Prospective motion correction in functional MRI

Advancements in MR hardware systems and magnetic field control: B0 shimming, RF coils, and gradient techniques for enhancing magnetic resonance imaging and spectroscopy

High magnetic field homogeneity is critical for magnetic resonance imaging (MRI), functional MRI, and magnetic resonance spectroscopy (MRS) applications. B0 inhomogeneity during MR scans is a long-standing problem resulting from magnet imperfections and site conditions, with the main issue being the inhomogeneity across the human body caused by differences in magnetic susceptibilities between tissues, resulting in signal loss, image distortion, and poor spectral resolution. Through a combination of passive and active shim techniques, as well as technological advances employing multi-coil techniques, optimal coil design, motion tracking, and real-time modifications, improved field homogeneity and image quality have been achieved in MRI/MRS. The integration of RF and shim coils brings a high shim efficiency due to the proximity of participants. This technique will potentially be applied to high-density RF coils with a high-density shim array for improved B0 homogeneity. Simultaneous shimming and image encoding can be achieved using multi-coil array, which also enables the development of novel encoding methods using advanced magnetic field control. Field monitoring enables the capture and real-time compensation for dynamic field perturbance beyond the static background inhomogeneity. These advancements have the potential to better use the scanner performance to enhance diagnostic capabilities and broaden applications of MRI/MRS in a variety of clinical and research settings. The purpose of this paper is to provide an overview of the latest advances in B0 magnetic field shimming and magnetic field control techniques as well as MR hardware, and to emphasize their significance and potential impact on improving the data quality of MRI/MRS.

Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.

Navigator-based motion compensation for liver BOLD measurement with five-echo SAGE EPI and breath-hold task

PURPOSE

The purpose of this work is to apply multi-echo spin- and gradient-echo (SAGE) echo-planar imaging (EPI) combined with a navigator-based (NAV) prospective motion compensation method for a quantitative liver blood oxygen level dependent (BOLD) measurement with a breath-hold (BH) task.

METHODS

A five-echo SAGE sequence was developed to quantitatively measure T2 and T2* to depict function with sufficient signal-to-noise ratio, spatial resolution and sensitivity to BOLD changes induced by the BH task. To account for respiratory motion, a navigator was employed in the form of a single gradient-echo projection readout, located at the diaphragm along the inferior-superior direction. Prior to each transverse imaging slice of the spin-echo EPI-based readouts, navigator acquisition and fat suppression were incorporated. Motion data was obtained from the navigator and transmitted back to the sequence, allowing real-time adjustments to slice positioning. Six healthy volunteers and three patients with liver carcinoma were included in this study. Quantitative T2 and T2* were calculated at each time point of the BH task. Parameters of t value from first-level analysis using a general linear model and hepatovascular reactivity (HVR) of Echo1, T2 and T2* were calculated.

RESULTS

The motion caused by respiratory activity was successfully compensated using the navigator signal. The average changes of T2 and T2* during breath-hold were about 1% and 0.7%, respectively. With the help of NAV prospective motion compensation whole liver t values could be obtained without motion artifacts. The quantified liver T2 (34.7 ± 0.7 ms) and T2* (29 ± 1.2 ms) values agreed with values from literature. In healthy volunteers, the distribution of statistical t value and HVR was homogeneous throughout the whole liver. In patients with liver carcinoma, the distribution of t value and HVR was inhomogeneous due to metastases or therapy.

CONCLUSIONS

This study demonstrates the feasibility of using a NAV prospective motion compensation technique in conjunction with five-echo SAGE EPI for the quantitative measurement of liver BOLD with a BH task.

Inverse radon transform with deep learning: an application in cardiac motion correction

Objective. This paper addresses performing inverse radon transform (IRT) with artificial neural network (ANN) or deep learning, simultaneously with cardiac motion correction (MC). The suggested application domain is cardiac image reconstruction in emission or transmission tomography where IRT is relevant. Our main contribution is in proposing an ANN architecture that is particularly suitable for this purpose.Approach. We validate our approach with two types of datasets. First, we use an abstract object that looks like a heart to simulate motion-blurred radon transform. With the known ground truth in hand, we then train our proposed ANN architecture and validate its effectiveness in MC. Second, we used human cardiac gated datasets for training and validation of our approach. The gating mechanism bins data over time using the electro-cardiogram (ECG) signals for cardiac motion correction.Main results. We have shown that trained ANNs can perform motion-corrected image reconstruction directly from a motion-corrupted sinogram. We have compared our model against two other known ANN-based approaches.Significance. Our method paves the way for eliminating any need for hardware gating in medical imaging.

Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI

The major issue faced by elderly people in society is the loss of memory, difficulty learning new things, and poor judgment. This is due to damage to brain tissues, which may lead to cognitive impairment and eventually Alzheimer's. Therefore, the detection of such mild cognitive impairment (MCI) becomes important. Usually, this is detected when it is converted into Alzheimer's disease (AD). AD is irreversible and cannot be cured whereas mild cognitive impairment (MCI) can be cured. The goal of this research is to diagnose Alzheimer's patients for timely treatment. For this purpose, functional MRI images from the publicly available dataset are used. Various deep-learning models have been used by the scientific community for the automatic detection of Alzheimer's subjects. These include the binary classification of scans of patients into MCI and AD stages, and limited work is carried out for multiclass classification of Alzheimer's disease up to six different stages. This study is divided into two steps. In the first step, a binary classification of the subject's scan is performed using Custom CNN. The second step involves the use of different deep learning models along with Custom CNN for multiclass classification of a subject's scan into one of the six stages of Alzheimer's disease. The models are evaluated based on different evaluation metrics, and the overall result of the models is improved using the max-voting ensembling technique. The experimental results show that an overall average accuracy of 98.8% is achieved for Alzheimer's stages classification.

Improving motion robustness of 3D MR fingerprinting with a fat navigator

PURPOSE

To develop a 3D MR fingerprinting (MRF) method in combination with fat navigators to improve its motion robustness for neuroimaging.

METHODS

A rapid fat navigator was developed using the stack-of-spirals acquisition and non-Cartesian spiral GRAPPA. The fat navigator module was implemented in the 3D MRF sequence with high scan efficiency. The developed method was first validated in phantoms and five healthy subjects with intentional head motion. The method was further applied to infants with neonatal opioid withdrawal symptoms. The 3D MRF scans with fat navigators acquired with and without acceleration along the partition-encoding direction were both examined in the study.

RESULTS

Both phantom and in vivo results demonstrated that the added fat navigator modules did not influence the quantification accuracy in MRF. In combination with non-Cartesian spiral GRAPPA, a rapid fat navigator sampling with whole-brain coverage was achieved in ˜0.5 s at 3T, reducing its sensitivity to potential motion. Based on the motion waveforms extracted from fat navigators, the motion robustness of the 3D MRF was largely improved. With the proposed method, the motion-corrupted MRF datasets yielded T1 and T2 maps with significantly reduced artifacts and high correlations with measurements from the reference motion-free MRF scans.

