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Biomedical subjects

J Ashburner

Publications and source records attributed to J Ashburner.

At least 19 recordsLinked to original sources
PubMed2005

The role of the medial temporal lobe in autistic spectrum disorders.

The neural basis of autistic spectrum disorders (ASDs) is poorly understood. Studies of mnemonic function in ASD suggest a profile of impaired episodic memory with relative preservation of semantic memory (at least in high-functioning individuals). Such a pattern is consistent with developmental hippocampal abnormality. However, imaging evidence for abnormality of the hippocampal formation in ASD is inconsistent. These inconsistencies led us to examine the memory profile of children with ASD and the relationship to structural abnormalities. A cohort of high-functioning individuals with ASD and matched controls completed a comprehensive neuropsychological memory battery and underwent magnetic resonance imaging for the purpose of voxel-based morphometric analyses. Correlations between cognitive/behavioural test scores and quantified results of brain scans were also carried out to further examine the role of the medial temporal lobe in ASD. A selective deficit in episodic memory with relative preservation of semantic memory was found. Voxel-based morphometry revealed bilateral abnormalities in several areas implicated in ASD including the hippocampal formation. A significant correlation was found between parental ratings reflecting autistic symptomatology and the measure of grey matter density in the junction area involving the amygdala, hippocampus and entorhinal cortex. The data reveal a pattern of impaired and relatively preserved mnemonic function that is consistent with a hippocampal abnormality of developmental origin. The structural imaging data highlight abnormalities in several brain regions previously implicated in ASD, including the medial temporal lobes.

Adolescent↗
PubMed2005

Progression of structural neuropathology in preclinical Huntington's disease: a tensor based morphometry study.

BACKGROUND AND OBJECTIVES: Regional cerebral atrophy occurs in carriers of the Huntington's disease (HD) gene mutation before clinical diagnosis is possible. The current inability to reliably measure progression of pathology in this preclinical phase impedes development of therapies to delay clinical onset. We hypothesised that longitudinal statistical imaging would detect progression of structural pathology in preclinical carriers of the HD gene mutation, in the absence of measurable clinical change. METHODS: Thirty subjects (17 preclinical mutation positive, 13 mutation negative) underwent serial clinical and magnetic resonance imaging (MRI) assessments over an interval of 2 years. Statistically significant changes in regional grey and white matter volume on MRI were analysed using tensor based morphometry (TBM). This technique derives a voxel-wise estimation of regional tissue volume change from the deformation field required to warp a subject's early to late T1 images. RESULTS: Over 2 years, there was progressive regional grey matter atrophy in mutation-positive relative to negative subjects, without significant clinical progression of disease. Significant grey matter volume loss was limited to bilateral putamen and globus pallidus externa (GPe), left caudate nucleus, and left ventral midbrain in the region of the substantia nigra. CONCLUSIONS: While these results are consistent with previous cross sectional pathologic and morphometric studies, significant progression of atrophy in HD before the onset of significant clinical decline is now demonstrable with longitudinal statistical imaging. Such measures could be used to assess the efficacy of potential disease modifying drugs in slowing the progression of pathology before confirmed clinical onset of HD.

Adult↗
PubMed2002

Classical and Bayesian inference in neuroimaging: theory.

This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that conventional analyses of neuroimaging data can be usefully extended within an empirical Bayesian framework. In particular we formulate the procedures used in conventional data analysis in terms of hierarchical linear models and establish a connection between classical inference and parametric empirical Bayes (PEB) through covariance component estimation. This estimation is based on an expectation maximization or EM algorithm. The key point is that hierarchical models not only provide for appropriate inference at the highest level but that one can revisit lower levels suitably equipped to make Bayesian inferences. Bayesian inferences eschew many of the difficulties encountered with classical inference and characterize brain responses in a way that is more directly predicated on what one is interested in. The motivation for Bayesian approaches is reviewed and the theoretical background is presented in a way that relates to conventional methods, in particular restricted maximum likelihood (ReML). This paper is a technical and theoretical prelude to subsequent papers that deal with applications of the theory to a range of important issues in neuroimaging. These issues include; (i) Estimating nonsphericity or variance components in fMRI time-series that can arise from serial correlations within subject, or are induced by multisubject (i.e., hierarchical) studies. (ii) Spatiotemporal Bayesian models for imaging data, in which voxels-specific effects are constrained by responses in other voxels. (iii) Bayesian estimation of nonlinear models of hemodynamic responses and (iv) principled ways of mixing structural and functional priors in EEG source reconstruction. Although diverse, all these estimation problems are accommodated by the PEB framework described in this paper.

