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Heunis S, Lamerichs R, Zinger S, Caballero‐Gaudes C, Jansen JFA, Aldenkamp B, Breeuwer M. Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review. Hum Brain Mapp 2020; 41:3439-3467. [PMID: 32333624 PMCID: PMC7375116 DOI: 10.1002/hbm.25010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/13/2020] [Accepted: 04/03/2020] [Indexed: 01/31/2023] Open
Abstract
Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.
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Affiliation(s)
- Stephan Heunis
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | - Rolf Lamerichs
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- Philips ResearchEindhovenThe Netherlands
| | - Svitlana Zinger
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
| | | | - Jacobus F. A. Jansen
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of RadiologyMaastricht University Medical CentreMaastrichtThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
| | - Bert Aldenkamp
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Research and DevelopmentEpilepsy Centre KempenhaegheHeezeThe Netherlands
- School for Mental Health and NeuroscienceMaastrichtThe Netherlands
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and NeuropsychologyGhent University HospitalGhentBelgium
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Marcel Breeuwer
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Philips HealthcareBestThe Netherlands
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Lu W, Dong K, Cui D, Jiao Q, Qiu J. Quality assurance of human functional magnetic resonance imaging: a literature review. Quant Imaging Med Surg 2019; 9:1147-1162. [PMID: 31367569 DOI: 10.21037/qims.2019.04.18] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Functional magnetic resonance imaging (fMRI) has been a popular approach in brain research over the past 20 years. It offers a noninvasive method to probe the brain and uses blood oxygenation level dependent (BOLD) signal changes to access brain function. However, the BOLD signal only represents a small fraction of the total MR signal. System instability and various noise have a strong impact on the BOLD signal. Additionally, fMRI applies fast imaging technique to record brain cognitive process over time, requiring high temporal stability of MR scanners. Furthermore, data acquisition, image quality, processing, and statistical analysis methods also have a great effect on the results of fMRI studies. Quality assurance (QA) programs for fMRI can test the stability of MR scanners, evaluate the quality of fMRI and help to find errors during fMRI scanning, thereby greatly enhancing the success rate of fMRI. In this review, we focus on previous studies which developed QA programs and methods in SCI/SCIE citation peer-reviewed publications over the last 20 years, including topics on existing fMRI QA programs, QA phantoms, image QA metrics, quality evaluation of existing preprocessing pipelines and fMRI statistical analysis methods. The summarized studies were classified into four categories: QA of fMRI systems, QA of fMRI data, quality evaluation of data processing pipelines and statistical methods and QA of task-related fMRI. Summary tables and figures of QA programs and metrics have been developed based on the comprehensive review of the literature.
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Affiliation(s)
- Weizhao Lu
- Medical Engineering and Technical Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Kejiang Dong
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Dong Cui
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Qing Jiao
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
| | - Jianfeng Qiu
- Medical Engineering and Technical Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China.,Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271016, China
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Hellrung L, Dietrich A, Hollmann M, Pleger B, Kalberlah C, Roggenhofer E, Villringer A, Horstmann A. Intermittent compared to continuous real-time fMRI neurofeedback boosts control over amygdala activation. Neuroimage 2017; 166:198-208. [PMID: 29100939 DOI: 10.1016/j.neuroimage.2017.10.031] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 10/09/2017] [Accepted: 10/16/2017] [Indexed: 12/20/2022] Open
Abstract
Real-time fMRI neurofeedback is a feasible tool to learn the volitional regulation of brain activity. So far, most studies provide continuous feedback information that is presented upon every volume acquisition. Although this maximizes the temporal resolution of feedback information, it may be accompanied by some disadvantages. Participants can be distracted from the regulation task due to (1) the intrinsic delay of the hemodynamic response and associated feedback and (2) limited cognitive resources available to simultaneously evaluate feedback information and stay engaged with the task. Here, we systematically investigate differences between groups presented with different variants of feedback (continuous vs. intermittent) and a control group receiving no feedback on their ability to regulate amygdala activity using positive memories and feelings. In contrast to the feedback groups, no learning effect was observed in the group without any feedback presentation. The group receiving intermittent feedback exhibited better amygdala regulation performance when compared with the group receiving continuous feedback. Behavioural measurements show that these effects were reflected in differences in task engagement. Overall, we not only demonstrate that the presentation of feedback is a prerequisite to learn volitional control of amygdala activity but also that intermittent feedback is superior to continuous feedback presentation.
