Saad Jbabdi

YNIMG Journal 2023 Journal Article

Tensor image registration library: Deformable registration of stand‐alone histology images to whole‐brain post‐mortem MRI data

• Istvan N. Huszar
• Menuka Pallebage-Gamarallage
• Sarah Bangerter-Christensen
• Hannah Brooks
• Sean Fitzgibbon
• Sean Foxley
• Marlies Hiemstra
• Amy F.D. Howard

BACKGROUND: Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data. METHODS: Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline. RESULTS: All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2-0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks. CONCLUSIONS: Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies.

YNIMG Journal 2022 Journal Article

Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

• Ying-Qiu Zheng
• Seyedeh-Rezvan Farahibozorg
• Weikang Gong
• Hossein Rafipoor
• Saad Jbabdi
• Stephen Smith

Modelling and predicting individual differences in task-fMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble learner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of task-fMRI scans, suggesting that it has potential to supplement traditional task localisers.

YNIMG Journal 2022 Journal Article

An automated pipeline for extracting histological stain area fraction for voxelwise quantitative MRI-histology comparisons

• Daniel Z.L. Kor
• Saad Jbabdi
• Istvan N. Huszar
• Jeroen Mollink
• Benjamin C. Tendler
• Sean Foxley
• Chaoyue Wang
• Connor Scott

The acquisition of MRI and histology in the same post-mortem tissue sample enables direct correlation between MRI and histologically-derived parameters. However, there still lacks a standardised automated pipeline to process histology data, with most studies relying on manual intervention. Here, we introduce an automated pipeline to extract a quantitative histological measure for staining density (stain area fraction, SAF) from multiple immunohistochemical (IHC) stains. The pipeline is designed to directly address key IHC artefacts related to tissue staining and slide digitisation. Here, the pipeline was applied to post-mortem human brain data from multiple subjects, relating MRI parameters (FA, MD, RD, AD, R2*, R1) to IHC slides stained for myelin, neurofilaments, microglia and activated microglia. Utilising high-quality MRI-histology co-registrations, we then performed whole-slide voxelwise comparisons (simple correlations, partial correlations and multiple regression analyses) between multimodal MRI- and IHC-derived parameters. The pipeline was found to be reproducible, robust to artefacts and generalisable across multiple IHC stains. Our partial correlation results suggest that some simple MRI-SAF correlations should be interpreted with caution, due to the co-localisation of other tissue features (e.g., myelin and neurofilaments). Further, we find activated microglia-a generic biomarker of inflammation-to consistently be the strongest predictor of high DTI FA and low RD, which may suggest sensitivity of diffusion MRI to aspects of neuroinflammation related to microglial activation, even after accounting for other microstructural changes (demyelination, axonal loss and general microglia infiltration). Together, these results show the utility of this approach in carefully curating IHC data and performing multimodal analyses to better understand microstructural relationships with MRI.

YNIMG Journal 2022 Journal Article

Estimating axial diffusivity in the NODDI model

• Amy FD Howard
• Michiel Cottaar
• Mark Drakesmith
• Qiuyun Fan
• Susie Y. Huang
• Derek K. Jones
• Frederik J. Lange
• Jeroen Mollink

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model ( d ∥ = 1. 7 μ m 2 /ms ). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼ 2 − 2. 5 μ m 2 /ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

YNIMG Journal 2022 Journal Article

Identifying microstructural changes in diffusion MRI; How to circumvent parameter degeneracy

• Hossein Rafipoor
• Ying-Qiu Zheng
• Ludovica Griffanti
• Saad Jbabdi
• Michiel Cottaar

Biophysical models that attempt to infer real-world quantities from data usually have many free parameters. This over-parameterisation can result in degeneracies in model inversion and render parameter estimation ill-posed. However, in many applications, we are not interested in quantifying the parameters per se, but rather in identifying changes in parameters between experimental conditions (e.g. patients vs controls). Here we present a Bayesian framework to make inference on changes in the parameters of biophysical models even when model inversion is degenerate, which we refer to as Bayesian EstimatioN of CHange (BENCH). We infer the parameter changes in two steps; First, we train models that can estimate the pattern of change in the measurements given any hypothetical direction of change in the parameters using simulations. Next, for any pair of real data sets, we use these pre-trained models to estimate the probability that an observed difference in the data can be explained by each model of change. BENCH is applicable to any type of data and models and particularly useful for biophysical models with parameter degeneracies, where we can assume the change is sparse. In this paper, we apply the approach in the context of microstructural modelling of diffusion MRI data, where the models are usually over-parameterised and not invertible without injecting strong assumptions. Using simulations, we show that in the context of the standard model of white matter our approach is able to identify changes in microstructural parameters from conventional multi-shell diffusion MRI data. We also apply our approach to a subset of subjects from the UK-Biobank Imaging to identify the dominant standard model parameter change in areas of white matter hyperintensities under the assumption that the standard model holds in white matter hyperintensities.

