neuRosim: An R Package for Generating fMRI Data

Studies that validate statistical methods for functional magnetic resonance imaging (fMRI) data often use simulated data to ensure that the ground truth is known. However, simulated fMRI data are almost always generated using in-house procedures because a well-accepted simulation method is lacking. In this article we describe the R package neuRosim, which is a collection of data generation functions for neuroimaging data. We will demonstrate the possibilities to generate data from simple time series to complete 4D images and the possibilities for the user to create her own data generation method

Investigation of spatial resolution, partial volume effects and smoothing in functional MRI using artificial 3D time series.

This work addresses the balance between temporal signal-to-noise ratio (tSNR) and partial volume effects (PVE) in functional magnetic resonance imaging (fMRI) and investigates the impact of the choice of spatial resolution and smoothing. In fMRI, since physiological time courses are monitored, tSNR is of greater importance than image SNR. Improving SNR by an increase in voxel volume may be of negligible benefit when physiological fluctuations dominate the noise. Furthermore, at large voxel volumes, PVE are more pronounced, leading to an overall loss in performance. Artificial fMRI time series, based on high-resolution anatomical data, were used to simulate BOLD activation in a controlled manner. The performance was subsequently quantified as a measure of how well the resulted activation matched the simulated activation. The performance was highly dependent on the spatial resolution. At high contrast-to-noise ratio (CNR), the optimal voxel volume was small, i.e. in the region of 2(3) mm(3). It was also shown that using a substantially larger voxel volume in this case could potentially negate the CNR benefits. The optimal smoothing kernel width was dependent on the CNR, being larger at poor CNR. At CNR >1, little or no smoothing proved advantageous. The use of artificial time series gave an opportunity to quantitatively investigate the effects of partial volume and smoothing in single subject fMRI. It was shown that a proper choice of spatial resolution and smoothing kernel width is important for fMRI performance

3T vs. 7T fMRI: capturing early human memory consolidation after motor task utilizing the observed higher functional specificity of 7T

ObjectiveFunctional magnetic resonance imaging (fMRI) visualizes brain structures at increasingly higher resolution and better signal-to-noise ratio (SNR) as field strength increases. Yet, mapping the blood oxygen level dependent (BOLD) response to distinct neuronal processes continues to be challenging. Here, we investigated the characteristics of 7 T-fMRI compared to 3 T-fMRI in the human brain beyond the effect of increased SNR and verified the benefits of 7 T-fMRI in the detection of tiny, highly specific modulations of functional connectivity in the resting state following a motor task.Methods18 healthy volunteers underwent two resting state and a stimulus driven measurement using a finger tapping motor task at 3 and 7 T, respectively. The SNR for each field strength was adjusted by targeted voxel size variation to minimize the effect of SNR on the field strength specific outcome. Spatial and temporal characteristics of resting state ICA, network graphs, and motor task related activated areas were compared. Finally, a graph theoretical approach was used to detect resting state modulation subsequent to a simple motor task.ResultsSpatial extensions of resting state ICA and motor task related activated areas were consistent between field strengths, but temporal characteristics varied, indicating that 7 T achieved a higher functional specificity of the BOLD response than 3 T-fMRI. Following the motor task, only 7 T-fMRI enabled the detection of highly specific connectivity modulations representing an “offline replay” of previous motor activation. Modulated connections of the motor cortex were directly linked to brain regions associated with memory consolidation.ConclusionThese findings reveal how memory processing is initiated even after simple motor tasks, and that it begins earlier than previously shown. Thus, the superior capability of 7 T-fMRI to detect subtle functional dynamics promises to improve diagnostics and therapeutic assessment of neurological diseases

