HOW TO CONDUCT A FUNCTIONAL MAGNETIC RESONANCE (FMRI) STUDY IN SOCIAL SCIENCE RESEARCH 1

Transcription

1 RESEARCH ESSAY HOW TO CONDUCT A FUNCTIONAL MAGNETIC RESONANCE (FMRI) STUDY IN SOCIAL SCIENCE RESEARCH 1 Angelika Dimoka Fox School of Business, Temple University, Philadelphia, PA U.S.A. {angelika@temple.edu) } This research essay outlines a set of guidelines for conducting functional Magnetic Resonance Imaging (fmri) studies in social science research in general and also, accordingly, in Information Systems research. Given the increased interest in using neuroimaging tools across the social sciences, this study aims at specifying the key steps needed to conduct an fmri study while ensuring that enough detail is provided to evaluate the methods and results. The outline of an fmri study consists of four key steps: (1) formulating the research question, (2) designing the fmri protocol, (3) analyzing fmri data, and (4) interpreting and reporting fmri results. These steps are described with an illustrative example of a published fmri study on trust and distrust in this journal (Dimoka 2010). The paper contributes to the methodological literature by (1) providing a set of guidelines for designing and conducting fmri studies, (2) specifying methodological details that should be included in fmri studies in academic venues, and (3) illustrating these practices with an exemplar fmri study. Future directions for conducting high-quality fmri studies in the social sciences are discussed. Keywords: fmri, decision neuroscience, neurois, brain imaging Introduction 1 The ability of researchers to measure and link brain activity to human processes, constructs, decisions, and behaviors has demonstrated the potential of brain research for the social sciences (e.g., Cacioppo et al. 2008; Camerer 2003; Dimoka et al. 2011; Lee et al. 2006). By allowing for a close surrogate of brain activity, functional neuroimaging tools, particularly fmri (functional Magnetic Resonance Imaging), have informed many unanswered questions in economics (e.g., Camerer et al. 2004; Glimcher and Rustichini 2004; King-Casas et al. 2005), marketing (e.g., Dietvost et al. 2009; Huettel and Payne 2009; Yoon et al.2009; Yoon et al. 2006), psychology (e.g., Crockett et al. 2008; Damasio 1994; Pessoa 1 Detmar Straub was the accepting senior editor for this paper. Eric Walden served as the associate editor. The appendices for this paper are located in the Online Supplements section of the MIS Quarterly s website ( 2008), and IS (e.g., Dimoka 2010, 2011; Dimoka et al. 2007), in addition to the cognitive sciences in general (e.g., Decety et al. 2004; Dulebohn et al. 2009). Accordingly, there has been a proliferation of fmri studies in social sciences research during the last few years. Despite the increased interest in neuroimaging tools and specifically fmri in the social sciences, functional neuroimaging for many social scientists is still overwhelmingly complex (Huettel et al. 2008). Furthermore, becoming skilled at neuroimaging tools, such as fmri, will require substantial time and effort (e.g., Culham 2006; Song et al. 2006). Thus, the purpose of this paper is to integrate the neuroscience literature outlining the process of designing, conducting, and evaluating an fmri study with practical guidelines on how to undertake fmri studies. These guidelines focus on four key steps typically used in an fmri study: (1) formulating the research question, (2) designing the fmri protocol, (3) analyzing fmri data, and (4) interpreting and reporting fmri results. These four steps are illustrated with a running MIS Quarterly Vol. 36 No. 3, pp /September

2 example of a published fmri study on trust and distrust in this journal (Dimoka 2010). In addition to this running example, other studies are employed to show how the proposed steps play out in research practice. The paper proceeds as follows: The next section provides a review of the basics of neuroscience and fmri. The following section describes how to conduct an fmri study (formulate research questions, design fmri protocol, analyze fmri data, and interpret and present fmri results) using an illustrative example of an fmri study. The contributions of the study and how to establish guidelines for conducting and evaluating fmri studies in the social sciences are then discussed. Basic Neuroscience Foundations fmri has become a method of choice in the decision neuroscience literature because it has superior spatial resolution and it is a noninvasive approach that is able to precisely localize a subject s activated brain areas. The MRI scanner produces cross sectional images of the brain, exploiting resonance signals that are emitted by tissue water enveloped within a strong magnetic field and excited by a high frequency electromagnetic pulse (Belliveau et al. 1991). 2 Specifically, while the magnetic field aligns the ions in the tissue water, the radio frequency pulses alter the alignment in a manner that produces a rotating magnetic field, which is captured in an MRI scanner as a three-dimensional anatomical brain image. Using the magnetic properties of blood (Ogawa et al. 1990), fmri also captures localized neural activity in response to decision and other mental processes by measuring changes during scanning in blood flow or oxygenation (proxies for brain activation). Neural activity increases blood oxygenation (termed hemodynamic response) (e.g., Logothetis et al. 2001), which is captured with an fmri scanner that keys off of the magnetic properties of blood (Logothetis and Wandell 2004; Song et al. 2006). Increased brain activity results in a higher concentration of deoxyhemoglobin, which emits a signal that can be detected with the fmri magnet. Signal intensity depends on the blood s magnetic properties, which is termed blood oxygen level dependent (BOLD) signal (Ogawa et al. 1992). The signal increase (BOLD effect) is proportional to brain activity (Bandettini and Ungerleider 2001), and through this mechanism, the BOLD signal can detect increases in neural activation in a specific brain area after a subject is stimulated. Specifically, the BOLD signal is a reasonable 2 For more details on the basics of fmri, see erefs/cabeza/c002/section1.html. proxy for blood flow and volume in that specific area of the brain responsible for undertaking the tasks related to a mental process (e.g., Huettel et al. 2008). Accordingly, the BOLD signal is used as an indirect measure or proxy of brain activity (Logothetis et al. 2001). As discussed in detail later in this paper ( Analyzing fmri Data ), brain activity can be statistically inferred via time series data of the activated voxels (volumetric pixels) in threedimensional space using multivariate statistics, such as the general linear model (GLM). Statistical parametric activation maps (SPMs) are then created based on statistical analyses of time-series images that compare the intensity of BOLD signals relative to a baseline condition, such as the brain at rest (e.g., Friston, Firth et al. 1995; Huettel et al. 2008). 3 In 3Tesla fmri scanners, 4 functional images have a spatial resolution of ~2 3 mm 3 voxels and temporal resolution of 2 4 seconds. The fmri scanner also acquires a single highresolution structural image of the brain. fmri does not capture absolute levels of blood oxygenation but the relative intensity of BOLD signals across different conditions or contrasts (Buckner and Logan 2006), as determined by the fmri protocol (see Designing the fmri Protocol ). Besides being noninvasive and having fine-grained spatial resolution, other advantages of fmri include strong signal fidelity, reproducibility, and consistency (e.g., Belliveau et al. 1991). Because of the pinpoint localization of brain activity coupled with fine-grained spatial resolution (2 3 mm 3 ) and good temporal resolution (a few seconds), fmri is currently the most widely used neuroimaging tool in the neuroscience literature (e.g., Dimoka et al. 2011; Huesing et al. 2006). Using fmri, the neuroscience literature has linked constructs and mental processes to specific brain areas, termed neural correlates (Camerer et al. 2004; Lieberman 2007; see Appendix B). This is based on the assumption in the literature that all mental processes have their origins in the brain (neural correlates) (Glimcher and Rustichini 2004). While it is often assumed that there is a simple one-to-one mapping between human processes and brain areas (Lorig 2009), the true picture is more complex with essentially many-to-many brain mapping among brain activations and mental processes and 3 SPMs are set of images with voxel values that are distributed according to a known probability density function, usually the subject s T or F distributions, under the null hypothesis. Those SPMs are referred to as the T- or F-maps. 4 3 Tesla refers to a certain level of magnetic field to which current commercial fmri scanners adhere. 812 MIS Quarterly Vol. 36 No. 3/September 2012