CONCLUSION

We developed a 3D MRF method coupled with rapid fat navigators to improve its motion robustness for quantitative neuroimaging. Our results demonstrate that (1) accurate tissue quantification was preserved with the fat navigator modules and (2) the motion robustness for quantitative tissue mapping was largely improved with the developed method.

Prediction of motion induced magnetic fields for human brain MRI at 3 T

OBJECTIVE

Maps of B0 field inhomogeneities are often used to improve MRI image quality, even in a retrospective fashion. These field inhomogeneities depend on the exact head position within the static field but acquiring field maps (FM) at every position is time consuming. Here we propose a forward simulation strategy to obtain B0 predictions at different head-positions.

METHODS

FM were predicted by combining (1) a multi-class tissue model for estimation of tissue-induced fields, (2) a linear k-space model for capturing gradient imperfections, (3) a dipole estimation for quantifying lower-body perturbing fields (4) and a position-dependent tissue mask to model FM alterations caused by large motion effects. The performance of the combined simulation strategy was compared with an approach based on a rigid body transformation of the FM measured in the reference position to the new position.

RESULTS

The transformed FM provided inconsistent results for large head movements (> 5° rotation, approximately), while the simulation strategy had a superior prediction accuracy for all positions. The simulated FM was used to optimize B0 shims with up to 22.2% improvement with respect to the transformed FM approach.

CONCLUSION

The proposed simulation strategy is able to predict movement-induced B0 field inhomogeneities yielding more precise estimates of the ground truth field homogeneity than the transformed FM.

Adaptive model-based Magnetic Resonance

PURPOSE

Conventional sequences are static in nature, fixing measurement parameters in advance in anticipation of a wide range of expected tissue parameter values. We set out to design and benchmark a new, personalized approach-termed adaptive MR-in which incoming subject data is used to update and fine-tune the pulse sequence parameters in real time.

METHODS

We implemented an adaptive, real-time multi-echo (MTE) experiment for estimating T₂ s. Our approach combined a Bayesian framework with model-based reconstruction. It maintained and continuously updated a prior distribution of the desired tissue parameters, including T₂ , which was used to guide the selection of sequence parameters in real time.

RESULTS

Computer simulations predicted accelerations between 1.7- and 3.3-fold for adaptive multi-echo sequences relative to static ones. These predictions were corroborated in phantom experiments. In healthy volunteers, our adaptive framework accelerated the measurement of T₂ for n-acetyl-aspartate by a factor of 2.5.

CONCLUSION

Adaptive pulse sequences that alter their excitations in real time could provide substantial reductions in acquisition times. Given the generality of our proposed framework, our results motivate further research into other adaptive model-based approaches to MRI and MRS.

Germany's journey toward 14 Tesla human magnetic resonance

Multiple sites within Germany operate human MRI systems with magnetic fields either at 7 Tesla or 9.4 Tesla. In 2013, these sites formed a network to facilitate and harmonize the research being conducted at the different sites and make this technology available to a larger community of researchers and clinicians not only within Germany, but also worldwide. The German Ultrahigh Field Imaging (GUFI) network has defined a strategic goal to establish a 14 Tesla whole-body human MRI system as a national research resource in Germany as the next progression in magnetic field strength. This paper summarizes the history of this initiative, the current status, the motivation for pursuing MR imaging and spectroscopy at such a high magnetic field strength, and the technical and funding challenges involved. It focuses on the scientific and science policy process from the perspective in Germany, and is not intended to be a comprehensive systematic review of the benefits and technical challenges of higher field strengths.

Systematic evaluation of head motion on resting-state functional connectivity MRI in the neonate

Reliability and robustness of resting state functional connectivity MRI (rs-fcMRI) relies, in part, on minimizing the influence of head motion on measured brain signals. The confounding effects of head motion on functional connectivity have been extensively studied in adults, but its impact on newborn brain connectivity remains unexplored. Here, using a large newborn data set consisting of 159 rs-fcMRI scans acquired in the Developing Brain Institute at Children's National Hospital and 416 scans from The Developing Human Connectome Project (dHCP), we systematically investigated associations between head motion and rs-fcMRI. Head motion during the scan significantly affected connectivity at sensory-related networks and default mode networks, and at the whole brain scale; the direction of motion effects varied across the whole brain. Comparing high- versus low-head motion groups suggested that head motion can impact connectivity estimates across the whole brain. Censoring of high-motion volumes using frame-wise displacement significantly reduced the confounding effects of head motion on neonatal rs-fcMRI. Lastly, in the dHCP data set, we demonstrated similar persistent associations between head motion and network connectivity despite implementing a standard denoising strategy. Collectively, our results highlight the importance of using rigorous head motion correction in preprocessing neonatal rs-fcMRI to yield reliable estimates of brain activity.

HALO: A software tool for real-time head alignment in the MR scanner

PURPOSE

MRI studies in human subjects often require multiple scanning sessions/visits. Changes in a subject's head position across sessions result in different alignment between brain tissues and the magnetic field which leads to changes in magnetic susceptibility. These changes can have considerable impacts on acquired signals. Head ALignment Optimization (HALO), a software tool was developed by the authors for active head alignment between sessions.

METHODS

HALO provides real-time visual feedback of a subject's current head position relative to the position in a previous session. The tool was evaluated in a pilot sample of seven healthy human subjects.

RESULTS

HALO was shown to enable subjects to actively align their head positions to the desired position of their initial sessions. The subjects were able to improve their head alignment significantly using HALO and achieved good alignment with their first session meeting stringent criteria similar to that used for within-run head motion (less than 2 mm translation or 2 degrees rotation in any direction from the desired position). Moreover, we found a negative correlation between the post-alignment rotation and similarity in inter-session BOLD patterns around the air-tissue interface near sinus which further highlighted the impact of tissue-field alignment on BOLD data quality.

CONCLUSION

Utilization of HALO in longitudinal studies may help to improve data quality by ensuring the consistency of susceptibility gradients in brain tissues across sessions. HALO has been made publicly available.

Camera-based Prospective Motion Correction in Paediatric Epilepsy Patients Enables EEG-fMRI Localization Even in High-motion States

BACKGROUND

EEG-fMRI is a useful additional test to localize the epileptogenic zone (EZ) particularly in MRI negative cases. However subject motion presents a particular challenge owing to its large effects on both MRI and EEG signal. Traditionally it is assumed that prospective motion correction (PMC) of fMRI precludes EEG artifact correction.

METHODS

Children undergoing presurgical assessment at Great Ormond Street Hospital were included into the study. PMC of fMRI was done using a commercial system with a Moiré Phase Tracking marker and MR-compatible camera. For retrospective EEG correction both a standard and a motion educated EEG artefact correction (REEGMAS) were compared to each other.

