Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition

PE R SP EC TI V ES O N P SY CH O L O G I CA L S CIE N CE Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social 1 Cognition Edward Vul,1 Christine Harris,2 Piotr Winkielman,2 & Harold Pashler2 1 Massachussetts Institute of Technology and 2University of California, San Diego ABSTRACT—Functional magnetic resonance imaging (fMRI) studies of emotion, personality, and social cognition have drawn much attention in recent years, with high-profile studies frequently reporting extremely high (e.g., >.8) correlations between brain activation and personality measures. We show that these correlations are higher than should be expected given the (evidently limited) reliability of both fMRI and personality measures. The high correlations are all the more puzzling because method sections rarely contain much detail about how the correlations were obtained. We surveyed authors of 55 articles that reported findings of this kind to determine a few details on how these correlations were computed. More than half acknowledged using a strategy that computes separate correlations for individual voxels and reports means of only those voxels exceeding chosen thresholds. We show how this nonindependent analysis inflates correlations while yielding reassuring-looking scattergrams. This analysis technique was used to obtain the vast majority of the implausibly high correlations in our survey sample. In addition, we argue that, in some cases, other analysis problems likely created entirely spurious correlations. We outline how the data from these studies could be reanalyzed with unbiased methods to provide accurate estimates of the correlations in question and urge authors to perform such reanalyses. The underlying problems described here appear to be common in fMRI research of many kinds—not just in studies of emotion, personality, and social cognition. 1 This article was formerly known as ‘‘Voodoo Correlations in Social Neuroscience.’’ Address correspondence to Harold Pashler, Department of Psychology 0109, University of California, San Diego, La Jolla, CA 92093; e-mail: hpashler@ucsd.edu. 274 Functional magnetic resonance imaging (fMRI) studies of emotion, personality, and social cognition scarcely existed 10 years ago, and yet the field has already achieved a remarkable level of attention and prominence. Within the space of a few years, it has spawned several new journals (Social Neuroscience, Social Cognitive and Affective Neuroscience) and is the focus of substantial new funding initiatives (National Institute of Mental Health, 2007) while receiving lavish attention from the popular press (Hurley, 2008) and the trade press of the psychological research community (e.g., Fiske, 2003). Perhaps even more impressive, however, is the number of papers from this area that have appeared in such prominent journals as Science, Nature, and Nature Neuroscience. Although the questions and methods used in such research are quite diverse, a substantial number of widely cited papers in this field have reported a specific type of empirical finding that appears to bridge the divide between mind and brain: extremely high correlations between measures of individual differences relating to personality, emotion, and social cognition and measures of brain activity obtained using fMRI. We focus on these studies2 here because this was the area where these correlations came to our attention; we have no basis for concluding that the problems discussed here are necessarily any worse in this area than in some other areas. The following are a few examples of many studies that will be discussed in this article: 2 Studies of the neural substrates of emotion, personality, and social cognition rely on many methods besides fMRI and positron emission tomography, including electroencephalography and magnetoencephalography, animal research (e.g., cross-species comparisons), and neuroendocrine and neuroimmunological investigations (Harmon-Jones & Winkielman, 2007). Copyright r 2009 Association for Psychological Science Volume 4—Number 3 Edward Vul, Christine Harris, Piotr Winkielman, and Harold Pashler 1. Eisenberger, Lieberman, and Williams (2003), writing in Science, described a game they created to expose individuals to social rejection in the laboratory. The authors measured the brain activity in 13 individuals while the actual rejection took place, and they later obtained a self-report measure of how much distress the subject had experienced. Distress was correlated at r 5 .88 with activity in the anterior cingulate cortex (ACC). 2. In another Science article, Singer et al. (2004) found that the magnitude of differential activation within the ACC and left insula induced by an empathy-related manipulation displayed a correlation between .52 and .72 with two scales of emotional empathy (the Empathic Concern Scale of Davis and the Balanced Emotional Empathy Scale of Mehrabian). 3. Writing in NeuroImage, Sander et al. (2005) reported that a subject’s proneness to anxiety reactions (as measured by an index of the Behavioral Inhibition System), correlated at r 5 .96 with the difference in activation of the right cuneus between attended and ignored angry speech. In this article, we will discuss many studies reporting similar sorts of correlations. The work that led to the present article began when we became puzzled about how such impressively high correlations could arise. We describe our efforts to resolve this puzzlement, and the conclusions that our inquiries have led us to. Why should it be puzzling to find high correlations between brain activity and social and emotional measures? After all, if new techniques are providing a deeper window on the link between brain and behavior, does it not make sense that researchers should be able to find the neural substrates of individual traits and thus potentially reveal stronger relationships than have often been found in purely behavioral studies? The problem is this: It is a statistical fact (first noted by researchers in the field of classical psychometric test theory) that the strength of the correlation observed between Measures A and B (rObservedA,ObservedB) reflects not only the strength of the relationship between the traits underlying A and B (rA,B), but also the reliability of the measures of A and B (reliabilityA and reliabilityB, respectively). In general, p rObservedA ; ObservedB ¼ rA;B " ðreliabilityA " reliabilityBÞ Thus, the reliabilities of two measures provide an upper bound on the possible correlation that can be observed between the two measures (Nunnally, 1970).3 3 This is the case because the correlation coefficient is defined as the ratio between the covariance of two measures and the product of their standard s deviations: rx;y ¼ sxxysy . Real-world measurements will be corrupted by (independent) noise, thus the standard deviations of the measured distributions will be increased by the additional noise (with a magnitude assessed by the measure’s reliability). This will make the measured correlation lower than the true underlying correlation by a factor equal to the geometric mean of reliabilities. Volume 4—Number 3 RELIABILITY ESTIMATES So what are the reliabilities of fMRI and personality and emotional measures likely to be?4 The reliability of personality and emotional scales varies between measures and according to the number of items used in a particular assessment. However, test– retest reliabilities as high as .8 seem to be relatively uncommon and are usually found only with large and highly refined scales. Viswesvaran and Ones (2000) surveyed many studies on the reliability of the Big Five factors of personality, and they concluded that the different scales have reliabilities ranging from .73 to .78. Hobbs and Fowler (1974) carefully assessed the reliability of the subscales of the MMPI and found numbers ranging between .66 and .94, with an average of .84. In general, a range of .7 to .8 would seem to be a somewhat optimistic estimate for the smaller and more ad hoc scales used in much of the research described below, which could well have substantially lower reliabilities. Less is known about the reliability of blood oxygenation level dependent (BOLD) signal measures in fMRI, but some relevant studies have recently been performed.5 Kong et al. (2006) had subjects engage in six sessions of a finger-tapping task while recording brain activation. They found test–retest correlations of the change in BOLD signal ranging between 0 and .76 for the set of areas that showed significant activity in all sessions.6 Manoach et al. (2001, their Fig. 1, p. 956) scanned subjects in two sessions of performance with the Sternberg memory scanning task and found reliabilities ranging between .23 to .93, with an average of .60. Aron, Gluck, and Poldrack (2006) had people perform a classification learning task on two separate occasions widely separated in time and found voxel-level reliabilities with modal values (see their Fig. 5, p. 1005) a little below .8.7 Johnstone et al. (2005, p. 1118) examined the stability of amygdala BOLD response to presentations of fearful faces in multiple sessions: Intraclass correlations for the left and right amygdale regions of interest were in the range of .4 to .7 for the two sessions (which were separated by 2 weeks). Thus, from the literature that does exist, it would seem reasonable to suppose that fMRI measures computed at the voxel level will not often have reliabilities greater than about .7. 4 We consider test–retest reliabilities here (rather than interitem, or split–half reliability) because, for the most part, the studies we discuss gathered behavioral measure at different points in time than the fMRI data. In any case, internal reliability measures, like coefficient alpha, do not generally appear to be much higher in this domain. 5 We focus here on studies that look at the reliability of BOLD signal activation measures rather than the reliability of patterns of voxels exceeding specific thresholds, which tend to be substantially lower (e.g., Stark et al., 2004). 6 It seems likely that restricting the reliability analysis to regions consistently active in all sessions would tend to overestimate the reliability of BOLD signal in general. 7 They found somewhat higher reliabilities for voxels within a frontostriatal system that they believed was most specifically involved in carrying out the probabilistic classification learning. 