3 constructs (Poldrack 2006). 5 Hence, a complex construct would typically map onto more than one brain areas and one brain area can map to many constructs. For a detailed review of the neuroscience literature using fmri, see Dimoka et al. (2011). The emerging fmri literature focuses on the localization and functionality of brain areas that underlie mental processes. For example, the mapping of decision processes to brain areas is accomplished by engaging subjects with specific decision tasks and observing their corresponding brain activations when making decisions. A basic assumption of the neuroscience literature is that there is similarity across subjects in the anatomy and functionality of the human brain (e.g., Aguirre et al. 1998), and localized activation in a certain brain area is likely to generalize to the broader population of healthy (right-handed) adults. 6 For example, certain brain areas are usually associated with certain mental processes, as demonstrated in meta-analyses of how processes are mapped on the brain (e.g., Krain et al. 2006; Phan et al. 2002; Phelps 2006). For a review of mental processes and their corresponding neural correlates, see Appendix B. As brain functionality is similar across people, the goal of fmri studies is to derive generalizable inferences about the population by consciously modeling intra-subject and intersubject variability (e.g., Holmes and Friston 1998; Mechelli et al. 2002). While the analysis of fmri data starts at the individual level, the goal is to produce aggregate results with group-level analyses (e.g., Thirion et al. 2007), as described in the section on Analyzing fmri Data. While fmri makes inferences about which brain area is implicated when subjects are engaged by a mental process, inferring that a certain mental process is solely engaged (based on activation in a brain area) is unwarranted (termed reverse inference; see Appendix A) (Poldrack 2006). As discussed above, brain activity involves complex many-tomany relationship (Price and Friston 2005), and thus inferring that activity in a brain area necessitates the existence of a certain mental process is fraught with logical problems. While reverse inference is not deductively valid (Poldrack 5 While all mental processes are assumed to have their origins in the brain, we herein focus on research constructs as the manifestation of these processes and their treatment in the literature as theoretical concepts. For example, the mental processes of trusting and distrusting an entity would correspond to the research constructs of trust and distrust. 6 While brain activations are similar across normal, right-handed individuals, severe brain injury may cause plasticity (reorganization of the brain s functionality) (Price and Friston 2005). This may result in differing functionality for injured patients since the healthy brain areas assume the functionality of the injured ones (termed degeneracy). 2006, p. 59), it often helps generate exploratory hypotheses (Cacioppo et al. 2008), which can later be tested empirically. Besides, it may also be used in conjunction with other studies to explain ex post some fmri results. Still, reverse inference must be used very cautiously to avoid over-interpretation of fmri data, as discussed in the exemplar study (Dimoka 2010). How to Conduct an fmri Study To provide a roadmap for conducting an fmri study, the various steps are categorized into four key phases: (1) formulating the research questions, (2) designing the fmri protocol, (3) analyzing fmri data, and (4) interpreting and reporting fmri results, as graphically illustrated in Figure 1. Formulating Research Questions for an fmri Study When formulating research questions for an fmri study, there are several important considerations: (1) obtaining unique data that extend existing sources of data, (2) leveraging the properties of brain data, (3) developing theories and hypotheses that correspond to the brain s functionality by integrating social science theories with neuroscience theories, while (4) accounting for the idiosyncrasies of the fmri environment. Obtaining Unique Data that Extend Existing Sources of Data First, the fmri data should provide unique input in order to complement existing data. Simply put, fmri data are valuable when they go above and beyond what existing sources of data can already provide. These include constructs that cannot be easily measured with existing methods, such sensitive issues as gender, race, religion, hidden emotions (like guilt, fear, anger), automated processes (e.g., automaticity), complex processes (cognitive overload), and moral issues (for instance, ethics) (Dimoka et al. 2011). Masked stimuli, which are not consciously observed but are still likely to trigger brain activity, might be captured with fmri (e.g., Vuilleumier et al. 2001) but may not be captured otherwise. fmri data are also valuable for constructs that may be subject to measurement biases. For example, with self-reports, subjectivity bias degrades the perfect measurement of emotions (e.g., LeDoux 2003). Moreover, social desirability may bias MIS Quarterly Vol. 36 No. 3/September

