John Ashburner

YNIMG Journal 2022 Journal Article

A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets

• Fabio S. Ferreira
• Agoston Mihalik
• Rick A. Adams
• John Ashburner
• Janaina Mourao-Miranda

Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging and behaviour). However, these methods have inherent limitations: (1) statistical inferences about the associations are often not robust; (2) the associations within each data modality are not modelled; (3) missing values need to be imputed or removed. Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations. Here, we propose an extension of GFA that handles missing data, and highlight that GFA can be used as a predictive model. We applied GFA to synthetic and real data consisting of brain connectivity and non-imaging measures from the Human Connectome Project (HCP). In synthetic data, GFA uncovered the underlying shared and specific factors and predicted correctly the non-observed data modalities in complete and incomplete data sets. In the HCP data, we identified four relevant shared factors, capturing associations between mood, alcohol and drug use, cognition, demographics and psychopathological measures and the default mode, frontoparietal control, dorsal and ventral networks and insula, as well as two factors describing associations within brain connectivity. In addition, GFA predicted a set of non-imaging measures from brain connectivity. These findings were consistent in complete and incomplete data sets, and replicated previous findings in the literature. GFA is a promising tool that can be used to uncover associations between and within multiple data modalities in benchmark datasets (such as, HCP), and easily extended to more complex models to solve more challenging tasks.

YNICL Journal 2022 Journal Article

Simultaneous assessment of regional distributions of atrophy across the neuraxis in MS patients

• Patrick Freund
• Nico Papinutto
• Antje Bischof
• Michela Azzarito
• Gina Kirkish
• John Ashburner
• Alan Thompson
• Stephen L. Hauser

BACKGROUND: The ability to assess brain and cord atrophy simultaneously would improve the efficiency of MRI to track disease evolution. OBJECTIVE: To test a promising tool to simultaneously map the regional distribution of atrophy in multiple sclerosis (MS) patients across the brain and cord. METHODS: Voxel-based morphometry combined with a statistical parametric mapping probabilistic brain-spinal cord (SPM-BSC) template was applied to standard T1-weighted magnetic resonance imaging (MRI) scans covering the brain and cervical cord from 37 MS patients and 20 healthy controls (HC). We also measured the cord area at C2-C3 with a semi-automatic segmentation method using (i) the same T1-weighted acquisitions used for the new voxel-based analysis and (ii) dedicated spinal cord phase sensitive inversion recovery (PSIR) acquisitions. Cervical cord findings derived from the three approaches were compared to each other and the goodness to fit to clinical scores was assessed by regression analyses. RESULTS: The SPM-BSC approach revealed a severity-dependent pattern of atrophy across the cervical cord and thalamus in MS patients when compared to HCs. The magnitude of cord atrophy was confirmed by the semi-automatic extraction approach at C2-C3 using both standard brain T1-weighted and advanced cord dedicated acquisitions. Associations between atrophy of cord and thalamus with disability and cognition were demonstrated. CONCLUSION: Atrophy in the brain and cervical cord of MS patients can be identified simultaneously and rapidly at the voxel-level. The SPM-BSC approach yields similar results as available standard processing tools with the added advantage of performing the analysis simultaneously and faster.

YNIMG Journal 2021 Journal Article

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging

• Pawel J. Markiewicz
• Julian C. Matthews
• John Ashburner
• David M. Cash
• David L. Thomas
• Enrico De Vita
• Anna Barnes
• M. Jorge Cardoso

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.

YNIMG Journal 2021 Journal Article

Ventralis intermedius nucleus anatomical variability assessment by MRI structural connectivity

• Francisca Ferreira
• Harith Akram
• John Ashburner
• Ludvic Zrinzo
• Hui Zhang
• Christian Lambert

The ventralis intermedius nucleus (Vim) is centrally placed in the dentato-thalamo-cortical pathway (DTCp) and is a key surgical target in the treatment of severe medically refractory tremor. It is not visible on conventional MRI sequences; consequently, stereotactic targeting currently relies on atlas-based coordinates. This fails to capture individual anatomical variability, which may lead to poor long-term clinical efficacy. Probabilistic tractography, combined with known anatomical connectivity, enables localisation of thalamic nuclei at an individual subject level. There are, however, a number of confounds associated with this technique that may influence results. Here we focused on an established method, using probabilistic tractography to reconstruct the DTCp, to identify the connectivity-defined Vim (cd-Vim) in vivo. Using 100 healthy individuals from the Human Connectome Project, our aim was to quantify cd-Vim variability across this population, measure the discrepancy with atlas-defined Vim (ad-Vim), and assess the influence of potential methodological confounds. We found no significant effect of any of the confounds. The mean cd-Vim coordinate was located within 1.88 mm (left) and 2.12 mm (right) of the average midpoint and 3.98 mm (left) and 5.41 mm (right) from the ad-Vim coordinates. cd-Vim location was more variable on the right, which reflects hemispheric asymmetries in the probabilistic DTC reconstructed. The method was reproducible, with no significant cd-Vim location differences in a separate test-retest cohort. The superior cerebellar peduncle was identified as a potential source of artificial variance. This work demonstrates significant individual anatomical variability of the cd-Vim that atlas-based coordinate targeting fails to capture. This variability was not related to any methodological confound tested. Lateralisation of cerebellar functions, such as speech, may contribute to the observed asymmetry. Tractography-based methods seem sensitive to individual anatomical variability that is missed by conventional neurosurgical targeting; these findings may form the basis for translational tools to improve efficacy and reduce side-effects of thalamic surgery for tremor.

YNIMG Journal 2020 Journal Article

A Symmetric Prior for the Regularisation of Elastic Deformations: Improved anatomical plausibility in nonlinear image registration

• Frederik J. Lange
• John Ashburner
• Stephen M. Smith
• Jesper L.R. Andersson

Nonlinear registration is critical to many aspects of Neuroimaging research. It facilitates averaging and comparisons across multiple subjects, as well as reporting of data in a common anatomical frame of reference. It is, however, a fundamentally ill-posed problem, with many possible solutions which minimise a given dissimilarity metric equally well. We present a regularisation method capable of selectively driving solutions towards those which would be considered anatomically plausible by penalising unlikely lineal, areal and volumetric deformations. This penalty is symmetric in the sense that geometric expansions and contractions are penalised equally, which encourages inverse-consistency. We demonstrate that this method is able to significantly reduce local volume changes and shape distortions compared to state-of-the-art elastic (FNIRT) and plastic (ANTs) registration frameworks. Crucially, this is achieved whilst simultaneously matching or exceeding the registration quality of these methods, as measured by overlap scores of labelled cortical regions. Extensive leveraging of GPU parallelisation has allowed us to solve this highly computationally intensive optimisation problem while maintaining reasonable run times of under half an hour.

YNIMG Journal 2020 Journal Article

The influence of microsatellite polymorphisms in sex steroid receptor genes ESR1, ESR2 and AR on sex differences in brain structure

• Geoffrey Chern-Yee Tan
• Carlton Chu
• Yu Teng Lee
• Clarence Chih King Tan
• John Ashburner
• Nicholas W. Wood
• Richard SJ. Frackowiak

The androgen receptor (AR), oestrogen receptor alpha (ESR1) and oestrogen receptor beta (ESR2) play essential roles in mediating the effect of sex hormones on sex differences in the brain. Using Voxel-based morphometry (VBM) and gene sizing in two independent samples (discovery n ​= ​173, replication ​= ​61), we determine the common and unique influences on brain sex differences in grey (GM) and white matter (WM) volume between repeat lengths (n) of microsatellite polymorphisms AR(CAG)n, ESR1(TA)n and ESR2(CA)n. In the hypothalamus, temporal lobes, anterior cingulate cortex, posterior insula and prefrontal cortex, we find increased GM volume with increasing AR(CAG)n across sexes, decreasing ESR1(TA)n across sexes and decreasing ESR2(CA)n in females. Uniquely, AR(CAG)n was positively associated with dorsolateral prefrontal and orbitofrontal GM volume and the anterior corona radiata, left superior fronto-occipital fasciculus, thalamus and internal capsule WM volume. ESR1(TA)n was negatively associated with the left superior corona radiata, left cingulum and left inferior longitudinal fasciculus WM volume uniquely. ESR2(CA)n was negatively associated with right fusiform and posterior cingulate cortex uniquely. We thus describe the neuroanatomical correlates of three microsatellite polymorphisms of steroid hormone receptors and their relationship to sex differences.

