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Alexander Leemans

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YNICL Journal 2026 Journal Article

Individualized functionnectome for the statistical assessment of white matter circuits underlying task-fMRI activations in glioma patients

  • Giovanni Sighinolfi
  • Alexander Leemans
  • David Neil Manners
  • Elena Cantoni
  • Gianfranco Vornetti
  • Lorenzo Motta
  • Enrico Franceschi
  • Caterina Tonon

The Functionnectome framework enables the projection of task-related fMRI signals onto the underlying white matter pathways, using anatomical priors derived from structural connectivity. However, the existing "standardized" priors are based on averaged tractograms from healthy controls and cannot accurately capture the altered anatomy found in patients with brain lesions. This study extends the Functionnectome framework by generating individualized anatomical priors from subject-specific diffusion MRI tractography, allowing for improved integration of functional and structural data in patients with brain tumors. Twenty-six patients with gliomas (9 females; mean age 43 ± 17 years) underwent 3 T MRI, including a multishell diffusion protocol and task-based fMRI targeting motor and/or language functions. Whole-brain tractography was reconstructed using three methods (TensorDet, PFT, and iFOD2) and converted into voxelwise individualized priors. Functionnectome maps were estimated using both standardized and individualized priors, and Z-statistic activation maps were obtained via GLM analysis. Similarity metrics and atlas-based consistency were used to compare standardized and individualized Functionnectomes. Functionnectomes derived from probabilistic tractography showed moderate correlations with standardized Functionnectomes (average Pearson's r ≈ 0.5), highlighting the influence of individual structural variability while maintaining comparable activation patterns. The PFT-based Functionnectome exhibited superior anatomical plausibility, with consistent overlap with expected motor white matter tracts and identification of relevant bundles in up to 100% of cases, compared to 50-60% with standardized priors. The individualized Functionnectome enhances the anatomical validity and subject specificity of structure-function mapping, advancing precision neuroimaging for clinical and neurosurgical applications.

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YNIMG Journal 2022 Journal Article

Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH)

  • Alberto de Luca
  • Suheyla Cetin Karayumak
  • Alexander Leemans
  • Yogesh Rathi
  • Stephan Swinnen
  • Jolien Gooijers
  • Amanda Clauwaert
  • Roald Bahr

Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.

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YNICL Journal 2022 Journal Article

Improved sensitivity and precision in multicentre diffusion MRI network analysis using thresholding and harmonization

  • Bruno M. de Brito Robalo
  • Alberto de Luca
  • Christopher Chen
  • Anna Dewenter
  • Marco Duering
  • Saima Hilal
  • Huiberdina L. Koek
  • Anna Kopczak

PURPOSE: To investigate if network thresholding and raw data harmonization improve consistency of diffusion MRI (dMRI)-based brain networks while also increasing precision and sensitivity to detect disease effects in multicentre datasets. METHODS: Brain networks were reconstructed from dMRI of five samples with cerebral small vessel disease (SVD; 629 patients, 166 controls), as a clinically relevant exemplar condition for studies on network integrity. We evaluated consistency of network architecture in age-matched controls, by calculating cross-site differences in connection probability and fractional anisotropy (FA). Subsequently we evaluated precision and sensitivity to disease effects by identifying connections with low FA in sporadic SVD patients relative to controls, using more severely affected patients with a pure form of genetically defined SVD as reference. RESULTS: In controls, thresholding and harmonization improved consistency of network architecture, minimizing cross-site differences in connection probability and FA. In patients relative to controls, thresholding improved precision to detect disrupted connections by removing false positive connections (precision, before: 0.09-0.19; after: 0.38-0.70). Before harmonization, sensitivity was low within individual sites, with few connections surviving multiple testing correction (k = 0-25 connections). Harmonization and pooling improved sensitivity (k = 38), while also achieving higher precision when combined with thresholding (0.97). CONCLUSION: We demonstrated that network consistency, precision and sensitivity to detect disease effects in SVD are improved by thresholding and harmonization. We recommend introducing these techniques to leverage large existing multicentre datasets to better understand the impact of disease on brain networks.

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YNIMG Journal 2022 Journal Article

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

  • Chiara Maffei
  • Gabriel Girard
  • Kurt G. Schilling
  • Dogu Baran Aydogan
  • Nagesh Adluru
  • Andrey Zhylka
  • Ye Wu
  • Matteo Mancini

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

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YNICL Journal 2022 Journal Article

Neuroanatomical markers of psychotic experiences in adolescents: A machine-learning approach in a longitudinal population-based sample

  • Joanne P.M. Kenney
  • Laura Milena Rueda-Delgado
  • Erik O. Hanlon
  • Lee Jollans
  • Ian Kelleher
  • Colm Healy
  • Niamh Dooley
  • Conor McCandless

It is important to identify accurate markers of psychiatric illness to aid early prediction of disease course. Subclinical psychotic experiences (PEs) are important risk factors for later mental ill-health and suicidal behaviour. This study used machine learning to investigate neuroanatomical markers of PEs in early and later stages of adolescence. Machine learning using logistic regression using Elastic Net regularization was applied to T1-weighted and diffusion MRI data to classify adolescents with subclinical psychotic experiences vs. controls across 3 timepoints (Time 1: 11–13 years, n = 77; Time 2: 14–16 years, n = 56; Time 3: 18–20 years, n = 40). Neuroimaging data classified adolescents aged 11–13 years with current PEs vs. controls returning an AROC of 0. 62, significantly better than a null model, p = 1. 73e-29. Neuroimaging data also classified those with PEs at 18–20 years (AROC = 0. 59; P = 7. 19e-10) but performance was at chance level at 14–16 years (AROC = 0. 50). Left hemisphere frontal regions were top discriminant classifiers for 11–13 years-old adolescents with PEs, particularly pars opercularis. Those with future PEs at 18–20 years-old were best distinguished from controls based on left frontal regions, right-hemisphere medial lemniscus, cingulum bundle, precuneus and genu of the corpus callosum (CC). Deviations from normal adolescent brain development in young people with PEs included an acceleration in the typical pattern of reduction in left frontal thickness and right parietal curvature, and accelerated progression of microstructural changes in right white matter and corpus callosum. These results emphasise the importance of multi-modal analysis for understanding adolescent PEs and provide important new insights into early phenotypes for psychotic experiences.

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YNICL Journal 2021 Journal Article

Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease

  • Bruno M. de Brito Robalo
  • Geert Jan Biessels
  • Christopher Chen
  • Anna Dewenter
  • Marco Duering
  • Saima Hilal
  • Huiberdina L. Koek
  • Anna Kopczak

OBJECTIVES: Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. METHODS: Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. RESULTS: = 0.60). CONCLUSIONS: We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.

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YNIMG Journal 2021 Journal Article

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge

  • Alberto de Luca
  • Andrada Ianus
  • Alexander Leemans
  • Marco Palombo
  • Noam Shemesh
  • Hui Zhang
  • Daniel C. Alexander
  • Markus Nilsson

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

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YNIMG Journal 2021 Journal Article

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

  • Kurt G. Schilling
  • François Rheault
  • Laurent Petit
  • Colin B. Hansen
  • Vishwesh Nath
  • Fang-Cheng Yeh
  • Gabriel Girard
  • Muhamed Barakovic

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

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YNIMG Journal 2020 Journal Article

Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data

  • Fenghua Guo
  • Alexander Leemans
  • Max A. Viergever
  • Flavio Dell’Acqua
  • Alberto de Luca

