Brain Imaging and Brain Privacy: a Realistic Concern?

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Neuroethics Publications Center for Neuroscience & Society

1-2010

Brain Imaging and Brain Privacy: A Realistic Concern?

Martha J. Farah University of Pennsylvania, [email protected]

M. Elizabeth Smith University of Pennsylvania

Cyrena Gawuga University of Pennsylvania

Dennis Lindsell University of Pennsylvania

Dean Foster University of Pennsylvania

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Recommended Citation Farah, M. J., Smith, M. E., Gawuga, C., Lindsell, D., & Foster, D. (2010). Brain Imaging and Brain Privacy: A Realistic Concern?. Retrieved from https://repository.upenn.edu/neuroethics_pubs/63

Suggested Citation: Farah, M.J. et al. (2010). Brain Imaging and Brain Privacy: A Realistic Concern? Journal of Cognitive Neuroscience. Vol. 21(1). pp. 119-127.

This paper is posted at ScholarlyCommons. https://repository.upenn.edu/neuroethics_pubs/63 For more information, please contact [email protected]. Brain Imaging and Brain Privacy: A Realistic Concern?

Abstract Functional neuroimaging has been used to study a wide array of psychological traits, including aspects of personality and intelligence. Progress in identifying the neural correlates of individual differences in such traits, for the sake of basic science, has moved us closer to the applied science goal of measuring them and thereby raised ethical concerns about privacy. How realistic are such concerns given the current state of the art? In this article, we describe the statistical basis of the measurement of psychological traits using functional neuroimaging and examine the degree to which current functional neuroimaging protocols could be used for this purpose. By analyzing the published data from 16 studies, we demonstrate that the use of imaging to gather information about an individual’s psychological traits is already possible, but to an extremely limited extent.

Disciplines Medicine and Health Sciences

Comments Suggested Citation: Farah, M.J. et al. (2010). Brain Imaging and Brain Privacy: A Realistic Concern? Journal of Cognitive Neuroscience. Vol. 21(1). pp. 119-127.

This journal article is available at ScholarlyCommons: https://repository.upenn.edu/neuroethics_pubs/63 Brain Imaging and Brain Privacy: A Realistic Concern?

Martha J. Farah, M. Elizabeth Smith, Cyrena Gawuga, Dennis Lindsell, and Dean Foster

Abstract & Functional neuroimaging has been used to study a wide ar- this article, we describe the statistical basis of the measurement ray of psychological traits, including aspects of personality and of psychological traits using functional neuroimaging and ex- intelligence. Progress in identifying the neural correlates of in- amine the degree to which current functional neuroimaging dividual differences in such traits, for the sake of basic science, protocols could be used for this purpose. By analyzing the pub- has moved us closer to the applied science goal of measuring lished data from 16 studies, we demonstrate that the use of them and thereby raised ethical concerns about privacy. How imaging to gather information about an individual’s psychological realistic are such concerns given the current state of the art? In traits is already possible, but to an extremely limited extent. &