YNIMG Journal 2019 Journal Article

hMRI – A toolbox for quantitative MRI in neuroscience and clinical research

• Karsten Tabelow
• Evelyne Balteau
• John Ashburner
• Martina F. Callaghan
• Bogdan Draganski
• Gunther Helms
• Ferath Kherif
• Tobias Leutritz

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R 1 and R 2 ⋆, proton density P D and magnetisation transfer M T saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.

YNIMG Journal 2018 Journal Article

A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods

• Gemma C. Monté-Rubio
• Carles Falcón
• Edith Pomarol-Clotet
• John Ashburner

There is a widespread interest in applying pattern recognition methods to anatomical neuroimaging data, but so far, there has been relatively little investigation into how best to derive image features in order to make the most accurate predictions. In this work, a Gaussian Process machine learning approach was used for predicting age, gender and body mass index (BMI) of subjects in the IXI dataset, as well as age, gender and diagnostic status using the ABIDE and COBRE datasets. MRI data were segmented and aligned using SPM12, and a variety of feature representations were derived from this preprocessing. We compared classification and regression accuracy using the different sorts of features, and with various degrees of spatial smoothing. Results suggested that feature sets that did not ignore the implicit background tissue class, tended to result in better overall performance, whereas some of the most commonly used feature sets performed relatively poorly.

YNICL Journal 2018 Journal Article

Connectivity derived thalamic segmentation in deep brain stimulation for tremor

• Harith Akram
• Viswas Dayal
• Philipp Mahlknecht
• Dejan Georgiev
• Jonathan Hyam
• Thomas Foltynie
• Patricia Limousin
• Enrico De Vita

The ventral intermediate nucleus (VIM) of the thalamus is an established surgical target for stereotactic ablation and deep brain stimulation (DBS) in the treatment of tremor in Parkinson's disease (PD) and essential tremor (ET). It is centrally placed on a cerebello-thalamo-cortical network connecting the primary motor cortex, to the dentate nucleus of the contralateral cerebellum through the dentato-rubro-thalamic tract (DRT). The VIM is not readily visible on conventional MR imaging, so identifying the surgical target traditionally involved indirect targeting that relies on atlas-defined coordinates. Unfortunately, this approach does not fully account for individual variability and requires surgery to be performed with the patient awake to allow for intraoperative targeting confirmation. The aim of this study is to identify the VIM and the DRT using probabilistic tractography in patients that will undergo thalamic DBS for tremor. Four male patients with tremor dominant PD and five patients (three female) with ET underwent high angular resolution diffusion imaging (HARDI) (128 diffusion directions, 1.5 mm isotropic voxels and b value = 1500) preoperatively. Patients received VIM-DBS using an MR image guided and MR image verified approach with indirect targeting. Postoperatively, using parallel Graphical Processing Unit (GPU) processing, thalamic areas with the highest diffusion connectivity to the primary motor area (M1), supplementary motor area (SMA), primary sensory area (S1) and contralateral dentate nucleus were identified. Additionally, volume of tissue activation (VTA) corresponding to active DBS contacts were modelled. Response to treatment was defined as 40% reduction in the total Fahn-Tolosa-Martin Tremor Rating Score (FTMTRS) with DBS-ON, one year from surgery. Three out of nine patients had a suboptimal, long-term response to treatment. The segmented thalamic areas corresponded well to anatomically known counterparts in the ventrolateral (VL) and ventroposterior (VP) thalamus. The dentate-thalamic area, lay within the M1-thalamic area in a ventral and lateral location. Streamlines corresponding to the DRT connected M1 to the contralateral dentate nucleus via the dentate-thalamic area, clearly crossing the midline in the mesencephalon. Good response was seen when the active contact VTA was in the thalamic area with highest connectivity to the contralateral dentate nucleus. Non-responders had active contact VTAs outside the dentate-thalamic area. We conclude that probabilistic tractography techniques can be used to segment the VL and VP thalamus based on cortical and cerebellar connectivity. The thalamic area, best representing the VIM, is connected to the contralateral dentate cerebellar nucleus. Connectivity based segmentation of the VIM can be achieved in individual patients in a clinically feasible timescale, using HARDI and high performance computing with parallel GPU processing. This same technique can map out the DRT tract with clear mesencephalic crossing.

YNIMG Journal 2018 Journal Article

Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction

• Claudia Blaiotta
• Patrick Freund
• M. Jorge Cardoso
• John Ashburner

In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications. In particular we validate the proposed method on both real and synthetic brain MR scans including the cervical cord and demonstrate that it yields accurate alignment of brain and spinal cord structures, as compared to state-of-the-art tools for medical image registration. At the same time we illustrate how the resulting tissue probability maps can readily be used to segment, bias correct and spatially normalise unseen data, which are all crucial pre-processing steps for MR imaging studies.

YNIMG Journal 2017 Journal Article

Multivariate dynamical modelling of structural change during development

• Gabriel Ziegler
• Gerard R. Ridgway
• Sarah-Jayne Blakemore
• John Ashburner
• Will Penny

Here we introduce a multivariate framework for characterising longitudinal changes in structural MRI using dynamical systems. The general approach enables modelling changes of states in multiple imaging biomarkers typically observed during brain development, plasticity, ageing and degeneration, e. g. regional gray matter volume of multiple regions of interest (ROIs). Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development. In particular, the inputs to the system are specified to account for known or latent developmental growth/decline factors, e. g. due to effects of growth hormones, puberty, or sudden behavioural changes etc. Because effects of developmental factors might be region-specific, the sensitivity of each ROI to contributions of each factor is explicitly modelled. In addition to the external effects of developmental factors on regional change, the framework enables modelling and inference about directed (potentially reciprocal) interactions between brain regions, due to competition for space, or structural connectivity, and suchlike. This approach accounts for repeated measures in typical MRI studies of development and aging. Model inversion and posterior distributions are obtained using earlier established variational methods enabling Bayesian evidence-based comparisons between various models of structural change. Using this approach we demonstrate dynamic cortical changes during brain maturation between 6 and 22 years of age using a large openly available longitudinal paediatric dataset with 637 scans from 289 individuals. In particular, we model volumetric changes in 26 bilateral ROIs, which cover large portions of cortical and subcortical gray matter. We account for (1) puberty-related effects on gray matter regions; (2) effects of an early transient growth process with additional time-lag parameter; (3) sexual dimorphism by modelling parameter differences between boys and girls. There is evidence that the regional pattern of sensitivity to dynamic hidden growth factors in late childhood is similar across genders and shows a consistent anterior-posterior gradient with strongest impact to prefrontal cortex (PFC) brain changes. Finally, we demonstrate the potential of the framework to explore the coupling of structural changes across a priori defined subnetworks using an example of previously established resting state functional connectivity.

YNIMG Journal 2017 Journal Article

OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis

• Yury Koush
• John Ashburner
• Evgeny Prilepin
• Ronald Sladky
• Peter Zeidman
• Sergei Bibikov
• Frank Scharnowski
• Artem Nikonorov

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user’s needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.

YNICL Journal 2017 Journal Article

Relationship between brainstem neurodegeneration and clinical impairment in traumatic spinal cord injury

• Patrick Grabher
• Claudia Blaiotta
• John Ashburner
• Patrick Freund

BACKGROUND: Brainstem networks are pivotal in sensory and motor function and in recovery following experimental spinal cord injury (SCI). OBJECTIVE: To quantify neurodegeneration and its relation to clinical impairment in major brainstem pathways and nuclei in traumatic SCI. METHODS: Quantitative MRI data of 30 chronic traumatic SCI patients (15 with tetraplegia and 15 with paraplegia) and 23 controls were acquired. Patients underwent a full neurological examination. We calculated quantitative myelin-sensitive (magnetisation transfer saturation (MT) and longitudinal relaxation rate (R1)) and iron-sensitive (effective transverse relaxation rate (R2*)) maps. We constructed brainstem tissue templates using a multivariate Gaussian mixture model and assessed volume loss, myelin reductions, and iron accumulation across the brainstem pathways (e.g. corticospinal tracts (CSTs) and medial lemniscus), and nuclei (e.g. red nucleus and periaqueductal grey (PAG)). The relationship between structural changes and clinical impairment were assessed using regression analysis. RESULTS: Volume loss was detected in the CSTs and in the medial lemniscus. Myelin-sensitive MT and R1 were reduced in the PAG, the CSTs, the dorsal medulla and pons. No iron-sensitive changes in R2* were detected. Lower pinprick score related to more myelin reductions in the PAG, whereas lower functional independence was related to more myelin reductions in the vestibular and pontine nuclei. CONCLUSION: Neurodegeneration, indicated by volume loss and myelin reductions, is evident in major brainstem pathways and nuclei following traumatic SCI; the magnitude of these changes relating to clinical impairment. Thus, quantitative MRI protocols offer new targets, which may be used as neuroimaging biomarkers in treatment trials.