YNIMG Journal 2021 Journal Article

Clinical applications of magnetic resonance imaging based functional and structural connectivity

• Chengyuan Wu
• Francisca Ferreira
• Michael Fox
• Noam Harel
• Jona Hattangadi-Gluth
• Andreas Horn
• Saad Jbabdi
• Joshua Kahan

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.

YNIMG Journal 2021 Journal Article

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

• Seyedeh-Rezvan Farahibozorg
• Janine D. Bijsterbosch
• Weikang Gong
• Saad Jbabdi
• Stephen M. Smith
• Samuel J. Harrison
• Mark W. Woolrich

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.

YNIMG Journal 2021 Journal Article

Modelling white matter in gyral blades as a continuous vector field

• Michiel Cottaar
• Matteo Bastiani
• Nikhil Boddu
• Matthew F. Glasser
• Suzanne Haber
• David C. Van Essen
• Stamatios N. Sotiropoulos
• Saad Jbabdi

Many brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called "gyral biases" limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemispheric connections when compared to tracers.

YNIMG Journal 2020 Journal Article

Improved fibre dispersion estimation using b-tensor encoding

• Michiel Cottaar
• Filip Szczepankiewicz
• Matteo Bastiani
• Moises Hernandez-Fernandez
• Stamatios N. Sotiropoulos
• Markus Nilsson
• Saad Jbabdi

Measuring fibre dispersion in white matter with diffusion magnetic resonance imaging (MRI) is limited by an inherent degeneracy between fibre dispersion and microscopic diffusion anisotropy (i. e. , the diffusion anisotropy expected for a single fibre orientation). This means that estimates of fibre dispersion rely on strong assumptions, such as constant microscopic anisotropy throughout the white matter or specific biophysical models. Here we present a simple approach for resolving this degeneracy using measurements that combine linear (conventional) and spherical tensor diffusion encoding. To test the accuracy of the fibre dispersion when our microstructural model is only an approximation of the true tissue structure, we simulate multi-compartment data and fit this with a single-compartment model. For such overly simplistic tissue assumptions, we show that the bias in fibre dispersion is greatly reduced (~5x) for single-shell linear and spherical tensor encoding data compared with single-shell or multi-shell conventional data. In in-vivo data we find a consistent estimate of fibre dispersion as we reduce the b-value from 3 to 1. 5 m s / μ m 2, increase the repetition time, increase the echo time, or increase the diffusion time. We conclude that the addition of spherical tensor encoded data to conventional linear tensor encoding data greatly reduces the sensitivity of the estimated fibre dispersion to the model assumptions of the tissue microstructure.

YNIMG Journal 2020 Journal Article

Towards HCP-Style macaque connectomes: 24-Channel 3T multi-array coil, MRI sequences and preprocessing

• Joonas A. Autio
• Matthew F. Glasser
• Takayuki Ose
• Chad J. Donahue
• Matteo Bastiani
• Masahiro Ohno
• Yoshihiko Kawabata
• Yuta Urushibata

Macaque monkeys are an important animal model where invasive investigations can lead to a better understanding of the cortical organization of primates including humans. However, the tools and methods for noninvasive image acquisition (e. g. MRI RF coils and pulse sequence protocols) and image data preprocessing have lagged behind those developed for humans. To resolve the structural and functional characteristics of the smaller macaque brain, high spatial, temporal, and angular resolutions combined with high signal-to-noise ratio are required to ensure good image quality. To address these challenges, we developed a macaque 24-channel receive coil for 3-T MRI with parallel imaging capabilities. This coil enables adaptation of the Human Connectome Project (HCP) image acquisition protocols to the in-vivo macaque brain. In addition, we adapted HCP preprocessing methods to the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI). The coil provides the necessary high signal-to-noise ratio and high efficiency in data acquisition, allowing four- and five-fold accelerations for dMRI and fMRI. Automated FreeSurfer segmentation of cortex, reconstruction of cortical surface, removal of artefacts and nuisance signals in fMRI, and distortion correction of dMRI all performed well, and the overall quality of basic neurobiological measures was comparable with those for the HCP. Analyses of functional connectivity in fMRI revealed high sensitivity as compared with those from publicly shared datasets. Tractography-based connectivity estimates correlated with tracer connectivity similarly to that achieved using ex-vivo dMRI. The resulting HCP-style in vivo macaque MRI data show considerable promise for analyzing cortical architecture and functional and structural connectivity using advanced methods that have previously only been available in studies of the human brain.

