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Keith J. Worsley

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16 papers
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16

YNIMG Journal 2010 Journal Article

General multivariate linear modeling of surface shapes using SurfStat

  • Moo K. Chung
  • Keith J. Worsley
  • Brendon M. Nacewicz
  • Kim M. Dalton
  • Richard J. Davidson

Although there are many imaging studies on traditional ROI-based amygdala volumetry, there are very few studies on modeling amygdala shape variations. This paper presents a unified computational and statistical framework for modeling amygdala shape variations in a clinical population. The weighted spherical harmonic representation is used to parameterize, smooth out, and normalize amygdala surfaces. The representation is subsequently used as an input for multivariate linear models accounting for nuisance covariates such as age and brain size difference using the SurfStat package that completely avoids the complexity of specifying design matrices. The methodology has been applied for quantifying abnormal local amygdala shape variations in 22 high functioning autistic subjects.

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YNIMG Journal 2009 Journal Article

Thalamo–cortical network pathology in idiopathic generalized epilepsy: Insights from MRI-based morphometric correlation analysis

  • Boris C. Bernhardt
  • Daniel A. Rozen
  • Keith J. Worsley
  • Alan C. Evans
  • Neda Bernasconi
  • Andrea Bernasconi

Epileptic activity underlying idiopathic generalized epilepsy (IGE) is related to abnormal thalamo–cortical interactions. Our purpose was to map in vivo the organization of the thalamo–cortical network in IGE. We measured cortical thickness and thalamic volumes on MRI in 23 IGE patients with generalized tonic–clonic seizures only and 46 healthy controls. Significant correlations between thalamic volumes and cortical thickness were interpreted as thalamo–cortical network connections. In controls, thickness of frontal, limbic, and occipital regions was positively correlated with the thalamic volumes, corresponding to known anatomical connections from sacrificial tracer studies in primates and human in vivo DTI data. In patients, thalamo–cortical network correlations increased in fronto-central and parietal regions, but decreased in limbic areas. Group analysis revealed that, compared to controls, IGE patients had bilateral thalamic atrophy and widespread cortical thinning that was most prominent in fronto-central areas, with a prevalence of up to 40%. Duration of epilepsy affected negatively thalamic volumes and thickness of fronto-central and limbic cortices. These effects were significantly different from aging in controls. Patients with poorly controlled seizures showed an even faster progression in these neocortical regions. Fronto-centro-parietal atrophy in IGE is likely the effect of generalized seizure activity inducing thalamo–cortical network remodeling. On the other hand, limbic abnormalities may take place secondary to thalamic disconnection.

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YNIMG Journal 2008 Journal Article

Guidelines for reporting an fMRI study

  • Russell A. Poldrack
  • Paul C. Fletcher
  • Richard N. Henson
  • Keith J. Worsley
  • Matthew Brett
  • Thomas E. Nichols

In this editorial, we outline a set of guidelines for the reporting of methods and results in functional magnetic resonance imaging studies and provide a checklist to assist authors in preparing manuscripts that meet these guidelines.

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YNIMG Journal 2008 Journal Article

Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: Insights on the relation between mesiotemporal connectivity and cortical atrophy

  • Boris C. Bernhardt
  • Keith J. Worsley
  • Pierre Besson
  • Luis Concha
  • Jason P. Lerch
  • Alan C. Evans
  • Neda Bernasconi

Temporal lobe epilepsy (TLE) is considered primarily a limbic disorder. Our purpose was to map limbic network organization in TLE and to statistically relate it to neocortical atrophy. We performed MRI-based cortical thickness analysis in 110 TLE patients (including 68 patients with hippocampal atrophy and 42 patients with normal hippocampal volume) and 46 healthy controls. Limbic connectivity was statistically inferred by correlating mean thickness of the entorhinal cortex (EC) with thickness at each vertex across the entire neocortex. The EC was chosen as seed region since it is the link between the neocortex and the hippocampal formation. Patients showed cortical thinning mainly in temporal and fronto-central neocortices, with a prevalence of atrophy in up to 35%. In controls, EC networks corresponded closely to known anatomical connections. In TLE the pattern of correlations was similar to controls, suggesting that pathological processes in the EC affect the same networks that co-vary with the EC in the healthy brain. Nevertheless, we found decreases in correlations mainly in the temporal lobe and increases mainly in orbitofrontal cortices. Although our analysis indicated alterations in the temporo-limbic network in TLE, there was no association between mesiotemporal connectivity and atrophy across the entire cortical surface. This divergence underlines the complexity of the pathophysiological mechanisms leading to neocortical atrophy in TLE.