INTRODUCTION Although a number of writers have commented on Progress in neuroscience raises a number of ethical con- the potential threat to brain privacy posed by functional cerns. Among the most prominent of these concerns is neuroimaging (New York City Bar Association, 2005; the potential use of functional neuroimaging to obtain Farah & Wolpe, 2004; Hyman, 2004; Kennedy, 2004; personal information about individuals. Recent studies have Illes, 2003; Canli & Amin, 2002), little attention has been reported neuroimaging correlates of personality, attitudes, paid to the question of whether this threat is realistic, and intelligence (for reviews, see Gray & Thompson, 2004; given the current state of the field. Exceptions include Duncan,2003;Illes,Kirschen,&Gabrieli,2003;Canli& Canli (2006), who argues that brain imaging data may Amin, 2002). By analogy with the term ‘‘genotyping,’’ the provide better measures of personality than convention- use of neuroimaging to determine features of brain func- al paper-and-pencil tests, and in contrast, Levy (2007, tion relevant to an individual’s traits could be called Chap. 4) and Gazzaniga (2006, Chap. 7), who argue that ‘‘brainotyping.’’ Many of the same ethical issues that current technologies for brain imaging are unlikely to have arisen with genotyping may also arise with braino- enable any kind of socially relevant ‘‘mind reading.’’ typing. For example, both genotyping and brainotyping, It is clear that measurement of psychological traits in principle, raise serious concerns about privacy (Illes & using neuroimaging is, in principle, possible, and that Racine, 2005). such a use raises ethical concerns. However, it is not Like the tissue samples used for genotyping, brain clear whether such measurement is a likely development images can be obtained with consent for one purpose in the near term, a more remote but still realistic goal for but later analyzed for other purposes. Correlations be- the future, or whether it remains effectively a science tween brain function and psychological traits are often fiction scenario. obtained in the context of tasks that lack an obvious The present article addresses this issue by answering relation to the trait being measured. For example, ex- the question: What can the current state of the art in traversion and unconscious racial attitudes are both functional neuroimaging of psychological traits tell us correlated with brain activity evoked by simply viewing about an individual? An explicit answer to this question pictures of faces (Canli, Sivers, Whitfield, Gotlieb, & is not available in the literature because published func- Gabrieli, 2002; Phelps et al., 2000). Hence, in these stud- tional neuroimaging research has not been directed to- ies, subjects could be told that the purpose of the scan ward the measurement of normal psychological traits was related to face perception. This feature of neuro- for the purpose of characterizing individuals. Rather, the imaging-based studies of psychological traits makes it goal of individual differences research with functional relatively easy to obtain information about a person with- neuroimaging has been to understand the brain bases of out their knowledge. variation in psychological traits across the population. In other words, current functional neuroimaging research on individual differences is not carried out for the University of Pennsylvania applied science goal of measuring levels of traits in

D 2008 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 21:1, pp. 119–127 individuals, but rather the basic science goal of identify- interest (ROI) (b and sb) and of the trait ( p and sp), and ing those features of brain function that underlie indi- the correlation between them, rho (r), we can use the vidual difference in psychological function. level of brain activity of a hypothetical new subject (b)to On the basis of this alone, one might conclude that predict his or her likely level of the psychological trait worries about brain privacy are premature. Just as the (pˆ): possibility of genotyping individuals to obtain useful information about psychological traits is, at present, re- p^ p b b mote, we might conclude that prospects for brainotyp- ¼ r ing individuals are also unrealistic. Genetic research on sp sb individual differences in psychological traits has yet to identify any specific genes with more than extremely weak correlations with such traits (Parens, 2004; Van s Gestel & Van Broeckhoven, 2003), and this research has p^ ¼ p þ r p b b had a few decades’ head start on functional neuroimag- sb ing research on individual differences. However, one should not dismiss the prospect of brainotyping on these grounds. Two considerations argue Of course, for r < 1, there will be an interval around for the possibility of current or near-term measurement the predicted value within which the true level of of psychological traits using functional neuroimaging. the psychological is likely to fall. This is illustrated in First, the analogy between functional neuroimaging and Figure 1. The distribution shown on the y-axis repre- genotyping is imperfect. Behavioral geneticists have con- sents the postscan prediction, that is, the range of trait cluded that most complex psychological traits involve values and associated probabilities for a subject, given a multiple genes and are determined by a combination of brain activation value. Its spread depends on the length genes and experience. This would account for the weak of the prescan prediction interval and the strength of the relations between individual genes and psychological correlation. traits, and predicts only moderate success, at best, in pre- The predictive value of the brain activation can be dicting such traits from a complete genetic analysis. In expressed in terms of the prediction interval, omega contrast, brain function is one causal step closer to be- (), which is the range of values within which the true havior, in that it reflects the joint influence of genome value will fall some set percentage of the time. A con- and experience. Although there is no guarantee that the ventional choice of percentage would be 95%, yielding most relevant aspects of brain function are captured by an interval that extends from roughly 2 standard devia- current functional neuroimaging methods, such methods tions above the predicted value to 2 standard deviations at least target a point on the final common pathway for below it. the full set of genetic and environmental influences on Before a scan, the prediction interval for a psycholog- psychology, and are therefore more likely to provide use- ical trait is simply the range of scores that 95% of the ful correlates. relevant normal population would be expected to have; Second, an empirical basis for brainotyping already its length is four times the standard deviation of that exists. Although imaging studies, to date, have indeed trait in this population. If a scan is informative about a focused on relations between psychological traits and psychological trait, then after the scan, we will be able to brain activity across groups of subjects, the data collect- define a shorter interval within which the subject has a ed in these studies are, in principle, as applicable to the goal of individual measurement as to the goal of gener- alizing about the neural bases of these traits. For either goal, one needs the same information, namely, information about the distributions of brain activation measures and psychological measures and the correlation between the two. Here we describe a statistical basis of the measurement of psychological traits using functional neuroimaging, and use it to determine the degree to which current functional neuroimaging protocols could be used for this purpose.

