Author name cluster

Tim B. Dyrby

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16 papers

1 author row

YNIMG Journal 2022 Journal Article

Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures?

Mariam Andersson
Marco Pizzolato
Hans Martin Kjer
Katrine Forum Skodborg
Henrik Lundell
Tim B. Dyrby

Noninvasive estimation of axon diameter with diffusion MRI holds the potential to investigate the dynamic properties of the brain network and pathology of neurodegenerative diseases. Recent studies use powder averaging to account for complex white matter architectures, but these have not been validated for real axonal geometries from regions that contain fibre crossings. Here, we present 120-304μm long segmented axons from X-ray nano-holotomography volumes of a splenium and crossing fibre region of a vervet monkey brain. We show that the axons in the complex crossing fibre region, which contains callosal, association, and corticospinal connections, exhibit a wider diameter distribution than those of the splenium region. To accurately estimate the axon diameter in these regions, therefore, sensitivity to a wide range of diameters is required. We demonstrate how the q-value, b-value, signal-to-noise ratio and the assumed intra-axonal parallel diffusivity influence the range of measurable diameters with powder average approaches. Furthermore, we show how Gaussian distributed noise results in a wider range of measurable diameter at high b-values than Rician distributed noise, even at high signal-to-noise ratios of 100. The number of gradient directions is also shown to impose a lower bound on measurable diameter. Our results indicate that axon diameter estimation can be performed with only few b-shells, and that additional shells do not improve the accuracy of the estimate. For strong gradients available on human Connectom and preclinical scanners, Monte Carlo simulations of diffusion confirm that powder averaging techniques succeed in providing accurate estimates of axon diameter across a range of diameters, sequence parameters and diffusion times, even in complex white matter architectures. At relatively low b-values, the diameter estimate becomes sensitive to axonal microdispersion and the intra-axonal parallel diffusivity shows time dependency at both in vivo and ex vivo intrinsic diffusivities.

YNICL Journal 2021 Journal Article

In vivo tensor-valued diffusion MRI of focal demyelination in white and deep grey matter of rodents

Yi He
Susana Aznar
Hartwig R. Siebner
Tim B. Dyrby

BACKGROUND: Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease leading to damage of white matter (WM) and grey matter (GM). Magnetic resonance imaging (MRI) is the modality of choice to assess brain damage in MS, but there is an unmet need in MRI for achieving higher sensitivity and specificity to MS-related microstructural alterations in WM and GM. OBJECTIVE: To explore whether tensor-valued diffusion MRI (dMRI) can yield sensitive microstructural read-outs for focal demyelination in cerebral WM and deep GM (DGM). METHODS: Eight rats underwent L-α-Lysophosphatidylcholine (LPC) injections in the WM and striatum to introduce focal demyelination. Multimodal MRI was performed at 7 Tesla after 7 days. Tensor-valued dMRI was complemented by diffusion tensor imaging, quantitative MRI and proton magnetic resonance spectroscopy (MRS). RESULTS: ) in DGM (P < 0.0001). CONCLUSION: increase may be particularly sensitive in detecting DGM lesions of MS.

YNIMG Journal 2021 Journal Article

Using connectomics for predictive assessment of brain parcellations

Kristoffer J. Albers
Karen S. Ambrosen
Matthew G. Liptrot
Tim B. Dyrby
Mikkel N. Schmidt
Morten Mørup

