Marta Correia

Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation

Multi-center studies using magnetic resonance imaging facilitate studying small effect sizes, global population variance and rare diseases. The reliability and sensitivity of these multi-center studies crucially depend on the comparability of the data generated at different sites and time points. The level of inter-site comparability is still controversial for conventional anatomical T1-weighted MRI data. Quantitative multi-parameter mapping (MPM) was designed to provide MR parameter measures that are comparable across sites and time points, i.e., 1 mm high-resolution maps of the longitudinal relaxation rate (R1 = 1/T1), effective proton density (PD*), magnetization transfer saturation (MT) and effective transverse relaxation rate (R2* = 1/T2*). MPM was validated at 3T for use in multi-center studies by scanning five volunteers at three different sites. We determined the inter-site bias, inter-site and intra-site coefficient of variation (CoV) for typical morphometric measures [i.e., gray matter (GM) probability maps used in voxel-based morphometry] and the four quantitative parameters. The inter-site bias and CoV were smaller than 3.1 and 8%, respectively, except for the inter-site CoV of R2* (<20%). The GM probability maps based on the MT parameter maps had a 14% higher inter-site reproducibility than maps based on conventional T1-weighted images. The low inter-site bias and variance in the parameters and derived GM probability maps confirm the high comparability of the quantitative maps across sites and time points. The reliability, short acquisition time, high resolution and the detailed insights into the brain microstructure provided by MPM makes it an efficient tool for multi-center imaging studies.

QuickBundles, a Method for Tractography Simplification

Diffusion MR data sets produce large numbers of streamlines which are hard to visualize, interact with, and interpret in a clinically acceptable time scale, despite numerous proposed approaches. As a solution we present a simple, compact, tailor-made clustering algorithm, QuickBundles (QB), that overcomes the complexity of these large data sets and provides informative clusters in seconds. Each QB cluster can be represented by a single centroid streamline; collectively these centroid streamlines can be taken as an effective representation of the tractography. We provide a number of tests to show how the QB reduction has good consistency and robustness. We show how the QB reduction can help in the search for similarities across several subjects.

White matter pathology in Parkinson's disease: The effect of imaging protocol differences and relevance to executive function

Diffusion magnetic resonance imaging is increasingly used as a non-invasive method to investigate white matter structure in neurological and neuropsychiatric disease. However, many options are available for the acquisition sequence and analysis method. Here we used Parkinsons disease as a model neurodegenerative disorder to compare imaging protocols and analysis options. We investigated fractional anisotropy and mean diffusivity of white matter in patients and age-matched controls, comparing two datasets acquired with different imaging protocols. One protocol prioritised the number of b value acquisitions, whilst the other prioritised the number of gradient directions. The dataset with more gradient directions was more sensitive to reductions in fractional anisotropy in Parkinsons disease, whilst the dataset with more b values was more sensitive to increases in mean diffusivity. Moreover, the areas of reduced fractional anisotropy were highly similar to areas of increased mean diffusivity in PD patients. Next, we compared two widely used analysis methods: tract-based spatial statistics identified reduced fractional anisotropy and increased mean diffusivity in Parkinsons disease in many of the major white matter tracts in the frontal and parietal lobes. Voxel-based analyses were less sensitive, with similar patterns of white matter pathology observed only at liberal statistical thresholds. We also used tract-based spatial statistics to identify correlations between a test of executive function (phonemic fluency), fractional anisotropy and mean diffusivity in prefrontal white matter in both Parkinsons disease patients and controls. These findings suggest that in Parkinsons disease there is widespread pathology of cerebral white matter, and furthermore, pathological white matter in the frontal lobe may be associated with executive dysfunction. Diffusion imaging protocols that prioritised the number of directions versus the number of b values were differentially sensitive to alternative markers of white matter pathology, such as fractional anisotropy and mean diffusivity.

Looking for the optimal DTI acquisition scheme given a maximum scan time : are more b-values a waste of time?

In this study we used simulated data to investigate how valuable the use of multiple b-values is, in terms of improving the accuracy and reproducibility of Diffusion Tensor Imaging (DTI) results. Our results show that the systematic bias of the estimated scalar diffusion parameters [apparent diffusion coefficient and fractional anisotropy (FA)] - due to the Rician distribution of magnetic resonance noise - can be minimized by increasing the number of b-values and not by increasing the number of sampling directions. In addition, the use of more than one b-value accounts better for the wide range of diffusivities found in the human brain by bringing closer together the FA estimates for fibres with different mean diffusivities. It is also shown that while for tractography studies we should use as many sampling directions as allowed by scan time limitations, for follow-up, intersubject or multicenter studies, the use of more than one b-value will improve the accuracy of the scalar diffusion parameters, as long as the minimum number of directions required for robust estimation of each parameter is still used.