275 High Correlations in fMRI Studies META-ANALYSIS METHODS We turned to the original articles to find out how common these remarkable correlations are and what analyses might yield them. Unfortunately, after a brief review of several articles, it became apparent that the analyses used varied greatly from one investigator to the next and that the exact methods were simply not made clear in the typically brief and sometimes opaque method sections. To probe the issue further, we conducted a survey of the investigators. We first attempted to pull together a large sample of the literature reporting correlations between evoked BOLD signal activity and behavioral measures of individual differences in personality, emotionality, social cognition, and related domains. We then e-mailed the authors of the articles we identified a brief survey to determine how the reported correlation values were computed. Frequency (number of significant correlations reported) 35 30 25 20 15 10 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Absolute correlation value 1 Fig. 1. A histogram of the correlations between evoked blood oxygenation level dependent response and behavioral measures of individual differences seen in the studies identified for analysis in the current article. THE PUZZLE This, then, is the puzzle. Measures of personality and emotion evidently do not often have reliabilities greater than .8. Neuroimaging measures seem typically to be reliable at .7 or less. If we assume that a neuroimaging study is performed in a case where the underlying correlation between activation in the brain area and the individual difference measure (i.e., the correlation that would be observed if there were no measurement error) is perfect,8 p then the highest expected correlation would be (.8 " .7), or .74. Surprisingly, correlations exceeding this upper bound are often reported in recent fMRI studies on emotion, personality, and social cognition. 8 There are several reasons why a true correlation of 1.0 seems highly unrealistic. First, it is far-fetched to suppose that only one brain area influences any behavioral trait. Second, even if the neural underpinnings of a trait were confined to one particular region, it would seem to require an extraordinarily favorable set of coincidences for the BOLD signal (basically a blood flow measure) assessed in one particular stimulus or task contrast to capture all functions relevant to the behavioral trait, which, after all, reflects the organization of complex neural circuitry residing in that brain area. 276 Literature Review Our literature review was conducted using the keyword fMRI (and variants) in conjunction with a list of social terms (e.g., jealousy, altruism, personality, grief ). Within the articles retrieved by these searches, we selected all the articles that reported across-subject correlations between a trait measure and evoked BOLD signal activity. This resulted in 55 articles, with 274 significant correlations between BOLD signal and a trait measure. It should be emphasized that we do not suppose this literature review to be exhaustive. Undoubtedly, we missed some articles reporting these kinds of numbers, but our sample seems likely to be quite representative and perhaps slanted toward articles that appeared in higher impact journals. A histogram of these significant correlations is displayed in Figure 1. One can see that correlations in excess of .74 are plentiful indeed. Next, we ask, ‘‘Where do these numbers come from?’’ Before doing so, we have to provide a bit of background for readers unfamiliar with methods in this area. Elements of fMRI Analysis For those not familiar with fMRI analysis, the essential steps in just about any neuroimaging study can be described rather simply (those familiar with the techniques may wish to skip this section). The output of an fMRI experiment typically consists of two types of ‘‘3D pictures’’ (image volumes): anatomical scans (a high-resolution scan that shows anatomical structure, not function) and functional scans. Functional image volumes are lower resolution scans showing measurements reflecting, among other things, the amount of deoxygenated hemoglobin in the blood (the BOLD signal). A functional image volume is composed of many measurements of the BOLD signal in small, roughly cube-shaped regions called voxels (‘‘volumetric pixels’’). The number of voxels in the whole image volume depends Volume 4—Number 3 Edward Vul, Christine Harris, Piotr Winkielman, and Harold Pashler on the scanner settings, but it typically ranges between 10 " 64 " 64 and 30 " 128 " 128 voxels. Thus, each functional image contains somewhere between 40,000 and 500,000 voxels, with each of these voxels covering between 1 mm3 (1 mm " 1 mm " 1 mm) to 125 mm3 (5 mm " 5 mm " 5 mm) of brain tissue (except for voxels outside of the brain). A new functional image volume is usually acquired every 2 or 3 s (which is referred to as the repetition time or TR) during a scan, so one ends up with a time series of these functional images. These data are typically preprocessed to reduce noise and to allow comparisons between different brains. The preprocessing usually includes smoothing (weighted averaging of each voxel with its neighbors where the weighting is provided by some function that falls off with distance, typically a Gaussian function). The studies we focus on here ultimately compute correlations across subjects: In this kind of study, the voxels are usually mapped onto an average brain (although not always; e.g., Yovel & Kanwisher, 2005). A number of average-brain models exist, the most famous being Talairach (Talairach & Tournoux, 1988) and Montreal Neurological Institute (Evans et al., 1993), but some investigators compute an average brain model for their particular subjects and normalize their functional image scans onto that model. Following preprocessing, some measure of the activation in a given voxel needs to be derived to assess if it is related to what the person is doing, seeing, or feeling. The simplest procedure is just to extract the average activation in the voxel while the person does a task. However, because any task will engage most of the brain (the visual cortex is needed to see the stimulus, the motor cortex is needed to produce a response, etc.), fMRI researchers typically focus not on the activation in particular voxels during one task, but rather on a contrast between the activation arising when the person performs one task versus the activation arising when they do another. This is usually measured as follows: While functional images are being acquired, the subject does a mixed sequence of two different tasks (A, B, B, A, A, B, A, and so forth, where A might be reading words and B might be looking at nonlinguistic patterns). Thus, the experimenter ends up with two different time series to compare: the sequence of tasks the person performed and the sequence of activation levels measured separately at each voxel. A regression analysis can now be performed to ask, ‘‘Is this voxel’s activity different when the subject was performing Task A than it was when the subject performed Task B?’’ These basic steps common to most fMRI data analyses yield matrices consisting of tens or hundreds of thousands of numbers indicating activation levels in different voxels. These can be (and indeed generally are) displayed as images. However, to obtain quantitative summaries of these results and do further statistics on them (such as correlating them with behavioral measures—the topic of the present article), an investigator must somehow select a subset of voxels and aggregate measurements across them. This can be done in various ways. A subset of voxels in the whole brain image may be selected based on purely an- Volume 4—Number 3 atomical constraints (e.g., all voxels in a region generally agreed to represent the amygdala, or all voxels within a certain radius of some a priori specified brain coordinates). Alternatively, regions can be selected based on ‘‘functional constraints,’’ meaning that voxels are selected based on their activity pattern in functional scans. For example, one could select all the voxels for a particular subject that responded more to reading than to nonlinguistic stimuli. Finally, voxels could be chosen based on some combination of anatomy and functional response. In the articles we are focusing on here, the final result, as we have seen, was always a correlation value—a correlation between each person’s score on some behavioral measure and some summary statistic of their brain activation. The latter summary statistic reflects the activation or activation contrast within a certain set of voxels. In either case, the critical question is, ‘‘How was this set of voxels selected?’’ As we have seen, voxels may be selected based on anatomical criteria, functional criteria, or both. Within these broad options, there are a number of additional more fine-grained choices. It is hardly surprising, then, that brief method sections rarely suffice to describe how the analyses were done in adequate detail to really understand what choices were being made. Survey Methods To learn more than the Method sections of these articles disclosed about the analyses that yielded these correlations, we e-mailed the corresponding authors of these articles. The exact wording of our questions is included in Appendix A, but we often needed to send customized follow-up questions to figure out the exact details when the survey questions were misunderstood or when we had trouble reconciling authors’ responses with what they had written in their methods section. In our survey, we first inquired whether the fMRI signal measure that was correlated across subjects with a behavioral measure represented the average of some number of voxels or the activity from just one voxel that was deemed most informative (referred to as the peak voxel). If it was the average of some number of voxels, we asked whether the voxels were selected on the basis of anatomy, or the activation seen in those voxels, or both. If activation was used to select voxels, or if one voxel was determined to be most informative based on its activation, we asked what measure of activation was used. Was it the difference in activation between two task conditions computed on individual subjects, or was it a measure of how this task contrast correlated with the individual difference measure? Finally, if functional data were used to select the voxels, we asked if the same functional data were used to compute the reported correlation. 