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Danielle van Westen

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13 papers

1 author row

YNIMG Journal 2024 Journal Article

Divergent functional connectivity changes associated with white matter hyperintensities

Alexander F. Santillo
Tor O. Strandberg
Nina H. Reislev
Markus Nilsson
Erik Stomrud
Nicola Spotorno
Danielle van Westen
Oskar Hansson

Age-related white matter hyperintensities are a common feature and are known to be negatively associated with structural integrity, functional connectivity, and cognitive performance. However, this has yet to be fully understood mechanistically. We analyzed multiple MRI modalities acquired in 465 non-demented individuals from the Swedish BioFINDER study including 334 cognitively normal and 131 participants with mild cognitive impairment. White matter hyperintensities were automatically quantified using fluid-attenuated inversion recovery MRI and parameters from diffusion tensor imaging were estimated in major white matter fibre tracts. We calculated fMRI resting state-derived functional connectivity within and between predefined cortical regions structurally linked by the white matter tracts. How change in functional connectivity is affected by white matter lesions and related to cognition (in the form of executive function and processing speed) was explored. We examined the functional changes using a measure of sample entropy. As expected hyperintensities were associated with disrupted structural white matter integrity and were linked to reduced functional interregional lobar connectivity, which was related to decreased processing speed and executive function. Simultaneously, hyperintensities were also associated with increased intraregional functional connectivity, but only within the frontal lobe. This phenomenon was also associated with reduced cognitive performance. The increased connectivity was linked to increased entropy (reduced predictability and increased complexity) of the involved voxels' blood oxygenation level-dependent signal. Our findings expand our previous understanding of the impact of white matter hyperintensities on cognition by indicating novel mechanisms that may be important beyond this particular type of brain lesions.

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YNICL Journal 2023 Journal Article

Meningioma microstructure assessed by diffusion MRI: An investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology

Jan Brabec
Magda Friedjungová
Daniel Vašata
Elisabet Englund
Johan Bengzon
Linda Knutsson
Filip Szczepankiewicz
Danielle van Westen

BACKGROUND: Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level. PURPOSE: To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters. MATERIALS AND METHODS: , respectively. RESULTS: is high in the presence of elongated and aligned cell structures, but low otherwise. CONCLUSION: across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.

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YNIMG Journal 2023 Journal Article

Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding

Björn Lampinen
Filip Szczepankiewicz
Jimmy Lätt
Linda Knutsson
Johan Mårtensson
Isabella M. Björkman-Burtscher
Danielle van Westen
Pia C. Sundgren

Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue ‘compartments’ such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current ‘neurite models’ of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.

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YNICL Journal 2023 Journal Article

Structural and microstructural thalamocortical network disruption in sporadic behavioural variant frontotemporal dementia

David Jakabek
Brian D. Power
Nicola Spotorno
Matthew D. Macfarlane
Mark Walterfang
Dennis Velakoulis
Christer Nilsson
Maria Landqvist Waldö

Using multi-block methods we combined multimodal neuroimaging metrics of thalamic morphology, thalamic white matter tract diffusion metrics, and cortical thickness to examine changes in behavioural variant frontotemporal dementia. (bvFTD). Twenty-three patients with sporadic bvFTD and 24 healthy controls underwent structural and diffusion MRI scans. Clinical severity was assessed using the Clinical Dementia Rating scale and behavioural severity using the Frontal Behaviour Inventory by patient caregivers. Thalamic volumes were manually segmented. Anterior and posterior thalamic radiation fractional anisotropy and mean diffusivity were extracted using Tract-Based Spatial Statistics. Finally, cortical thickness was assessed using Freesurfer. We used shape analyses, diffusion measures, and cortical thickness as features in sparse multi-block partial least squares (PLS) discriminatory analyses to classify participants within bvFTD or healthy control groups. Sparsity was tuned with five-fold cross-validation repeated 10 times. Final model fit was assessed using permutation testing. Additionally, sparse multi-block PLS was used to examine associations between imaging features and measures of dementia severity. Bilateral anterior-dorsal thalamic atrophy, reduction in mean diffusivity of thalamic projections, and frontotemporal cortical thinning, were the main features predicting bvFTD group membership. The model had a sensitivity of 96%, specificity of 68%, and was statistically significant using permutation testing (p = 0.012). For measures of dementia severity, we found similar involvement of regional thalamic and cortical areas as in discrimination analyses, although more extensive thalamo-cortical white matter metric changes. Using multimodal neuroimaging, we demonstrate combined structural network dysfunction of anterior cortical regions, cortical-thalamic projections, and anterior thalamic regions in sporadic bvFTD.