4 Figure 1. The Four Basic Steps for Conducting an fmri Study truthful self-reported responses on sensitive issues (Fossati et al. 2003), while brain responses are, arguably, not as susceptible to deliberate manipulation by subjects (Dimoka et al. 2011). Thus, fmri data may be able to provide physiological responses to different traits that subjects may struggle to selfreport due to knowledge bias (subjects may lack knowledge on construct score) and rating bias (subjects cannot provide a good estimate of the true construct score) (Burton-Jones 2009). For example, cognitive overload, utility, or emotions are responses that are arguably difficult to measure with self reports, but fmri can provide an alternative form of measurement. Demand effects, characteristics, or artifacts may also be overcome with brain data that is less affected by conscious manipulation. By seeking to capture constructs that are difficult to measure otherwise, fmri data might also reduce omitted variable bias. In sum, fmri data should be used when it is challenging to use existing sources of data to capture mental processes or social science constructs. In such cases, complementing traditional and new sources of data with fmri data allows triangulation across data sources and improves the overall quality of measurement in the social sciences. Hence, fmri data should be unique in the sense that they supplement existing sources of data by offering a different measurement perspective. Leveraging the Properties of Brain Data Second, because fmri data, in response to experimental stimuli, can be obtained essentially in near real-time (accounting for the few seconds delay in the hemodynamic response), temporal and even causal (i.e., temporal precedence) relationships among constructs can also be readily inferred with fmri data; this is achieved by observing the temporal order of brain activations associated with mental processes and constructs. Notably, fmri data can be captured nearly simultaneously to when subjects observe a stimulus, thus measuring brain activity immediately after observation, decisions, and behaviors take place, rather than waiting to capture reactions long after subjects have observed a stimulus. By matching a task or stimulus to brain data with temporal precision in real time, fmri studies can also be used to infer temporal or even causal relationships among processes, constructs, decisions, and behaviors. Since temporal precedence greatly helps to infer causality (Cook and Campbell 1979; Zheng and Pavlou 2010), fmri data can also help to verify causal relationships among constructs. Further, because experience can play a role in how the human brain works, fmri studies may be useful in examining whether brain activity that corresponds to various constructs differs as subjects vary by level of experience or gain experience over time. Moreover, automated or routine activities performed by experts may be linked to distinct brain activations. For example, fmri studies can compare experts versus novices or track novices over time as they become experts. Tracking computer users over time or comparing expert versus novice system users could give insights on how system use evolves over time with experience and habituation. Finally, fmri data could also help predict decisions and behaviors (e.g., Bernheim 2009; Lo and Repin 2002), especially in situations where questionnaires, verbal reports, and interviews are not good predictors (e.g., Mast and Zaltman 2005). By acquiring behavioral data together with fmri data, virtually in real- 814 MIS Quarterly Vol. 36 No. 3/September 2012

5 time, it may be possible to link brain activity with behavioral responses and thereby to predict behavior in situations where alternative means have failed. Developing Social Science Theories and Hypotheses to Correspond to Brain s Functionality Third, fmri data can be used to challenge assumptions or test competing theories. For example, Smith et al. (2002) challenged a long-held assumption in the economics literature that payoffs and outcomes are independent. By showing that a certain brain area (such as the insular cortex, which is usually activated by fear of loss; Wicker et al. 2003) is only activated in response to a certain stimulus (ambiguous gambles) but not to a different stimulus (uncertain gambles), Hsu et al. (2005) helped explain why people avoid gambles with ambiguous probabilities and prefer gambles with uncertain probabilities. Moreover, fmri data can help identify the most likely mediators, thus guiding theory selection to better correspond to brain functionality. Notably, Dimoka, Pavlou, Benbasat, and Qiu (2012) used fmri data to show that similarityattraction is a more likely explanation for why people prefer similar avatars for male users while dissimilarity-aversion is a more likely explanation for female users. fmri data can therefore guide the selection of constructs for social science theories by identifying whether or not there is brain activity linked to certain constructs (thus suggesting that a construct should be included in a model). For example, Deppe et al. (2005) used fmri to show that a consumer s first brand choice activates an area linked to social decision making while the consumer s second brand choice activates an area linked to cognitive decision making. These results imply that social considerations guide impulse purchases while cognitive considerations guide planned purchases. Hence, fmri data should ideally be used to challenge existing social science theories that are less neurologically plausible and do not readily correspond to brain functionality, and the fmri data should be used to develop new social science theories that are more in keeping with how the brain actually works. Neuroscience theories, moreover, can be used to guide biologically plausible hypotheses. Having specified how fmri data sheds light on phenomena in the social sciences, it is useful to draw upon this literature to develop hypotheses that link specific brain activations to social science constructs or behaviors (Hedgcock and Rao 2009; Huettel and Payne 2009; Yoon et al. 2009). Ideally hypotheses should have some tension in the sense that not all hypotheses are straightforward and support the same theory. fmri studies can be open to non-hypothesized results by allowing the brain data to infer surprising findings that call for new theories. This notwithstanding, in proposing testable hypotheses, it is often necessary to assume that certain brain areas are associated with certain social science constructs (termed earlier as reverse inference). Drawing upon extant neuroscience literature, reverse inference may be tentatively used to generate hypotheses for exploratory testing (Cacioppo et al. 2008). Nonetheless, scholars should be extremely wary of using reverse inference for fmri studies when there is minimal or no relevant literature, either in the social science or the fmri literature. In such cases, two common mistakes are possible: (1) either proposing hypotheses that relate to miniscule behavioral differences that are difficult to infer neurologically (particularly if no guidance is provided by the fmri literature), or (2) proposing effects that spawn activations throughout the brain, making it difficult to pinpoint specific areas of interest. Therefore, the author recommends heavy reliance on relevant literature to specify expected areas of brain activation and only developing tentative hypotheses about differences in brain activations across different conditions. fmri data ideally should be used to test theories or hypotheses that are hard to address with existing tools. Thus, simply replicating an existing behavioral study without expecting to see some neurological findings that would help illuminate the phenomenon may be unwarranted. Localization of neural correlates is seldom useful unless it informs social sciences theories. Specifically, localization of the neural correlates of many constructs can shed light on the underlying nature or dimensionality by drawing upon the properties of these brain areas from the neuroscience literature. For example, while perceived usefulness is generally viewed as a cognitive construct, Dimoka et al. (2011) found its neural correlates to reside in brain areas also linked to emotions. Moreover, fmri data can give guidance on whether constructs are distinct if their neural correlates are functionally disassociated from each other in the brain, or whether they are similar if they share very similar or the same neural correlates. For example, Yoon et al. (2006) showed that person and brand judgments have distinct neural correlates, implying that they may be distinct constructs. Furthermore, it may be informative to examine whether two constructs involve the same or a different set of brain areas. For example, eye movement and attention span the same brain areas (Corbetta et al. 1998), implying that these two processes may be very similar to each other, at least at a neurological level. Other studies have shown that processes such as vision for perception and vision for action reside in clearly distinct brain areas (e.g., Culham et al. 2003). Similarly, Kuhnen and Knutson (2005) showed that the prospect of a monetary gain activated a different brain area compared to the prospect of a monetary loss of equal magnitude (neurologically supporting prospect theory). McClure et al. (2004) found immediate (impulse pur- MIS Quarterly Vol. 36 No. 3/September

