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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.

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.

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.

We introduce MACACC-Mapping Anatomical Correlations Across Cerebral Cortex-to study correlated changes within and across different cortical networks. The principal topic of investigation is whether the thickness of one area of the cortex changes in a statistically correlated fashion with changes in thickness of other cortical regions. We further extend these methods by introducing techniques to test whether different population groupings exhibit significantly varying MACACC patterns. The methods are described in detail and applied to a normal childhood development population (n = 292), and show that association cortices have the highest correlation strengths. Taking Brodmann Area (BA) 44 as a seed region revealed MACACC patterns strikingly similar to tractography maps obtained from diffusion tensor imaging. Furthermore, the MACACC map of BA 44 changed with age, older subjects featuring tighter correlations with BA 44 in the anterior portions of the superior temporal gyri. Lastly, IQ-dependent MACACC differences were investigated, revealing steeper correlations between BA 44 and multiple frontal and parietal regions for the higher IQ group, most significantly (t = 4.0) in the anterior cingulate.

Several authors have suggested allowing for unknown latency of the hemodynamic response by incorporation of hemodynamic derivative terms into the linear model for the statistical analysis of fMRI data. In this paper, we show how to use random field theory to provide a P value for local maxima of two test statistics that have been recently proposed for detecting activation based on this analysis.

Electrophysiological (EEG/MEG) imaging challenges statistics by providing two views of the same spatiotemporal data: topographic and tomographic. Until now, statistical tests for these two situations have developed separately. This work introduces statistical tests for assessing simultaneously the significance of spatiotemporal event-related potential/event-related field (ERP/ERF) components and that of their sources. The test for detecting a component at a given time instant is provided by a Hotelling's T(2) statistic. This statistic is constructed in such a manner to be invariant to any choice of reference and is based upon a generalized version of the average reference transform of the data. As a consequence, the proposed test is a generalization of the well-known Global Field Power statistic. Consideration of tests at all time instants leads to a multiple comparison problem addressed by the use of Random Field Theory (RFT). The Union-Intersection (UI) principle is the basis for testing hypotheses about the topographic and tomographic distributions of such ERP/ERF components. The performance of the method is illustrated with actual EEG recordings obtained from a visual experiment of pattern reversal stimuli.

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.

In multiple sclerosis (MS), the correlation between disability and the volume of white matter lesions on magnetic resonance imaging (MRI) is usually weak. This may be because lesion location also influences the extent and type of functional disability. We applied an automatic lesion-detection algorithm to 452 MRI scans of patients with relapsing-remitting MS to identify the regions preferentially responsible for different types of clinical deficits. Statistical parametric maps were generated by performing voxel-wise linear regressions between lesion probability and different clinical disability scores. There was a clear distinction between lesion locations causing physical and cognitive disability. Lesion likelihood correlated with the Expanded Disability Status Scale (EDSS) in the left internal capsule and in periventricular white matter mostly in the left hemisphere. Pyramidal deficits correlated with only one area in the left internal capsule that was also present in the EDSS correlation. Cognitive dysfunction correlated with lesion location at the grey-white junction of associative, limbic, and prefrontal cortex. Coordination impairment correlated with areas in interhemispheric and pyramidal periventricular white matter tracts, and in the inferior and superior longitudinal fascicles. Bowel and bladder scores correlated with lesions in the medial frontal lobes, cerebellum, insula, dorsal midbrain, and pons, areas known to be involved in the control of micturition. This study demonstrates for the first time a relationship between the site of lesions and the type of disability in large scale MRI data set in MS.

In this paper we present an approach to making inferences about generic activations in groups of subjects using fMRI. In particular we suggest that activations common to all subjects reflect aspects of functional anatomy that may be “typical” of the population from which that group was sampled. These commonalities can be identified by a conjunction analysis of the activation effects in which the contrasts, testing for an activation, are specified separately for each subject. A conjunction is the joint refutation of multiple null hypotheses, in this instance, of no activation in any subject. The motivation behind this use of conjunctions is that fixed-effect analyses are generally more “sensitive” than equivalent random-effect analyses. This is because fixed-effect analyses can harness the large degrees of freedom and small scan-to-scan variability (relative to the variability in responses from subject to subject) when assessing the significance of an estimated response. The price one pays for the apparent sensitivity of fixed-effect analyses is that the ensuing inferences pertain to, and only to, the subjects studied. However, a conjunction analysis, using a fixed-effect model, allows one to infer: (i) that every subject studied activated and (ii) that at least a certain proportion of the population would have shown this effect. The second inference depends upon a meta-analytic formulation in terms of a confidence region for this proportion. This approach retains the sensitivity of fixed-effect analyses when the inference that only a substantial proportion of the population activates is sufficient.