YNIMG Journal 2020 Journal Article

Use of multi-flip angle measurements to account for transmit inhomogeneity and non-Gaussian diffusion in DW-SSFP

• Benjamin C. Tendler
• Sean Foxley
• Moises Hernandez-Fernandez
• Michiel Cottaar
• Connor Scott
• Olaf Ansorge
• Karla L. Miller
• Saad Jbabdi

Diffusion-weighted steady-state free precession (DW-SSFP) is an SNR-efficient diffusion imaging method. The improved SNR and resolution available at ultra-high field has motivated its use at 7T. However, these data tend to have severe B1 inhomogeneity, leading not only to spatially varying SNR, but also to spatially varying diffusivity estimates, confounding comparisons both between and within datasets. This study proposes the acquisition of DW-SSFP data at two-flip angles in combination with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. Data were acquired from five fixed whole human post-mortem brains with a pair of flip angles that jointly optimize the diffusion contrast-to-noise (CNR) across the brain. We compared one- and two-flip angle DW-SSFP data using a tensor model that incorporates the full DW-SSFP Buxton signal, in addition to tractography performed over the cingulum bundle and pre-frontal cortex using a ball & sticks model. The two-flip angle DW-SSFP data produced angular uncertainty and tractography estimates close to the CNR optimal regions in the single-flip angle datasets. The two-flip angle tensor estimates were subsequently fitted using a modified DW-SSFP signal model that incorporates a gamma distribution of diffusivities. This allowed us to generate tensor maps at a single effective b-value yielding more consistent SNR across tissue, in addition to eliminating the B1 dependence on diffusion coefficients and orientation maps. Our proposed approach will allow the use of DW-SSFP at 7T to derive diffusivity estimates that have greater interpretability, both within a single dataset and between experiments.

YNIMG Journal 2020 Journal Article

XTRACT - Standardised protocols for automated tractography in the human and macaque brain

• Shaun Warrington
• Katherine L. Bryant
• Alexandr A. Khrapitchev
• Jerome Sallet
• Marina Charquero-Ballester
• Gwenaëlle Douaud
• Saad Jbabdi
• Rogier B. Mars

We present a new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science.

YNIMG Journal 2019 Journal Article

Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project

• Matteo Bastiani
• Jesper L.R. Andersson
• Lucilio Cordero-Grande
• Maria Murgasova
• Jana Hutter
• Anthony N. Price
• Antonios Makropoulos
• Sean P. Fitzgibbon

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age.

YNIMG Journal 2019 Journal Article

Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction

• Matteo Bastiani
• Michiel Cottaar
• Sean P. Fitzgibbon
• Sana Suri
• Fidel Alfaro-Almagro
• Stamatios N. Sotiropoulos
• Saad Jbabdi
• Jesper L.R. Andersson

Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts.

YNIMG Journal 2019 Journal Article

Joint modelling of diffusion MRI and microscopy

• Amy FD. Howard
• Jeroen Mollink
• Michiel Kleinnijenhuis
• Menuka Pallebage-Gamarallage
• Matteo Bastiani
• Michiel Cottaar
• Karla L. Miller
• Saad Jbabdi

The combination of diffusion MRI (dMRI) with microscopy provides unique opportunities to study microstructural features of tissue, particularly when acquired in the same sample. Microscopy is frequently used to validate dMRI microstructure models, addressing the indirect nature of dMRI signals. Typically, these modalities are analysed separately, and microscopy is taken as a gold standard against which dMRI-derived parameters are validated. Here we propose an alternative approach in which we combine dMRI and microscopy data obtained from the same tissue sample to drive a single, joint model. This simultaneous analysis allows us to take advantage of the breadth of information provided by complementary data acquired from different modalities. By applying this framework to a spherical-deconvolution analysis, we are able to overcome a known degeneracy between fibre dispersion and radial diffusion. Spherical-deconvolution based approaches typically estimate a global fibre response function to determine the fibre orientation distribution in each voxel. However, the assumption of a ‘brain-wide’ fibre response function may be challenged if the diffusion characteristics of white matter vary across the brain. Using a generative joint dMRI-histology model, we demonstrate that the fibre response function is dependent on local anatomy, and that current spherical-deconvolution based models may be overestimating dispersion and underestimating the number of distinct fibre populations per voxel.

YNIMG Journal 2019 Journal Article

Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes

• Moises Hernandez-Fernandez
• Istvan Reguly
• Saad Jbabdi
• Mike Giles
• Stephen Smith
• Stamatios N. Sotiropoulos

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, a single GPU achieves better performance than 200 CPU cores thanks to our parallel designs.

YNIMG Journal 2018 Journal Article

A gyral coordinate system predictive of fibre orientations

• Michiel Cottaar
• Matteo Bastiani
• Charles Chen
• Krikor Dikranian
• David Van Essen
• Timothy E. Behrens
• Stamatios N. Sotiropoulos
• Saad Jbabdi