YNIMG Journal 2017 Journal Article

Spinal cord grey matter segmentation challenge

• Ferran Prados
• John Ashburner
• Claudia Blaiotta
• Tom Brosch
• Julio Carballido-Gamio
• Manuel Jorge Cardoso
• Benjamin N. Conrad
• Esha Datta

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.

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 2015 Journal Article

Accurate automatic estimation of total intracranial volume: A nuisance variable with less nuisance

• Ian B. Malone
• Kelvin K. Leung
• Shona Clegg
• Josephine Barnes
• Jennifer L. Whitwell
• John Ashburner
• Nick C. Fox
• Gerard R. Ridgway

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5. 3. 0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R2 =0. 940, 95% Confidence Interval (0. 924, 0. 953)), with a small mean difference (SPM12 40. 4±35. 4ml lower than manual, amounting to 2. 8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p<0. 001) than for either SPM8 (R2 =0. 577 CI (0. 500, 0. 644)) or FreeSurfer (R2 =0. 801 CI (0. 744, 0. 843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV.

YNIMG Journal 2015 Journal Article

Do we need to revise the tripartite subdivision hypothesis of the human subthalamic nucleus (STN)? Response to Alkemade and Forstmann

• Christian Lambert
• Ludvic Zrinzo
• Zoltan Nagy
• Antoine Lutti
• Marwan Hariz
• Thomas Foltynie
• Bogdan Draganski
• John Ashburner

Recently in this journal, Alkemade and Forstmann again challenged the evidence for a tripartite organisation to the subthalamic nucleus (STN) (Alkemade & Forstmann 2014). Additionally, they raised specific issues with the earlier published results using 3T MRI to perform in vivo diffusion weighted imaging (DWI) based segmentation of the STN (Lambert et al. 2012). Their comments reveal a common misconception related to the underlying methodologies used, which we clarify in this reply, in addition to highlighting how their current conclusions are synonymous with our original paper. The ongoing debate, instigated by the controversies surrounding STN parcellation, raises important implications for the assumptions and methodologies employed in mapping functional brain anatomy, both in vivo and ex vivo, and reveals a fundamental emergent problem with the current techniques. These issues are reviewed, and potential strategies that could be developed to manage them in the future are discussed further.

YNICL Journal 2013 Journal Article

Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians

• Christian Lambert
• Antoine Lutti
• Gunther Helms
• Richard Frackowiak
• John Ashburner

The human brainstem is a densely packed, complex but highly organised structure. It not only serves as a conduit for long projecting axons conveying motor and sensory information, but also is the location of multiple primary nuclei that control or modulate a vast array of functions, including homeostasis, consciousness, locomotion, and reflexive and emotive behaviours. Despite its importance, both in understanding normal brain function as well as neurodegenerative processes, it remains a sparsely studied structure in the neuroimaging literature. In part, this is due to the difficulties in imaging the internal architecture of the brainstem in vivo in a reliable and repeatable fashion. A modified multivariate mixture of Gaussians (mmMoG) was applied to the problem of multichannel tissue segmentation. By using quantitative magnetisation transfer and proton density maps acquired at 3 T with 0.8 mm isotropic resolution, tissue probability maps for four distinct tissue classes within the human brainstem were created. These were compared against an ex vivo fixated human brain, imaged at 0.5 mm, with excellent anatomical correspondence. These probability maps were used within SPM8 to create accurate individual subject segmentations, which were then used for further quantitative analysis. As an example, brainstem asymmetries were assessed across 34 right-handed individuals using voxel based morphometry (VBM) and tensor based morphometry (TBM), demonstrating highly significant differences within localised regions that corresponded to motor and vocalisation networks. This method may have important implications for future research into MRI biomarkers of pre-clinical neurodegenerative diseases such as Parkinson's disease.

YNIMG Journal 2012 Journal Article

Confirmation of functional zones within the human subthalamic nucleus: Patterns of connectivity and sub-parcellation using diffusion weighted imaging

• Christian Lambert
• Ludvic Zrinzo
• Zoltan Nagy
• Antoine Lutti
• Marwan Hariz
• Thomas Foltynie
• Bogdan Draganski
• John Ashburner

The subthalamic nucleus (STN) is a small, glutamatergic nucleus situated in the diencephalon. A critical component of normal motor function, it has become a key target for deep brain stimulation in the treatment of Parkinson's disease. Animal studies have demonstrated the existence of three functional sub-zones but these have never been shown conclusively in humans. In this work, a data driven method with diffusion weighted imaging demonstrated that three distinct clusters exist within the human STN based on brain connectivity profiles. The STN was successfully sub-parcellated into these regions, demonstrating good correspondence with that described in the animal literature. The local connectivity of each sub-region supported the hypothesis of bilateral limbic, associative and motor regions occupying the anterior, mid and posterior portions of the nucleus respectively. This study is the first to achieve in-vivo, non-invasive anatomical parcellation of the human STN into three anatomical zones within normal diagnostic scan times, which has important future implications for deep brain stimulation surgery.

YNIMG Journal 2012 Journal Article

SPM: A history

• John Ashburner

YNIMG Journal 2011 Journal Article

Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation

• John Ashburner
• Karl J. Friston

This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM), but with a more efficient optimisation scheme — both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss–Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data.

YNIMG Journal 2011 Journal Article

Kernel regression for fMRI pattern prediction

• Carlton Chu
• Yizhao Ni
• Geoffrey Tan
• Craig J. Saunders
• John Ashburner

This paper introduces two kernel-based regression schemes to decode or predict brain states from functional brain scans as part of the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007, in which our team was awarded first place. Our procedure involved image realignment, spatial smoothing, detrending of low-frequency drifts, and application of multivariate linear and non-linear kernel regression methods: namely kernel ridge regression (KRR) and relevance vector regression (RVR). RVR is based on a Bayesian framework, which automatically determines a sparse solution through maximization of marginal likelihood. KRR is the dual-form formulation of ridge regression, which solves regression problems with high dimensional data in a computationally efficient way. Feature selection based on prior knowledge about human brain function was also used. Post-processing by constrained deconvolution and re-convolution was used to furnish the prediction. This paper also contains a detailed description of how prior knowledge was used to fine tune predictions of specific “feature ratings, ” which we believe is one of the key factors in our prediction accuracy. The impact of pre-processing was also evaluated, demonstrating that different pre-processing may lead to significantly different accuracies. Although the original work was aimed at the PBAIC, many techniques described in this paper can be generally applied to any fMRI decoding works to increase the prediction accuracy.

YNIMG Journal 2011 Journal Article

Multivariate models of inter-subject anatomical variability

• John Ashburner
• Stefan Klöppel

This paper presents a very selective review of some of the approaches for multivariate modelling of inter-subject variability among brain images. It focusses on applying probabilistic kernel-based pattern recognition approaches to pre-processed anatomical MRI, with the aim of most accurately modelling the difference between populations of subjects. Some of the principles underlying the pattern recognition approaches of Gaussian process classification and regression are briefly described, although the reader is advised to look elsewhere for full implementational details. Kernel pattern recognition methods require matrices that encode the degree of similarity between the images of each pair of subjects. This review focusses on similarity measures derived from the relative shapes of the subjects' brains. Pre-processing is viewed as generative modelling of anatomical variability, and there is a special emphasis on the diffeomorphic image registration framework, which provides a very parsimonious representation of relative shapes. Although the review is largely methodological, excessive mathematical notation is avoided as far as possible, as the paper attempts to convey a more intuitive understanding of various concepts. The paper should be of interest to readers wishing to apply pattern recognition methods to MRI data, with the aim of clinical diagnosis or biomarker development. It also tries to explain that the best models are those that most accurately predict, so similar approaches should also be relevant to basic science. Knowledge of some basic linear algebra and probability theory should make the review easier to follow, although it may still have something to offer to those readers whose mathematics may be more limited.