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.

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.

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.

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.

Although there are many imaging studies on traditional ROI-based amygdala volumetry, there are very few studies on modeling amygdala shape variations. This paper present a unified computational and statistical framework for modeling amygdala shape variations in a clinical population. The weighted spherical harmonic representation is used as to parameterize, to smooth out, and to 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 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.

Numerous applications have been reported for the stereotactic mapping of focal changes in cerebral blood flow during sensory and cognitive activation as measured with positron emission tomography (PET) subtraction images. Since these images lack significant anatomical information, analysis of these kinds of data has been restricted to an automated search for peaks in the PET subtraction dataset and localization of the peak coordinates within a standardized stereotactic atlas. This method is designed to identify isolated foci with dimensions smaller than the image resolution. Details of activation patterns that may extend over finite distances, following the underlying anatomical structures, will not be apparent. We describe the combined mapping into stereotactic coordinate space of magnetic resonance imaging (MRI) and PET information from each of a set of subjects such that the major features of the activation pattern, particularly extended tracts of increased blood flow, can be immediately assessed within their true anatomical context as opposed to that presumed using a standard atlas alone. Near areas of high anatomical variability, e.g., central sulcus, or of sharp curvature, e.g., frontal and temporal poles, this information can be essential to the localization of a focus to the correct gyrus or for the rejection of extracerebral peaks. It also allows for the removal from further analysis of data from cognitively-normal subjects with abnormal anatomy such as enlarged ventricles. In patients with neuropathology, e.g., Alzheimer's disease, arteriovenous malformation, stroke, or neoplasm, the use of correlated MRI is mandatory for correct localization of functional activation.

In this technical note, we describe and validate a topological false discovery rate (FDR) procedure for statistical parametric mapping. This procedure is designed to deal with signal that is continuous and has, in principle, unbounded spatial support. We therefore infer on topological features of the signal, such as the existence of local maxima or peaks above some threshold. Using results from random field theory, we assign a p-value to each maximum in an SPM and identify an adaptive threshold that controls false discovery rate, using the Benjamini and Hochberg (BH) procedure (1995). This provides a natural complement to conventional family wise error (FWE) control on local maxima. We use simulations to contrast these procedures; both in terms of their relative number of discoveries and their spatial accuracy (via the distribution of the Euclidian distance between true and discovered activations). We also assessed two other procedures: cluster-wise and voxel-wise FDR procedures. Our results suggest that (a) FDR control of maxima or peaks is more sensitive than FWE control of peaks with minimal cost in terms of false-positives, (b) voxel-wise FDR is substantially less accurate than topological FWE or FDR control. Finally, we present an illustrative application using an fMRI study of visual attention.

Contradictory results from the efforts for detecting evoked neuronal currents have left the feasibility of neuronal current MRI (ncMRI) an open question. Most of the previous ncMRI studies in human subjects are suspect due to their inability to separate or eliminate the hemodynamic effects. In this study, we used a bloodless turtle brain that eliminates hemodynamic effects, to explore the feasibility of detecting visual-evoked ncMRI signals at 9.4T. The turtle brain, with its eyes attached, was dissected from the cranium and placed in artificial cerebral spinal fluid. Light flashes were delivered to the eyes, which produced visual-evoked neuronal activity in the brain. Local field potential (LFP) and MRI signals in the turtle brain were measured in an interleave fashion. Although robust neuronal responses to the visual stimulation were observed in the LFP signals, no significant signal changes synchronized with neuronal currents were found in the MRI images. Analysis of the temporal stability of the MRI time courses indicated that the detectable effect sizes are 0.11% and 0.09° for the magnitude and phase, respectively, and the visual-evoked ncMRI signals in the turtle brain are below these levels.