6 chases) versus delayed (planned purchase) rewards to activate distinct brain areas, implying a distinction. Conscious and unconscious visual stimuli possess distinct neural correlates (e.g., Rees et al. 2002). In sum, brain data could be used to offer complementary data, which, when coupled with existing knowledge from the neuroscience literature, can help inform and ideally extend existing social science theories. Accounting for the Idiosyncrasies of the fmri Environment Last but not least, researchers need to be able to test research questions and hypotheses within the constraints of the fmri scanner. Because the fmri scanner is a full-body circular tube (Figure 2), researchers have to design the fmri protocol so that subjects can perform the experimental tasks while lying down flat. In the fmri scanner, subjects can view stimuli projected through goggles on a computer screen or projected on a screen through a mirror. Subjects provide feedback in several ways, such as a mouse or keyboard, using hands, or verbal responses, using a microphone. Furthermore, given that the head must stay physically still during data collection, subjects are required to avoid excessive movement. Finally, the fmri scanner is noisy, and subjects not wearing ear plugs can be distracted and, hence, yield poor responses. In sum, fmri studies are currently restricted to relatively simple protocols to account for the inverse prone position, the projected stimuli, and response option. They should be relatively short (~45 60 seconds) to account for subject fatigue from the constrained movement and scanner noise. 7 In sum, the fmri environment is unique because of the idiosyncrasies of the fmri scanner, and not all studies can be adapted for the fmri context. Experience with fmri studies can lead to more complicated fmri protocols, particularly given the emergence of sophisticated fmri-compatible (fiber optic) input and output devices that enable subjects to better view more complex visual stimuli (e.g., high resolution goggles). Increasingly today, scholars are capturing subject responses through more advanced output devices (e.g., joystick, trackball). Nonetheless, researchers will likely have to compare the behavioral responses obtained within the fmri scanner to traditional lab experiments and ensure that the idiosyncrasies of the fmri environment do not result in any significant behavioral or perceptual (self-reported) contradictions. Inventive researchers can work around these constraints to fit their particular needs, always keeping in mind that (1) some experimental protocols may not be amenable to 7 These are some current technological limitations that will, hopefully, be overcome in the future. At that point, it may be possible to scan subjects while they are in more natural working and thinking positions. fmri, (2) each fmri protocol is unique as a result of its study design and research hypotheses, and (3) there is no single, perfect recipe for all fmri studies. Taken together, while fmri experiments share many similarities with traditional lab experiments, researchers must carefully account for the idiosyncratic fmri environment. Illustrative Exemplar: Neural Correlates of Trust and Distrust To illustrate these guidelines, we use throughout the following section the Dimoka (2010) study as a running example that examined the neural correlates of trust and distrust and shed light on whether these are distinct constructs or the opposite ends of the same continuum. Using fmri to complement psychometric measurement scales of trust and distrust, Dimoka captured the location, timing, and level of brain activity associated with trust and distrust when subjects interacted with four manipulated online seller profiles. These seller profiles differed on their level of trust and distrust in the context of online auctions. The fmri data showed that trust and distrust activate different areas of the brain. Specifically, the neural correlates of trust were shown to be the caudate nucleus (consistent with confident expectations about anticipated positive rewards from trust) (King-Casas et al. 2005), the anterior paracingulate cortex (predicting how the trustee will act in the future) (McCabe et al. 2001) and the orbitofrontal cortex (uncertainty from the trustor s willingness to be vulnerable) (e.g., Krain et al. 2006). Distrust was shown to be linked to brain areas linked to intense negative emotions (amygdala) and fear of loss (insular cortex). Dimoka also showed a clear distinction in the brain areas associated with the corresponding dimensions of trust and distrust, with credibility and discredibility mostly associated with the brain s more cognitive areas whereas benevolence and malevolence were mostly associated with the brain s more emotional areas (amygdala and insular cortex), differences that were more exacerbated in women than in men. 8 Dimoka s study helps address the proposed guidelines for conducting fmri studies (Table 1). First, in terms of obtaining unique data, fmri data supplemented self-reported data on trust and distrust and used both sources of data to test whether trust and distrust are distinct constructs. As noted by Benbasat et al. (2010), trust and particularly distrust are difficult to measure with existing methods, such as self-reports because they are subject to measurement biases. Dimoka com- 8 Because of limitations of sample size, this inference was tentative. 816 MIS Quarterly Vol. 36 No. 3/September 2012