Spherical deconvolution is a widely used approach to quantify the fiber orientation distribution (FOD) from diffusion MRI data of the brain. The damped Richardson-Lucy (dRL) is an algorithm developed to perform robust spherical deconvolution on single-shell diffusion MRI data while suppressing spurious FOD peaks due to noise or partial volume effects. Due to recent progress in acquisition hardware and scanning protocols, it is becoming increasingly common to acquire multi-shell diffusion MRI data, which allows for the modelling of multiple tissue types beyond white matter. While the dRL algorithm could, in theory, be directly applied to multi-shell data, it is not optimised to exploit its information content to model the signal from multiple tissue types. In this work, we introduce a new framework based on dRL – dubbed generalized Richardson-Lucy (GRL) – that uses multi-shell data in combination with user-chosen tissue models to disentangle partial volume effects and increase the accuracy in FOD estimation. Further, GRL estimates signal fraction maps associated to each user-selected model, which can be used during fiber tractography to dissect and terminate the tracking without need for additional structural data. The optimal weighting of multi-shell data in the fit and the robustness to noise and to partial volume effects of GRL was studied with synthetic data. Subsequently, we investigated the performance of GRL in comparison to dRL and to multi-shell constrained spherical deconvolution (MSCSD) on a high-resolution diffusion MRI dataset from the Human Connectome Project and on an MRI dataset acquired at 3T on a clinical scanner. In line with previous studies, we described the signal of the cerebrospinal-fluid and of the grey matter with isotropic diffusion models, whereas four diffusion models were considered to describe the white matter. With a third dataset including small diffusion weightings, we studied the feasibility of including intra-voxel incoherent motion effects due to blood pseudo-diffusion in the modelling. Further, the reliability of GRL was demonstrated with a test-retest scan of a subject acquired at 3T. Results of simulations show that GRL can robustly disentangle different tissue types at SNR above 20 with respect to the non-weighted image, and that it improves the angular accuracy of the FOD estimation as compared to dRL. On real data, GRL provides signal fraction maps that are physiologically plausible and consistent with those obtained with MSCSD, with correlation coefficients between the two methods up to 0. 96. When considering IVIM effects, a high blood pseudo-diffusion fraction is observed in the medial temporal lobe and in the sagittal sinus. In comparison to dRL and MSCSD, GRL provided sharper FODs and less spurious peaks in presence of partial volume effects, but the FOD reconstructions are also highly dependent on the chosen modelling of white matter. When performing fiber tractography, GRL allows to terminate fiber tractography using the signal fraction maps, which results in a better tract termination at the grey-white matter interface or at the outer cortical surface. In terms of inter-scan reliability, GRL performed similarly to or better than compared methods. In conclusion, GRL offers a new modular and flexible framework to perform spherical deconvolution of multi-shell data.

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YNICL Journal 2020 Journal Article

Perinatal thalamic injury: MRI predictors of electrical status epilepticus in sleep and long-term neurodevelopment

  • Bart van den Munckhof
  • Anne F. Zwart
  • Lauren C. Weeke
  • Nathalie H.P. Claessens
  • Joost D.J. Plate
  • Alexander Leemans
  • Hugo J. Kuijf
  • Heleen C. van Teeseling

OBJECTIVE: Perinatal thalamic injury is associated with epilepsy with electrical status epilepticus in sleep (ESES). The aim of this study was to prospectively quantify the risk of ESES and to assess neuroimaging predictors of neurodevelopment. METHODS: We included patients with perinatal thalamic injury. MRI scans were obtained in the neonatal period, around three months of age and during childhood. Thalamic and total brain volumes were obtained from the three months MRI. Diffusion characteristics were assessed. Sleep EEGs distinguished patients into ESES (spike-wave index (SWI) >85%), ESES-spectrum (SWI 50-85%) or no ESES (SWI < 50%). Serial Intelligence Quotient (IQ)/Developmental Quotient (DQ) scores were obtained during follow-up. Imaging and EEG findings were correlated to neurodevelopmental outcome. RESULTS: Thirty patients were included. Mean thalamic volume at three months was 8.11 (±1.67) ml and mean total brain volume 526.45 (±88.99) ml. In the prospective cohort (n = 23) 19 patients (83%) developed ESES (-spectrum) abnormalities after a mean follow-up of 96 months. In the univariate analysis, larger thalamic volume, larger total brain volume and lower SWI correlated with higher mean IQ/DQ after 2 years (Pearson's r = 0.74, p = 0.001; Pearson's r = 0.64, p = 0.005; and Spearman's rho -0.44, p = 0.03). In a multivariable mixed model analysis, thalamic volume was a significant predictor of IQ/DQ (coefficient 9.60 [p < 0.001], i.e., corrected for total brain volume and SWI and accounting for repeated measures within patients, a 1 ml higher thalamic volume was associated with a 9.6 points higher IQ). Diffusion characteristics during childhood correlated with IQ/DQ after 2 years. SIGNIFICANCE: Perinatal thalamic injury is followed by electrical status epilepticus in sleep in the majority of patients. Thalamic volume and diffusion characteristics correlate to neurodevelopmental outcome.

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YNIMG Journal 2020 Journal Article

Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs)

  • Alberto de Luca
  • Fenghua Guo
  • Martijn Froeling
  • Alexander Leemans

In diffusion MRI, spherical deconvolution approaches can estimate local white matter (WM) fiber orientation distributions (FOD) which can be used to produce fiber tractography reconstructions. The applicability of spherical deconvolution to gray matter (GM), however, is still limited, despite its critical role as start/endpoint of WM fiber pathways. The advent of multi-shell diffusion MRI data offers additional contrast to model the GM signal but, to date, only isotropic models have been applied to GM. Evidence from both histology and high-resolution diffusion MRI studies suggests a marked anisotropic character of the diffusion process in GM, which could be exploited to improve the description of the cortical organization. In this study, we investigated whether performing spherical deconvolution with tissue specific models of both WM and GM can improve the characterization of the latter while retaining state-of-the-art performances in WM. To this end, we developed a framework able to simultaneously accommodate multiple anisotropic response functions to estimate multiple, tissue-specific, fiber orientation distributions (mFODs). As proof of principle, we used the diffusion kurtosis imaging model to represent the WM signal, and the neurite orientation dispersion and density imaging (NODDI) model to represent the GM signal. The feasibility of the proposed approach is shown with numerical simulations and with data from the Human Connectome Project (HCP). The performance of our method is compared to the current state of the art, multi-shell constrained spherical deconvolution (MSCSD). The simulations show that with our new method we can accurately estimate a mixture of two FODs at SNR≥50. With HCP data, the proposed method was able to reconstruct both tangentially and radially oriented FODs in GM, and performed comparably well to MSCSD in computing FODs in WM. When performing fiber tractography, the trajectories reconstructed with mFODs reached the cortex with more spatial continuity and for a longer distance as compared to MSCSD and allowed to reconstruct short trajectories tangential to the cortical folding. In conclusion, we demonstrated that our proposed method allows to perform spherical deconvolution of multiple anisotropic response functions, specifically improving the performances of spherical deconvolution in GM tissue.

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YNIMG Journal 2020 Journal Article

The adverse effect of gradient nonlinearities on diffusion MRI: From voxels to group studies

  • Hamed Y. Mesri
  • Szabolcs David
  • Max A. Viergever
  • Alexander Leemans

Nonlinearities of gradient magnetic fields in diffusion MRI (dMRI) can introduce systematic errors in estimates of diffusion measures. While there are correction methods that can compensate for these errors, as presented in the Human Connectome Project, such nonlinear effects are often assumed to be negligible for typical applications, and as a result, gradient nonlinearities are mostly left uncorrected. In this work, we perform a systematic analysis to investigate the effect of gradient nonlinearities on dMRI studies, from voxel-wise estimates to group study outcomes. We present a novel framework to quantify and visualize these effects by decomposing them into their magnitude and angle components. Mean magnitude deviation and fractional gradient anisotropy are introduced to quantify the distortions in the size and shape of gradient vector distributions. By means of Monte-Carlo simulations and real data from the Human Connectome Project, the errors on dMRI measures derived from the diffusion tensor imaging and diffusional kurtosis imaging are highlighted. We perform a group study to showcase the alteration in the significance and effect size due to ignoring gradient nonlinearity correction. Our results indicate that the effect of gradient field nonlinearities on dMRI studies can be significant and may complicate the interpretation of the results and conclusions.

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YNIMG Journal 2019 Journal Article

Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms

  • Chantal MW. Tax
  • Francesco Grussu
  • Enrico Kaden
  • Lipeng Ning
  • Umesh Rudrapatna
  • C. John Evans
  • Samuel St-Jean
  • Alexander Leemans

Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain ‘truly quantitative measures’ and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with ‘standard’ and ‘state-of-the-art’ protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques.

