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Cerebrovascular reactivity (CVR) is an important indicator of cerebrovascular health and is commonly studied with the Blood Oxygenation Level Dependent functional MRI (BOLD-fMRI) response to a vasoactive stimulus. There is theoretical and empirical evidence to suggest that baseline cerebral blood flow (CBF) modulates the BOLD signal amplitude, and that baseline CBF may influence BOLD-CVR estimates. We address how some pertinent data acquisition and modelling choices affect the relationship between baseline CBF and BOLD-CVR: whether BOLD-CVR is modelled with breathing task data or just resting-state data, and whether BOLD-CVR amplitudes are optimized for hemodynamic lag effects. For the relationship between baseline CBF and BOLD-CVR, we assessed both between-subject correlations of average GM values and within-subject spatial correlations across cortical regions. Our results suggest that a simple breathing task addition to a resting-state scan, alongside lag-optimization within BOLD-CVR modelling, can improve BOLD-CVR correlations with baseline CBF, both between- and within-subjects, likely because these CVR estimates are more physiologically accurate. We report positive coupling between baseline CBF and BOLD-CVR, both between and within subjects; the physiological explanation of this positive coupling is unclear, and future research with larger sample sizes and more tightly controlled vasoactive stimuli is needed. Understanding how baseline vascular physiology relates to dynamic cerebrovascular processes will bring further insights into what drives between and within subject participant variability in BOLD-CVR measurements and related measurements of cerebrovascular function. These insights are particularly relevant when interpreting results in populations with altered vascular and/or metabolic baselines or impaired cerebrovascular reserve.

This work introduces a novel algorithm for deconvolution of the BOLD signal in multiecho fMRI data: Multiecho Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the transverse relaxation without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.

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.

This work introduces a novel method for the temporal deconvolution of the BOLD signal for multiecho functional MRI (fMRI) data: Multiecho Paradigm Free Mapping (ME-PFM). By solving an inverse problem with the Generalized LASSO, this method obtains voxelwise time-varying estimates of the changes in the transverse relaxation (R) and the net magnetization (So) of the fMRI signal without prior knowledge of the timings of the BOLD responses. Our results with experimental data demonstrate that the maps of R changes obtained with ME-PFM at the times of the stimulus trials closely resemble the maps obtained with a standard approach that is aware of the onsets and durations of the events. Compared with previous deconvolution approaches for BOLD fMRI, this method yields estimates of R changes in physiologically interpretable units, which is particularly relevant for the quantification of the dynamics of the BOLD fMRI signal during tasks and resting state.

During the monitoring of pharmacoresistant epilepsy patients prior to surgery interictal epileptic discharges (IEDs) are analyzed to locate possible sources of epileptic activity. In order to compensate low spatial resolution of EEG simultaneous EEG fMRI recordings can be used. Conventional methods typically deploy an EEG informed analysis of the fMRI data; i.e. EEG derived IED onset timings are used to setup regressors for linear regression. Recently we have proposed a new fMRI analysis method total activation (TA) which is able to deconvolve the underlying activity inducing signal without prior information on the onset timing and duration of events. Here we demonstrate that TA can locate the epileptogenic regions from fMRI data. We compare and validate our results with conventional methods performed by experts prior to surgery. © 2013 IEEE.

Paradigm-free mapping enables to map the haemodynamic response in space and time without prior knowledge of the timing of the underlying neuronal events (i.e., no stimulation paradigm). Such deconvolution approach can take advantage of modern sparsity-promoting regularization. Here we extend this concept using structured sparsity approaches in order to gain robustnesss against model mismatch. Specifically, we extend the haemodynamic dictionary with the informed basis set (i.e., canonical HRF, and its temporal and dispersion derivatives) and we deploy state-of-the art structured sparsity functionals. In addition, we propose the group-weighted fusion penalty. We demonstrate the feasibility of the proposed approach for both synthetic and experimental data, showing superior abilities to characterize the single-trial BOLD response with no timing information.

The ability to detect single trial responses in functional magnetic resonance imaging (fMRI) studies is essential, particularly if investigating learning or adaptation processes or unpredictable events. We recently introduced paradigm free mapping (PFM), an analysis method that detects single trial blood oxygenation level dependent (BOLD) responses without specifying prior information on the timing of the events. PFM is based on the deconvolution of the fMRI signal using a linear hemodynamic convolution model. Our previous PFM method (Caballero‐Gaudes et al., 2011: Hum Brain Mapp) used the ridge regression estimator for signal deconvolution and required a baseline signal period for statistical inference. In this work, we investigate the application of sparse regression techniques in PFM. In particular, a novel PFM approach is developed using the Dantzig selector estimator, solved via an efficient homotopy procedure, along with statistical model selection criteria. Simulation results demonstrated that, using the Bayesian information criterion to select the regularization parameter, this method obtains high detection rates of the BOLD responses, comparable with a model‐based analysis, but requiring no information on the timing of the events and being robust against hemodynamic response function variability. The practical operation of this sparse PFM method was assessed with single‐trial fMRI data acquired at 7T, where it automatically detected all task‐related events, and was an improvement on our previous PFM method, as it does not require the definition of a baseline state and amplitude thresholding and does not compromise on specificity and sensitivity. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.