A Simplified Crossing Fiber Model in Diffusion Weighted Imaging

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models

Interactive Effects of Physical Activity and APOE-ε4 On White Matter Tract Diffusivity in Healthy Elders

Older adult apolipoprotein-E epsilon 4 (APOE-ε4) allele carriers vary considerably in the expression of clinical symptoms of Alzheimer\u27s disease (AD), suggesting that lifestyle or other factors may offer protection from AD-related neurodegeneration. We recently reported that physically active APOE-ε4 allele carriers exhibit a stable cognitive trajectory and protection from hippocampal atrophy over 18 months compared to sedentary ε4 allele carriers. The aim of this study was to examine the interactions between genetic risk for AD and physical activity (PA) on white matter (WM) tract integrity, using diffusion tensor imaging (DTI) MRI, in this cohort of healthy older adults (ages of 65 to 89). Four groups were compared based on the presence or absence of an APOE-ε4 allele (High Risk; Low Risk) and self-reported frequency and intensity of leisure time physical activity (PA) (High PA; Low PA). As predicted, greater levels of PA were associated with greater fractional anisotropy (FA) and lower radial diffusivity in healthy older adults who did not possess the APOE-ε4 allele. However, the effects of PA were reversed in older adults who were at increased genetic risk for AD, resulting in significant interactions between PA and genetic risk in several WM tracts. In the High Risk-Low PA participants, who had exhibited episodic memory decline over the previous 18-months, radial diffusivity was lower and fractional anisotropy was higher, compared to the High Risk-High PA participants. In WM tracts that subserve learning and memory processes, radial diffusivity (DR) was negatively correlated with episodic memory performance in physically inactive APOE-ε4 carriers, whereas DR was positively correlated with episodic memory performance in physically active APOE-ε4 carriers and the two Low Risk groups. The common model of demyelination-induced increase in radial diffusivity cannot directly explain these results. Rather, we hypothesize that PA may protect APOE-ε4 allele carriers from selective neurodegeneration of individual fiber populations at locations of crossing fibers within projection and association WM fiber tracts

Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom.

International audienceAs it provides the only method for mapping white matter fibers in vivo, diffusion MRI tractography is gaining importance in clinical and neuroscience research. However, despite the increasing availability of different diffusion models and tractography algorithms, it remains unclear how to select the optimal fiber reconstruction method, given certain imaging parameters. Consequently, it is of utmost importance to have a quantitative comparison of these models and algorithms and a deeper understanding of the corresponding strengths and weaknesses. In this work, we use a common dataset with known ground truth and a reproducible methodology to quantitatively evaluate the performance of various diffusion models and tractography algorithms. To examine a wide range of methods, the dataset, but not the ground truth, was released to the public for evaluation in a contest, the "Fiber Cup". 10 fiber reconstruction methods were evaluated. The results provide evidence that: 1. For high SNR datasets, diffusion models such as (fiber) orientation distribution functions correctly model the underlying fiber distribution and can be used in conjunction with streamline tractography, and 2. For medium or low SNR datasets, a prior on the spatial smoothness of either the diffusion model or the fibers is recommended for correct modelling of the fiber distribution and proper tractography results. The phantom dataset, the ground truth fibers, the evaluation methodology and the results obtained so far will remain publicly available on: http://www.lnao.fr/spip.php?rubrique79 to serve as a comparison basis for existing or new tractography methods. New results can be submitted to [email protected] and updates will be published on the webpage

A Simplified Crossing Fiber Model in Diffusion Weighted Imaging

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models

Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification

Purpose To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating. Methods The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments. Results The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved. Conclusion The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency

Stratifying chronic stroke patients based on the influence of contralesional motor cortices: an inter-hemispheric inhibition study

Objective: A recent “bimodal-balance recovery” model suggests that contralesional influence varies based on the amount of ipsilesional reserve: inhibitory when there is a large reserve, but supportive when there is a low reserve. Here, we investigated the relationships between contralesional influence (inter-hemispheric inhibition, IHI) and ipsilesional reserve (corticospinal damage/impairment), and also defined a criterion separating subgroups based on the relationships. Methods: Twenty-four patients underwent assessment of IHI using Transcranial Magnetic Stimulation (ipsilateral silent period method), motor impairment using Upper Extremity Fugl-Meyer (UEFM), and corticospinal damage using Diffusion Tensor Imaging and active motor threshold. Assessments of UEFM and IHI were repeated after 5 week-rehabilitation (n=21). Results: Relationship between IHI and baseline UEFM was quadratic with criterion at UEFM 43 (95%conference interval: 40-46). Patients less impaired than UEFM=43 showed stronger IHI with more impairment, whereas patients more impaired than UEFM=43 showed lower IHI with more impairment. Of those made clinically-meaningful functional gains in rehabilitation (n=14), more-impaired patients showed further IHI reduction. Conclusions: A criterion impairment-level can be derived to stratify patient-subgroups based on the bimodal influence of contralesional cortex. Contralesional influence also evolves differently across subgroups following rehabilitation. Significance: The criterion may be used to stratify patients to design targeted, precision treatments