7 Figure 2. Full Body fmri Scanner pared self-reported data on trust and distrust with corresponding fmri data to identify differences and similarities. While the self reports did not reliably distinguish between trust and distrust, the fmri data showed trust and distrust to have distinct neural correlates that span different brain networks, showing their functional distinction at the neurological level. Second, in terms of leveraging the properties of brain data, Dimoka measured trust and distrust in the brain in near realtime while subjects observed trust/distrust stimuli, thus showing that the neural correlates of distrust precede those of trust. The neural correlates of trust and distrust were shown to better predict price premiums relative to the corresponding self-reported data on trust and distrust perceptions, with the neural correlates of distrust (amygdala and insular cortex) having stronger relative effects. Third, in terms of guiding social science theories so that they accurately reflect the brain s functionality, Dimoka challenged social science theories that have suggested that trust and distrust are merely the opposite ends of the same continuum. In terms of integrating social science with neuroscience theories, Dimoka explicitly hypothesized the neural correlates of trust and distrust and their dimensions, thus contributing to our understanding of the dimensionality of trust and distrust. The study showed that brain activity associated with trust and distrust is a better predictor of economic outcomes (price premiums) than the corresponding selfreported psychometric scales of trust and distrust, thereby extending Pavlou and Dimoka s (2006) work on the role of trust on price premiums and the neuroscience literature on comparing the predictive power of neurological versus selfreported data. 9 Finally, it contributed by showing that trust is more cognitive and calculative in nature while distrust is more emotional in nature. Overall, Dimoka s paper called for future reexamination of the nature, dimensionality, distinction, and relative effects of social science constructs. Fourth, in terms of accounting for the constraints of the fmri environment, Dimoka asked subjects to view and read traditional Likert-type measurement items projected onto a computer screen. Subjects used a custom-made fmri-compatible device (fiber-optic mouse) to respond their choices. The device required minimal movement and was accommodated in the fmri scanner. The fmri protocol was relatively simple, as required by fmri constrained settings, and it was pretested with a behavioral study, which showed some very similar responses to the self-reported measures of trust and distrust within and outside the fmri scanner. Designing the fmri Protocol The fundamental issue to consider when designing an fmri protocol is that researchers should assess brain activation rela- 9 This logic depends on the assumption that there is a theoretical basis for why trust and distrust predict price premiums. Given this assumption, brain data better predicted price premiums than self-reported data. MIS Quarterly Vol. 36 No. 3/September

8 Table 1. Key Guidelines When Formulating Research Questions for fmri Studies Guideline Running Example (Dimoka 2010) Obtaining unique data that extend beyond existing sources of data Overcoming potential measurement biases (e.g., subjectivity bias, social desirability bias) Examining data differences and similarities among different sources of data Leveraging the unique properties of brain data Making causal inferences across constructs Capturing brain data in near real-time Predicting decisions and behavior Developing social science theories to correspond to brain s functionality Using fmri data to test competing theories, guide theory development, and develop new social science theories Challenging neurally implausible theories that do not correspond to brain s functionality and building new social science theories that are consistent with the brain s functionality Developing testable hypotheses about which specific brain areas correspond to constructs by integrating social science theories with neuroscience theories Accounting for fmri environment Designing protocols that can be undertaken while subjects are lying down flat Offering responses using custom fiber optic devices that can be used in fmri scanner Avoiding the need for excessive movement Relatively short experimental protocols Complementing perceptual (self-reported) measures of trust and distrust with brain (fmri) data Measuring trust and distrust is subject to several measurement biases Comparing self-reported measures of trust and distrust with corresponding fmri data for differences and similarities Conducting real-time measurement of trust and distrust and predicting price premiums Examining the timing of the neural correlates of trust and distrust Comparing brain scan and psychometric measures of trust and distrust with respect to their ability to predict a common dependent variable (price premiums) Predicting relative effects of trust and distrust on price premiums Developing trust and distrust theories to correspond to brain s functionality Examining whether trust/distrust have the same neural correlates and whether they span similar brain networks and challenging theories that suggest that trust and distrust are the opposite ends of the same (continuous) construct Theories on the nature, dimensionality, distinction, and relationship between trust and distrust should be guided by the premise that trust and distrust have distinct neural networks Integrating social science theories with neuroscience literature to hypothesize the neural correlates of trust (i.e., caudate nucleus, orbitofrontal cortex, anterior paracingulate cortex) and of distrust (i.e., amygdala and insular cortex) Designing the fmri protocol to account for fmri constraints The protocol had subjects read trust and distrust stimuli on computer screen through fiber-optic goggles Responding to trust and distrust stimuli using a custom fiber-optic device required the minimal movement of a single finger. Completing the experimental protocol in minutes to avoid subject fatigue tive to a baseline condition because absolute levels of brain activation are non-interpretable (Song et al. 2006). The baseline condition is captured while the subject is in a state of relative rest. Therefore, the design of an fmri protocol should subtract the baseline from the experimental condition, and researchers should focus on selecting a proper baseline condition in order to partial out activations that are not part of the experimental task (Friston 2002). This subtraction logic is based on the idea that the difference between two tasks can result in a difference in hemodynamic response to identify a functionally active area (Culham 2006). Selecting Appropriate Subjects In fmri studies, it is important to explain the rules for including or excluding subjects, attributes such as age, sex, handedness, or medical history. Subjects should not be using psychotropic medications, and they need to have normal or corrected-normal visual acuity. Subjects should be recused from fmri studies for excessive head motion, structural problems, or failure to follow the fmri protocol. Incorporating behavioral performance criteria in the protocol ensures that subjects in the fmri data analysis properly 818 MIS Quarterly Vol. 36 No. 3/September 2012

