheinin NM, Lehtola S, Merisaari H, Saunavaara J, Parkkola R, Sehlstedt I, Karlsson L, Karlsson H, Björnsdotter M. Neural correlates of gentle skin stroking in early infancy. Dev Cogn Neurosci 2017; 35:36-41. [PMID: 29241822 PMCID: PMC6968958 DOI: 10.1016/j.dcn.2017.10.004] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 10/14/2017] [Accepted: 10/16/2017] [Indexed: 12/21/2022] Open
Abstract
The infant brain is sensitive to gentle skin stroking within the first weeks of age. The postcentral gyrus and posterior insular cortex are responsive to stroking. Social touch activates both somatosensory and socio-affective brain areas in infancy.
Physical expressions of affection play a foundational role in early brain development, but the neural correlates of affective touch processing in infancy remain unclear. We examined brain responses to gentle skin stroking, a type of tactile stimulus associated with affectionate touch, in young infants. Thirteen term-born infants aged 11–36 days, recruited through the FinnBrain Birth Cohort Study, were included in the study. Soft brush strokes, which activate brain regions linked to somatosensory as well as socio-affective processing in children and adults, were applied to the skin of the right leg during functional magnetic resonance imaging. We examined infant brain responses in two regions-of-interest (ROIs) known to process gentle skin stroking – the postcentral gyrus and posterior insular cortex – and found significant responses in both ROIs. These results suggest that the neonate brain is responsive to gentle skin stroking within the first weeks of age, and that regions linked to primary somatosensory as well as socio-affective processing are activated. Our findings support the notion that social touch may play an important role in early life sensory processing. Future research will elucidate the significance of these findings for human brain development.
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Affiliation(s)
- Jetro J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland; Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Noora M Scheinin
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland; Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Satu Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Isac Sehlstedt
- Center for Social and Affective Neuroscience, Linköping University, Sweden; Department of Psychology, University of Gothenburg, Sweden
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland; Department of Child Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland; Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Malin Björnsdotter
- Center for Social and Affective Neuroscience, Linköping University, Sweden; Institute of Neuroscience and Physiology, University of Gothenburg, Sweden.
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