When axonal fibres approach or leave the cortex, their trajectories tend to closely follow the cortical convolutions. To quantify this tendency, we propose a three-dimensional coordinate system based on the gyral geometry. For every voxel in the brain, we define a “radial” axis orthogonal to nearby surfaces, a “sulcal” axis along the sulcal depth gradient that preferentially points from deep white matter to the gyral crown, and a “gyral” axis aligned with the long axis of the gyrus. When compared with high-resolution, in-vivo diffusion MRI data from the Human Connectome Project, we find that in superficial white matter the apparent diffusion coefficient (at b = 1000) along the sulcal axis is on average 16% larger than along the gyral axis and twice as large as along the radial axis. This is reflected in the vast majority of observed fibre orientations lying close to the tangential plane (median angular offset < 7°), with the dominant fibre orientation typically aligning with the sulcal axis. In cortical grey matter, fibre orientations transition to a predominantly radial orientation. We quantify the width and location of this transition and find strong reproducibility in test-retest data, but also a clear dependence on the resolution of the diffusion data. The ratio of radial to tangential diffusion is fairly constant throughout most of the cortex, except for a decrease of the diffusivitiy ratio in the sulcal fundi and the primary somatosensory cortex (Brodmann area 3) and an increase in the primary motor cortex (Brodmann area 4). Although only constrained by cortical folds, the proposed gyral coordinate system provides a simple and intuitive representation of white and grey matter fibre orientations near the cortex, and may be useful for future studies of white matter development and organisation.

YNIMG Journal 2018 Journal Article

Concurrent white matter bundles and grey matter networks using independent component analysis

• Jonathan O'Muircheartaigh
• Saad Jbabdi

Developments in non-invasive diffusion MRI tractography techniques have permitted the investigation of both the anatomy of white matter pathways connecting grey matter regions and their structural integrity. In parallel, there has been an expansion in automated techniques aimed at parcellating grey matter into distinct regions based on functional imaging. Here we apply independent component analysis to whole-brain tractography data to automatically extract brain networks based on their associated white matter pathways. This method decomposes the tractography data into components that consist of paired grey matter ‘nodes’ and white matter ‘edges’, and automatically separates major white matter bundles, including known cortico-cortical and cortico-subcortical tracts. We show how this framework can be used to investigate individual variations in brain networks (in terms of both nodes and edges) as well as their associations with individual differences in behaviour and anatomy. Finally, we investigate correspondences between tractography-based brain components and several canonical resting-state networks derived from functional MRI.

YNIMG Journal 2018 Journal Article

Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects

• Michael P. Harms
• Leah H. Somerville
• Beau M. Ances
• Jesper Andersson
• Deanna M. Barch
• Matteo Bastiani
• Susan Y. Bookheimer
• Timothy B. Brown

The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full lifespan, collecting structural, resting-state fMRI, task-fMRI, diffusion, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5–21), and HCP-A is enrolling 1200+ healthy adults (ages 36–100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges. The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. We strove to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, aged 22–35), but some imaging-related changes were motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations. Here, we provide an overview of the common HCP-D/A imaging protocol including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multi-modal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging related changes in the healthy human brain.

YNIMG Journal 2018 Journal Article

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank

• Fidel Alfaro-Almagro
• Mark Jenkinson
• Neal K. Bangerter
• Jesper L.R. Andersson
• Ludovica Griffanti
• Gwenaëlle Douaud
• Stamatios N. Sotiropoulos
• Saad Jbabdi

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100, 000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10, 000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

YNIMG Journal 2018 Journal Article

Neuroimage special issue on brain segmentation and parcellation - Editorial

• R. Cameron Craddock
• Pierre Bellec
• Saad Jbabdi

YNIMG Journal 2017 Journal Article

Evaluating fibre orientation dispersion in white matter: Comparison of diffusion MRI, histology and polarized light imaging

• Jeroen Mollink
• Michiel Kleinnijenhuis
• Anne-Marie van Cappellen van Walsum
• Stamatios N. Sotiropoulos
• Michiel Cottaar
• Christopher Mirfin
• Mattias P. Heinrich
• Mark Jenkinson

Diffusion MRI is an exquisitely sensitive probe of tissue microstructure, and is currently the only non-invasive measure of the brain's fibre architecture. As this technique becomes more sophisticated and microstructurally informative, there is increasing value in comparing diffusion MRI with microscopic imaging in the same tissue samples. This study compared estimates of fibre orientation dispersion in white matter derived from diffusion MRI to reference measures of dispersion obtained from polarized light imaging and histology. Three post-mortem brain specimens were scanned with diffusion MRI and analyzed with a two-compartment dispersion model. The specimens were then sectioned for microscopy, including polarized light imaging estimates of fibre orientation and histological quantitative estimates of myelin and astrocytes. Dispersion estimates were correlated on region – and voxel-wise levels in the corpus callosum, the centrum semiovale and the corticospinal tract. The region-wise analysis yielded correlation coefficients of r = 0. 79 for the diffusion MRI and histology comparison, while r = 0. 60 was reported for the comparison with polarized light imaging. In the corpus callosum, we observed a pattern of higher dispersion at the midline compared to its lateral aspects. This pattern was present in all modalities and the dispersion profiles from microscopy and diffusion MRI were highly correlated. The astrocytes appeared to have minor contribution to dispersion observed with diffusion MRI. These results demonstrate that fibre orientation dispersion estimates from diffusion MRI represents the tissue architecture well. Dispersion models might be improved by more faithfully incorporating an informed mapping based on microscopy data.

