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Sinha H, Raamana PR. Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA. Neuroinformatics 2024:10.1007/s12021-024-09668-4. [PMID: 38861098 DOI: 10.1007/s12021-024-09668-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2024] [Indexed: 06/12/2024]
Abstract
Pooling data across diverse sources acquired by multisite consortia requires compliance with a predefined reference protocol i.e., ensuring different sites and scanners for a given project have used identical or compatible MR physics parameter values. Traditionally, this has been an arduous and manual process due to difficulties in working with the complicated DICOM standard and lack of resources allocated towards protocol compliance. Moreover, issues of protocol compliance is often overlooked for lack of realization that parameter values are routinely improvised/modified locally at various sites. The inconsistencies in acquisition protocols can reduce SNR, statistical power, and in the worst case, may invalidate the results altogether. An open-source tool, mrQA was developed to automatically assess protocol compliance on standard dataset formats such as DICOM and BIDS, and to study the patterns of non-compliance in over 20 open neuroimaging datasets, including the large ABCD study. The results demonstrate that the lack of compliance is rather pervasive. The frequent sources of non-compliance include but are not limited to deviations in Repetition Time, Echo Time, Flip Angle, and Phase Encoding Direction. It was also observed that GE and Philips scanners exhibited higher rates of non-compliance relative to the Siemens scanners in the ABCD dataset. Continuous monitoring for protocol compliance is strongly recommended before any pre/post-processing, ideally right after the acquisition, to avoid the silent propagation of severe/subtle issues. Although, this study focuses on neuroimaging datasets, the proposed tool mrQA can work with any DICOM-based datasets.
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Affiliation(s)
- Harsh Sinha
- Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, USA
| | - Pradeep Reddy Raamana
- Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, USA.
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA.
- Department of Radiology, University of Pittsburgh, Pittsburgh, USA.
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2
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Oberman LM, Francis SM, Beynel L, Hynd M, Jaime M, Robins PL, Deng ZD, Stout J, van der Veen JW, Lisanby SH. Design and methodology for a proof of mechanism study of individualized neuronavigated continuous Theta burst stimulation for auditory processing in adolescents with autism spectrum disorder. Front Psychiatry 2024; 15:1304528. [PMID: 38389984 PMCID: PMC10881663 DOI: 10.3389/fpsyt.2024.1304528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
It has been suggested that aberrant excitation/inhibition (E/I) balance and dysfunctional structure and function of relevant brain networks may underlie the symptoms of autism spectrum disorder (ASD). However, the nomological network linking these constructs to quantifiable measures and mechanistically relating these constructs to behavioral symptoms of ASD is lacking. Herein we describe a within-subject, controlled, proof-of-mechanism study investigating the pathophysiology of auditory/language processing in adolescents with ASD. We utilize neurophysiological and neuroimaging techniques including magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG) metrics of language network structure and function. Additionally, we apply a single, individually targeted session of continuous theta burst stimulation (cTBS) as an experimental probe of the impact of perturbation of the system on these neurophysiological and neuroimaging outcomes. MRS, fMRI, and MEG measures are evaluated at baseline and immediately prior to and following cTBS over the posterior superior temporal cortex (pSTC), a region involved in auditory and language processing deficits in ASD. Also, behavioral measures of ASD and language processing and DWI measures of auditory/language network structures are obtained at baseline to characterize the relationship between the neuroimaging and neurophysiological measures and baseline symptom presentation. We hypothesize that local gamma-aminobutyric acid (GABA) and glutamate concentrations (measured with MRS), and structural and functional activity and network connectivity (measured with DWI and fMRI), will significantly predict MEG indices of auditory/language processing and behavioral deficits in ASD. Furthermore, a single session of cTBS over left pSTC is hypothesized to lead to significant, acute changes in local glutamate and GABA concentration, functional activity and network connectivity, and MEG indices of auditory/language processing. We have completed the pilot phase of the study (n=20 Healthy Volunteer adults) and have begun enrollment for the main phase with adolescents with ASD (n=86; age 14-17). If successful, this study will establish a nomological network linking local E/I balance measures to functional and structural connectivity within relevant brain networks, ultimately connecting them to ASD symptoms. Furthermore, this study will inform future therapeutic trials using cTBS to treat the symptoms of ASD.