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YNICL Journal 2019 Journal Article

Magnetic resonance imaging of the cervical spinal cord in spinal muscular atrophy

  • Marloes Stam
  • Wieke Haakma
  • Lidy Kuster
  • Martijn Froeling
  • Marielle E.P. Philippens
  • Clemens Bos
  • Alexander Leemans
  • Louise A.M. Otto

OBJECTIVE: In this study we investigated the potential value of magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) in characterizing changes in the cervical spinal cord and peripheral nerve roots in vivo in patients with spinal muscular atrophy (SMA). METHODS: We developed an MRI protocol with 4 sequences to investigate the cervical spinal cord and nerve roots on a 3 Tesla MRI system. We used 2 anatomical MRI sequences to investigate cross-sectional area (CSA) at each spinal segment and the diameter of ventral and dorsal nerve roots, and two diffusion tensor imaging (DTI) techniques to estimate the fractional anisotropy (FA), mean (MD), axial (AD) and radial diffusivity (RD) in 10 SMA patients and 20 healthy controls. RESULTS: There were no significant differences in CSA (p > .1), although an 8.5% reduction of CSA in patients compared to healthy controls was apparent at segment C7. DTI data showed a higher AD in grey matter of patients compared to healthy controls (p = .033). Significantly lower MD, AD and RD values were found in rostral nerve roots (C3-C5) in patients (p < .045). CONCLUSIONS: We showed feasibility of an advanced 3 T MRI protocol that allowed differences to be determined between patients and healthy controls, confirming the potential of this technique to assess pathological mechanisms in SMA. After further development and confirmation of findings in a larger sample, these techniques may be used to study disease course of SMA in vivo and evaluate response to survival motor neuron (SMN) augmenting therapy.

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YNIMG Journal 2019 Journal Article

Reducing variability in along-tract analysis with diffusion profile realignment

  • Samuel St-Jean
  • Maxime Chamberland
  • Max A. Viergever
  • Alexander Leemans

Diffusion weighted magnetic resonance imaging (dMRI) provides a non invasive virtual reconstruction of the brain's white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tract-averaged measurements. However, performing group analyses with this along-tract strategy requires correspondence between points of tract pathways across subjects. This is usually achieved by creating a new common space where the representative streamlines from every subject are resampled to the same number of points. If the underlying anatomy of some subjects was altered due to, e. g. , disease or developmental changes, such information might be lost by resampling to a fixed number of points. In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR). Experiments on synthetic datasets show that DPR reduces the coefficient of variation for the mean diffusivity, fractional anisotropy and apparent fiber density when compared to the unaligned case. Using 100 in vivo datasets from the human connectome project, we simulated changes in mean diffusivity, fractional anisotropy and apparent fiber density. Independent Student's t-tests between these altered subjects and the original subjects indicate that regional changes are identified after realignment with the DPR algorithm, while preserving differences previously detected in the unaligned case. This new correction strategy contributes to revealing effects of interest which might be hidden by misalignment and has the potential to improve the specificity in longitudinal population studies beyond the traditional region of interest based analysis and along-tract analysis workflows.

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YNICL Journal 2019 Journal Article

Widespread white matter connectivity abnormalities in narcolepsy type 1: A diffusion tensor imaging study

  • Jari K. Gool
  • Rolf Fronczek
  • Alexander Leemans
  • Dennis A. Kies
  • Gert Jan Lammers
  • Ysbrand D. van der Werf

Narcolepsy type 1 is caused by a selective loss of hypothalamic hypocretin-producing neurons, resulting in severely disturbed sleep-wake control and cataplexy. Hypocretin-producing neurons project widely throughout the brain, influencing different neural networks. We assessed the extent of microstructural white matter organization and brain-wide structural connectivity abnormalities in a homogeneous group of twelve drug-free patients with narcolepsy type 1 and eleven matched healthy controls using diffusion tensor imaging with multimodal analysis techniques. First, tract-based spatial statistics (TBSS) was carried out using fractional anisotropy (FA) and mean, axial and radial diffusivity (MD, AD, RD). Second, quantitative analyses of mean FA, MD, AD and RD were conducted in predefined regions-of-interest, including sleep-wake regulation-related, limbic and reward system areas. Third, we performed hypothalamus-seeded tractography towards the thalamus, amygdala and midbrain. TBSS analyses yielded brain-wide significantly lower FA and higher RD in patients. Localized significantly lower FA and higher RD in the left ventral diencephalon and lower AD in the midbrain, were seen in patients. Lower FA was also found in patients in left hypothalamic fibers connecting with the midbrain. No significant MD and AD differences nor a correlation with disease duration were found. The brain-wide, localized ventral diencephalon (comprising the hypothalamus and different sleep- and motor-related nuclei) and hypothalamic connectivity differences clearly show a heretofore underestimated direct and/or indirect effect of hypocretin deficiency on microstructural white matter composition, presumably resulting from a combination of lower axonal density, lower myelination and/or greater axon diameter.

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YNIMG Journal 2018 Journal Article

Associations between prenatal, childhood, and adolescent stress and variations in white-matter properties in young men

  • Sarah K.G. Jensen
  • Melissa Pangelinan
  • Lassi Björnholm
  • Anja Klasnja
  • Alexander Leemans
  • Mark Drakesmith
  • C.J. Evans
  • Edward D. Barker

Objective Previous studies have shown that both pre- and post-natal adversities, the latter including exposures to stress during childhood and adolescence, explain variation in structural properties of white matter (WM) in the brain. While previous studies have examined effects of independent stress exposures within one developmental period, such as childhood, we examine effects of stress across development using data from a prospective longitudinal study. More specifically, we ask how stressful events during prenatal development, childhood, and adolescence relate to variation in WM properties in early adulthood in young men recruited from a birth cohort. Method Using data from 393 mother-son pairs from a community-based birth cohort from England (Avon Longitudinal Study of Parents and Children), we examined how stressful life events relate to variation in different structural properties of WM in the corpus callosum and across the whole brain in early adulthood in men aged 18–21 years. We distinguish between stress occurring during three developmental periods: a) prenatal maternal stress, b) postnatal stress within the first four years of life, c) stress during adolescence (age 12–16 years). To obtain a comprehensive quantification of variation in WM, we assess structural properties of WM using four different measures, namely fractional anisotropy (FA), mean diffusivity (MD), magnetization transfer ratio (MTR) and myelin water fraction (MWF). Results The developmental model shows that prenatal stress is associated with lower MTR and MWF in the genu and/or splenium of the corpus callosum, and with lower MTR in global (lobar) WM. Stress during early childhood is associated with higher MTR in the splenium, and stress during adolescence is associated with higher MTR in the genu and lower MD in the splenium. We see no associations between postnatal stress and variation in global (lobar) WM. Conclusions The current study found evidence for independent effects of stress on WM properties during distinct neurodevelopmental periods. We speculate that these independent effects are due to differences in the developmental processes unfolding at different developmental time points. We suggest that associations between prenatal stress and WM properties may relate to abnormalities in neurogenesis, affecting the number and density of axons, while postnatal stress may interfere with processes related to myelination or radial growth of axons. Potential consequences of prenatal glucocorticoid exposure should be considered in obstetric care.

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YNICL Journal 2018 Journal Article

The cumulative effect of small vessel disease lesions is reflected in structural brain networks of memory clinic patients

  • Rutger Heinen
  • Naomi Vlegels
  • Jeroen de Bresser
  • Alexander Leemans
  • Geert Jan Biessels
  • Yael D. Reijmer

Background and purpose: Mechanisms underlying cognitive impairment in patients with small vessel disease (SVD) are still unknown. We hypothesized that cognition is affected by the cumulative effect of multiple SVD-related lesions on brain connectivity. We therefore assessed the relationship between the total SVD burden on MRI, global brain network efficiency, and cognition in memory clinic patients with vascular brain injury. Methods: 173 patients from the memory clinic of the University Medical Center Utrecht underwent a 3 T brain MRI scan (including diffusion MRI sequences) and neuropsychological testing. MRI markers for SVD were rated and compiled in a previously developed total SVD score. Structural brain networks were reconstructed using fiber tractography followed by graph theoretical analysis. The relationship between total SVD burden score, global network efficiency and cognition was assessed using multiple linear regression analyses. Results: = .12). Conclusion: Global network efficiency is sensitive to the cumulative effect of multiple manifestations of SVD on brain connectivity. Global network efficiency may therefore serve as a useful marker for functionally relevant SVD-related brain injury in clinical trials.

