Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review

First published: 25 April 2020Neurofeedback 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.LSH‐TKI, Grant/Award Number: LSHM16053‐SGF; Philips Researc

Spatial and Temporal Quality of Brain Networks for Different Multi-Echo fMRI Combination Methods

The application of multi-echo functional magnetic resonance imaging (fMRI) studies has considerably increased in the last decade due to superior BOLD sensitivity compared to single-echo fMRI. Various methods have been developed that combine fMRI data derived at different echo times to improve data quality. Here, we evaluated five multi-echo combination schemes: ‘optimal combination’ (OC, ${\text {T}_{2}}^{\ast }$ -weighted), ${\text {T}_{2}}^{\ast }$ -FIT ( ${\text {T}_{2}}^{\ast }$ -weighted, calculated per volume), average-weighted (Avg), temporal Signal-to-Noise Ratio (tSNR) weighted, and temporal Contrast-to-Noise Ratio weighted combination. The effect of these combinations, with and without additional postprocessing, on the quality of functional resting-state networks was assessed. Sixteen healthy volunteers were scanned during a 5-minutes resting-state fMRI session. After network extraction, several quality metrics in the temporal and spatial domain were calculated for their respective time-series and spatial maps. Our results showed that OC and ${\text {T}_{2}}^{\ast }$ -FIT outperformed the other methods in both domains. Whereas the OC and ${\text {T}_{2}}^{\ast }$ -FIT time-series were found to be the least associated with artifacts, OC resulted in the highest quality spatial maps. Furthermore, spatial smoothing, bandpass filtering and ICA-AROMA merely improved networks derived from the least performing combinations (Avg and tSNR). Because similar network quality was obtained following OC and ${\text {T}_{2}}^{\ast }$ -FIT without postprocessing, we recommend future studies to implement these combinations without these postprocessing steps. This minimizes the amount of image modifications and processing, potentially leading to enhanced BOLD contrast. The results highlight the benefits of ${\text {T}_{2}}^{\ast }$ -weighted multi-echo combinations on resting-state network quality and raise its potential value in dynamic fMRI analyses or for diagnosis and prognosis purposes of neuropsychiatric disorders

Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI

Background and Purpose The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. Methods In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. Results We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. Conclusion Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity

Neu3CA-RT: a framework for real-time fMRI analysis

Real-time functional magnetic resonance imaging (rtfMRI) allows visualisation of ongoing brain activity of the subject in the scanner. Denoising algorithms aim to rid acquired data of confounding effects, enhancing the blood oxygenation level-dependent (BOLD) signal. Further image processing and analysis methods, like general linear models (GLM) or multivariate analysis, then present application-specific information to the researcher. These processes are typically applied to regions of interest but, increasingly, rtfMRI techniques extract and classify whole brain functional networks and dynamics as correlates for brain states or behaviour, particularly in neuropsychiatric and neurocognitive disorders. We present Neu3CA-RT: a Matlab-based rtfMRI analysis framework aiming to advance scientific knowledge on real-time cognitive brain activity and to promote its translation into clinical practice. Design considerations are listed based on reviewing existing rtfMRI approaches. The toolbox integrates established SPM preprocessing routines, real-time GLM mapping of fMRI data to a basis set of spatial brain networks, correlation of activity with 50 behavioural profiles from the BrainMap database, and an intuitive user interface. The toolbox is demonstrated in a task-based experiment where a subject executes visual, auditory and motor tasks inside a scanner. In three out of four experiments, resulting behavioural profiles agreed with the expected brain state

Delayed convergence between brain network structure and function in rolandic epilepsy