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YNIMG Journal 2007 Journal Article

Focal cortical atrophy in multiple sclerosis: Relation to lesion load and disability

  • Arnaud Charil
  • Alain Dagher
  • Jason P. Lerch
  • Alex P. Zijdenbos
  • Keith J. Worsley
  • Alan C. Evans

Multiple sclerosis (MS) is thought to predominantly affect white matter (WM). Recently, however, loss of cortical gray matter has also been described. Little is known about the cause of cortical atrophy in MS, whether it occurs early in the disease course, and whether it affects all cortical regions equally or if there is a preferential pattern of focal cortical atrophy. An automated method was used to compute the thickness at every vertex of the cortical surface of the brains of 425 early relapsing–remitting MS patients. We correlated cortical thickness with the WM lesion load and the Expanded Disability Status Scale score. Mean cortical thickness correlated with WM lesion load and disability. The correlations of cortical thickness with total lesion load and disability were most significant in cingulate gyrus, insula, and associative cortical regions. Conversely, primary sensory, visual, and motor areas showed a less significant relationship. The highest amount of atrophy per lesion volume or disability scale unit was in the anterior cingulate cortex. This study confirms the relation between cortical atrophy, WM lesion load, and disability in MS, and suggests that cortical atrophy occurs even in MS patients with only mild disability. Most interestingly, we show a specific regional pattern of focal atrophy in MS that is distinctively different from the one in normal aging. The predilection of the atrophic process for areas that are heavily inter-connected with other brain regions suggests that interruption of WM tracts by MS plaques contributes, at least in part, to the development of cortical atrophy.

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YNIMG Journal 2004 Journal Article

Nonstationary cluster-size inference with random field and permutation methods

  • Satoru Hayasaka
  • K.Luan Phan
  • Israel Liberzon
  • Keith J. Worsley
  • Thomas E. Nichols

Because of their increased sensitivity to spatially extended signals, cluster-size tests are widely used to detect changes and activations in brain images. However, when images are nonstationary, the cluster-size distribution varies depending on local smoothness. Clusters tend to be large in smooth regions, resulting in increased false positives, while in rough regions, clusters tend to be small, resulting in decreased sensitivity. Worsley et al. proposed a random field theory (RFT) method that adjusts cluster sizes according to local roughness of images [Worsley, K. J. , 2002. Nonstationary FWHM and its effect on statistical inference of fMRI data. Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2–6, 2002, Sendai, Japan. Available on CD-ROM in NeuroImage 16 (2) 779–780; Hum. Brain Mapp. 8 (1999) 98]. In this paper, we implement this method in a permutation test framework, which requires very few assumptions, is known to be exact [J. Cereb. Blood Flow Metab. 16 (1996) 7] and is robust [NeuroImage 20 (2003) 2343]. We compared our method to stationary permutation, stationary RFT, and nonstationary RFT methods. Using simulated data, we found that our permutation test performs well under any setting examined, whereas the nonstationary RFT test performs well only for smooth images under high df. We also found that the stationary RFT test becomes anticonservative under nonstationarity, while both nonstationary RFT and permutation tests remain valid under nonstationarity. On a real PET data set we found that, though the nonstationary tests have reduced sensitivity due to smoothness estimation variability, these tests have better sensitivity for clusters in rough regions compared to stationary cluster-size tests. We include a detailed and consolidated description of Worsley nonstationary RFT cluster-size test.

