Alexander Leemans

Microstructural maturation of the human brain from childhood to adulthood.

Brain maturation is a complex process that continues well beyond infancy, and adolescence is thought to be a key period of brain rewiring. To assess structural brain maturation from childhood to adulthood, we charted brain development in subjects aged 5 to 30 years using diffusion tensor magnetic resonance imaging, a novel brain imaging technique that is sensitive to axonal packing and myelination and is particularly adept at virtually extracting white matter connections. Age-related changes were seen in major white matter tracts, deep gray matter, and subcortical white matter, in our large (n=202), age-distributed sample. These diffusion changes followed an exponential pattern of maturation with considerable regional variation. Differences observed in developmental timing suggest a pattern of maturation in which areas with fronto-temporal connections develop more slowly than other regions. These in vivo results expand upon previous postmortem and imaging studies and provide quantitative measures indicative of the progression and magnitude of regional human brain maturation.

The B-Matrix Must Be Rotated When Correcting for Subject Motion in DTI Data

To estimate diffusion tensor MRI (DTI) measures, such as fractional anisotropy and fiber orientation, reliably, a large number of diffusion‐encoded images is needed, preferably cardiac gated to reduce pulsation artifacts. However, the concomitant longer acquisition times increase the chances of subject motion adversely affecting the estimation of these measures. While correcting for motion artifacts improves the accuracy of DTI, an often overlooked step in realigning the images is to reorient the B‐matrix so that orientational information is correctly preserved. To the best of our knowledge, most research groups and software packages currently omit this reorientation step. Given the recent explosion of DTI applications including, for example, neurosurgical planning (in which errors can have drastic consequences), it is important to investigate the impact of neglecting to perform the B‐matrix reorientation. In this work, a systematic study to investigate the effect of neglecting to reorient the B‐matrix on DTI data during motion correction is presented. The consequences for diffusion fiber tractography are also discussed. Magn Reson Med, 61:1336–1349, 2009.

Diffusion tensor imaging and beyond

The diffusion of water molecules inside organic tissues is often anisotropic (1). Namely, if there are aligned structures in the tissue, the apparent diffusion coefficient (ADC) of water may vary depending on the orientation along which the diffusion-weighted (DW) measurements are taken. In the late 1980s, diffusion-weighted imaging (DWI) became possible by combining MR diffusion measurements with imaging, enabling the mapping of both diffusion constants and diffusion anisotropy inside the brain and revealing valuable information about axonal architectures (2-14). In the beginning of the 1990s, the diffusion tensor model was introduced to describe the degree of anisotropy and the structural orientation information quantitatively (15,16). This diffusion tensor imaging (DTI) approach provided a simple and elegant way to model this complex neuroanatomical information using only six parameters. Since then, we have witnessed a tremendous amount of growth in this research field, including more sophisticated nontensor models to describe diffusion properties and to extract finer anatomical information from each voxel. Three-dimensional (3D) reconstruction technologies for white matter tracts are also developing beyond the initial deterministic line-propagation models (17-20). As these new reconstruction methods are an area of very active research, it is important to remember that the theory cannot be dissociated from practical aspects of the technology. Importantly, DWI is inherently a noise-sensitive and artifact-prone technique (Fig. 1). Thus, we cannot overemphasize the importance of image quality assurance and robust image analysis techniques. Last but not least, data acquisition technologies have also been steadfastly evolving. In this article, we review the recent advances in these areas since 2000. FIG. 1 Examples of typical artifacts: (i) signal/slice dropouts, (ii) eddy-current induced geometric distortions, (iii) systematic vibration artifacts, and (iv) ghosting (insufficient/incorrect fat-suppression).

Investigating the Prevalence of Complex Fiber Configurations in White Matter Tissue with Diffusion Magnetic Resonance Imaging

It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor‐derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor‐derived analyses, using large, high‐quality diffusion‐weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be ∼90% (using CSD) and 63% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures. Hum Brain Mapp 34:2747–2766, 2013.

Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution.

Constrained spherical deconvolution (CSD) is a new technique that, based on high‐angular resolution diffusion imaging (HARDI) MR data, estimates the orientation of multiple intravoxel fiber populations within regions of complex white matter architecture, thereby overcoming the limitations of the widely used diffusion tensor imaging (DTI) technique. One of its main applications is fiber tractography. The noisy nature of diffusion‐weighted (DW) images, however, affects the estimated orientations and the resulting fiber trajectories will be subject to uncertainty. The impact of noise can be large, especially for HARDI measurements, which employ relatively high b‐values. To quantify the effects of noise on fiber trajectories, probabilistic tractography was introduced, which considers multiple possible pathways emanating from one seed point, taking into account the uncertainty of local fiber orientations. In this work, a probabilistic tractography algorithm is presented based on CSD and the residual bootstrap. CSD, which provides accurate and precise estimates of multiple fiber orientations, is used to extract the local fiber orientations. The residual bootstrap is used to estimate fiber tract probability within a clinical time frame, without prior assumptions about the form of uncertainty in the data. By means of Monte Carlo simulations, the performance of the CSD fiber pathway uncertainty estimator is measured in terms of accuracy and precision. In addition, the performance of the proposed method is compared to state‐of‐the‐art DTI residual bootstrap tractography and to an existing probabilistic CSD tractography algorithm using clinical DW data. Hum Brain Mapp, 2011.