YNIMG Journal 2011 Journal Article

Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT)

• Nikolaus Weiskopf
• Antoine Lutti
• Gunther Helms
• Marianne Novak
• John Ashburner
• Chloe Hutton

Quantitative mapping of the longitudinal relaxation rate (R1=1/T1) in the human brain enables the investigation of tissue microstructure and macroscopic morphology which are becoming increasingly important for clinical and neuroimaging applications. R1 maps are now commonly estimated from two fast high-resolution 3D FLASH acquisitions with variable excitation flip angles, because this approach is fast and does not rely on special acquisition techniques. However, these R1 maps need to be corrected for bias due to RF transmit field (B1+) inhomogeneities, requiring additional B1+ mapping which is usually time consuming and difficult to implement. We propose a technique that simultaneously estimates the B1+ inhomogeneities and R1 values from the uncorrected R1 maps in the human brain without need for B1+ mapping. It employs a probabilistic framework for unified segmentation based correction of R1 maps for B1+ inhomogeneities (UNICORT). The framework incorporates a physically informed generative model of smooth B1+ inhomogeneities and their multiplicative effect on R1 estimates. Extensive cross-validation with the established standard using measured B1+ maps shows that UNICORT yields accurate B1+ and R1 maps with a mean deviation from the standard of less than 4. 3% and 5%, respectively. The results of different groups of subjects with a wide age range and different levels of atypical brain anatomy further suggest that the method is robust and generalizes well to wider populations. UNICORT is easy to apply, as it is computationally efficient and its basic framework is implemented as part of the tissue segmentation in SPM8.

YNIMG Journal 2011 Journal Article

Utilizing temporal information in fMRI decoding: Classifier using kernel regression methods

• Carlton Chu
• Janaina Mourão-Miranda
• Yu-Chin Chiu
• Nikolaus Kriegeskorte
• Geoffrey Tan
• John Ashburner

This paper describes a general kernel regression approach to predict experimental conditions from activity patterns acquired with functional magnetic resonance image (fMRI). The standard approach is to use classifiers that predict conditions from activity patterns. Our approach involves training different regression machines for each experimental condition, so that a predicted temporal profile is computed for each condition. A decision function is then used to classify the responses from the testing volumes into the corresponding category, by comparing the predicted temporal profile elicited by each event, against a canonical hemodynamic response function. This approach utilizes the temporal information in the fMRI signal and maintains more training samples in order to improve the classification accuracy over an existing strategy. This paper also introduces efficient techniques of temporal compaction, which operate directly on kernel matrices for kernel classification algorithms such as the support vector machine (SVM). Temporal compacting can convert the kernel computed from each fMRI volume directly into the kernel computed from beta-maps, average of volumes or spatial–temporal kernel. The proposed method was applied to three different datasets. The first one is a block-design experiment with three conditions of image stimuli. The method outperformed the SVM classifiers of three different types of temporal compaction in single-subject leave-one-block-out cross-validation. Our method achieved 100% classification accuracy for six of the subjects and an average of 94% accuracy across all 16 subjects, exceeding the best SVM classification result, which was 83% accuracy (p=0. 008). The second dataset is also a block-design experiment with two conditions of visual attention (left or right). Our method yielded 96% accuracy and SVM yielded 92% (p=0. 005). The third dataset is from a fast event-related experiment with two categories of visual objects. Our method achieved 77% accuracy, compared with 72% using SVM (p=0. 0006).

YNIMG Journal 2010 Journal Article

Normal variation in fronto-occipital circuitry and cerebellar structure with an autism-associated polymorphism of CNTNAP2

• Geoffrey C.Y. Tan
• Thomas F. Doke
• John Ashburner
• Nicholas W. Wood
• Richard S.J. Frackowiak

Recent genetic studies have implicated a number of candidate genes in the pathogenesis of Autism Spectrum Disorder (ASD). Polymorphisms of CNTNAP2 (contactin-associated like protein-2), a member of the neurexin family, have already been implicated as a susceptibility gene for autism by at least 3 separate studies. We investigated variation in white and grey matter morphology using structural MRI and diffusion tensor imaging. We compared volumetric differences in white and grey matter and fractional anisotropy values in control subjects characterised by genotype at rs7794745, a single nucleotide polymorphism in CNTNAP2. Homozygotes for the risk allele showed significant reductions in grey and white matter volume and fractional anisotropy in several regions that have already been implicated in ASD, including the cerebellum, fusiform gyrus, occipital and frontal cortices. Male homozygotes for the risk alleles showed greater reductions in grey matter in the right frontal pole and in FA in the right rostral fronto-occipital fasciculus compared to their female counterparts who showed greater reductions in FA of the anterior thalamic radiation. Thus a risk allele for autism results in significant cerebral morphological variation, despite the absence of overt symptoms or behavioural abnormalities. The results are consistent with accumulating evidence of CNTNAP2's function in neuronal development. The finding suggests the possibility that the heterogeneous manifestations of ASD can be aetiologically characterised into distinct subtypes through genetic-morphological analysis.

YNIMG Journal 2010 Journal Article

Predicting clinical scores from magnetic resonance scans in Alzheimer's disease

• Cynthia M. Stonnington
• Carlton Chu
• Stefan Klöppel
• Clifford R. Jack
• John Ashburner
• Richard S.J. Frackowiak

Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and mild cognitive impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent data sets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other data set; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale—Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P <0. 0001). Training with one data set and testing with another demonstrated stability between data sets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome.

YNIMG Journal 2009 Journal Article

A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging

• Chloe Hutton
• Bogdan Draganski
• John Ashburner
• Nikolaus Weiskopf

The morphology of cortical grey matter is commonly assessed using T1-weighted MRI together with automated computerised methods such as voxel-based morphometry (VBM) and cortical thickness measures. In the presented study we investigate how grey matter changes identified using voxel-based cortical thickness (VBCT) measures compare with local grey matter volume changes identified using VBM. We use data from a healthy aging population to perform the comparison, focusing on brain regions where age-related changes have been observed in previous studies. Our results show that overall, in healthy aging, VBCT and VBM yield very consistent results but VBCT provides a more sensitive measure of age-associated decline in grey matter compared with VBM. Our findings suggest that while VBCT selectively investigates cortical thickness, VBM provides a mixed measure of grey matter including cortical surface area or cortical folding, as well as cortical thickness. We therefore propose that used together, these techniques can separate the underlying grey matter changes, highlighting the utility of combining these complementary methods.

YNIMG Journal 2009 Journal Article

Computing average shaped tissue probability templates

• John Ashburner
• Karl J. Friston

This note presents a framework for generating tissue probability maps that represent the average shape of a number of subjects' brain images. The procedure is formulated as finding maximum a posteriori estimates within a probabilistic generative model. Estimating the parameters involves alternating between estimating the deformations that match tissue class images of individual subjects to template, and updating the template according to the latest estimates of the deformations. A multinomial matching criterion is used, such that multiple tissue class images (e. g. grey and white matter) are registered simultaneously with the current template estimate. In order to generalise the resulting template to a broader range of subjects, a template blurriness prior is included within the model.

YNIMG Journal 2009 Journal Article

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration

• Arno Klein
• Jesper Andersson
• Babak A. Ardekani
• John Ashburner
• Brian Avants
• Ming-Chang Chiang
• Gary E. Christensen
• D. Louis Collins

All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45, 000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms (“SPM2-type” and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http: //www. mindboggle. info/papers/ website.

YNIMG Journal 2009 Journal Article

Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps

• Gunther Helms
• Bogdan Draganski
• Richard Frackowiak
• John Ashburner
• Nikolaus Weiskopf

Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures.