277 High Correlations in fMRI Studies Response No Response Mean of a Subset of Voxels One Peak Voxel Individual Subject Contrast Regression Across Subjects Anatomical Only Functional Only Functional and Anatomical Same Data Different Data Something Else Fig. 2. The results of our survey of individual-difference correlation methods between fMRI signals and measures of emotion, personality, and social cognition. Of the 55 articles surveyed, the authors of 53 provided responses. Of those, 23 reported a correlation between behavior and one peak voxel, and 30 reported the mean of a number of voxels. For those that reported the mean of a subset of voxels, 7 defined this subset purely anatomically, 11 used only functional constraints, and 12 used anatomical and functional constraints. Of the 45 studies that used functional constraints to choose voxels (either for averaging or for finding the peak voxel), 10 said they used functional measures defined within a given subject, 28 used the across-subject correlation to find voxels, and 7 did something else. All of the studies using functional constraints used the same data to select voxels and measure the correlation. Notably, 53% of the surveyed studies selected voxels based on a correlation with the behavioral individual-differences measure and then used those same data to compute a correlation within that subset of voxels. Survey Participants Of the 55 articles we found in our review, we received methodological details from 53, and 2 did not respond to repeated requests. SURVEY RESULTS We display the raw results from our survey as the proportion of studies that investigators described with a particular answer to each question (see Fig. 2). As some questions only applied to a subset of participants, we display only the proportion of the relevant subset of studies. The raw answers to our survey do not by themselves explain how respondents arrived at the (implausibly high, or so we have argued) correlations. The key, we believe, lies in the 53% of 278 respondents who said that ‘‘regression across subjects’’ was the functional constraint used to select voxels, indicating that voxels were selected because they correlated highly with the behavioral measure of interest.9 Figure 3 shows very concretely the sequence of steps that these respondents reported following when analyzing their data. A separate correlation across subjects was performed for each voxel within a specified brain region. Each correlation relates some measure of brain activity in that voxel (which might be a difference between responses in two tasks or in two conditions) with the behavioral measure for that individual. Thus, the 9 It is important to note that all of these studies also reported using the same data to compute the correlation that they initially used to select the subset of voxels. Volume 4—Number 3 Edward Vul, Christine Harris, Piotr Winkielman, and Harold Pashler a Subject Behavioral measure Voxel 1 Voxel 2 Voxel 3 Voxel n 1 2 –3 1 0.1 –0.4 0.2 0.5 0.3 0.8 –0.7 0.3 3 5 1 0.1 –0.1 –0.2 K B(K) V2(K) V3(K) Vn(K) b V1(K) Rb,V1 Rb,V2 Rb,V3 Rb,Vn c Frequency d Rb,V* Rb,Vx Fig. 3. An illustration of the analysis employed by 53% of the articles surveyed. A: From each subject, the researchers obtain a behavioral measure as well as blood oxygenation level dependent (BOLD) signal measures from many voxels. B: The activity in each voxel is correlated with the behavioral measure of interest across subjects. C: From this set of correlations, researchers select those voxels that pass a statistical threshold. D: Researchers aggregate the fMRI signal across the selected voxels to derive a final measure of the correlation of BOLD signal and the behavioral measure. number of correlations computed was equal to the number of voxels, meaning that thousands of correlations were computed in many cases. At the next stage, researchers selected the set of voxels for which this correlation exceeded a certain threshold, and reported the correlation within this set of voxels. What are the implications of selecting voxels in this fashion? Such an analysis will inflate observed across-subject correlations and can even produce significant measures out of pure noise. The problem is illustrated in the simple simulation displayed in Figure 4. First, the investigator computes a separate correlation of the behavioral measure of interest with each of the voxels (Fig. 4a). Then, he or she selects those voxels that exhibited a sufficiently high correlation (by passing a statistical threshold; Fig. 4b). Finally, an ostensible measure of the ‘‘true’’ correlation is aggregated from the voxels that showed high correlations (e.g., by taking the mean of the voxels over the threshold). With enough voxels, such a biased analysis is guaranteed to produce high correlations even if none are truly present (Fig. 4). Moreover, this analysis will produce visually pleasing scattergrams (e.g., Fig. 4c) that will provide (quite meaningless) reassurance to the viewer that s/he is looking at a result that is solid, is ‘‘not driven by outliers,’’ and so on. Volume 4—Number 3 THE NONINDEPENDENCE ERROR The fault seen in glaring form in Figure 4 will be referred to henceforth as the nonindependence error. This approach amounts to selecting one or more voxels based on a functional analysis and then reporting the results of the same analysis and functional data from just the selected voxels. This analysis distorts the results by selecting noise that exhibits the effect being searched for, and any measures obtained from such a nonindependent analysis are biased and untrustworthy (for a formal discussion, see Vul & Kanwisher, in press). It may be easier to appreciate the gravity of the nonindependence error by transposing it outside of neuroimaging. We have identified a weather station whose temperature readings predict daily changes in the value of a specific set of stocks with a correlation of r 5 %0.87. For $50, we will provide the list of stocks to any interested reader. That way, you can buy the stocks every morning when the weather station posts a drop in temperature and sell when the temperature goes up. Obviously, your potential profits here are enormous. But you may wonder, ‘‘How did they find this correlation?’’ We arrived at %0.87 by separately computing the correlation between the readings of the weather station in Adak Island, AK, with each of the 3,315 279 High Correlations in fMRI Studies –1 c 0 Correlation (Rb,Vx) 1 Frequency b –1 0 Correlation (Rb,V*) 1 Mean Voxel Activity Frequency a 2 0 –2 –2 0 Behavioral Measure 2 Fig. 4. A simulation of a nonindependent analysis on pure noise data. We simulated 1,000 experiments, each with 10 subjects, 10,000 voxels, and one individual difference measure. Each subject’s voxel activity and behavioral measure were independent zero-mean Gaussian noise. Thus, the true distribution of correlations between the behavioral measure and simulated voxel activity is around 0, with random fluctuations resulting in a distribution that spans the range of possible correlations (Panel A). When a subset of voxels are selected for passing a statistical threshold (a positive correlation with p < .01), the observed correlation of the mean activity of those voxels is very high indeed (Panel B). If the blood oxygenation level dependent activity from that subset of voxels is plotted as a function of the behavioral measure, a compelling scattergram may be produced (Panel C). (For similar exercises in other neuroimaging domains, see Appendix B; Baker et al., 2007; Kriegeskorte et al., in press). financial instruments available for the New York Stock Exchange (through the Mathematica function FinancialData; Wolfram Research, Champaign, IL) over the 10 days that the market was open between November 18 and December 3, 2008. We then averaged the correlation values of the stocks whose correlation exceeded a high threshold of our choosing, thus yielding the figure of %0.87. Should you pay us for this investment strategy? Probably not: Of the 3,315 stocks assessed, some were sure to be correlated with the Adak Island temperature measurements simply by chance—and if we select just those (as our selection process would do), there is no doubt we would find a high average correlation. Thus, the final measure (the average correlation of a subset of stocks) was not independent of the selection criteria (how stocks were chosen): this, in essence, is the nonindependence error. The fact that random noise in previous stock fluctuations aligned with the temperature readings is no reason to suspect that future fluctuations can be predicted by the same measure, and one would be wise to keep one’s money far away from us or any other such investment adviser.10 Variants of the nonindependence error occur in many different types of neuroimaging studies and in many different domains. The nonindependence error is by no means confined to fMRI studies on emotion, personality, and social cognition, nor to 10 See Taleb (2004) for a sustained and engaging argument that this error, in subtler and more disguised form, is actually a common one within the world of market trading and investment advising. 