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For: Seidel P, Levine SM, Tahedl M, Schwarzbach JV. Temporal Signal-to-Noise Changes in Combined Multislice- and In-Plane-Accelerated Echo-Planar Imaging with a 20- and 64-Channel Coil. Sci Rep 2020;10:5536. [PMID: 32218476 PMCID: PMC7099092 DOI: 10.1038/s41598-020-62590-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 03/17/2020] [Indexed: 11/08/2022] Open

For:	Seidel P, Levine SM, Tahedl M, Schwarzbach JV. Temporal Signal-to-Noise Changes in Combined Multislice- and In-Plane-Accelerated Echo-Planar Imaging with a 20- and 64-Channel Coil. Sci Rep 2020;10:5536. [PMID: 32218476 PMCID: PMC7099092 DOI: 10.1038/s41598-020-62590-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 03/17/2020] [Indexed: 11/08/2022] Open

Number

Cited by Other Article(s)

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] [MESH Headings] [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

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
Suchandrima Banerjee GE HealthCare, Menlo Park, CA, USA
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
Arvind Caprihan The Mind Research Network, Albuquerque, NM, USA
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
Yulin Chang Siemens Medical Solutions USA Inc, PA, 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
Steven Kecskemeti Waisman Center, University of Wisconsin-Madison, Madison, WI, 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
Andrew R Mayer The Mind Research Network, Albuquerque, NM, 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
Ralph Noeske GE HealthCare, Munich, Germany
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
Russell A Poldrack Department of Psychology, Stanford University, Stanford, CA, 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
Dan W Rettmann GE HealthCare, Rochester, MN, 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
Maarten Versluis MR Clinical Science, Philips Healthcare, Best, the Netherlands
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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Quinones JF, Schmitt T, Pavan T, Hildebrandt A, Heep A. Customization of neonatal functional magnetic resonance imaging: A preclinical phantom-based study. PLoS One 2024;19:e0313192. [PMID: 39485821 PMCID: PMC11530025 DOI: 10.1371/journal.pone.0313192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Accepted: 10/18/2024] [Indexed: 11/03/2024] Open

Nakamura Y, Ishida T. The effect of multiband sequences on statistical outcome measures in functional magnetic resonance imaging using a gustatory stimulus. Neuroimage 2024;300:120867. [PMID: 39322093 DOI: 10.1016/j.neuroimage.2024.120867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 09/19/2024] [Accepted: 09/23/2024] [Indexed: 09/27/2024] Open

Abstract

Recent technical developments have led to the invention of multiband functional magnetic resonance imaging (fMRI) sequences that allow for faster sampling rates. However, some studies have highlighted problems with these sequences, leading to a decreased temporal signal-to-noise ratio (tSNR). In addition, this temporal noise may interfere with detecting reward-related responses in mesolimbic regions. The blood-oxygen-level-dependent signal utilized in the majority of fMRI measurements is relatively slow. Furthermore, the cerebral response to gustatory stimuli would also be relatively slow. Therefore, given the temporal noise issues with multiband sequences, it is unclear whether multiband sequences are necessary for fMRI studies using gustatory stimuli. We thus conducted an fMRI experiment using a gustatory stimulus to investigate the effects of multiband sequences and increased sampling rates on statistical outcome measures. A single-band sequence with a repetition time (TR) of 2 s of phantom fMRI data and gustatory fMRI data from the gustatory regions exhibited the highest tSNR, although the tSNR of this sequence of gustatory fMRI was not statistically different from tSNR of multiband sequences with a TR of 2 s in any of the selected region of interests. Conventional general linear model analysis of fMRI showed that single-band sequences are more advantageous than multiband sequences for detecting brain responses to gustatory stimuli in the primary gustatory cortex. In addition, a Bayesian data comparison showed that data derived from a single-band sequence with a TR of 2 s was optimal for inferring neuronal connectivity in gustatory processing. Therefore, a conventional single-band sequence with a TR of 2 s is more appropriate for fMRI with gustatory stimuli. Image acquisition sequences should be selected aligned with the study objectives and target brain regions.

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Wein S, Riebel M, Seidel P, Brunner LM, Wagner V, Nothdurfter C, Rupprecht R, Schwarzbach JV. Local and global effects of sedation in resting-state fMRI: a randomized, placebo-controlled comparison between etifoxine and alprazolam. Neuropsychopharmacology 2024;49:1738-1748. [PMID: 38822128 PMCID: PMC11399242 DOI: 10.1038/s41386-024-01884-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 06/02/2024]