Statistical analysis of complex neuroimaging data

This dissertation is composed of two major topics: a) regression models for identifying noise sources in magnetic resonance images, and b) multiscale Adaptive method in neuroimaging studies. The first topic is covered by the first thesis paper. In this paper, we formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion weighted imaging (DWI) and functional MRI (fMRI). Estimation algorithms are introduced to maximize the likelihood function of the three regression models. We also develop a diagnostic procedure for systematically exploring MR images to identify noise components other than simple stochastic noise, and to detect discrepancies between the fitted regression models and MRI data. The diagnostic procedure includes goodness-of-fit statistics, measures of influence, and tools for graphical display. The goodness-of-fit statistics can assess the key assumptions of the three regression models, whereas measures of influence can isolate outliers caused by certain noise components, including motion artifact. The tools for graphical display permit graphical visualization of the values for the goodness-of-fit statistic and influence measures. Finally, we conduct simulation studies to evaluate performance of these methods, and we analyze a real dataset to illustrate how our diagnostic procedure localizes subtle image artifacts by detecting intravoxel variability that is not captured by the regression models. The second topic, multiscale adaptive methods for neuroimaging data, consists of two thesis papers.The goal of the first paper is to develop a multiscale adaptive regression model (MARM) for spatial and adaptive analysis of neuroimaging data. Compared with the existing voxel-wise approach in the analysis of imaging data,MARM has three unique features: being spatial, being hierarchical, and being adaptive. MARM creates a small sphere with a given radius at each location (called voxel), analyzes all observations in the sphere of each voxel, and then uses these consecutively connected spheres across all voxels to capture spatial dependence among imaging observations. MARM builds hierarchically nested spheres by increasing the radius of a spherical neighborhood around each voxel and utilizes information in each of the nested spheres at each voxel. Finally, MARM combine imaging observations with adaptive weights in the voxels within the sphere of the current voxel to adaptively calculate parameter estimates and test statistics. Theoretically, we establish the consistency and asymptotic normality of adaptive estimates and the asymptotic distributions of adaptive test statistics under some mild conditions. Three sets of simulation studies are used to demonstrate the methodology and examine the finite sample performance of the adaptive estimates and test statistics in MARM. We apply MARM to quantify spatiotemporal white matter maturation patterns in early postnatal population using diffusion tensor imaging. Our simulation studies and real data analysis confirm that the MARM significantly outperforms the voxel-wise methods. The goal of the second paper is to develop a multiscale adaptive generalized estimation equation (MAGEE) for spatial and adaptive analysis of longitudinal neuroimaging data. Longitudinal imaging studies have been valuable for better understanding disease progression and normal brain development/aging. Compared to cross-sectional imaging studies, longitudinal imaging studies can increase the statistical power in detecting subtle spatiotemporal changes of brain structure and function. MAGEE is a hierarchical, spatial, semiparametric, and adaptive procedure, compared with the existing voxel-wise approach. The key ideas of MAGEE are to build hierarchically nested spheres with increasing radii at each location, to analyze all observations in the sphere of each voxel using weighted generalized estimating equations, and to use the consecutively connected spheres across all voxels to adaptively capture spatial pattern. Simulation studies and real data analysis clearly show the advantage of MAGEE method over the existing voxel-wise methods. Our results also reveal i) the increase of fractional anisotropy in this early postnatal stage, and ii) five different growth patterns in the brain regions under examination

Developing advanced MR imaging to assess spinal cord function and tract integrity.

The overall purpose of this thesis is to develop a way to match diffusion and functional acquisition techniques in the spinal cord (SC) in order to offer a comprehensive assessment of factors responsible for functional and structural integrity. I began by optimising a pipeline to acquire and process spinal functional data and I finished by matching the functional information with that derived from diffusion imaging (DI) performed during the same scan session as fM RI. In order to characterize the interactions between local structural connections (derived from DI) and functional activation of the SC it has been necessary to develop an imaging protocol that acquires transverse SC images with both modalities, matching their spatial and geometrical characteristics. This is because transverse cord images possess the relevant anatomical information in terms of grey-white matter structure and allow better localisation of the functional response and structural properties within the spinal cord. My main contribution to the field has been: 1. To demonstrate that it is possible to use the “ZOOM” sequence for spinal fM RI 2. To characterize the signal obtained and the comparison of different image analysis approaches 3. To propose a final pipeline for acquisition and analysis of spinal fM RI 4. To demonstrate that there is a dependency of pathological functional and structural changes The same ZOOM-EPI sequence has been applied for all the functional studies reported in this thesis. The outcome of the optimisation for spinal fMRI has been matched by a DI protocol, using standard DI parameters for spinal microstructural characterization and constitutes the final MR protocol used in a pilot study including a group of healthy controls and a group of patients affected by multiple sclerosis (MS). Based on the gathered experience and results from data acquired and analysed over the years I have concluded with some recommendations for future studies and development strategies for structural and functional MRI of the spinal cor