YNIMG Journal 2008 Journal Article

Bayesian decoding of brain images

• Karl Friston
• Carlton Chu
• Janaina Mourão-Miranda
• Oliver Hulme
• Geraint Rees
• Will Penny
• John Ashburner

This paper introduces a multivariate Bayesian (MVB) scheme to decode or recognise brain states from neuroimages. It resolves the ill-posed many-to-one mapping, from voxel values or data features to a target variable, using a parametric empirical or hierarchical Bayesian model. This model is inverted using standard variational techniques, in this case expectation maximisation, to furnish the model evidence and the conditional density of the model’s parameters. This allows one to compare different models or hypotheses about the mapping from functional or structural anatomy to perceptual and behavioural consequences (or their deficits). We frame this approach in terms of decoding measured brain states to predict or classify outcomes using the rhetoric established in pattern classification of neuroimaging data. However, the aim of MVB is not to predict (because the outcomes are known) but to enable inference on different models of structure–function mappings; such as distributed and sparse representations. This allows one to optimise the model itself and produce predictions that outperform standard pattern classification approaches, like support vector machines. Technically, the model inversion and inference uses the same empirical Bayesian procedures developed for ill-posed inverse problems (e. g. , source reconstruction in EEG). However, the MVB scheme used here extends this approach to include a greedy search for sparse solutions. It reduces the problem to the same form used in Gaussian process modelling, which affords a generic and efficient scheme for model optimisation and evaluating model evidence. We illustrate MVB using simulated and real data, with a special focus on model comparison; where models can differ in the form of the mapping (i. e. , neuronal representation) within one region, or in the (combination of) regions per se.

YNIMG Journal 2008 Journal Article

Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns

• Federico De Martino
• Giancarlo Valente
• Noël Staeren
• John Ashburner
• Rainer Goebel
• Elia Formisano

In functional brain mapping, pattern recognition methods allow detecting multivoxel patterns of brain activation which are informative with respect to a subject's perceptual or cognitive state. The sensitivity of these methods, however, is greatly reduced when the proportion of voxels that convey the discriminative information is small compared to the total number of measured voxels. To reduce this dimensionality problem, previous studies employed univariate voxel selection or region-of-interest-based strategies as a preceding step to the application of machine learning algorithms. Here we employ a strategy for classifying functional imaging data based on a multivariate feature selection algorithm, Recursive Feature Elimination (RFE) that uses the training algorithm (support vector machine) recursively to eliminate irrelevant voxels and estimate informative spatial patterns. Generalization performances on test data increases while features/voxels are pruned based on their discrimination ability. In this article we evaluate RFE in terms of sensitivity of discriminative maps (Receiver Operative Characteristic analysis) and generalization performances and compare it to previously used univariate voxel selection strategies based on activation and discrimination measures. Using simulated fMRI data, we show that the recursive approach is suitable for mapping discriminative patterns and that the combination of an initial univariate activation-based (F-test) reduction of voxels and multivariate recursive feature elimination produces the best results, especially when differences between conditions have a low contrast-to-noise ratio. Furthermore, we apply our method to high resolution (2 × 2 × 2mm3) data from an auditory fMRI experiment in which subjects were stimulated with sounds from four different categories. With these real data, our recursive algorithm proves able to detect and accurately classify multivoxel spatial patterns, highlighting the role of the superior temporal gyrus in encoding the information of sound categories. In line with the simulation results, our method outperforms univariate statistical analysis and statistical learning without feature selection.

YNIMG Journal 2008 Journal Article

Interpreting scan data acquired from multiple scanners: A study with Alzheimer's disease

• Cynthia M. Stonnington
• Geoffrey Tan
• Stefan Klöppel
• Carlton Chu
• Bogdan Draganski
• Clifford R. Jack
• Kewei Chen
• John Ashburner

Large, multi-site studies utilizing MRI-derived measures from multiple scanners present an opportunity to advance research by pooling data. On the other hand, it remains unclear whether or not the potential confound introduced by different scanners and upgrades will devalue the integrity of any results. Although there are studies of scanner differences for the purpose of calibration and quality control, the current literature is devoid of studies that describe the analysis of multi-scanner data with regard to the interaction of scanner(s) with effects of interest. We investigated a data-set of 136 subjects, 62 patients with mild to moderate Alzheimer's disease and 74 cognitively normal elderly controls, with MRI scans from one center that were acquired over 10 years with 6 different scanners and multiple upgrades over time. We used a whole-brain voxel-wise analysis to evaluate the effect of scanner, effect of disease, and the interaction of scanner and disease for the 6 different scanners. The effect of disease in patients showed the expected significant reduction of grey matter in the medial temporal lobe. Scanner differences were substantially less than the group differences and only significant in the thalamus. There was no significant interaction of scanner with disease group. We describe the rationale for concluding that our results were not confounded by scanner differences. Similar analyses in other multi-scanner data-sets could be used to justify the pooling of data when needed, such as in studies of rare disorders or in multi-center designs.

YNIMG Journal 2008 Journal Article

Voxel-based cortical thickness measurements in MRI

• Chloe Hutton
• Enrico De Vita
• John Ashburner
• Ralf Deichmann
• Robert Turner

The thickness of the cerebral cortex can provide valuable information about normal and abnormal neuroanatomy. High resolution MRI together with powerful image processing techniques has made it possible to perform these measurements automatically over the whole brain. Here we present a method for automatically generating voxel-based cortical thickness (VBCT) maps. This technique results in maps where each voxel in the grey matter is assigned a thickness value. Sub-voxel measurements of thickness are possible using sub-sampling and interpolation of the image information. The method is applied to repeated MRI scans of a single subject from two MRI scanners to demonstrate its robustness and reproducibility. A simulated data set is used to show that small focal differences in thickness between two groups of subjects can be detected. We propose that the analysis of VBCT maps can provide results that are complementary to other anatomical analyses such as voxel-based morphometry.

YNIMG Journal 2007 Journal Article

A fast diffeomorphic image registration algorithm

• John Ashburner

This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg–Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.

YNIMG Journal 2007 Journal Article

A tensor based morphometry study of longitudinal gray matter contraction in FTD

• Simona M. Brambati
• Natasha C. Renda
• Katherine P. Rankin
• Howard J. Rosen
• William W. Seeley
• John Ashburner
• Michael W. Weiner
• Bruce L. Miller

Frontotemporal dementia (FTD) is a neurodegenerative disease characterized by progressive behavioural abnormalities and frontotemporal atrophy. Here we used tensor based morphometry (TBM) to identify regions of longitudinal progression of gray matter atrophy in FTD compared to controls. T1-weighted MRI images were acquired at presentation and 1-year follow-up from 12 patients with mild to moderate FTD and 12 healthy controls. Using TBM as implemented in SPM2, a voxel-wise estimation of regional tissue volume change was derived from the deformation field required to warp a subject’s late to early anatomical images. A whole brain analysis was performed, in which a level of significance of p <0. 05 corrected for multiple comparisons (family wise error-FWE) was accepted. Based on prior studies, a region of interest (ROI) analysis was also performed, including in the search area bilateral medial and orbital frontal regions, anterior cingulate gyrus, insula, amygdala and hippocampus. Within this ROI a level of significance of p <0. 001 uncorrected was accepted. In the whole brain analysis, the anterior cingulate/paracingulate gyri were the only regions that showed significant atrophy change over 1 year. In the ROI analysis, the left ventro-medial frontal cortex, right medial superior frontal gyrus, anterior insulae and left amygdala/hippocampus showed significant longitudinal changes. In conclusion, limbic and paralimbic regions showed detectable gray matter contraction over 1 year in FTD, confirming the susceptibility of these regions to the disease and the consistency with their putative role in causing typical presenting behaviours. These results suggest that TBM might be useful in tracking progression of regional atrophy in FTD.

YNIMG Journal 2007 Journal Article

Spatial normalization of lesioned brains: Performance evaluation and impact on fMRI analyses

• Jenny Crinion
• John Ashburner
• Alex Leff
• Matthew Brett
• Cathy Price
• Karl Friston

A key component of group analyses of neuroimaging data is precise and valid spatial normalization (i. e. , inter-subject image registration). When patients have structural brain lesions, such as a stroke, this process can be confounded by the lack of correspondence between the subject and standardized template images. Current procedures for dealing with this problem include regularizing the estimate of warping parameters used to match lesioned brains to the template, or “cost function masking”; both these solutions have significant drawbacks. We report three experiments that identify the best spatial normalization for structurally damaged brains and establish whether differences among normalizations have a significant effect on inferences about functional activations. Our novel protocols evaluate the effects of different normalization solutions and can be applied easily to any neuroimaging study. This has important implications for users of both structural and functional imaging techniques in the study of patients with structural brain damage.