A Statistical Basis for Brainotyping For a particular neuroimaging study of individual differences in a psychological trait, given the mean and standard deviation of brain activity in the relevant region of Figure 1.

120 Journal of Cognitive Neuroscience Volume 21, Number 1 95% probability of falling, that is, an interval that is some measure of the scan’s informativeness concerning an number smaller than 4 standard deviations of the trait in individual. the population. The length of the postscan prediction interval depends on the prescan interval and the strength of the METHODS correlation between the brain activation and psychological trait as follows (Sheskin, 1997, Equation 22.12): Search Method Medline was searched using the following combined ffiffiffiffiffiffiffiffi keywords: [individual differences OR intelligence OR ¼ p postscan prescan 1r2 personality] and fMRI, for articles published in 2006 or before. Additional articles were identified by colleague recommendation. This search procedure yielded a total Thus, the reduction in the prediction interval from pre- of 1875 articles. On the basis of the articles’ abstracts, scan to postscan is a straightforward measure of a scan’s a set of 64 articles was identified as being potentially informativeness, which depends on the strength of cor- relevant, in that they reported a relationship between relation between the relevant measure of brain activity individual differences in brain activation and in a psy- and the trait. chological trait for a sample of normal human subjects. The foregoing assumes that the correlation between Four additional criteria were then applied. brain activity and trait is known with certainty, which First, we required that the psychological trait under would be a reasonable approximation for studies with study has some social or personal relevance, such that large samples of subjects. However, in the current lit- privacy could conceivably be a concern, according to erature, the correlations between brain activation and our intuitions. An example of a study excluded by this psychological trait are estimated with small groups of criterion is Urry et al.’s (2006) study of individual differ- subjects and, therefore, have errors of their own, as ences in the diurnal slope of salivary cortisol secretion. illustrated in Figure 2. Although the analyses just pre- Second, we required that the subject sample be un- sented take into account error in the individual meas- selected for the trait under study (e.g., we excluded ures (s and s ), they do not take into account error in b p extreme group designs). An example of a study excluded the correlation coefficient (r). by this criterion is Bertolino et al.’s (2005) study of fear In order to take into account the error of estimating responsiveness in which subjects were selected for being the correlation between brain activity and psychological prone to phobia. trait, as well as the error in the two measures being cor- The third criterion concerned the number of inde- related, we need to begin with the individual data points, pendent tests carried out in each study. We adopted this from which the published correlations above were com- criterion to limit the number of spurious results in the puted, and build a regression model of the relation be- review, and thus, avoid overestimating current braino- tween the measured activations and traits. The model typing capabilities. The most common reason for exclu- takes the form of an equation converting a subject’s brain sion was insufficient constraint on ROIs. Only results activation value into a psychological trait level, along with from a priori ROIs, obtained in studies with just one or a confidence interval around that prediction, in other two such ROIs, were included. ROIs could be defined words, the prediction interval, . The proportional re- anatomically or functionally. The ROI criterion excluded duction in prediction interval resulting from the scan is a studies that used whole-brain analyses and also excluded studies with several a priori ROIs. Some of the latter found predominantly null results (e.g., Matthews, Ward, & Lawrence, 2004, examined 22 a priori ROIs in a study of attentional control and fearfulness and found a reliable relationship between brain activation and behavioral inhibition in only one ROI), whereas others found predominantly positive results (e.g., Beaver et al., 2006, examined nine ROIs and found reliable relationships between brain activation and reward drive in five of them). In addition to limiting the number of ROIs, we also excluded studies in which multiple traits were examined for correlation with brain activation. An example of an article eliminated by this criterion is Stark et al.’s (2005) study of disgust sensitivity, in which five different subscales of their Questionnaire for Disgust Sensitivity were analyzed separately. A final restriction aimed at lim- Figure 2. iting the possibility of spurious findings was to confine

