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Valevicius D, Beck N, Kasper L, Boroday S, Bayer J, Rioux P, Caron B, Adalat R, Evans AC, Khalili-Mahani N. Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN. Front Neuroinform 2023; 17:1251023. [PMID: 37841811 PMCID: PMC10569687 DOI: 10.3389/fninf.2023.1251023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/05/2023] [Indexed: 10/17/2023] Open
Abstract
Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN's unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN's infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines.
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Affiliation(s)
- Darius Valevicius
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Natacha Beck
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Lars Kasper
- BRAIN-TO Lab, Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sergiy Boroday
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Johanna Bayer
- Center for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
- Orygen Youth Health, Orygen, Melbourne, VIC, Australia
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Bryan Caron
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Reza Adalat
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Alan C. Evans
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
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Krishnamurthy V, Krishnamurthy LC, Schwam DM, Ealey A, Shin J, Greenberg D, Morris RD. Retrospective Correction of Physiological Noise: Impact on Sensitivity, Specificity, and Reproducibility of Resting-State Functional Connectivity in a Reading Network Model. Brain Connect 2019; 8:94-105. [PMID: 29226700 DOI: 10.1089/brain.2017.0513] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
It is well accepted that physiological noise (PN) obscures the detection of neural fluctuations in resting-state functional connectivity (rsFC) magnetic resonance imaging. However, a clear consensus for an optimal PN correction (PNC) methodology and how it can impact the rsFC signal characteristics is still lacking. In this study, we probe the impact of three PNC methods: RETROICOR: (Glover et al., 2000 ), ANATICOR: (Jo et al., 2010 ), and RVTMBPM: (Bianciardi et al., 2009 ). Using a reading network model, we systematically explore the effects of PNC optimization on sensitivity, specificity, and reproducibility of rsFC signals. In terms of specificity, ANATICOR was found to be effective in removing local white matter (WM) fluctuations and also resulted in aggressive removal of expected cortical-to-subcortical functional connections. The ability of RETROICOR to remove PN was equivalent to removal of simulated random PN such that it artificially inflated the connection strength, thereby decreasing sensitivity. RVTMBPM maintained specificity and sensitivity by balanced removal of vasodilatory PN and local WM nuisance edges. Another aspect of this work was exploring the effects of PNC on identifying reading group differences. Most PNC methods accounted for between-subject PN variability resulting in reduced intersession reproducibility. This effect facilitated the detection of the most consistent group differences. RVTMBPM was most effective in detecting significant group differences due to its inherent sensitivity to removing spatially structured and temporally repeating PN arising from dense vasculature. Finally, results suggest that combining all three PNC resulted in "overcorrection" by removing signal along with noise.
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Affiliation(s)
- Venkatagiri Krishnamurthy
- 1 Department of Neurology, Emory University , Atlanta, Georgia .,2 Center for Visual and Neurocognitive Rehabilitation , Atlanta VAMC, Decatur, Georgia .,3 Center for Advanced Brain Imaging, Georgia State University and Georgia Institute of Technology , Atlanta, Georgia
| | - Lisa C Krishnamurthy
- 2 Center for Visual and Neurocognitive Rehabilitation , Atlanta VAMC, Decatur, Georgia .,3 Center for Advanced Brain Imaging, Georgia State University and Georgia Institute of Technology , Atlanta, Georgia .,4 Department of Physics and Astronomy, Georgia State University , Atlanta, Georgia
| | - Dina M Schwam
- 5 Department of Educational Psychology, Special Education, and Communication Disorders, Georgia State University , Atlanta, Georgia
| | - Ashley Ealey
- 6 Department of Biology, Neuroscience Program, Agnes Scott College , Decatur, Georgia
| | - Jaemin Shin
- 3 Center for Advanced Brain Imaging, Georgia State University and Georgia Institute of Technology , Atlanta, Georgia
| | - Daphne Greenberg
- 5 Department of Educational Psychology, Special Education, and Communication Disorders, Georgia State University , Atlanta, Georgia
| | - Robin D Morris
- 3 Center for Advanced Brain Imaging, Georgia State University and Georgia Institute of Technology , Atlanta, Georgia .,7 Department of Psychology, Georgia State University , Atlanta, Georgia
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Marchitelli R, Minati L, Marizzoni M, Bosch B, Bartrés-Faz D, Müller BW, Wiltfang J, Fiedler U, Roccatagliata L, Picco A, Nobili F, Blin O, Bombois S, Lopes R, Bordet R, Sein J, Ranjeva JP, Didic M, Gros-Dagnac H, Payoux P, Zoccatelli G, Alessandrini F, Beltramello A, Bargalló N, Ferretti A, Caulo M, Aiello M, Cavaliere C, Soricelli A, Parnetti L, Tarducci R, Floridi P, Tsolaki M, Constantinidis M, Drevelegas A, Rossini PM, Marra C, Schönknecht P, Hensch T, Hoffmann KT, Kuijer JP, Visser PJ, Barkhof F, Frisoni GB, Jovicich J. Test-retest reliability of the default mode network in a multi-centric fMRI study of healthy elderly: Effects of data-driven physiological noise correction techniques. Hum Brain Mapp 2016; 37:2114-32. [PMID: 26990928 DOI: 10.1002/hbm.23157] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 02/16/2016] [Accepted: 02/17/2016] [Indexed: 12/31/2022] Open