The organization of the human brain remains elusive, yet is of great importance to the mechanisms of integrative brain function. At the macroscale, its structural and functional interpretation is conventionally assessed at the level of cortical units. However, the definition and validation of such cortical parcellations are problematic due to the absence of a true gold standard. We propose a framework for quantitative evaluation of brain parcellations via statistical prediction of connectomics data. Specifically, we evaluate the extent in which the network representation at the level of cortical units (defined as parcels) accounts for high-resolution brain connectivity. Herein, we assess the pertinence and comparative ranking of ten existing parcellation atlases to account for functional (FC) and structural connectivity (SC) data based on data from the Human Connectome Project (HCP), and compare them to data-driven as well as spatially-homogeneous geometric parcellations including geodesic parcellations with similar size distributions as the atlases. We find substantial discrepancy in parcellation structures that well characterize FC and SC and differences in what well represents an individual's functional connectome when compared against the FC structure that is preserved across individuals. Surprisingly, simple spatial homogenous parcellations generally provide good representations of both FC and SC, but are inferior when their within-parcellation distribution of individual parcel sizes is matched to that of a valid atlas. This suggests that the choice of fine grained and coarse representations used by existing atlases are important. However, we find that resolution is more critical than the exact border location of parcels.

YNICL Journal 2020 Journal Article

Motor fatigue is associated with asymmetric connectivity properties of the corticospinal tract in multiple sclerosis

Christian Bauer
Tim B. Dyrby
Finn Sellebjerg
Kathrine Skak Madsen
Olivia Svolgaard
Morten Blinkenberg
Hartwig Roman Siebner
Kasper Winther Andersen

Multiple Sclerosis (MS) is characterized by demyelination and neurodegeneration of the central nervous system and causes excessive fatigue in more than 80% of the patients. The pathophysiologic mechanisms causing fatigue are still largely unknown. In 46 right-handed patients with relapsing-remitting MS and 25 right-handed controls, we performed diffusion MRI and applied streamline based probabilistic tractography to derive unilateral anatomical connectivity maps for the white matter of the right and left hemispheres. The maps provide an indication how often a streamline has passed through a given voxel. Since tractography based anatomical connectivity mapping (ACM) is sensitive to disease-induced changes in anatomical connectivity, we used ACM to test whether motor fatigue is associated with altered ipsi-hemispherical anatomical connectivity in the major motor output pathway, the corticospinal tract (CST). Patients had higher mean ACM values in the CST than healthy controls. This indicated that a higher number of streamlines, starting from voxels in the same hemisphere, travelled through the CST and may reflect an accumulated disease-induced disintegration of CST. The motor subscale of the Fatigue Scale for Motor and Cognitive functions (FSMCMOTOR) was used to define sub-groups with (n = 29, FSMCMOTOR score ≥ 27) and without motor fatigue (n = 17, FSMSMOTOR score ≤ 26). Patients without fatigue only showed higher ACM values in right CST, while mean ACM values were unaltered in left CST. The higher the mean ACM values in the left relative to the right CST, the more patients reported motor fatigue. Left-right asymmetry in anatomical connectivity outside the CST did not scale with individual motor fatigue. Our results link lateralized changes of tractography-based microstructural properties in the CST with motor fatigue in relapsing-remitting MS.

YNIMG Journal 2020 Journal Article

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Gabriel Girard
Roberto Caminiti
Alexandra Battaglia-Mayer
Etienne St-Onge
Karen S. Ambrosen
Simon F. Eskildsen
Kristine Krug
Tim B. Dyrby

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from “bottleneck” white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

YNIMG Journal 2020 Journal Article

Validation of structural brain connectivity networks: The impact of scanning parameters

Karen S. Ambrosen
Simon F. Eskildsen
Max Hinne
Kristine Krug
Henrik Lundell
Mikkel N. Schmidt
Marcel A.J. van Gerven
Morten Mørup