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YNIMG Journal 2016 Journal Article

Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI

  • Chantal M.W. Tax
  • Tom Dela Haije
  • Andrea Fuster
  • Carl-Fredrik Westin
  • Max A. Viergever
  • Luc Florack
  • Alexander Leemans

The question whether our brain pathways adhere to a geometric grid structure has been a popular topic of debate in the diffusion imaging and neuroscience societies. Wedeen et al. (2012a, b) proposed that the brain's white matter is organized like parallel sheets of interwoven pathways. Catani et al. (2012) concluded that this grid pattern is most likely an artifact, resulting from methodological biases that cause the tractography pathways to cross in orthogonal angles. To date, ambiguities in the mathematical conditions for a sheet structure to exist (e. g. its relation to orthogonal angles) combined with the lack of extensive quantitative evidence have prevented wide acceptance of the hypothesis. In this work, we formalize the relevant terminology and recapitulate the condition for a sheet structure to exist. Note that this condition is not related to the presence or absence of orthogonal crossing fibers, and that sheet structure is defined formally as a surface formed by two sets of interwoven pathways intersecting at arbitrary angles within the surface. To quantify the existence of sheet structure, we present a novel framework to compute the sheet probability index (SPI), which reflects the presence of sheet structure in discrete orientation data (e. g. fiber peaks derived from diffusion MRI). With simulation experiments we investigate the effect of spatial resolution, curvature of the fiber pathways, and measurement noise on the ability to detect sheet structure. In real diffusion MRI data experiments we can identify various regions where the data supports sheet structure (high SPI values), but also areas where the data does not support sheet structure (low SPI values) or where no reliable conclusion can be drawn. Several areas with high SPI values were found to be consistent across subjects, across multiple data sets obtained with different scanners, resolutions, and degrees of diffusion weighting, and across various modeling techniques. Under the strong assumption that the diffusion MRI peaks reflect true axons, our results would therefore indicate that pathways do not form sheet structures at every crossing fiber region but instead at well-defined locations in the brain. With this framework, sheet structure location, extent, and orientation could potentially serve as new structural features of brain tissue. The proposed method can be extended to quantify sheet structure in directional data obtained with techniques other than diffusion MRI, which is essential for further validation.

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YNIMG Journal 2016 Journal Article

Trade-off between angular and spatial resolutions in in vivo fiber tractography

  • Sjoerd B. Vos
  • Murat Aksoy
  • Zhaoying Han
  • Samantha J. Holdsworth
  • Julian Maclaren
  • Max A. Viergever
  • Alexander Leemans
  • Roland Bammer

Tractography is becoming an increasingly popular method to reconstruct white matter connections in vivo. The diffusion MRI data that tractography is based on requires a high angular resolution to resolve crossing fibers whereas high spatial resolution is required to distinguish kissing from crossing fibers. However, scan time increases with increasing spatial and angular resolutions, which can become infeasible in clinical settings. Here we investigated the trade-off between spatial and angular resolutions to determine which of these factors is most worth investing scan time in. We created a unique diffusion MRI dataset with 1. 0mm isotropic resolution and a high angular resolution (100 directions) using an advanced 3D diffusion-weighted multi-slab EPI acquisition. This dataset was reconstructed to create subsets of lower angular (75, 50, and 25 directions) and lower spatial (1. 5, 2. 0, and 2. 5mm) resolution. Using all subsets, we investigated the effects of angular and spatial resolutions in three fiber bundles—the corticospinal tract, arcuate fasciculus and corpus callosum—by analyzing the volumetric bundle overlap and anatomical correspondence between tracts. Our results indicate that the subsets of 25 and 50 directions provided inferior tract reconstructions compared with the datasets with 75 and 100 directions. Datasets with spatial resolutions of 1. 0, 1. 5, and 2. 0mm were comparable, while the lowest resolution (2. 5mm) datasets had discernible inferior quality. In conclusion, we found that angular resolution appeared to be more influential than spatial resolution in improving tractography results. Spatial resolutions higher than 2. 0mm only appear to benefit multi-fiber tractography methods if this is not at the cost of decreased angular resolution.

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YNIMG Journal 2016 Journal Article

White matter abnormalities and impaired attention abilities in children born very preterm

  • Andrea L. Murray
  • Deanne K. Thompson
  • Leona Pascoe
  • Alexander Leemans
  • Terrie E. Inder
  • Lex W. Doyle
  • Jacqueline F.I. Anderson
  • Peter J. Anderson

While attention impairments are commonly observed in very preterm (<32weeks' gestational age) children, neuroanatomical correlates of these difficulties are unclear. We aimed to determine whether the microstructural organization of key white matter tracts thought to be involved in attention (cingulum bundle, superior longitudinal fasciculi, reticular activating system, and corpus callosum) were altered in very preterm children compared with term-born controls. We also aimed to determine whether alterations in microstructural organization of these tracts were associated with attention functioning in very preterm children. One hundred and forty-nine very preterm children and 36 term-born controls underwent neuroimaging and assessment of their attention abilities at 7years. Constrained spherical deconvolution and probabilistic tractography was used to identify the key white matter tracts. Altered microstructural organization and reduced tract volume within reticular activating system and corpus callosum were found in the very preterm group compared with the control group. Diffusion and volume changes in the cingulum bundle, superior longitudinal fasciculi, reticular activating system, and corpus callosum were related to variations in attention functioning in the very preterm children. These findings emphasize that white matter tract integrity is associated with later attentional abilities in very preterm children.

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YNICL Journal 2015 Journal Article

Dynamics of the connectome in Huntington's disease: A longitudinal diffusion MRI study

  • Omar F.F. Odish
  • Karen Caeyenberghs
  • Hadi Hosseini
  • Simon J.A. van den Bogaard
  • Raymund A.C. Roos
  • Alexander Leemans

OBJECTIVES: To longitudinally investigate the connectome in different stages of Huntington's disease (HD) by applying graph theoretical analysis to diffusion MRI data. EXPERIMENTAL DESIGN: We constructed weighted structural networks and calculated their topological properties. Twenty-two premanifest (preHD), 10 early manifest HD and 24 healthy controls completed baseline and 2 year follow-up scans. We stratified the preHD group based on their predicted years to disease onset into a far (preHD-A) and near (preHD-B) to disease onset group. We collected clinical and behavioural measures per assessment time point. PRINCIPLE OBSERVATIONS: We found a significant reduction over time in nodal betweenness centrality both in the early manifest HD and preHD-B groups as compared to the preHD-A and control groups, suggesting a decrease of importance of specific nodes to overall network organization in these groups (FDR adjusted ps < 0.05). Additionally, we found a significant longitudinal decrease of the clustering coefficient in preHD when compared to healthy controls (FDR adjusted p < 0.05), which can be interpreted as a reduced capacity for internodal information processing at the local level. Furthermore, we demonstrated dynamic changes to hub-status loss and gain both in preHD and early manifest HD. Finally, we found significant cross-sectional as well as longitudinal relationships between graph metrics and clinical and neurocognitive measures. CONCLUSIONS: This study demonstrates divergent longitudinal changes to the connectome in (pre) HD compared to healthy controls. This provides novel insights into structural correlates associated with clinical and cognitive functions in HD and possible compensatory mechanisms at play in preHD.

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YNIMG Journal 2015 Journal Article

The effect of Gibbs ringing artifacts on measures derived from diffusion MRI

  • Daniele Perrone
  • Jan Aelterman
  • Aleksandra Pižurica
  • Ben Jeurissen
  • Wilfried Philips
  • Alexander Leemans

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a unique method to investigate microstructural tissue properties noninvasively and is one of the most popular methods for studying the brain white matter in vivo. To obtain reliable statistical inferences with diffusion MRI, however, there are still many challenges, such as acquiring high-quality DW-MRI data (e. g. , high SNR and high resolution), careful data preprocessing (e. g. , correcting for subject motion and eddy current induced geometric distortions), choosing the appropriate diffusion approach (e. g. , diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), or diffusion spectrum MRI), and applying a robust analysis strategy (e. g. , tractography based or voxel based analysis). Notwithstanding the numerous efforts to optimize many steps in this complex and lengthy diffusion analysis pipeline, to date, a well-known artifact in MRI – i. e. , Gibbs ringing (GR) – has largely gone unnoticed or deemed insignificant as a potential confound in quantitative DW-MRI analysis. Considering the recent explosion of diffusion MRI applications in biomedical and clinical applications, a systematic and comprehensive investigation is necessary to understand the influence of GR on the estimation of diffusion measures. In this work, we demonstrate with simulations and experimental DW-MRI data that diffusion estimates are significantly affected by GR artifacts and we show that an off-the-shelf GR correction procedure based on total variation already can alleviate this issue substantially.