Abstract
Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within-site test-retest reliability and the across-site reproducibility consistency of DMN-derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue-based regression, PESTICA and FSL-FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z-scores and, albeit less markedly, the cluster-size in the DMN; in particular, FSL-FIX tended to increase the DMN z-scores compared to others. Within-site test-retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5-11% for DMN z-scores and cluster-size reliability. DMN pattern overlap was in the range 60-65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL-FIX and Tissue-based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z-scores relative to NPC. Overall these findings support the use of rPNC methods like tissue-based or FSL-FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114-2132, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Rocco Marchitelli
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
| | - Ludovico Minati
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy.,Scientific Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Moira Marizzoni
- LENITEM Laboratory of Epidemiology, Neuroimaging, & Telemedicine-IRCCS San Giovanni Di Dio-FBF, Brescia, Italy
| | - Beatriz Bosch
- Alzheimer's Disease and Other Cognitive Disorders Unit, Department of Neurology, Hospital Clínic, and IDIBAPS, Barcelona, Spain
| | - David Bartrés-Faz
- Department of Psychiatry and Clinical Psychobiology, Universitat De Barcelona and IDIBAPS, Barcelona, Spain
| | - Bernhard W Müller
- LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany
| | - Jens Wiltfang
- LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center (UMG), Georg August University, Göttingen, Germany
| | - Ute Fiedler
- LVR-Clinic for Psychiatry and Psychotherapy, Institutes and Clinics of the University Duisburg-Essen, Essen, Germany
| | - Luca Roccatagliata
- Department of Neuroradiology, IRCSS San Martino University Hospital and IST, Genoa, Italy.,Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Agnese Picco
- Department of Neuroscience, Ophthalmology, Genetics and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Flavio Nobili
- Department of Neuroscience, Ophthalmology, Genetics and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Oliver Blin
- Pharmacology, Assistance Publique - Hôpitaux De Marseille, Aix-Marseille University-CNRS, UMR, Marseille, 7289, France
| | - Stephanie Bombois
- University of Lille, INSERM, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - Renaud Lopes
- University of Lille, INSERM, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - Régis Bordet
- University of Lille, INSERM, CHU Lille, U1171 - Degenerative and Vascular Cognitive Disorders, Lille, France
| | - Julien Sein
- CRMBM-CEMEREM, UMR 7339, Aix Marseille Université-CNRS, Marseille, France
| | | | - Mira Didic
- APHM, CHU Timone, Service De Neurologie Et Neuropsychologie, Marseille, France.,Aix-Marseille Université, INSERM INS UMR_S 1106, Marseille, 13005, France
| | - Hélène Gros-Dagnac
- INSERM, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, Toulouse, France.,Université De Toulouse, UPS, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, CHU Purpan, Place Du Dr Baylac, Toulouse Cedex 9, France
| | - Pierre Payoux
- INSERM, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, Toulouse, France.,Université De Toulouse, UPS, Imagerie Cérébrale Et Handicaps Neurologiques, UMR 825, CHU Purpan, Place Du Dr Baylac, Toulouse Cedex 9, France
| | | | | | | | - Núria Bargalló
- Department of Neuroradiology and Magnetic Resonace Image Core Facility, Hospital Clínic De Barcelona, IDIBAPS, Barcelona, Spain
| | - Antonio Ferretti
- Department of Neuroscience Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti, Italy.,Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio" of Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti, Italy.,Institute for Advanced Biomedical Technologies (ITAB), University "G. d'Annunzio" of Chieti, Italy
| | | | | | - Andrea Soricelli
- IRCCS SDN, Naples, Italy.,University of Naples Parthenope, Naples, Italy
| | - Lucilla Parnetti
- Section of Neurology, Centre for Memory Disturbances, University of Perugia, Perugia, Italy
| | | | - Piero Floridi
- Perugia General Hospital, Neuroradiology Unit, Perugia, Italy
| | - Magda Tsolaki
- 3rd Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Antonios Drevelegas
- Interbalkan Medical Center of Thessaloniki, Thessaloniki, Greece.,Department of Radiology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paolo Maria Rossini
- Department of Geriatrics, Neuroscience & Orthopaedics, Catholic University, Policlinic Gemelli, Rome, Italy.,IRCSS S.Raffaele Pisana, Rome, Italy
| | - Camillo Marra
- Center for Neuropsychological Research, Catholic University, Rome, Italy
| | - Peter Schönknecht
- Department of Psychiatry, University Hospital Leipzig, Leipzig, Germany
| | - Tilman Hensch
- Department of Psychiatry, University Hospital Leipzig, Leipzig, Germany
| | | | - Joost P Kuijer
- Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, the Netherlands
| | - Pieter Jelle Visser
- Alzheimer Centre and Department of Neurology, Vrije Universiteit University Medical Center, Amsterdam, the Netherlands.,Department of Psychiatry and Neuropsychology, Alzheimer Center Limburg, University of Maastricht, Maastricht, the Netherlands
| | - Frederik Barkhof
- Alzheimer Centre and Department of Neurology, Vrije Universiteit University Medical Center, Amsterdam, the Netherlands
| | - Giovanni B Frisoni
- LENITEM Laboratory of Epidemiology, Neuroimaging, & Telemedicine-IRCCS San Giovanni Di Dio-FBF, Brescia, Italy.,Memory Clinic and LANVIE, Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
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