Farah et al. 121 the search to studies relating psychological traits to rel- the subject is the mean of the distribution of trait levels, atively simple and commonly used imaging measures, in- plus or minus 2 standard deviations of that trait. After the cluding percent signal change, number of suprathreshold scan, we get a new and narrower interval. The ratio of voxels, and adaptation, rather than to more complex these two is our measure of the scan’s informativeness. functions derived from these measures such as asymmetry ratios or rate of activation change across conditions. RESULTS An example of an article eliminated by this criterion is Xue, Chen, Jin, and Dong’s (2006) study relating language Table 2 shows the prediction interval for a hypothetical learning with asymmetry of fusiform activation. new subject before and after being scanned using the Fourth, we selected those studies for which the pub- protocol of each of the 16 studies reviewed here. The lished articles included scatterplots showing the rela- reduction of the prediction interval, shown in the right- tionship between trait scores and brain activations. most column, indicates by how much the range of pos- These scatterplots provided the data for the analyses sible trait levels would be narrowed as a result of the to be presented here. Specifically, they provided the pair- scan. ing of a trait value and a brain activation value for each The reduction varies across studies, ranging from less individual subject in each ROI. We also required that the than 10% to more than half. For 7 of the 16 studies, a brain activations shown in the scatterplots be from the scan would be expected to narrow the range of a per- entirety of each a priori ROI, rather than from voxels son’s possible trait levels by at least one third; for 3 of within an ROI chosen to illustrate the relationship at its the 16 studies, the range would be reduced by at least strongest, in order not to bias our results. An example of half. To put these numbers in perspective, consider an article eliminated by this criterion is Singer et al.’s what we could expect to learn from some of the scan- (2004) study of empathy. ning protocols reviewed here. Sixteen studies met all four criteria, and these are The least informative scanning protocol identified by shown in Table 1. The psychological traits measured in us concerns reading span (Reichle, Carpenter, & Just, these studies ranged from personality traits and other 2000). If a new subject was scanned and the activity in aspects of affective functioning to intelligence and spe- the relevant region of the left inferior frontal cortex was cific cognitive aptitudes. As shown in Table 1, these were one standard deviation below the average of the sample studied in the contexts of tasks that lacked obvious re- reported by Reichle et al. (2000), then we would know lationships to the traits. that his or her reading span score lies between 2.3 and 5.5 with a probability of 95%. Because the range of reading span scores for the sample is approximately the Data Transcoding and Analysis same, namely, from 2.0 to 5.0, the brain imaging proto- Published scatterplots were enlarged up to 300% in col would have told us very little about the new subject’s order to obtain a range of at least 5 cm for the range likely reading span. of psychological trait values. The enlargements were A more informative protocol concerns the personality printed onto transparent sheets, which were superim- trait of extraversion as studied in an emotional Stroop posed on graph paper to facilitate reconstruction of the task (Canli, Amin, Haas, Omura, & Constable, 2004). A coordinates of each point. Note that measurement error subject whose anterior cingulate activity was one stan- will have the effect of underestimating the predictive dard deviation below average in this task would be ex- power of brain imaging. pected, with 95% probability, to have an extraversion The goal of data analysis was to determine the pro- score between 18 and 35. Although this remains a fairly portion of reduction in the prediction interval for a wide range, it does exclude a substantial segment of the hypothetical new subject as a result of scanning that extraversion scale. For example, Canli’s subjects spanned subject. For each study, the brain activations were the 21 to 42 on the scale. Simply on the basis of this hy- independent variables and psychological trait level was pothetical subject’s scan, one could reasonably infer that the dependent variable in univariate regression analyses the subject is not a particularly extraverted individual. using JMP IN 5.1. Regression analysis was used to model The most informative protocol identified here concerns the relation between measures of brain activity in a navigational ability (Epstein, Higgins, & Thompson- specific ROI and levels of the trait in question, yielding Schill, 2005). If the brain activation of a new subject in an equation that expresses the predicted level of a psy- this scanning protocol was one standard deviation below chological trait as a function of brain activation in the normal, then we could infer that his or her navigational ROI. Crucially, these analyses yield the expected error of ability is 95% certain to lie between 2.5 and 4.5. In com- the predicted level of trait. parison, the range for the sample is 3.2 to 6.1. Simply The informativeness of a scan was measured as the by measuring a person’s brain responses when viewing reduction in the range of possible values of a trait for a pictures of scenes, without requiring any real or simu- hypothetical new individual subjected to the scanning lated navigation, we can learn a fair amount about his or protocol. Before the scan, the 95% confidence interval for her navigational ability—for example, in this hypotheti-

