Familial atrial fibrillation mutation M1875T-SCN5A increases early sodium current and dampens the effect of flecainide.

AIMS: Atrial fibrillation (AF) is the most common cardiac arrhythmia. Pathogenic variants in genes encoding ion channels are associated with familial AF. The point mutation M1875T in the SCN5A gene, which encodes the α-subunit of the cardiac sodium channel Nav1.5, has been associated with increased atrial excitability and familial AF in patients. METHODS AND RESULTS: We designed a new murine model carrying the Scn5a-M1875T mutation enabling us to study the effects of the Nav1.5 mutation in detail in vivo and in vitro using patch clamp and microelectrode recording of atrial cardiomyocytes, optical mapping, electrocardiogram, echocardiography, gravimetry, histology, and biochemistry. Atrial cardiomyocytes from newly generated adult Scn5a-M1875T+/- mice showed a selective increase in the early (peak) cardiac sodium current, larger action potential amplitude, and a faster peak upstroke velocity. Conduction slowing caused by the sodium channel blocker flecainide was less pronounced in Scn5a-M1875T+/- compared to wildtype atria. Overt hypertrophy or heart failure in Scn5a-M1875T+/- mice could be excluded. CONCLUSION: The Scn5a-M1875T point mutation causes gain-of-function of the cardiac sodium channel. Our results suggest increased atrial peak sodium current as a potential trigger for increased atrial excitability.

Measurement and visualisation of focal cartilage thickness change by MRI in a study of knee osteoarthritis using a novel image analysis tool.

We describe the application of a novel analysis method that provides detailed maps of changes in cartilage thickness measured from MRI scans for individuals and cohorts of patients together with regional measures. A cohort of osteoarthritis patients was imaged using a 1.0 T MR scanner over a 36-month period. Hyaline cartilage was manually segmented from a three-dimensional (3D) spoiled gradient-echo sequence with fat suppression. Representative outlines of the bone surfaces of the distal femur and proximal tibia were automatically generated from T2 weighted images using statistical models of the shape and appearance of the bones. Cartilage thickness was measured from a dense set of points representing the bony surface. The models of the bones provided a common frame of reference, relative to which change maps were generated and aggregated across the cohort and anatomically corresponding subregions of the joint to be identified. In the reproducibility arm involving six patients, the thickness of cartilage had coefficients of variation of 2.66% within the tibiofemoral joint and 2.94% within the medial femoral condyle region. In the 9 patients (6 female, 3 male) who completed the 36-month study, the most striking observation was that lack of change in global measures of cartilage thickness concealed substantial focal changes. Specifically, the cartilage thickness within the tibiofemoral joint decreased by 0.85% per annum (95% CI -2.13% to 0.45%) with the medial femoral condyle as the region with the most significant change, decreasing by 2.43% per annum (uncorrected 95% CI -4.31% to 0.51%).

Can cartilage loss be detected in knee osteoarthritis (OA) patients with 3-6 months' observation using advanced image analysis of 3T MRI?

PURPOSE: Prior investigations of magnetic resonance imaging (MRI) biomarkers of cartilage loss in knee osteoarthritis (OA) suggest that trials of interventions which affect this biomarker with adequate statistical power would require large clinical studies of 1-2 years duration. We hypothesized that smaller, shorter duration, "Proof of Concept" (PoC) studies might be achievable by: (1) selecting a population at high risk of rapid medial tibio-femoral (TF) progression, in conjunction with; (2) high-field MRI (3T), and; (3) using advanced image analysis. The primary outcome was the cartilage thickness in the central medial femur. METHODS: Multi-centre, non-randomized, observational cohort study at four sites in the US. Eligible participants were females with knee pain, a body mass index (BMI)> or =25 kg/m(2), symptomatic radiographic evidence of medial TF OA, and varus mal-alignment. The 29 participants had a mean age of 62 years, mean BMI of 36 kg/m(2), with eight index knees graded as Kellgren-Lawrence (K&L)=2 and 21 as K&L=3. Eligible participants had four MRI scans of one knee: two MRIs (1 week apart) were acquired as a baseline with follow-up MRI at 3 and 6 months. A trained operator, blind to time-point but not subject, manually segmented the cartilage from the Dual Echo Steady State water excitation MR images. Anatomically corresponding regions of interest were identified on each image by using a three-dimensional statistical shape model of the endosteal bone surface, and the cartilage thickness (with areas denuded of cartilage included as having zero thickness - ThCtAB) within each region was calculated. The percentage change from baseline at 3 and 6 months was assessed using a log-scale analysis of variance (ANOVA) model including baseline as a covariate. The primary outcome was the change in cartilage thickness within the aspect of central medial femoral condyle exposed within the meniscal window (w) during articulation, neglecting cartilage edges [nuclear (n)] (nwcMF x ThCtAB), with changes in other regions considered as secondary endpoints. RESULTS: Anatomical mal-alignment ranged from -1.9 degrees to 6.3 degrees , with mean 0.9 degrees . With one exception, no changes in ThCtAB were detected at the 5% level for any of the regions of interest on the TF joint at 3 or 6 months of follow-up. The change in the primary variable (nwcMF x ThCtAB) from (mean) baseline at 3 months from the log-scale ANOVA model was -2.1% [95% confidence interval (CI) (-4.4%, +0.2%)]. The change over 6 months was 0.0% [95% CI (-2.7%, +2.8%)]. The 95% CI for the change from baseline did not include zero for the cartilage thickness within the meniscal window of the lateral tibia (wLT x ThCtAB) at 6 month follow-up (-1.5%, 95% CI [-2.9, -0.2]), but was not significant at the 5% level after correction for multiple comparisons. CONCLUSIONS: The small inconsistent compartment changes, and the relatively high variabilities in cartilage thickness changes seen over time in this study, provide no additional confidence for a 3- or 6-month PoC study using a patient population selected on the basis of risk for rapid progression with the MRI acquisition and analyses employed.

