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.

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, Akerman 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.

Object shape differences reflected by somatosensory cortical activation.

Humans can easily by touch discriminate fine details of the shapes of objects. The computation of representations and the representations of objects differing in shape are, when the differences are not founded in different sensory cues or the objects belong to different categories, assumed to take place in a series of cortical areas, which only show differences at the single-neuron level. How the somatosensory cortex computes shape is unknown, but theoretically it should depend heavily on the curvatures of the object surfaces. We measured regional cerebral blood flow (rCBF) of normal volunteers with positron emission tomography (PET) as an index of neuronal activation. One group discriminated a round set of ellipsoids having a narrow spectrum of curvatures and an oblong set of ellipsoids having a broad spectrum of curvatures. Another group discriminated curvatures. When the rCBF from the conditions round and oblong ellipsoid discrimination was contrasted, part of the cortex lining the postcentral sulcus had significantly higher rCBF when ellipsoids having a broader spectrum of curvatures were discriminated. This cortex was also activated by curvature discrimination. The activation is therefore regarded as crucial for the computation of curvature and in accordance with curvature being a major determinant of object form; this cortex is also crucially active in somatosensory shape perception. A comparison of the activation with cytoarchitectural maps, in the anatomical format of the standard brain for both PET and cytoarchitectural brain images, revealed that this part of the cortex lining the postcentral sulcus is situated caudally from cytoarchitectural area 1 and may involve presumptive area 2 on the posterior bank of the sulcus.

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).

Two different areas within the primary motor cortex of man.

The primary motor area (M1) of mammals has long been considered to be structurally and functionally homogeneous. This area corresponds to Brodmann's cytoarchitectural area 4. A few reports showing that arm and hand are doubly represented in M1 of macaque monkeys and perhaps man, and that each subarea has separate connections from somatosensory areas, have, with a few exceptions, gone largely unnoticed. Here we show that area 4 in man can be subdivided into areas '4 anterior' (4a) and '4 posterior' (4p) on the basis of both quantitative cytoarchitecture and quantitative distributions of transmitter-binding sites. We also show by positron emission tomography that two representations of the fingers exist, one in area 4a and one in area 4p. Roughness discrimination activated area 4p significantly more than a control condition of self-generated movements. We therefore suggest that the primary motor area is subdivided on the basis of anatomy, neurochemistry and function.

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 than can be activated by roughness.

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. © 1994 Wiley-Liss, Inc.