Exploring the 3D geometry of the diffusion kurtosis tensor—Impact on the development of robust tractography procedures and novel biomarkers

Diffusion kurtosis imaging (DKI) is a diffusion-weighted technique which overcomes limitations of the commonly used diffusion tensor imaging approach. This technique models non-Gaussian behaviour of water diffusion by the diffusion kurtosis tensor (KT), which can be used to provide indices of tissue heterogeneity and a better characterisation of the spatial architecture of tissue microstructure. In this study, the geometry of the KT is elucidated using synthetic data generated from multi-compartmental models, where diffusion heterogeneity between intra- and extra-cellular media is taken into account, as well as the sensitivity of the results to each model parameter and to synthetic noise. Furthermore, based on the assumption that the maxima of the KT are distributed perpendicularly to the direction of well-aligned fibres, a novel algorithm for estimating fibre direction directly from the KT is proposed and compared to the fibre directions extracted from DKI-based orientation distribution function (ODF) estimates previously proposed in the literature. Synthetic data results showed that, for fibres crossing at high intersection angles, direction estimates extracted directly from the KT have smaller errors than the DKI-based ODF estimation approaches (DKI-ODF). Nevertheless, the proposed method showed smaller angular resolution and lower stability to changes of the simulation parameters. On real data, tractography performed on these KT fibre estimates suggests a higher sensitivity than the DKI-based ODF in resolving lateral corpus callosum fibres reaching the pre-central cortex when diffusion acquisition is performed with five b-values. Using faster acquisition schemes, KT-based tractography did not show improved performance over the DKI-ODF procedures. Nevertheless, it is shown that direct KT fibre estimates are more adequate for computing a generalised version of radial kurtosis maps.

Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI: A Diffusion Tensor Imaging Study

Objective. Traumatic brain injury (TBI) is not a single insult with monophasic resolution, but a chronic disease, with dynamic processes that remain active for years. We aimed to assess patient trajectories over the entire disease narrative, from ictus to late outcome. Methods. Twelve patients with moderate-to-severe TBI underwent magnetic resonance imaging in the acute phase (within 1 week of injury) and twice in the chronic phase of injury (median 7 and 21 months), with some undergoing imaging at up to 2 additional time points. Longitudinal imaging changes were assessed using structural volumetry, deterministic tractography, voxel-based diffusion tensor analysis, and region of interest analyses (including corpus callosum, parasagittal white matter, and thalamus). Imaging changes were related to behavior. Results. Changes in structural volumes, fractional anisotropy, and mean diffusivity continued for months to years postictus. Changes in diffusion tensor imaging were driven by increases in both axial and radial diffusivity except for the earliest time point, and were associated with changes in reaction time and performance in a visual memory and learning task (paired associates learning). Dynamic structural changes after TBI can be detected using diffusion tensor imaging and could explain changes in behavior. Conclusions. These data can provide further insight into early and late pathophysiology, and begin to provide a framework that allows magnetic resonance imaging to be used as an imaging biomarker of therapy response. Knowledge of the temporal pattern of changes in TBI patient populations also provides a contextual framework for assessing imaging changes in individuals at any given time point.

Diffusion tensor imaging in the study of language and aphasia

Background: Diffusion tensor imaging (DTI) is an emerging research technique that is used to map and characterise white matter tracts in the healthy and damaged brain. Aims: The aim of this paper is to familiarise the readers with DTI while giving the tools to understand and evaluate recent developments in aphasia research that use DTI methodology. Main Contribution: Principles of DTI technology as well as its main caveats are described. An overview of studies that used DTI to explore the language system and aphasia is given. Future directions and the potential contribution of DTI to the understanding of aphasia diagnosis and recovery are highlighted. Conclusions: DTI is an emerging technology, increasingly being applied to further our understanding of aphasia and its recovery. So far it has contributed to our knowledge in four areas of research. In the area of brain anatomy it is used to redefine the borders between various parts of the cortex based on their structural connectivity, to acquire a more accurate map of the tracts connecting the various parts of the language system, and to measure hemispheric asymmetry. Future studies might be able to further our understanding of language anatomy and relate hemispheric asymmetry to recovery potential. Second, DTI can help in relating structure to function. So far many studies focused on repetition deficits and conduction aphasia. Future studies can explore the anatomy of other language deficits. Third, DTI has been used in the study of brain damage and recovery. Studies have documented the damage that occurs to white matter following stroke and other insults, and the spontaneous reorganisation that follows. In the future DTI might contribute to the debate about the role of the right hemisphere in recovery from aphasia. Lastly, in the area of aphasia rehabilitation there is great lack of data. The studies reviewed here have shown that rehabilitation potential is dependent on white matter integrity and that white matter changes can occur as a result of therapy. Future studies should further our understanding of the role of white matter integrity in recovery, therefore contributing to the question of why some patients show good recovery while others do not. Future studies should also try and map white matter changes that are associated with successful versus unsuccessful rehabilitation, and with different stages of recovery.

A watershed model of individual differences in fluid intelligence

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes.

Vascular contributions to pattern analysis: comparing gradient and spin echo fMRI at 3T.

Multivariate pattern analysis is often assumed to rely on signals that directly reflect differences in the distribution of particular neural populations. The source of the signal used in these analyses remains unclear however, and an alternative model suggests that signal from larger draining veins may play a significant role. The current study was designed to investigate the vascular contribution to pattern analyses at 3T by comparing the results obtained from gradient and spin echo data. Classification analyses were carried out comparing line orientations in V1, tone frequencies in A1, and responses from different fingers in M1. In all cases, classification accuracy in the spin echo data was not significantly different from chance. In contrast, classification accuracies in the gradient echo data were significantly above chance, and significantly higher than the accuracies observed for the spin echo data. These results suggest that at the field strength and spatial resolution used for the majority of fMRI studies, a considerable proportion of the signal used by pattern analysis originates in the vasculature.

Contributions of bilateral white matter to chronic aphasia symptoms as assessed by diffusion tensor MRI

Highlights • We investigated the role of the arcuate fasciculus (AF) in post-stroke aphasia.• We found that left hemispheric AF damage correlates with aphasia symptoms.• We found no evidence for a role of the right hemispheric AF in aphasia symptoms.