Abstract

TSPO ligands are promising alternatives to benzodiazepines in the treatment of anxiety, as they display less pronounced side effects such as sedation, cognitive impairment, tolerance development and abuse potential. In a randomized double-blind repeated-measures study we compare a benzodiazepine (alprazolam) to a TSPO ligand (etifoxine) by assessing side effects and acquiring resting-state fMRI data from 34 healthy participants after 5 days of taking alprazolam, etifoxine or a placebo. To study the effects of the pharmacological interventions in fMRI in detail and across different scales, we combine in our study complementary analysis strategies related to whole-brain functional network connectivity, local connectivity analysis expressed in regional homogeneity, fluctuations in low-frequency BOLD amplitudes and coherency of independent resting-state networks. Participants reported considerable adverse effects such as fatigue, sleepiness and concentration impairments, related to the administration of alprazolam compared to placebo. In resting-state fMRI we found a significant decrease in functional connection density, network efficiency and a decrease in the networks rich-club coefficient related to alprazolam. While observing a general decrease in regional homogeneity in high-level brain networks in the alprazolam condition, we simultaneously could detect an increase in regional homogeneity and resting-state network coherence in low-level sensory regions. Further we found a general increase in the low-frequency compartment of the BOLD signal. In the etifoxine condition, participants did not report any significant side effects compared to the placebo, and we did not observe any corresponding modulations in our fMRI metrics. Our results are consistent with the idea that sedation globally disconnects low-level functional networks, but simultaneously increases their within-connectivity. Further, our results point towards the potential of TSPO ligands in the treatment of anxiety and depression.

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Alahäivälä ALI, Thaploo D, Wein S, Seidel P, Riebel M, Hummel T, Schwarzbach JV. Inhalation-modulated detection of olfactory BOLD responses in the human brain. FRONTIERS IN NEUROIMAGING 2023;2:1260893. [PMID: 38107774 PMCID: PMC10725246 DOI: 10.3389/fnimg.2023.1260893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/02/2023] [Indexed: 12/19/2023]

Meyer NK, Kang D, Ahmed Z, In MH, Shu Y, Huston J, Bernstein MA, Trzasko JD. Locally Low-Rank Denoising of Multi-Echo Functional MRI Data With Application in Resting-State Analysis. Top Magn Reson Imaging 2023;32:37-49. [PMID: 37796647 PMCID: PMC10549890 DOI: 10.1097/rmr.0000000000000307] [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: 06/23/2023] [Revised: 07/28/2023] [Accepted: 08/06/2023] [Indexed: 09/29/2023]

Abstract

OBJECTIVES

Locally low-rank (LLR) denoising of functional magnetic resonance imaging (fMRI) time series image data is extended to multi-echo (ME) data. The proposed method extends the capabilities of non-physiologic noise suppression beyond single-echo applications with a dedicated ME algorithm.

MATERIALS AND METHODS

Following an institutional review board (IRB) approved protocol, resting-state fMRI data were acquired in 7 healthy subjects. A compact 3T scanner enabled whole-brain acquisition of multiband ME fMRI data at high spatial resolution (1.4 × 1.4 × 2.8 mm 3 ) with a 1810 ms repetition time (TR). Image data were denoised with ME-LLR preceding functional processing. The results of connectivity maps generated from denoised data were compared with maps generated with equivalent processing of non-denoised images. To assess ME-LLR as a method to reduce scan time, comparisons were made between maps computed from image data with full and retrospectively truncated durations. Assessments were completed with seed-based connectivity analyses using echo-combined image data. In a feasibility assessment, nondenoised and denoised full-duration echo-combined data were equivalently processed with independent component analysis (ICA) and compared.

RESULTS

ME-LLR denoising yielded strengthened resting-state network connectivity maps after nuisance regression and seed-based connectivity analysis. In assessing ME-LLR as a scan reduction mechanism, maps generated from denoised data at half scan time showed comparable quality with maps generated from full-duration, non-denoised data, at both single subject and group levels. ME-LLR substantially increased temporal signal-to-noise ratio (tSNR) for image data respective to each individual echo and for image data after nuisance regression. Among echo-specific image volumes, increases in tSNR yielded by ME-LLR were most pronounced for image data with the longest echo time and thereby lowest SNR. ICA showed resting-state networks consistently identified between non-denoised and denoised data, with clearer demarcation of networks for ME-LLR.

CONCLUSIONS

ME-LLR is demonstrated to suppress non-physiologic noise, enhance functional connectivity map quality, and could potentially facilitate scan time reduction in ME-fMRI.

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Meyer NK, Kang D, Black DF, Campeau NG, Welker KM, Gray EM, In MH, Shu Y, Huston III J, Bernstein MA, Trzasko JD. Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data. Neuroradiol J 2023;36:273-288. [PMID: 36063799 PMCID: PMC10268095 DOI: 10.1177/19714009221122171] [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] [Indexed: 11/15/2022] Open

Abstract

OBJECTIVE

This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps.

METHODS

Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject.

RESULTS

fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant (p = 4.88×10-4 to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data.

CONCLUSION

LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.