122 Journal of Cognitive Neuroscience Volume 21, Number 1 Table 1. The 16 Studies Analyzed Here, Grouped According to General Type of Psychological Trait

Trait Study, Sample Size Trait Measure Regions of Interest Activation Task and Contrast(s) Intelligence Navigational ability Epstein et al., 2005 Santa Barbara Parahippocampal Place-change relative to (n = 12) Sense of place area viewpoint-change of an Direction Scale environmental scene Verbal intelligence Geake & Hansen, Verbal Left BA 9 Letter-string analogy task 2005 (n = 12) Intelligence and 45/46 score from the National Adult Reading Test Visual–spatial skill Reichle et al., 2000 Vandenberg Left and right Sentence–picture verification task (n = 12) Mental Rotation parietal using a visual-imagery strategy Task Reading span Reichle et al., 2000 Reading Span Left and right Sentence–picture verification task (n = 12) Test inferior frontal using a verbal strategy

Negative Affectivity Anxiety Critchley, Wiens, Hamilton Right anterior Perception of self heartbeat Rotshtein, Ohman, Anxiety Scale insular/opercular & Dolan, 2004 region (n = 11) State anxiety Bishop, Duncan, & Spielberger Left and right (I) Subliminal perception of fearful Lawrence, 2004 State–Trait amygdala faces relative to neutral faces (n =27) Anxiety Inventory (II) Attentional modulation of fearful vs. neutral attended faces relative to fearful vs. neutral unattended faces Threat sensitivity Cools et al., 2005 Behavioral Left and right Gender categorization of fearful (n = 12) Inhibition Scale amygdala faces relative to happy faces Pessimism (tendency to Fischer, Tillfors, Attributional Left and right Viewing films of snakes believe that negative Furmark, & Style amygdala life events occur for Fredrikson, 2001 Questionnaire reasons likely to (n = 13) persist, whereas positive life events are exceptions and special cases) Harm avoidance Paulus, Rogalsky, Temperament Left and right Response to punishment in simple Simmons, Feinstein, and character insula gambling game of high-risk & Stein, 2003 inventory gambles (n = 15) Neuroticism Paulus et al., 2003 NEO Five Factor Left and right Response to punishment in simple (n = 15) Inventory insula gambling game of high-risk gambles Negative bias in Phelps et al., 2000 Implicit Bilateral Viewing of facial photographs of racial evaluation (n = 12) Association Test amygdala unfamiliar black faces, (the tendency relative to unfamiliar white to evaluate other-race strangers more negatively than same race)

Farah et al. 123 Table 1. (continued)

Trait Study, Sample Size Trait Measure Regions of Interest Activation Task and Contrast(s)

Trait rumination Ray et al., 2005 Ruminative Left and right Cognitive reappraisal when viewing (n = 24) Responses amygdala a negative or neutral photo Scale; Rumination subscale of the Rumination and Reflection Questionnaire; Anger Rumination Scale

