Evaluating the accuracy of diffusion MRI models in white matter

Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of some of the models that are commonly used in analyzing human white matter have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking

A theoretical framework for the assessment of water fraction-dependent longitudinal decay rates and magnetisation transfer in membrane lipid phantoms

Phantom systems consisting of liposome suspensions are widely employed to investigate quantitative MRI parameters mimicking cellular membranes. The proper physical understanding of the measurement results, however, requires proper models for liposomes and their interaction with the surrounding water molecules. Here, we present an MD-based approach for the theoretical prediction of R1=1/T1, the dependence of R1 on water concentration and the magnetization exchange between lipids and interacting water layer in lipids and lipid mixtures. Moreover, a new parameter is introduced which quantitatively measures the amount of hydration water (hydration water fraction, f_HW) based on conventional spoiled gradient echo MR acquisitions. Both f_HW and the magnetisation exchange rate between lipids and hydration water were determined quantitatively from spoiled gradient echo data. We observed that liposome systems behaved similarly, apart from PLPC which showed both lower hydration water fraction and lower exchange rate. The extracted parameters accurately predicted the measured water fraction-dependent R1 rates and allowed for a theoretical understanding of MR parameters in liposomes of different composition

The glial framework reveals white matter fiber architecture in human and primate brains

How to quantify local axonal orientations Mapping the axonal trajectories of the brain’s white matter at cellular resolution is a long-standing goal of neuroscience. However, existing methods for mapping the axons are either limited to animal studies or require highly specialized equipment for data acquisition and processing. Nissl staining identifies cell nuclei and has been used extensively to investigate parcellations of the cortical gray matter, but the white matter has largely been neglected with this technique. Schurr and Mezer now show that Nissl staining, together with structure tensor analysis, can be used to study white matter architecture and the organization of the glial cell framework around axons over the whole brain. This technique greatly advances our knowledge regarding the organization of glial cells and the fine-grained organization of axonal projections in the brain. —PRS</jats:p

Mapping microstructural gradients of the human striatum in normal aging and Parkinson’s disease

Mapping structural spatial change (i.e., gradients) in the striatum is essential for understanding the function of the basal ganglia in both health and disease. We developed a method to identify and quantify gradients of microstructure in the single human brain in vivo. We found spatial gradients in the putamen and caudate nucleus of the striatum that were robust across individuals, clinical conditions, and datasets. By exploiting multiparametric quantitative MRI, we found distinct, spatially dependent, aging-related alterations in water content and iron concentration. Furthermore, we found cortico-striatal microstructural covariation, showing relations between striatal structural gradients and cortical hierarchy. In Parkinson’s disease (PD) patients, we found abnormal gradients in the putamen, revealing changes in the posterior putamen that explain patients’ dopaminergic loss and motor dysfunction. Our work provides a noninvasive approach for studying the spatially varying, structure-function relationship in the striatum in vivo, in normal aging and PD.</jats:p

Neurobiological underpinnings of rapid white matter plasticity during intensive reading instruction

AbstractDiffusion MRI is a powerful tool for imaging brain structure, but it is challenging to discern the biological underpinnings of plasticity inferred from these and other non-invasive MR measurements. Biophysical modeling of the diffusion signal aims to render a more biologically rich image of tissue microstructure, but the application of these models comes with important caveats. A separate approach for gaining biological specificity has been to seek converging evidence from multi-modal datasets. Here we use metrics derived from diffusion kurtosis imaging (DKI) and the white matter tract integrity (WMTI) model along with quantitative MRI measurements of T1 relaxation to characterize changes throughout the white matter during an 8-week, intensive reading intervention (160 total hours of instruction). Behavioral measures, multi-shell diffusion MRI data, and quantitative T1 data were collected at regular intervals during the intervention in a group of 33 children with reading difficulties (7-12 years old), and over the same period in an age-matched non-intervention control group. Throughout the white matter, mean ‘extra-axonal’ diffusivity was inversely related to intervention time. In contrast, model estimated axonal water fraction (AWF), overall diffusion kurtosis, and T1 relaxation time showed no significant change over the intervention period. Both diffusion and quantitative T1 based metrics were correlated with pre-intervention reading performance, albeit with distinct anatomical distributions. These results are consistent with the view that rapid changes in diffusion properties reflect phenomena other than widespread changes in myelin density. We discuss this result in light of recent work highlighting non-axonal factors in experience-dependent plasticity and learning.HighlightsDiffusion MRI measurements in white matter show changes linked to an educational intervention.Tissue modeling results point to changes within the extra-axonal space.Complementary MRI measurements fail to suggest a widespread change in white matter in myelination over the intervention period.Both diffusion and quantitative T1 measures correlate with pre-intervention reading skill.</jats:sec