Introduction Rolandic epilepsy (RE) manifests during a critical phase of brain development, and has been associated with language impairments. Concordant abnormalities in structural and functional connectivity (SC and FC) have been described before. As SC and FC are under mutual influence, the current study investigates abnormalities in the SC-FC synergy in RE. Methods Twenty-two children with RE (age, mean±SD: 11.3±2.0 y) and 22 healthy controls (age 10.5±1.6 y) underwent structural, diffusion weighted, and functional MRI at 3T. The probabilistic anatomical landmarks atlas was used to parcellate the (sub)cortical gray matter. Constrained spherical deconvolution tractography and correlation of time series were used to assess SC and FC, respectively. The SC-FC correlation was assessed as a function of age for the non-zero structural connections over a range of sparsity values (0.01-0.75). A modularity analysis was performed on the mean SC network of the controls to localize potential global effects to subnetworks. SC and FC were also assessed separately using graph analysis.Results The SC-FC correlation was significantly reduced in children with RE compared to healthy controls, especially for the youngest participants. This effect was most pronounced in a left and a right centro-temporal network, as well as in a medial parietal network. Graph analysis revealed no prominent abnormalities in SC or FC network organization.Conclusion Since SC and FC converge during normal maturation, our finding of reduced SC-FC correlation illustrates impaired synergy between brain structure and function. More specifically, since this effect was most pronounced in the youngest participants, RE may represent a developmental disorder of delayed brain network maturation. The observed effects seem especially attributable to medial parietal connections, which forms an intermediate between bilateral centro-temporal modules of epileptiform activity, and bear relevance for language function

Quality and denoising in real-time fMRI neurofeedback: a methods review

Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localized 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. Maximization of neurofeedback learning effects in accordance with operant conditioning requires the feedback signal to be closely contingent on real brain activity, which necessitates the use of effective real-time fMRI denoising methods to prevent sham feedback. In this work, we present the first extensive review of acquisition, data processing and quality reporting methods available to improve the quality of the rtfMRI neurofeedback signal. Furthermore, we investigated the state of denoising and quality control practices in a set of 128 recently published rtfMRI-NF studies. We found: (i) that less than a third of the studies reported implementing standard real-time fMRI denoising steps; (ii) significant room for improvement with regards to methods reporting; and (iii) the need for methodological studies quantifying and comparing the contribution of denoising steps to the quality of the neurofeedback signal. Advances in the field of rtfMRI-NF research depend on reproducibility of methods and results. To this end, we recommend that future rtfMRI-NF studies: (i) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/); (ii) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks; and (iii) strive to adopt transparent principles in the form of methods and data sharing and the support of open-source rtfMRI-NF software

A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect

Several studies claim that listening to Mozart music affects cognition and can be used to treat neurological conditions like epilepsy. Research into this Mozart effect has not addressed how dynamic interactions between brain networks, i.e. effective connectivity, is affected. Granger-causality analysis is often used to infer effective connectivity. First, we investigate if a new method, Bayesian topology identification, can be used as an alternative. Both methods are evaluated on simulation data, where the Bayesian method outperforms the Granger-causality analysis in the inference of connectivity graphs of dynamic networks, especially for short data lengths. In the second part, the Bayesian method is extended to enable the inference of changes in effective connectivity between groups of subjects. Next, we apply both methods to fMRI scans of 16 healthy subjects, who were scanned before and after exposure to Mozart’s sonata K448 at least 2 hours a day for 7 days. Here, we investigate if the effective connectivity of the subjects significantly changed after listening to Mozart music. The Bayesian method detected changes in effective connectivity between networks related to cognitive processing and control: First, in the connection from the central executive to the superior sensori-motor network. Second, in the connection from the posterior default mode to the fronto-parietal right network. Finally, in the connection from the anterior default mode to the dorsal attention network, but only in a subgroup of subjects with a longer listening duration. Only in this last connection an effect was found by the Granger-causality analysis

Functional network abnormalities consistent with behavioral profile in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in which the severity of symptoms varies over subjects. The iCAPs model (innovation-driven co-activation patterns) is a recently developed spatio-temporal model to describe fMRI data. In this study, the iCAPs model was employed to find functional imaging biomarkers for ASD in resting-state fMRI data. MRI data from 125 ASD patients and 243 healthy controls was selected from the online ABIDE data repository. Following standard fMRI preprocessing steps, the iCAP patterns were fitted to the data to obtain network time series. Furthermore, specific combinations of iCAPs were mapped to behavioral domain time series. To quantify to which extent the time series contribute to the fMRI dynamics, their (temporal) standard deviation was calculated and compared between patients and controls. Abnormalities were found in networks involving subcortical and limbic areas and default mode network regions. When mapping the network dynamics to behavioral domain time series, abnormalities were found in emotional and visual behavioral subdomains, and within the ASD spectrum were more pronounced in subjects with autism compared to Asperger's syndrome. Also a trend towards impairment in networks facilitating social cognition was found. The functional imaging abnormalities are consistent with the behavioral impairments typical for ASD