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YNICL Journal 2015 Journal Article

Training-induced improvements in postural control are accompanied by alterations in cerebellar white matter in brain injured patients

  • David Drijkoningen
  • Karen Caeyenberghs
  • Inge Leunissen
  • Catharine Vander Linden
  • Alexander Leemans
  • Stefan Sunaert
  • Jacques Duysens
  • Stephan P. Swinnen

We investigated whether balance control in young TBI patients can be promoted by an 8-week balance training program and whether this is associated with neuroplastic alterations in brain structure. The cerebellum and cerebellar peduncles were selected as regions of interest because of their importance in postural control as well as their vulnerability to brain injury. Young patients with moderate to severe TBI and typically developing (TD) subjects participated in balance training using PC-based portable balancers with storage of training data and real-time visual feedback. An additional control group of TD subjects did not attend balance training. Mean diffusivity and fractional anisotropy were determined with diffusion MRI scans and were acquired before, during (4 weeks) and at completion of training (8 weeks) together with balance assessments on the EquiTest® System (NeuroCom) which included the Sensory Organization Test, Rhythmic Weight Shift and Limits of Stability protocols. Following training, TBI patients showed significant improvements on all EquiTest protocols, as well as a significant increase in mean diffusivity in the inferior cerebellar peduncle. Moreover, in both training groups, diffusion metrics in the cerebellum and/or cerebellar peduncles at baseline were predictive of the amount of performance increase after training. Finally, amount of training-induced improvement on the Rhythmic Weight Shift test in TBI patients was positively correlated with amount of change in fractional anisotropy in the inferior cerebellar peduncle. This suggests that training-induced plastic changes in balance control are associated with alterations in the cerebellar white matter microstructure in TBI patients.

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YNICL Journal 2014 Journal Article

Alterations in the optic radiations of very preterm children—Perinatal predictors and relationships with visual outcomes

  • Deanne K. Thompson
  • Dolly Thai
  • Claire E. Kelly
  • Alexander Leemans
  • Jacques-Donald Tournier
  • Michael J. Kean
  • Katherine J. Lee
  • Terrie E. Inder

Children born very preterm (VPT) are at risk for visual impairments, the main risk factors being retinopathy of prematurity and cerebral white matter injury, however these only partially account for visual impairments in VPT children. This study aimed to compare optic radiation microstructure and volume between VPT and term-born children, and to investigate associations between 1) perinatal variables and optic radiations; 2) optic radiations and visual function in VPT children. We hypothesized that optic radiation microstructure would be altered in VPT children, predicted by neonatal cerebral white matter abnormality and retinopathy of prematurity, and associated with visual impairments. 142 VPT children and 32 controls underwent diffusion-weighted magnetic resonance imaging at 7 years of age. Optic radiations were delineated using constrained spherical deconvolution tractography. Tract volume and average diffusion tensor values for the whole optic radiations and three sub-regions were compared between the VPT and control groups, and correlated with perinatal variables and 7-year visual outcome data. Total tract volumes and average diffusion values were similar between VPT and control groups. On regional analysis of the optic radiation, mean and radial diffusivity were higher within the middle sub-regions in VPT compared with control children. Neonatal white matter abnormalities and retinopathy of prematurity were associated with optic radiation diffusion values. Lower fractional anisotropy in the anterior sub-regions was associated with poor visual acuity and increased likelihood of other visual defects. This study presents evidence for microstructural alterations in the optic radiations of VPT children, which are largely predicted by white matter abnormality or severe retinopathy of prematurity, and may partially explain the higher rate of visual impairments in VPT children.

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YNIMG Journal 2014 Journal Article

Automated longitudinal intra-subject analysis (ALISA) for diffusion MRI tractography

  • Saskia H. Aarnink
  • Sjoerd B. Vos
  • Alexander Leemans
  • Terry L. Jernigan
  • Kathrine Skak Madsen
  • William F.C. Baaré

Fiber tractography (FT), which aims to reconstruct the three-dimensional trajectories of white matter (WM) fibers non-invasively, is one of the most popular approaches for analyzing diffusion tensor imaging (DTI) data given its high inter- and intra-rater reliability and scan-rescan reproducibility. The major disadvantage of manual FT segmentations, unfortunately, is that placing regions-of-interest for tract selection can be very labor-intensive and time-consuming. Although there are several methods that can identify specific WM fiber bundles in an automated way, manual FT segmentations across multiple subjects performed by a trained rater with neuroanatomical expertise are generally assumed to be more accurate. However, for longitudinal DTI analyses it may still be beneficial to automate the FT segmentation across multiple time points, but then for each individual subject separately. Both the inter-subject and intra-subject automation in this situation are intended for subjects without gross pathology. In this work, we propose such an automated longitudinal intra-subject analysis (dubbed ALISA) approach, and assessed whether ALISA could preserve the same level of reliability as obtained with manual FT segmentations. In addition, we compared ALISA with an automated inter-subject analysis. Based on DTI data sets from (i) ten healthy subjects that were scanned five times (six-month intervals, aged 7. 6–8. 6years at the first scan) and (ii) one control subject that was scanned ten times (weekly intervals, 12. 2years at the first scan), we demonstrate that the increased efficiency provided by ALISA does not compromise the high degrees of precision and accuracy that can be achieved with manual FT segmentations. Further automation for inter-subject analyses, however, did not provide similarly accurate FT segmentations.

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YNIMG Journal 2014 Journal Article

Can diffusion kurtosis imaging improve the sensitivity and specificity of detecting microstructural alterations in brain tissue chronically after experimental stroke? Comparisons with diffusion tensor imaging and histology

  • S. Umesh Rudrapatna
  • Tadeusz Wieloch
  • Kerstin Beirup
  • Karsten Ruscher
  • Wouter Mol
  • Pavel Yanev
  • Alexander Leemans
  • Annette van der Toorn

Imaging techniques that provide detailed insights into structural tissue changes after stroke can vitalize development of treatment strategies and diagnosis of disease. Diffusion-weighted MRI has been playing an important role in this regard. Diffusion kurtosis imaging (DKI), a recent addition to this repertoire, has opened up further possibilities in extending our knowledge about structural tissue changes related to injury as well as plasticity. In this study we sought to discern the microstructural alterations characterized by changes in diffusion tensor imaging (DTI) and DKI parameters at a chronic time point after experimental stroke. Of particular interest was the question of whether DKI parameters provide additional information in comparison to DTI parameters in understanding structural tissue changes, and if so, what their histological origins could be. Region-of-interest analysis and a data-driven approach to identify tissue abnormality were adopted to compare DTI- and DKI-based parameters in post mortem rat brain tissue, which were compared against immunohistochemistry of various cellular characteristics. The unilateral infarcted area encompassed the ventrolateral cortex and the lateral striatum. Results from region-of-interest analysis in the lesion borderzone and contralateral tissue revealed significant differences in DTI and DKI parameters between ipsi- and contralateral sensorimotor cortex, corpus callosum, internal capsule and striatum. This was reflected by a significant reduction in ipsilateral mean diffusivity (MD) and fractional anisotropy (FA) values, accompanied by significant increases in kurtosis parameters in these regions. Data-driven analysis to identify tissue abnormality revealed that the use of kurtosis-based parameters improved the detection of tissue changes in comparison with FA and MD, both in terms of dynamic range and in being able to detect changes to which DTI parameters were insensitive. This was observed in gray as well as white matter. Comparison against immunohistochemical stainings divulged no straightforward correlation between diffusion-based parameters and individual neuronal, glial or inflammatory tissue features. Our study demonstrates that DKI allows sensitive detection of structural tissue changes that reflect post-stroke tissue remodeling. However, our data also highlights the generic difficulty in unambiguously asserting specific causal relationships between tissue status and MR diffusion parameters.