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Affiliation(s)
- Lindsay M Oberman
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Sunday M Francis
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Lysianne Beynel
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Megan Hynd
- Clinical Affective Neuroscience Laboratory, Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, NC, United States
| | - Miguel Jaime
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Pei L Robins
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Jeff Stout
- Magnetoencephalography Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Jan Willem van der Veen
- Magnetic Resonance Spectroscopy Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Sarah H Lisanby
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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3
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Chen Y, Fernandez Z, Scheel N, Gifani M, Zhu DC, Counts SE, Dorrance AM, Razansky D, Yu X, Qian W, Qian C. Novel inductively coupled ear-bars (ICEs) to enhance restored fMRI signal from susceptibility compensation in rats. Cereb Cortex 2024; 34:bhad479. [PMID: 38100332 PMCID: PMC10793587 DOI: 10.1093/cercor/bhad479] [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: 08/17/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023] Open
Abstract
Functional magnetic resonance imaging faces inherent challenges when applied to deep-brain areas in rodents, e.g. entorhinal cortex, due to the signal loss near the ear cavities induced by susceptibility artifacts and reduced sensitivity induced by the long distance from the surface array coil. Given the pivotal roles of deep brain regions in various diseases, optimized imaging techniques are needed. To mitigate susceptibility-induced signal losses, we introduced baby cream into the middle ear. To enhance the detection sensitivity of deep brain regions, we implemented inductively coupled ear-bars, resulting in approximately a 2-fold increase in sensitivity in entorhinal cortex. Notably, the inductively coupled ear-bar can be seamlessly integrated as an add-on device, without necessitating modifications to the scanner interface. To underscore the versatility of inductively coupled ear-bars, we conducted echo-planner imaging-based task functional magnetic resonance imaging in rats modeling Alzheimer's disease. As a proof of concept, we also demonstrated resting-state-functional magnetic resonance imaging connectivity maps originating from the left entorhinal cortex-a central hub for memory and navigation networks-to amygdala hippocampal area, Insular Cortex, Prelimbic Systems, Cingulate Cortex, Secondary Visual Cortex, and Motor Cortex. This work demonstrates an optimized procedure for acquiring large-scale networks emanating from a previously challenging seed region by conventional magnetic resonance imaging detectors, thereby facilitating improved observation of functional magnetic resonance imaging outcomes.
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Affiliation(s)
- Yi Chen
- Department of High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tuebingen 72076, Germany
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
| | - Zachary Fernandez
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
- Neuroscience Program, Michigan State University, East Lansing, MI 48824, United States
| | - Norman Scheel
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
| | - Mahsa Gifani
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, United States
| | - David C Zhu
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
- Neuroscience Program, Michigan State University, East Lansing, MI 48824, United States
| | - Scott E Counts
- Neuroscience Program, Michigan State University, East Lansing, MI 48824, United States
- Department of Translational Neuroscience, Michigan State University, Grand Rapids, MI 49503, United States
- Department of Family Medicine, Michigan State University, Grand Rapids, MI 49503, United States
- Department of Hauenstein Neurosciences Center, Mercy Health Saint Mary’s Hospital, Grand Rapids, MI 49508, United States
- Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI 48105, United States
| | - Anne M Dorrance
- Neuroscience Program, Michigan State University, East Lansing, MI 48824, United States
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, United States
| | - Daniel Razansky
- Institute of Pharmacology and Toxicology and Institute for Biomedical Engineering, Faculty of Medicine, University of Zurich, Zurich 8006, Switzerland
- Department of Information Technology and Electrical Engineering, ETH Zurich, Institute for Biomedical Engineering, , Zurich 8092, Switzerland
- Zurich Neuroscience Center, Zurich 8057, Switzerland
| | - Xin Yu
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02114, United States
| | - Wei Qian
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States
| | - Chunqi Qian
- Department of Radiology and Cognitive Imaging Research Center, Michigan State University, East Lansing, MI 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States
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Chen G, Taylor PA, Reynolds RC, Leibenluft E, Pine DS, Brotman MA, Pagliaccio D, Haller SP. BOLD Response is more than just magnitude: Improving detection sensitivity through capturing hemodynamic profiles. Neuroimage 2023; 277:120224. [PMID: 37327955 PMCID: PMC10527035 DOI: 10.1016/j.neuroimage.2023.120224] [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: 02/13/2023] [Revised: 05/21/2023] [Accepted: 06/11/2023] [Indexed: 06/18/2023] Open
Abstract
