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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.)

Brain imaging studies on duration perception usually report the activation of a network that includes the frontal and mesiofrontal cortex (supplementary motor area, SMA), parietal cortex, and subcortical areas (basal ganglia, thalamus, and cerebellum). To address the question of the specific involvement of these structures in temporal processing, we contrasted two visual discrimination tasks in which the relevant stimulus dimension was either its intensity or its duration. Eleven adults had to indicate (by pressing one of two keys) whether they thought the duration or the intensity of a light (LED) was equal to (right hand) or different from (left hand) that of a previously presented standard. In a control task, subjects had to press one of the two keys at random. A similar broad network was observed in both the duration-minus-control and intensity-minus-control comparisons. The intensity-minus-duration comparison pointed out activation in areas known to participate in cognitive operations on visual stimuli: right occipital gyrus, fusiform gyri, hippocampus, precuneus, and intraparietal sulcus. In contrast, the duration-minus-intensity comparison indicated activation of a complex network that included the basal ganglia, SMA, ventrolateral prefrontal cortex, inferior parietal cortex, and temporal cortex. These structures form several subnetworks, each possibly in charge of specific time-coding operations in humans. The SMA and basal ganglia may be implicated in the time-keeping mechanism, and the frontal-parietal areas may be involved in the attentional and mnemonic operations required for encoding and retrieving duration information.

Purpose: Childhood autism is a severe developmental disorder that impairs the acquisition of some of the most important skills in human life. Progress in understanding the neural basis of childhood autism requires clear and reliable data indicating specific neuroanatomical or neurophysiological abnormalities. The purpose of the present study was to research localized brain dysfunction in autistic children using functional brain imaging., Patients and Methods: Regional cerebral blood flow (rCBF) was measured with positron emission tomography (PET) in 21 primary autistic children and 10 age-matched non autistic children., Results: A statistical parametric analysis of rCBF images revealed significant bilateral temporal hypoperfusion in the associative auditory cortex (superior temporal gyrus) and in the multimodal cortex (superior temporal sulcus) in the autistic group (p<0.001). In addition, temporal hypoperfusion was detected individually in 77% of autistic children., Conclusion: These findings provide robust evidence of well localized functional abnormalities in autistic children located in the superior temporal lobe. Such localized abnormalities were not detected with the low resolution PET camera,. This study suggests that high resolution PET camera combined with statistical parametric mapping is useful to understand developmental disorders.

A new simple method is proposed to detect, using PET and [(11)C]raclopride, changes in striatal extracellular dopamine concentration during a rewarded effortful task. This approach aimed to increase the sensitivity in detection of these effects. It requires a single-dynamic PET study and combines the classic kinetic compartmental model with the general linear model of SPM to provide statistical inference on changes in [(11)C]raclopride time-activity curve due to endogenous dopamine release during two short periods of activation. Kinetic simulations predicted that 100% dopamine increase during two 5-min periods starting at 30 and 60 min after the injection can be detected. Moreover the effects of dopamine release on the [(11)C]raclopride time-activity-curve are different from those induced by CBF increase. These simulated curves were used to construct the statistical linear model and to test voxel-by-voxel in healthy subjects the hypothesis that dopamine is released in the ventral striatum during periods of unexpected monetary gains, but not during periods of unexpected monetary loss. The experimental results are in line with the expected results although the amplitude of the effects due to dopamine release is moderate. The advantages and the limits of this method as well as the relevance of the results for dopamine involvement in reward processing are discussed.