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Affiliation(s)
- Lydia Hellrung
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.
| | - Anja Dietrich
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Maurice Hollmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Burkhard Pleger
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neurology, BG University Clinic Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Christian Kalberlah
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Elisabeth Roggenhofer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neuroscience Clinique's, University Hospital Genève, Genève, Switzerland
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinics for Cognitive Neurology, University Hospital, Leipzig, Germany; Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany; Mind and Brain Institute, Berlin School of Mind and Brain, Humboldt-University and Charité, Berlin, Germany
| | - Annette Horstmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany
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Hellrung L, Hollmann M, Zscheyge O, Schlumm T, Kalberlah C, Roggenhofer E, Okon-Singer H, Villringer A, Horstmann A. Flexible adaptive paradigms for fMRI using a novel software package 'Brain Analysis in Real-Time' (BART). PLoS One 2015; 10:e0118890. [PMID: 25837719 PMCID: PMC4383593 DOI: 10.1371/journal.pone.0118890] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Accepted: 01/09/2015] [Indexed: 11/18/2022] Open
Abstract
In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject’s compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject’s gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by the post-hoc removal of erroneous trials. Here, we propose an a priori adaptation of the paradigm during the experiment’s runtime. Our fMRI findings clearly show the benefits of an adapted paradigm in terms of statistical power and higher effect sizes in emotion-related brain regions. This can be of special interest for all experiments with low statistical power due to a limited number of subjects, a limited amount of time, costs or available data to analyze, as is the case with real-time fMRI.
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Affiliation(s)
- Lydia Hellrung
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,Germany
- Leipzig University Medical Center, Leipzig, Germany
- * E-mail:
| | - Maurice Hollmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,Germany
| | - Oliver Zscheyge
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,Germany
| | - Torsten Schlumm
- NMR Unit, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Christian Kalberlah
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,Germany
- Leipzig University Medical Center, Leipzig, Germany
| | - Elisabeth Roggenhofer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany
- Clinic for Cognitive Neurology, University Hospital, Leipzig, Germany
| | | | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany
- Clinic for Cognitive Neurology, University Hospital, Leipzig, Germany
- Mind and Brain Institute, Berlin School of Mind and Brain, Humboldt-University and Charite, Berlin, Germany
| | - Annette Horstmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,Germany
- Leipzig University Medical Center, Leipzig, Germany
- Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig, Germany
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Baecke S, Lützkendorf R, Mallow J, Luchtmann M, Tempelmann C, Stadler J, Bernarding J. A proof-of-principle study of multi-site real-time functional imaging at 3T and 7T: Implementation and validation. Sci Rep 2015; 5:8413. [PMID: 25672521 PMCID: PMC4325335 DOI: 10.1038/srep08413] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Accepted: 01/19/2015] [Indexed: 11/09/2022] Open
Abstract
Real-time functional Magnetic Resonance Imaging (rtfMRI) is used mainly for neurofeedback or for brain-computer interfaces (BCI). But multi-site rtfMRI could in fact help in the application of new interactive paradigms such as the monitoring of mutual information flow or the controlling of objects in shared virtual environments. For that reason, a previously developed framework that provided an integrated control and data analysis of rtfMRI experiments was extended to enable multi-site rtfMRI. Important new components included a data exchange platform for analyzing the data of both MR scanners independently and/or jointly. Information related to brain activation can be displayed separately or in a shared view. However, a signal calibration procedure had to be developed and integrated in order to permit the connecting of sites that had different hardware and to account for different inter-individual brain activation levels. The framework was successfully validated in a proof-of-principle study with twelve volunteers. Thus the overall concept, the calibration of grossly differing signals, and BCI functionality on each site proved to work as required. To model interactions between brains in real-time, more complex rules utilizing mutual activation patterns could easily be implemented to allow for new kinds of social fMRI experiments.
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Affiliation(s)
- Sebastian Baecke
- Institute for Biometry and Medical Informatics, Otto-von-Guericke-University Magdeburg
| | - Ralf Lützkendorf
- Institute for Biometry and Medical Informatics, Otto-von-Guericke-University Magdeburg
| | - Johannes Mallow
- Institute for Biometry and Medical Informatics, Otto-von-Guericke-University Magdeburg
| | | | | | | | - Johannes Bernarding
- Institute for Biometry and Medical Informatics, Otto-von-Guericke-University Magdeburg
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Reichert C, Fendrich R, Bernarding J, Tempelmann C, Hinrichs H, Rieger JW. Online tracking of the contents of conscious perception using real-time fMRI. Front Neurosci 2014; 8:116. [PMID: 24904260 PMCID: PMC4033165 DOI: 10.3389/fnins.2014.00116] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Accepted: 05/02/2014] [Indexed: 11/13/2022] Open
Abstract
Perception is an active process that interprets and structures the stimulus input based on assumptions about its possible causes. We use real-time functional magnetic resonance imaging (rtfMRI) to investigate a particularly powerful demonstration of dynamic object integration in which the same physical stimulus intermittently elicits categorically different conscious object percepts. In this study, we simulated an outline object that is moving behind a narrow slit. With such displays, the physically identical stimulus can elicit categorically different percepts that either correspond closely to the physical stimulus (vertically moving line segments) or represent a hypothesis about the underlying cause of the physical stimulus (a horizontally moving object that is partly occluded). In the latter case, the brain must construct an object from the input sequence. Combining rtfMRI with machine learning techniques we show that it is possible to determine online the momentary state of a subject's conscious percept from time resolved BOLD-activity. In addition, we found that feedback about the currently decoded percept increased the decoding rates compared to prior fMRI recordings of the same stimulus without feedback presentation. The analysis of the trained classifier revealed a brain network that discriminates contents of conscious perception with antagonistic interactions between early sensory areas that represent physical stimulus properties and higher-tier brain areas. During integrated object percepts, brain activity decreases in early sensory areas and increases in higher-tier areas. We conclude that it is possible to use BOLD responses to reliably track the contents of conscious visual perception with a relatively high temporal resolution. We suggest that our approach can also be used to investigate the neural basis of auditory object formation and discuss the results in the context of predictive coding theory.