9 performed the experimental tasks and their behavioral responses are reasonable. If any subjects should be excluded from the fmri data analysis, the reasons for exclusion should be clearly stated in the scientific reporting. Calculating Power Specifying sample size and statistical power in fmri studies is challenging. Power calculation is complex because the fmri images have tens of thousands of correlated voxels that raise the number of time-series data (Friston et al. 1996; Friston, Holmes, and Worsley 1999). Researchers should balance the need for a large number of subjects to detect true insignificant effects while at the same time minimizing study costs in that fmri studies are still generally high. The number of subjects is chosen to ensure adequate power of analysis for obtaining statistically significant activations at the individual (first-level analysis) and, with more difficulty, group (second-level analysis) levels. There are many guidelines to calculate the number of subjects (e.g., Desmond and Glover 2002; Mumford and Nichols 2008; Murphy and Garavan 2004). Within-subjects (repeated measures) designs are recommended for fmri studies to allow comparison across the same brains. Within-subject designs also help increase the power of analysis for comparison with other sources of data. Besides, the statistical power (a priori estimated or post hoc detected) analysis can be enhanced through separation of the trial events and orthogonality of the design parameters. Table 2 shows how to estimate power (and adequate sample sizes), assuming certain means and standard deviations a priori. 10 Despite concerns for Type II errors (false negatives) due to small sample sizes, Type I errors (false positives) are more common in fmri studies in that they produce over 30,000 scans of the brain, thus having to conduct thousands of significance tests across numerous voxels. To control for false positives there is a need to correct for family-wise error, and adjustment methods, such as Bonferroni correction or false discovery rate (FDR) should be used to account for the multiple comparisons across voxels (Bhatt and Camerer 2003). In the Dimoka study, for a threshold of p <.05 with.80 power for a Level 2 analysis, N = 12 was needed for the first level 10 For post hoc power calculations, researchers should use the realized explained variances in research findings as a substitute for the estimated effect sizes with a power calculator like default.aspx. analysis 11 (assuming mean difference #.5 and STD #.75). In fact, 15 subjects (6 of whom were women) participated in the study, all being recruited from a major university in a metropolitan area using an open ad. Subjects were paid U.S. $35. All subjects were right-handed and were prescreened for fmri safety issues (no medical implants, metal piercings, or medical problems). All ex post tests showed that no subjects should have been excluded from the study (e.g., abnormalities or excessive movement). A detailed source for calculating the number of subjects required for an fmri study is Desmond and Glover (2002). Selecting Trial Design The fmri protocol should also clearly specify the trial design, which includes the number of trials per condition, trial duration, and intervals between trials (Figure 3). The time between two trials is referred to as the inter-stimulus interval (ISI). There are two major types of trial designs (Figure 4), blocked (fixed sequential order of presentation of the experimental conditions using extended time intervals) and event-related (randomized order of the presentation of experimental conditions in relatively short time intervals). First, blocked designs present the stimuli sequentially within each condition and alternating the conditions as a boxcar of distinct blocks (Caplan 2009). The main advantage of blocked designs is high power of detection due to the repetitive stimuli that creates an additive effect on the resulting brain activations. Second, event-related designs present the stimuli in a randomized order for a simultaneous presentation of many conditions (Dale 1999). Simply put, event-related designs study brain activation when a particular stimulus is present (in a random order) versus not present. By allowing randomization across conditions, event-related designs minimize anticipation, habituation effects, and order effects; however, this benefit comes at a cost of lower detection power than blocked designs (and difficulty in testing confounding from order effects). Thus, to increase the detection power of event-related designs, temporal randomization of the intervals among successive presen- 11 The second level analysis in Dimoka would have required the use of nonparametric statistics since one cell had only six subjects (women) (Chi-square tests require a minimum of five units of analysis per cell; Siegel and Castellan 1988). Nevertheless, because of the more stringent sample size requirements of parametric tests that would have had to be met, Dimoka (p. 388) adopted the scientifically conservative approach of interpreting only mean differences so as not to venture into more definitive statistical inferences about differences between women and men. MIS Quarterly Vol. 36 No. 3/September

10 Table 2. Power Calculations for fmri Analysis Analysis Level First Second Third Test by Design Type Description A-priori Power Estimates* Assumptions** Within-subjects (i.e., repeated measures) Between subjects (i.e., between groups) Between subjects (i.e., between studies) Comparison of each subject s voxels between a resting and stimulated condition (across many trials of each condition) Summation across trials for subjects, then comparison between groupings of subjects Comparison of the summative findings of one study versus those of another.80 with subjects [STD (between subjects) =.5; STD (within subjects) =.75; α =.05].80 with 6 subjects [STD (between subjects)=.5; STD (within subjects) =.75; α =.05].80 with subjects [STD (between subjects) =.5; STD (within subjects) =.75; α =.05] Use standard power calculations based on total number of subjects (post hoc) and effect sizes in the literature (a priori) Use standard power calculations based on the number of subjects across studies (post hoc) and effect sizes in the literature (a priori) Mean difference =.50 Mean difference =.75 Mean difference =.25 *First level based on Desmond and Glover s (2011) simulations. **According to Desmond and Glover (2001, p. 121):, With a mud and sigmab of 0.5, subjects are needed to achieve 80% at a 0.05, assuming a value of 0.75 for sigmaw, typically observed in spatially smoothed data, and 100 time points per condition (n). Note that at mud /0.75, approximately six subjects are needed to achieve 80% power; a decrease of 0.25 in mud (from 0.75 to 0.5) requires an additional 5 /6 subjects to maintain 80% power, whereas an additional decrease in mud of 0.25 (from 0.5 to 0.25), requires over 20 more subjects to maintain 80% power. Figure 3. Graphical Representation of a Trial Design of an fmri Protocol Figure 4. Graphical Representation of a Block and Event Related Design of an fmri Protocol 820 MIS Quarterly Vol. 36 No. 3/September 2012

