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Abstract: Many event-related functional magnetic resonance imaging paradigms performed so far have been designed to study a limited part of the brain with high temporal resolution. However, event-related paradigms can be exploratory, therefore requiring whole brain scans and so repetition times (TR) of several seconds. For these large TR values, the slice acquisition order may have an important effect on the detection of event-related activation. Indeed, when the scanning is interleaved, the temporal delay between the acquisition of two contiguous slices can reach a few seconds. During this time, the subject is likely to move, and the haemodynamic response will vary significantly. In this case, the interpolation applied between contiguous slices for motion correction induces a temporal smoothing between voxels that are spatially close but temporally sampled a few seconds apart. This should modify the frequency structure of the response and may impair the detection of short events. We, therefore tested the effect of three acquisition schemes (sequential, sequential with gap and interleaved. INT) at two repetition times (TR = 3 and 6 s on six and seven subjects, respectively) on activation detection and frequency content in a visual motion event-related paradigm. Unexpectedly, tor large TR (6 s), results were found in favour of the INT acquisition scheme(P <0.05). For smaller TR. no strong bias could be found. Generally, intra-subject variability (across acquisition schemes) is found to be much smaller than inter-subject variability, confirming the importance of multi-subjects analyses. Our study also shows that important physiological information is carried by high frequency components that should not be filtered out.

Left medial temporal lobe epilepsy (MTLE) is associated with verbal memory impairment usually related to hippocampal damage. We used functional MRI (fMRI) to investigate the patterns of functional activity in healthy volunteers and MTLE patients engaged in verbal episodic memory tasks to look for evidence of a reallocation of verbal memory in epileptic patients. fMRI data were collected from seven MTLE patients with left-sided hippocampal sclerosis and 10 healthy right-handed control subjects on a 3T scanner. Subjects were instructed to learn a list of 17 words (encoding) and then to recall them (retrieval) on successive trials. Healthy volunteers and patients both exhibited bilateral activation (right higher than left) of the parahippocampal gyrus during the retrieval. This effect was more marked in the control subjects. In contrast to the control subjects, patients exhibited consistent and extensive left prefrontal activations in all the memory tasks. These findings show that verbal memory tasks did not involve the same functional patterns in patients and healthy volunteers. This may be interpreted as a dysfunctional response due to the epilepsy and left hippocampal sclerosis, and could reflect the early onset and progressive course of the disease.

This paper concerns temporal filtering in fMRI time-series analysis. Whitening serially correlated data is the most efficient approach to parameter estimation. However, if there is a discrepancy between the assumed and the actual correlations, whitening can render the analysis exquisitely sensitive to bias when estimating the standard error of the ensuing parameter estimates. This bias, although not expressed in terms of the estimated responses, has profound effects on any statistic used for inference. The special constraints of fMRI analysis ensure that there will always be a misspecification of the assumed serial correlations. One resolution of this problem is to filter the data to minimize bias, while maintaining a reasonable degree of efficiency. In this paper we present expressions for efficiency (of parameter estimation) and bias (in estimating standard error) in terms of assumed and actual correlation structures in the context of the general linear model. We show that: (i) Whitening strategies can result in profound bias and are therefore probably precluded in parametric fMRI data analyses. (ii) Band-pass filtering, and implicitly smoothing, has an important role in protecting against inferential bias.

We present here a new method for cerebral activation detection over a group of subjects. This method is performed using individual activation maps of any sort. It aims at processing a group analysis while preserving individual information and at overcoming as far as possible limitations of the spatial normalization used to compare different subjects. We designed it such that it provides the individual occurrence of the activations detected at a group level. The localization can then be performed on the individual anatomy of each subject. The analysis starts with a hierarchical multiscale object-based description of each individual map. These descriptions are then compared, rather than comparing the images directly. The analysis is thus performed at an object level instead of voxel by voxel. It is made using a comparison graph, on which a labeling process is performed. The label field on the graph is modeled by a Markov random field, which allows us to introduce high-level rules of interrogation of the data. The process has been evaluated on simulated data and real data from a PET protocol.

This paper presents a general approach to the analysis of functional MRI time-series from one or more subjects. The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing. This extension uses the effective degrees of freedom associated with the error term. The effective degrees of freedom are a simple function of the number of scans and the temporal autocorrelation function. A specific form for the latter can be assumed if the data are smoothed, in time, to accentuate hemodynamic responses with a neural basis. This assumption leads to an expedient implementation of a flexible statistical framework. The importance of this small extension is that, in contradistinction to our previous approach, any parametric statistical analysis can be implemented. We demonstrate this point using a multiple regression analysis that tests for effects of interest (activations due to word generation), while taking explicit account of some obvious confounds. Copyright 1995, 1999 Academic Press

We present an experimental evaluation of a new algorithm for the detection of activated areas in brain functional maps. The new algorithm, named HMSD, is based on a hierarchical multiscale description of the difference image in terms of connected objects. Size and magnitude of each object are simultaneously tested with respect to a bidimensional frequency distribution derived using Monte-Carlo simulations under the null hypothesis. In the present work. HMSD was applied to the analysis of a silent verb generation PET activation protocol conducted in six right-handed subjects. Applied to single-subject data. HMSD reveals activation located in the left inferior frontal gyrus in three subjects (two in the pars opercularis, one in the pars triangularis), and in the pars opercularis of the right inferior frontal gyrus in one case, the latter being combined to a crossed cerebellar activation. Overall, single-case results were consistent with the analyses of stereotactically averaged data. Despite a 2D implentation. HMSD detection performances of averaged data were better than that obtained with the 2D version of statistical parametric mapping (SPM) and comparable to that of the 3D version of SPM. Copyright 1995, 1999 Academic Press

This paper presents a new method for characterizing brain responses in both PET and fMRI data. The aim is to capture the correlations between the scans of an experiment and a set of external predictor variables that are thought to affect the scans, such as type, intensity, or shape of stimulus response. Its main feature is a Canonical Variates Analysis (CVA) of the estimated effects of the predictors from a multivariate linear model (MLM). The advantage of this over current methods is that temporal correlations can be incorporated into the model, making the MLM method suitable for fMRI as well as PET data. Moreover, tests for the presence of any correlation, and inference about the number of canonical variates needed to capture that correlation, can be based on standard multivariate statistics, rather than simulations. When applied to an fMRI data set previously analyzed by another CVA method, the MLM method reveals a pattern of responses that is closer to that detected in an earlier non-CVA analysis.

Parametric study designs can reveal information about the relationship between a study parameter (e.g., word presentation rate) and regional cerebral blood flow (rCBF) in functional imaging. The brain's responses in relation to study parameters might be nonlinear, therefore the (linear) correlation coefficient as often used in the analysis of parametric studies might not be a proper characterization. We present a noninteractive method, which fits nonlinear functions of stimulus or task parameters to rCBF responses, using second-order polynomial expansions. This technique is implemented in the context of the general linear model and statistical parametric mapping. We also consider the usefulness of statistical inferences, based onFfields, about similarities and differences of these nonlinear responses in different groups. This approach is illustrated with a 12-run H215O PET activation study using an auditory paradigm of increasing word presentation rates. A patient who had recovered from severe aphasia and a normal control were studied. We demonstrate the ability of this new technique to identify brain regions where rCBF is closely related to increasing word presentation rate in both subjects without constraining the nature of this relationship and where these nonlinear responses differ.

Boecker, H., A. Dagher, A. O. Ceballos-Baumann, R. E. Passingham, M. Samuel, K. J. Friston, J.-B. Poline, C. Dettmers, B. Conrad, and D. J. Brooks.Role of the human rostral supplementary motor area and the basal ganglia in motor sequence control: investigations with H215O PET. J. Neurophysiol.79: 1070–1080, 1998. The aim of this study was to investigate the functional anatomy of distributed cortical and subcortical motor areas in the human brain that participate in the central control of overlearned complex sequential unimanual finger movements. On the basis of previous research in nonhuman primates, a principal involvement of basal ganglia (medial premotor loops) was predicted for central control of finger sequences performed automatically. In pertinent areas, a correlation of activation levels with the complexity of a motor sequence was hypothesized. H215O positron emission tomography (PET) was used in a group of seven healthy male volunteers [mean age 32.0 ± 10.4 yr] to determine brain regions where levels of regional cerebral blood flow (rCBF) correlated with graded complexity levels of five different key-press sequences. All sequences were overlearned before PET and involved key-presses of fingers II–V of the right hand. Movements of individual fingers were kept constant throughout all five conditions by external pacing at 1-Hz intervals. Positive correlations of rCBF with increasing sequence complexity were identified in the contralateral rostral supplementary motor area (pre-SMA) and the associated pallido-thalamic loop, as well as in right parietal area 7 and ipsilateral primary motor cortex (M1). In contrast, while rCBF in contralateral M1 and and extensive parts of caudal SMA was increased compared with rest during task performance, significant correlated increases of rCBF with sequence complexity were not observed. Inverse correlations of rCBF with increasing sequence complexity were identified in mesial prefrontal-, medial temporal-, and anterior cingulate areas. The findings provide further evidence in humans supporting the notion of a segregation of SMA into functionally distinct subcomponents: although pre-SMA was differentially activated depending on the complexity of a sequence of learned finger movements, such modulation was not detectable in caudal SMA (except the most antero-superior part), implicating a motor executive role. Our observations of complexity-correlated rCBF increases in anterior globus palllidus suggest a specific role for the basal ganglia in the process of sequence facilitation and control. They may act to filter and focus input from motor cortical areas as patterns of action become increasingly complex.

Local cerebral hemodynamics and oxygen metabolism were measured by positron emission tomography (PET) with the oxygen-15 (15O) steady-state method in baboons, immediately before (TO), 1 (T1), and 3–4 (T2) h after permanent middle cerebral artery occlusion (MCAO). At T1, there was a marked fall in both cerebral blood flow (CBF) and the CBF/cerebral blood volume (CBV) ratio in the occluded territory; these changes were sustained at T2, indicating stable reduction in cerebral perfusion pressure and lack of spontaneous reperfusion within this time range. Compared with preocclusion conditions, the oxygen extraction fraction (OEF) in the occluded territory was elevated at both T1 and T2, indicative of a persistent oligemia/ischemia for up to 3 h after MCAO. At T2, however, this OEF increase had lessened, concomitantly with a decline in cerebral metabolic rate of oxygen (CMRO2). This impairment of oxidative metabolism occurred earlier in the deep, compared with the cortical, MCA territories; in the latter, the CMRO2was essentially preserved at T1 and only moderately reduced at T2, possibly suggesting prolonged viability. Finally, no significant changes in CBF or CMRO2were observed in the contralateral MCA territory in this time range after MCAO. Despite methodological limitations (mainly partial volume effects related to PET imaging, which may have resulted in an underestimation of true changes and an overlooking of heterogeneous changes) our study demonstrates the feasibility of the combined PET-MCAO paradigm in baboons; this experimental approach should be valuable in investigating the pathophysiology and therapy of acute stroke.

PET activation studies are performed widely to study human brain function. The question of reproducibility, reliability, and comparability of the results of such experiments has never been addressed on a large scale. Recently, 12 European PET centers performed the same cognitive activation experiment in a European Union funded concerted action. The experiment involved a standardized and validated cross-lingual experimental and control task involving verbal fluency. Each center contributed at least 6 subjects. In total there were 77 subjects and 247 scans in each of the two conditions, giving 494 scans in total. We have analyzed each center's dataset and pooled datasets using statistical parametric mapping. We present results that address the consistency of these analyses, discuss the factors that influence their sensitivity, and comment on a number of related methodological issues. We used a MANOVA to test for center, condition, and centre by condition effects and found a strong condition and center effect and weaker interactions. The main effect determining reproducibility was the overall sensitivity of the experiment, to which the scanner and number of scans contribute in a major way, with a marked advantage for 3D scanners and a large field of view. An important conclusion is that data from different centers can be pooled to improve the reliability of results, which is of particular importance for studies in patients with rare conditions.

Within the framework of statistical mapping, there are up to now only two tests used to assess the regional significance in functional images. One is based on the magnitude of the foci and tends to detect high intensity signals, while the second is based on the spatial extent of regions defined by a simple thresholding of the statistical map, a test that is more sensitive to extended signals. The aim of this paper is to combine the two tests into a single test that is more sensitive to a wider range of signals. This combined test is based on an analytical approximation of the distribution of these two parameters (size and height) and is applied in the context of statistical maps. The risk of error in noise-only 2D or 3D volumes is assessed under a wide range of experimental conditions obtained by varying both the resolution of the map and the threshold at which clusters are defined. In addition, we have investigated this new test on simulated signals, and applied it to an experimental PET dataset. The experimental risk of error is close to the predicted one, and the overall sensitivity increases when analyzing a volume containing different types of signals.

This paper is about detecting activations in statistical parametric maps and considers the relative sensitivity of a nested hierarchy of tests that we have framed in terms of the level of inference (voxel level, cluster level, and set level). These tests are based on the probability of obtainingc, or more, clusters withk, or more, voxels, above a thresholdu. This probability has a reasonably simple form and is derived using distributional approximations from the theory of Gaussian fields. The most important contribution of this work is the notion ofset-level inference.Set-level inference refers to the statistical inference that the number of clusters comprising an observed activation profile is highly unlikely to have occurred by chance. This inference pertains to the set of activations reaching criteria and represents a new way of assigningPvalues to distributed effects. Cluster-level inferences are a special case of set-level inferences, which obtain when the number of clustersc= 1. Similarly voxel-level inferences are special cases of cluster-level inferences that result when the cluster can be very small (i.e.,k=0). Using a theoretical power analysis of distributed activations, we observed that set-level inferences are generally more powerful than cluster-level inferences and that cluster-level inferences are generally more powerful than voxel-level inferences. The price paid for this increased sensitivity is reduced localizing power: Voxel-level tests permit individual voxels to be identified as significant, whereas cluster- and set-level inferences only allow clusters or sets of clusters to be so identified. For all levels of inference the spatial size of the underlying signalf(relative to resolution) determines the most powerful thresholds to adopt. For set-level inferences iffis large (e.g., fMRI) then the optimum extent threshold should be greater than the expected number of voxels for each cluster. Iffis small (e.g., PET) the extent threshold should be small. We envisage that set-level inferences will find a role in making statistical inferences about distributed activations, particularly in fMRI.

Most approaches to detecting changes in functional brain images assume that activations are focal or very localized. However, the brain's response to cognitive of sensorimotor challenge may be spatially or anatomically distributed. In this paper we consider the use-fulness of a test based on the mean sum of squares of statistical parametric maps. The performance of this test is evaluated using simulated and real data and is compared to the γ₂ test, a test of the size of the activated region, and a focal activation test based on the intensity of local maxima. We demonstrate that the mean sum of squares test is more sensitive to nonfocal signals and propose that it could be used to complement approaches that are more sensitive to focal activations. Copyright 1995, 1999 Academic Press

We describe a novel technique for characterizing regional cerebral gray and white matter differences in structural magnetic resonance images by the application of methods derived from functional imaging. The technique involves automatic scalp-editing of images followed by segmentation, smoothing, and spatial normalization to a symmetrical template brain in stereotactic Talairach space. The basic idea is (i) to convert structural magnetic resonance image data into spatially normalized images of gray (or white) matter density, effected by segmenting the images and smoothing, and then (ii) to use Statistical Parametric Mapping to make inferences about the relationship between gray (or white) matter density and symptoms (or other pathophysiological measures) in a regionally specific fashion. Because the whole brain sum of gray (or white) matter indices is treated as a confound, the analysis reduces to a characterization of relative gray (or white) matter density on a voxel by voxel basis. We suggest that this is a powerful approach to voxel-based statistical anatomy. Using the technique, we constructed maps of the regional cerebral gray and white matter density correlates of syndrome scores (distinct psychotic symptoms) in a group of 15 schizophrenic patients. There was a negative correlation between the score for the reality distortion syndrome and regional gray matter density in the left superior temporal lobe (P = 0.01) and regional white matter density in the corpus callosum (P &lt; 0.001). These abnormalities may be associated with functional changes predisposing to auditory hallucinations and delusions. This method permits the detection of structural differences within the entire brain (as opposed to selected regions of interest) and may be of value in the investigation of structural gray and white matter abnormalities in a variety of brain diseases. Copyright 1995, 1999 Academic Press

Le syndrome de Blau (SB) est une affection monogénique à début pédiatrique caractérisée par une triade clinique (atteinte articulaire, oculaire et cutanée), une histologie granulomateuse et des mutations du gène NOD2/CARD15. L’atteinte cutanée de ce syndrome auto-inflammatoire très rare est observée chez environ deux tiers des patients. Cependant, les données sur la présentation clinique initiale des manifestations dermatologiques et leur évolution sont très limitées.

We objectively delineated the extent of abnormalities in central benzodiazepine receptor (cBZR) binding using [11C]flumazenil (FMZ)-PET and high resolution volumetric MRI in 12 patients with mesial temporal lobe epilepsy (mTLE) associated with unilateral hippocampal sclerosis, who underwent presurgical evaluation and subsequent temporal lobe surgery. We compared parametric 3D-images of regional cerebral [11C]FMZ volume of distribution (Vd) obtained from individuals and groups of patients with [11C]FMZ-Vd from a group of 17 healthy controls using statistical parametric mapping (SPM). The [12C]FMZ-Vd was significantly reduced (P < 0.001) in the affected hippocampus in the group of 12 patients with unilateral hippocampal sclerosis. Analysis of individual patients reflected the group finding and there was no significant abnormality of cBZR binding outside the hippocampus in any patient. We conclude that SPM is an useful objective method for detecting changes in regional cerebral [11C]FMZ binding and that in patients with mTLE and unilateral hippocampal sclerosis reduction of cBZR binding is restricted to the area of hippocampal sclerosis with no abnormalities being detected in the temporal neocortex or elsewhere in the neocortex.