Evaluation of the structural connectivity (SC) of the brain based on tractography has mainly focused on the choice of diffusion model, tractography algorithm, and their respective parameter settings. Here, we systematically validate SC derived from a post mortem monkey brain, while varying key acquisition parameters such as the b-value, gradient angular resolution and image resolution. As gold standard we use the connectivity matrix obtained invasively with histological tracers by Markov et al. (2014). As performance metric, we use cross entropy as a measure that enables comparison of the relative tracer labeled neuron counts to the streamline counts from tractography. We find that high angular resolution and high signal-to-noise ratio are important to estimate SC, and that SC derived from low image resolution (1. 03 mm3) are in better agreement with the tracer network, than those derived from high image resolution (0. 53 mm3) or at an even lower image resolution (2. 03 mm3). In contradiction, sensitivity and specificity analyses suggest that if the angular resolution is sufficient, the balanced compromise in which sensitivity and specificity are identical remains 60–64% regardless of the other scanning parameters. Interestingly, the tracer graph is assumed to be the gold standard but by thresholding, the balanced compromise increases to 70–75%. Hence, by using performance metrics based on binarized tracer graphs, one risks losing important information, changing the performance of SC graphs derived by tractography and their dependence of different scanning parameters.

YNIMG Journal 2019 Journal Article

Limits to anatomical accuracy of diffusion tractography using modern approaches

Kurt G. Schilling
Vishwesh Nath
Colin Hansen
Prasanna Parvathaneni
Justin Blaber
Yurui Gao
Peter Neher
Dogu Baran Aydogan

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets – a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

YNIMG Journal 2018 Journal Article

Validation strategies for the interpretation of microstructure imaging using diffusion MRI

Tim B. Dyrby
Giorgio M. Innocenti
Martin Bech
Henrik Lundell

Extracting microanatomical information beyond the image resolution of MRI would provide valuable tools for diagnostics and neuroscientific research. A number of mathematical models already suggest microstructural interpretations of diffusion MRI (dMRI) data. Examples of such microstructural features could be cell bodies and neurites, e. g. the axon's diameter or their orientational distribution for global connectivity analysis using tractography, and have previously only been possible to access through conventional histology of post mortem tissue or invasive biopsies. The prospect of gaining the same knowledge non-invasively from the whole living human brain could push the frontiers for the diagnosis of neurological and psychiatric diseases. It could also provide a general understanding of the development and natural variability in the healthy brain across a population. However, due to a limited image resolution, most of the dMRI measures are indirect estimations and may depend on the whole chain from experimental parameter settings to model assumptions and implementation. Here, we review current literature in this field and highlight the integrative work across anatomical length scales that is needed to validate and trust a new dMRI method. We encourage interdisciplinary collaborations and data sharing in regards to applying and developing new validation techniques to improve the specificity of future dMRI methods.

YNIMG Journal 2017 Journal Article

Image quality transfer and applications in diffusion MRI

Daniel C. Alexander
Darko Zikic
Aurobrata Ghosh
Ryutaro Tanno
Viktor Wottschel
Jiaying Zhang
Enrico Kaden
Tim B. Dyrby

This paper introduces a new computational imaging technique called image quality transfer (IQT). IQT uses machine learning to transfer the rich information available from one-off experimental medical imaging devices to the abundant but lower-quality data from routine acquisitions. The procedure uses matched pairs to learn mappings from low-quality to corresponding high-quality images. Once learned, these mappings then augment unseen low quality images, for example by enhancing image resolution or information content. Here, we demonstrate IQT using a simple patch-regression implementation and the uniquely rich diffusion MRI data set from the human connectome project (HCP). Results highlight potential benefits of IQT in both brain connectivity mapping and microstructure imaging. In brain connectivity mapping, IQT reveals, from standard data sets, thin connection pathways that tractography normally requires specialised data to reconstruct. In microstructure imaging, IQT shows potential in estimating, from standard “single-shell” data (one non-zero b-value), maps of microstructural parameters that normally require specialised multi-shell data. Further experiments show strong generalisability, highlighting IQT's benefits even when the training set does not directly represent the application domain. The concept extends naturally to many other imaging modalities and reconstruction problems.