11 tations must be used (Ollinger et al. 2001). Event-related designs are more complex than blocked designs, and there are techniques to counter-balance across events by using fmri protocols with irregular intervals that vary (jitter). 12 To create and subsequently capture the cognitive strategies and/or strategy types used by a subject to perform a given task, researchers are advised to use a blocked design since such design offers the repeated opportunity to develop (and thus capture) the corresponding brain activity. When researchers want to create and measure distinct conditions that may be contrasted to observe differential brain activity, event-related designs are preferred. Thus, both trial designs have advantages and disadvantages. In the Dimoka study, an event-related design was used. First, a randomly selected stimulus (seller profile) was presented to the subjects for 3 seconds. This was followed by a randomly selected measurement item for a randomly-selected construct (trust or distrust) for each seller. Each item was shown for 5 seconds. Then, a seven-point Likert-type scale appeared. After the subjects made their choice, a new randomly selected seller profile was shown followed by a randomly selected item. This procedure was repeated for four sellers and ten measurement items for each seller in a random order. The reason an event-related design was used was to compare trust and distrust across the four sellers that were manipulated to vary in their trust/distrust, and the measurement items on trust and distrust were used to trigger trust/distrust perceptions across sellers. For a detailed example on how to select a trial design for fmri studies, see Decety et al. (2004) in the context of cooperation/competition and Yoon et al. (2006) in the context of person and brand judgments. An example of an eventrelated trial design is offered by Bhatt and Camerer (2005) and Ferstl et al. (2005), while examples of a blocked design are given by Barch et al. (1999) and Soltysik and Hyde (2008). A detailed comparison between event-related and blocked trial designs is given by Chee et al. (2003). Specifying Experimental Tasks The basic objective of an fmri study is to pinpoint the exact area of brain activation in response to a particular experimental task. Hence, an fmri protocol should engage subjects in a set of experimental tasks, such as viewing a visual stimulus, making a decision, responding to a question, or engaging in a behavior, that aims at manipulating certain 12 Jittering refers to using varying delays between the beginning of sampling of brain images relative to the start of the stimulus presentation to the subject. cognitive, emotional, or social processes while the corresponding brain activations are recorded within an fmri scanner. Relative to lab experiments, experimental tasks in fmri studies must be simpler to enable a straightforward link between the experimental tasks and the observed brain activations. The experimental tasks should also be broken down into experimental conditions, procedures intended to create variation in the independent variables of the study (Mandeville and Rosen 2002). The experimental conditions are governed by the trial design that specifies the experimental conditions (Figure 3), with each trial consisting of one or more experimental condition that aims to either ask subjects to actively perform a task or passively activate the brain using a visual, auditory, or other type of stimulus (e.g., Huettel and Payne 2009; Yoon et al. 2009). The basic paradigm is to contrast brain activity between an experimental task versus a baseline (control) task (Huettel et al. 2006). The baseline task is used to cancel out spurious brain activation due to visual stimuli, movement, and other sources of noise, and thus isolate brain activation associated only with the experimental stimuli (Song et al. 2006). It is important to specify the nature and number of the experimental stimuli and conditions, the number of trials per condition, the duration of each trial, and the interstimulus interval among trials (Birn et al. 2002). In the Dimoka study, the experimental conditions consisted of the two dimensions of trust and distrust for four seller profiles (2 4 = 16 conditions). These four seller profiles differed on the dimensions of trust and distrust (high/low trust/distrust). Nine trials, using one measurement item per trial, per condition were used. Manipulation checks showed that the four sellers differed on their degree of trust and distrust. The use of measurement items was similar to psychometric scales in lab experiments. 13 It is also consistent with the neuroscience literature (Winston et al. 2002) that used numerical scales to 13 There are multiple forms of experimental stimuli that are used in the neuroscience literature to elicit brain activation. The use of existing, wellvalidated measurement items, which capture a particular construct with Likert-type scales, was proposed by Dimoka (2011) by integrating the theory of measurement in the social sciences with the neuroscience literature to localize the neural correlates of theoretical constructs with psychometric scales with measurement items as stimuli to elicit activation in the brain areas responsible for these constructs. The multi-item scales serve as the stimuli that trigger brain activation while subjects read the measurement items and process the information that pertains to a particular construct, such as trust and distrust. This method is based on the logic that subjects engage the brain area that corresponds to the construct measured by the psychometric measurement items that were specifically developed to capture the construct, thus eliciting activation specifically in the brain areas that relate to the focal construct. MIS Quarterly Vol. 36 No. 3/September

12 Figure 5. Graphical Representation of fmri Experimental Procedure rate the trustworthiness of faces and used the numerical ratings as parametric covariates to contrast with the level of brain activity. The trial was repeated for all four constructs, four seller profiles, and nine measurement items (Figure 5). In addition to the Dimoka study, other fmri studies that offer detailed information on the nature and sequencing of the experimental tasks include Decety et al. (2004), Hsu et al. (2005), and Yoon et al. (2006). Designing Appropriate Contrasts As explained earlier, fmri data do not capture absolute levels (blood always flows in the brain), but the relative intensity of blood flow (BOLD signal) across conditions. Since fmri is based on a subtraction logic between an experimental and a baseline condition, the difference in the BOLD signal is a function of the contrasts across conditions. fmri experiments are similar in spirit to behavioral studies that must assure that the observed measures (brain activations) are due to the experimental conditions and not due to any confounding or spurious factors that threaten internal validity (Song et al. 2006). Accordingly, the baseline condition must subtract all brain activation other than the process of interest (Mandeville and Rosen 2002). fmri protocols depend on an appropriate contrast between either the experimental and baseline condition or between two experimental conditions (e.g., high and low values of a stimulus or independent variable). Also, since any experimental task, such as moving a finger, viewing an image, or hearing a sound may spawn brain activation (e.g., motor, visual, and auditory cortex), it is necessary to create a contrast with the experimental condition that differs from spurious activation. Spurious activations are highly problematic because they may raise the total level of activity in the brain, thereby potentially statistically suppressing true brain activations. In the Dimoka study, a contrast was created by subtracting the study experimental primary condition (reading and responding to the measurement items for trust and distrust) from the baseline condition (a set of statements that resembled study measurement items in terms of format type and number of words, which the subjects read, processed, and were asked to choose by pressing one of the seven buttons via a fiber-optic mouse. The contrast for each condition was created between the measurement items for each of the four constructs (dimensions of trust and distrust) and the corresponding control statements with the same numerical score. To prevent spurious brain activation in the measurement items of the focal constructs, the complexity, format, and length of the measurement items of these constructs were very similar across the four sellers. A detailed example of how contrasts should be designed can be found in Phan et al. (2004). Repetition and Duration of fmri Studies fmri studies require repetition (multiple trials) to help obtain statistically significant brain activity since repetition helps overcome changes in cerebral blood flow across experimental tasks to reduce standard error. While fmri scanning could theoretically last as long as necessary, the fmri protocol should ideally constrain the number of tasks to reduce fatigue and prevent subjects from becoming disengaged from the experiment. The experimental tasks should not be too mundane to maintain similar brain activation throughout the study. Typically fmri protocols are about 30 to 60 minutes depending on the nature of the experimental tasks. In the Dimoka study, a set of nine similar, yet not identical, measurement items based on the literature were used to both measure the two dimensions of trust and distrust across four seller profiles and serve as repetitive stimuli to spawn brain activation associated with the two dimensions of trust and 822 MIS Quarterly Vol. 36 No. 3/September 2012