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YNIMG Journal 2004 Journal Article

Unified univariate and multivariate random field theory

  • Keith J. Worsley
  • Jonathan E. Taylor
  • Francesco Tomaiuolo
  • Jason Lerch

We report new random field theory P values for peaks of canonical correlation SPMs for detecting multiple contrasts in a linear model for multivariate image data. This completes results for all types of univariate and multivariate image data analysis. All other known univariate and multivariate random field theory results are now special cases, so these new results present a true unification of all currently known results. As an illustration, we use these results in a deformation-based morphometry (DBM) analysis to look for regions of the brain where vector deformations of nonmissile trauma patients are related to several verbal memory scores, to detect regions of changes in anatomical effective connectivity between the trauma patients and a group of age- and sex-matched controls, and to look for anatomical connectivity in cortical thickness.

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YNIMG Journal 2003 Journal Article

Deformation-based surface morphometry applied to gray matter deformation

  • Moo K. Chung
  • Keith J. Worsley
  • Steve Robbins
  • Tomáš Paus
  • Jonathan Taylor
  • Jay N. Giedd
  • Judith L. Rapoport
  • Alan C. Evans

We present a unified statistical approach to deformation-based morphometry applied to the cortical surface. The cerebral cortex has the topology of a 2D highly convoluted sheet. As the brain develops over time, the cortical surface area, thickness, curvature, and total gray matter volume change. It is highly likely that such age-related surface changes are not uniform. By measuring how such surface metrics change over time, the regions of the most rapid structural changes can be localized. We avoided using surface flattening, which distorts the inherent geometry of the cortex in our analysis and it is only used in visualization. To increase the signal to noise ratio, diffusion smoothing, which generalizes Gaussian kernel smoothing to an arbitrary curved cortical surface, has been developed and applied to surface data. Afterward, statistical inference on the cortical surface will be performed via random fields theory. As an illustration, we demonstrate how this new surface-based morphometry can be applied in localizing the cortical regions of the gray matter tissue growth and loss in the brain images longitudinally collected in the group of children and adolescents.

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YNIMG Journal 1999 Journal Article

How Many Subjects Constitute a Study?

  • Karl J. Friston
  • Andrew P. Holmes
  • Keith J. Worsley

In fMRI there are two classes of inference: one aims to make a comment about the “typical” characteristics of a population, and the other about “average” characteristics. The first pertains to studies of normal subjects that try to identify some qualitative aspect of normal functional anatomy. The second class necessarily applies to clinical neuroscience studies that want to make an inference about quantitative differences of a regionally specific nature. The first class of inferences is adequately serviced by conjunction analyses and fixed-effects models with relatively small numbers of subjects. The second requires random-effect analyses and larger cohorts.

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YNIMG Journal 1999 Journal Article

Robust Smoothness Estimation in Statistical Parametric Maps Using Standardized Residuals from the General Linear Model

  • Stefan J. Kiebel
  • Jean-Baptiste Poline
  • Karl J. Friston
  • Andrew P. Holmes
  • Keith J. Worsley

The assessment of significant activations in functional imaging using voxel-based methods often relies on results derived from the theory of Gaussian random fields. These results solve the multiple comparison problem and assume that the spatial correlation or smoothness of the data is known or can be estimated. End results (i. e. , P values associated with local maxima, clusters, or sets of clusters) critically depend on this assessment, which should be as exact and as reliable as possible. In some earlier implementations of statistical parametric mapping (SPM) (SPM94, SPM95) the smoothness was assessed on Gaussianized t-fields (Gt-f) that are not generally free of physiological signal. This technique has two limitations. First, the estimation is not stable (the variance of the estimator being far from negligible) and, second, physiological signal in the Gt-f will bias the estimation. In this paper, we describe an estimation method that overcomes these drawbacks. The new approach involves estimating the smoothness of standardized residual fields which approximates the smoothness of the component fields of the associated t-field. Knowing the smoothness of these component fields is important because it allows one to compute corrected P values for statistical fields other than the t-field or the Gt-f (e. g. , the F-map) and eschews bias due to deviation from the null hypothesis. We validate the method on simulated data and demonstrate it using data from a functional MRI study.

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v2026.09.13