Introduction to fMRI: experimental design and data analysis

This provides an introduction to functional MRI, experimental design and data analysis procedures using statistical parametric mapping approach

Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

Parallel MRI is a fast imaging technique that enables the acquisition of highly resolved images in space or/and in time. The performance of parallel imaging strongly depends on the reconstruction algorithm, which can proceed either in the original k-space (GRAPPA, SMASH) or in the image domain (SENSE-like methods). To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated. In this paper, we extend this approach using 3D-wavelet representations in order to handle all slices together and address reconstruction artifacts which propagate across adjacent slices. The gain induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE: 3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal acquisition is considered. Another important extension accounts for temporal correlations that exist between successive scans in functional MRI (fMRI). In addition to the case of 2D+t acquisition schemes addressed by some other methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and 4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that all regularization parameters are estimated in the maximum likelihood sense on a reference scan. The gain induced by such extensions is illustrated on both anatomical and functional image reconstruction, and also measured in terms of statistical sensitivity for the 4D-UWR-SENSE approach during a fast event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE reconstruction at the subject and group levels (15 subjects) for different contrasts of interest (eg, motor or computation tasks) and using different parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353

Optimal Correction of The Slice Timing Problem and Subject Motion Artifacts in fMRI

Functional magnetic resonance imaging (fMRI) is an extremely popular investigative and clinical imaging tool that allows safe and noninvasive study of the functional living brain. Fundamentally, fMRI measures a physiological signal as it changes over time. The manner in which this spatio-temporal signal is acquired can create technical challenges during image reconstruction that must be corrected for if any meaningful information is to be extracted from the data. Two particular challenges that are fundamentally intertwined with each other are temporal misalignment and spatial misalignment. Temporal misalignment is due to the nature of fMRI acquisition protocols themselves: a 3D volume is created by sampling and stacking multiple 2D slices. However, these slices are not acquired simultaneously or sequentially, and therefore will always be temporally misaligned with each other. Spatial misalignment arises when subject motion is present during the scan, resulting in individual volumes being spatially misaligned with each other. Spatial and temporal misalignment are not independent from each other, and their interaction can cause additional artifacts and reconstruction challenges if not addressed properly. The purpose of this thesis is to critically examine the problem of both spatial and temporal misalignment from a signal processing perspective, while considering the physical nature and origin of the signal itself, and develop optimal correction routines for spatial and temporal misalignment and their associated artifacts. One of the most immediate problems associated with temporal misalignment is that the order in which the slices are acquired must be known in order for correction to be possible. Surprisingly, this information is rarely provided with old or shared data, meaning that this critical preprocessing step must be skipped, significantly lowering the value of the data. We use the spatio-temporal properties of the fMRI signal to develop a robust and accurate algorithm to infer the slice acquisition order retrospectively from any fMRI scan. The ability to extract the interleave parameter from any data set allows us to perform slice timing correction even if this information had been lost, or was not provided with the scan. In the next section of this work, we develop a new optimal method of slice timing correction (Filter-Shift) based on the fundamental properties of sampling theory in digital signal processing. By examining the properties of the signal of interest (The blood oxygen level depended signal: BOLD signal), we are able to design and implement an effective FIR filter to simultaneously remove noise and reconstruct the signal of interest at any shifted offset, without the need for sub-optimal interpolation. In the final section, we investigate the effects of different motion types on the MR signal based on the Bloch equation, in order to develop a theoretical foundation from which we can create an optimal correction method. We devise a novel method to remove these artifacts: Discrete reconstruction of irregular fMRI trajectory (DRIFT). Our method calculates the exact displacement of the k-space samples due to motion at each dwell time and retrospectively corrects each slice of the fMRI volume using an inverse nonuniform Fourier transform. We conclude that a hybrid approach with both prospective and retrospective components are essentially required for optimal removal of motion artifacts from the fMRI data. The combined work of this thesis provides two theoretically sound and extremely effective correction routines, that both remove artifacts and restore the underlying sampled signal. Motion correction and slice timing correction are typically the first two preprocessing steps to be applied to any fMRI data, and thus provide the foundation for any further analysis. While many other preprocessing steps can be omitted or included depending on the analysis, motion correction and slice timing correction are unequivocally beneficial and necessary for accurate and reliable results. This work provides a theoretical and quantitative framework that describes the optimal removal of artifacts associated with motion and slice timing

Following one's heart: cardiac rhythms gate central initiation of sympathetic reflexes