Extraversion and Related Personality Traits Extraversion (high Canli et al., 2002 NEO Five Left and right Viewing of happy relative to energy level, sociability, (n = 15) Factor Inventory amygdala neutral faces assertiveness and a tendency to seek enjoyment; a prime dimension in virtually all theories of personality Extraversion Canli et al., 2004 NEO Five Factor Bilateral anterior Emotional Stroop with positive (n = 12) Inventory cingulate words relative to neutral words Behavioral Approach Gray & Braver, 2002 Behavioral Caudal anterior 3-back working memory task (tendency to approach (n = 14) Approach Scale cingulate cortex and be activated by rewarding stimuli)

cal case, that it is not very good and, more likely than periments. A measure that correlates well in one sample not, below average. will not necessarily correlate as well with people of other On the basis of this small sample of studies, certain ages and backgrounds. A similar caution applies con- brain regions appear to be more predictive than others cerning the generality of fMRI measures across different of individual differences, in some cases, predicting indi- circumstances. There is no guarantee that a correlation vidual differences in multiple different traits. For exam- found under one set of circumstances, for example vol- ple, the amygdala appears in numerous rows of Table 2, untary participation in a cognitive neuroscience experi- in association with traits as diverse as pessimism, auto- ment, will remain equally predictive under a different matic racial evaluations, and extraversion. Of course, set, for example the stress of employment screening. Of these diverse traits correlate with amygdala activations course, concerns about generalizability are not unique measured in very different tasks. The neurobiological to brain imaging measures. The specific questions asked correlate of these traits is not activation of an area per se, on a paper-and-pencil test may be more effective at mea- but activation of an area by a certain task. suring personality or ability in one population than in another, and differences in the stress or distractions of the testing situation can influence paper-and-pencil test results as well. DISCUSSION Another caution regarding the present results con- The present results indicate that a modest degree of cerns a possible bias in the studies reviewed. If other brainotyping capability already exists. The potential use similar studies were carried out and produced null re- of functional brain imaging to gain knowledge of some- sults that were not published, then a review of published one’s psychological traits is not science fiction, but rather results would overestimate the predictive power of func- a realistic possibility, albeit limited in important ways. tional neuroimaging. Again, this is a concern that applies One limitation concerns generalizability of the rela- equally well to other measures. It will be either born out tionship between brain activation and psychological trait or dispelled with continuing empirical work. to populations other than the typical volunteer subject In some ways, the present review underestimates for functional magnetic resonance imaging (fMRI) ex- the predictive power of the current state of the art in

124 Journal of Cognitive Neuroscience Volume 21, Number 1 Table 2. The Prescan and Postscan Prediction Intervals for Each Study and Each ROI, Measured on the Scale of the Psychological Trait, and the Percentage Reduction of Prediction Interval from Pre- to Postscan

Prediction Intervals Reduction of Trait Study Regions of Interest Prescan, Postscan Prediction Interval Intelligence Navigational ability Epstein et al., 2005 Parahippocampal 0.814, 0.496 64% place area Verbal intelligence Geake & Hansen, 2005 BA 9 6.56, 3.54 46% BA 45/46 6.56, 3.21 51% Visual–spatial skill Reichle et al., 2000 L. Parietal 6.14, 5.51 63% R. Parietal 6.21, 5.20 19% Reading span Reichle et al., 2000 L. Inferior frontal 0.868, 0.804 7% R. Inferior frontal – –

Negative Affectivity Anxiety Critchley et al., 2004 R. Anterior insular/ 4.88, 3.55 27% opercular region State anxiety (I) Bishop et al., 2004 L. Amygdala 9.00, 8.10 11% R. Amygdala – – State anxiety (II) Bishop et al., 2004 L. Amygdala 8.11, 7.04 13% R. Amygdala – – Threat sensitivity Cools et al., 2005 L. Amygdala – – R. Amygdala 3.77, 2.76 37% Pessimism Fischer et al., 2001 L. Amygdala 2.27, 2.03 11% R. Amygdala 2.32, 1.94 13% Harm avoidance Paulus et al., 2003 L. Insula – – R. Insula 4.39, 3.88 12% Neuroticism Paulus et al., 2003 L. Insula – – R. Insula 0.412, 0.347 16% Racial evaluation Phelps et al., 2000 Bilateral amygdala 71.3, 60.9 15% Trait rumination Ray et al., 2005 L. Amygdala 1.44, 1.05 37% R. Amygdala 1.42, 1.13 26%