Objective: Disturbed neuronal interactions may be involved in schizophrenia because it is without clear regional pathology. Aberrant connectivity is further suggested by theoretical formulations and neurochemical and neuroanatomical data. The authors applied to schizophrenia a recently available functional neuroimaging analytic method that permits characterization of cooperative action on the systems level., Method: Thirteen medication-free patients and 13 matched healthy comparison subjects performed a working memory (n-back) task and sensorimotor baseline task during positron emission tomography. "Functional connectivity" patterns, reflecting distributed correlated activity that differed most between groups, were extracted by a canonical variates analysis., Results: More than half the variance was explained by a single pattern showing inferotemporal, (para-)hippocampal, and cerebellar loadings for patients versus dorsolateral prefrontal and anterior cingulate activity for comparison subjects. Expression of this pattern perfectly separated all patient scans from comparison scans, thus showing promise as a trait marker. This result was validated prospectively by successfully classifying unrelated scans from the same patients and data from a new cohort. An additional 19% of variance corresponded to the pattern activated by the working memory task. Expression of this pattern was more variable in patients during working memory but not the control condition, suggesting inability to sustain a task-adequate neural network, consistent with the disconnection hypothesis., Conclusions: Pronounced disruptions of distributed cooperative activity in schizophrenia were found. A pattern showing disturbed frontotemporal interactions showed promise as a trait marker and may be useful for future investigations.

In a previous functional magnetic resonance imaging (fMRI) study, we suggested that in left medial temporal lobe epilepsy (LTLE) poor verbal episodic memory performances were sustained by abnormal neocortical and mesiotemporal activations. In the present study, we attempted to examine the evolution of these abnormal neocortical and mesiotemporal activations over 24 h. We thus observed the fMRI brain regions activated during the 24-h-delayed retrieval of a word list in the same sample of healthy control subjects and LTLE patients. In control subjects, a similar left occipitotemporofrontal network was activated during both immediate and 24-h-delayed retrieval conditions. In addition, the 24-h-delayed retrieval also activated a larger parietal region and the right hippocampus. This distributed neocortical and mesiotemporal network was very poorly activated during the 24-h-delayed retrieval in LTLE patients, suggesting the inability to reactivate areas that are keys to retrieving stored information., (Copyright 2001 Academic Press.)

Visual exploration is organized in sequences of saccadic eye movements that depend on both perceptual and cognitive context. Using functional magnetic resonance imaging, we studied the neural basis of sequential oculomotor behavior and its dependence on different types of memory by analyzing cerebral activity during performance of newly learned and familiar sequences of eye movements. Compared to a resting condition, both types of sequences activated a common fronto-parietal network, including frontal and supplementary eye fields, and several parietal areas. Within this network, newly learned sequences induced stronger activation than familiar sequences, probably reflecting higher attentional demands. In addition, specific regions were recruited for the performance of new sequences, including pre-supplementary eye fields, the precuneus and the caudate nucleus. This indicates that in addition to attentional modulation, novelty of saccadic sequences requires specific cortical resources, probably related to effortful sequence preparation and coordination as well as to spatial working memory. For familiar sequences, recalled from long-term memory, we observed specific right medial temporo-occipital activation in the vicinity of the boundary between the parahippocampal and lingual gyri, as well as an activation site in the parieto-occipital fissure. We conclude that neuronal resources recruited by the gaze system can change with the familiarity of the scanpath to be executed. This study is important to better understand how the brain implements memorized scanpaths for visual exploration and orienting.

Using MRI, we report the observation of a transient decrease of the apparent diffusion coefficient (ADC) of water in the human brain visual cortex during activation by a black and white 8-Hz-flickering checkerboard. The ADC decrease was small (<1%), but significant and reproducible, and closely followed the time course of the activation paradigm. Based on the known sensitivity of diffusion MRI to cell size in tissues and on optical imaging studies that have revealed changes in the shape of neurons and glial cells during activation, the observed ADC findings have been tentatively ascribed to a transient swelling of cortical cells. These preliminary results suggest a new approach to produce images of brain activation with MRI from signals directly associated with neuronal activation, and not through changes in local blood flow.

We used functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) to visualize the cerebral processing of unseen masked words. Within the areas associated with conscious reading, masked words activated left extrastriate, fusiform and precentral areas. Furthermore, masked words reduced the amount of activation evoked by a subsequent conscious presentation of the same word. In the left fusiform gyrus, this repetition suppression phenomenon was independent of whether the prime and target shared the same case, indicating that case-independent information about letter strings was extracted unconsciously. In comparison to an unmasked situation, however, the activation evoked by masked words was drastically reduced and was undetectable in prefrontal and parietal areas, correlating with participants' inability to report the masked words.