YNIMG Journal 2015 Journal Article

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data

Alessandro Daducci
Erick J. Canales-Rodríguez
Hui Zhang
Tim B. Dyrby
Daniel C. Alexander
Jean-Philippe Thiran

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i. e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0. 9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

YNIMG Journal 2014 Journal Article

Interpolation of diffusion weighted imaging datasets

Tim B. Dyrby
Henrik Lundell
Mark W. Burke
Nina L. Reislev
Olaf B. Paulson
Maurice Ptito
Hartwig R. Siebner

Diffusion weighted imaging (DWI) is used to study white-matter fibre organisation, orientation and structural connectivity by means of fibre reconstruction algorithms and tractography. For clinical settings, limited scan time compromises the possibilities to achieve high image resolution for finer anatomical details and signal-to-noise-ratio for reliable fibre reconstruction. We assessed the potential benefits of interpolating DWI datasets to a higher image resolution before fibre reconstruction using a diffusion tensor model. Simulations of straight and curved crossing tracts smaller than or equal to the voxel size showed that conventional higher-order interpolation methods improved the geometrical representation of white-matter tracts with reduced partial-volume-effect (PVE), except at tract boundaries. Simulations and interpolation of ex-vivo monkey brain DWI datasets revealed that conventional interpolation methods fail to disentangle fine anatomical details if PVE is too pronounced in the original data. As for validation we used ex-vivo DWI datasets acquired at various image resolutions as well as Nissl-stained sections. Increasing the image resolution by a factor of eight yielded finer geometrical resolution and more anatomical details in complex regions such as tract boundaries and cortical layers, which are normally only visualized at higher image resolutions. Similar results were found with typical clinical human DWI dataset. However, a possible bias in quantitative values imposed by the interpolation method used should be considered. The results indicate that conventional interpolation methods can be successfully applied to DWI datasets for mining anatomical details that are normally seen only at higher resolutions, which will aid in tractography and microstructural mapping of tissue compartments.

YNIMG Journal 2013 Journal Article

The CONNECT project: Combining macro- and micro-structure

Yaniv Assaf
Daniel C. Alexander
Derek K. Jones
Albero Bizzi
Tim E.J. Behrens
Chris A. Clark
Yoram Cohen
Tim B. Dyrby

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome.

YNIMG Journal 2011 Journal Article

Distribution of collateral fibers in the monkey cervical spinal cord detected with diffusion-weighted magnetic resonance imaging

Henrik Lundell
Jens Bo Nielsen
Maurice Ptito
Tim B. Dyrby

Diffusion anisotropy monitored with diffusion-weighted magnetic resonance imaging (DWMRI) is a sensitive marker to monitor developmental or pathological microstructural changes in spinal cord. The white matter is often treated as a unidirectional axonal bundle but collateral fibers branching off the main spinal pathways contradicts this assumption and affects the diffusion anisotropy. It is the aim of this study to investigate to what extent collateral fibers are apparent in diffusion tensor data and if collaterals can be detected as individual fiber directions using crossing fiber detection techniques. We calculate the diffusion tensor and the persistent angular structure (PAS), a multi-fiber reconstruction technique, from high quality post mortem data of a perfusion-fixed vervet monkey cervical spinal cord sample and simulated crossing fiber data. Our results show that (i) cylindrical geometry in the white matter of the spinal cord is an invalid assumption due to collateral fibers. We also demonstrate that (ii) collateral fibers can be resolved as distinct peaks in the water diffusion propagator in white matter using multi-fiber models. Finally, we show that (iii) crossing fibers are mainly located laterally and increase towards the cervical enlargement.

