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Human brain anatomy is strikingly diverse and highly inheritable: genetic factors may explain up to 80% of its variability. Prior studies have tried to detect genetic variants with a large effect on neuroanatomical diversity, but those currently identified account for <5% of the variance. Here, based on our analyses of neuroimaging and whole-genome genotyping data from 1765 subjects, we show that up to 54% of this heritability is captured by large numbers of single-nucleotide polymorphisms of small-effect spread throughout the genome, especially within genes and close regulatory regions. The genetic bases of neuroanatomical diversity appear to be relatively independent of those of body size (height), but shared with those of verbal intelligence scores. The study of this genomic architecture should help us better understand brain evolution and disease.

Despite the recognition that cortical thickness is heritable and correlates with intellectual ability in children and adolescents, the genes contributing to individual differences in these traits remain unknown. We conducted a large-scale association study in 1583 adolescents to identify genes affecting cortical thickness. Single-nucleotide polymorphisms (SNPs; n=54,837) within genes whose expression changed between stages of growth and differentiation of a human neural stem cell line were selected for association analyses with average cortical thickness. We identified a variant, rs7171755, associating with thinner cortex in the left hemisphere (P=1.12 × 10(-)(7)), particularly in the frontal and temporal lobes. Localized effects of this SNP on cortical thickness differently affected verbal and nonverbal intellectual abilities. The rs7171755 polymorphism acted in cis to affect expression in the human brain of the synaptic cell adhesion glycoprotein-encoding gene NPTN. We also found that cortical thickness and NPTN expression were on average higher in the right hemisphere, suggesting that asymmetric NPTN expression may render the left hemisphere more sensitive to the effects of NPTN mutations, accounting for the lateralized effect of rs7171755 found in our study. Altogether, our findings support a potential role for regional synaptic dysfunctions in forms of intellectual deficits.

The last two decades have seen an unprecedented development of human brain mapping approaches at various spatial and temporal scales. Together, these have provided a large fundus of information on many different aspects of the human brain including micro- and macrostructural segregation, regional specialization of function, connectivity, and temporal dynamics. Atlases are central in order to integrate such diverse information in a topographically meaningful way. It is noteworthy, that the brain mapping field has been developed along several major lines such as structure vs. function, postmortem vs. in vivo, individual features of the brain vs. population-based aspects, or slow vs. fast dynamics. In order to understand human brain organization, however, it seems inevitable that these different lines are integrated and combined into a multimodal human brain model. To this aim, we held a workshop to determine the constraints of a multi-modal human brain model that are needed to enable (i) an integration of different spatial and temporal scales and data modalities into a common reference system, and (ii) efficient data exchange and analysis. As detailed in this report, to arrive at fully interoperable atlases of the human brain will still require much work at the frontiers of data acquisition, analysis, and representation. Among them, the latter may provide the most challenging task, in particular when it comes to representing features of vastly different scales of space, time and abstraction. The potential benefits of such endeavor, however, clearly outweigh the problems, as only such kind of multi-modal human brain atlas may provide a starting point from which the complex relationships between structure, function, and connectivity may be explored., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Although the patterns of structural and metabolic brain alterations in Alzheimer's disease are being refined and discrepancies between them are being underlined, the exact relationships between atrophy and hypometabolism are still unclear. In this study, we aimed to provide a direct comparison between grey matter atrophy and hypometabolism in a sample of patients with clinically probable Alzheimer's disease, using a voxel-based method specially designed to statistically compare the two imaging modalities. Eighteen patients with probable Alzheimer's disease of mild severity and 15 healthy aged controls underwent both high-resolution T1 MRI and resting-state (18)FDG-PET. The MRI data sets were handled using optimized VBM. The PET data were coregistered to their corresponding MRI, corrected voxel-wise for partial volume averaging and spatially normalized using the same parameters as those of their corresponding MRI volume. A differential smoothing was applied on the MRI and PET data sets to equalize their effective smoothness and resolution. For each patient, Z-score maps of atrophy and hypometabolism were created by comparing to the controls data set, respectively averaged to provide the profile of hypometabolism and atrophy, and entered in a voxel-by-voxel SPM analysis to assess the statistical differences between hypometabolism and atrophy. The observed patterns of hypometabolism and atrophy were consistent with previous studies. However, the direct comparison revealed marked regional variability in the relationship between hypometabolism and atrophy. Thus, the hypometabolism significantly exceeded atrophy in most altered structures, particularly in the posterior cingulate-precuneus, orbitofrontal, inferior temporo-parietal, parahippocampal, angular and fusiform areas. In contrast, a few hypometabolic structures among which the hippocampus exhibited similar degrees of atrophy and hypometabolism, a profile that significantly differed from the posterior cingulate. Excessive hypometabolism relative to atrophy suggests the intervention of additional hypometabolism-inducing factors, such as disconnection and amyloid deposition, resulting in genuine functional perturbation ahead of actual atrophy and perhaps of pathology as well. Conversely, in the hippocampus, where disconnection processes are also likely to occur, relative synaptic compensatory mechanisms may be taking place, maintaining neuronal activity in the face of structural alterations.