280 studies correlating individual behavioral differences with evoked fMRI activity. (For broader discussions of how nonindependent analyses produce misleading results in other domains, see Baker, Hutchinson, & Kanwisher, 2007; Baker, Simmons, Bellgowan, & Kriegeskorte, 2007; Kriegeskorte, Simmons, Bellgowan, & Baker, in press; Vul & Kanwisher, in press.) Our survey allows us to determine which of the studies were committing variants of the nonindependence error by finding analyses in which researchers selected voxels (i.e., they answered A or B to Question 1) based on correlation with the across-subject behavioral measure of interest (i.e., they answered B or C to Question 2 and B to Question 3) and then plotted or reported the observed correlations from just those voxels (i.e., they answered A to Question 4). RESULTS AND DISCUSSION For maximum clarity, we will present the results of our survey, as well as our overall analysis of how these results should be interpreted, in the form of a number of questions and answers. Are the Correlation Values Reported in This Literature Meaningful? Of the 53 articles we successfully surveyed, 28 provided responses indicating that a nonindependent analysis, like the one portrayed in Figures 3 and 4, was used to obtain the across- Volume 4—Number 3 Edward Vul, Christine Harris, Piotr Winkielman, and Harold Pashler subject correlations between evoked BOLD signal activity and a measure of individual differences. As we saw in Figure 4, a nonindependent analysis systematically distorts any true correlations that might exist. Thus, in half of the studies we surveyed, the reported correlation coefficients mean almost nothing, because they are systematically inflated by the biased analysis. The magnitude of this distortion depends on variables that a reader would have no way of knowing (such as the number of voxels within the brain, noise and signal variance, etc.), so it is not possible to correct for it. The problem is exacerbated in the case of the 38% of our respondents who reported the correlation of the peak voxel (the voxel with the highest observed correlation) rather than the average of all voxels in a cluster passing some threshold. Figure 5 shows the histogram of correlation values with which our investigation started,11 this time color coded by whether or not such a nonindependent analysis was used in the article (see Table 1 for the key to the color-coding). It is reassuring to see that the mode of independently acquired (i.e., valid) correlation values (coded green) is indeed below the ‘‘theoretical upper bound’’ we anticipated from classical test theory and the limited information we have on test reliability (described in the introduction). The overwhelming trend is for the larger correlations to be emerging from nonindependent analyses that are statistically guaranteed to inflate the measured correlation values. In looking at Figure 5, it is tempting to assume that the nonindependent (red) correlations, had they been measured properly, would have values around the central tendency of the independent (green) correlations (around .6). Thus, one might say, ‘‘It is very unfortunate that the numbers were seriously exaggerated, but the real relationships here are still pretty impressive.’’ In our view, any such inference is unwarranted; many of the real relationships are probably far lower than the ones shown in green. After all, the published studies reporting independent measures of correlations are still predominantly those that found significant effects (resulting in the well-known publication bias for significant results; cf. Ioannidis, 2005), and correlations much lower than .5 would often not have been significant with these sample sizes. We would speculate that, properly measured, many of the red correlations would have been far lower still. (For a discussion of the relationship between the nonindependence error and the use of spatial clustering thresholds, see Appendix B.) Is the Problem Being Discussed Here Anything Different Than the Well-Known Problem of Multiple Comparisons Raising the Probability of False Alarms? Every fMRI study involves vast numbers of voxels, and comparisons of one task to another involve computing a t statistic 11 Thanks to Lieberman, Berkman, and Wager (2009, this issue) for pointing out that clerical errors in an earlier version of this histogram that circulated on the Internet had resulted in omissions (now corrected). In the course of reviewing our files, we also realized that Study 55, surveyed in April 2008, was inadvertently omitted from the earlier histogram distributed online. Volume 4—Number 3 Fig. 5. The histogram of the correlations values from the studies we surveyed (same data as Figure 1), this time, color coded by whether or not the article used nonindependent analyses. Correlations coded in green correspond to those that used independent analyses, avoiding the bias described in this article. However, those in red correspond to the 53% of articles surveyed that reported conducting nonindependent analyses— these correlation values are certain to be inflated. Entries in orange indicate articles from authors who chose not to respond to our survey. (See Appendix C for the key to article numbers.) The color coding corresponds to whether or not the one correlation we focused on in a particular article was nonindependent. There are varying gradations of nonindependence; for instance, Study 26 carried out a slightly different, nonindependent analysis: instead of explicitly selecting for a correlation between the implicit association test (IAT) and activation, they split the data into two groups: those with high IAT scores and those with low IAT scores. They then found voxels that showed a main effect between these two groups and computed a correlation within those voxels. In Study 23, voxels were selected on a behavioral measure that correlated with the final behavioral measure of interest. These procedures are also not independent and will also inflate correlations, though perhaps to a lesser degree. and comparing it with some threshold. When numerous comparisons are made, adjustments of threshold are needed and are commonly used. The conventional approach involves finding voxels that exceed some arbitrarily high threshold of significance on a particular contrast (e.g., reading a word vs. looking at random shapes). This multiple comparisons correction problem is well known and has received much attention. The problem we describe arises when authors then report secondary statistics on the data in the voxels that were selected 281 High Correlations in fMRI Studies originally. In the case discussed in this article, correlations are both the selection criterion and the secondary statistic. When people compare reading a word with reading a letter and find brain areas with a t value of 13.2 (with 11 degrees of freedom, comparable to an r of .97 or an effect size of d 5 2.4), few people would interpret the t value as a measure of effect size. On the other hand, we would contend that essentially everyone interprets the r values under discussion here in that way. What May Be Inferred From the Scattergrams Often Exhibited in Connection With Nonindependent Analyses? Many of the articles reporting biased correlation values display scattergram plots of evoked activity as a function of the behavioral measure. These plots are presumably included to show the reader that the correlation is not being driven by a few outliers or by other aberrations in the data. However, when nonindependent selection criteria are used to pick out a subset of voxels, the voxels passing this criterion will inevitably contain a large admixture of noise favoring the correlation (see the scattergram in Figure 4c for an example of a case in which the relationship is pure noise). Thus, the shape of the resulting scattergrams provides no reliable indication about the nature of the possible correlation signal underlying the noise, if any. How Can These Same Methods Sometimes Produce No Correlations? It may be as surprising to some readers, as it was to us, that a few articles reporting extraordinarily high correlations arrived at through nonindependent analyses also reported some negative results (correlations that failed to reach significance). If the same analysis methods were applied to each correlation investigated, shouldn’t the same correlation-amplifying bias apply to each one? Indeed it should. However, with a bit of investigation, we were able to track down the source of (at least some of) the inconsistency: in certain articles, the bias inherent in nonindependent analyses was sometimes wielded selectively, in such a way as to inflate certain correlations but not others. Take for instance Takahashi et al. (2006), reporting an interaction in the presence of a correlation between evoked BOLD signal activity and rated jealousy in men and women: Activity in the insula correlated with self-reported jealousy about emotional infidelity in men (r 5 .88), but not women (r 5 %.03). The opposite was true of the activity in the posterior superior temporal sulcus, which correlated with self-reported jealousy in women (r 5 .88), but not men (r 5 %.07). At first blush, the scattergrams and correlations exhibit a very striking interaction (reported as significant at p < .001). However, the insula activity corresponds to the peak voxel of a cluster that passed statistical threshold for the correlation between rated jealousy and BOLD signal in males; thus, the observed correlation with rated jealousy in males was nonindependent and biased, whereas the 282 same correlation for rated jealousy in females was independent. The posterior superior temporal sulcus activity was selected because of the correlation with rated jealousy in females, and thus only the jealousy correlation in males was independent in that region. It should come as no surprise, therefore, that such nonindependently selected data produced a striking interaction in which the nonindependent analyses showed high correlations, whereas the independent analyses showed no correlation. Thus, the presence of the interaction, along with the magnitude of the correlations themselves, is quite meaningless and could have been obtained with completely random data like those utilized in the simulation shown in Figure 4. But Is There Really Any Viable Alternative to These Nonindependent Analyses? It is all very well to point out ways in which research methods fall short of the ideal. However, the ideal experiment and the ideal analysis are often out of reach, especially in fields like psychology and cognitive neuroscience. Perhaps we must settle for somewhat imperfect designs and methods to get any information whatsoever about across-subject brain–behavior correlations. Are any better methods available? We contend that the answer is a clear-cut ‘‘Yes.’’ These kinds of brain–behavior linkages can be readily investigated with methods that do not produce any of the rather disastrous complications that accompany the use of nonindependent analyses. One method is to select the voxels comprising different regions of interest in a principled way that is ‘‘blind’’ to the correlations of those voxels with the behavioral measure and also mindful of the fact that individuals’ brains are far from identical. For instance, to assess the relationship between ACC activity during exclusion and reactions to social rejection measured in a questionnaire, one would first put the social rejection data aside and not ‘‘peek’’ at it while analyzing the fMRI data. The researcher can then define regions of interest in individual subjects in whatever way seems appropriate (e.g., by identifying voxels within the anatomical confines of the ACC that were significantly active for the excluded–included contrast, or, even better, by using a different contrast or different data altogether). Once a subset of voxels is defined within an individual subject, one number should be aggregated from these voxels (e.g., the mean signal change). Only then can one examine the behavioral data and compute an unbiased correlation between the ACC region of interest and the behavioral measure. This method was used by a few of the authors of the current studies (e.g., Kross, Egner, Ochsner, Hirsch, & Downey, 2007). In addition to providing an unbiased measure of any relationships between evoked activity and individual differences, this ‘‘functional Region of Interest’’ method avoids implausible assumptions about voxel-wise correspondence across different Volume 4—Number 3 Edward Vul, Christine Harris, Piotr Winkielman, and Harold Pashler individuals’ functional anatomy12 (Saxe, Brett, & Kanwisher, 2006). If one feels that it makes sense to draw voxel-wise correspondences between the functional anatomy of one subject and another, a second alternative exists: a ‘‘split half’’ analysis. Here, half of the data are used to select a subset of voxels exhibiting the correlation of interest, and the other half of the data are used to measure the effect (examining the same voxels, but looking at different runs of the scanner). For example, if there are four runs in the social exclusion and four runs in the neutral condition, one can use two exclusion runs and two neutral runs to identify voxels that maximize the correlation, and then test the correlation of the behavioral trait with these same voxels while looking only at the other two pairs of runs. Such a procedure uses independent data for voxel selection and the subsequent correlation test and thus avoids the nonindependence error.13 This straightforward analysis may be computed on all of the suspect results noted in our article thus far and can be used to provide unbiased estimates of the correlations reported in these articles. Techniques of this kind (hold out validation and crossvalidation) are used in a variety of fields (including fMRI) to evaluate the generality of conclusions when overfitting is a possibility (Geisser, 1993), as is the case when picking a small subset of many measured correlations as a measure of the true correlation. It may often be advisable to use both of the methods just described, because they may find slightly different kinds of (real) patterns in the data. The first type of analysis focuses on the voxels that are most active in the task contrast at issue. This is a sensible place to look first to find relationships with individual differences. However, it is possible that the behavioral individual differences may be most closely associated with activity in some subset of voxels that may not show the greatest activity in this contrast. For example, it is possible that there could be neural structures within the ACC whose magnitude of 12 Although it is possible for voxels registered to the ‘‘average brain’’ to be functionally matched across subjects, the variability in anatomical location of well-studied regions in early visual processing (V1, MT) and visual cognition (FFA) suggests to us that higher level functions determining individual differences in personality and emotionality are not likely to be anatomically uniform across individuals (Saxe, Brett, & Kanwisher, 2006). 13 At first blush, one might worry that using only half of the data to select the correlated regions will greatly decrease statistical power. However, there are two reasons why this should not be a concern. First, removing half of the data from each subject does not reduce the number of data points that go into the across-subject correlation—it simply makes the p estimate of BOLD activity for an individual subject more noisy by a factor of 2. This is not as detrimental to the ability to evaluate a correlation as is the reduction of the number of data points. Second, stringent corrections for multiple comparisons are unnecessary for an independent split-half analysis; thus, a (reasonable) liberal threshold may be chosen to select the subset of voxels that correlate with the behavioral measure in the first half of the data. The statistical inference relies on the magnitude of the correlation observed in those voxels in the second half of the data using a single comparison, which will have ample power to detect any effect that may be close to significant in a properly corrected whole-brain analysis. For an even more data-efficient (but computationally intensive) independent validation technique, variants of the ‘‘k-fold method’’ can also be used (Brieman & Spector, 1992). Volume 4—Number 3 response is related to rejection, even if the mean activation in those structures across subjects does not differ from zero. Even If Correlations Were Overestimated Due to Nonindependent Analyses, Can’t We At Least Be Sure the Correlations Are Statistically Significant and That a Real, Nonzero Correlation Exists? In most of the nonindependent analyses, the voxels included in the computation of the reported correlation were those that passed a threshold for significance that was based on some combination of the correlation value for each voxel and the spatial contiguity between the voxel and other above: threshold voxels—a threshold that typically included some ostensible adjustment for multiple comparisons. Given that, can we not be sure that there is a real correlation, albeit one that is weaker than reported? In principle, this ought to be the case, but only if the correction for multiple comparisons is appropriately implemented. We did not explicitly survey the authors about their multiple comparisons correction procedures, but we do see evidence that some of the corrections used in this literature may be less than trustworthy. The most common method of correcting for multiple comparisons used in this literature is family-wise error correction relying on ‘‘minimum cluster size thresholds.’’14 In this approach, the correlation in clusters of voxels is determined to be significant if the cluster contains a sufficiently large number of contiguous voxels, each of which exceed some statistical threshold. This procedure ‘‘relies on the assumption that areas of true neural activity will tend to stimulate signal changes over contiguous pixels’’ (Forman et al., 1995; i.e., ‘‘signal’’ will tend to show up as activity that extends beyond a single voxel, whereas statistical noise will generally be independent from one voxel to its neighboring voxel and thus will not usually appear in large clusters).15 Given particular scan parameters,16 one can use various sophisticated techniques to compute the probability of falsely detecting a cluster of voxels (Type I error). This probability may be estimated using the AlphaSim tool from the AFNI (Analysis for Functional NeuroImaging) program (Cox, 1996).17 We 14 See Appendix B for a discussion of whether the problem of inflated correlations is eliminated by the use of a cluster-based threshold. 15 Technically, the rationale is somewhat more complicated and relies on estimates of the spatial correlations known to be present in the voxels (e.g., due to smoothing). The smoothness assumption defines how likely it is for pure noise observations with these spatial statistics to contain clusters with a particular number of contiguous voxels exceeding statistical threshold. 16 These parameters include voxel dimensions, volume dimensions, smoothing parameter (sometimes data smoothness as estimated from the data), minimum cluster size, and minimum single-voxel p threshold. 17 AlphaSim allows users to enter an estimate of smoothness of the data (the literal smoothing kernel is often an underestimate, and a better estimate is to use the output of the FWHMx function, which computes a measure of smoothness by measuring the spatial correlation in the data in addition to the smoothing parameter applied—this is the default in the Statistical Parametric Mapping software package). Thus, simply entering the smoothing kernel into AlphaSim underestimates the smoothness of the data and underestimates the probability of a falsely detected cluster. For our purposes, this means that the numbers obtained from AlphaSim will actually underestimate how large the clusters must be to reach a certain false alarm probability. 283 High Correlations in fMRI Studies noticed that many articles in our sample used p thresholds of .005 and cluster size thresholds of 10, and the researchers stated that these choices were made relying upon Forman et al. (1995) as an authority. For instance, Eisenberger et al. (2003) claimed that their analysis had a per-voxel false positive probability of less than 0.000001. They used these thresholds on 19 " 64 " 64 voxel imaging volumes at 3.125 mm " 3.125 mm " 4.000 mm, smoothed with 8 mm full-width at half-max Gaussian kernel. We were puzzled that these parameters would be able to reduce the rate of false alarms to the degree claimed, and so we used AlphaSim to investigate. According to the AlphaSim simulations, pure noise data is likely to yield a cluster passing this threshold in nearly 100% of all runs (a per-voxel false alarm probability of 0.002)! To hold the false detection probability for a particular cluster below .00003 (thus keeping the overall probability of a false positive in the analysis below the commonly desired alpha level of 0.05), a far larger cluster size (namely, 41 voxels) would need to be used.18 Thus, we suspect that the .000001 figure cited by Eisenberger et al. (2003) and other authors actually reflects a misinterpretation of Forman’s simulations results.19 It seems that ostensible corrections for multiple comparisons with the cluster size method are at least sometimes misapplied, and thus even the statistical significance of some correlations in this literature may be questionable. In general, it is important to keep in mind what statistics the conclusions of a particular paper rely on. In many papers, researchers use a liberal threshold to select a region of interest (ROI; one that would be insufficiently conservative to address the multiple comparisons problem) and then compute an independent test on the ROI voxels. The conclusions of such papers usually rest on the secondary statistic computed within the ROI; the threshold used to select the ROI voxels does not matter as much. In the cases we discuss in this article, the secondary statistics are nonindependent and are thus biased and meaningless. In these cases, the criteria used to select voxels becomes the only statistic that may legitimately be used to evaluate the probability of a false alarm in the results; thus, the selection criteria are of utmost importance for the conclusions of the article. It should be emphasized that we certainly do not contend that problems with corrections for multiple comparisons exist in all 18 Even if the brain occupied just one tenth of the imaging volume (7,700 voxels), the parameters described would falsely detect a cluster 60% of the time in pure noise—in this case, the appropriate minimum cluster-size threshold would need to be 27, rather than 10, to reach a false detection rate of 0.05. 