RESULTS

Ten children underwent simultaneous EEG-fMRI. Overall head movement was high (mean RMS velocity < 1.5 mm/s) and showed high inter- and intra-individual variability. Comparing motion measured by the PMC camera and the (uncorrected residual) motion detected by realignment of fMRI images, there was a five-fold reduction in motion from its prospective correction. Retrospective EEG correction using both standard approaches and REEGMAS allowed the visualization and identification of physiological noise and epileptiform discharges. Seven of 10 children had significant maps, which were concordant with the clinical EZ hypothesis in 6 of these 7.

CONCLUSION

To our knowledge this is the first application of camera-based PMC for MRI in a pediatric clinical setting. Despite large amount of movement PMC in combination with retrospective EEG correction recovered data and obtained clinically meaningful results during high levels of subject motion. Practical limitations may currently limit the widespread use of this technology.

Clinical Evaluation of Scout Accelerated Motion Estimation and Reduction Technique for 3D MR Imaging in the Inpatient and Emergency Department Settings

BACKGROUND AND PURPOSE

A scout accelerated motion estimation and reduction (SAMER) framework has been developed for efficient retrospective motion correction. The goal of this study was to perform an initial evaluation of SAMER in a series of clinical brain MR imaging examinations.

MATERIALS AND METHODS

Ninety-seven patients who underwent MR imaging in the inpatient and emergency department settings were included in the study. SAMER motion correction was retrospectively applied to an accelerated T1-weighted MPRAGE sequence that was included in brain MR imaging examinations performed with and without contrast. Two blinded neuroradiologists graded images with and without SAMER motion correction on a 5-tier motion severity scale (none = 1, minimal = 2, mild = 3, moderate = 4, severe = 5).

RESULTS

The median SAMER reconstruction time was 1 minute 47 seconds. SAMER motion correction significantly improved overall motion grades across all examinations (P < .005). Motion artifacts were reduced in 28% of cases, unchanged in 64% of cases, and increased in 8% of cases. SAMER improved motion grades in 100% of moderate motion cases and 75% of severe motion cases. Sixty-nine percent of nondiagnostic motion cases (grades 4 and 5) were considered diagnostic after SAMER motion correction. For cases with minimal or no motion, SAMER had negligible impact on the overall motion grade. For cases with mild, moderate, and severe motion, SAMER improved the motion grade by an average of 0.3 (SD, 0.5), 1.1 (SD, 0.3), and 1.1 (SD, 0.8) grades, respectively.

CONCLUSIONS

SAMER improved the diagnostic image quality of clinical brain MR imaging examinations with motion artifacts. The improvement was most pronounced for cases with moderate or severe motion.

A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI

Patient movement during Magnetic Resonance Imaging (MRI) scan can cause severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several echoes are typically measured during a single repetition period, where the earliest echoes show less contrast between various tissues, while the later echoes are more susceptible to artifacts and signal dropout. In this paper, we propose a knowledge interaction paradigm that jointly learns feature details from multiple distorted echoes by sharing their knowledge with unified training parameters, thereby simultaneously reducing motion artifacts of all echoes. This is accomplished by developing a new scheme that boosts a Single Encoder with Multiple Decoders (SEMD), which assures that the generated features not only get fused but also learned together. We called the proposed method Knowledge Interaction Learning between Multi-Echo data (KIL-ME-based SEMD). The proposed KIL-ME-based SEMD allows to share information and gain an understanding of the correlations between the multiple echoes. The main purpose of this work is to correct the motion artifacts and maintain image quality and structure details of all motion-corrupted echoes towards generating high-resolution susceptibility enhanced contrast images, i.e., SWI, using a weighted average of multi-echo motion-corrected acquisitions. We also compare various potential strategies that might be used to address the problem of reducing artifacts in multi-echoes data. The experimental results demonstrate the feasibility and effectiveness of the proposed method, reducing the severity of motion artifacts and improving the overall clinical image quality of all echoes with their associated SWI maps. Significant improvement of image quality is observed using both motion-simulated test data and actual volunteer data with various motion severity strengths. Eventually, by enhancing the overall image quality, the proposed network can increase the effectiveness of the physicians' capability to evaluate and correctly diagnose brain MR images.

Speaking in gestures: Left dorsal and ventral frontotemporal brain systems underlie communication in conducting

Conducting constitutes a well-structured system of signs anticipating information concerning the rhythm and dynamic of a musical piece. Conductors communicate the musical tempo to the orchestra, unifying the individual instrumental voices to form an expressive musical Gestalt. In a functional magnetic resonance imaging (fMRI) experiment, 12 professional conductors and 16 instrumentalists conducted real-time novel pieces with diverse complexity in orchestration and rhythm. For control, participants either listened to the stimuli or performed beat patterns, setting the time of a metronome or complex rhythms played by a drum. Activation of the left superior temporal gyrus (STG), supplementary and premotor cortex and Broca's pars opercularis (F3op) was shared in both musician groups and separated conducting from the other conditions. Compared to instrumentalists, conductors activated Broca's pars triangularis (F3tri) and the STG, which differentiated conducting from time beating and reflected the increase in complexity during conducting. In comparison to conductors, instrumentalists activated F3op and F3tri when distinguishing complex rhythm processing from simple rhythm processing. Fibre selection from a normative human connectome database, constructed using a global tractography approach, showed that the F3op and STG are connected via the arcuate fasciculus, whereas the F3tri and STG are connected via the extreme capsule. Like language, the anatomical framework characterising conducting gestures is located in the left dorsal system centred on F3op. This system reflected the sensorimotor mapping for structuring gestures to musical tempo. The ventral system centred on F3Tri may reflect the art of conductors to set this musical tempo to the individual orchestra's voices in a global, holistic way.

Neurofunctional differences and similarities between persistent postural-perceptual dizziness and anxiety disorder

INTRODUCTION

Persistent postural-perceptual dizziness (PPPD) (ICD-11) and anxiety disorders (ANX) share behavioural symptoms like anxiety, avoidance, social withdrawal, hyperarousal, or palpitation as well as neurological symptoms like vertigo, stance and gait disorders. Furthermore, previous studies have shown a bidirectional link between vestibulo-spatial and anxiety neural networks. So far, there have been no neuroimaging-studies comparing these groups.

OBJECTIVES

The aim of this explorative study was to investigate differences and similarities of neural correlates between these two patient groups and to compare their findings with a healthy control group.

METHODS

63 participants, divided in two patient groups (ANX = 20 and PPPD = 14) and two sex and age matched healthy control groups (HC-A = 16, HC-P = 13) were included. Anxiety and dizziness related pictures were shown during fMRI-measurements in a block-design in order to induce emotional responses. All subjects filled in questionnaires regarding vertigo (VSS, VHQ), anxiety (STAI), depression (BDI-II), alexithymia (TAS), and illness-perception (IPQ). After modelling the BOLD response with a standard canonical HRF, voxel-wise t-tests between conditions (emotional-negative vs neutral stimuli) were used to generate statistical contrast maps and identify relevant brain areas (pFDR < 0.05, cluster size >30 voxels). ROI-analyses were performed for amygdala, cingulate gyrus, hippocampus, inferior frontal gyrus, insula, supramarginal gyrus and thalamus (p ≤ 0.05).

