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

Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking.

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2×2×3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p&lt;0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies

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

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

Reprint of: Minimizing noise in pediatric task-based functional MRI; Adolescents with developmental disabilities and typical development

Functional Magnetic Resonance Imaging (fMRI) represents a powerful tool with which to examine brain functioning and development in typically developing pediatric groups as well as children and adolescents with clinical disorders. However, fMRI data can be highly susceptible to misinterpretation due to the effects of excessive levels of noise, often related to head motion. Imaging children, especially with developmental disorders, requires extra considerations related to hyperactivity, anxiety and the ability to perform and maintain attention to the fMRI paradigm. We discuss a number of methods that can be employed to minimize noise, in particular movement-related noise. To this end we focus on strategies prior to, during and following the data acquisition phase employed primarily within our own laboratory. We discuss the impact of factors such as experimental design, screening of potential participants and pre-scan training on head motion in our adolescents with developmental disorders and typical development. We make some suggestions that may minimize noise during data acquisition itself and finally we briefly discuss some current processing techniques that may help to identify and remove noise in the data. Many advances have been made in the field of pediatric imaging, particularly with regard to research involving children with developmental disorders. Mindfulness of issues such as those discussed here will ensure continued progress and greater consistency across studies

Improving Data Quality for High Resolution Functional MRI in Cognitive Neuroscience Applications

Since the first successful Magnetic Resonance Imaging (MRI) image was produced by Paul Lauterbur in 1973, the field of MRI has been improving by leaps and bounds. The number of MRI and functional MRI (fMRI) papers have sky rocketed over the last decade, alongside with advancements in MRI field strength and techniques. In this thesis, I explore various methods for improving data quality for high resolution fMRI in 3T and 7T MRI scanners. Firstly, I studied the effect of Prospective Motion Correction (PMC) on 3T data using a simple visual paradigm. In contrast to most conventional techniques that use retrospective motion correction (RMC), PMC collects real-time motion data and uses it to update the acquisition field of view prior to each radiofrequency (RF) pulse. This allows for the correction of spin-history effects and intra-volume distortions. In this study, I utilized a secondary optical camera in the bore of the scanner to track a Moiré phase marker attached to the participant via a custom-moulded dental mouthpiece. I demonstrated that the camera is capable of accurately tracking the participant’s head motion. While simple metrics such as temporal signal-to-noise ratio (tSNR) and functional contrast-to-noise ratio (fCNR) showed no difference between the two methods, more complex analysis such as the Linear Discriminant Contrast (LDC) showed that the PMC data was indeed cleaner than the RMC data for higher resolution data. Next, I compared the sensitivity of two multi-voxel pattern analysis (MVPA) methods, Support Vector Machines (SVM) and Linear Discriminant Contrast (LDC). MVPA attempts to capture the relationship between the spatial fMRI activity and the experimental manipulations by treating it as a supervised learning problem. This is a promising technique that can capture spatial activation patterns that are lost in univariate analysis. I demonstrated through both actual fMRI data and computer simulations that LDC is a better MVPA metric than SVM. This agrees with our theory that SVM has more inherent variability and less sensitivity due to its limitations, discretization of results, rigid decision boundaries and ceiling effects. Subsequently, I analysed the quality of fMRI data acquired in a 3T Prisma scanner vs a 7T Terra scanner using a visual attention paradigm. While 7T scanners are becoming increasingly commonplace with over 70 of them worldwide now, the higher field strength also comes with its own host of problems. Field inhomogeneities and artefacts are a larger problem at 7T, and the smaller voxel sizes also cause data to be more susceptible to motion. As such, it is important to establish if there is a real benefit to using a 7T scanner. I observed that both 3T and 7T data showed similar trends with comparable z-scores and concluded that both scanners yielded comparable results. However, the 7T data was acquired at a much higher resolution (64x smaller volume per voxel) and thus, these results indicate a benefit of 7T as comparable results were achieved in spite of the smaller voxel volume. I hypothesized that acquiring data in a 7T scanner would be informative if studies sought to probe further into laminar or columnal structures which require submillimetre resolution, while a 3T scanner should suffice for studies looking at coarse regional activations. I did not explore the benefits of using 7T MRI at coarser resolutions. I also assessed the utility of boundary-based registration (BBR) realignment to improve on conventional RMC techniques to realign fMRI time series. Some motion artefacts affect the image in non-rigid ways and thus, voxel-based registration (VBR), generally utilized in conventional RMC, might be insufficient to properly realign fMRI time series. I demonstrated that BBR realignment outperforms VBR realignment across multiple metrics at submillimetre resolution, but no difference was observed at lower resolutions. Lastly, I examined the process of cleaning up 7T fMRI data for laminar analysis. Gradient echo (GE) sequences have been widely used for fMRI studies due to the high signal-to-noise ratio (SNR) and low specific absorption rate (SAR) relative to other sequences. However, GE sequences have been shown to exhibit superficial bias due to the presence of draining veins. I employed two methods- excluding venous voxels and utilizing a regression analysis, to remove superficial bias in an attempt to unmask any laminar effects for a visual attention task. In summary, I have explored various methods of optimizing fMRI data, ranging from initial setup decisions, such as which field strength scanner to use, to final MVPA analysis methods. I also analysed methods to remove motion artefacts, through both PMC and RMC, as well as post-processing methods to remove superficial bias in laminar data.A*STAR PhD Scholarshi