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YNICL Journal 2014 Journal Article

Characterizing the microstructural basis of “unidentified bright objects” in neurofibromatosis type 1: A combined in vivo multicomponent T2 relaxation and multi-shell diffusion MRI analysis

  • Thibo Billiet
  • Burkhard Mädler
  • Felice D'Arco
  • Ronald Peeters
  • Sabine Deprez
  • Ellen Plasschaert
  • Alexander Leemans
  • Hui Zhang

INTRODUCTION: The histopathological basis of "unidentified bright objects" (UBOs) (hyperintense regions seen on T2-weighted magnetic resonance (MR) brain scans in neurofibromatosis-1 (NF1)) remains unclear. New in vivo MRI-based techniques (multi-exponential T2 relaxation (MET2) and diffusion MR imaging (dMRI)) provide measures relating to microstructural change. We combined these methods and present previously unreported data on in vivo UBO microstructure in NF1. METHODS: 3-Tesla dMRI data were acquired on 17 NF1 patients, covering 30 white matter UBOs. Diffusion tensor, kurtosis and neurite orientation and dispersion density imaging parameters were calculated within UBO sites and in contralateral normal appearing white matter (cNAWM). Analysis of MET2 parameters was performed on 24 UBO-cNAWM pairs. RESULTS: No significant alterations in the myelin water fraction and intra- and extracellular (IE) water fraction were found. Mean T2 time of IE water was significantly higher in UBOs. UBOs furthermore showed increased axial, radial and mean diffusivity, and decreased fractional anisotropy, mean kurtosis and neurite density index compared to cNAWM. Neurite orientation dispersion and isotropic fluid fraction were unaltered. CONCLUSION: Our results suggest that demyelination and axonal degeneration are unlikely to be present in UBOs, which appear to be mainly caused by a shift towards a higher T2-value of the intra- and extracellular water pool. This may arise from altered microstructural compartmentalization, and an increase in 'extracellular-like', intracellular water, possibly due to intramyelinic edema. These findings confirm the added value of combining dMRI and MET2 to characterize the microstructural basis of T2 hyperintensities in vivo.

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YNIMG Journal 2014 Journal Article

Methodological considerations on tract-based spatial statistics (TBSS)

  • Michael Bach
  • Frederik B. Laun
  • Alexander Leemans
  • Chantal M.W. Tax
  • Geert J. Biessels
  • Bram Stieltjes
  • Klaus H. Maier-Hein

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.

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YNIMG Journal 2014 Journal Article

Microstructural brain development between 30 and 40 weeks corrected age in a longitudinal cohort of extremely preterm infants

  • Karina J. Kersbergen
  • Alexander Leemans
  • Floris Groenendaal
  • Niek E. van der Aa
  • Max A. Viergever
  • Linda S. de Vries
  • Manon J.N.L. Benders

Diffusion tensor imaging (DTI) is frequently used to assess brain development in preterm infants. This study investigates maturational changes in diffusivity measures in 122 regions of the brain between 30 and 40weeks postmenstrual age (PMA) using the neonatal atlas of Oishi and colleagues (Oishi et al. , 2011). Forty infants without cerebral injury and with normal neurodevelopmental outcome were selected from a cohort of preterm infants (gestational age<28weeks), scanned longitudinally at 30 and 40weeks PMA. Fractional anisotropy (FA) changed significantly in 84 brain regions, with the largest increase in the central brain regions; by contrast, the cortical brain regions showed a decrease in FA. Mean, radial and axial diffusivity all showed a clear decrease in the majority of brain regions. This study provides longitudinal reference diffusivity values in a cohort of extremely preterm infants, showing a central to peripheral and posterior to anterior directed gradient, in line with our current understanding of brain maturation, and adding to this knowledge. This study further elucidates brain maturation in preterm infants during the last 10weeks prior to term equivalent age. The presented values can be used as a reference for assessing brain development in other cohorts, when investigating the effects of brain injury in this vulnerable period, and to evaluate the effect of future neuroprotective strategies.

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YNIMG Journal 2014 Journal Article

Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data

  • Chantal M.W. Tax
  • Ben Jeurissen
  • Sjoerd B. Vos
  • Max A. Viergever
  • Alexander Leemans

There is accumulating evidence that at current acquisition resolutions for diffusion-weighted (DW) MRI, the vast majority of white matter voxels contains “crossing fibers”, referring to complex fiber configurations in which multiple and distinctly differently oriented fiber populations exist. Spherical deconvolution based techniques are appealing to characterize this DW intra-voxel signal heterogeneity, as they provide a balanced trade-off between constraints on the required hardware performance and acquisition time on the one hand, and the reliability of the reconstructed fiber orientation distribution function (fODF) on the other hand. Recent findings, however, suggest that an inaccurate calibration of the response function (RF), which represents the DW signal profile of a single fiber orientation, can lead to the detection of spurious fODF peaks which, in turn, can have a severe impact on tractography results. Currently, the computation of this RF is either model-based or estimated from selected voxels that have a fractional anisotropy (FA) value above a predefined threshold. For both approaches, however, there are user-defined settings that affect the RF and, consequently, fODF estimation and tractography. Moreover, these settings still rely on the second-rank diffusion tensor, which may not be the appropriate model, especially at high b-values. In this work, we circumvent these issues for RF calibration by excluding “crossing fibers” voxels in a recursive framework. Our approach is evaluated with simulations and applied to in vivo and ex vivo data sets with different acquisition settings. The results demonstrate that with the proposed method the RF can be calibrated in a robust and automated way without needing to define ad-hoc FA threshold settings. Our framework facilitates the use of spherical deconvolution approaches in data sets in which it is not straightforward to define RF settings a priori.

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YNIMG Journal 2014 Journal Article

Structural neuroimaging correlates of allelic variation of the BDNF val66met polymorphism

  • Natalie J. Forde
  • Lisa Ronan
  • John Suckling
  • Cathy Scanlon
  • Simon Neary
  • Laurena Holleran
  • Alexander Leemans
  • Roger Tait

Background The brain-derived neurotrophic factor (BDNF) val66met polymorphism is associated with altered activity dependent secretion of BDNF and a variable influence on brain morphology and cognition. Although a met-dose effect is generally assumed, to date the paucity of met-homozygotes have limited our understanding of the role of the met-allele on brain structure. Methods To investigate this phenomenon, we recruited sixty normal healthy subjects, twenty in each genotypic group (val/val, val/met and met/met). Global and local morphology were assessed using voxel based morphometry and surface reconstruction methods. White matter organisation was also investigated using tract-based spatial statistics and constrained spherical deconvolution tractography. Results Morphological analysis revealed an “inverted-U” shaped profile of cortical changes, with val/met heterozygotes most different relative to the two homozygous groups. These results were evident at a global and local level as well as in tractography analysis of white matter fibre bundles. Conclusion In contrast to our expectations, we found no evidence of a linear met-dose effect on brain structure, rather our results support the view that the heterozygotic BDNF val66met genotype is associated with cortical morphology that is more distinct from the BDNF val66met homozygotes. These results may prove significant in furthering our understanding of the role of the BDNF met-allele in disorders such as Alzheimer's disease and depression.

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YNIMG Journal 2013 Journal Article

Investigating the need of triggering the acquisition for infant diffusion MRI: A quantitative study including bootstrap statistics

  • Lajos R. Kozák
  • Szabolcs Dávid
  • Gábor Rudas
  • Zoltán Vidnyánszky
  • Alexander Leemans
  • Zoltán Nagy

Diffusion weighted magnetic resonance imaging is increasingly being used for neonatal and young pediatric subjects. Our purpose was to investigate a) whether cardiac triggering was needed to reduce variability of diffusion (tensor) imaging data, b) how pulsation artifacts affect the fitted diffusion tensor when triggering is not used and c) the feasibility of triggered data acquisition in neonates and young children. Data were collected from 11 infants and 7 adults. In seven infants and seven adults, diffusion encoding was applied solely along the z gradient direction with and without cardiac triggering. Non-parametric bootstrap statistical methods were applied to investigate the dependence of variance on triggering. One infant and all adults served as test–retest controls. From the remaining three infants diffusion tensor imaging data were acquired with and without triggering. Our findings that used the repeated measurements in a single diffusion-encoding direction indicated that without triggering the variability in the data was increased significantly both in infants and adults. When collecting diffusion tensor data in infants, this increased variability results in erroneous fractional anisotropy values and artifactual fiber direction estimates. Contrary to previous reports but supported by our findings involving adults, pulsation artifacts were present in a larger extent of the brain in the infant population. In conclusion, triggering is feasible in young subjects and is preferred when acquiring diffusion MRI data. In doing so, the amount of erroneous estimations due to image artifacts will be minimized, which in turn will lead to more specific and less ambiguous interpretations. Although fitting the pulse-monitoring device requires additional set-up time, the total imaging time is usually shorter than acquiring multiple data sets to compile a single, artifact-free set.