Awareness Lost: a neuroimaging-based comparison between pathological and pharmacological loss of consciousness

Consciousness is a phenomenon that has so far evaded detailed description. However, with the help of modern brain imaging techniques, we can examine some of the mechanisms underlying changes in its prominence. In the experiments described in this thesis, we used structural and functional magnetic resonance imaging to discover new brain alterations occurring during two types of loss of consciousness: pharmacological (propofol anesthesia) and pathological (disorders of consciousness: vegetative state/unresponsive wakefulness syndrome, in which no awareness is assumed, and minimally conscious state, with fluctuating low-level consciousness). In both cases, we found loss of consciousness to be associated with a breakdown of three brain networks involved in higher-order processing: the default mode network, external control network, and salience network. These networks have been associated with internal awareness, external awareness, and saliency detection, respectively. Furthermore, their connectivity with the thalamus was severely disrupted. Our findings suggest that these changes could be a general hallmark of loss of consciousness. Additionally, we developed several novel techniques to examine changing brain dynamics, which could be used to search for other mechanisms underlying loss of consciousness. In contrast to anesthesia, loss of consciousness in patients with disorders of consciousness is the result of structural brain damage. We performed an analysis of white and gray matter damage occurring in these patients and found it to be widespread, with damage in midline default mode network regions potentially discriminating between unconscious and conscious patients.Our results indicate that structural and resting state functional magnetic resonance imaging might have the potential to improve differential diagnosis in disorders of consciousness

Méthodologie et application de l'imagerie de la perfusion cérébrale et de la vasoréactivité par IRM

Le travail méthodologique mis en place durant cette thèse a consisté en l'optimisation des acquisitions et des traitements de données pour l'imagerie quantitative de la perfusion cérébrale et de la vasoréactivité en ASL. Dans un premier temps, une méthode originale pour mesurer la largeur du bolus des spins marqués appelée BoTuS (Bolus Turbo Sampling) a été mise en place et validée afin d'améliorer la quantification de la perfusion cérébrale basale en ASL. Les acquisitions en ASL pulsé ont été comparé aux mesures de perfusion en premier passage gadolinium et en premier passage de produit de contraste iodé en scanner X sur des patients atteints de tumeurs cérébrales. Dans un second temps, afin d'améliorer la qualité des cartes de vasoréactivité en ASL, des analyses des variations physiologiques des sujets ont été implémenté afin d'être utilisé comme modèle dans l'analyse statistique des données. Enfin, ces méthodes optimisées au niveau de l'acquisition et de la chaîne traitement ont été appliquées sur des populations de sujets sains et de patients afin de les valider. Les applications cliniques ont été menées sur des patients porteurs de la maladie d'Alzheimer où l'on a montré une baisse de la vasoréactivité par rapport aux témoins âgés. La perfusion cérébrale et la vasoréactivité de sujets atteints de sténoses a été étudié. Enfin, une étude avant et après acclimatation à l'altitude a montré qu'un séjour de 7 jours à 4365 m augmente le débit sanguin cérébral et diminue la vasoréactivité cérébrale.The methodological aspects implemented during this Ph.D. thesis consisted of the optimization of the acquisitions and data processing of ASL imaging for quantitative assessment of cerebral perfusion and vasoreactivity. First of all, an original technique called BoTuS (Bolus Turbo Sampling) was implemented and validated,with the aim to render the quantification of the pulsed ASL signal more robust. Cerebral blood flow measurements obtained using pulsed ASL were compared to gold standard techniques such as the first passage of gadolinium MRI and CT-scan perfusion in a population of patients with treated brain tumors. Secondly, a new processing technique was tested, taking into account the physiological state of the subject during the exam to model the ASL signal during the vasoreactivity paradigm, and thus to provide more reliable maps at the subject level. Finally, these methods were applied in various studies on healthy subjects and patients. A decrease in vasoreactivity was found in Alzheimer disease patients compared to elderly subjects. Studies on patients with severe stenosis were conducted to test our methods at the subject level. An increase in CBF and a decrease in vasoreactivity in subjects exposed to high altitude at 4365 m during 7 days was demonstrated and correlated to transcranial Doppler results.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF