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Recent studies of functional brain imaging have shown the involvement of the basal ganglia in executive processes such as planning and set‐shifting. However, the specific contributions of the striatum in those processes remain unknown. This study aimed to test the hypothesis that the caudate nucleus is primarily involved in the preparation of a novel action and not in set‐shifting per se. In the present event‐related functional magnetic resonance imaging (fMRI) study, a new task was developed that permitted, for the first time, to distinguish between shifts in classification when the rule is implicitly given by the task from shifts that require cognitive comparison and planning. Significantly increased activity in the caudate nucleus and the putamen was observed only in conditions in which cognitive planning was required to perform a set‐shift, whereas significant activation was seen in the subthalamic nucleus (another region of the basal ganglia) in all shifting conditions whether or not planning was required. We suggest that the caudate nucleus and the putamen are particularly important, respectively, in the planning and the execution of a self‐generated novel action, whereas the subthalamic nucleus may be required when a new motor program is solicited independently of the choice of strategy. Ann Neurol 2006;59:257–264

We compare two common methods for detecting functional connectivity: thresholding correlations and singular value decomposition (SVD). We find that thresholding correlations are better at detecting focal regions of correlated voxels, whereas SVD is better at detecting extensive regions of correlated voxels. We apply these results to resting state networks in an fMRI dataset to look for connectivity in cortical thickness.

The aim of this study was to investigate long-term consequences of severe non-missile traumatic brain injury (nmTBI) in patients without macroscopic focal brain lesions (>1.6 cm3) on regional white-matter density (WMd), and possible correlations with days of coma and memory performances. T1-weighted magnetic-resonance images (MRI) were acquired in 19 nmTBI patients, 3–113 months following the injury, and in 19 control subjects matched for age and gender. In addition, nmTBI patients underwent a battery of standardised memory tests. The MRIs were processed in a fully automatic system using voxel-by-voxel methods. Corpus callosum, fornix, anterior limb of the internal capsule, superior frontal gyrus, para-hippocampal gyrus, optic radiation and chiasma showed significant WMd reduction in nmTBI when compared to control subjects. None of the correlations between days of coma and memory performance scores with nmTBI voxels value that showed WMd reduction reached significance, with the exception of a significant negative correlation between WMd in the mid body of corpus callosum and shortstory delayed recall. We detected reductions in WM density in several brain locations similar to those described in previous post mortem investigations. In addition, we observed WMd reduction in the optic chiasma and in the optic radiations; this finding may reflect transneural degeneration along the visual pathway. The weak correlations between specific anatomical sites of the reduced WMd and behavior may reflect the diffuse nature of the brain damage and/or the different time of onset between behavioral manifestations and neuropathological modifications occurring in nmTBI.

Siegmund and Worsley (1995) considered the problem of testing for signals with unknown location and scale in a Gaussian random field defined on RN. The test statistic was the maximum of a Gaussian random field in an N+1 dimensional ‘scale space’, Ndimensions for location and 1 dimension for the scale of a smoothing filter. Scale space is identical to a continuous wavelet transform with a kernel smoother as the wavelet, though the emphasis here is on signal detection rather than image compression or enhancement. Two methods were used to derive an approximate null distribution for N=2 and N=3: one based on the method of volumes of tubes, the other based on the expected Euler characteristic of the excursion set. The purpose of this paper is two-fold: to show how the latter method can be extended to higher dimensions, and to apply this more general result to ?2fields. The result of Siegmund and Worsley (1995) then follows as a special case. In this paper the results are applied to the problem of searching for activation in brain images obtained by functional magnetic resonance imaging (fMRI).

A methodology for fMRI data analysis confined to the cortex, Cortical Surface Mapping (CSM), is presented. CSM retains the flexibility of the General Linear Model based estimation, but the procedures involved are adapted to operate on the cortical surface, while avoiding to resort to explicit flattening. The methodology is tested by means of simulations and application to a real fMRI protocol. The results are compared with those obtained with a standard, volume‐oriented approach (SPM), and it is shown that CSM leads to local differences in sensitivity, with generally higher sensitivity for CSM in two of the three subjects studied. The discussion provided is focused on the benefits of the introduction of anatomical information in fMRI data analysis, and the relevance of CSM as a step toward this goal. Hum. Brain Mapping 12:79–93, 2001. © 2001 Wiley‐Liss, Inc.

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., Pvalues 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 Pvalues 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.

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.

We present a new approach to studying functional connectivity in the human brain. This approach is based on the observation that when we engage in motor activity, a discharge corollary to the motor command is sent from motor to sensory structures. Thus, as long as movement-related sensory input is either prevented or masked, modulation of neuronal activity in sensory structures would indicate the presence of functional connectivity between the motor and the sensory regions. Using positron emission tomography, such a central interaction between motor and sensory regions can be assessed by measuring regional changes in cerebral blood flow (CBF) in sensory regions. In this paper, we describe the experimental design and the results of two studies of corollary discharges, namely those generated during eye movements and speech. In these studies, a graded approach was used to establish the relationship between the number of eye movements or utterances and CBF in visual or auditory regions, respectively. Significant covariations between the number of movements and CBF in sensory regions were found, thus indicating the presence of functional connectivity between motor and sensory regions. In addition, interregional CBF covariations were computed and the effect of removing the intersubject variance on these covariations was evaluated. The corollary-discharge-based approach to studying functional connectivity is discussed in the context of more traditional computational approaches to network analysis in functional brain imaging.

Abstract: In van der Heijden and de Leeuw (1985) it was proposed to use loglinear analysis to detect interactions in a multiway contingency table, and to explore the form of these interactions with correspondence analysis. After performing the exploratory phase of the analysis, we will show here how the results found in this phase can be used for confirmation.

The first part of this paper gives an overview of a simplified approach to the statistical analysis of PET and fMRI data, including new developments and future directions. The second part outlines a new method, based on multivariate linear models (MLM), for characterising the response in PET and fMRI data, which overcomes some of the drawbacks of current methods such as SSM, SVD, PLS and CVA. Hum. Brain Mapping 5:254–258, 1997. © 1997 Wiley-Liss, Inc.

Functional connectivity between two voxels or regions of voxels can be measured by the correlation between voxel measurements from either PET CBF or BOLD fMRI images in 3D. We propose to look at the entire 6D matrix of correlations between all voxels and search for 6D local maxima. The main result is a new theoretical formula based on random field theory for the p-value of these local maxima, which distinguishes true correlations from background noise. This can be applied to crosscorrelations between two different sets of images—such as activations under two different tasks, as well as autocorrelations within the same set of images. Hum. Brain Mapping 6:364–367, 1998. © 1998 Wiley-Liss, Inc.

The first part of this paper gives an overview of a simplified approach to the statistical analysis of PET and fMRI data, including new developments and future directions. The second part outlines a new method, based on multivariate linear models (MLM), for characterising the response in PET and fMRI data, which overcomes some of the drawbacks of current methods such as SSM, SVD, PLS and CVA. Hum. Brain Mapping 5:254–258, 1997.© 1997 Wiley‐Liss, Inc.

Functional connectivity between two voxels or regions of voxels can be measured by the correlation between voxel measurements from either PET CBF or BOLD fMRI images in 3D. We propose to look at the entire 6D matrix of correlations between all voxels and search for 6D local maxima. The main result is a new theoretical formula based on random field theory for the p‐value of these local maxima, which distinguishes true correlations from background noise. This can be applied to crosscorrelations between two different sets of images—such as activations under two different tasks, as well as autocorrelations within the same set of images. Hum. Brain Mapping 6:364–367, 1998.© 1998 Wiley‐Liss, Inc.