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YNIMG Journal 2021 Journal Article

Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

Meera Srikrishna
Joana B. Pereira
Rolf A. Heckemann
Giovanni Volpe
Danielle van Westen
Anna Zettergren
Silke Kern
Lars-Olof Wahlund

Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.

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YNICL Journal 2020 Journal Article

Medial temporal atrophy in preclinical dementia: Visual and automated assessment during six year follow-up

Gustav Mårtensson
Claes Håkansson
Joana B. Pereira
Sebastian Palmqvist
Oskar Hansson
Danielle van Westen
Eric Westman

Medial temporal lobe (MTL) atrophy is an important morphological marker of many dementias and is closely related to cognitive decline. In this study we aimed to characterize longitudinal progression of MTL atrophy in 93 individuals with subjective cognitive decline and mild cognitive impairment followed up over six years, and to assess if clinical rating scales are able to detect these changes. All MRI images were visually rated according to Scheltens' scale of medial temporal atrophy (MTA) by two neuroradiologists and AVRA, a software for automated MTA ratings. The images were also segmented using FreeSurfer's longitudinal pipeline in order to compare the MTA ratings to volumes of the hippocampi and inferior lateral ventricles. We found that MTL atrophy rates increased with CSF biomarker abnormality, used to define preclinical stages of Alzheimer's Disease. Both AVRA's and the radiologists' MTA ratings showed similar longitudinal trends as the subcortical volumes, suggesting that visual rating scales provide a valid alternative to automatic segmentations. Our results further showed that it took more than 8 years on average for individuals with mild cognitive impairment, and an Alzheimer's disease biomarker profile, to increase the MTA score by one. This suggests that discrete MTA ratings are too coarse for tracking individual MTL atrophy in short time spans. While the MTA scores from each radiologist showed strong correlations to subcortical volumes, the inter-rater agreement was low. We conclude that the main limitation of quantifying MTL atrophy with visual ratings in clinics is the subjectiveness of the assessment.

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YNIMG Journal 2018 Journal Article

Imaging brain tumour microstructure

Markus Nilsson
Elisabet Englund
Filip Szczepankiewicz
Danielle van Westen
Pia C. Sundgren

Imaging is an indispensable tool for brain tumour diagnosis, surgical planning, and follow-up. Definite diagnosis, however, often demands histopathological analysis of microscopic features of tissue samples, which have to be obtained by invasive means. A non-invasive alternative may be to probe corresponding microscopic tissue characteristics by MRI, or so called ‘microstructure imaging’. The promise of microstructure imaging is one of ‘virtual biopsy’ with the goal to offset the need for invasive procedures in favour of imaging that can guide pre-surgical planning and can be repeated longitudinally to monitor and predict treatment response. The exploration of such methods is motivated by the striking link between parameters from MRI and tumour histology, for example the correlation between the apparent diffusion coefficient and cellularity. Recent microstructure imaging techniques probe even more subtle and specific features, providing parameters associated to cell shape, size, permeability, and volume distributions. However, the range of scenarios in which these techniques provide reliable imaging biomarkers that can be used to test medical hypotheses or support clinical decisions is yet unknown. Accurate microstructure imaging may moreover require acquisitions that go beyond conventional data acquisition strategies. This review covers a wide range of candidate microstructure imaging methods based on diffusion MRI and relaxometry, and explores advantages, challenges, and potential pitfalls in brain tumour microstructure imaging.