YNIMG Journal 2017 Journal Article

Improved tractography using asymmetric fibre orientation distributions

• Matteo Bastiani
• Michiel Cottaar
• Krikor Dikranian
• Aurobrata Ghosh
• Hui Zhang
• Daniel C. Alexander
• Timothy E. Behrens
• Saad Jbabdi

Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and –x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity.

YNICL Journal 2017 Journal Article

Pathology of callosal damage in ALS: An ex-vivo, 7 T diffusion tensor MRI study

• Agustin M. Cardenas
• Joelle E. Sarlls
• Justin Y. Kwan
• Devin Bageac
• Zachary S. Gala
• Laura E. Danielian
• Abhik Ray-Chaudhury
• Hao-Wei Wang

OBJECTIVES: The goal of this study was to better understand the changes in tissue microstructure that underlie white matter diffusion changes in ALS patients. METHODS: Diffusion tensor imaging was carried out in postmortem brains of 4 ALS patients and two subjects without neurological disease on a 7 T MRI scanner using steady-state free precession sequences. Fractional anisotropy (FA) was measured in the genu, body, and splenium of the corpus callosum in formalin-fixed hemispheres. FA of the body and genu was expressed as ratio to FA of the splenium, a region unaffected in ALS. After imaging, tissue sections of the same segments of the callosum were stained for markers of different tissue components. Coded image fields were rated for pathological changes by blinded raters. RESULTS: mutations, and increased reactive astrocytes throughout the callosum. CONCLUSION: Reduced FA of the corpus callosum in ALS results from complex changes in tissue microstructure. Callosal segments with reduced FA had large numbers of microglia-macrophages in addition to loss of myelinated axons and astrogliosis. Microglial inflammation contributed to reduced FA in ALS, and may contribute to a pro-inflammatory state, but further work is needed to determine their role.

YNIMG Journal 2017 Journal Article

Subthalamic deep brain stimulation sweet spots and hyperdirect cortical connectivity in Parkinson's disease

• Harith Akram
• Stamatios N. Sotiropoulos
• Saad Jbabdi
• Dejan Georgiev
• Philipp Mahlknecht
• Jonathan Hyam
• Thomas Foltynie
• Patricia Limousin

Objectives Firstly, to identify subthalamic region stimulation clusters that predict maximum improvement in rigidity, bradykinesia and tremor, or emergence of side-effects; and secondly, to map-out the cortical fingerprint, mediated by the hyperdirect pathways which predict maximum efficacy. Methods High angular resolution diffusion imaging in twenty patients with advanced Parkinson's disease was acquired prior to bilateral subthalamic nucleus deep brain stimulation. All contacts were screened one-year from surgery for efficacy and side-effects at different amplitudes. Voxel-based statistical analysis of volumes of tissue activated models was used to identify significant treatment clusters. Probabilistic tractography was employed to identify cortical connectivity patterns associated with treatment efficacy. Results All patients responded well to treatment (46% mean improvement off medication UPDRS-III [p < 0. 0001]) without significant adverse events. Cluster corresponding to maximum improvement in tremor was in the posterior, superior and lateral portion of the nucleus. Clusters corresponding to improvement in bradykinesia and rigidity were nearer the superior border in a further medial and posterior location. The rigidity cluster extended beyond the superior border to the area of the zona incerta and Forel-H2 field. When the clusters where averaged, the coordinates of the area with maximum overall efficacy was X = −10(−9. 5), Y = −13(-1) and Z = −7(−3) in MNI(AC-PC) space. Cortical connectivity to primary motor area was predictive of higher improvement in tremor; whilst that to supplementary motor area was predictive of improvement in bradykinesia and rigidity; and connectivity to prefrontal cortex was predictive of improvement in rigidity. Interpretation These findings support the presence of overlapping stimulation sites within the subthalamic nucleus and its superior border, with different cortical connectivity patterns, associated with maximum improvement in tremor, rigidity and bradykinesia.

YNIMG Journal 2016 Journal Article

Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project

• Stamatios N. Sotiropoulos
• Moisés Hernández-Fernández
• An T. Vu
• Jesper L. Andersson
• Steen Moeller
• Essa Yacoub
• Christophe Lenglet
• Kamil Ugurbil

Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually.

YNIMG Journal 2015 Journal Article

Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data

• Peter Kochunov
• Neda Jahanshad
• Daniel Marcus
• Anderson Winkler
• Emma Sprooten
• Thomas E. Nichols
• Susan N. Wright
• L. Elliot Hong

The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h2 =0. 53–0. 90, p<10−5), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application.