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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 2013 Journal Article

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

  • Jelle Veraart
  • Jan Sijbers
  • Stefan Sunaert
  • Alexander Leemans
  • Ben Jeurissen

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.

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YNIMG Journal 2012 Journal Article

Architectural configuration and microstructural properties of the sacral plexus: A diffusion tensor MRI and fiber tractography study

  • Pasquelle K.N. van der Jagt
  • Pieter Dik
  • Martijn Froeling
  • Thomas C. Kwee
  • Rutger A.J. Nievelstein
  • Bennie ten Haken
  • Alexander Leemans

The ability to investigate microstructural properties of the central nervous system with diffusion tensor imaging (DTI) has been shown in many studies. More recently, DTI is being applied outside the brain showing promising results, for instance, for investigating muscle tissue. In this work, we demonstrate the feasibility of diffusion tensor imaging (DTI) and fiber tractography to study the nerves of the sacral plexus in humans in vivo and to assess the architectural configuration and microstructural properties of these peripheral nerves. For this research goal we optimized the acquisition parameters of a DTI sequence and acquired data from 10 healthy adults and one 12-year patient having spina bifida and neurogenic bladder dysfunction. For the healthy volunteers, we estimated the fractional anisotropy (FA) and mean (MD), axial (AD), and radial diffusivities (RD) of the sacral plexus nerves which may serve as a baseline for future studies. We demonstrated that tractography of the sacral plexus on a 3 Tesla MR scanner is feasible, giving 3D insight in the general anatomy and organization of the nerves L4 to S3. In addition, branches to the pudendal nerve were also found in 4 volunteers. There were no significant differences in any of the estimated diffusion measures between the right and left sided nerves or between the nerves L4 to S3 on an intra-subject basis. Furthermore, clinical feasibility of DTI and tractography in a child having spina bifida and neurogenic bladder dysfunction is demonstrated. The architectural configuration of the child's sacral plexus was comparable with the healthy volunteers and no significant disrupted nerve fibers were observed. However, there are strong indications that abnormal diffusion characteristics are present at the level of the neural tube defect due to incomplete segments of the nerves that are close to the vertebrae. These findings are encouraging for using DTI as a means to investigate changes in microstructural properties of the nerves of the sacral plexus. Moreover, this new methodology may provide a new avenue to a better analysis and diagnosis of neurogenic bladder dysfunctions.

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YNIMG Journal 2012 Journal Article

Microstructural white matter abnormalities in type 2 diabetes mellitus: A diffusion tensor imaging study

  • Jung-Lung Hsu
  • Yen-Ling Chen
  • Jyu-Gang Leu
  • Fu-Shan Jaw
  • Cheng-Hui Lee
  • Yuh-Feng Tsai
  • Chien-Yeh Hsu
  • Chyi-Huey Bai

This study investigated whether diffusion tensor imaging (DTI) could identify potential abnormalities in type 2 diabetes mellitus (T2DM) patients without cognitive complaints compared to healthy controls. In addition, the existence of associations between diffusion measures and clinical parameters was examined. Forty T2DM patients and 97 non-diabetic controls completed a clinical and biochemistry examination. Structural MRI scans (DTI, T1, T2, FLAIR) were subsequently acquired with a 1. 5 Tesla scanner. In addition to a global DTI analysis, voxel-based analysis was performed on the fractional anisotropy (FA), mean diffusivity (MD), and axial (AD) and transverse (TD) diffusivity maps to investigate regions that exhibit (i) WM differences between patients and controls; and (ii) associations between clinical measurements and these DTI indices. There were no significant differences in age, gender, and WM hyperintensity scores derived by the conventional MRI scans between controls and T2DM patients. For the T2DM patients, however, the MD of the brain parenchyma was significantly increased compared to controls and was positively correlated with disease duration. The voxel based analyses revealed (i) a significantly decreased FA in the bilateral frontal WM compared to controls which was mainly caused by an increased TD and not a decreased AD within these regions; (ii) a significant association between disease duration and microstructural properties in several brain regions including bilateral cerebellum, temporal lobe WM, right caudate, bilateral cingulate gyrus, pons, and parahippocampal gyrus. Our findings indicate that microstructural WM abnormalities and associations with clinical measurements can be detected with DTI in T2DM patients.

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YNIMG Journal 2012 Journal Article

The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain

  • Sjoerd B. Vos
  • Derek K. Jones
  • Ben Jeurissen
  • Max A. Viergever
  • Alexander Leemans

In diffusion tensor magnetic resonance imaging (DT-MRI), limitations concerning complex fiber architecture (when an image voxel contains fiber populations with more than one dominant orientation) are well-known. Fractional anisotropy (FA) values are lower in such areas because of a lower directionality of diffusion on the voxel-scale, which makes the interpretation of FA less straightforward. Moreover, the interpretation of the axial and radial diffusivities is far from trivial when there is more than one dominant fiber orientation within a voxel. In this work, using (i) theoretical considerations, (ii) simulations, and (iii) experimental data, it is demonstrated that the mean diffusivity (or the trace of the diffusion tensor) is lower in complex white matter configurations, compared with tissue where there is a single dominant fiber orientation within the voxel. We show that the magnitude of this reduction depends on various factors, including configurational and microstructural properties (e. g. , the relative contributions of different fiber populations) and acquisition settings (e. g. , the b-value). These results increase our understanding of the quantitative metrics obtained from DT-MRI and, in particular, the effect of the microstructural architecture on the mean diffusivity. More importantly, they reinforce the growing awareness that differences in DT-MRI metrics need to be interpreted cautiously.

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YNIMG Journal 2011 Journal Article

A comparison of MR based segmentation methods for measuring brain atrophy progression

  • Jeroen de Bresser
  • Marileen P. Portegies
  • Alexander Leemans
  • Geert Jan Biessels
  • L. Jaap Kappelle
  • Max A. Viergever

Automated brain segmentation methods with a good precision and accuracy are required to detect subtle changes in brain volumes over time in clinical applications. However, the ability of established methods such as SIENA, US and kNN to estimate brain volume change have not been compared on the same data, nor been evaluated with ground-truth manual segmentations. We compared measurements of brain volume change between SIENA, US and kNN in terms of precision (repeatability) and accuracy (ground-truth) using one baseline and two repeated follow-up 1. 5T MRI scans after 4years of 10 subjects. The coefficient of repeatability (brain volume/volume change) was larger for US (29. 6cc/2. 84%) than for kNN (4. 9cc/0. 31%) and SIENA (−/0. 92%). In terms of absolute brain volume measurements US and kNN showed good correlation with the manual segmentations and with each other (all Spearman's correlation coefficients ρ ≥0. 96; all p <0. 001). Concerning brain volume changes, SIENA showed a good (ρ =0. 82; p =0. 004), kNN a moderate (ρ =0. 60; p =0. 067) and US a weak (ρ =0. 50; p =0. 138) correlation with the manual segmentations. For measurements of volume change, SIENA–US (mean correlation coefficient and p-value: ρ =0. 28; p =0. 442) and US–kNN (ρ =0. 17; p =0. 641) showed a weak correlation, but correlation was fairly good for kNN–SIENA (ρ =0. 65; p =0. 048). In conclusion, US and kNN showed a good precision, accuracy and comparability for brain volume measurements. For measurements of volume change, SIENA showed the best performance. kNN is a good alternative if volume change measurements of other brain structures are required.