YNIMG Journal 2007 Journal Article

Variational free energy and the Laplace approximation

• Karl Friston
• Jérémie Mattout
• Nelson Trujillo-Barreto
• John Ashburner
• Will Penny

This note derives the variational free energy under the Laplace approximation, with a focus on accounting for additional model complexity induced by increasing the number of model parameters. This is relevant when using the free energy as an approximation to the log-evidence in Bayesian model averaging and selection. By setting restricted maximum likelihood (ReML) in the larger context of variational learning and expectation maximisation (EM), we show how the ReML objective function can be adjusted to provide an approximation to the log-evidence for a particular model. This means ReML can be used for model selection, specifically to select or compare models with different covariance components. This is useful in the context of hierarchical models because it enables a principled selection of priors that, under simple hyperpriors, can be used for automatic model selection and relevance determination (ARD). Deriving the ReML objective function, from basic variational principles, discloses the simple relationships among Variational Bayes, EM and ReML. Furthermore, we show that EM is formally identical to a full variational treatment when the precisions are linear in the hyperparameters. Finally, we also consider, briefly, dynamic models and how these inform the regularisation of free energy ascent schemes, like EM and ReML.

YNIMG Journal 2005 Journal Article

Unified segmentation

• John Ashburner
• Karl J. Friston

A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.

YNIMG Journal 2003 Journal Article

Changes in cerebral morphology consequent to peripheral autonomic denervation

• Hugo D Critchley
• Catriona D Good
• John Ashburner
• Richard S Frackowiak
• Christopher J Mathias
• Raymond J Dolan

Pure autonomic failure (PAF) is characterized by an acquired, selective, peripheral denervation of the autonomic nervous system. Patients with PAF fail to generate bodily states of arousal via the autonomic nervous system in response to physical or cognitive effort. We used voxel-based morphometry to test the hypothesis that changes in the morphology of brain regions involved in autonomic control would arise as a consequence to the longstanding absence of peripheral autonomic responses in PAF patients. Optimized voxel-based morphometry of structural magnetic resonance scans was used to test for regional differences in grey and white matter in 15 PAF patients and matched controls. There were no group differences observed in global measures of grey matter, white matter, or cerebrospinal fluid (CSF). We identified morphometric differences reflecting regional decreases in grey matter volume and concentration in anterior cingulate and insular cortices in PAF patients relative to controls. Morphometric differences in brainstem and subcortical regions did not reach statistical significance. Our findings suggest that peripheral autonomic denervation is associated with grey matter loss in cortical regions encompassing areas that we have previously shown are functionally involved in generation and representation of bodily states of autonomic arousal. The nature of these changes cannot be determined from morphometric analysis alone, but we suggest that they reflect experience-dependent change consequent upon loss of afferent input to brain regions involved in representation of autonomic states.

YNIMG Journal 2003 Journal Article

How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging

• Jesper L.R. Andersson
• Stefan Skare
• John Ashburner

Diffusion tensor imaging is often performed by acquiring a series of diffusion-weighted spin-echo echo-planar images with different direction diffusion gradients. A problem of echo-planar images is the geometrical distortions that obtain near junctions between tissues of differing magnetic susceptibility. This results in distorted diffusion-tensor maps. To resolve this we suggest acquiring two images for each diffusion gradient; one with bottom-up and one with top-down traversal of k-space in the phase-encode direction. This achieves the simultaneous goals of providing information on the underlying displacement field and intensity maps with adequate spatial sampling density even in distorted areas. The resulting DT maps exhibit considerably higher geometric fidelity, as assessed by comparison to an image volume acquired using a conventional 3D MR technique.

YNIMG Journal 2003 Journal Article

Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution

• Darren R. Gitelman
• William D. Penny
• John Ashburner
• Karl J. Friston

The analysis of functional magnetic resonance imaging (fMRI) time-series data can provide information not only about task-related activity, but also about the connectivity (functional or effective) among regions and the influences of behavioral or physiologic states on that connectivity. Similar analyses have been performed in other imaging modalities, such as positron emission tomography. However, fMRI is unique because the information about the underlying neuronal activity is filtered or convolved with a hemodynamic response function. Previous studies of regional connectivity in fMRI have overlooked this convolution and have assumed that the observed hemodynamic response approximates the neuronal response. In this article, this assumption is revisited using estimates of underlying neuronal activity. These estimates use a parametric empirical Bayes formulation for hemodynamic deconvolution.

YNIMG Journal 2002 Journal Article

Automatic Differentiation of Anatomical Patterns in the Human Brain: Validation with Studies of Degenerative Dementias

• Catriona D. Good
• Rachael I. Scahill
• Nick C. Fox
• John Ashburner
• Karl J. Friston
• Dennis Chan
• William R. Crum
• Martin N. Rossor

We compared voxel-based morphometry (VBM) with independent accurate region-of-interest (ROI) measurements of temporal lobe structures in order to validate the usefulness of this fully automated and unbiased technique in Alzheimer's disease (AD) and semantic dementia (SD). In AD, ROI analyses appear more sensitive to volume loss in the amygdalae, whereas VBM analyses appear more sensitive to right middle temporal gyrus and regional hippocampal volume loss. In SD, ROI analyses appear more sensitive to left middle and inferior temporal gyrus volume loss, whereas VBM appears more sensitive to regional hippocampal volume loss. In addition the significance of volume reductions was generally less in VBM owing to more stringent corrections for multiple comparisons. In conclusion, the automated technique detects a general trend of atrophy similar to that of expertly labeled ROI measurements in AD and SD, although there are discrepancies in the ranking of severity and in the significance of volume reductions that are more marked in AD.

YNIMG Journal 2002 Journal Article

Image Distortion Correction in fMRI: A Quantitative Evaluation

• Chloe Hutton
• Andreas Bork
• Oliver Josephs
• Ralf Deichmann
• John Ashburner
• Robert Turner

A well-recognized problem with the echo-planar imaging (EPI) technique most commonly used for functional magnetic resonance imaging (fMRI) studies is geometric distortion caused by magnetic field inhomogeneity. This makes it difficult to achieve an accurate registration between a functional activation map calculated from an EPI time series and an undistorted, high resolution anatomical image. A correction method based on mapping the spatial distribution of field inhomogeneities can be used to reduce these distortions. This approach is attractive in its simplicity but requires postprocessing to improve the robustness of the acquired field map and reduce any secondary artifacts. Furthermore, the distribution of the internal magnetic field throughout the head is position dependent resulting in an interaction between distortion and head motion. Therefore, a single field map may not be sufficient to correct for the distortions throughout a whole fMRI time series. In this paper we present a quantitative evaluation of image distortion correction for fMRI at 2T. We assess (i) methods for the acquisition and calculation of field maps, (ii) the effect of image distortion correction on the coregistration between anatomical and functional images, and (iii) the interaction between distortion and head motion, assessing the feasibility of using field maps to reduce this effect. We propose that field maps with acceptable noise levels can be generated easily using a dual echo-time EPI sequence and demonstrate the importance of distortion correction for anatomical coregistration, even for small distortions. Using a dual echo-time series to generate a unique field map at each time point, we characterize the interaction between head motion and geometric distortion. However, we suggest that the variance between successively measured field maps introduces additional unwanted variance in the voxel time-series and is therefore not adequate to correct for time-varying distortions.

YNIMG Journal 2001 Journal Article

A Global Estimator Unbiased by Local Changes

• Jesper L.R. Andersson
• John Ashburner
• Karl Friston

The global activity is an important confound when analyzing PET data in that its inclusion in the statistical model can substantially reduce error variance and increase sensitivity. However, by defining global activity as the average over all voxels one introduces a bias that is collinear with experimental factors. This leads to an underestimation of true activations and the introduction of artefactual deactivations. We propose a novel estimator for the global activity based on the notion of finding a maximally nonlocal mode in a multivariate characterization of the data, while maximizing the locality of the remaining modes. The approach uses singular value decomposition (SVD) to find a provisional set of modes, which are subsequently rotated such that a metric based on the above heuristic is maximized. This metric is a version of the stochastic sign change (SSC) criterion that has been used previously for normalizing medical images with focal defects. The estimator was evaluated on simulated and real functional imaging (PET) data. The simulations show that the bias of the global mean, introduced by focal activations, is reduced by 80–90% with the new estimator. Comparison with a previous unbiased estimator, using the empirical data, yielded similar results. The advantage of the new estimator is that it is not informed of experimental design and relies only on general assumptions regarding the nature of the signal.

