Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN.

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.

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration.

Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject-specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 min, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.

Retrospective Correction of Physiological Noise: Impact on Sensitivity, Specificity, and Reproducibility of Resting-State Functional Connectivity in a Reading Network Model.

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.

Physiology recording with magnetic field probes for fMRI denoising.

Physiological noise originating in cardiovascular and respiratory processes is a substantial confound in BOLD fMRI. When unaccounted for it reduces the temporal SNR and causes error in inferred brain activity and connectivity. Physiology correction typically relies on auxiliary measurements with peripheral devices such as ECG, pulse oximeters, and breathing belts. These require direct skin contact or at least a tight fit, impairing subject comfort and adding to the setup time. In this work, we explore a touch-free alternative for physiology recording, using magnetic detection with NMR field probes. Placed close to the chest such probes offer high sensitivity to cardiovascular and respiratory dynamics without mechanical contact. This is demonstrated by physiology regression in a typical fMRI scenario at 7T, including validation against standard devices. The study confirms essentially equivalent performance of noise models based on conventional recordings and on field probes. It is shown that the field probes may be positioned in the subject's back such that they could be readily integrated in the patient table.

The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data.

BACKGROUND: Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. NEW METHODS: We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention. RESULTS: We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35). COMPARISON WITH EXISTING METHODS: The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. CONCLUSIONS: Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.

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.

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.

Real-time fMRI processing with physiological noise correction - Comparison with off-line analysis.

BACKGROUND: While applications of real-time functional magnetic resonance imaging (rtfMRI) are growing rapidly, there are still limitations in real-time data processing compared to off-line analysis. NEW METHODS: We developed a proof-of-concept real-time fMRI processing (rtfMRIp) system utilizing a personal computer (PC) with a dedicated graphic processing unit (GPU) to demonstrate that it is now possible to perform intensive whole-brain fMRI data processing in real-time. The rtfMRIp performs slice-timing correction, motion correction, spatial smoothing, signal scaling, and general linear model (GLM) analysis with multiple noise regressors including physiological noise modeled with cardiac (RETROICOR) and respiration volume per time (RVT). RESULTS: The whole-brain data analysis with more than 100,000voxels and more than 250volumes is completed in less than 300ms, much faster than the time required to acquire the fMRI volume. Real-time processing implementation cannot be identical to off-line analysis when time-course information is used, such as in slice-timing correction, signal scaling, and GLM. We verified that reduced slice-timing correction for real-time analysis had comparable output with off-line analysis. The real-time GLM analysis, however, showed over-fitting when the number of sampled volumes was small. COMPARISON WITH EXISTING METHODS: Our system implemented real-time RETROICOR and RVT physiological noise corrections for the first time and it is capable of processing these steps on all available data at a given time, without need for recursive algorithms. CONCLUSIONS: Comprehensive data processing in rtfMRI is possible with a PC, while the number of samples should be considered in real-time GLM.

Understanding the contribution of neural and physiological signal variation to the low repeatability of emotion-induced BOLD responses.

Previous studies have reported low repeatability of BOLD activation measures during emotion processing tasks. It is not clear, however, whether low repeatability is a result of changes in the underlying neural signal over time, or due to insufficient reliability of the acquired BOLD signal caused by noise contamination. The aim of this study was to investigate the influence of "cleaning" the BOLD signal, by correcting for physiological noise and for differences in BOLD responsiveness, on measures of repeatability. Fifteen healthy volunteers were scanned on two different occasions, performing an emotion provocation task with faces (neutral, 50% fearful, 100% fearful) followed by a breath-hold paradigm to provide a marker of BOLD responsiveness. Repeatability of signal distribution (spatial repeatability) and repeatability of signal amplitude within two regions of interest (amygdala and fusiform gyrus) were estimated by calculating the intraclass correlation coefficient (ICC). Significant repeatability of signal amplitude was only found within the right amygdala during the perception of 50% fearful faces, but disappeared when physiological noise correction was performed. Spatial repeatability was higher within the fusiform gyrus than within the amygdala, and better at the group level than at the participant level. Neither physiological noise correction, nor consideration of BOLD responsiveness, assessed through the breath-holding, increased repeatability. The findings lead to the conclusion that low repeatability of BOLD response amplitude to emotional faces is more likely to be explained by the lack of stability in the underlying neural signal than by physiological noise contamination. Furthermore, reported repeatability might be a result of repeatability of task-correlated physiological variation rather than neural activity. This means that the emotion paradigm used in this study might not be useful for studies that require the BOLD response to be a stable measure of emotional processing, for example in the context of biomarkers.

Advances in functional magnetic resonance imaging of the human brainstem.

The brainstem is of tremendous importance for our daily survival, and yet the functional relationships between various nuclei, their projection targets, and afferent regulatory areas remain poorly characterized. The main reason for this lies in the sub-optimal performance of standard neuroimaging methods in this area. In particular, fMRI signals are much harder to detect in the brainstem region compared to cortical areas. Here we describe and validate a new approach to measure activation of brainstem nuclei in humans using standard fMRI sequences and widely available tools for statistical image processing. By spatially restricting an independent component analysis to an anatomically defined brainstem mask, we excluded those areas from the analysis that were strongly affected by physiological noise. This allowed us to identify for the first time intrinsic connectivity networks in the human brainstem and to map brainstem-cortical connectivity purely based on functionally defined regions of interest.