19 The per-voxel false detection probabilities described by Eisenberger et al. (2003) and others seem to come from Forman et al.’s Table 2C. Values in Forman et al.’s table report the probability of false alarms that cluster within a single 2D slice (a single 128 " 128 voxel slice, smoothed with a Gaussian kernel with a full-width at half max of 0.6 " voxel size). However, the statistics of clusters in a single 2D slice are very different from those of a 3D volume: There are many more opportunity for spatially clustering false alarm voxels in the 3D case than in the 2D case. Moreover, the smoothing parameter used in the articles in question was much larger than 0.6 " voxel size assumed by Forman in Table 2C (in Eisenberger et al., 2003, this was >2 " voxel size). The smoothing, too, increases the chances of false alarms appearing in larger spatial clusters. 284 (or even a majority) of the articles surveyed. Many comparisons are corrected in a defensible fashion. Moreover, even articles using multiple comparisons corrections that, strictly speaking, rely on assumptions that were not really met likely report relationships that do indeed exist at least to some nonzero extent. In any case, we argue that (a) the actual correlation values reported by the nonindependent analyses comprising over half of the studies we examined are sure to be inflated to the point of being completely untrustworthy; (b) assertions of statistical significance of the whole-brain analysis used to select voxels require careful scrutiny—which does not always appear to have been done in the publication process; and, perhaps most importantly, (c) if researchers would use the approaches recommended on p. 282, they could avoid the whole treacherous terrain of nonindependent analyses and its attendant uncertainties and complexities. In this way, the statistics would only need to be done once, the false alarm risk would be completely transparent, and there would be no need to use highly complex corrections for multiple comparisons that rest on hard-to-assess assumptions. Isn’t a Significant (Nonzero) Correlation Really What Matters, Anyway? Does the Actual Correlation Value Really Matter So Much? We contend that the magnitude, rather than the mere existence, of the correlation is what really matters. A correlation of 0.96 (as in Sander et al., 2005), indicates that 92% of the variance in proneness to anxiety is predicted by the right cuneus response to angry speech. A relationship of such strength would be a milestone in the understanding of brain–behavior linkages and would promise potential diagnostic and therapeutic spin-offs. In contrast, suppose—and here we speak purely hypothetically— that the true correlation in this case were 0.1, accounting for 1% of the variance. The practical implications would be far less, and the scientific interest would be greatly reduced as well. A correlation of 0.1 could be mediated by a wide variety of highly indirect relationships devoid of any generality or interest. For instance, proneness to anxiety may lead people to breathe faster, drink more coffee, or make slightly different choices in which lipids they ingest. All of these are known to have effects on BOLD responses (Mulderink, Gitelman, Mesulam, & Parrish, 2002; Noseworthy, Alfonsi, & Bells, 2003; Weckesser et al., 1999), and those effects could easily interact slightly with the specific hemodynamic responses of different brain areas. Or perhaps anxious people are more afraid of failing to follow task instructions and thus attend ever so slightly more to the required auditory stream. The weaker the correlation, the greater the number of indirect and uninteresting causal chains that might be accounting for it, and the greater the chance that the effect itself will appear and disappear in different samples in a completely inscrutable fashion (e.g., if the dietary propensities of anxious people in England differ from those of anxious people in Japan). We suspect that it is for this reason that the field of risk-factor Volume 4—Number 3 Edward Vul, Christine Harris, Piotr Winkielman, and Harold Pashler epidemiology is said to have reached some consensus that findings involving modest but statistically significant risk ratios (e.g., ratios between 1.0 and 2.0) have not generally proven to be robust or important. It seems likely to us that most reviewers in behavioral and brain sciences also implicitly view correlation magnitude as important, and we suspect that the very fact that so many of the studies reviewed here appeared in high-impact journals partly reflects the high correlation values they reported. CONCLUDING REMARKS We began this article by arguing that many correlations reported in recent fMRI studies on emotion, personality, and social cognition are ‘‘impossibly high.’’ Correlations of this magnitude are unlikely to occur even if one makes the (implausible) assumption that the true underlying correlations—the correlations that would be observed if there were no measurement error— are perfect. We then described our efforts to figure out how these impossible results could possibly be arising. Although the method sections of articles in this area did not provide much information about how analyses were being done, a survey of researchers provided a clear and worrisome picture. Over half of the investigators in this area used methods that are guaranteed to offer greatly inflated estimates of correlations. As seen in Figure 5, these procedures turn out to be associated with the great majority of the correlations in the literature that struck us as impossibly high.20 We suspect that the problems brought to light here are ones that most editors and reviewers of studies using purely behavioral measures would usually be quite sensitive to. Suppose an author reported that a questionnaire measure was correlated with some target behavioral measure at r 5 .85 and that he or she arrived at this number by separately computing the correlation between the target measure and each of the items on the questionnaire and reporting just the average of the highest correlated questionnaire items. Moreover, to assess whether these highest correlated questionnaire items were just the tail of a chance distribution across the many items, the author used a filtering procedure with properties too complex to derive analytically. We believe that few prestigious psychology journals would publish such findings. It may be that the problems are not being recognized in this field because of the relative unfamiliarity of the measures and the relatively greater complexity of the data analyses. Moreover, perhaps the fact that the articles report using procedures that include some precautions relating to the issue of multiple comparisons leads reviewers to assume that such matters are all well taken care of. As discussed above, one thing our conclusions leave open is whether there is at least some real relationship behind any given inflated correlation (i.e., a true correlation higher than zero). 20 The other studies (high green numbers in Figure 5) could simply reflect normal sampling variability of the sort found with any kind of imperfect measurement. Volume 4—Number 3 Most investigators used thresholds that ostensibly correct for multiple comparisons, but we have argued that these corrections were seriously misapplied in some cases. Based on the analysis described above, we suspect that although the reported relationships probably reflect some underlying relationship in many cases (albeit a much weaker relationship than the numbers in the articles implied), it is quite possible that a considerable number of relationships reported in this literature are entirely illusory. To sum up, then, we are led to conclude that a disturbingly large, and quite prominent, segment of fMRI research on emotion, personality, and social cognition is using seriously defective research methods and producing a profusion of numbers that should not be believed. Although we have focused here on studies relating to emotion, personality, and social cognition, we suspect that the questionable analysis methods discussed here are also widespread in other fields that use fMRI to study individual differences, such as cognitive neuroscience, clinical neuroscience, and neurogenetics. Despite the dismal scenario painted in the last paragraph, we can end on a much more positive note. We pointed out earlier how investigators could have explored these behavioral-trait/ brain-activity correlations using methods that do not have any of the logical and statistical deficiencies described here. The good news is that in almost all cases the correct (and simpler) analyses can still be performed. It is routine for researchers to archive large neuroimaging data sets (which have usually been collected at great cost to public agencies), and journals and funders often require it. Therefore, in most cases, it is not too late to perform the analyses advocated here (or possibly others that also avoid the problem of nonindependence). Thus, we urge investigators whose results have been