YNIMG Journal 2001 Journal Article

A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains

• Catriona D. Good
• Ingrid S. Johnsrude
• John Ashburner
• Richard N.A. Henson
• Karl J. Friston
• Richard S.J. Frackowiak

Voxel-based-morphometry (VBM) is a whole-brain, unbiased technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. We describe an optimized method of VBM to examine the effects of age on grey and white matter and CSF in 465 normal adults. Global grey matter volume decreased linearly with age, with a significantly steeper decline in males. Local areas of accelerated loss were observed bilaterally in the insula, superior parietal gyri, central sulci, and cingulate sulci. Areas exhibiting little or no age effect (relative preservation) were noted in the amygdala, hippocampi, and entorhinal cortex. Global white matter did not decline with age, but local areas of relative accelerated loss and preservation were seen. There was no interaction of age with sex for regionally specific effects. These results corroborate previous reports and indicate that VBM is a useful technique for studying structural brain correlates of ageing through life in humans.

YNIMG Journal 2001 Journal Article

Cerebral Asymmetry and the Effects of Sex and Handedness on Brain Structure: A Voxel-Based Morphometric Analysis of 465 Normal Adult Human Brains

• Catriona D. Good
• Ingrid Johnsrude
• John Ashburner
• Richard N.A. Henson
• Karl J. Friston
• Richard S.J. Frackowiak

We used voxel-based morphometry (VBM) to examine human brain asymmetry and the effects of sex and handedness on brain structure in 465 normal adults. We observed significant asymmetry of cerebral grey and white matter in the occipital, frontal, and temporal lobes (petalia), including Heschl's gyrus, planum temporale (PT) and the hippocampal formation. Males demonstrated increased leftward asymmetry within Heschl's gyrus and PT compared to females. There was no significant interaction between asymmetry and handedness and no main effect of handedness. There was a significant main effect of sex on brain morphology, even after accounting for the larger global volumes of grey and white matter in males. Females had increased grey matter volume adjacent to the depths of both central sulci and the left superior temporal sulcus, in right Heschl's gyrus and PT, in right inferior frontal and frontomarginal gyri and in the cingulate gyrus. Females had significantly increased grey matter concentration extensively and relatively symmetrically in the cortical mantle, parahippocampal gyri, and in the banks of the cingulate and calcarine sulci. Males had increased grey matter volume bilaterally in the mesial temporal lobes, entorhinal and perirhinal cortex, and in the anterior lobes of the cerebellum, but no regions of increased grey matter concentration.

YNIMG Journal 2001 Journal Article

Gene deletion mapping of the X chromosome

• Catriona D. Good
• Jonna Kuntsi
• Rebecca Akers
• Kate Elgar
• Cathy Price
• John Ashburner
• Karl J. Friston
• Richard S.J. Frackowiak

YNIMG Journal 2001 Journal Article

Herpes simplex encephalitis: Structural changes analyzed with voxel-based morphometry

• Darren R. Gitelman
• John Ashburner
• Karl J. Friston
• Lorraine K. Tyler
• Cathy J. Price

YNIMG Journal 2001 Journal Article

Hippocampal shape in schizophrenia: A deformation-based morphometric analysis

• Andrew J. Saykin
• Laura A. Flashman
• Li Shen
• John Ashburner
• Molly Sparling
• Annette Donnelly
• Fillia Makedon
• David Isecke

YNIMG Journal 2001 Journal Article

Image warping using empirical Bayes

• John Ashburner
• K.J. Friston

YNIMG Journal 2001 Journal Article

Learning Arbitrary Visuomotor Associations: Temporal Dynamic of Brain Activity

• Ivan Toni
• Narender Ramnani
• Oliver Josephs
• John Ashburner
• Richard E. Passingham

Primates can give behavioral responses on the basis of arbitrary, context-dependent rules. When sensory instructions and behavioral responses are associated by arbitrary rules, these rules need to be learned. This study investigates the temporal dynamics of functional segregation at the basis of visuomotor associative learning in humans, isolating specific learning-related changes in neurovascular activity across the whole brain. We have used fMRI to measure human brain activity during performance of two tasks requiring the association of visual patterns with motor responses. Both tasks were learned by trial and error, either before (visuomotor control) or during (visuomotor learning) the scanning session. Epochs of tasks performance (∼30 s) were alternated with a baseline period over the whole scanning session (∼50 min). We have assessed both linear and nonlinear modulations in the differential signal between tasks, independently from overall task differences. The performance indices of the visuomotor learning task smoothly converged onto the values of a steady-state control condition, according to nonlinear timecourses. Specific visuomotor learning-related activity has been found over a distributed cortical network, centred on a temporo-prefrontal circuit. These cortical time-modulated activities were supported early in learning by the hippocampal/parahippocampal complex, and late in learning by the basal ganglia system. These findings suggest the inferior temporal and the ventral prefrontal cortex are critical neural nodes for integrating perceptual information with executive processes.

YNIMG Journal 2001 Journal Article

Modeling Geometric Deformations in EPI Time Series

• Jesper L.R. Andersson
• Chloe Hutton
• John Ashburner
• Robert Turner
• Karl Friston

Even after realignment there is residual movement-related variance present in fMRI time-series, causing loss of sensitivity and, potentially, also specificity. One cause is the differential deformation of the sampling matrix, by field inhomogeneities, at different object positions, i. e. , a movement-by-inhomogeneity interaction. This has been addressed previously by using empirical field measurements. In the present paper we suggest a forward model of how data is affected by an inhomogeneous field at different object positions. From this model we derive a method to solve the inverse problem of estimating the field inhomogeneities and their derivatives with respect to object position, directly from the EPI data and estimated realignment parameters. The field is modeled as a linear combination of cosine basis fields, which facilitates a fast way of implementing the necessary matrix operations. Simulations suggest that the solution is tractable and that the fields are estimable given the deformed images and knowledge of the relative positions at which they have been acquired. An experiment on a subject performing voluntary movements in the scanner yielded plausible estimates of the deformation fields and their application to “unwarp” the time series significantly reduced movement-related variance.

YNIMG Journal 2001 Journal Article

Patterns of cerebral atrophy in Alzheimer's disease and semantic dementia: A comparison of voxel based morphometry and region of interest measurements

• Catriona D. Good
• Rachael I. Scahill
• Nick C. Fox
• John Ashburner
• Dennis C. Chan
• William R. Crum
• Martin N. Rossor
• Richard S.J. Frackowiak

YNIMG Journal 2001 Journal Article

Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking

• Matthew Brett
• Alexander P. Leff
• Chris Rorden
• John Ashburner

In studies of patients with focal brain lesions, it is often useful to coregister an image of the patient's brain to that of another subject or a standard template. We refer to this process as spatial normalization. Spatial normalization can improve the presentation and analysis of lesion location in neuropsychological studies; it can also allow other data, for example from functional imaging, to be compared to data from other patients or normal controls. In functional imaging, the standard procedure for spatial normalization is to use an automated algorithm, which minimizes a measure of difference between image and template, based on image intensity values. These algorithms usually optimize both linear (translations, rotations, zooms, and shears) and nonlinear transforms. In the presence of a focal lesion, automated algorithms attempt to reduce image mismatch between template and image at the site of the lesion. This can lead to significant inappropriate image distortion, especially when nonlinear transforms are used. One solution is to use cost-function masking—masking the areas used in the calculation of image difference—to exclude the area of the lesion, so that the lesion does not bias the transformations. We introduce and evaluate this technique using normalizations of a selection of brains with focal lesions and normal brains with simulated lesions. Our results suggest that cost-function masking is superior to the standard approach to this problem, which is affine-only normalization; we propose that cost-function masking should be used routinely for normalizations of brains with focal lesions.

YNIMG Journal 2001 Journal Article

Voxel-Based Morphometry of Herpes Simplex Encephalitis

• Darren R. Gitelman
• John Ashburner
• Karl J. Friston
• Lorraine K. Tyler
• Cathy J. Price

Voxel-based morphometry (VBM) is a powerful tool for analyzing changes in gray or white matter density of the brain. By using an automated segmentation procedure and standardized parametric statistics it avoids biases inherent in operator-dependent morphological operations (J. Ashburner and K. J. Friston, 2000, NeuroImage 11, 805–821). Since its introduction in 1995, VBM has been used to examine anatomical changes in a variety of diseases associated with neurologic and psychiatric dysfunction. Given the power of this technique for discerning subtle anatomical changes, we wanted to assess its performance on brains with gross structural abnormalities. Such results could have implications regarding the difficulties to be faced when examining other types of distorted brains (e. g. , brains with changes due to degenerative disease). This report describes the use of VBM for examining individual and group changes in gray matter concentration in five patients who had recovered from herpes simplex encephalitis (HSE) compared with age- and sex-matched controls. Because HSE tends to affect a specific set of brain regions we thought that this would (1) provide an opportunity to assess the anatomical face validity of VBM, (2) allow us to assess the problems of this technique when used on distorted brains, and (3) provide an in vivo demonstration of the gray matter changes due to HSE. We found that, despite problems in normalizing and segmenting these severely distorted brains, VBM was able to identify correctly a number of the regional gray matter abnormalities in HSE. The results, while consistent with the well-known histopathology of the disease, also demonstrate potential difficulties with this method.