Typical fMRI analyses often assume a canonical hemodynamic response function (HRF) that primarily focuses on the peak height of the overshoot, neglecting other morphological aspects. Consequently, reported analyses often reduce the overall response curve to a single scalar value. In this study, we take a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a response profile at the individual level. We then employ a roughness penalty at the population level to estimate the response curve, aiming to enhance predictive accuracy, inferential efficiency, and cross-study reproducibility. By examining a fast event-related FMRI dataset, we demonstrate the shortcomings and information loss associated with adopting the canonical approach. Furthermore, we address the following key questions: 1) To what extent does the HRF shape vary across different regions, conditions, and participant groups? 2) Does the data-driven approach improve detection sensitivity compared to the canonical approach? 3) Can analyzing the HRF shape help validate the presence of an effect in conjunction with statistical evidence? 4) Does analyzing the HRF shape offer evidence for whole-brain response during a simple task?
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Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA.
| | - Paul A Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Richard C Reynolds
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Ellen Leibenluft
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, USA
| | - Daniel S Pine
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, USA
| | - Melissa A Brotman
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, USA
| | | | - Simone P Haller
- Neuroscience and Novel Therapeutics Unit, Emotion and Development Branch, National Institute of Mental Health, USA
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5
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Taylor PA, Reynolds RC, Calhoun V, Gonzalez-Castillo J, Handwerker DA, Bandettini PA, Mejia AF, Chen G. Highlight results, don't hide them: Enhance interpretation, reduce biases and improve reproducibility. Neuroimage 2023; 274:120138. [PMID: 37116766 PMCID: PMC10233921 DOI: 10.1016/j.neuroimage.2023.120138] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 04/30/2023] Open
Abstract
Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples and extensions of this approach for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams' results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice-focusing on highlighting results, rather than hiding all but the strongest ones-can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.
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Affiliation(s)
- Paul A Taylor
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, MD, USA.
| | | | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, and Emory University, Atlanta, GA, USA
| | | | | | - Peter A Bandettini
- Section on Functional Imaging Methods, NIMH, NIH, Bethesda, MD, USA; Functional MRI Core Facility, NIMH, NIH, Bethesda, MD, USA
| | | | - Gang Chen
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, MD, USA
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6
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Taylor PA, Glen DR, Reynolds RC, Basavaraj A, Moraczewski D, Etzel JA. Editorial: Demonstrating quality control (QC) procedures in fMRI. Front Neurosci 2023; 17:1205928. [PMID: 37325035 PMCID: PMC10264898 DOI: 10.3389/fnins.2023.1205928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/12/2023] [Indexed: 06/17/2023] Open
Affiliation(s)
- Paul A. Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Daniel R. Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Richard C. Reynolds
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Arshitha Basavaraj
- Data Science and Sharing Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Dustin Moraczewski
- Data Science and Sharing Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Joset A. Etzel
- Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
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Teves JB, Gonzalez-Castillo J, Holness M, Spurney M, Bandettini PA, Handwerker DA. The art and science of using quality control to understand and improve fMRI data. Front Neurosci 2023; 17:1100544. [PMID: 37090794 PMCID: PMC10117661 DOI: 10.3389/fnins.2023.1100544] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/17/2023] [Indexed: 04/08/2023] Open
Abstract
Designing and executing a good quality control (QC) process is vital to robust and reproducible science and is often taught through hands on training. As FMRI research trends toward studies with larger sample sizes and highly automated processing pipelines, the people who analyze data are often distinct from those who collect and preprocess the data. While there are good reasons for this trend, it also means that important information about how data were acquired, and their quality, may be missed by those working at later stages of these workflows. Similarly, an abundance of publicly available datasets, where people (not always correctly) assume others already validated data quality, makes it easier for trainees to advance in the field without learning how to identify problematic data. This manuscript is designed as an introduction for researchers who are already familiar with fMRI, but who did not get hands on QC training or who want to think more deeply about QC. This could be someone who has analyzed fMRI data but is planning to personally acquire data for the first time, or someone who regularly uses openly shared data and wants to learn how to better assess data quality. We describe why good QC processes are important, explain key priorities and steps for fMRI QC, and as part of the FMRI Open QC Project, we demonstrate some of these steps by using AFNI software and AFNI’s QC reports on an openly shared dataset. A good QC process is context dependent and should address whether data have the potential to answer a scientific question, whether any variation in the data has the potential to skew or hide key results, and whether any problems can potentially be addressed through changes in acquisition or data processing. Automated metrics are essential and can often highlight a possible problem, but human interpretation at every stage of a study is vital for understanding causes and potential solutions.