YNIMG Journal 2014 Journal Article

Improving diffusion-weighted imaging of post-mortem human brains: SSFP at 7T

• Sean Foxley
• Saad Jbabdi
• Stuart Clare
• Wilfred Lam
• Olaf Ansorge
• Gwenaelle Douaud
• Karla Miller

Post-mortem diffusion imaging of whole, human brains has potential to provide data for validation or high-resolution anatomical investigations. Previous work has demonstrated improvements in data acquired with diffusion-weighted steady-state free precession (DW-SSFP) compared with conventional diffusion-weighted spin echo at 3T. This is due to the ability of DW-SSFP to overcome signal-to-noise and diffusion contrast losses brought about by tissue fixation related decreases in T2 and ADC. In this work, data of four post-mortem human brains were acquired at 3T and 7T, using DW-SSFP with similar effective b-values (beff ~5150s/mm2) for inter-field strength comparisons; in addition, DW-SSFP data were acquired at 7T with higher beff (~8550s/mm2) for intra-field strength comparisons. Results demonstrate that both datasets acquired at 7T had higher SNR and diffusion contrast than data acquired at 3T, and data acquired at higher beff had improved diffusion contrast than at lower beff at 7T. These results translate to improved estimates of secondary fiber orientations leading to higher fidelity tractography results compared with data acquired at 3T. Specifically, tractography streamlines of cortical projections originating from the corpus callosum, corticospinal tract, and superior longitudinal fasciculus were more successful at crossing the centrum semiovale and projected closer to the cortex. Results suggest that DW-SSFP at 7T is a preferential method for acquiring diffusion-weighted data of post-mortem human brain, specifically where the primary region of interest involves crossing white matter tracts.

YNIMG Journal 2014 Journal Article

MSM: A new flexible framework for Multimodal Surface Matching

• Emma C. Robinson
• Saad Jbabdi
• Matthew F. Glasser
• Jesper Andersson
• Gregory C. Burgess
• Michael P. Harms
• Stephen M. Smith
• David C. Van Essen

Surface-based cortical registration methods that are driven by geometrical features, such as folding, provide sub-optimal alignment of many functional areas due to variable correlation between cortical folding patterns and function. This has led to the proposal of new registration methods using features derived from functional and diffusion imaging. However, as yet there is no consensus over the best set of features for optimal alignment of brain function. In this paper we demonstrate the utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity. The versatility of the framework originates from adapting the discrete Markov Random Field (MRF) registration method to surface alignment. This has the benefit of being very flexible in the choice of a similarity measure and relatively insensitive to local minima. The method offers significant flexibility in the choice of feature set, and we demonstrate the advantages of this by performing registrations using univariate descriptors of surface curvature and myelination, multivariate feature sets derived from resting fMRI, and multimodal descriptors of surface curvature and myelination. We compare the results with two state of the art surface registration methods that use geometric features: FreeSurfer and Spherical Demons. In the future, the MSM technique will allow explorations into the best combinations of features and alignment strategies for inter-subject alignment of cortical functional areas for a wide range of neuroimaging data sets.

YNIMG Journal 2013 Journal Article

Advances in diffusion MRI acquisition and processing in the Human Connectome Project

• Stamatios N. Sotiropoulos
• Saad Jbabdi
• Junqian Xu
• Jesper L. Andersson
• Steen Moeller
• Edward J. Auerbach
• Matthew F. Glasser
• Moises Hernandez

The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013.

YNIMG Journal 2013 Journal Article

Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

• Kamil Uğurbil
• Junqian Xu
• Edward J. Auerbach
• Steen Moeller
• An T. Vu
• Julio M. Duarte-Carvajalino
• Christophe Lenglet
• Xiaoping Wu

The Human Connectome Project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3T, leading to whole brain coverage with 2mm isotropic resolution in 0. 7s for fMRI, and 1. 25mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.

YNIMG Journal 2013 Journal Article

Spatially constrained hierarchical parcellation of the brain with resting-state fMRI

• Thomas Blumensath
• Saad Jbabdi
• Matthew F. Glasser
• David C. Van Essen
• Kamil Ugurbil
• Timothy E.J. Behrens
• Stephen M. Smith

We propose a novel computational strategy to partition the cerebral cortex into disjoint, spatially contiguous and functionally homogeneous parcels. The approach exploits spatial dependency in the fluctuations observed with functional Magnetic Resonance Imaging (fMRI) during rest. Single subject parcellations are derived in a two stage procedure in which a set of (~1000 to 5000) stable seeds is grown into an initial detailed parcellation. This parcellation is then further clustered using a hierarchical approach that enforces spatial contiguity of the parcels. A major challenge is the objective evaluation and comparison of different parcellation strategies; here, we use a range of different measures. Our single subject approach allows a subject-specific parcellation of the cortex, which shows high scan-to-scan reproducibility and whose borders delineate clear changes in functional connectivity. Another important measure, on which our approach performs well, is the overlap of parcels with task fMRI derived clusters. Connectivity-derived parcellation borders are less well matched to borders derived from cortical myelination and from cytoarchitectonic atlases, but this may reflect inherent differences in the data.

YNIMG Journal 2013 Journal Article

The minimal preprocessing pipelines for the Human Connectome Project

• Matthew F. Glasser
• Stamatios N. Sotiropoulos
• J. Anthony Wilson
• Timothy S. Coalson
• Bruce Fischl
• Jesper L. Andersson
• Junqian Xu
• Saad Jbabdi

The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines.