RESULTS

Patient groups differed from both HC groups regarding anxiety, dizziness, depression and alexithymia scores; ratings of the PPPD group and the ANX group did differ significantly only in the VSS subscale 'vertigo and related symptoms' (VSS-VER). The PPPD group showed increased neural responses in the vestibulo-spatial network, especially in the supramarginal gyrus (SMG), and superior temporal gyrus (STG), compared to ANX and HC-P group. The PPPD group showed increased neural responses compared to the HC-P group in the anxiety network including amygdala, insula, lentiform gyrus, hippocampus, inferior frontal gyrus (IFG) and brainstem. Neuronal responses were enhanced in visual structures, e.g. fusiform gyrus, middle occipital gyrus, and in the medial orbitofrontal cortex (mOFC) in healthy controls compared to patients with ANX and PPPD, and in the ANX group compared to the PPPD group.

CONCLUSIONS

These findings indicate that neuronal responses to emotional information in the PPPD and the ANX group are comparable in anxiety networks but not in vestibulo-spatial networks. Patients with PPPD revealed a stronger neuronal response especially in SMG and STG compared to the ANX and the HC group. These results might suggest higher sensitivity and poorer adaptation processes in the PPPD group to anxiety and dizziness related pictures. Stronger activation in visual processing areas in HC subjects might be due to less emotional and more visual processing strategies.

Task-based functional MRI challenges in clinical neuroscience: Choice of the best head motion correction approach in multiple sclerosis

Introduction

Functional MRI (fMRI) is commonly used for understanding brain organization and connectivity abnormalities in neurological conditions, and in particular in multiple sclerosis (MS). However, head motion degrades fMRI data quality and influences all image-derived metrics. Persistent controversies regarding the best correction strategy motivates a systematic comparison, including methods such as scrubbing and volume interpolation, to find optimal correction models, particularly in studies with clinical populations prone to characterize by high motion. Moreover, strategies for correction of motion effects gain more relevance in task-based designs, which are less explored compared to resting-state, have usually lower sample sizes, and may have a crucial role in describing the functioning of the brain and highlighting specific connectivity changes.

Methods

We acquired fMRI data from 17 early MS patients and 14 matched healthy controls (HC) during performance of a visual task, characterized motion in both groups, and quantitatively compared the most used and easy to implement methods for correction of motion effects. We compared task-activation metrics obtained from: (i) models containing 6 or 24 motion parameters (MPs) as nuisance regressors; (ii) models containing nuisance regressors for 6 or 24 MPs and motion outliers (scrubbing) detected with Framewise Displacement or Derivative or root mean square VARiance over voxelS; and (iii) models with 6 or 24 MPs and motion outliers corrected through volume interpolation. To our knowledge, volume interpolation has not been systematically compared with scrubbing, nor investigated in task fMRI clinical studies in MS.

Results

No differences in motion were found between groups, suggesting that recently diagnosed MS patients may not present problematic motion. In general, models with 6 MPs perform better than models with 24 MPs, suggesting the 6 MPs as the best trade-off between correction of motion effects and preservation of valuable information. Parsimonious models with 6 MPs and volume interpolation were the best combination for correcting motion in both groups, surpassing the scrubbing methods. A joint analysis regardless of the group further highlighted the value of volume interpolation.

Discussion

Volume interpolation of motion outliers is an easy to implement technique, which may be an alternative to other methods and may improve the accuracy of fMRI analyses, crucially in clinical studies in MS and other neurological populations.

Mesoscopic in vivo human T2* dataset acquired using quantitative MRI at 7 Tesla

Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for advancing neuroscience and bridging the resolution gap with animal models. Despite the variety of MRI contrasts measured in recent years at the mesoscopic scale, in vivo quantitative imaging of T₂^* has not been performed. Here we provide a dataset containing empirical T₂^* measurements acquired at 0.35 × 0.35 × 0.35 mm³ voxel resolution using 7 Tesla MRI. To demonstrate unique features and high quality of this dataset, we generate flat map visualizations that reveal fine-scale cortical substructures such as layers and vessels, and we report quantitative depth-dependent T₂^* (as well as R₂^*) values in primary visual cortex and auditory cortex that are highly consistent across subjects. This dataset is freely available at https://doi.org/10.17605/OSF.IO/N5BJ7, and may prove useful for anatomical investigations of the human brain, as well as for improving our understanding of the basis of the T₂^*-weighted (f)MRI signal.

Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI

In resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinson's Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test-retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust artifact removal metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test-retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.

The association between head motion during functional magnetic resonance imaging and executive functioning in older adults

Minimizing head motion during functional magnetic resonance imaging (fMRI) is important for maintaining the integrity of neuroimaging data. While there are a variety of techniques to control for head motion, oftentimes, individuals with excessive in-scanner motion are removed from analyses. Movement in the scanner tends to increase with age; however, the cognitive profile of these "high-movers" in older adults has yet to be explored. This study aimed to assess the association between in-scanner head motion (i.e., number of "invalid scans" flagged as motion outliers) and cognitive functioning (e.g., executive functioning, processing speed, and verbal memory performance) in a sample of 282 healthy older adults. Spearman's Rank-Order correlations showed that a higher number of invalid scans was significantly associated with poorer performance on tasks of inhibition and cognitive flexibility and with older age. Since performance in these domains tend to decline as a part of the non-pathological aging process, these findings raise concerns regarding the potential systematic exclusion due to motion of older adults with lower executive functioning in neuroimaging samples. Future research should continue to explore prospective motion correction techniques to better ensure the collection of quality neuroimaging data without excluding informative participants from the sample.

Tracking of rigid head motion during MRI using an EEG system

Purpose

To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients.

Theory and Methods

Gradient switching during simultaneous EEG–fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high‐impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators.

Results

Head‐pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]‐mm translation, [0.30 0.27 0.22]‐degree rotation). Retrospective correction of 3D gradient‐echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with CapTrack pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees).

Conclusion

Motion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions.

A Descriptive Review of the Impact of Patient Motion in Early Childhood Resting-State Functional Magnetic Resonance Imaging

Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient’s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.

Self-navigated prospective motion correction for 3D-EPI acquisition

PURPOSE

Although 3D EPI is more susceptible to motion artifacts than 2D EPI, it presents some benefits for functional MRI, including the absence of spin-history artifacts, greater potential for parallel imaging acceleration, and better functional sensitivity in high-resolution imaging. Here we present a self-navigated 3D-EPI sequence suitable for prospective motion-corrected functional MRI without additional hardware or pulses.