Algorithms↗
PubMed2002

Classical and Bayesian inference in neuroimaging: applications.

In Friston et al. ((2002) Neuroimage 16: 465-483) we introduced empirical Bayes as a potentially useful way to estimate and make inferences about effects in hierarchical models. In this paper we present a series of models that exemplify the diversity of problems that can be addressed within this framework. In hierarchical linear observation models, both classical and empirical Bayesian approaches can be framed in terms of covariance component estimation (e.g., variance partitioning). To illustrate the use of the expectation-maximization (EM) algorithm in covariance component estimation we focus first on two important problems in fMRI: nonsphericity induced by (i) serial or temporal correlations among errors and (ii) variance components caused by the hierarchical nature of multisubject studies. In hierarchical observation models, variance components at higher levels can be used as constraints on the parameter estimates of lower levels. This enables the use of parametric empirical Bayesian (PEB) estimators, as distinct from classical maximum likelihood (ML) estimates. We develop this distinction to address: (i) The difference between response estimates based on ML and the conditional means from a Bayesian approach and the implications for estimates of intersubject variability. (ii) The relationship between fixed- and random-effect analyses. (iii) The specificity and sensitivity of Bayesian inference and, finally, (iv) the relative importance of the number of scans and subjects. The forgoing is concerned with within- and between-subject variability in multisubject hierarchical fMRI studies. In the second half of this paper we turn to Bayesian inference at the first (within-voxel) level, using PET data to show how priors can be derived from the (between-voxel) distribution of activations over the brain. This application uses exactly the same ideas and formalism but, in this instance, the second level is provided by observations over voxels as opposed to subjects. The ensuing posterior probability maps (PPMs) have enhanced anatomical precision and greater face validity, in relation to underlying anatomy. Furthermore, in comparison to conventional SPMs they are not confounded by the multiple comparison problem that, in a classical context, dictates high thresholds and low sensitivity. We conclude with some general comments on Bayesian approaches to image analysis and on some unresolved issues.

Algorithms↗
PubMed2002

The precision of anatomical normalization in the medial temporal lobe using spatial basis functions.

We investigated the accuracy of spatial basis function normalization using anatomical landmarks to determine how precisely homologous regions are colocalized. We examined precision in terms of: (1) the number of nonlinear basis functions used by the normalization procedure; (2) the degree of (Bayesian) regularization; and (3) the effect of substituting different templates and how this interacted with the number of basis functions. The face validity of spatial normalization was assessed as a function of these parameters, using the colocalization of homologous landmarks in a test sample of 20 normally developing children and 5 children with bilateral hippocampal pathology. Our results suggest that when optimal normalization parameters are used, anatomical landmarks in the medial temporal lobes are colocalized to within a standard deviation of about 1 mm. When suboptimal parameters are used this standard deviation can increase up to 3 mm. Interestingly the optimal parameters are those that provide a rather constrained normalization as opposed to those that optimize intensity matching at the expense of rendering the warps "unlikely." The implications of our results, for users of voxel-based morphometry, are discussed.

Adolescent↗
PubMed2002

MRI analysis of an inherited speech and language disorder: structural brain abnormalities.