YNIMG Journal 2012 Journal Article

Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI

• Stamatios N. Sotiropoulos
• Timothy E.J. Behrens
• Saad Jbabdi

A number of methods have been proposed for resolving crossing fibers from diffusion-weighted (DW) MRI. However, other complex fiber geometries have drawn minimal attention. In this study, we focus on fiber orientation dispersion induced by within-voxel fanning. We use a multi-compartment, model-based approach to estimate fiber dispersion. Bingham distributions are employed to represent continuous distributions of fiber orientations, centered around a main orientation, and capturing anisotropic dispersion. We evaluate the accuracy of the model for different simulated fanning geometries, under different acquisition protocols and we illustrate the high SNR and angular resolution needs. We also perform a qualitative comparison between our parametric approach and five popular non-parametric techniques that are based on orientation distribution functions (ODFs). This comparison illustrates how the same underlying geometry can be depicted by different methods. We apply the proposed model on high-quality, post-mortem macaque data and present whole-brain maps of fiber dispersion, as well as exquisite details on the local anatomy of fiber distributions in various white matter regions.

YNIMG Journal 2012 Journal Article

Diffusion tractography of post-mortem human brains: Optimization and comparison of spin echo and steady-state free precession techniques

• Karla L. Miller
• Jennifer A. McNab
• Saad Jbabdi
• Gwenaëlle Douaud

Diffusion imaging of post-mortem brains could provide valuable data for validation of diffusion tractography of white matter pathways. Long scans (e. g. , overnight) may also enable high-resolution diffusion images for visualization of fine structures. However, alterations to post-mortem tissue (T2 and diffusion coefficient) present significant challenges to diffusion imaging with conventional diffusion-weighted spin echo (DW-SE) acquisitions, particularly for imaging human brains on clinical scanners. Diffusion-weighted steady-state free precession (DW-SSFP) has been proposed as an alternative acquisition technique to ameliorate this tradeoff in large-bore clinical scanners. In this study, both DWSE and DW-SSFP are optimized for use in fixed white matter on a clinical 3-Tesla scanner. Signal calculations predict superior performance from DW-SSFP across a broad range of protocols and conditions. DW-SE and DW-SSFP data in a whole, post-mortem human brain are compared for 6- and 12-hour scan durations. Tractography is performed in major projection, commissural and association tracts (corticospinal tract, corpus callosum, superior longitudinal fasciculus and cingulum bundle). The results demonstrate superior tract-tracing from DW-SSFP data, with 6-hour DW-SSFP data performing as well as or better than 12-hour DW-SE scans. These results suggest that DW-SSFP may be a preferred method for diffusion imaging of post-mortem human brains. The ability to estimate multiple fibers in imaging voxels is also demonstrated, again with greater success in DW-SSFP data.

YNIMG Journal 2011 Journal Article

Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner

• Karla L. Miller
• Charlotte J. Stagg
• Gwenaëlle Douaud
• Saad Jbabdi
• Stephen M. Smith
• Timothy E.J. Behrens
• Mark Jenkinson
• Steven A. Chance

Diffusion imaging of post mortem brains has great potential both as a reference for brain specimens that undergo sectioning, and as a link between in vivo diffusion studies and “gold standard” histology/dissection. While there is a relatively mature literature on post mortem diffusion imaging of animals, human brains have proven more challenging due to their incompatibility with high-performance scanners. This study presents a method for post mortem diffusion imaging of whole, human brains using a clinical 3-Tesla scanner with a 3D segmented EPI spin-echo sequence. Results in eleven brains at 0. 94×0. 94×0. 94mm resolution are presented, and in a single brain at 0. 73×0. 73×0. 73mm resolution. Region-of-interest analysis of diffusion tensor parameters indicate that these properties are altered compared to in vivo (reduced diffusivity and anisotropy), with significant dependence on post mortem interval (time from death to fixation). Despite these alterations, diffusion tractography of several major tracts is successfully demonstrated at both resolutions. We also report novel findings of cortical anisotropy and partial volume effects.

YNIMG Journal 2010 Journal Article

Connectivity-based segmentation of the substantia nigra in human and its implications in Parkinson's disease

• Ricarda A. Menke
• Saad Jbabdi
• Karla L. Miller
• Paul M. Matthews
• Mojtaba Zarei

The aims of this study were to i) identify substantia nigra subregions i. e. pars reticulata (SNr) and pars compacta (SNc), in human, and ii) to assess volumetric changes in these subregions in the diagnosis of Parkinson's disease. Current MR imaging techniques are unable to distinguish SNr and SNc. Segmentation of these regions may be clinically useful in Parkinson's disease (PD) as substantia nigra is invariably affected in PD. We acquired quantitative T1 as well as diffusion tensor imaging (DTI) data from ten healthy subjects and ten PD patients. For each subject, the left and right SN were manually outlined on T1 images and then classified into two discrete regions based on the characteristics of their connectivity with the rest of the brain using an automated clustering method on the DTI data. We identified two regions in each subjects' SN: an internal region that is likely to correspond with SNc because it was mainly connected with posterior striatum, pallidum, anterior thalamus, and prefrontal cortex; and an external region that corresponds with SNr because it was chiefly connected with posterior thalamus, ventral thalamus, and motor cortex. Volumetric study of these regions in PD patients showed a general atrophy in PD particularly in the right SNr. This pilot study showed that automated DTI-based parcellation of SN subregions may provide a useful tool for in-vivo identification of SNc and SNr and might therefore assist to detect changes that occur in patients with PD.