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YNIMG Journal 2011 Journal Article

Partial volume effect as a hidden covariate in DTI analyses

  • Sjoerd B. Vos
  • Derek K. Jones
  • Max A. Viergever
  • Alexander Leemans

During the last decade, diffusion tensor imaging (DTI) has been used extensively to investigate microstructural properties of white matter fiber pathways. In many of these DTI-based studies, fiber tractography has been used to infer relationships between bundle-specific mean DTI metrics and measures-of-interest (e. g. , when studying diffusion changes related to age, cognitive performance, etc.) or to assess potential differences between populations (e. g. , comparing males vs. females, healthy vs. diseased subjects, etc.). As partial volume effects (PVEs) are known to affect tractography and, subsequently, the estimated DTI measures sampled along these reconstructed tracts in an adverse way, it is important to gain insight into potential confounding factors that may modulate this PVE. For instance, for thicker fiber bundles, the contribution of PVE-contaminated voxels to the mean metric for the entire fiber bundle will be smaller, and vice-versa — which means that the extent of PVE-contamination will vary from bundle to bundle. With the growing popularity of tractography-based methods in both fundamental research and clinical applications, it is of paramount importance to examine the presence of PVE-related covariates, such as thickness, orientation, curvature, and shape of a fiber bundle, and to investigate the extent to which these hidden confounds affect diffusion measures. To test the hypothesis that these PVE-related covariates modulate DTI metrics depending on the shape of a bundle, we performed simulations with synthetic diffusion phantoms and analyzed bundle-specific DTI measures of the cingulum and the corpus callosum in 55 healthy subjects. Our results indicate that the estimated bundle-specific mean values of diffusion metrics, including the frequently used fractional anisotropy and mean diffusivity, were indeed modulated by fiber bundle thickness, orientation, and curvature. Correlation analyses between gender and diffusion measures yield different results when volume is included as a covariate. This indicates that incorporating these PVE-related factors in DTI analyses is imperative to disentangle changes in “true microstructural” tissue properties from these hidden covariates.

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YNIMG Journal 2011 Journal Article

The effect of template selection on diffusion tensor voxel-based analysis results

  • Wim Van Hecke
  • Alexander Leemans
  • Caroline A. Sage
  • Louise Emsell
  • Jelle Veraart
  • Jan Sijbers
  • Stefan Sunaert
  • Paul M. Parizel

Diffusion tensor imaging (DTI) is increasingly being used to study white matter (WM) degeneration in patients with psychiatric and neurological disorders. In order to compare diffusion measures across subjects in an automated way, voxel-based analysis (VBA) methods were introduced. In VBA, all DTI data are transformed to a template, after which the diffusion measures of control subjects and patients are compared quantitatively in each voxel. Although VBA has many advantages compared to other post-processing approaches, such as region of interest analysis or tractography, VBA results need to be interpreted cautiously, since it has been demonstrated that they depend on the different parameter settings that are applied in the VBA processing pipeline. In this paper, we examine the effect of the template selection on the VBA results of DTI data. We hypothesized that the choice of template to which all data are transformed would also affect the VBA results. To this end, simulated DTI data sets as well as DTI data from control subjects and multiple sclerosis patients were aligned to (i) a population-specific DTI template, (ii) a subject-based DTI atlas in MNI space, and (iii) the ICBM-81 DTI atlas. Our results suggest that the highest sensitivity and specificity to detect WM abnormalities in a VBA setting was achieved using the population-specific DTI atlas, presumably due to the better spatial image alignment to this template.

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YNIMG Journal 2010 Journal Article

Microstructural white matter changes in normal aging: A diffusion tensor imaging study with higher-order polynomial regression models

  • Jung-Lung Hsu
  • Wim Van Hecke
  • Chyi-Huey Bai
  • Cheng-Hui Lee
  • Yuh-Feng Tsai
  • Hou-Chang Chiu
  • Fu-Shan Jaw
  • Chien-Yeh Hsu

Diffusion tensor imaging (DTI) has already proven to be a valuable tool when investigating both global and regional microstructural white matter (WM) brain changes in the human aging process. Although subject to many criticisms, voxel-based analysis is currently one of the most common and preferred approaches in such DTI aging studies. In this context, voxel-based DTI analyses have assumed a ‘linear’ correlation when finding the significant brain regions that relate age with a particular diffusion measure of interest. Recent literature, however, has clearly demonstrated ‘non-linear’ relationships between age and diffusion metrics by using region-of-interest and tractography-based approaches. In this work, we incorporated polynomial regression models in the voxel-based DTI analysis framework to assess age-related changes in WM diffusion properties (fractional anisotropy and axial, transverse, and mean diffusivity) in a large cohort of 346 subjects (25 to 81 years old). Our novel approach clearly demonstrates that voxel-based DTI analyses can greatly benefit from incorporating higher-order regression models when investigating potential relationships between aging and diffusion properties.

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YNIMG Journal 2009 Journal Article

On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods

  • Wim Van Hecke
  • Jan Sijbers
  • Steve De Backer
  • Dirk Poot
  • Paul M. Parizel
  • Alexander Leemans

Although many studies are starting to use voxel-based analysis (VBA) methods to compare diffusion tensor images between healthy and diseased subjects, it has been demonstrated that VBA results depend heavily on parameter settings and implementation strategies, such as the applied coregistration technique, smoothing kernel width, statistical analysis, etc. In order to investigate the effect of different parameter settings and implementations on the accuracy and precision of the VBA results quantitatively, ground truth knowledge regarding the underlying microstructural alterations is required. To address the lack of such a gold standard, simulated diffusion tensor data sets are developed, which can model an array of anomalies in the diffusion properties of a predefined location. These data sets can be employed to evaluate the numerous parameters that characterize the pipeline of a VBA algorithm and to compare the accuracy, precision, and reproducibility of different post-processing approaches quantitatively. We are convinced that the use of these simulated data sets can improve the understanding of how different diffusion tensor image post-processing techniques affect the outcome of VBA. In turn, this may possibly lead to a more standardized and reliable evaluation of diffusion tensor data sets of large study groups with a wide range of white matter altering pathologies. The simulated DTI data sets will be made available online (http: //www. dti. ua. ac. be).

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YNIMG Journal 2008 Journal Article

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

  • Jung-Lung Hsu
  • Alexander Leemans
  • Chyi-Huey Bai
  • Cheng-Hui Lee
  • Yuh-Feng Tsai
  • Hou-Chang Chiu
  • Wei-Hung Chen

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.

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YNIMG Journal 2008 Journal Article

On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain

  • Wim Van Hecke
  • Jan Sijbers
  • Emiliano d'Agostino
  • Frederik Maes
  • Steve De Backer
  • Evert Vandervliet
  • Paul M. Parizel
  • Alexander Leemans

Voxel based morphometry (VBM) has been increasingly applied to detect diffusion tensor (DT) image abnormalities in patients for different pathologies. An important requisite for a robust VBM analysis is the availability of a high-dimensional non-rigid coregistration technique that is able to align both the spatial and the orientational DT information. Consequently, there is a need for an inter-subject DTI atlas as a group specific reference frame that also contains this orientational DT information. In this work, a population based DTI atlas has been developed that incorporates such orientational DT information with high accuracy and precision. The proposed methodology for constructing such an atlas is compared with a subject based DTI atlas, in which a single subject is selected as the reference image. Our results demonstrate that the population based atlas framework is more accurate with respect to the underlying diffusion information.

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YNIMG Journal 2006 Journal Article

In vivo diffusion tensor imaging (DTI) of brain subdivisions and vocal pathways in songbirds

  • Geert De Groof
  • Marleen Verhoye
  • Vincent Van Meir
  • Ilse Tindemans
  • Alexander Leemans
  • Annemie Van der Linden

The neural substrate for song behavior in songbirds, the song control system (SCS), is thus far the best-documented brain circuit in which to study neuroplasticity and adult neurogenesis. Not only does the volume of the key song control nuclei change in size, but also the density of the connections between them changes as a function of seasonal and hormonal influences. This study explores the potentials of in vivo Diffusion-Tensor MRI (DT-MRI or DTI) to visualize the distinct, concentrated connections of the SCS in the brain of the starling (Sturnus vulgaris). In vivo DTI on starling was performed on a 7T MR system using sagittal and coronal slices. DTI was accomplished with diffusion gradients applied in seven non-collinear directions. Fractional Anisotropy (FA)-maps allowed us to distinguish most of the grey matter and white matter-tracts, including the laminae subdividing the avian telencephalon and the tracts connecting the major song control nuclei (e. g. , HVC with RA and X). The FA-maps also allowed us to discern a number of song control, auditory and visual nuclei. Fiber tracking was implemented to illustrate the discrimination of all tracts running from and to RA. Because of the remarkable plasticity inherent to the songbird brain, the successful implementation of DTI in this model could represent a useful tool for the in vivo exploration of fiber degeneration and regeneration and the biological mechanisms involved in brain plasticity.

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v2026.09.13