YNIMG Journal 2001 Journal Article

Why Voxel-Based Morphometry Should Be Used

• John Ashburner
• Karl J. Friston

Thisarticle has been written in response to Dr. Fred L. Bookstein's article entitled ‘“Voxel-Based Morphometry” Should Not Be Used with Imperfectly Registered Images’ in this issue of NeuroImage. We will address three main issues: (i) Dr. Bookstein appears to have misunderstood the objective of voxel-based morphometry (VBM) and the nature of the continuum we referred to. (ii) We agree with him when he states that findings from VBM can pertain to systematic registration errors during spatial normalization. (iii) His argument about voxelwise tests on smooth data holds in the absence of error variance, but is of no consequence when using actual data. We first review the tenets of VBM, paying particular attention to the relationship between VBM and tensor-based morphometry. The last two sections of this response deal with the specific concerns raised by Dr. Bookstein.

YNIMG Journal 2000 Journal Article

A comprehensive framework for time series realignment

• Jesper Andersson
• John Ashburner
• Karl Friston

YNIMG Journal 2000 Journal Article

Assessing study-specific regional variation in fMRI signal

• Brigitte Lipschutz
• John Ashburner
• Karl Friston
• Cathy Price

YNIMG Journal 2000 Journal Article

Automated nonlinear coregistration of damaged brains to a normal template using cost function masking

• Matthew Brett
• Alex Leff
• John Ashburner

YNIMG Journal 2000 Journal Article

Diffusion tensor imaging with high spatial resolution

• Jiun-Jie Wang
• Ralf Deichmann
• John Ashburner
• Robert Turner
• Roger Ordidge

YNIMG Journal 2000 Journal Article

Effect of head motion and non-linear distortions on fMRI time series

• Chloe Hutton
• Andreas Bork
• Oliver Josephs
• John Ashburner
• Robert Turner

YNIMG Journal 2000 Journal Article

Tensor based morphometry

• John Ashburner
• Catriona Good
• Karl J. Friston

YNIMG Journal 2000 Journal Article

Voxel based morphometry of 465 normal adult human brains

• Catriona Good
• Ingrid Johnsrude
• John Ashburner
• Karl Friston
• Richard Frackowiak

YNIMG Journal 2000 Journal Article

Voxel-Based Morphometry—The Methods

• John Ashburner
• Karl J. Friston

At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.

YNIMG Journal 1999 Journal Article

High-Dimensional Image Registration Using Symmetric Priors

• John Ashburner
• Jesper L.R. Andersson
• Karl J. Friston

This paper is about warping a brain image from one subject (the object image) so that it matches another (the template image). A high-dimensional model is used, whereby a finite element approach is employed to estimate translations at the location of each voxel in the template image. Bayesian statistics are used to obtain amaximum a posteriori(MAP) estimate of the deformation field. The validity of any registration method is largely based upon the constraints or, in this instance, priors incorporated into the model describing the transformations. In this approach we assume that the priors should have some form of symmetry, in that priors describing the probability distribution of the deformations should be identical to those for the inverses (i. e. , warping brain A to brain B should not be different probabilistically from warping B to A). The fundamental assumption is that the probability of stretching a voxel by a factor ofnis considered to be the same as the probability of shrinkingnvoxels by a factor ofn −1. In the Bayesian framework adopted here, the priors are assumed to have a Gibbs form, where the Gibbs potential is a penalty function that embodies this symmetry. The penalty function of choice is based upon the singular values of the Jacobian having a lognormal distribution. This enforces a continuous one-to-one mapping. A gradient descent algorithm is presented that incorporates the above priors in order to obtain a MAP estimate of the deformations. We demonstrate this approach for the two-dimensional case, but the principles can be extended to three dimensions. A number of examples are given to demonstrate how the method works.

YNIMG Journal 1999 Journal Article

Voxel-by-Voxel Comparison of Automatically Segmented Cerebral Gray Matter—A Rater-Independent Comparison of Structural MRI in Patients with Epilepsy

• Friedrich G. Woermann
• Samantha L. Free
• Matthias J. Koepp
• John Ashburner
• John S. Duncan

Quantitative evaluation of MRI in patients with epilepsy can give more information than qualitative assessment. Previously developed volume-of-interest-based methods identified subtle widespread structural changes in the neocortex beyond the visualized lesions in patients with malformations of cortical development (MCD) and hippocampal sclerosis (HS) and also in MRI-negative patients with juvenile myoclonic epilepsy (JME). This study evaluates a voxel-based automated analysis of structural MRI in epilepsy. After fully automated segmentation of cerebral gray matter from structural T1-weighted, high-resolution MRI scans, we applied the automated and objective technique of statistical parametric mapping (SPM) to the analysis of gray matter of 35 control subjects, 10 patients with partial seizures and MCD, 10 patients with left temporal lobe epilepsy (TLE) and HS, 10 patients with left TLE and normal MR quantitation of the hippocampus, and 20 patients with JME. At a corrected threshold of P < 0. 05, significant abnormalities were found in 3/35 controls; in all 10 patients with MCD, 6 of whom had additional lesions beyond the margins of the visualized abnormalities; in 2/10 TLE patients with HS; in 2/10 MRI-negative TLE; and in 4/20 JME patients. Group comparisons between control subjects and HS patients identified the affected left temporal lobe with an increase in gray matter in the posterior temporal lobe, but did not identify hippocampal atrophy. The group of MRI-negative TLE patients showed no abnormalities compared with control subjects. Group comparison between control subjects and JME patients identified a mesial frontal increase in gray matter. The SPM-based voxel-by-voxel comparison of gray matter distribution identified MCD and abnormalities beyond the visualized lesion in individual MCD patients. The method did not reliably identify HS in individual patients or identify abnormalities in individual MRI-negative patients with TLE or JME in a proportion larger than the chance findings in the control group. Using group comparisons, structural abnormalities in the neocortical gray matter of patients with TLE and HS were lateralized to the affected temporal lobe. In patients with JME as a group, an increase in gray matter was localized to the mesial frontal area, corroborating earlier quantitative MRI findings.

YNIMG Journal 1998 Journal Article

High-Dimensional Nonlinear Image Registration

• John Ashburner
• Karl Friston

YNIMG Journal 1998 Journal Article

Identifying Global Anatomical Differences: Deformation-Based Morphometry

• John Ashburner
• Chloe Hutton
• Ingrid Johnsrude
• Cathy Price
• Richard Frackowiak
• Karl Friston

YNIMG Journal 1998 Journal Article

MRI Sensitivity Correction and Tissue Classification

• John Ashburner
• Karl Friston

YNIMG Journal 1997 Journal Article

MRI and PET Coregistration—A Cross Validation of Statistical Parametric Mapping and Automated Image Registration

• Stefan J. Kiebel
• John Ashburner
• Jean-Baptiste Poline
• Karl J. Friston

Coregistration of functional PET and T1-weighted MR images is a necessary step for combining functional information from PET images with anatomical information in MR images. Several coregistration algorithms have been published and are used in functional brain imaging studies. In this paper, we present a comparison and cross validation of the two most widely used coregistration routines (Fristonet al. ,1995, Hum. Brain Map. 2: 165–189; Woodset al. ,1993, J. Comput. Assisted Tomogr. 17: 536–546). Several transformations were applied to high-resolution anatomical MR images to generate simulated PET images so that the exact (rigid body) transformations between each MR image and its associated simulated PET images were known. The estimation error of a coregistration in relation to the known transformation allows a comparison of the performance of different coregistration routines. Under the assumption that the simulated PET images embody the salient features of real PET images with respect to coregistration, this study shows that the routines examined reliably solve the MRI to PET coregistration problem.

YNIMG Journal 1996 Journal Article

Analysis of fMRI data using the general linear statistical model

• Robert Turner
• Karl Friston
• John Ashburner
• Oliver Josephs
• Alistair Howseman