METHODS

For each volume acquisition, the first 24 of the 52 partitions being acquired are accumulated to a new feedback block that was added to the image reconstruction pipeline. After zero-filling the remaining partitions, the feedback block constructs a volumetric self-navigator (vSNav), co-registers it to the reference vSNav acquired during the first volume acquisition, and sends motion estimates to the sequence. The sequence then updates its FOV and acquires subsequent partitions with the adjusted FOV, until the next update is received. The sequence was validated without and with intentional motion in phantom and in vivo on a 3T Skyra.

RESULTS

For phantom scans, the FOV was updated 0.704 s after acquisition of the vSNav partitions, and for in vivo scans after 0.768 s. Both phantom and in vivo data demonstrated stable motion estimates in the absence of motion. For in vivo acquisitions, prospective head-pose estimates using the vSNav's and retrospective estimates with FLIRT (FMRIB's Linear Image Registration Tool) agreed to within 0.23 mm (< 10% of the slice thickness) and 0.14° in all directions.

CONCLUSION

Depending when motion occurs during a volume acquisition, the proposed method fully corrects the FOV and recovers image quality within one volume acquisition.

A line through the brain: implementation of human line-scanning at 7T for ultra-high spatiotemporal resolution fMRI

Functional magnetic resonance imaging (fMRI) is a widely used tool in neuroscience to detect neurally evoked responses, e.g. the blood oxygenation level-dependent (BOLD) signal. Typically, BOLD fMRI has millimeter spatial resolution and temporal resolution of one to few seconds. To study the sub-millimeter structures and activity of the cortical gray matter, the field needs an fMRI method with high spatial and temporal resolution. Line-scanning fMRI achieves very high spatial resolution and high sampling rate, at the cost of a sacrifice in volume coverage. Here, we present a human line-scanning implementation on a 7T MRI system. First, we investigate the quality of the saturation pulses that suppress MR signal outside the line. Second, we established the best coil combination for reconstruction. Finally, we applied the line-scanning method in the occipital lobe during a visual stimulation task, showing BOLD responses along cortical depth, every 250 µm with a 200 ms repetition time (TR). We found a good correspondence of t-statistics values with 2D gradient-echo echo planar imaging (GE-EPI) BOLD fMRI data with the same temporal resolution and voxel volume (R = 0.6 ± 0.2). In summary, we demonstrate the feasibility of line-scanning in humans and this opens line-scanning fMRI for applications in cognitive and clinical neuroscience.

Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion

The data quality of simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) can be strongly affected by motion. Recent work has shown that the quality of fMRI data can be improved by using a Moiré-Phase-Tracker (MPT)-camera system for prospective motion correction. The use of the head position acquired by the MPT-camera-system has also been shown to correct motion-induced voltages, ballistocardiogram (BCG) and gradient artefact residuals separately. In this work we show the concept of an integrated framework based on the general linear model to provide a unified motion informed model of in-MRI artefacts. This model (retrospective EEG motion educated gradient artefact suppression, REEG-MEGAS) is capable of correcting voltage-induced, BCG and gradient artefact residuals of EEG data acquired simultaneously with prospective motion corrected fMRI. In our results, we have verified that applying REEG-MEGAS correction to EEG data acquired during subject motion improves the data quality in terms of motion induced voltages and also GA residuals in comparison to standard Artefact Averaging Subtraction and Retrospective EEG Motion Artefact Suppression. Besides that, we provide preliminary evidence that although adding more regressors to a model may slightly affect the power of physiological signals such as the alpha-rhythm, its application may increase the overall quality of a dataset, particularly when strongly affected by motion. This was verified by analysing the EEG traces, power spectra density and the topographic distribution from two healthy subjects. We also have verified that the correction by REEG-MEGAS improves higher frequency artefact correction by decreasing the power of Gradient Artefact harmonics. Our method showed promising results for decreasing the power of artefacts for frequencies up to 250 Hz. Additionally, REEG-MEGAS is a hybrid framework that can be implemented for real time prospective motion correction of EEG and fMRI data. Among other EEG-fMRI applications, the approach described here may benefit applications such as EEG-fMRI neurofeedback and brain computer interface, which strongly rely on the prospective acquisition and application of motion artefact removal.

Advances in resting state fMRI acquisitions for functional connectomics

Resting state functional magnetic resonance imaging (rs-fMRI) is based on spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal, which occur simultaneously in different brain regions, without the subject performing an explicit task. The low-frequency oscillations of the rs-fMRI signal demonstrate an intrinsic spatiotemporal organization in the brain (brain networks) that may relate to the underlying neural activity. In this review article, we briefly describe the current acquisition techniques for rs-fMRI data, from the most common approaches for resting state acquisition strategies, to more recent investigations with dedicated hardware and ultra-high fields. Specific sequences that allow very fast acquisitions, or multiple echoes, are discussed next. We then consider how acquisition methods weighted towards specific parts of the BOLD signal, like the Cerebral Blood Flow (CBF) or Volume (CBV), can provide more spatially specific network information. These approaches are being developed alongside the commonly used BOLD-weighted acquisitions. Finally, specific applications of rs-fMRI to challenging regions such as the laminae in the neocortex, and the networks within the large areas of subcortical white matter regions are discussed. We finish the review with recommendations for acquisition strategies for a range of typical applications of resting state fMRI.

Combining prospective and retrospective motion correction based on a model for fast continuous motion

PURPOSE

Prospective motion correction (PMC) and retrospective motion correction (RMC) have different advantages and limitations. The present work aims to combine the advantages of both for rigid body motion, aiming at correcting for faster motions than was previously achievable. Additionally, it provides insights into the effects of motion on pulse sequences and MR signals with a goal of further improving motion correction in the future.

METHODS

The effective encoding trajectory and a global phase offset in a moving object are calculated based on complete gradient waveforms of an arbitrary sequence and a continuous motion model. These data are used to feed the forward signal model, which is then used in iterative image reconstruction to suppress the artifacts still present after the PMC.

RESULTS

Verification experiments with a rotation phantom and in vivo were performed. Predictions of simulated motion artifacts for PMC based on sequence waveforms are very accurate. The performance at combined PMC+RMC is limited by Nyquist violations in the sampled k-space and can be compensated by oversampling.

CONCLUSION

The combined correction results in better images than pure PMC in the presence of fast motion. The predictions of artifacts are very accurate, allowing for comparing sequences or protocols in simulations. The observed artifacts due to Nyquist violations are expected to be corrected by utilizing parallel imaging.

Real-time motion and retrospective coil sensitivity correction for CEST using volumetric navigators (vNavs) at 7T

PURPOSE

To explore the impact of temporal motion-induced coil sensitivity changes on CEST-MRI at 7T and its correction using interleaved volumetric EPI navigators, which are applied for real-time motion correction.