Haemoglobin level in Scottish intensive care patients--a national one day survey.

UNLABELLED: Objectives were to determine haemoglobin (Hb) levels present in patients and blood ordering habits of clinicians within Scottish Intensive Care Units (ICUs) on one typical day. A questionnaire survey (February 29 2000) was sent to all adult Scottish ICUs. All patients present in the responding adult ICUs in Scotland on the above date were included. MEASUREMENTS AND MAIN RESULTS: Nineteen (73%) of the 26 Scottish Adult Intensive Care Units (ICUs) responded to the questionnaire. Data were received from 78 patients, 8 (10%) received blood. Mean initial Hb was 102 g/l (range 63-138). Modal transfusion trigger haemoglobin was 80 g/l in 38% of subjects at first trigger, 100 g/l in 24% of cases. No intensive care unit allowed haemoglobin to fall below 70 g/l and no patients were transfused when measured Hb was greater than 100 g/l. The presence of ischaemic heart disease was the second most important trigger to transfuse after haemoglobin level. Modal transfusion was 2 units (n = 7). Only one patient received a single unit transfusion. CONCLUSIONS: Scottish ICUs maintain Hb between 70 and 100 g/l but clinicians are currently not consistent when ordering blood. More investigation is required to determine the optimal haemoglobin in our ICU population.

To smooth or not to smooth? Bias and efficiency in fMRI time-series analysis.

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

Variability in fMRI: an examination of intersession differences.

The results from a single functional magnetic resonance imaging session are typically reported as indicative of the subject's functional neuroanatomy. Underlying this interpretation is the implicit assumption that there are no responses specific to that particular session, i.e., that the potential variability of response between sessions is negligible. The present study sought to examine this assumption empirically. A total of 99 sessions, comprising 33 repeats of simple motor, visual, and cognitive paradigms, were collected over a period of 2 months on a single male subject. For each paradigm, the inclusion of session-by-condition interactions explained a significant amount of error variance (P < 0.05 corrected for multiple comparisons) over a model assuming a common activation magnitude across all sessions. However, many of those voxels displaying significant session-by-condition interactions were not seen in a multisession fixed-effects analysis of the same data set; i.e., they were not activated on average across all sessions. Most voxels that were both significantly variable and activated on average across all sessions did not survive a random-effects analysis (modeling between-session variance). We interpret our results as demonstrating that correct inference about subject responses to activation tasks can be derived through the use of a statistical model which accounts for both within- and between-session variance, combined with an appropriately large session sample size. If researchers have access to only a single session from a single subject, erroneous conclusions are a possibility, in that responses specific to this single session may be claimed to be typical responses for this subject.

A study of analysis parameters that influence the sensitivity of event-related fMRI analyses.