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Affiliation(s)
- Joshua B. Teves
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Micah Holness
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Megan Spurney
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Peter A. Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
- Functional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Daniel A. Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
- *Correspondence: Daniel A. Handwerker,
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8
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Reynolds RC, Taylor PA, Glen DR. Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI. Front Neurosci 2023; 16:1073800. [PMID: 36793774 PMCID: PMC9922690 DOI: 10.3389/fnins.2022.1073800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 12/16/2022] [Indexed: 01/31/2023] Open
Abstract
Quality control (QC) is a necessary, but often an under-appreciated, part of FMRI processing. Here we describe procedures for performing QC on acquired or publicly available FMRI datasets using the widely used AFNI software package. This work is part of the Research Topic, "Demonstrating Quality Control (QC) Procedures in fMRI." We used a sequential, hierarchical approach that contained the following major stages: (1) GTKYD (getting to know your data, esp. its basic acquisition properties), (2) APQUANT (examining quantifiable measures, with thresholds), (3) APQUAL (viewing qualitative images, graphs, and other information in systematic HTML reports) and (4) GUI (checking features interactively with a graphical user interface); and for task data, and (5) STIM (checking stimulus event timing statistics). We describe how these are complementary and reinforce each other to help researchers stay close to their data. We processed and evaluated the provided, publicly available resting state data collections (7 groups, 139 total subjects) and task-based data collection (1 group, 30 subjects). As specified within the Topic guidelines, each subject's dataset was placed into one of three categories: Include, exclude or uncertain. The main focus of this paper, however, is the detailed description of QC procedures: How to understand the contents of an FMRI dataset, to check its contents for appropriateness, to verify processing steps, and to examine potential quality issues. Scripts for the processing and analysis are freely available.
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Provins C, MacNicol E, Seeley SH, Hagmann P, Esteban O. Quality control in functional MRI studies with MRIQC and fMRIPrep. FRONTIERS IN NEUROIMAGING 2023; 1:1073734. [PMID: 37555175 PMCID: PMC10406249 DOI: 10.3389/fnimg.2022.1073734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 12/19/2022] [Indexed: 08/10/2023]
Abstract
The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and "MRI schools", ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community.
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Affiliation(s)
- Céline Provins
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Eilidh MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Saren H. Seeley
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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10
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Lu B, Yan CG. Demonstrating quality control procedures for fMRI in DPABI. Front Neurosci 2023; 17:1069639. [PMID: 36895416 PMCID: PMC9989208 DOI: 10.3389/fnins.2023.1069639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/01/2023] [Indexed: 02/25/2023] Open
Abstract
Quality control (QC) is an important stage for functional magnetic resonance imaging (fMRI) studies. The methods for fMRI QC vary in different fMRI preprocessing pipelines. The inflating sample size and number of scanning sites for fMRI studies further add to the difficulty and working load of the QC procedure. Therefore, as a constituent part of the Demonstrating Quality Control Procedures in fMRI research topic in Frontiers, we preprocessed a well-organized open-available dataset using DPABI pipelines to illustrate the QC procedure in DPABI. Six categories of DPABI-derived reports were used to eliminate images without adequate quality. After the QC procedure, twelve participants (8.6%) were categorized as excluded and eight participants (5.8%) were categorized as uncertain. More automatic QC tools were needed in the big-data era while visually inspecting images was still indispensable now.
