A LOW RANK AND SPARSE PARADIGM FREE MAPPING ALGORITHM FOR DECONVOLUTION OF FMRI DATA

Date Added to IEEE Xplore: 25 May 2021Current deconvolution algorithms for functional magnetic resonance imaging (fMRI) data are hindered by widespread signal changes arising from motion or physiological processes (e.g. deep breaths) that can be interpreted incorrectly as neuronal-related hemodynamic events. This work proposes a novel deconvolution approach that simultaneously estimates global signal fluctuations and neuronalrelated activity with no prior information about the timings of the blood oxygenation level-dependent (BOLD) events by means of a low rank plus sparse decomposition algorithm. The performance of the proposed method is evaluated on simulated and experimental fMRI data, and compared with state-of-the-art sparsity-based deconvolution approaches and with a conventional analysis that is aware of the temporal model of the neuronal-related activity. We demonstrate that the novel low-rank and sparse paradigm free mapping algorithm can estimate global signal fluctuations related to motion in our task, while estimating the neuronal-related activity with high fidelity

Introduction to quantitative susceptibility mapping and susceptibility weighted imaging.

Quantitative Susceptibility Mapping (QSM) and Susceptibility Weighted Imaging (SWI) are magnetic resonance imaging techniques that measure and display differences in the magnetisation that is induced in tissues, i.e. their magnetic susceptibility, when placed in the strong external magnetic field of an MRI system. SWI produces images in which the contrast is heavily weighted by the intrinsic tissue magnetic susceptibility. It has been applied in a wide range of clinical applications. QSM is a further advancement of this technique that requires sophisticated post-processing in order to provide quantitative maps of tissue susceptibility. This review explains the steps involved in both SWI and QSM as well as describing some of their uses in both clinical and research applications.Mr Ruetten is funded by a Medical Research Council/Sackler Stipend. The project was supported by the Addenbrooke’s Charitable Trust and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust]

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

Experimental MRI monitoring of renal blood volume fraction variations en route to renal magnetic resonance oximetry

Diagnosis of early-stage acute kidney injury (AKI) will benefit from a timely identification of local tissue hypoxia. Renal tissue hypoxia is an early feature in AKI pathophysiology, and renal oxygenation is increasingly being assessed through T(2)*-weighted magnetic resonance imaging (MRI). However, changes in renal blood volume fraction (BVf) confound renal T(2)*. The aim of this study was to assess the feasibility of intravascular contrast-enhanced MRI for monitoring renal BVf during physiological interventions that are concomitant with variations in BVf and to explore the possibility of correcting renal T(2)* for BVf variations. A dose-dependent study of the contrast agent ferumoxytol was performed in rats. BVf was monitored throughout short-term occlusion of the renal vein, which is known to markedly change renal blood partial pressure of O(2) and BVf. BVf calculated from MRI measurements was used to estimate oxygen saturation of hemoglobin (SO(2)). BVf and SO(2) were benchmarked against cortical data derived from near-infrared spectroscopy. As estimated from magnetic resonance parametric maps of T(2) and T(2)*, BVf was shown to increase, whereas SO(2) was shown to decline during venous occlusion (VO). This observation could be quantitatively reproduced in test-retest scenarios. Changes in BVf and SO(2) were in good agreement with data obtained from near-infrared spectroscopy. Our findings provide motivation to advance multiparametric MRI for studying AKIs, with the ultimate goal of translating MRI-based renal BVf mapping into clinical practice en route noninvasive renal magnetic resonance oximetry as a method of assessing AKI and progression to chronic damage

The Graded Change in Connectivity across the Ventromedial Prefrontal Cortex Reveals Distinct Subregions.

The functional heterogeneity of the ventromedial prefrontal cortex (vmPFC) suggests it may include distinct functional subregions. To date these have not been well elucidated. Regions with differentiable connectivity (and as a result likely dissociable functions) may be identified using emergent data-driven approaches. However, prior parcellations of the vmPFC have only considered hard splits between distinct regions, although both hard and graded connectivity changes may exist. Here we determine the full pattern of change in structural and functional connectivity across the vmPFC for the first time and extract core distinct regions. Both structural and functional connectivity varied along a dorsomedial to ventrolateral axis from relatively dorsal medial wall regions to relatively lateral basal orbitofrontal cortex. The pattern of connectivity shifted from default mode network to sensorimotor and multimodal semantic connections. This finding extends the classical distinction between primate medial and orbital regions by demonstrating a similar gradient in humans for the first time. Additionally, core distinct regions in the medial wall and orbitofrontal cortex were identified that may show greater correspondence to functional differences than prior hard parcellations. The possible functional roles of the orbitofrontal cortex and medial wall are discussed.This work was supported by a doctoral prize from the Engineering and Physical Sciences Research Council and a British Academy Postdoctoral Fellowship (pf170068) to RLJ, a studentship from the BBSRC (BB/J014478/1) to CJB, and programme grants from the Medical Research Council (MR/J004146/1 & MR/R023883/1) to MALR