To assess the effect of various analysis parameters on the sensitivity of event-related fMRI analyses, 36 analyses were performed on a single fMRI data-set, varying parameters along four axes: (1) resampled voxel size; (2) spatial smoothing; (3) temporal smoothing; and (4) the set of basis functions used to model event-related responses. Sensitivity (i.e., the probability of detecting an activation given it exists) was assessed in terms of Z scores and by a metric for corrected P values, the negative log of the expected Euler characteristic. Sixteen brain regions distributed across cortical and subcortical areas were included in the meta-analysis. Main effects on sensitivity were found for resampled voxel size, spatial smoothing, temporal smoothing, and the set of basis functions chosen. The analysis parameters that generally produced the most sensitive analyses were a 2-mm(3) resampled voxel size, 10-mm spatial smoothing, 4-s temporal smoothing, and a basis set comprising a hemodynamic response function and its temporal derivative.

Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model.

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.

Multisubject fMRI studies and conjunction analyses.

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.

Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models.

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.

Statistical limitations in functional neuroimaging. II. Signal detection and statistical inference.

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.

Plurality and resemblance in fMRI data analysis.

We apply nine analytic methods employed currently in imaging neuroscience to simulated and actual BOLD fMRI signals and compare their performances under each signal type. Starting with baseline time series generated by a resting subject during a null hypothesis study, we compare method performance with embedded focal activity in these series of three different types whose magnitudes and time courses are simple, convolved with spatially varying hemodynamic responses, and highly spatially interactive. We then apply these same nine methods to BOLD fMRI time series from contralateral primary motor cortex and ipsilateral cerebellum collected during a sequential finger opposition study. Paired comparisons of results across methods include a voxel-specific concordance correlation coefficient for reproducibility and a resemblance measure that accommodates spatial autocorrelation of differences in activity surfaces. Receiver-operating characteristic curves show considerable model differences in ranges less than 10% significance level (false positives) and greater than 80% power (true positives). Concordance and resemblance measures reveal significant differences between activity surfaces in both data sets. These measures can assist researchers by identifying groups of models producing similar and dissimilar results, and thereby help to validate, consolidate, and simplify reports of statistical findings. A pluralistic strategy for fMRI data analysis can uncover invariant and highly interactive relationships between local activity foci and serve as a basis for further discovery of organizational principles of the brain. Results also suggest that a pluralistic empirical strategy coupled formally with substantive prior knowledge can help to uncover new brain-behavior relationships that may remain hidden if only a single method is employed.

How many subjects constitute a study?

In fMRI there are two classes of inference: one aims to make a comment about the "typical" characteristics of a population, and the other about "average" characteristics. The first pertains to studies of normal subjects that try to identify some qualitative aspect of normal functional anatomy. The second class necessarily applies to clinical neuroscience studies that want to make an inference about quantitative differences of a regionally specific nature. The first class of inferences is adequately serviced by conjunction analyses and fixed-effects models with relatively small numbers of subjects. The second requires random-effect analyses and larger cohorts.

The effect of slice order and thickness on fMRI activation data using multislice echo-planar imaging.

Multislice echo-planar imaging (EPI) is a commonly used technique for fMRI studies. Brain activation images acquired using fMRI are sensitive to T2* changes, reflecting the level of blood oxygenation (BOLD contrast), and may also contain an element of T1 contrast which detects blood flow changes in large vessels. If slice inflow (T1) effects are significant in multislice EPI, then as the order in which the slices are acquired is changed, differences in the activation maps are predicted. However, in experiments presented here using visual stimulation, the data demonstrate that highly consistent results can be achieved for repetition times (TR) of 6.0, 3.0, and 1.5 s. This suggests that, for whole-brain multislice EPI, fMRI activation is dominated by T2*, BOLD contrast. The thickness of the imaging slice is also an important parameter in these studies, having implications for spatial resolution, sensitivity, and acquisition time. In separate visual cortex experiments the effect on the values of the fMRI Z scores and the number of activated voxels is investigated as a function of slice thickness (from 1 to 8 mm). The maximum Z scores in the data are similar for all slice thicknesses and, after resampling to allow a direct comparison to be made, the volume of visual cortex detected as significantly activated increases with slice thickness.

Characterizing stimulus-response functions using nonlinear regressors in parametric fMRI experiments.