Methods for Physiological Artifact Correction in Oscillating Steady State Imaging

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that provides an unparalleled ability to non-invasively study brain activity. Since its inception in the early 1990s, fMRI has become a dominant tool in studying neurological responses to tasks and stimuli and has been critical in our evolving understanding of brain mapping. These achievements in neuroscience would not be possible without critical breakthroughs in MRI theory and hardware advancements, which continue to increase the speed and resolution of fMRI acquisitions. This dissertation explores a highly signal efficient fMRI imaging strategy known as Oscillating Steady-State Imaging (OSSI) and presents specialized artifact compensation strategies for addressing the practical challenges of the OSSI method. First, we develop analytical models and simulations of OSSI, which describe how the signal magnitude varies as a function of frequency. These simulations are then used to study how respiration-induced frequency changes cause artifactual signal fluctuations to a signal timecourse. Our simulations show that the severity of respiration artifacts changes with initial off-resonance. Furthermore, we show that respiration artifacts are primarily caused by transient signal effects rather than changes to steady-state magnitude. These findings inform the two correction strategies proposed in the remainder of the dissertation. The second portion of this work describes "OSSCOR," a retrospective method to correct timecourse magnitude changes caused by temporally varying frequency. We show how the OSSI signal exhibits a frequency-time duality which can be used to reshape structured physiological noise into a low-rank matrix. We then use principal component analysis in a data-driven correction strategy to create nuisance regressors for subsequent fMRI analysis. We show that free induction decay (FID) signals can also be used to create nuisance regressors in the same way in a variation of our method, referred to as "F-OSSCOR." Both OSSCOR and F-OSSCOR were found to significantly improve the functional sensitivity and signal stability compared to polynomial detrending alone. OSSCOR was also found to significantly outperform a standard data-driven correction method, CompCor. Finally, we present a prospective correction method which utilizes FID measurements to estimate and correct for B0 changes in real-time. Prospective correction has the potential to outperform retrospective correction methods by directly reducing perturbations to steady-state magnetization during acquisition. We first present the results of a feasibility analysis where simulation was used to determine how scan parameters would affect correction performance. We then developed a prospective correction application using a specialized scanner control platform to perform data analysis and parameter adjustment in real-time. Our initial fMRI proof-of-concept shows that real-time correction can increase the number of activated voxels and improve overall image stability as measured by tSNR.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155132/1/amoscao_1.pd

Abnormal functional activation and maturation of ventromedial prefrontal cortex and cerebellum during temporal discounting in autism spectrum disorder

People with autism spectrum disorder (ASD) have poor decision-making and temporal foresight. This may adversely impact on their everyday life, mental health, and productivity. However, the neural substrates underlying poor choice behavior in people with ASD, or its' neurofunctional development from childhood to adulthood, are unknown. Despite evidence of atypical structural brain development in ASD, investigation of functional brain maturation in people with ASD is lacking. This cross-sectional developmental fMRI study investigated the neural substrates underlying performance on a temporal discounting (TD) task in 38 healthy (11–35 years old) male adolescents and adults with ASD and 40 age, sex, and IQ-matched typically developing healthy controls. Most importantly, we assessed group differences in the neurofunctional maturation of TD across childhood and adulthood. Males with ASD had significantly poorer task performance and significantly lower brain activation in typical regions that mediate TD for delayed choices, in predominantly right hemispheric regions of ventrolateral/dorsolateral prefrontal cortices, ventromedial prefrontal cortex, striatolimbic regions, and cerebellum. Importantly, differential activation in ventromedial frontal cortex and cerebellum was associated with abnormal functional brain maturation; controls, in contrast to people with ASD, showed progressively increasing activation with increasing age in these regions; which furthermore was associated with performance measures and clinical ASD measures (stereotyped/restricted interests). Findings provide first cross-sectional evidence that reduced activation of TD mediating brain regions in people with ASD during TD is associated with abnormal functional brain development in these regions between childhood and adulthood, and this is related to poor task performance and clinical measures of ASD

Mechanisms of motor learning: by humans, for robots

Whenever we perform a movement and interact with objects in our environment, our central nervous system (CNS) adapts and controls the redundant system of muscles actuating our limbs to produce suitable forces and impedance for the interaction. As modern robots are increasingly used to interact with objects, humans and other robots, they too require to continuously adapt the interaction forces and impedance to the situation. This thesis investigated the motor mechanisms in humans through a series of technical developments and experiments, and utilized the result to implement biomimetic motor behaviours on a robot. Original tools were first developed, which enabled two novel motor imaging experiments using functional magnetic resonance imaging (fMRI). The first experiment investigated the neural correlates of force and impedance control to understand the control structure employed by the human brain. The second experiment developed a regressor free technique to detect dynamic changes in brain activations during learning, and applied this technique to investigate changes in neural activity during adaptation to force fields and visuomotor rotations. In parallel, a psychophysical experiment investigated motor optimization in humans in a task characterized by multiple error-effort optima. Finally a computational model derived from some of these results was implemented to exhibit human like control and adaptation of force, impedance and movement trajectory in a robot