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Affiliation(s)
- Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
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11
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Jung B, Taylor PA, Seidlitz J, Sponheim C, Perkins P, Ungerleider LG, Glen D, Messinger A. A comprehensive macaque fMRI pipeline and hierarchical atlas. Neuroimage 2021; 235:117997. [PMID: 33789138 PMCID: PMC9272767 DOI: 10.1016/j.neuroimage.2021.117997] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 01/27/2021] [Accepted: 03/23/2021] [Indexed: 12/14/2022] Open
Abstract
Functional neuroimaging research in the non-human primate (NHP) has been advancing at a remarkable rate. The increase in available data establishes a need for robust analysis pipelines designed for NHP neuroimaging and accompanying template spaces to standardize the localization of neuroimaging results. Our group recently developed the NIMH Macaque Template (NMT), a high-resolution population average anatomical template and associated neuroimaging resources, providing researchers with a standard space for macaque neuroimaging . Here, we release NMT v2, which includes both symmetric and asymmetric templates in stereotaxic orientation, with improvements in spatial contrast, processing efficiency, and segmentation. We also introduce the Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM), a hierarchical parcellation of the macaque cerebral cortex with varying degrees of detail. These tools have been integrated into the neuroimaging analysis software AFNI to provide a comprehensive and robust pipeline for fMRI processing, visualization and analysis of NHP data. AFNI's new @animal_warper program can be used to efficiently align anatomical scans to the NMT v2 space, and afni_proc.py integrates these results with full fMRI processing using macaque-specific parameters: from motion correction through regression modeling. Taken together, the NMT v2 and AFNI represent an all-in-one package for macaque functional neuroimaging analysis, as demonstrated with available demos for both task and resting state fMRI.
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Affiliation(s)
- Benjamin Jung
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA; Department of Neuroscience, Brown University, Providence, RI, USA
| | - Paul A Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD, USA
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Caleb Sponheim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Pierce Perkins
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Leslie G Ungerleider
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD, USA.
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA.
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Different activation signatures in the primary sensorimotor and higher-level regions for haptic three-dimensional curved surface exploration. Neuroimage 2021; 231:117754. [PMID: 33454415 DOI: 10.1016/j.neuroimage.2021.117754] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 01/05/2021] [Accepted: 01/09/2021] [Indexed: 01/03/2023] Open
Abstract
Haptic object perception begins with continuous exploratory contact, and the human brain needs to accumulate sensory information continuously over time. However, it is still unclear how the primary sensorimotor cortex (PSC) interacts with these higher-level regions during haptic exploration over time. This functional magnetic resonance imaging (fMRI) study investigates time-dependent haptic object processing by examining brain activity during haptic 3D curve and roughness estimations. For this experiment, we designed sixteen haptic stimuli (4 kinds of curves × 4 varieties of roughness) for the haptic curve and roughness estimation tasks. Twenty participants were asked to move their right index and middle fingers along the surface twice and to estimate one of the two features-roughness or curvature-depending on the task instruction. We found that the brain activity in several higher-level regions (e.g., the bilateral posterior parietal cortex) linearly increased as the number of curves increased during the haptic exploration phase. Surprisingly, we found that the contralateral PSC was parametrically modulated by the number of curves only during the late exploration phase but not during the early exploration phase. In contrast, we found no similar parametric modulation activity patterns during the haptic roughness estimation task in either the contralateral PSC or in higher-level regions. Thus, our findings suggest that haptic 3D object perception is processed across the cortical hierarchy, whereas the contralateral PSC interacts with other higher-level regions across time in a manner that is dependent upon the features of the object.
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