Doctor of Philosophy

dissertationThis dissertation presents original research that improves the ability of magnetic resonance imaging (MRI) to measure temperature in aqueous tissue using the proton resonance frequency (PRF) shift and T1 measurements in fat tissue in order to monitor focused ultrasound (FUS) treatments. The inherent errors involved in measuring the longitudinal relaxation time T1 using the variable flip angle method with a two-dimensional (2D) acquisition are presented. The edges of the slice profile can contribute a significant amount of signal for large flip angles at steady state, which causes significant errors in the T1 estimate. Only a narrow range of flip angle combinations provided accurate T1 estimates. Respiration motion causes phase artifacts, which lead to errors when measuring temperature changes using the PRF method. A respiration correction method for 3D imaging temperature of the breast is presented. Free induction decay (FID) navigators were used to measure and correct phase offsets induced by respiration. The precision of PRF temperature measurements within the breast was improved by an average factor of 2.1 with final temperature precision of approximately 1 °C. Locating the position of the ultrasound focus in MR coordinates of an ultrasound transducer with multiple degrees of freedom can be difficult. A rapid method for predicting the position using 3 tracker coils with a special MRI pulse iv sequence is presented. The Euclidean transformation of the coil's current positions to their calibration positions was used to predict the current focus position. The focus position was predicted to within approximately 2.1 mm in less than 1 s. MRI typically has tradeoffs between imaging field of view and spatial and temporal resolution. A method for acquiring a large field of view with high spatial and temporal resolution is presented. This method used a multiecho pseudo-golden angle stack of stars imaging sequence to acquire the large field of view with high spatial resolution and k-space weighted image contrast (KWIC) to increase the temporal resolution. The pseudo-golden angle allowed for removal of artifacts introduced by the KWIC reconstruction algorithm. The multiple echoes allowed for high readout bandwidth to reduce blurring due to off resonance and chemical shift as well as provide separate water/fat images, estimates of the initial signal magnitude M(0), T2 * time constant, and combination of echo phases. The combined echo phases provided significant improvement to the PRF temperature precision, and ranged from ~0.3-1.0 °C within human breast. M(0) and T2 * values can possibly be used as a measure of temperature in fat

Signature for Pain Recovery IN Teens (SPRINT): protocol for a multisite prospective signature study in chronic musculoskeletal pain

INTRODUCTION: Current treatments for chronic musculoskeletal (MSK) pain are suboptimal. Discovery of robust prognostic markers separating patients who recover from patients with persistent pain and disability is critical for developing patient-specific treatment strategies and conceiving novel approaches that benefit all patients. Given that chronic pain is a biopsychosocial process, this study aims to discover and validate a robust prognostic signature that measures across multiple dimensions in the same adolescent patient cohort with a computational analysis pipeline. This will facilitate risk stratification in adolescent patients with chronic MSK pain and more resourceful allocation of patients to costly and potentially burdensome multidisciplinary pain treatment approaches. METHODS AND ANALYSIS: Here we describe a multi-institutional effort to collect, curate and analyse a high dimensional data set including epidemiological, psychometric, quantitative sensory, brain imaging and biological information collected over the course of 12 months. The aim of this effort is to derive a multivariate model with strong prognostic power regarding the clinical course of adolescent MSK pain and function. ETHICS AND DISSEMINATION: The study complies with the National Institutes of Health policy on the use of a single internal review board (sIRB) for multisite research, with Cincinnati Children's Hospital Medical Center Review Board as the reviewing IRB. Stanford's IRB is a relying IRB within the sIRB. As foreign institutions, the University of Toronto and The Hospital for Sick Children (SickKids) are overseen by their respective ethics boards. All participants provide signed informed consent. We are committed to open-access publication, so that patients, clinicians and scientists have access to the study data and the signature(s) derived. After findings are published, we will upload a limited data set for sharing with other investigators on applicable repositories. TRIAL REGISTRATION NUMBER: NCT04285112

The graded change in connectivity across the ventromedial prefrontal cortex reveals distinct subregions

The functional heterogeneity of the ventromedial prefrontal cortex (vmPFC) suggests it may include distinct functional subregions. To date these have not been well elucidated. Regions with differentiable connectivity (and as a result likely dissociable functions) may be identified using emergent data-driven approaches. However, prior parcellations of the vmPFC have only considered hard splits between distinct regions, although both hard and graded connectivity changes may exist. Here we determine the full pattern of change in structural and functional connectivity across the vmPFC for the first time and extract core distinct regions. Both structural and functional connectivity varied along a dorsomedial to ventrolateral axis from relatively dorsal medial wall regions to relatively lateral basal orbitofrontal cortex. The pattern of connectivity shifted from default mode network to sensorimotor and multimodal semantic connections. This finding extends the classical distinction between primate medial and orbital regions by demonstrating a similar gradient in humans for the first time. Additionally, core distinct regions in the medial wall and orbitofrontal cortex were identified that may show greater correspondence to functional differences than prior hard parcellations. The possible functional roles of the orbitofrontal cortex and medial wall are discussed.peer-reviewe

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

Available online 6 March 2021.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.This research was supported by the European Union’s Horizon 2020 research and innovation program ( Marie Sk ł odowska-Curie grant agreement No. 713673 ), a fellowship from La Caixa Foundation (ID 100010434 , fellowship code LCF/BQ/IN17/11620063 ), the Spanish Ministry of Economy and Competitiveness ( Ramon y Cajal Fellowship, RYC-2017- 21845 ), the Spanish State Research Agency (BCBL “Severo Ochoa ”excellence accreditation, SEV- 2015-490 ), the Basque Govern- ment ( BERC 2018-2021 and PIBA_2019_104 ), the Spanish Ministry of Science, Innovation and Universities (MICINN; PID2019-105520GB-100 and FJCI-2017-31814 ), and the Eunice Kennedy Shriver National Insti- tute of Child Health and Human Development of the National Institutes of Health under award number K12HD073945