This paper proposes a method to infer a high level model of the white matter organization from a population of subjects using MR diffusion imaging. This method takes as input for each subject a set of trajectories stemming from any tracking algorithm. Then the inference results from two nested clustering stages. The first clustering converts each individual set of trajectories into a set of bundles supposed to represent large white matter pathways. The second clustering matches these bundles across subjects in order to provide a list of candidates for the bundle model. The method is applied on a population of eleven subjects and leads to the inference of 17 such candidates.

A basic issue in neurosciences is to look for possible relationships between brain architecture and cognitive models. The lack of architectural information in magnetic resonance images, however, has led the neuroimaging community to develop brain mapping strategies based on various coordinate systems without accurate architectural content. Therefore, the relationships between architectural and functional brain organizations are difficult to study when analyzing neuroimaging experiments. This paper advocates that the design of new brain image analysis methods inspired by the structural strategies often used in computer vision may provide better ways to address these relationships. The key point underlying this new framework is the conversion of the raw images into structural representations before analysis. These representations are made up of data-driven elementary features like activated clusters, cortical folds or fiber bundles. Two classes of methods are introduced. Inference of structural models via matching across a set of individuals is described first. This inference problem is illustrated by the group analysis of functional statistical parametric maps (SPMs). Then, the matching of new individual data with a priori known structural models is described, using the recognition of the cortical sulci as a prototypical example.

Do the neural circuits that subserve language acquisition lose plasticity as they become tuned to the maternal language? We tested adult subjects born in Korea and adopted by French families in childhood; they have become fluent in their second language and report no conscious recollection of their native language. In behavioral tests assessing their memory for Korean, we found that they do not perform better than a control group of native French subjects who have never been exposed to Korean. We also used event-related functional magnetic resonance imaging to monitor cortical activations while the Korean adoptees and native French listened to sentences spoken in Korean, French and other, unknown, foreign languages. The adopted subjects did not show any specific activations to Korean stimuli relative to unknown languages. The areas activated more by French stimuli than by foreign stimuli were similar in the Korean adoptees and in the French native subjects, but with relatively larger extents of activation in the latter group. We discuss these data in light of the critical period hypothesis for language acquisition.

We propose a fast, efficient, general, simple, valid, and robust method of estimating and making inference about the delay of the fMRI response modeled as a temporal shift of the hemodynamic response function (HRF). We estimate the shift unbiasedly using two optimally chosen basis functions for a spectrum of time shifted HRFs. This is done at every voxel, to create an image of estimated delays and their standard deviations. This can be used to compare delays for the same stimulus at different voxels, or for different stimuli at the same voxel. Our method is compared to other alternatives and validated on an fMRI data set from an experiment in pain perception.