The dorsolateral prefrontal cortex (DLPFC) plays a key role in working memory (WM). Yet its precise contribution (the storage, manipulation and/or utilization of information for the forthcoming response) remains to be determined. To test the hypothesis that the DLPFC is more involved in the preparation of actions than in the maintenance of information in short-term memory (STM), we undertook a functional magnetic resonance imaging investigation in normal subjects performing two delayed response tasks (matching and reproduction tasks) in a visuospatial task sequence (presentation, delay, response). In the two tasks, the presentation and delay phases were similar, but the expected response was different: in the matching task, subjects had to indicate whether a visuospatial sequence matched the sequence presented before the delay period; in the reproduction task, subjects had to reproduce the sequence and, therefore, to mentally organize their response during the delay. Using a fMRI paradigm focusing on the delay period, we observed a significant DLPFC activation when subjects were required to mentally prepare a sequential action based on the information stored in STM. When subjects had only to maintain a visuospatial stimulus in STM, no DLPFC activation was found. These results suggest that a parietal-premotor network is sufficient to store visuospatial information in STM whereas the DLPFC is involved when it is necessary to mentally prepare a forthcoming sequential action based on the information stored in STM.

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.

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

Objective: The nature of the underlying brain dysfunction of childhood autism, a life-long severe developmental disorder, is not well understood. Although researchers using functional brain imaging have attempted to contribute to this debate, previous studies have failed to report consistent localized neocortical brain dysfunction. The authors reasoned that early methods may have been insensitive to such dysfunction, which may now be detectable with improved technology., Method: To test this hypothesis, regional cerebral blood flow was measured with positron emission tomography (PET) in 21 children with primary autism and in 10 nonautistic children with idiopathic mental retardation. Autistic and comparison groups were similar in average age and developmental quotients. The authors first searched for focal brain dysfunction in the autistic group by using a voxel-based whole brain analysis and then assessed the sensitivity of the method to detect the abnormality in individual children. An extension study was then performed in an additional group of 12 autistic children., Results: The first autistic group had a highly significant hypoperfusion in both temporal lobes centered in associative auditory and adjacent multimodal cortex, which was detected in 76% of autistic children. Virtually identical results were found in the second autistic group in the extension study., Conclusions: PET and voxel-based image analysis revealed a localized dysfunction of the temporal lobes in school-aged children with idiopathic autism. Further studies will clarify the relationships between these temporal abnormalities and the perceptive, cognitive, and emotional developmental abnormalities characteristic of this disorder.

Neuroimagery findings have shown similar cerebral networks associated with imagination and execution of a movement. On the other hand, neuropsychological studies of parietal-lesioned patients suggest that these networks may be at least partly distinct. In the present study, normal subjects were asked to either imagine or execute auditory-cued hand movements. Compared with rest, imagination and execution showed overlapping networks, including bilateral premotor and parietal areas, basal ganglia and cerebellum. However, direct comparison between the two experimental conditions showed that specific cortico-subcortical areas were more engaged in mental simulation, including bilateral premotor, prefrontal, supplementary motor and left posterior parietal areas, and the caudate nuclei. These results suggest that a specific neuronal substrate is involved in the processing of hand motor representations.

Some models of word comprehension postulate that the processing of words presented in different modalities and languages ultimately converges toward common cerebral systems associated with semantic-level processing and that the localization of these systems may vary with the category of semantic knowledge being accessed. We used functional magnetic resonance imaging to investigate this hypothesis with two categories of words, numerals, and body parts, for which the existence of distinct category-specific areas is debated in neuropsychology. Across two experiments, one with a blocked design and the other with an event-related design, a reproducible set of left-hemispheric parietal and prefrontal areas showed greater activation during the manipulation of topographical knowledge about body parts and a right-hemispheric parietal network during the manipulation of numerical quantities. These results complement the existing neuropsychological and brain-imaging literature by suggesting that within the extensive network of bilateral parietal regions active during both number and body-part processing, a subset shows category-specific responses independent of the language and modality of presentation., (Copyright 2000 Academic Press.)

