Anders Ledberg

Human brain atlas : For high-resolution functional and anatomical mapping

We present the new computerized Human Brain Atlas (HBA) for anatomical and functional mapping studies of the human brain. The HBA is based on many high‐resolution magnetic resonance images of normal subjects and provides continuous updating of the mean shape and position of anatomical structures of the human brain. The structures are transformable by linear and nonlinear global and local transformations applied anywhere in 3‐D pictures to fit the anatomical structures of individual brains, which, by reformatting, are transformed into a high‐resolution standard anatomical format. The power of the HBA to reduce anatomical variations was evaluated on a randomized selection of anatomical landmarks in brains of 27 young normal male volunteers who were different from those on whom the standard brain was selected. The HBA, even when based only on standard brain surface and central structures, reduced interindividual anatomical variance to the level of the variance in structure position between the right and left hemisphere in individual brains.

Estimation of the Probabilities of 3D Clusters in Functional Brain Images

The interpretation of functional brain images is often hampered by the presence of noise. This problem is most commonly solved by using a statistical method and only considering signals that are unlikely to occur by chance. The method used should be specific and sensitive, specific because only true signals are of interest and sensitive because this will enable more information to be extracted from each experiment. Here we present a modification of the cluster analysis proposed by Roland et al. (Human Brain Mapping 1: 3-19, 1993). A covariance model is used to test hypotheses for each voxel. The generated statistical images are searched for the largest clusters. From the same data set noise images are generated. For each of these noise images the autocorrelation function is estimated. These estimates are subsequently used to generate simulated noise images, from which a distribution of cluster sizes is derived. The derived distribution is used to estimate probabilities for the clusters detected in the statistical images generated by testing the hypothesis. This presented method is shown to be specific and is further compared with SPM96 and the nonparametric method of Holmes et al. (J. Cereb. Blood Flow Metab. 16: 7-22, 1996).

Somatosensory Activations of the Parietal Operculum of Man. A PET Study

We tested the hypothesis that somatosensory discrimination of roughness (microgeometry) but not of shape (macrogeometry) would activate the parietal operculum (PO) in man. It was also investigated whether a simple square pulse indentation of the skin on the index finger would activate the PO. Regional cerebral blood flow was measured with [15O]butanol and positron emission tomography in a total of 20 normal volunteers. Ten subjects used their right hand to discriminate objects that differed in roughness and similar smooth objects that differed in length. Ten other subjects pressed a button when they felt a square pulse indentation of the skin on their right index finger in a somatosensory reaction time task. Discrimination of roughness activated one field in the PO contralaterally and two fields ipsilaterally to the stimulated hand. The discrimination of length activated one field in the PO located ipsilaterally to the stimulated hand. The somatosensory reaction time task also activated one contralateral and two ipsilateral fields in the PO, and these fields partially overlapped the activated fields in the roughness discrimination task. Based on the extension of these fields and their overlaps we conclude that there exist at least one part of the contralateral PO and at least two parts of the ipsilateral PO that can be activated by somatosensory stimulation of the right hand. We argue further that the contralateral activated part contains a region that can be activated by roughness.

Robust estimation of the probabilities of 3-D clusters in functional brain images: Application to PET data

Recently, we presented a method (the CS method) for estimating the probability distributions of the sizes of supra threshold clusters in functional brain images [Ledberg A, Åkerman S, Roland PE. 1998 . Estimating the significance of 3D clusters in functional brain images. NeuroImage 8:113–128]. In that method, the significance of the observed test statistic (cluster size) is assessed by comparing it with a sample of the test statistic obtained from simulated statistical images (SSIs). These images are generated to have the same spatial autocorrelation as the observed statistical image (t‐image) would have under the null hypothesis. The CS method relies on the assumptions that the t‐images are stationary and that they can be transformed to have a normal distribution. These assumptions are not always valid, and thus limit the applicability of the method. The purpose of this paper is to present a modification of the previous method, that does not depend on these assumptions. This modified CS method (MCS) uses the residuals in the linear model as a model of a dataset obtained under the null hypothesis. Subsequently, datasets with the same distribution as the residuals are generated, and from these datasets the SSIs are derived. These SSIs are t‐distributed. Thus, a conversion to normal distribution is no longer needed. Furthermore, no assumptions concerning the stationarity of the statistical images are needed. The MCS method is validated on both synthetical images and PET images and is shown to give accurate estimates of the probability distribution of the cluster size statistic. Hum. Brain Mapping 9:143–155, 2000.

A 4D approach to the analysis of functional brain images: application to FMRI data.

This paper presents a new approach to functional magnetic resonance imaging (FMRI) data analysis. The main difference lies in the view of what comprises an observation. Here we treat the data from one scanning session (comprising t volumes, say) as one observation. This is contrary to the conventional way of looking at the data where each session is treated as t different observations. Thus instead of viewing the v voxels comprising the 3D volume of the brain as the variables, we suggest the usage of the vt hypervoxels comprising the 4D volume of the brain‐over‐session as the variables. A linear model is fitted to the 4D volumes originating from different sessions. Parameter estimation and hypothesis testing in this model can be performed with standard techniques. The hypothesis testing generates 4D statistical images (SIs) to which any relevant test statistic can be applied. In this paper we describe two test statistics, one voxel based and one cluster based, that can be used to test a range of hypotheses. There are several benefits in treating the data from each session as one observation, two of which are: (i) the temporal characteristics of the signal can be investigated without an explicit model for the blood oxygenation level dependent (BOLD) contrast response function, and (ii) the observations (sessions) can be assumed to be independent and hence inference on the 4D SI can be made by nonparametric or Monte Carlo methods. The suggested 4D approach is applied to FMRI data and is shown to accurately detect the expected signal. Hum. Brain Mapping 13:185–198, 2001.

Exact multivariate tests for brain imaging data

In positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data sets, the number of variables is larger than the number of observations. This fact makes application of multivariate linear model analysis difficult, except if a reduction of the data matrix dimension is performed prior to the analysis. The reduced data set, however, will in general not be normally distributed and therefore, the usual multivariate tests will not be necessarily applicable. This problem has not been adequately discussed in the literature concerning multivariate linear analysis of brain imaging data. No theoretical foundation has been given to support that the null distributions of the tests are as claimed. Our study addresses this issue by introducing a method of constructing test statistics that follow the same distributions as when the data matrix is normally distributed. The method is based on the invariance of certain tests over a large class of distributions of the data matrix. This implies that the method is very general and can be applied for different reductions of the data matrix. As an illustration we apply a test statistic constructed by the method now presented to test a multivariate hypothesis on a PET data set. The test rejects the null hypothesis of no significant differences in measured brain activity between two conditions. The effect responsible for the rejection of the hypothesis is characterized using canonical variate analysis (CVA) and compared with the result obtained by using univariate regression analysis for each voxel and statistical inference based on size of activations. The results obtained from CVA and the univariate method are similar. Hum. Brain Mapping 16:24–35, 2002.