We test the hypothesis that motivational and cognitive processes are linked by a specific neural system to reach maximal efficiency. We studied six normal subjects performing a working memory paradigm (n-back tasks) associated with different levels of monetary reward during an fMRI session. The study showed specific brain activation in relation with changes in both the cognitive loading and the reward associated with task performance. First, the working memory tasks activated a network including the dorsolateral prefrontal cortex [Brodmann area (BA) 9/46] and, in addition, in the lateral frontopolar areas (BA 10), but only in the more demanding condition (3-back task). This result suggests that lateral prefrontal areas are organized in a caudo-rostral continuum in relation with the increase in executive requirement. Second, reward induces an increased activation in the areas already activated by working memory processing and in a supplementary region, the medial frontal pole (BA 10), regardless of the level of cognitive processing. It is postulated that the latter region plays a specific role in monitoring the reward value of ongoing cognitive processes. Third, we detected areas where the signal decreases (ventral-BA 11/47 and subgenual prefrontal cortices) in relation with both the increase of cognitive demand and the reward. The deactivation may represent an emotional gating aimed at inhibiting adverse emotional signals to maximize the level of performance. Taken together, these results suggest a balance between increasing activity in cortical cognitive areas and decreasing activity in the limbic and paralimbic structures during ongoing higher cognitive processing.

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, for 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.

Few magnetic resonance imaging studies of schizophrenia have investigated brain tissue volumes and their relation to clinical symptoms in patients with an early age at illness onset. The twofold purpose of the study was to investigate both gray and white matter volumes in schizophrenic men with an early age at illness onset, and to determine whether clinical features correlated with tissue volume changes, using an automated voxel-by-voxel image analysis procedure. Twenty male patients with DSM-IV diagnoses of schizophrenia, and an early age at onset (m+/-SD=19+/-2) were compared with 20 age-matched health men. Magnetic resonance (1.5-T) scans were obtained with an Inversion-Recovery prepared fast gradient echo sequence enhancing gray and white matter contrast. Statistical Parametric Mapping was used for image segmentation and comparison. Patients had significant gray matter reductions in medial frontal gyri, left insula, left parahippocampus, and left fusiform gyrus; bilateral white matter reductions in frontal lobes, and increased total cerebrospinal fluid volume were also observed. Negative symptom scores were negatively related to white matter volumes in cingulate regions, and in the right internal capsule. These findings emphasize a pattern of left-hemisphere gray matter abnormalities, and suggest that fronto-paralimbic connectivity may be altered in men with early onset schizophrenia.

Little is currently known about the brain regions involved in central processing of dyspnea. We performed a functional imaging study with positron emission tomography (PET) to assess brain activation associated with an important component of dyspnea, respiratory discomfort during loaded breathing. We induced respiratory discomfort in eight healthy volunteers by adding external resistive loads during inspiration and expiration. Brain activation was characterized by a significant increase in regional cerebral blood flow (rCBF) (Z score of peak activation > 3.09). As compared with the unloaded control condition, high loaded breathing was associated with neural activation in three distinct brain regions, the right anterior insula, the cerebellar vermis, and the medial pons (respective Z scores = 4.75, 4.44, 4.41). For these brain regions, we further identified a positive correlation between rCBF and the perceived intensity of respiratory discomfort (respective Z scores = 4.45, 4.75, 4.74) as well as between rCBF and the mean amplitude of mouth pressure swings (DeltaPm), the index of the main generating mechanism of the sensation (respective Z scores = 4.67, 4.36, 4.31), suggesting a common activation by these two parameters. Furthermore, we identified an area in the right posterior cingulate cortex where neural activation was specifically associated with perceived intensity of respiratory discomfort that is not related to DeltaPm (Z score = 4.25). Our results suggest that respiratory discomfort related to loaded breathing may be subserved by two distinct neural networks, the first being involved in the concomitant processing of the genesis and perception of respiratory discomfort and the second in the modulation of perceived intensity of the sensation by various factors other than its main generating mechanism, which may include emotional processing.

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., (Copyright 2000 Academic Press.)

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., P values 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 P values 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., (Copyright 1999 Academic Press.)