questioned here to perform such analyses and to correct the record by publishing follow-up errata that provide valid numbers. At present, all studies performed using these methods have large question marks over them. Investigators can erase these question marks by reanalyzing their data with appropriate methods. Acknowledgments—Phil Nguyen provided invaluable assistance with literature review and management of the survey of researchers reported here, and Shirley Leong provided capable assistance with data management and analysis. We thank all the researchers who responded to our questionnaire. This work was supported by the National Institute of Mental Health (Grant P50 MH0662286-01A1), Institute of Education Sciences (Grants R305H020061 and R305H040108 to H. Pashler), and the National Science Foundation (Grant BCS-0720375 to H. Pashler; Grant SBE-0542013 to G. Cottrell) and by a collaborative activity grant to H. Pashler from the James S. McDonnell Foundation. The authors gratefully acknowledge comments and suggestions from Chris Baker, Jon Baron, Hart Blanton, John Cacioppo, Max Coltheart, Danny Dilks, Victor Ferreira, Timothy Gentner, Michael Gorman, Alex Holcombe, David Huber, Richard Ivry, 285 High Correlations in fMRI Studies James C. Johnston, Nancy Kanwisher, Brian Knutson, Niko Kriegeskorte, James Kulik, Hans Op de Beeck, Russ Poldrack, Anina Rich, Seth Roberts, Rebecca Saxe, Jay Schulkin, John Serences, Marty Sereno, Mark Williams, John Wixted, Steven Yantis, and Galit Yovel. REFERENCES Aron, A.R., Gluck, M.A., & Poldrack, R.A. (2006). Long-term testretest reliability of functional MRI in a classification learning task. NeuroImage, 29, 1000–1006. Baker, C.I., Hutchison, T.L., & Kanwisher, N. (2007). Does the fusiform face area contain subregions highly selective for nonfaces? Nature Neuroscience, 10, 3–4. Baker, C.I., Simmons, W.K., Bellgowan, P.S., & Kriegeskorte, N. (2007, November). 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Magnetic Resonance Medicine, 41, 213–216. Yovel, G., & Kanwisher, N. (2005). The neural basis of the behavioral face-inversion effect. Current Biology, 15, 2256–2262. Volume 4—Number 3 Edward Vul, Christine Harris, Piotr Winkielman, and Harold Pashler APPENDIX A: FMRI SURVEY QUESTION TEST Would you please be so kind as to answer a few very quick questions about the analysis that produced, i.e., the correlations on page XX. We expect this will just take you a minute or two at most. To make this as quick as possible, we have framed these as multiple choice questions and listed the more common analysis procedures as options, but if you did something different, we’d be obliged if you would describe what you actually did. The data plotted reflect the percent signal change or difference in parameter estimates (according to some contrast) of . . . 1. . . . the average of a number of voxels. 2. . . . one peak voxel that was most significant according to some functional measure. 3. . . . something else? If 1: The voxels whose data were plotted (i.e., the ‘‘region of interest’’) were selected based on . . . 1a. . . . only anatomical constraints (no functional data were used to define the region, e.g., all voxels representing the hippocampus). 1b. . . . only functional constraints (voxels were selected if they passed some threshold according to a functional measure – no anatomical constraints were used; e.g., all voxels significant at p<.0001, or all voxels within a 5 mm radius of the peak voxel) 1c. . . . anatomical and functional constraints (voxels were selected if they were within a particular region of the brain and passed some threshold according to a functional measure; e.g., all voxels significant at p<.0001 in the anterior cingulate) 1d. . . . something else? If you picked [1b, 1c, or 2] above could you please advise us about the following: The functional measure used to select the voxel(s) plotted in the figure was . . . [A]. . . . a contrast within individual subjects (e.g., condition A greater than condition B at some p value for a given subject) [B]. . . . the result of running a regression, across subjects, of the behavioral measure of interest against brain activation (for a contrast) at each voxel. [C]. . . . something else? Finally: the fMRI data (runs/blocks/trials) displayed in the figure were . . . [A]. . . . the same data as those employed in the analysis used to select voxels (the functional localizer). [B]. . . . different data from those employed in the analysis used to select voxels (the functional localizer). Thank you very much for giving us this information so that we can describe your study accurately in our review. Volume 4—Number 3 APPENDIX B: CLUSTER-SIZE CORRECTION AND CORRELATION INFLATION Most papers use cluster size, not just a high threshold, to capture correlations. Does the inflation of correlation problem still exist in this case? Yes. The problem arises from imposing any threshold that does not capture the full distribution of the ‘‘true effect.’’ As any true signal will also be corrupted by measurement noise, measurements of voxels that really do correlate with the behavioral measure of interest will also produce a distribution (although in this case the distribution will have a mean with a value that differs from zero). Imposing a threshold on this distribution will select only some samples: those with more favorable patterns of noise. If nearly the whole distribution is selected (statistical power is nearly 1) and there are no false alarm clusters, there would be no inflation. However, the lower the power, the more biased the selected subsample. Although cluster-size correction methods effectively increase power, they do not increase it sufficiently to mitigate bias. For simple whole-brain contrasts, cluster-size methods appear to provide power that does not exceed 0.4 (and will more likely be substantially lower than that; Friston, Holmes, Poline, Price, & Frith, 1995). If statistical power is at 0.4, that means that only the top 40% of the true distribution will be selected—the mean of these selected samples will be very much higher than the true mean. For the moderately technical audience, we provide a simplified cluster-size threshold simulation to show the magnitude with which the underlying signal can be inflated by an analysis procedure of roughly the sort we describe in this article (see Fig. B1). We generated a random 1000 " 1000 voxel slice (300 " 300 subset shown; the dimensions are irrelevant in our case, because we had a constant proportion of signal voxels) by generating random noise for each voxel (Gaussian noise with mean 0 and standard deviation of 3.5). We blurred this slice with Gaussian smoothing (kernel standard deviation of 2), thus inducing a spatial correlation between voxels; this resulted in an effective standard deviation of 0.5 per voxel. We then added ‘‘signals’’ to this noise: Signals were square ‘‘pulses’’ added to randomly chosen 5 " 5 subregions of the matrix. Within one simulated matrix, 25% of the voxels were increased by 1. The color map shows measured intensity of a given voxel, with 0 being the noise average and 1 (marked with an asterisk) being the signal average. We then did a simple cluster search (finding 5 " 5 regions in which every voxel exceeded a particular threshold). We tried a number of different height thresholds, and for each threshold we measured the probability of a false alarm (the probability that a voxel that was within a 5 " 5 region in which all voxels passed threshold did not contain a true signal)—the logarithm (base 10) of this probability is the x axis (%2 corresponds to p(FA) 5 0.01, %0.3: p(FA) 5 0.5). We also computed the inflation of the measured signal and compared it with the true signal in the detected voxels, 287 High Correlations in fMRI Studies noise =3.50 kernel=2 region=52, EffNoise = 0.49 1* 0 Percent overestimation of true signal 3 240 220 200 180 160 140 120 100 80 60 40 ** –2 –2.5 –2 –1.5 –1 log10( p(FA I detection) ) –0.5 Fig. B1. Simulation of cluster size correction and measure variable inflation. The double asterisks on the x axis correspond to simulated thresholds that did not produce any false alarm voxels in our simulations; thus, they reflect only regions that were entirely composed of signals. Error bars correspond to & 1.96 standard deviations across simulations for each threshold. as a percentage of true mean voxel amplitude; this is plotted on the y axis. Naturally, low thresholds are on the right of the graph, producing many false alarms, and high thresholds are on the left, producing few, if any, false alarms. A crude summary of the results of this simulation is that the use of only those signals that pass a threshold will always seriously inflate the underlying signal (given thresholds that have a reasonable probability of false alarm), and as thresholds are raised to decrease false alarms, the signal inflation becomes even greater. (MatLab code available upon request.) APPENDIX C: KEY TO THE 55 ARTICLES REPORTED IN FIGURE 5 TABLE C1 Nonindependent Articles Number 1 2 3 4 5 6 7 8 9 10 11 288 Article Sander, D., Grandjean, D., Pourtois, G., Schwartz, S., Seghier, M.L., Scherer, K.R., & Vuilleumier, P. (2005). Emotion and attention interactions in social cognition: Brain regions involved in processing anger prosody. NeuroImage, 28, 848–858. Najib, A., Lorberbaum, J.P., Kose, S., Bohning, D.E., & George, M.S. (2004). Regional brain activity in women grieving a romantic relationship breakup. American Journal of Psychiatry, 161, 2245–2256. Amin, Z., Constable, R.T., & Canli, T. (2004). Attentional bias for valenced stimuli as a function of personality in the dot-probe task. Journal of Research in Personality, 38, 15–23. Ochsner, K.N., Ludlow, D.H., Knierim, K., Hanelin, J., Ramachandran, T., Glover, G.C., & Mackey, S.C. (2006). Neural correlates of individual differences in pain-related fear and anxiety. Pain, 120, 69–77. Goldstein, R.Z., Tomasi, D., Alia-Klein, N., Cottone, L.A., Zhang, L., Telang, F., & Volkow, N.D. (2007). Subjective sensitivity to monetary gradients is associated with frontolimbic activation to reward in cocaine abusers. Drug and Alcohol Dependence, 87, 233–240. Eisenberger, N.I., Lieberman, M.D., & Williams, K.D. (2003). Does rejection hurt? An FMRI study of social exclusion. Science, 302, 290–292. Hooker, C.I., Verosky, S.C., Miyakawa, A., Knight, R.T., & D’Esposito, M. (2008). The