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Affiliation(s)
- Christoph Reichert
- Department of Neurology, University Medical Center A.ö.R. Magdeburg, Germany ; Department of Knowledge and Language Processing, Otto-von-Guericke University Magdeburg, Germany ; Forschungscampus STIMULATE Magdeburg, Germany
| | - Robert Fendrich
- Department of Neurology, University Medical Center A.ö.R. Magdeburg, Germany ; Department of Psychological and Brain Sciences, Dartmouth College Hanover, NH, USA
| | - Johannes Bernarding
- Institute for Biometry and Medical Informatics, Medical Faculty, Otto-von-Guericke University Magdeburg, Germany
| | - Claus Tempelmann
- Department of Neurology, University Medical Center A.ö.R. Magdeburg, Germany
| | - Hermann Hinrichs
- Department of Neurology, University Medical Center A.ö.R. Magdeburg, Germany ; Forschungscampus STIMULATE Magdeburg, Germany ; Department of Behavioral Neurology, Leibniz Institute for Neurobiology Magdeburg, Germany ; German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Germany ; Center for Behavioral Brain Sciences Magdeburg, Germany
| | - Jochem W Rieger
- Department of Applied Neurocognitive Psychology, Carl-von-Ossietzky University Oldenburg, Germany ; Research Center for Neurosensory Sciences, Carl-von-Ossietzky University Oldenburg, Germany
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8
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Mueller C, Luehrs M, Baecke S, Adolf D, Luetzkendorf R, Luchtmann M, Bernarding J. Building virtual reality fMRI paradigms: A framework for presenting immersive virtual environments. J Neurosci Methods 2012; 209:290-8. [DOI: 10.1016/j.jneumeth.2012.06.025] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2012] [Revised: 05/18/2012] [Accepted: 06/23/2012] [Indexed: 10/28/2022]
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Real-time fMRI and its application to neurofeedback. Neuroimage 2012; 62:682-92. [DOI: 10.1016/j.neuroimage.2011.10.009] [Citation(s) in RCA: 227] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2011] [Accepted: 10/06/2011] [Indexed: 11/20/2022] Open
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Hollmann M, Rieger JW, Baecke S, Lützkendorf R, Müller C, Adolf D, Bernarding J. Predicting decisions in human social interactions using real-time fMRI and pattern classification. PLoS One 2011; 6:e25304. [PMID: 22003388 PMCID: PMC3189203 DOI: 10.1371/journal.pone.0025304] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2011] [Accepted: 08/31/2011] [Indexed: 11/18/2022] Open
Abstract
Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.
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Affiliation(s)
- Maurice Hollmann
- Medical Faculty, Institute for Biometry and Medical Computer Science, University Magdeburg, Magdeburg, Germany.
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Abstract
Real-time fMRI permits simultaneous measurement and observation of brain activity during an ongoing task. One of the most challenging applications of real-time fMRI in neuroscientific and clinical research is the possibility of acquiring volitional control of localized brain activity using real-time fMRI-based neurofeedback protocols. Real-time fMRI allows the experimenter to noninvasively manipulate brain activity as an independent variable to observe the effects on behavior. Real-time fMRI neurofeedback studies demonstrated that learned control of the local brain activity leads to specific changes in behavior. Here, the authors describe the implementation and application of real-time fMRI with particular emphasis on the self-regulation of local brain activity and the investigation of brain-function relationships. Real-time fMRI represents a promising new approach to cognitive neuroscience that could complement traditional neuroimaging techniques by providing more causal insights into the functional role of circumscribed brain regions in behavior.
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Affiliation(s)
- Andrea Caria
- Institute of Medical Psychology and Behavioral Neurobiology, Eberhard-Karls-University of Tübingen, Tübingen, Germany.
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