Gender differences and age-related white matter changes of the human brain: A diffusion tensor imaging study

Cerebral white matter undergoes various changes with normal aging. This study investigated the association between age, gender, and the global and regional fractional anisotropy (FA) and mean diffusivity (MD) in 145 adults (30 to 80 years old) using diffusion tensor magnetic resonance imaging. We studied sixteen regions of interest in both hemispheres to search for regions that display age- and gender-related white matter changes and also performed a complementary voxel-based analysis without any hypothesis a priori. On a global scale, our results indicate that the full brain FA was negatively correlated with age. The regional analysis showed that the anterior corpus callosum, the bilateral anterior and posterior internal capsule, and the posterior periventricular regions had the most significant age-related FA decrease. On the other hand, the FA in the temporal and occipital regions was not correlated with age. However, in contrast to males, females overall had a significantly lower FA in the right deep temporal regions. More gender differences in precentral, cingulate, and anterior temporal white matter areas were also found, suggesting that microstructural white matter organization in these regions may have a sexual dimorphism. Such differences were mainly due to the increase in diffusion perpendicular to fiber tracts.

Longitudinal Assessment of Chemotherapy-Induced Structural Changes in Cerebral White Matter and Its Correlation With Impaired Cognitive Functioning

PURPOSE To uncover the neural substrate of cognitive impairment related to adjuvant chemotherapy, we studied cerebral white matter (WM) integrity before and after chemotherapy by using magnetic resonance diffusion tensor imaging (DTI) in combination with detailed cognitive assessment. PATIENTS AND METHODS Thirty-four young premenopausal women with early-stage breast cancer who were exposed to chemotherapy underwent neuropsychologic testing and DTI before the start of chemotherapy (t1) and 3 to 4 months after treatment (t2). Sixteen patients not exposed to chemotherapy and 19 age-matched healthy controls underwent the same assessment at matched intervals. In all groups, we used paired t tests to study changes in neuropsychologic test scores and whole-brain voxel-based paired t tests to study changes in WM fractional anisotropy (FA; a DTI measure that reflects WM tissue organization), with depression scores and intelligence quotient as included covariates. We correlated changes of neuropsychologic test scores with the mean change of FA for regions that survived the paired t tests in patients treated with chemotherapy. RESULTS In contrast to controls, the chemotherapy-treated group performed significantly worse on attention tests, psychomotor speed, and memory at t2 compared with t1 (P < .05). In the chemotherapy-treated group, we found significant decreases of FA in frontal, parietal, and occipital WM tracts after treatment (familywise error P < .05), whereas for both control groups, FA values were the same between t1 and t2. Furthermore, performance changes in attention and verbal memory correlated with mean regional FA changes in chemotherapy-treated patients (P < .05). CONCLUSION We report evidence of longitudinal changes in cognitive functioning and cerebral WM integrity after chemotherapy as well as an association between both.

Chemotherapy-induced structural changes in cerebral white matter and its correlation with impaired cognitive functioning in breast cancer patients

A subgroup of patients with breast cancer suffers from mild cognitive impairment after chemotherapy. To uncover the neural substrate of these mental complaints, we examined cerebral white matter (WM) integrity after chemotherapy using magnetic resonance diffusion tensor imaging (DTI) in combination with detailed cognitive assessment. Postchemotherapy breast cancer patients (n = 17) and matched healthy controls (n = 18) were recruited for DTI and neuropsychological testing, including the self‐report cognitive failure questionnaire (CFQ). Differences in DTI WM integrity parameters [fractional anisotropy (FA) and mean diffusivity (MD)] between patients and healthy controls were assessed using a voxel‐based two‐sample‐t‐test. In comparison with healthy controls, the patient group demonstrated decreased FA in frontal and temporal WM tracts and increased MD in frontal WM. These differences were also confirmed when comparing this patient group with an additional control group of nonchemotherapy‐treated breast cancer patients (n = 10). To address the heterogeneity observed in cognitive function after chemotherapy, we performed a voxel‐based correlation analysis between FA values and individual neuropsychological test scores. Significant correlations of FA with neuropsychological tests covering the domain of attention and processing/psychomotor speed were found in temporal and parietal WM tracts. Furthermore, CFQ scores correlated negatively in frontal and parietal WM. These studies show that chemotherapy seems to affect WM integrity and that parameters derived from DTI have the required sensitivity to quantify neural changes related to chemotherapy‐induced mild cognitive impairment. Hum Brain Mapp 32:480–493, 2011.

Methodological considerations on tract-based spatial statistics (TBSS)

Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically.

Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls

PURPOSE Linear least squares estimators are widely used in diffusion MRI for the estimation of diffusion parameters. Although adding proper weights is necessary to increase the precision of these linear estimators, there is no consensus on how to practically define them. In this study, the impact of the commonly used weighting strategies on the accuracy and precision of linear diffusion parameter estimators is evaluated and compared with the nonlinear least squares estimation approach. METHODS Simulation and real data experiments were done to study the performance of the weighted linear least squares estimators with weights defined by (a) the squares of the respective noisy diffusion-weighted signals; and (b) the squares of the predicted signals, which are reconstructed from a previous estimate of the diffusion model parameters. RESULTS The negative effect of weighting strategy (a) on the accuracy of the estimator was surprisingly high. Multi-step weighting strategies yield better performance and, in some cases, even outperformed the nonlinear least squares estimator. CONCLUSION If proper weighting strategies are applied, the weighted linear least squares approach shows high performance characteristics in terms of accuracy/precision and may even be preferred over nonlinear estimation methods.