NMR studies of acetylene absorbed on supported platinum catalysts

This thesis describes NMR measurements involving carbon-13, deuterons, and protons in acetylene (C2H2) adsorbed at room temperature on supported platinum catalysts. Measurements from 77"K to 158°K of the spin-lattice relaxation behavior of carbon-13 reveal multiple-exponential behavior. Calculations rule out an anisotropic T 1 as an explanation for the multiple-exponential behavior. Carbon-proton SEDOR measurements indicate that each time constant, T 1, in the spin-lattice relaxation of carbon corresponds to a mixture of adsorbate structures. The T 1 seems to exhibit a linear temperature dependence, indicating interaction with conduction electrons of the platinum . clusters. However, the spectrum measurements indicate no Knight shift, a deviation from the Korringa relation. Such a deviation implies that electronic states at the Fermi energy involve hybrids of the platinum conduction band with adsorbate molecular orbitals with no spatial density at the carbon nucleus. Measurements of the deuterium spectrum reveal no obvious features that would prove useful in identifying different adsorbate structures. Spectrum and spin-locking measurements indicate no motion of deuteron groups at 77°K The deuterium spin-lattice relaxation rate exhibits multiple-exponential behavior. Measurements of the coupling between the magnetic dipole moments of deuterons determine bond angles and internuclear distances. A single-resonance "slow beat" measurement finds, in deuterated ethylidyne (CCD3) , a distance between deuterons ofr00 = 1.673±0.004A. A double resonance method, demonstrated in a test sample ofD20 in gypsum, can provide bond angle and internuclear distance information. Coherence transfer between carbon and deuterium indicates that deuterium NMR measurements at lower temperatures may offer significant advantages in terms of signal to noise ratios.U of I OnlyThesi

MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan

Purpose: Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time. Methods: We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps. Results: T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts. Conclusion: We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans

Evaluation of a connectivity-based imaging metric that reflects functional decline in Multiple Sclerosis.

Cognitive impairment is a common symptom in individuals with Multiple Sclerosis (MS), but meaningful, reliable biomarkers relating to cognitive decline have been elusive, making evaluation of the impact of therapeutics on cognitive function difficult. Here, we combine pathway-based MRI measures of structural and functional connectivity to construct a metric of functional decline in MS. The Structural and Functional Connectivity Index (SFCI) is proposed as a simple, z-scored metric of structural and functional connectivity, where changes in the metric have a simple statistical interpretation and may be suitable for use in clinical trials. Using data collected at six time points from a 2-year longitudinal study of 20 participants with MS and 9 age- and sex-matched healthy controls, we probe two common symptomatic domains, motor and cognitive function, by measuring structural and functional connectivity in the transcallosal motor pathway and posterior cingulum bundle. The SFCI is significantly lower in participants with MS compared to controls (p = 0.009) and shows a significant decrease over time in MS (p = 0.012). The change in SFCI over two years performed favorably compared to measures of brain parenchymal fraction and lesion volume, relating to follow-up measures of processing speed (r = 0.60, p = 0.005), verbal fluency (r = 0.57, p = 0.009), and score on the Multiple Sclerosis Functional Composite (r = 0.67, p = 0.003). These initial results show that the SFCI is a suitable metric for longitudinal evaluation of functional decline in MS

Evidence of Neural Microstructure Abnormalities in Type I Chiari Malformation: Associations Among Fiber Tract Integrity, Pain, and Cognitive Dysfunction.

BackgroundPrevious case-control investigations of type I Chiari malformation (CMI) have reported cognitive deficits and microstructural white matter abnormalities, as measured by diffusion tensor imaging (DTI). CMI is also typically associated with pain, including occipital headache, but the relationship between pain symptoms and microstructure is not known.MethodsEighteen CMI patients and 18 adult age- and education-matched control participants underwent DTI, were tested using digit symbol coding and digit span tasks, and completed a self-report measure of chronic pain. Tissue microstructure indices were used to examine microstructural abnormalities in CMI as compared with healthy controls. Group differences in DTI parameters were then reassessed after controlling for self-reported pain. Finally, DTI parameters were correlated with performance on the digit symbol coding and digit span tasks within each group.ResultsCMI patients exhibited greater fractional anisotropy (FA), lower radial diffusivity, and lower mean diffusivity in multiple brain regions compared with controls in diffuse white matter regions. Group differences no longer existed after controlling for self-reported pain. A significant correlation between FA and the Repeatable Battery for the Assessment of Neuropsychological Status coding performance was observed for controls but not for the CMI group.ConclusionsDiffuse microstructural abnormalities appear to be a feature of CMI, manifesting predominantly as greater FA and less diffusivity on DTI sequences. These white matter changes are associated with the subjective pain experience of CMI patients and may reflect reactivity to neuroinflammatory responses. However, this hypothesis will require further deliberate testing in future studies