YNIMG Journal 2010 Journal Article

Crossing fibres in tract-based spatial statistics

• Saad Jbabdi
• Timothy E.J. Behrens
• Stephen M. Smith

Voxelwise analysis of white matter properties typically relies on scalar measurements derived, for example, from a tensor model fit to diffusion MRI data. These are spatially matched across subjects prior to statistical modelling. In this paper, we show why and how this can be improved through the use of directionally dependent measurements. In the case where different orientations relate to different fibre populations (e. g. , in the presence of crossing fibres), distinguishing and matching those populations of fibres across subjects are important prior to any statistical modelling. It allows one to compare measurements that are related to the same fibres across subjects. We show how this framework applies to the parameters of a crossing fibre model and discuss its implications for voxelwise analysis of the white matter.

YNIMG Journal 2009 Journal Article

Bayesian analysis of neuroimaging data in FSL

• Mark W. Woolrich
• Saad Jbabdi
• Brian Patenaude
• Michael Chappell
• Salima Makni
• Timothy Behrens
• Christian Beckmann
• Mark Jenkinson

Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.

YNIMG Journal 2009 Journal Article

MRI characteristics of the substantia nigra in Parkinson's disease: A combined quantitative T1 and DTI study

• Ricarda A. Menke
• Jan Scholz
• Karla L. Miller
• Sean Deoni
• Saad Jbabdi
• Paul M. Matthews
• Mojtaba Zarei

The substantia nigra contains dopaminergic cells that project to the striatum and are affected by the neurodegenerative process that appears in Parkinson's disease (PD). For accurate differential diagnosis and for disease monitoring the availability of a sensitive and non-invasive biomarker for Parkinson's disease would be essential. Although there has been notable progress in studying correlates of nigral degeneration by means of magnetic resonance imaging (MRI) in the past decade, MRI and analysis techniques that allow accurate high-resolution mapping of the SN within a clinically acceptable acquisition time are still elusive. The main purpose of the preliminary study was to evaluate the potential role of the driven equilibrium single pulse observation of T1 (DESPOT1) method for delineation of the SN and differentiation of PD patients from healthy control subjects (n =10 in each group). We also investigated whether additional measures that can be obtained with diffusion tensor imaging (DTI) can further improve the MRI-guided discrimination between PD patients and controls. Our results show that the DESPOT1 method allows for a clear visualisation of the SN as a whole. Volumetric comparisons between ten PD patients and ten healthy subjects revealed significantly smaller volumes in patients for both the left and the right sides when the whole SN was considered. Combining SN volumetry and its connectivity with the thalamus improved the classification sensitivity to 100% and specificity to 80% for PD (discriminant function analysis with leave-one-out cross validation). Combining DESPOT1 imaging and DTI could therefore serve as a diagnostic marker for idiopathic Parkinson's disease in the future.

YNIMG Journal 2007 Journal Article

Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework

• Jean Daunizeau
• Christophe Grova
• Guillaume Marrelec
• Jérémie Mattout
• Saad Jbabdi
• Mélanie Pélégrini-Issac
• Jean-Marc Lina
• Habib Benali

In this work, we propose a symmetrical multimodal EEG/fMRI information fusion approach dedicated to the identification of event-related bioelectric and hemodynamic responses. Unlike existing, asymmetrical EEG/fMRI data fusion algorithms, we build a joint EEG/fMRI generative model that explicitly accounts for local coupling/uncoupling of bioelectric and hemodynamic activities, which are supposed to share a common substrate. Under a dedicated assumption of spatio-temporal separability, the spatial profile of the common EEG/fMRI sources is introduced as an unknown hierarchical prior on both markers of cerebral activity. Thereby, a devoted Variational Bayesian (VB) learning scheme is derived to infer common EEG/fMRI sources from a joint EEG/fMRI dataset. This yields an estimate of the common spatial profile, which is built as a trade-off between information extracted from EEG and fMRI datasets. Furthermore, the spatial structure of the EEG/fMRI coupling/uncoupling is learned exclusively from the data. The proposed data generative model and devoted VBEM learning scheme thus provide an un-supervised well-balanced approach for the fusion of EEG/fMRI information. We first demonstrate our approach on synthetic data. Results show that, in contrast to classical EEG/fMRI fusion approach, the method proved efficient and robust regardless of the EEG/fMRI discordance level. We apply the method on EEG/fMRI recordings from a patient with epilepsy, in order to identify brain areas involved during the generation of epileptic spikes. The results are validated using intracranial EEG measurements.