The functional architecture of human auditory cortex is still poorly understood compared with that of visual cortex, yet anatomical and electrophysiological studies in non-human primates suggest that the auditory cortex also might be functionally specialized, in a model of parallel and hierarchical organization. In particular, spectral changes such as the formant transitions of speech, or spectral motion (SM) by analogy with visual motion, could be processed in specialized cortical regions. In this study, positron emission tomography (PET) was used to identify which auditory cortical region are involved in SM analysis. We found that a bilateral secondary auditory cortical region, located in the caudal-lateral belt of auditory cortex, was more sensitive to auditory stimuli containing spectral changes than to matched stimuli with a stationary spectral profile. This result suggests that analogies between sensory systems could prove useful in the research into the functional organization of the auditory cortex.

Objective: The authors' goal was to investigate brain regions involved in the deficiency of working memory control processes in patients with schizophrenia., Method: Regional cerebral blood flow was measured with positron emission tomography in eight men with stabilized schizophrenia and eight healthy men while they were performing a graded random number generation task. Twelve scans were made for each subject. Covariations between randomness of responses and regional activation were analyzed., Results: The pattern of covariation between randomness of responses and activation in the anterior cingulate and superior parietal regions differed between patients and healthy subjects., Conclusions: These results suggest a cinguloparietal dysfunction underlying the impairment of working memory control processes during a random number generation task in patients with schizophrenia.

Functional magnetic resonance imaging was used to study the cortical bases of 3-D structure perception from visual motion in human. Nine subjects underwent three experiments designed to locate the areas involved in (i) motion processing (random motion versus static dots), (ii) coherent motion processing (expansion/ contraction versus random motion) and (iii) 3-D shape from motion reconstruction (3-D surface oscillating in depth versus random motion). Two control experiments tested the specific influence of speed distribution and surface curvature on the activation results. All stimuli consisted of random dots so that motion parallax was the only cue available for 3-D shape perception. As expected, random motion compared with static dots induced strong activity in areas V1/V2, V5+ and the superior occipital gyrus (SOG; presumptive V3/V3A). V1/V2 and V5+ showed no activity increase when comparing coherent motion (expansion or 3-D surface) with random motion. Conversely, V3/V3A and the dorsal parieto-occipital junction were highlighted in both comparisons and showed gradually increased activity for random motion, coherent motion and a curved surface rotating in depth, which suggests their involvement in the coding of 3-D shape from motion. Also, the ventral aspect of the left occipito-temporal junction was found to be equally responsive to random and coherent motion stimuli, but showed a specific sensitivity to curved 3-D surfaces compared with plane surfaces. As this region is already known to be involved in the coding of static object shape, our results suggest that it might integrate various cues for the perception of 3-D shape.

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.

Although it is largely accepted that visual-mental imagery and perception draw on many of the same neural structures, the existence and nature of neural processing in the primary visual cortex (or area V1) during visual imagery remains controversial. We tested two general hypotheses: The first was that V1 is activated only when images with many details are formed and used, and the second was that V1 is activated whenever images are formed, even if they are not necessarily used to perform a task. We used event-related functional magnetic resonance imaging (ER-fMRI) to detect and characterize the activity in the calcarine sulcus (which contains the primary visual cortex) during single instances of mental imagery. The results revealed reproducible transient activity in this area whenever participants generated or evaluated a mental image. This transient activity was strongly enhanced when participants evaluated characteristics of objects, whether or not details actually needed to be extracted from the image to perform the task. These results show that visual imagery processing commonly involves the earliest stages of the visual system.

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.