Previous studies of cortical asymmetry have relied mainly on voxel-based morphometry (VBM), or manual segmentation of regions of interest. This study uses fully automated, surface-based techniques to analyse position and surface area asymmetry for the mid-surfaces of 112 right-handed subjects' cortical hemispheres from a cohort of young adults. Native space measurements of local surface area asymmetry and vertex position asymmetry were calculated from surfaces registered to a previously validated hemisphere-unbiased surface-based template. Our analysis confirms previously identified hemispheric asymmetries (Yakovlevian torque, frontal and occipital petalia) in enhanced detail. It does not support previous findings of gender/asymmetry interactions or rightward planum parietale areal increase. It reveals several new findings, including a striking leftward increase in surface area of the supramarginal gyrus (peak effect 18%), compared with a smaller areal increase in the left Heschl's gyrus and planum temporale region (peak effect 8%). A second finding was rightward increase in surface area (peak effect 10%) in a band around the medial junction between the occipital lobe, and parietal and temporal lobes. By clearly separating out the effects of structural translocation and surface area change from those of thickness and curvature, this study resolves the confound of these variables inherent in VBM studies.

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.

We present a new continuity correction to the P value for local maxima of a statistical parametric map that bridges the gap between small FWHM , when the Bonferroni correction is accurate, and large FWHM , when random field theory is accurate. The new method, based on discrete local maxima, is always an upper bound (like the Bonferroni), but lower and hence more accurate for large FWHM , without increasing false positives. It resulted in P values that were ∼43% lower than the best of Bonferroni or random field theory methods when applied to a typical fMRI data set.

In the statistical analysis of fMRI data, the parameter of primary interest is the effect of a contrast; of secondary interest is its standard error, and of tertiary interest is the standard error of this standard error, or equivalently, the degrees of freedom (df). In a ReML (Restricted Maximum Likelihood) analysis, we show how spatial smoothing of temporal autocorrelations increases the effective df (but not the smoothness of primary or secondary parameter estimates), so that the amount of smoothing can be chosen in advance to achieve a target df, typically 100. This has already been done at the second level of a hierarchical analysis by smoothing the ratio of random to fixed effects variances (Worsley, K.J., Liao, C., Aston, J.A.D., Petre, V., Duncan, G.H., Morales, F., Evans, A.C., 2002. A general statistical analysis for fMRI data. NeuroImage, 15:1-15); we now show how to do it at the first level, by smoothing autocorrelation parameters. The proposed method is extremely fast and it does not require any image processing. It can be used in conjunction with other regularization methods (Gautama, T., Van Hulle, M.M., in press. Optimal spatial regularisation of autocorrelation estimates in fMRI analysis. NeuroImage.) to avoid unnecessary smoothing beyond 100 df. Our results on a typical 6-min, TR = 3, 1.5-T fMRI data set show that 8.5-mm smoothing is needed to achieve 100 df, and this results in roughly a doubling of detected activations.

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.

Our aim was to quantify with functional magnetic resonance imaging (fMRI) changes in brain activity in concussed athletes and compare the results with those of normal control subjects. Regional brain activations associated with a working memory task were obtained from a group of concussed athletes (15 symptomatic, 1 asymptomatic) and eight matched control subjects, using blood oxygen level dependent (BOLD) fMRI. The average percent signal change from baseline to working memory condition in each region of interest was computed. Symptomatic concussed athletes demonstrated task-related activations in some but not all the regions of interest, even when they performed as well as the control subjects. Furthermore, several concussed athletes had additional increases in activity outside the regions of interest, not seen in the control group. Quantitative analysis of BOLD signals within regions of interest revealed that, in general, concussed athletes had different BOLD responses compared to the control subjects. The task-related activation pattern of the one symptom-free athlete was comparable to that of the control group. We also repeated the study in one athlete whose symptoms had resolved. On the first study, when he was still symptomatic, less task-related activations were observed. On follow-up, once his symptoms had disappeared, the task-related activations became comparable to those of the control group. These results demonstrate the potential of fMRI, in conjunction with the working memory task, to identify an underlying pathology in symptomatic concussed individuals with normal structural imaging results.