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YNIMG Journal 2017 Journal Article

Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding

Björn Lampinen
Filip Szczepankiewicz
Johan Mårtensson
Danielle van Westen
Pia C. Sundgren
Markus Nilsson

In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersion. Separation of these properties is of high importance, since it could allow dMRI to non-invasively probe elongated structures such as neurites (axons and dendrites). However, conventional dMRI, based on single diffusion encoding (SDE), entangles microscopic anisotropy and orientation dispersion with intra-voxel variance in isotropic diffusivity. SDE-based methods for estimating microscopic anisotropy, such as the neurite orientation dispersion and density imaging (NODDI) method, must thus rely on model assumptions to disentangle these features. An alternative approach is to directly quantify microscopic anisotropy by the use of variable shape of the b-tensor. Along those lines, we here present the ‘constrained diffusional variance decomposition’ (CODIVIDE) method, which jointly analyzes data acquired with diffusion encoding applied in a single direction at a time (linear tensor encoding, LTE) and in all directions (spherical tensor encoding, STE). We then contrast the two approaches by comparing neurite density estimated using NODDI with microscopic anisotropy estimated using CODIVIDE. Data were acquired in healthy volunteers and in glioma patients. NODDI and CODIVIDE differed the most in gray matter and in gliomas, where NODDI detected a neurite fraction higher than expected from the level of microscopic diffusion anisotropy found with CODIVIDE. The discrepancies could be explained by the NODDI tortuosity assumption, which enforces a connection between the neurite density and the mean diffusivity of tissue. Our results suggest that this assumption is invalid, which leads to a NODDI neurite density that is inconsistent between LTE and STE data. Using simulations, we demonstrate that the NODDI assumptions result in parameter bias that precludes the use of NODDI to map neurite density. With CODIVIDE, we found high levels of microscopic anisotropy in white matter, intermediate levels in structures such as the thalamus and the putamen, and low levels in the cortex and in gliomas. We conclude that accurate mapping of microscopic anisotropy requires data acquired with variable shape of the b-tensor.

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YNIMG Journal 2016 Journal Article

Q-space trajectory imaging for multidimensional diffusion MRI of the human brain

Carl-Fredrik Westin
Hans Knutsson
Ofer Pasternak
Filip Szczepankiewicz
Evren Özarslan
Danielle van Westen
Cecilia Mattisson
Mats Bogren

This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i. e. , a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture.

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YNIMG Journal 2016 Journal Article

The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE)

Filip Szczepankiewicz
Danielle van Westen
Elisabet Englund
Carl-Fredrik Westin
Freddy Ståhlberg
Jimmy Lätt
Pia C. Sundgren
Markus Nilsson

The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MKT), and DIVIDE was used to decompose MKT into components caused by microscopic anisotropy (MKA) and isotropic heterogeneity (MKI). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed on the excised tumor tissue, where structural anisotropy and cell density were quantified by structure tensor analysis and cell nuclei segmentation, respectively. In order to validate the DIVIDE parameters they were correlated to the corresponding parameters derived from microscopy. We found an excellent agreement between the DIVIDE parameters and corresponding microscopy parameters; MKA correlated with cell eccentricity (r =0. 95, p <10−7) and MKI with the cell density variance (r =0. 83, p <10−3). The diffusion anisotropy correlated with structure tensor anisotropy on the voxel-scale (FA, r =0. 80, p <10−3) and microscopic scale (μFA, r =0. 93, p <10−6). A multiple regression analysis showed that the conventional MKT parameter reflects both variable cell eccentricity and cell density, and therefore lacks specificity in terms of microstructure characteristics. However, specificity was obtained by decomposing the two contributions; MKA was associated only to cell eccentricity, and MKI only to cell density variance. The variance in meningiomas was caused primarily by microscopic anisotropy (mean±s. d.) MKA =1. 11±0. 33 vs MKI =0. 44±0. 20 (p <10−3), whereas in the gliomas, it was mostly caused by isotropic heterogeneity MKI =0. 57±0. 30 vs MKA =0. 26±0. 11 (p <0. 05). In conclusion, DIVIDE allows non-invasive mapping of parameters that reflect variable cell eccentricity and density. These results constitute convincing evidence that a link exists between specific aspects of tissue heterogeneity and parameters from dMRI. Decomposing effects of microscopic anisotropy and isotropic heterogeneity facilitates an improved interpretation of tumor heterogeneity as well as diffusion anisotropy on both the microscopic and macroscopic scale.