In human vision, two features of the same object can be identified concurrently without loss of accuracy. Performance declines, however, when the features belong to different objects in opposite visual fields. We hypothesized that different positron emission tomography activation patterns would reflect these behavioral results. We first delineated an attention network for single discriminations in left or right visual field and then compared this with the activation pattern when subjects divided attention over two features of a single object or over two objects in opposite hemifields. When subjects attended to a single feature, parietal, premotor, and anterior cingulate cortex were activated. These effects were strongest in the right hemisphere and were, remarkably, unaffected by the direction of attention. In contrast, direction of attention affected occipital and frontal activity: right occipital and left lateral frontal activity were higher with attention to the left, whereas right lateral frontal activity was higher with attention to the right. When subjects identified two features of the same object, parietal, premotor, and anterior cingulate activity was enhanced further, predominantly this time in the left hemisphere. Again, there was no direction sensitivity. Direction-sensitive activation of lateral frontal cortex also was increased. Finally, when subjects divided their attention over opposite hemifields, activity in the direction-sensitive occipital and frontal regions fell to a level midway between those seen during exclusively leftward or rightward attention. Thus, the behavioral efficiency with which we attend to multiple features of a single peripheral object is paralleled by enhanced activity in structures generally active during peripheral selective attention as well as in structures that depend on the specific direction of attention, most notably lateral frontal cortex. In addition, in the direction-sensitive regions, dividing attention over hemifields causes a compromise pattern between the extreme levels obtained during unilateral attention.

Coregistration of functional PET and T1-weighted MR images is a necessary step for combining functional information from PET images with anatomical information in MR images. Several coregistration algorithms have been published and are used in functional brain imaging studies. In this paper, we present a comparison and cross validation of the two most widely used coregistration routines (Friston et al., 1995, Hum. Brain Map. 2: 165-189; Woods et al., 1993, J. Comput. Assisted Tomogr: 17: 536-546). Several transformations were applied to high-resolution anatomical MR images to generate simulated PET images so that the exact (rigid body) transformations between each MR image and its associated simulated PET images were known. The estimation error of a coregistration in relation to the known transformation allows a comparison of the performance of different coregistration routines. Under the assumption that the simulated PET images embody the salient features of real PET images with respect to coregistration, this study shows that the routines examined reliably solve the MRI to PET coregistration problem.

In this paper we present a general multivariate approach to the analysis of functional imaging studies. This analysis uses standard multivariate techniques to make statistical inferences about activation effects and to describe the important features of these effects. More specifically, the proposed analysis uses multivariate analysis of covariance (ManCova) with Wilk's lambda to test for specific effects of interest (e.g., differences among activation conditions), and canonical variates analysis (CVA) to characterize differential responses in terms of distributed brain systems. The data are subject to ManCova after transformation using their principal components or eigenimages. After significance of the activation effect has been assessed, underlying changes are described in terms of canonical images. Canonical images are like eigenimages but take explicit account of the effects of error or noise. The generality of this approach is assured by the general linear model used in the ManCova. The design and inferences sought are embodied in the design matrix and can, in principle, accommodate most parametric statistical analyses. This multivariate analysis may provide a statistical approach to PET activation studies that 1) complements univariate approaches like statistical parametric mapping, and 2) may facilitate the extension of existing multivariate techniques, like the scaled subprofile model and eigenimage analysis, to include hypothesis testing and statistical inference., (Copyright (c) 1996 Wiley-Liss, Inc.)

Objective: The smoothness parameter that characterises the spatial dependence of pixel values in functional brain images is usually estimated empirically from the data. Since this parameter is essential for the assessment of significant changes in brain activity, it is important to know (a) the variance of its estimator and (b) how this variability affects the results of the ensuing statistical analysis., Materials and Methods: In this article, we derive an approximate expression for the variance of the smoothness estimator and investigate the effects of this variability on assessing the significance of cerebral activation in statistical parametric maps using a verbal fluency PET activation experiment., Results: Our results suggest that, for p values around 0.05, the variability in the p value (due to smoothness estimation) is approximately 20%., Conclusion: The effect of the assessment of the spatial dependency of the data is far from being negligible, and this suggests a more comprehensive methodology for functional imaging than the one used so far. This work provides a simple tool for taking into account this effect.

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.

The authors propose a new method for the analysis of brain activation images that aims at detecting activated volumes rather than pixels. The method is based on Poisson process modeling, hierarchical description, and multiscale detection (MSD). Its performances have been assessed using both Monte Carlo simulated images and experimental PET brain activation data. As compared to other methods, the MSD approach shows enhanced sensitivity with a controlled overall type I error, and has the ability to provide an estimate of the spatial limits of the detected signals. It is applicable to any kind of difference image for which the spatial autocorrelation function can be approximated by a stationary Gaussian function.

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.