Analyses of brain structure in genetic speech and language disorders provide an opportunity to identify neurobiological phenotypes and further elucidate the neural bases of language and its development. Here we report such investigations in a large family, known as the KE family, half the members of which are affected by a severe disorder of speech and language, which is transmitted as an autosomal-dominant monogenic trait. The structural brain abnormalities associated with this disorder were investigated using two morphometric methods of MRI analysis. A voxel-based morphometric method was used to compare the amounts of grey matter in the brains of three groups of subjects: the affected members of the KE family, the unaffected members and a group of age-matched controls. This method revealed a number of mainly motor- and speech-related brain regions in which the affected family members had significantly different amounts of grey matter compared with the unaffected and control groups, who did not differ from each other. Several of these regions were abnormal bilaterally, including the caudate nucleus, which was of particular interest because this structure was also found to show functional abnormality in a related PET study. We performed a more detailed volumetric analysis of this structure. The results confirmed that the volume of this nucleus was reduced bilaterally in the affected family members compared with both the unaffected members and the group of age-matched controls. This reduction in volume was most evident in the superior portion of the nucleus. The volume of the caudate nucleus was significantly correlated with the performance of affected family members on a test of oral praxis, a test of non-word repetition and the coding subtest of the Wechsler Intelligence Scale. These results thus provide further evidence of a relationship between the abnormal development of this nucleus and the impairments in oromotor control and articulation reported in the KE family.

Adolescent↗
PubMed2002

Distributional assumptions in voxel-based morphometry.

In this paper we address the assumptions about the distribution of errors made by voxel-based morphometry. Voxel-based morphometry (VBM) uses the general linear model to construct parametric statistical tests. In order for these statistics to be valid, a small number of assumptions must hold. A key assumption is that the model's error terms are normally distributed. This is usually ensured through the Central Limit Theorem by smoothing the data. However, there is increasing interest in using minimal smoothing (in order to sensitize the analysis to regional differences at a small spatial scale). The validity of such analyses is investigated. In brief, our results indicate that nonnormality in the error terms can be an issue in VBM. However, in balanced designs, provided the data are smoothed with a 4-mm FWHM kernel, nonnormality is sufficiently attenuated to render the tests valid. Unbalanced designs appear to be less robust to violations of normality: a significant number of false positives arise at a smoothing of 4 and 8 mm when comparing a single subject to a group. This is despite the fact that conventional group comparisons appear to be robust, remaining valid even with no smoothing. The implications of the results for researchers using voxel-based morphometry are discussed.

Brain Mapping↗
PubMed2001

Assessing study-specific regional variations in fMRI signal.

In this paper, we present a post hoc method for identifying regions where functional MRI data are subject to signal reduction that may compromise sensitivity to activations. The motivation for developing this technique derives from recent language studies that showed responses in the inferior temporal lobe that could be detected by PET but not by fMRI. Reduced signal is due mostly to susceptibility artifacts and can be identified by comparing the T2* images (which are subject to susceptibility artifacts) with T2 images (which are not). However, T2 images are not usually acquired in fMRI studies. Therefore, we propose that areas with reduced signal can be identified by comparing T2* images that are corrected for nonuniformity with the original uncorrected images. The technique provides a voxel-wise characterization of signal reduction that pertains to the particular data that enter into a statistical model. It requires only the functional data and can thus be applied post hoc and without any additional scans.

Artifacts↗
PubMed2001

Voxel-based morphometry of herpes simplex encephalitis.

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.

Adult↗
PubMed2001

Modeling geometric deformations in EPI time series.

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.

Adult↗
PubMed2001

A global estimator unbiased by local changes.

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.

Brain↗
PubMed2001

A voxel-based morphometric study of ageing in 465 normal adult human brains.

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.

Adolescent↗
PubMed2001

Spatial normalization of brain images with focal lesions using cost function masking.

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.

Adult↗
PubMed2001

Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains.

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.

Adolescent↗
PubMed2001

Learning arbitrary visuomotor associations: temporal dynamic of brain activity.

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 ( approximately 30 s) were alternated with a baseline period over the whole scanning session ( approximately 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.

Adult↗
PubMed2001

Why voxel-based morphometry should be used.

This article 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.

Artifacts↗
PubMed2001

Computational neuroanatomy: new perspectives for neuroradiology.

Computational neuroanatomy is emerging as an exciting new methodology to characterise shape and neuroanatomical configuration of different brains. It encompasses a triad of techniques: Voxel-based morphometry (VBM), which compares neuroanatomical differences on a voxel by voxel basis, Deformation-based morphometry (DBM), which provides information about global differences in brain shape and Tensor-based morphometry (TBM) which provides information about local shape differences. This review will describe the methodology and clinical applications of these techniques.

Brain↗