scholarly journals Quality and denoising in real‐time functional magnetic resonance imaging neurofeedback: A methods review

2020 ◽  
Vol 41 (12) ◽  
pp. 3439-3467 ◽  
Author(s):  
Stephan Heunis ◽  
Rolf Lamerichs ◽  
Svitlana Zinger ◽  
Cesar Caballero‐Gaudes ◽  
Jacobus F. A. Jansen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Real Time ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Real-Time Functional Magnetic Resonance Imaging Neurofeedback for the Relief of Distressing Auditory-Verbal Hallucinations: Methodological and Empirical Advances

2020 ◽  
Vol 46 (6) ◽  
pp. 1409-1417 ◽  
Author(s):  
Clara Humpston ◽  
Jane Garrison ◽  
Natasza Orlov ◽  
André Aleman ◽  
Renaud Jardri ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Real Time ◽  
Best Practice ◽  
Mechanistic Models ◽  
Schizophrenia Spectrum Disorders ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Auditory Verbal Hallucinations

Abstract Auditory-verbal hallucinations (AVH) are often associated with high levels of distress and disability in individuals with schizophrenia-spectrum disorders. In around 30% of individuals with distressing AVH and diagnosed with schizophrenia, traditional antipsychotic drugs have little or no effect. Thus, it is important to develop mechanistic models of AVH to inform new treatments. Recently a small number of studies have begun to explore the use of real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) for the treatment of AVH in individuals with schizophrenia. rtfMRI-NF protocols have been developed to provide feedback about brain activation in real time to enable participants to progressively achieve voluntary control over their brain activity. We offer a conceptual review of the background and general features of neurofeedback procedures before summarizing and evaluating existing mechanistic models of AVH to identify feasible neural targets for the application of rtfMRI-NF as a potential treatment. We consider methodological issues, including the choice of localizers and practicalities in logistics when setting up neurofeedback procedures in a clinical setting. We discuss clinical considerations relating to the use of rtfMRI-NF for AVH in individuals distressed by their experiences and put forward a number of questions and recommendations about best practice. Lastly, we conclude by offering suggestions for new avenues for neurofeedback methodology and mechanistic targets in relation to the research and treatment of AVH.

Real-time functional magnetic resonance imaging: methods and applications

2007 ◽  
Vol 25 (6) ◽  
pp. 989-1003 ◽  
Author(s):  
Nikolaus Weiskopf ◽  
Ranganatha Sitaram ◽  
Oliver Josephs ◽  
Ralf Veit ◽  
Frank Scharnowski ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Real Time ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Imaging Methods

scholarly journals Early Stopping in Experimentation with Real-time Functional Magnetic Resonance Imaging Using the Sequential Probability Ratio Test

2021 ◽  
Author(s):  
Sarah J. A. Carr ◽  
Weicong Chen ◽  
Jeremy Fondran ◽  
Harry Friel ◽  
Curtis Tatsuoka
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Real Time ◽  
Ratio Test ◽  
Early Stopping ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Probability Ratio ◽  
Sequential Probability

AbstractIntroductionFunctional magnetic resonance imaging (fMRI) often involves long scanning durations to ensure the associated brain activity can be detected. However, excessive experimentation can lead to many undesirable effects, such as from learning and/or fatigue effects, as well as undue discomfort for the subject, which can lead to motion artifact and loss of sustained attention on task. Overly long experimentation ironically can thus have a detrimental effect on signal quality and accurate voxel activation detection. Here, we propose a method of dynamic experimentation with real-time fMRI using a novel statistically-driven approach to fMRI analytics. This new approach to experimental design invokes early stopping when sufficient statistical evidence for assessing the task-related activation is observed.MethodsVoxel-level sequential probability ratio test (SPRT) statistics based on general linear models (GLM) were implemented on fMRI scans of a mathematical 1-back task from 25 subjects, 14 healthy controls and 11 subjects born extremely preterm. This approach is based on likelihood ratios and allows for systematic early stopping based on statistical error thresholds being satisfied. We explored voxel-level serial covariance estimation in real-time using the “sandwich” estimator. We adopted a two-stage estimation approach that allows for the hypothesis tests to be formulated in terms of t-statistic scale, which enhances interpretability. Scan data was collected using a dynamic feedback system that allowed for adaptive experimentation. Numerical parallelization was employed to facilitate completion of computations involving a new scan within every repetition time (TR).ResultsSPRT analytics demonstrate the feasibility and efficiency gains of automated early stopping, while performing comparably in activation detection with full protocols analyzed through standard fMRI software. Dynamic stopping of stimulus administration was achieved in all subjects with typical time savings between 33 - 66% (4 – 8 minutes on a 12 minute scan).ConclusionA systematic statistical approach for early stopping with real-time fMRI experimentation has been implemented. This allows for great savings in scan times, while still eliciting comparable activation patterns as full protocols. This dynamic approach has promise for reducing subject burden and fatigue effects.

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