YNIMG Journal 2010 Journal Article

Orientationally invariant indices of axon diameter and density from diffusion MRI

Daniel C. Alexander
Penny L. Hubbard
Matt G. Hall
Elizabeth A. Moore
Maurice Ptito
Geoff J.M. Parker
Tim B. Dyrby

This paper proposes and tests a technique for imaging orientationally invariant indices of axon diameter and density in white matter using diffusion magnetic resonance imaging. Such indices potentially provide more specific markers of white matter microstructure than standard indices from diffusion tensor imaging. Orientational invariance allows for combination with tractography and presents new opportunities for mapping brain connectivity and quantifying disease processes. The technique uses a four-compartment tissue model combined with an optimized multishell high-angular-resolution pulsed-gradient-spin-echo acquisition. We test the method in simulation, on fixed monkey brains using a preclinical scanner and on live human brains using a clinical 3T scanner. The human data take about one hour to acquire. The simulation experiments show that both monkey and human protocols distinguish distributions of axon diameters that occur naturally in white matter. We compare the axon diameter index with the mean axon diameter weighted by axon volume. The index differs from this mean and is protocol dependent, but correlation is good for the monkey protocol and weaker, but discernible, for the human protocol where greater diffusivity and lower gradient strength limit sensitivity to only the largest axons. Maps of axon diameter and density indices from the monkey and human data in the corpus callosum and corticospinal tract reflect known trends from histology. The results show orientationally invariant sensitivity to natural axon diameter distributions for the first time with both specialist and clinical hardware. This demonstration motivates further refinement, validation, and evaluation of the precise nature of the indices and the influence of potential confounds.

YNIMG Journal 2008 Journal Article

Segmentation of age-related white matter changes in a clinical multi-center study

Tim B. Dyrby
Egill Rostrup
William F.C. Baaré
Elisabeth C.W. van Straaten
Frederik Barkhof
Hugo Vrenken
Stefan Ropele
Reinhold Schmidt

Age-related white matter changes (WMC) are thought to be a marker of vascular pathology, and have been associated with motor and cognitive deficits. In the present study, an optimized artificial neural network was used as an automatic segmentation method to produce probabilistic maps of WMC in a clinical multi-center study. The neural network uses information from T1- and T2-weighted and fluid attenuation inversion recovery (FLAIR) magnetic resonance (MR) scans, neighboring voxels and spatial location. Generalizability of the neural network was optimized by including the Optimal Brain Damage (OBD) pruning method in the training stage. Six optimized neural networks were produced to investigate the impact of different input information on WMC segmentation. The automatic segmentation method was applied to MR scans of 362 non-demented elderly subjects from 11 centers in the European multi-center study Leukoaraiosis And Disability (LADIS). Semi-manually delineated WMC were used for validating the segmentation produced by the neural networks. The neural network segmentation demonstrated high consistency between subjects and centers, making it a promising technique for large studies. For WMC volumes less than 10 ml, an increasing discrepancy between semi-manual and neural network segmentation was observed using the similarity index (SI) measure. The use of all three image modalities significantly improved cross-center generalizability compared to neural networks using the FLAIR image only. Expert knowledge not available to the neural networks was a minor source of discrepancy, while variation in MR scan quality constituted the largest source of error.

YNIMG Journal 2007 Journal Article

Validation of in vitro probabilistic tractography

Tim B. Dyrby
Lise V. Søgaard
Geoffrey J. Parker
Daniel C. Alexander
Nanna M. Lind
William F.C. Baaré
Anders Hay-Schmidt
Nina Eriksen

Diffusion weighted imaging (DWI) and tractography allow the non-invasive study of anatomical brain connectivity. However, a gold standard for validating tractography of complex connections is lacking. Using the porcine brain as a highly gyrated brain model, we quantitatively and qualitatively assessed the anatomical validity and reproducibility of in vitro multi-fiber probabilistic tractography against two invasive tracers: the histochemically detectable biotinylated dextran amine and manganese enhanced magnetic resonance imaging. Post mortem DWI was used to ensure that most of the sources known to degrade the anatomical accuracy of in vivo DWI did not influence the tracking results. We demonstrate that probabilistic tractography reliably detected specific pathways. Moreover, the applied model allowed identification of the limitations that are likely to appear in many of the current tractography methods. Nevertheless, we conclude that DWI tractography can be a precise tool in studying anatomical brain connectivity.