BOLD fMRI Simulation

Background: Brain functional magnetic resonance imaging (fMRI) is sensitive to changes in blood oxygenation level dependent (BOLD) brain magnetic states. The fMRI scanner produces a complex-valued image, but the calculation of the original BOLD magnetic source is not a mathematically tractable problem. We conduct numeric simulations to understand the BOLD fMRI model

Modeling brain dynamics in brain tumor patients using the virtual brain

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance

Motion Correction in fMRI by Mapping Slice-to-Volume with Concurrent Field-Inhomogeneity Correction

Head motion is the major source of error in measuring intensity changes related to given stimuli in fMRI. The effects of head motion are image shifts and field inhomogeneity variations which cause local changes in geometric distortions. The previously developed motion correction method, mapping slice-to-volume (MSV), retrospectively remaps slices that are shifted by head motion to their spatially correct locations in an anatomical reference. Images exhibiting spatially varying geometric distortions require non-linear mapping solutions. An accurate field map can be used for the correction of such spatial distortions. However, field-map changes with head motion and, in practice, only one field-map is available typically. This work evaluates the improved motion correction capability of MSV with concurrent iterative field-corrected reconstruction using only an initial field-map. The results from simulated motion data show effective convergence and accuracy in image registration for the correction of image artifacts complicated by the motion induced field effects.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85942/1/Fessler206.pd

Investigation of Spatio-Temporal Effects of fMRI Visual Field Mapping Techniques on V1

Blood oxygenation level dependent functional magnetic resonance imaging has been used extensively for mapping the representation of the visual field within the human brain. Visual field mapping using fMRI has been used clinically to assess patients with cortical pathology and to plan surgical treatment impacting the visual system. The accuracy of fMRI-based visual field mapping methods needs to be better understood for clinical use. This accuracy can be important for presurgical mapping of brain function near a tumor resection site since inaccurate rendition of the underlying neural function could lead to inappropriate resection of viable brain tissue. The most widely used method for visual field mapping is temporal phase mapping. This dissertation investigates the accuracy of temporal phase mapping, specifically focused on the detection of polar angle visual field locations in primary visual cortex. Early studies show that polar angle positions are not uniformly distributed as suggested by animal studies. These non-uniformities are seen as relatively under-represented areas in the visual field maps used to display the fMRI data. This dissertation shows that temporal phase mapping is susceptible to hemodynamic distortions that lead to missassignment of visual field locations. Further analysis of the non-uniformity in the frequency distribution of voxels representing different angular position within the visual field shows an under-representation of locations near the vertical meridia in V1. These results led to the development of a new retinotopic mapping technique, code-based mapping. The main reason for developing a new retinotopic mapping technique was to reduce the under-representations of vertical meridia posed by using temporal phase mapping when assigning a stimulus location to a voxel. This dissertation shows that code-based mapping is a viable method for mapping visual field locations and produces a uniform distribution of voxels representing different angular positions within the visual field. Furthermore, the code-based mapping method is less susceptible to the hemodynamic biases than temporal phase mapping. With respect to clinical utility of fMRI mapping techniques, the code-based mapping shows a greater potential to accurately map a patient\u27s visual field in the presence of a tumor or other malformations that can induce large noise effects in the fMRI voxel responses

Mitigating susceptibility-induced distortions in high-resolution 3DEPI fMRI at 7T

Geometric distortion is a major limiting factor for spatial specificity in high-resolution fMRI using EPI readouts and is exacerbated at higher field strengths due to increased B0 field inhomogeneity. Prominent correction schemes are based on B0 field-mapping or acquiring reverse phase-encoded (reversed-PE) data. However, to date, comparisons of these techniques in the context of fMRI have only been performed on 2DEPI data, either at lower field or lower resolution. In this study, we investigate distortion compensation in the context of sub-millimetre 3DEPI data at 7T. B0 field-mapping and reversed-PE distortion correction techniques were applied to both partial coverage BOLD-weighted and whole brain MT-weighted 3DEPI data with matched distortion. Qualitative assessment showed overall improvement in cortical alignment for both correction techniques in both 3DEPI fMRI and whole-brain MT-3DEPI datasets. The distortion-corrected MT-3DEPI images were quantitatively evaluated by comparing cortical alignment with an anatomical reference using dice coefficient (DC) and correlation ratio (CR) measures. These showed that B0 field-mapping and reversed-PE methods both improved correspondence between the MT-3DEPI and anatomical data, with more substantial improvements consistently obtained using the reversed-PE approach. Regional analyses demonstrated that the largest benefit of distortion correction, and in particular of the reversed-PE approach, occurred in frontal and temporal regions where susceptibility-induced distortions are known to be greatest, but had not led to complete signal dropout. In conclusion, distortion correction based on reversed-PE data has shown the greater capacity for achieving faithful alignment with anatomical data in the context of high-resolution fMRI at 7T using 3DEPI