METHODS

Five healthy volunteers were scanned via CEST. A 4-fold correction pipeline allowed the mitigation of (1) motion, (2) motion-induced coil sensitivity variations, ΔB1- , (3) motion-induced static magnetic field inhomogeneities, ΔB₀ , and (4) spatially varying transmit RF field fluctuations, ΔB1+ . Four CEST measurements were performed per session. For the first 2, motion correction was turned OFF and then ON in absence of voluntary motion, whereas in the other 2 controlled head rotations were performed. During post-processing ΔB1- was removed additionally for the motion-corrected cases, resulting in a total of 6 scenarios to be compared. In all cases, retrospective ∆B₀ and - ΔB1+ corrections were performed to compute artifact-free magnetization transfer ratio maps with asymmetric analysis (MTR_asym ).

RESULTS

Dynamic ΔB1- correction successfully mitigated signal deviations caused by head motion. In 2 frontal lobe regions of volunteer 4, induced relative signal errors of 10.9% and 3.9% were reduced to 1.1% and 1.0% after correction. In the right frontal lobe, the motion-corrected MTR_asym contrast deviated 0.92%, 1.21%, and 2.97% relative to the static case for Δω = 1, 2, 3 ± 0.25 ppm. The additional application of ΔB1- correction reduced these deviations to 0.10%, 0.14%, and 0.42%. The fully corrected MTR_asym values were highly consistent between measurements with and without intended head rotations.

CONCLUSION

Temporal ΔB1- cause significant CEST quantification bias. The presented correction pipeline including the proposed retrospective ΔB1- correction significantly reduced motion-related artifacts on CEST-MRI.

Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT)

PURPOSE

Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems.

THEORY AND METHODS

Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner.

RESULTS

The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data.

CONCLUSION

Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.

High-resolution fMRI at 7 Tesla: challenges, promises and recent developments for individual-focused fMRI studies

Limited detection power has been a bottleneck for subject-specific functional MRI (fMRI) studies, however the higher signal-to-noise ratio afforded by ultra-high magnetic fields (≥ 7 Tesla) provides levels of sensitivity and resolution needed to study individual subjects. What may be surprising is that higher imaging resolution may provide both higher specificity and sensitivity due to reductions in partial volume effects and reduced physiological noise. However, challenges remain to ensure high data quality and to reduce variability in ultra-high field fMRI. We discuss session-specific biases including those caused by factors related to instrumentation, anatomy, and physiology-which can translate into variability across sessions-and how to minimize these to help ultra-high field fMRI reach its full potential for individual-focused studies.

Quantity and quality: Normative open-access neuroimaging databases

The focus of this article is to compare twenty normative and open-access neuroimaging databases based on quantitative measures of image quality, namely, signal-to-noise (SNR) and contrast-to-noise ratios (CNR). We further the analysis through discussing to what extent these databases can be used for the visualization of deeper regions of the brain, such as the subcortex, as well as provide an overview of the types of inferences that can be drawn. A quantitative comparison of contrasts including T1-weighted (T1w) and T2-weighted (T2w) images are summarized, providing evidence for the benefit of ultra-high field MRI. Our analysis suggests a decline in SNR in the caudate nuclei with increasing age, in T1w, T2w, qT1 and qT2* contrasts, potentially indicative of complex structural age-dependent changes. A similar decline was found in the corpus callosum of the T1w, qT1 and qT2* contrasts, though this relationship is not as extensive as within the caudate nuclei. These declines were accompanied by a declining CNR over age in all image contrasts. A positive correlation was found between scan time and the estimated SNR as well as a negative correlation between scan time and spatial resolution. Image quality as well as the number and types of contrasts acquired by these databases are important factors to take into account when selecting structural data for reuse. This article highlights the opportunities and pitfalls associated with sampling existing databases, and provides a quantitative backing for their usage.

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

Restricted Water Diffusion in Diffusion-Weighted Magnetic Resonance Imaging in Pancreatic Cancer is Associated with Tumor Hypoxia

Simple Summary

Pancreatic cancer is characterized by a dense network of connective tissue surrounding clusters of cancer cells, the so-called stroma. This ubiquitous connective tissue impairs the delivery of oxygen to cancer cells. This results in hypoxia, which renders the cancer more aggressive and more resistant to treatment. In the present study, we investigated whether the extent of hypoxia in pancreatic cancer can be predicted by magnetic resonance imaging (MRI), a widely used medical imaging technique. More specifically, we used an MRI sequence which can quantitate the random motion (i.e., diffusion) of water molecules within the cancer tissue, namely diffusion-weighted (DW) MRI. We found that the random motion of water molecules is lower in cancer lesions with high hypoxia compared to those with low hypoxia. The findings from our study imply that DW-MRI can be used to identify pancreatic cancer lesions with high hypoxia which are at high risk for treatment failure.

Abstract

Hypoxia is a hallmark of pancreatic cancer (PDAC) due to its compact and extensive fibrotic tumor stroma. Hypoxia contributes to high lethality of this disease, by inducing a more malignant phenotype and resistance to radiation and chemotherapy. Thus, non-invasive methods to quantify hypoxia could be helpful for treatment decisions, for monitoring, especially in non-resectable tumors, or to optimize personalized therapy. In the present study, we investigated whether tumor hypoxia in PDAC is reflected by diffusion-weighted magnetic resonance imaging (DW-MRI), a functional imaging technique, frequently used in clinical practice for identification and characterization of pancreatic lesions. DW-MRI assesses the tissue microarchitecture by measuring the diffusion of water molecules, which is more restricted in highly compact tissues. As reliable surrogate markers for hypoxia, we determined Blimp-1 (B-lymphocyte induced maturation protein), a transcription factor, as well as vascular endothelial growth factor (VEGF), which are up-regulated in response to hypoxia. In 42 PDAC patients, we observed a close association between restricted water diffusion in DW-MRI and tumor hypoxia in matched samples, as expressed by high levels of Blimp-1 and VEGF in tissue samples of the respective patients. In summary, our data show that DW-MRI is well suited for the evaluation of tumor hypoxia in PDAC and could potentially be used for the identification of lesions with a high hypoxic fraction, which are at high risk for failure of radiochemotherapy.

Ground-truth "resting-state" signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics

The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T₂* changes, using sensors that provide a ground-truth with respect to cardiac, respiratory, and head movement dynamics. However, blood oxygenation level-dependent (BOLD) time-series dynamics are also confounded by scanner artifacts, in complex ways that can vary not only between scanners but even, for the same scanner, between sessions. Unfortunately, the lack of an equivalent ground truth for BOLD time-series has thus far stymied the development of reliable methods for identification and removal of scanner-induced noise, a problem that we have previously shown to severely impact detection sensitivity of resting-state brain networks. To address this problem, we first designed and built a phantom capable of providing dynamic signals equivalent to that of the resting-state brain. Using the dynamic phantom, we then compared the ground-truth time-series with its measured fMRI data. Using these, we introduce data-quality metrics: Standardized Signal-to-Noise Ratio (ST-SNR) and Dynamic Fidelity that, unlike currently used measures such as temporal SNR (tSNR), can be directly compared across scanners. Dynamic phantom data acquired from four "best-case" scenarios: high-performance scanners with MR-physicist-optimized acquisition protocols, still showed scanner instability/multiplicative noise contributions of about 6-18% of the total noise. We further measured strong non-linearity in the fMRI response for all scanners, ranging between 8-19% of total voxels. To correct scanner distortion of fMRI time-series dynamics at a single-subject level, we trained a convolutional neural network (CNN) on paired sets of measured vs. ground-truth data. The CNN learned the unique features of each session's noise, providing a customized temporal filter. Tests on dynamic phantom time-series showed a 4- to 7-fold increase in ST-SNR and about 40-70% increase in Dynamic Fidelity after denoising, with CNN denoising outperforming both the temporal bandpass filtering and denoising using Marchenko-Pastur principal component analysis. Critically, we observed that the CNN temporal denoising pushes ST-SNR to a regime where signal power is higher than that of noise (ST-SNR > 1). Denoising human-data with ground-truth-trained CNN, in turn, showed markedly increased detection sensitivity of resting-state networks. These were visible even at the level of the single-subject, as required for clinical applications of fMRI.

Dissimilarity of functional connectivity uncovers the influence of participant's motion in functional magnetic resonance imaging studies

Head motion is a major confounding factor impairing the quality of functional magnetic resonance imaging (fMRI) data. In particular, head motion can reduce analytical efficiency, and its effects are still present even after preprocessing. To examine the validity of motion removal and to evaluate the remaining effects of motion on the quality of the preprocessed fMRI data, a new metric of group quality control (QC), dissimilarity of functional connectivity, is introduced. Here, we investigate the association between head motion, represented by mean framewise displacement, and dissimilarity of functional connectivity by applying four preprocessing methods in two independent resting‐state fMRI datasets: one consisting of healthy participants (N = 167) scanned in a 3T GE‐Discovery 750 with longer TR (2.5 s), and the other of chronic back pain patients (N = 143) in a 3T Siemens Magnetom Prisma scanner with shorter TR (0.555 s). We found that dissimilarity of functional connectivity uncovers the influence of participant's motion, and this relationship is independent of population, scanner, and preprocessing method. The association between motion and dissimilarity of functional connectivity, and how the removal of high‐motion participants affects this association, is a new strategy for group‐level QC following preprocessing.

Prospective motion correction for diffusion weighted EPI of the brain using an optical markerless tracker

PURPOSE

To enable motion-robust diffusion weighted imaging of the brain using well-established imaging techniques.

METHODS

An optical markerless tracking system was used to estimate and correct for rigid body motion of the head in real time during scanning. The imaging coordinate system was updated before each excitation pulse in a single-shot EPI sequence accelerated by GRAPPA with motion-robust calibration. Full Fourier imaging was used to reduce effects of motion during diffusion encoding. Subjects were imaged while performing prescribed motion patterns, each repeated with prospective motion correction on and off.

RESULTS

Prospective motion correction with dynamic ghost correction enabled high quality DWI in the presence of fast and continuous motion within a 10° range. Images acquired without motion were not degraded by the prospective correction. Calculated diffusion tensors tolerated the motion well, but ADC values were slightly increased.

CONCLUSIONS

Prospective correction by markerless optical tracking minimizes patient interaction and appears to be well suited for EPI-based DWI of patient groups unable to remain still including those who are not compliant with markers.

SLIMM: Slice localization integrated MRI monitoring

Functional MRI (fMRI) is extremely challenging to perform in subjects who move because subject motion disrupts blood oxygenation level dependent (BOLD) signal measurement. It has become common to use retrospective framewise motion detection and censoring in fMRI studies to eliminate artifacts arising from motion. Data censoring results in significant loss of data and statistical power unless the data acquisition is extended to acquire more data not corrupted by motion. Acquiring more data than is necessary leads to longer than necessary scan duration, which is more expensive and may lead to additional subject non-compliance. Therefore, it is well established that real-time prospective motion monitoring is crucial to ensure data quality and reduce imaging costs. In addition, real-time monitoring of motion allows for feedback to the operator and the subject during the acquisition, to enable intervention to reduce the subject motion. The most widely used form of motion monitoring for fMRI is based on volume-to-volume registration (VVR), which quantifies motion as the misalignment between subsequent volumes. However, motion is not constrained to occur only at the boundaries of volume acquisition, but instead may occur at any time. Consequently, each slice of an fMRI acquisition may be displaced by motion, and assessment of whole volume to volume motion may be insensitive to both intra-volume and inter-volume motion that is revealed by displacement of the slices. We developed the first slice-by-slice self-navigated motion monitoring system for fMRI by developing a real-time slice-to-volume registration (SVR) algorithm. Our real-time SVR algorithm, which is the core of the system, uses a local image patch-based matching criterion along with a Levenberg-Marquardt optimizer, all accelerated via symmetric multi-processing, with interleaved and simultaneous multi-slice acquisition schemes. Extensive experimental results on real motion data demonstrated that our fast motion monitoring system, named Slice Localization Integrated MRI Monitoring (SLIMM), provides more accurate motion measurements than a VVR based approach. Therefore, SLIMM offers improved online motion monitoring which is particularly important in fMRI for challenging patient populations. Real-time motion monitoring is crucial for online data quality control and assurance, for enabling feedback to the subject and the operator to act to mitigate motion, and in adaptive acquisition strategies that aim to ensure enough data of sufficient quality is acquired without acquiring excess data.

Effects of using different software packages for BOLD analysis in planning a neurosurgical treatment in patients with brain tumours

BACKGROUND

The authors of the present thesis carried out a comparative analysis of three different computer software packages - FSL, SPM and FuncTool - for the processing of fMRI scans.

PURPOSE

The aim of the thesis was the analysis of the volume of regions functionally active during the stimulation of the centres evaluated as well as the location of those regions in relation to the tumour boundaries, and then the comparison of the results.

MATERIAL AND METHODS

Thirty eight patients with a diagnosed tumour of the left hemisphere, qualified for a neurosurgical operation, underwent fMRI. The functions of speech, motion and sensation were evaluated. Imaging data were processed for all the acquired scans with the use of each of the three software packages assessed.

RESULTS

For the FuncTool software package the calculated differences in the distances were several times greater than those calculated using FSL and SPM. The differences in the volume of the functionally active regions derived from the calculations with the use of the FSL and SPM software packages were statistically different for four out of the six functions evaluated.

CONCLUSIONS

The conclusions of the analysis in question showed that the FSL and SPM packages could be used interchangeably in the functional mapping of the brain with the purpose of the planning of neurosurgical operations. The FuncTool software package is less precise than FSL and SPM.

A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies

This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like "Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties". Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.

A Series Registration Framework to Recover Resting-State Functional Magnetic Resonance Data Degraded By Motion

Data retention is a significant problem in the medical imaging domain. For example, resting-state functional magnetic resonance images (rs-fMRIs) are invaluable for studying neurodevelopment but are highly susceptible to corruption due to patient motion. The effects of patient motion can be reduced through post-acquisition techniques such as volume registration. Traditional volume registration minimizes the global differences between all volumes in the rs-fMRI sequence and a designated reference volume. We suggest using the spatiotemporal relationships between subsequent image volumes to inform the registration: they are used initialize each volume registration to reduce local differences between volumes while minimizing global differences. We apply both the traditional and novel registration methods to a set of healthy human neonatal rs-fMRIs with significant motion artifacts (N=17). Both methods impacted the mean and standard deviation of the image sequences' correlation ratio matrices similarly; however, the novel framework was more effective in meeting gold standard motion thresholds.

Improved motion correction of submillimetre 7T fMRI time series with Boundary-Based Registration (BBR)

Ultra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. For instance, the method results in a 15% increase in linear discriminant contrast, a cross-validated estimate of multivariate discriminability. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.

Reliability of BOLD signals in chronic stroke-induced aphasia

Investigating the neurobiology of language impairment and treatment in chronic stroke aphasia using fMRI requires an understanding of measurement variability within and between participants. In this multicenter study, we evaluated the scan-rescan reliability of an auditory and visual (written) story comprehension paradigm in stroke participants with aphasia (N = 65) and healthy controls (N = 22). The multi-modal task was conducted twice (~1 week apart) on separate visits upon study enrolment and twice again at completion three months later. A non-language visuomotor task was studied in the aphasia group only, which was conducted once per time point (3 months apart). While participants were asked to make responses during the comprehension task, these in-scanner responses were not recorded. Reliability was assessed using intraclass correlation coefficient (ICC) at both group and individual participant levels. The visual story comprehension condition had higher reliability than the auditory condition in both groups, with participants with aphasia exhibiting lower reliability than controls in both conditions (stroke ICC = .43, healthy ICC = .81). Differences in reliability within the group of participants with aphasia were found to be partially explained by overall language impairment as well as greater head motion. In the participants with aphasia, the visuomotor paradigm was found to have greater reliability than the story comprehension task at equivalent interscan intervals (visuomotor = 0.50, comprehension = 0.34), and its reliability was not associated with language impairment. This work highlights the importance of considering the reliability of fMRI tasks in aphasia research, provides strategies to improve reliability and has potential implications for the field of clinical neuroimaging in general.

Correction of respiratory artifacts in MRI head motion estimates

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion

The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 ​mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.

Functional magnetic resonance imaging

Among the range of methods available to assess neurodevelopmental disorders, functional MRI (fMRI) has been a preferred tool of choice. Indeed, fMRI can reveal functional alterations in brain networks, irrespective of their structural integrity. Yet, whether fMRI studies have provided unique added value and influenced the clinical care and assessments in children with these conditions remains controversial. This chapter aims to give an overview of the clinical use of task-based as well as resting-state fMRI in children with neurodevelopmental disorders, such as dyslexia, DLD, and epilepsy. We introduce analysis methods that appear promising (namely PPI and machine learning) and describe strengths and limitations of fMRI in the field of pediatrics. Altogether, we suggest that fMRI has provided us with a unique understanding of some developmental conditions. Indeed, findings from group studies have both informed neuroanatomical models and revealed compensation mechanisms. In addition, improvements have made fMRI an increasingly child-friendly method. Nevertheless, clinicians should be aware of limitations, including (1) lack of replication of results, (2) the limited specificity as a diagnostic tool, and (3) difficulties with interpretation of findings. The use of fMRI in the clinic currently remains restricted, with the exception of epilepsy surgery planning, where it is used routinely.

Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI

The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses. Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics. Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data. Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.

Educational fMRI: From the Lab to the Classroom

Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.

Multi-band SWIFT enables quiet and artefact-free EEG-fMRI and awake fMRI studies in rat

Functional magnetic resonance imaging (fMRI) studies in animal models provide invaluable information regarding normal and abnormal brain function, especially when combined with complementary stimulation and recording techniques. The echo planar imaging (EPI) pulse sequence is the most common choice for fMRI investigations, but it has several shortcomings. EPI is one of the loudest sequences and very prone to movement and susceptibility-induced artefacts, making it suboptimal for awake imaging. Additionally, the fast gradient-switching of EPI induces disrupting currents in simultaneous electrophysiological recordings. Therefore, we investigated whether the unique features of Multi-Band SWeep Imaging with Fourier Transformation (MB-SWIFT) overcome these issues at a high 9.4 T magnetic field, making it a potential alternative to EPI. MB-SWIFT had 32-dB and 20-dB lower peak and average sound pressure levels, respectively, than EPI with typical fMRI parameters. Body movements had little to no effect on MB-SWIFT images or functional connectivity analyses, whereas they severely affected EPI data. The minimal gradient steps of MB-SWIFT induced significantly lower currents in simultaneous electrophysiological recordings than EPI, and there were no electrode-induced distortions in MB-SWIFT images. An independent component analysis of the awake rat functional connectivity data obtained with MB-SWIFT resulted in near whole-brain level functional parcellation, and simultaneous electrophysiological and fMRI measurements in isoflurane-anesthetized rats indicated that MB-SWIFT signal is tightly linked to neuronal resting-state activity. Therefore, we conclude that the MB-SWIFT sequence is a robust preclinical brain mapping tool that can overcome many of the drawbacks of conventional EPI fMRI at high magnetic fields.

Distinctions among real and apparent respiratory motions in human fMRI data

Head motion estimates in functional magnetic resonance imaging (fMRI) scans appear qualitatively different with sub-second image sampling rates compared to the multi-second sampling rates common in the past. Whereas formerly the head appeared still for much of a scan with brief excursions from baseline, the head now appears to be in constant motion, and motion estimates often seem to divulge little information about what is happening in a scan. This constant motion has been attributed to respiratory oscillations that do not alias at faster sampling rates, and investigators are divided on the extent to which such motion is "real" motion or only "apparent" pseudomotion. Some investigators have abandoned the use of motion estimates entirely due to these considerations. Here we investigate the properties of motion in several fMRI datasets sampled at rates between 720 and 1160 ms, and describe 5 distinct kinds of respiratory motion: 1) constant real respiratory motion in the form of head nodding most evident in vertical position and pitch, which can be very large; 2) constant pseudomotion at the same respiratory rate as real motion, occurring only in the phase encode direction; 3) punctate real motions occurring at times of very deep breaths; 4) a low-frequency pseudomotion in only the phase encode direction at and after very deep breaths; 5) slow modulation of vertical and anterior-posterior head position by the respiratory envelope. We reformulate motion estimates in light of these considerations and obtain good concordance between motion estimates, physiologic records, image quality measures, and events evident in the fMRI signals. We demonstrate how variables describing respiration or body habitus separately scale with distinct kinds of head motion. We also note heritable aspects of respiration and motion.