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YNIMG Journal 2015 Journal Article

Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors

Filip Szczepankiewicz
Samo Lasič
Danielle van Westen
Pia C. Sundgren
Elisabet Englund
Carl-Fredrik Westin
Freddy Ståhlberg
Jimmy Lätt

The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for “tissue integrity”, especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.

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YNIMG Journal 2013 Journal Article

Variability in diffusion kurtosis imaging: Impact on study design, statistical power and interpretation

Filip Szczepankiewicz
Jimmy Lätt
Ronnie Wirestam
Alexander Leemans
Pia Sundgren
Danielle van Westen
Freddy Ståhlberg
Markus Nilsson

Diffusion kurtosis imaging (DKI) is an emerging technique with the potential to quantify properties of tissue microstructure that may not be observable using diffusion tensor imaging (DTI). In order to help design DKI studies and improve interpretation of DKI results, we employed statistical power analysis to characterize three aspects of variability in four DKI parameters; the mean diffusivity, fractional anisotropy, mean kurtosis, and radial kurtosis. First, we quantified the variability in terms of the group size required to obtain a statistical power of 0. 9. Second, we investigated the relative contribution of imaging and post-processing noise to the total variance, in order to estimate the benefits of longer scan times versus the inclusion of more subjects. Third, we evaluated the potential benefit of including additional covariates such as the size of the structure when testing for differences in group means. The analysis was performed in three major white matter structures of the brain: the superior cingulum, the corticospinal tract, and the mid-sagittal corpus callosum, extracted using diffusion tensor tractography and DKI data acquired in a healthy cohort. The results showed heterogeneous variability across and within the white matter structures. Thus, the statistical power varies depending on parameter and location, which is important to consider if a pathogenesis pattern is inferred from DKI data. In the data presented, inter-subject differences contributed more than imaging noise to the total variability, making it more efficient to include more subjects rather than extending the scan-time per subject. Finally, strong correlations between DKI parameters and the structure size were found for the cingulum and corpus callosum. Structure size should thus be considered when quantifying DKI parameters, either to control for its potentially confounding effect, or as a means of reducing unexplained variance.

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YNIMG Journal 2006 Journal Article

fMRI activity in the medial temporal lobe during famous face processing

Christina Elfgren
Danielle van Westen
Ulla Passant
Elna-Marie Larsson
Peter Mannfolk
Peter Fransson

The current event-related fMRI study examined the relative involvement of different parts of the medial temporal lobe (MTL), particularly the contribution of hippocampus and perirhinal cortex, in either intentional or incidental recognition of famous faces in contrast to unfamiliar faces. Our intention was to further explore the controversial contribution of MTL in the processing of semantic memory tasks. Subjects viewed a sequence of famous and unfamiliar faces. Two tasks were used encouraging attention to either fame or gender. In the fame task, the subjects were requested to identify the person when seeing his/her face and also to try to generate the name of this person. In the gender task, the subjects were asked to conduct a judgement of a person's gender when seeing his/her face. The visual processing was hence directed to gender and thereby expected to diminish attention to semantic information leading only to a “passive” registration of famous and non-familiar faces. Recognition of famous faces, in both contrasts, produced significant activations in the MTL. First, during the intentional recognition (the person identification task) increased activity was observed in the anterolateral part of left hippocampus, in proximity to amygdala. Second, during the incidental recognition of famous faces (the gender classification task), there was increased activity in the left posterior MTL with focus in the perirhinal cortex. Our results suggest that the hippocampus may be centrally involved in the intentional retrieval of semantic memories while the perirhinal cortex is associated with the incidental recognition of semantic information.

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