Extraversion and Related Personality Traits Extraversion Canli et al., 2002 L. Amygdala 6.26, 4.58 27% R. Amygdala – – Extraversion Canli et al., 2004 Bilateral anterior 11.6, 7.14 39% cingulate Behavioral approach Gray & Braver, 2002 Caudal anterior 6.65, 4.07 39% cingulate cortex functional neuroimaging. A simple way of boosting pre- sure of extraversion obtained from the left amygdala in dictive power involves examining relationships between Canli et al.’s (2002) face-viewing protocol is not entirely traits and higher-order patterns combining brain areas redundant with the measure obtained from the anterior and different task conditions. For example, if the mea- cingulate cortex in Canli et al.’s (2004) Stroop protocol,

Farah et al. 125 then one could narrow the extraversion prediction in- information about the human mind (McCabe & Castel, terval further by administering both tasks to a subject. 2008; Weisberg, Keil, Goodstein, Rawson, & Gray, 2008; The results of our review suggest that brainotyping Racine, Bar-Ilan, & Illes, 2005; Dumit, 2004). This must using functional neuroimaging can provide more pre- be taken into account when considering the social con- dictive biological correlates of psychological traits than sequences of brain imaging. genotyping. This is not surprising, in principle, because Whether the foregoing issues will take on real-world of the difference between the two mentioned earlier, relevance depends on another empirical issue: whether namely, that brain function is a causal step closer to be- brain imaging, in fact, poses any actual or foreseeable havior than are genes. It is also consistent with the threat to brain privacy, given the current state of the art. empirical observation that the percentage of variance The present study is a first attempt to address this issue, in psychological traits accounted for by single genes is and indicates that functional neuroimaging is, indeed, typically 5% or less (Parens, 2004; Van Gestel & Van already capable of delivering a modest amount of infor- Broeckhoven, 2003), whereas the correlations between mation about personality, intelligence, and other socially imaging measures and such traits are often reported to relevant psychological traits. be .6 and higher, accounting for 36% and higher of the trait variance. Do our results have implications for current research Acknowledgments practice? We believe that they do not, in the sense of We thank Eric Racine and two anonymous reviewers for their requiring any additional protection of research partic- insightful comments on an earlier draft of this article, Pauline ipants’ privacy. Rather, they suggest that existing IRB Baniqued for assistance with manuscript preparation, and guidelines designed to maintain confidentiality of imag- Geena Sankoorikal for assistance reviewing the 1875 abstracts. The research reported here was supported by NIH grants R21- ing data, which might once have seemed to be regula- DA01586, R01-HD043078, R01-DA14129, and R01-DA18913, tory overkill for cognitive neuroscience researchers, are and the MacArthur Project on Neuroscience and the Law. in fact prudent measures. Reprint requests should be sent to Martha J. Farah, Center for The measurement of socially relevant psychological Cognitive Neuroscience, University of Pennsylvania, 3720 traits with fMRI should be distinguished from the mea- Walnut St., Philadelphia, PA 19104, or via e-mail: mfarah@ surement of socially relevant psychological states, de- psych.upenn.edu. fined as more transient characteristics of the mind such as mood, momentary emotional states, and the contents of thought. Brain imaging has also been used in the REFERENCES study of mental states. Examples of states with social rel- Beaver, J. D., Lawrence, A. D., van Ditzhuijzen, J., Davis, M. H., evance include deception, false memory, and consumer Woods, A., & Calder, A. J. (2006). Individual differences in interest. For example, the possibility of a brain-based lie reward drive predict neural responses to images of food. detector has captured the imagination of the public and Journal of Neuroscience, 26, 5160–5166. 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