influence of personality on neural mechanisms of observational fear and reward learning. Neuropsychologia, 466, 2709–2724. Takahashi, H., Matsuura, M., Yahata, N., Koeda, M., Suhara, T., & Okubo, Y. (2006). Men and women show distinct brain activations during imagery of sexual and emotional infidelity. NeuroImage, 32, 1299–1307. Canli, T., Amin, Z., Haas, B., Omura, K., & Constable, R.T. (2004). A double dissociation between mood states and personality traits in the anterior cingulate. Behavioral Neuroscience, 118, 897–904. Canli, T., Zhao, Z., Desmond, J.E., Kang, E., Gross, J., & Gabrieli, J.D.E. (2001). An fMRI study of personality influences on brain reactivity to emotional stimuli. Behavioral Neuroscience, 115, 33–42. Eisenberger, N.I., Lieberman, M.D., & Satpute, A.B. (2005). Personality from a controlled processing perspective: An fMRI study of neuroticism, extraversion, and self-consciousness. Cognitive, Affective & Behavioral Neuroscience, 5, 169–181. Volume 4—Number 3 Edward Vul, Christine Harris, Piotr Winkielman, and Harold Pashler Table C1. (Contd.) Number 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 30 31 55 Article Takahashi, H., Kato, M., Matsuura, M., Koeda, M., Yahata, N., Suhara, T., & Okubo, Y. (2008). Neural correlates of human virtue judgment. Cerebral Cortex, 18, 1886–1891. Britton, J.C., Ho, S.H., Taylor, S.F., & Liberzon, I. (2007). Neuroticism associated with neural activation patterns to positive stimuli. Psychiatry Research: Neuroimaging, 156, 263–267. Straube, T., Mentzel, H.J., & Miltner, W.H. (2007). Waiting for spiders: Brain activation during anticipatory anxiety in spider phobics. NeuroImage, 37, 1427–1436. Jabbi, M., Swart, M., & Keysers, C. (2007). Empathy for positive and negative emotions in the gustatory cortex. NeuroImage, 34, 1744– 1753. Killgore, W.D., Gruber, S.A., & Yurgelun-Todd, D.A. (2007). Depressed mood and lateralized prefrontal activity during a Stroop task in adolescent children. Neuroscience Letters, 416, 43–48. Takahashi, H., Yahata, N., Koeda, M., Matsuda, T., Asai, K., & Okubo, Y. (2004). Brain activation associated with evaluative processes of guilt and embarrassment: An fMRI study. NeuroImage, 23, 967–974. Aron, A., Fisher, H., Mashek, D.J., Strong, G., Li, H., & Brown, L.L. (2005). Reward, motivation, and emotion systems associated with early-stage intense romantic love. Journal of Neurophysiology, 94, 327–337. Singer, T., Seymour, B., O’Doherty, J., Kaube, H., Dolan, R.J., & Frith, C.D. (2004). Empathy for pain involves the affective but not sensory components of pain. Science, 303, 1157–1162. Canli, T., Sivers, H., Whitfield, S.L., Gotlib, I.H., & Gabrieli, J.D.E. (2002). Amygdala response to happy faces as a function of extraversion. Science, 296, 2191. Rilling, J.K., Glenn, A.L., Jairam, M.R., Pagnoni, G., Goldsmith, D.R., Elfenbein, H.A., & Lilienfeld, S.O. (2007). Neural correlates of social cooperation and non-cooperation as a function of psychopathy. Biological Psychiatry, 61, 1260–1271. Mobbs, D., Hagan, C.C., Azim, E., Menon, V., & Reiss, A.L. (2005). Personality predicts activity in reward and emotional regions associated with humor. Proceedings of the National Academy of Sciences, USA, 102, 16502–16506. Somerville, L.H., Kim, H., Johnstone, T., Alexander, A.L., & Whalen, P.J. (2004). Human amygdala responses during presentation of happy and neutral faces: Correlations with state anxiety. Biological Psychiatry, 55, 897–903. Mantani, T., Okamoto, Y., Shirao, N., Okada, G., & Yamawaki, S. (2005). Reduced activation of posterior cingulate cortex during imagery in subjects with high degrees of alexithymia: A functional magnetic resonance imaging study. Biological Psychiatry, 57, 982– 990. Barrett, J., Pike, G.B., & Paus, T. (2004). The role of the anterior cingulate cortex in pitch variation during sad affect. European Journal of Neuroscience, 19, 458–464. Mitchell, J.P., Macrae, C.M., & Banaji, M.R. (2006). Dissociable medial prefrontal contributions to judgments of similar and dissimilar others. Neuron, 50, 655–663. Reuter, M., Stark, R., Hennig, J., Walter, B., Kirsch, P., Schienle, A., & Vaitl, D. (2004). Personality and emotion: Test of Gray’s personality theory by means of an fMRI study. Behavioral Neuroscience, 118, 462–469. Singer, T., Seymour, B., O’Doherty, J.P., Stephan, K.E., Dolan, R.J., & Frith, C.D. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature, 439, 466–469. Sareen, J., Campbell, D.W., Leslie, W.D., Malisza, K.L., Stein, M.B., Paulus, M.P., et al. (2007). Striatal function in generalized social phobia: A functional magnetic resonance imaging study. Biological Psychiatry, 61, 396–404. TABLE C2 No-Response Articles (Authors Who Did Not Respond) Number 27 34 Article Abler, B., Erk, S., Herwig, U., & Walter, H. (2007). Anticipation of aversive stimuli activates extended amygdala in unipolar depression. Journal of Psychiatric Research, 41, 511–522. Glahn, D.C., Lovallo, W.R., & Fox, P.T. (2007). Reduced amygdala activation in young adults at high risk of alcoholism: Studies from the Oklahoma Family Health Patterns Project. Biological Psychiatry, 61, 1306–1309. Volume 4—Number 3 289 High Correlations in fMRI Studies TABLE C3 Independent Articles Number 28 29 32 33 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 290 Article Phelps, E.A., O’Connor, K.J., Gatenby, J.C., Gore, J.C., Grillon, C., & Davis, M. (2001). Activation of the left amygdala to a cognitive representation of fear. Nature Neuroscience, 4, 437–441. Lee, K.H., Brown ,W.H., Egleston, P.N., Green, R.D.J., Farrow, T.F.D., Hunter, M.D., et al. (2006). A functional magnetic resonance imaging study of social cognition in schizophrenia during an acute episode and after recovery. American Journal of Psychiatry, 163, 1926–1933. Phelps, E., O’Connor, K., Cunningham, W., Funayama, E., Gatenby, J., Gore, J., & Banaji, M.R. (2000). Performance on indirect measures of race evaluation predicts amygdala activation. Journal of Cognitive Neuroscience, 12, 729–738. Coccaro, E.F., McCloskey, M.S., Fitzgerald, D.A., & Phan, K.L. (2007). Amygdala and orbitofrontal reactivity to social threat in individuals with impulsive aggression. Biological Psychiatry, 62, 168–178. McClernon, F.J., Hiott, F.B., Huettel, S.A., & Rose, J.E. (2005) Abstinence-induced changes in self-report craving correlate with eventrelated FMRI responses to smoking cues. Neuropsychopharmacology, 30, 1940–1947. Herwig, U., Kaffenberger, T., Baumgartner, T., & Jancke, L. (2007). Neural correlates of a ‘‘pessimistic’’ attitude when anticipating events of unknown emotional valence. NeuroImage, 34, 848–858. Nitschke, J.B., Nelson, E.E., Rusch, B.D., Fox, A.S., Oakes, T.R., & Davidson, R.J. (2004). Orbitofrontal cortex tracks positive mood in mothers viewing pictures of their newborn infants. NeuroImage, 21, 583–592. Lee, B.T., Cho, S.W., Khang, H.S., Lee, B.C., Choi, I.G., Lyoo, I.K., & Ham, B.J. (2007). The neural substrates of affective processing toward positive and negative affective pictures in patients with major depressive disorder. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 31, 1487–1492. Posse, S., Fitzgerald, D., Gao, K., Habel, U., Rosenberg, D., Moore, G.J., & Schneider, F. (2003). Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness. NeuroImage, 18, 760–768. Paulus, M.P., Rogalsky, C., Simmons, A., Feinstein, J.S., & Stein, M.B. (2003). Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism. NeuroImage, 19, 1439–1448. Richeson, J.A., Baird, A.A., Gordon, H.L., Heatherton, T.F., Wyland, C.L., Trawalter, S., & Shelton, J.N. (2003). An fMRI investigation of the impact of interracial contact on executive function. Nature Neuroscience, 6, 1323–1328. Rilling, J.K., Gutman, D.A., Zeh, T.R., Pagnoni, G., Berns, G.S., & Kilts, C.D. (2002). A neural basis for social cooperation. Neuron, 35, 395–405. Heinz, A., Wrase, J., Kahnt, T., Beck, A., Bromand, Z., Grüsser, S.M., et al. (2007). Brain activation elicited by affectively positive stimuli is associated with a lower risk of relapse in detoxified alcoholic subjects. Alcoholism Clinical and Experimental Research, 31, 1138–1147. Schneider, F., Habel, U., Kessler, C., Salloum, J.B., & Posse, S. (2000). Gender differences in regional cerebral activity during sadness. Human Brain Mapping, 9, 226–238. Leland, D., Arce, E., Feinstein, J., & Paulus, M. (2006). Young adult stimulant users increased striatal activation during uncertainty is related to impulsivity. NeuroImage, 33, 725–731. Schneider, F., Weiss, U., Kessler, C., Salloum, J.B., Posse, S., Grodd, W., & Müller-Gärtner, H.W. (1998). Differential amygdala activation in schizophrenia during sadness. Schizophrenia Research, 34, 133–142. Yucel, M., Lubman, D.I., Harrison, B.J., Fornito, A., Allen, N.B., Wellard, R.M., et al. (2007). A combined spectroscopic and functional MRI investigation of the dorsal anterior cingulate region in opiate addiction. Molecular Psychiatry, 12, 691–702. Stein, M.B., Simmons, A.N., Feinstein, J.S., & Paulus, M.P. (2007). Increased amygdala and insula activation during emotion processing in anxiety-prone subjects. American Journal of Psychiatry, 164, 318–327. Dannlowski, U., Ohrmann, P., Bauer, J., Kugel, H., Arolt, V., Heindel, W., & Suslow, T. (2007). Amygdala reactivity predicts automatic negative evaluations for facial emotions. Psychiatry Research: Neuroimaging, 154, 13–20. Moriguchi, Y., Ohnishi, T., Lane, R.D., Maeda, M., Mori, T., Nemoto, K., et al. (2006). Impaired self-awareness and theory of mind: an fMRI study of mentalizing in alexithymia. NeuroImage, 32, 1472–1482. Habel, U.,Windischberger, C., Derntl, B., Robinson, S., Kryspin-Exner, I., Gur, R.C., & Moser, E. (2007). Amygdala activation and facial expressions: Explicit emotion discrimination versus implicit emotion processing. Neuropsychologia, 45, 2369–2377. Samanez-Larkin, G.R., Gibbs, S.E.B., Khanna, K., Nielsen, L., Carstensen, L.L., & Knutson, B. (2007). Anticipation of monetary gain but not loss in healthy older adults. Nature Neuroscience, 10, 787–791. Kross, E., Egner, T., Ochsner, K., Hirsch, J., & Downey, G. (2007). Neural dynamics of rejection sensitivity. Journal of Cognitive Neuroscience, 19, 945–956. Sanfey, A.G., Rilling, J.K., Aronson, J.A., Nystrom, L.E., & Cohen, J.D. (2003). The neural basis of economic decision-making in the Ultimatum Game. Science, 300, 1755–1758. Volume 4—Number 3