Central nervous processing of environmental stimuli requires integration of sensory information with ongoing autonomic control of cardiovascular function. Rhythmic feedback of cardiac and baroreceptor activity contributes dynamically to homeostatic autonomic control. We examined how the processing of brief somatosensory stimuli is altered across the cardiac cycle to evoke differential changes in bodily state. Using functional magnetic resonance imaging of brain and noninvasive beat-to-beat cardiovascular monitoring, we show that stimuli presented before and during early cardiac systole elicited differential changes in neural activity within amygdala, anterior insula and pons, and engendered different effects on blood pressure. Stimulation delivered during early systole inhibited blood pressure increases. Individual differences in heart rate variability predicted magnitude of differential cardiac timing responses within periaqueductal gray, amygdala and insula. Our findings highlight integration of somatosensory and phasic baroreceptor information at cortical, limbic and brainstem levels, with relevance to mechanisms underlying pain control, hypertension and anxiety

Simultaneous intracranial EEG and fMRI of interictal epileptic discharges in humans

Simultaneous scalp EEG–fMRI measurements allow the study of epileptic networks and more generally, of the coupling between neuronal activity and haemodynamic changes in the brain. Intracranial EEG (icEEG) has greater sensitivity and spatial specificity than scalp EEG but limited spatial sampling. We performed simultaneous icEEG and functional MRI recordings in epileptic patients to study the haemodynamic correlates of intracranial interictal epileptic discharges (IED). Two patients undergoing icEEG with subdural and depth electrodes as part of the presurgical assessment of their pharmaco-resistant epilepsy participated in the study. They were scanned on a 1.5 T MR scanner following a strict safety protocol. Simultaneous recordings of fMRI and icEEG were obtained at rest. IED were subsequently visually identified on icEEG and their fMRI correlates were mapped using a general linear model (GLM). On scalp EEG–fMRI recordings performed prior to the implantation, no IED were detected. icEEG–fMRI was well tolerated and no adverse health effect was observed. intra-MR icEEG was comparable to that obtained outside the scanner. In both cases, significant haemodynamic changes were revealed in relation to IED, both close to the most active electrode contacts and at distant sites. In one case, results showed an epileptic network including regions that could not be sampled by icEEG, in agreement with findings from magneto-encephalography, offering some explanation for the persistence of seizures after surgery. Hence, icEEG–fMRI allows the study of whole-brain human epileptic networks with unprecedented sensitivity and specificity. This could help improve our understanding of epileptic networks with possible implications for epilepsy surgery

Substantia nigra activity level predicts trial-to-trial adjustments in cognitive control

Effective adaptation to the demands of a changing environment requires flexible cognitive control. The medial and the lateral frontal cortices are involved in such control processes, putatively in close interplay with the BG. In particular, dopaminergic projections from the midbrain (i.e., from the substantia nigra [SN] and the ventral tegmental area) have been proposed to play a pivotal role in modulating the activity in these areas for cognitive control purposes. In that dopaminergic involvement has been strongly implicated in reinforcement learning, these ideas suggest functional links between reinforcement learning, where the outcome of actions shapes behavior over time, and cognitive control in a more general context, where no direct reward is involved. Here, we provide evidence from functional MRI in humans that activity in the SN predicts systematic subsequent trial-to-trial RT prolongations that are thought to reflect cognitive control in a stop-signal paradigm. In particular, variations in the activity level of the SN in one trial predicted the degree of RT prolongation on the subsequent trial, consistent with a modulating output signal from the SN being involved in enhancing cognitive control. This link between SN activity and subsequent behavioral adjustments lends support to theoretical accounts that propose dopaminergic control signals that shape behavior both in the presence and in the absence of direct reward. This SN-based modulatory mechanism is presumably mediated via a wider network that determines response speed in this task, including frontal and parietal control regions, along with the BG and the associated subthalamic nucleus

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

Methods for cleaning the BOLD fMRI signal

Available online 9 December 2016 http://www.sciencedirect.com/science/article/pii/S1053811916307418?via%3Dihubhttp://www.sciencedirect.com/science/article/pii/S1053811916307418?via%3DihubBlood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.This work was supported by the Spanish Ministry of Economy and Competitiveness [Grant PSI 2013–42343 Neuroimagen Multimodal], the Severo Ochoa Programme for Centres/Units of Excellence in R & D [SEV-2015-490], and the research and writing of the paper were supported by the NIMH and NINDS Intramural Research Programs (ZICMH002888) of the NIH/HHS