In this note we draw attention to a source of potential ambiguity in functional neuroimaging results when data analysis is based on the resolution of a linear model. This ambiguity arises whenever there exists correlation between the model covariates. A single-subject PET activation experiment helps to illustrate to what extent correlation can affect statistical results interpretation, possibly leading to misinterpretation of part of the activation pattern. This note is intended to clarify this point and to suggest the use of a simple and well-known procedure to deal with these situations. In the Appendix, we suggest a convenient mathematical formulation for statistical tests particularly useful in such cases., (Copyright 1999 Academic Press.)

The field of functional neuroimaging (FNI) methodology has developed into a mature but evolving area of knowledge and its applications have been extensive. A general problem in the analysis of FNI data is finding a signal embedded in noise. This is sometimes called signal detection. Signal detection theory focuses in general on issues relating to the optimization of conditions for separating the signal from noise. When methods from probability theory and mathematical statistics are directly applied in this procedure it is also called statistical inference. In this paper we briefly discuss some aspects of signal detection theory relevant to FNI and, in addition, some common approaches to statistical inference used in FNI. Low-pass filtering in relation to functional-anatomical variability and some effects of filtering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In turn, random field, scale space, non-parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of non-inferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Significant differences in results between different methods are most apparent in extreme parameter ranges, for example at low effective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference.

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., (Copyright 1997 Academic Press.)

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 obtaining c, or more, clusters with k, or more, voxels, above a threshold u. 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 of set-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 assigning P values to distributed effects. Cluster-level inferences are a special case of set-level inferences, which obtain when the number of clusters c = 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 signal f (relative to resolution) determines the most powerful thresholds to adopt. For set-level inferences if f is large (e.g., fMRI) then the optimum extent threshold should be greater than the expected number of voxels for each cluster. If f is 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.

Methods for analysing functional imaging data have evolved rapidly over the last ten years. Standardized techniques based on formal mathematical and statistical theory and rigorous empirical validation have been proposed to facilitate comparisons of biological results between laboratories. This paper examines an image analysis technique that appears to identify unexpectedly large numbers of activated brain areas for the data collected. It concludes that the method may not adequately control for false positives, rendering interpretation of the functional anatomy difficult.

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 on F fields, 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.

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.

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 < 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.

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 usefulness 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 gamma 2 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.

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 auto correlation 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.

Current methods for detecting activation foci in positron emission tomography difference images include a low pass filtering step aimed at improving the signal-to-noise ratio. However, we show that detection sensitivity depends both on the activation signal and the filter sizes. Therefore, we propose to improve current detection methods by using a multifiltering strategy that is shown to be more sensitive when various kinds of signals are present in the brain activation images.

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 (T0), 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 CMRO2 was essentially preserved at T1 and only moderately reduced at T2, possibly suggesting prolonged viability. Finally, no significant changes in CBF or CMRO2 were 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.

We present a new method for the analysis of individual brain positron emission tomography (PET) activation maps that looks for activated areas of a certain size rather than pixels with maximum values. High signal-to-noise-ratio pixel clusters (HSC) are identified and their sizes are statistically tested with respect to a Monte-Carlo-derived distribution of cluster sizes in pure noise images. From multiple HSC size tests, a strategy is proposed for control of the overall type I error. The sensitivity and specificity of this method have been assessed using realistic Monte Carlo simulations of brain activation maps. When compared with the gamma 2 statistic of the local maxima distribution, the proposed method showed enhanced sensitivity, particularly for signals of low magnitude and/or large size. Its potential for the individual analysis of PET activation studies is presented in two sets of subjects who underwent two cognitive protocols. Although it can be viewed as an alternative to the classical stereotactic averaging approach, this new method is intended to be a first step toward the analysis of single-subject PET activation studies.

Fluent readers recognize visual words across changes in case and retinal location, while maintaining a high sensitivity to the arrangement of letters. To evaluate the automaticity and functional anatomy of invariant word recognition, we measured brain activity during subliminal masked priming. By preceding target words with an unrelated prime, a repeated prime, or an anagram made of the same letters, we separated letter-level and whole-word codes. By changing the case and the retinal location of primes and targets, we evaluated the invariance of those codes. Our results indicate that an invariant binding of letters into words is achieved unconsciously through a series of increasingly invariant stages in the left occipitotemporal pathway. [ABSTRACT FROM AUTHOR]