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De Rosa AP, Esposito F, Valsasina P, d'Ambrosio A, Bisecco A, Rocca MA, Tommasin S, Marzi C, De Stefano N, Battaglini M, Pantano P, Cirillo M, Tedeschi G, Filippi M, Gallo A. Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative. J Neurol 2023;270:1047-1066. [PMID: 36350401 PMCID: PMC9886598 DOI: 10.1007/s00415-022-11479-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022]

Affiliation(s)

Alessandro Pasquale De Rosa Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
Fabrizio Esposito Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy.
Paola Valsasina Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
Alessandro d'Ambrosio Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
Alvino Bisecco Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
Maria A Rocca Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
Silvia Tommasin Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
Chiara Marzi Institute of Applied Physics "Nello Cararra" (IFAC), National Research Council (CNR), Via Madonna del Piano, 10, Sesto Fiorentino, 50019, Florence, Italy
Nicola De Stefano Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
Marco Battaglini Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
Patrizia Pantano Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
Mario Cirillo Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
Gioacchino Tedeschi Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
Massimo Filippi Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
Antonio Gallo Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy

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Schmitt T, Rieger JW. Recommendations of Choice of Head Coil and Prescan Normalize Filter Depend on Region of Interest and Task. Front Neurosci 2021;15:735290. [PMID: 34776844 PMCID: PMC8585748 DOI: 10.3389/fnins.2021.735290] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/21/2021] [Indexed: 11/23/2022] Open

Risk BB, Murden RJ, Wu J, Nebel MB, Venkataraman A, Zhang Z, Qiu D. Which multiband factor should you choose for your resting-state fMRI study? Neuroimage 2021;234:117965. [PMID: 33744454 PMCID: PMC8159874 DOI: 10.1016/j.neuroimage.2021.117965] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/05/2021] [Accepted: 03/06/2021] [Indexed: 12/30/2022] Open

Khajehim M, Christen T, Tam F, Graham SJ. Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation. Neuroimage 2021;238:118237. [PMID: 34091035 DOI: 10.1016/j.neuroimage.2021.118237] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/26/2021] [Accepted: 06/02/2021] [Indexed: 01/02/2023] Open

Abstract

Magnetic resonance fingerprinting (MRF) is a quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current challenges exist in the forms of (1) scan time; (2) need for custom image reconstruction; (3) large dictionary sizes; (4) long dictionary-matching time. This study aims to introduce a novel streamlined magnetic-resonance fingerprinting (sMRF) framework based on a single-shot echo-planar imaging (EPI) sequence to simultaneously estimate tissue T1, T2, and T2* with integrated B1⁺ correction. Encouraged by recent work on EPI-based MRF, we developed a method that combines spin-echo EPI with gradient-echo EPI to achieve T2 in addition to T1 and T2* quantification. To this design, we add simultaneous multi-slice (SMS) acceleration to enable full-brain coverage in a few minutes. Moreover, in the parameter-estimation step, we use deep learning to train a deep neural network (DNN) to accelerate the estimation process by orders of magnitude. Notably, due to the high image quality of the EPI scans, the training process can rely simply on Bloch-simulated data. The DNN also removes the need for storing large dictionaries. Phantom scans along with in-vivo multi-slice scans from seven healthy volunteers were acquired with resolutions of 1.1×1.1×3 mm³ and 1.7×1.7×3 mm³, and the results were validated against ground truth measurements. Excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches. In the phantom scan, a strong linear relationship (R = 1-1.04, R²>0.96) was found for all parameter estimates, with a particularly high agreement for T2 estimation (R²>0.99). Similar findings are reported for the in-vivo human data for all of our parameter estimates. Incorporation of DNN results in a reduction of parameter estimation time on the order of 1000 x and a reduction in storage requirements on the order of 2500 x while achieving highly similar results as conventional dictionary matching (%differences of 7.4 ± 0.4%, 3.6 ± 0.3% and 6.0 ± 0.4% error in T1, T2, and T2* estimation). Thus, sMRF has the potential to be the method of choice for future MRF studies by providing ease of implementation, fast whole-brain coverage, and ultra-fast T1/T2/T2* estimation.

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Kang D, Jo HJ, In MH, Yarach U, Meyer NK, Bardwell Speltz LJ, Gray EM, Trzasko JD, Huston Iii J, Bernstein MA, Shu Y. The benefit of high-performance gradients on echo planar imaging for BOLD-based resting-state functional MRI. Phys Med Biol 2020;65:235024. [PMID: 33245051 DOI: 10.1088/1361-6560/abb2ec] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]

Levine SM, Kumpf M, Rupprecht R, Schwarzbach JV. Supracategorical fear information revealed by aversively conditioning multiple categories. Cogn Neurosci 2020;12:28-39. [PMID: 33135598 DOI: 10.1080/17588928.2020.1839039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]

Bollmann S, Barth M. New acquisition techniques and their prospects for the achievable resolution of fMRI. Prog Neurobiol 2020;207:101936. [PMID: 33130229 DOI: 10.1016/j.pneurobio.2020.101936] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/10/2020] [Accepted: 10/18/2020] [Indexed: 01/17/2023]