Neural mechanisms of reactivation-induced updating that enhance and distort memory

We remember a considerable number of personal experiences because we are frequently reminded of them, a process known as memory reactivation. Although memory reactivation helps to stabilize and update memories, reactivation may also introduce distortions if novel information becomes incorporated with memory. Here we used functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms mediating reactivation-induced updating in memory for events experienced during a museum tour. During scanning, participants were shown target photographs to reactivate memories from the museum tour followed by a novel lure photograph from an alternate tour. Later, participants were presented with target and lure photographs and asked to determine whether the photographs showed a stop they visited during the tour. We used a subsequent memory analysis to examine neural recruitment during reactivation that was associated with later true and false memories. We predicted that the quality of reactivation, as determined by online ratings of subjective recollection, would increase subsequent true memories but also facilitate incorporation of the lure photograph, thereby increasing subsequent false memories. The fMRI results revealed that the quality of reactivation modulated subsequent true and false memories via recruitment of left posterior parahippocampal, bilateral retrosplenial, and bilateral posterior inferior parietal cortices. However, the timing of neural recruitment and the way in which memories were reactivated contributed to differences in whether memory reactivation led to distortions or not. These data reveal the neural mechanisms recruited during memory reactivation that modify how memories will be subsequently retrieved, supporting the flexible and dynamic aspects of memory

Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement

Most motion correction methods work by aligning a set of volumes together, or to a volume that represents a reference location. These are based on an implicit assumption that the subject remains motionless during the several seconds it takes to acquire all slices in a volume, and that any movement occurs in the brief moment between acquiring the last slice of one volume and the first slice of the next. This is clearly an approximation that can be more or less good depending on how long it takes to acquire one volume and in how rapidly the subject moves. In this paper we present a method that increases the temporal resolution of the motion correction by modelling movement as a piecewise continous function over time. This intra-volume movement correction is implemented within a previously presented framework that simultaneously estimates distortions, movement and movement-induced signal dropout. We validate the method on highly realistic simulated data containing all of these effects. It is demonstrated that we can estimate the true movement with high accuracy, and that scalar parameters derived from the data, such as fractional anisotropy, are estimated with greater fidelity when data has been corrected for intra-volume movement. Importantly, we also show that the difference in fidelity between data affected by different amounts of movement is much reduced when taking intra-volume movement into account. Additional validation was performed on data from a healthy volunteer scanned when lying still and when performing deliberate movements. We show an increased correspondence between the “still” and the “movement” data when the latter is corrected for intra-volume movement. Finally we demonstrate a big reduction in the telltale signs of intra-volume movement in data acquired on elderly subjects

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

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 explore different ways to obtain B0 predictions at different head positions. Methods FM were predicted from iterative simulations with four field factors: 1) sample induced B0 field, 2) system's spherical harmonic shim field, 3) perturbing field originating outside the field of view, 4) sequence phase errors. The simulation was improved by including local susceptibility sources estimated from UTE scans and position-specific masks. The estimation performance of the simulated FMs and a transformed FM, obtained from the measured reference FM, were compared with the actual FM at different head positions. Results The transformed FM provided inconsistent results for large head movements (>5 degree rotation), 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

Image Artifacts in Concurrent Transcranial Magnetic Stimulation (TMS) and fMRI Caused by Leakage Currents: Modeling and Compensation

Purpose: To characterize and eliminate a new type of image artifact in concurrent transcranial magnetic stimulation and functional MRI (TMS-fMRI) caused by small leakage currents originating from the high-voltage capacitors in the TMS stimulator system.Materials and Methods: The artifacts in echo-planar images (EPI) caused by leakage currents were characterized and quantified in numerical simulations and phantom studies with different phantom-coil geometries. A relay-diode combination was devised and inserted in the TMS circuit that shorts the leakage current. Its effectiveness for artifact reduction was assessed in a phantom scan resembling a realistic TMS-fMRI experiment.Results: The leakage-current-induced signal changes exhibited a multipolar spatial pattern and the maxima exceeded 1% at realistic coil-cortex distances. The relay-diode combination effectively reduced the artifact to a negligible level.Conclusion: The leakage-current artifacts potentially obscure effects of interest or lead to false-positives. Since the artifact depends on the experimental setup and design (eg. amplitude of the leakage current, coil orientation, paradigm. EPI parameters), we recommend its assessment for each experiment. The relay-diode combination can eliminate the artifacts if necessary

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