Parametric study designs proved very useful in characterizing the relationship between experimental parameters (e.g., word presentation rate) and regional cerebral blood flow in positron emission tomography studies. In a previous paper we presented a method that fits nonlinear functions of stimulus or task parameters to hemodynamic responses, using second-order polynomial expansions. Here we expand this approach to model nonlinear relationships between BOLD responses and experimental parameters, using fMRI. We present a framework that allows this technique to be implemented in the context of the general linear model employed by statistical parametric mapping (SPM). Statistical inferences, in this instance, are based on F statistics and in this respect we emphasize the use of corrected P values for F fields (i.e., SPM¿F¿). The approach is illustrated with a fMRI study that looked at the effect of increasing auditory word-presentation rate. Our parametric design allowed us to characterize different forms of rate-dependent responses in three critical regions: (i) bilateral frontal regions showed a categorical response to the presence of words irrespective of rate, suggesting a role for this region in establishing cognitive (e.g., attentional) set; (ii) in bilateral occipitotemporal regions activations increased linearly with increasing word rate; and (iii) posterior auditory association cortex exhibited a nonlinear (inverted U) relationship to word rate.

Pallidotomy in Parkinson's disease increases supplementary motor area and prefrontal activation during performance of volitional movements an H2(15)O PET study.

Supplementary motor area and right dorsal prefrontal cortex activation in Parkinson's disease is selectively impaired during volitional limb movements. Since posteroventral pallidotomy improves motor performance in Parkinson's disease patients 'off' medication (i.e. off medication for 9-12 h), we hypothesized that it would also concomitantly increase supplementary motor area and dorsal prefrontal cortex activation. Six Parkinson's disease patients with a median total motor Unified Parkinson's Disease Rating Scale (UPDRS) of 52.5 (range 34-66) 'off' medication underwent unilateral right posteroventral pallidotomy. The patients had H2(15)O PET when 'off' medication before and 3-4 months after surgery. Each PET study comprised four to six measurements of regional cerebral blood flow either at rest or while performing regularly paced joystick movements in freely selected directions (forward, backward, left or right) using the left hand. Pre- and postoperative scans were performed in an identical manner and the associated levels of activation were compared using statistical parametric mapping. After pallidotomy, the median total motor UPDRS score 'off' medication decreased by 34.7% (P = 0.03) and mean response times of joystick movements following the pacing tones improved by 13.8% (P = 0.08). Relative increases in activation of the supplementary motor area and right dorsal prefrontal cortex were observed during joystick movements (P < 0.001). Decreased activation was seen in the region of the right pallidum (P = 0.001). We conclude that pallidotomy reduces pallidal inhibition of thalamocortical circuits and reverses, at least partially, the impairment of supplementary motor area and dorsal prefrontal cortex activation associated with Parkinson's disease.

Reproducibility of PET activation studies: lessons from a multi-center European experiment. EU concerted action on functional imaging.

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

Nonparametric analysis of statistic images from functional mapping experiments.

The analysis of functional mapping experiments in positron emission tomography involves the formation of images displaying the values of a suitable statistic, summarising the evidence in the data for a particular effect at each voxel. These statistic images must then be scrutinised to locate regions showing statistically significant effects. The methods most commonly used are parametric, assuming a particular form of probability distribution for the voxel values in the statistic image. Scientific hypotheses, formulated in terms of parameters describing these distributions, are then tested on the basis of the assumptions. Images of statistics are usually considered as lattice representations of continuous random fields. These are more amenable to statistical analysis. There are various shortcomings associated with these methods of analysis. The many assumptions and approximations involved may not be true. The low numbers of subjects and scans, in typical experiments, lead to noisy statistic images with low degrees of freedom, which are not well approximated by continuous random fields. Thus, the methods are only approximately valid at best and are most suspect in single-subject studies. In contrast to the existing methods, we present a nonparametric approach to significance testing for statistic images from activation studies. Formal assumptions are replaced by a computationally expensive approach. In a simple rest-activation study, if there is really no activation effect, the labelling of the scans as "active" or "rest" is artificial, and a statistic image formed with some other labelling is as likely as the observed one. Thus, considering all possible relabellings, a p value can be computed for any suitable statistic describing the statistic image. Consideration of the maximal statistic leads to a simple nonparametric single-threshold test. This randomisation test relies only on minimal assumptions about the design of the experiment, is (almost) exact, with Type I error (almost) exactly that specified, and hence is always valid. The absence of distributional assumptions permits the consideration of a wide range of test statistics, for instance, "pseudo" t statistic images formed with smoothed variance images. The approach presented extends easily to other paradigms, permitting nonparametric analysis of most functional mapping experiments. When the assumptions of the parametric methods are true, these new nonparametric methods, at worst, provide for their validation. When the assumptions of the parametric methods are dubious, the nonparametric methods provide the only analysis that can be guaranteed valid and exact.

A multivariate analysis of PET activation studies.

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.