13 distrust. The total duration of the fmri study was = 2,160 seconds or about 36 minutes (6 conditions (5 constructs + 1 baseline) 9 measurement items for each experimental condition 4 sellers about seconds for each cycle). Along with 3 to 4 minutes for placing the subject in the scanner and obtaining anatomical (nonfunctional) brain images at the beginning of the fmri session (which are used to superimpose the functional images onto, as discussed in the section Acquiring fmri Images ), the 2-second ISI, the total time subjects spent in the fmri scanner was about 45 minutes. No evidence of fatigue or disengagement was reported by subjects. Pretesting fmri Protocol with a Behavioral Study Before executing the fmri protocol in the fmri scanner, the author advises researchers to pretest a behavioral study with a similar protocol to ensure that subjects correctly perceive the study tasks, understand the manipulations, and perform the tasks properly (relative to the baseline or control condition). This helps ensure that the experimental protocol is clear to the subjects during the fmri study. The behavioral study should include manipulation checks for the experimental conditions. The experimental protocol should then be modified for the fmri environment with as little deviation as possible to allow for comparison between any behavioral data collected during the lab and the fmri study, either with the same set of subjects (to train subjects for the fmri study) or with a different set of subjects (for between-subjects comparison). Another purpose of running the study both inside and outside the fmri scanner is to compare the behavioral data within and outside the fmri scanner to ensure study validity and address a potential concern that the fmri scanner may have altered the subject responses. Notably, Yoon et al. (2009, p. 19) advises that researchers should seek convergent validity by linking fmri data to other behavioral measures. Therefore, researchers are advised to undertake a behavioral study outside the fmri scanner with the same procedures as the fmri protocol to test the veracity of the experimental tasks inside and outside the scanning environment. In Dimoka, the behavioral data on the study s constructs were similar across the traditional lab and fmri studies (p <.01), implying that the fmri context did not affect subjects behavioral responses. of subjects, approximately three to five subjects. Besides fine tuning the experimental tasks and potentially identifying problems in the experimental stimuli or procedures, the analysis of the pilot fmri data can reveal potential problems that would necessitate refining or even revising the fmri protocol. Furthermore, such pilot studies may include additional conditions to test whether it is possible to uncover any subtler brain effects. Indeed, the pilot can also be used to calculate the minimum amount of subjects and number of repetitions needed to extract the hypothesized effects (Calhoun 2006). In Dimoka, a pilot fmri study with five subjects was conducted before the full study with 15 subjects. An example of an fmri study with an extensive pretest is offered by Kriegeskorte et al. (2006). Specifying Procedures Before fmri Scan The fmri protocol should also include the procedures the subjects perform before the fmri session, including the study instructions and any self-reported behavioral data obtained outside the fmri scanner. Given the time constraints of the fmri in terms of cost and lack of movement, researchers should provide, according to the author s experience, all relevant pertinent information about the experimental procedures, and describe the familiarization exercises for subjects before participating in the study, and they should elaborate on any activities that require close inspection prior to the subject entering the scanner. In the Dimoka study, before the fmri session, subjects were instructed to buy an MP3 player (IPod Nano) from four madeup electronics auction sellers in ebay s marketplace, which served as the study context. The seller profiles differed on trust and distrust by manipulating feedback text comments and ratings. Subjects were given ample time to browse the four seller profiles, depicted as the candidates from whom to buy the IPod Nano, and they spent about 20 minutes browsing the sellers preparatory to the fmri session. Other examples where additional behavioral data were collected and analyzed before the fmri session as a means of informing the subsequent fmri study include Linden et al. (2003), McCabe et al. (2001), and Sanfey et al. (2005). Conducting a Small-Scale fmri Study with Small Number of Subjects Before undertaking the full fmri study with all subjects, it is strongly advised to conduct pilot studies with a small number Specifying Procedures During fmri Scan During an fmri session, subjects lay on their back in an MRI scanner. During the first 3 to 5 minutes, anatomical (structural) images of the brain are acquired while subjects lay still, MIS Quarterly Vol. 36 No. 3/September

14 not perform any activities. The structural images serve both to provide a high resolution image of a specific brain s anatomy on which the fmri data can be overlaid, and also to specify where the fmri data should be collected. Functional data are then collected while subjects respond to experimental stimuli, such as visual, auditory, or tactile stimuli, all the while the fmri scanner records the BOLD signal throughout the brain in approximately 2-second intervals. Behavioral responses during the fmri session can also be used for interpretation and comparison with the fmri data (which is a common empirical goal). The fmri procedures need to be described in adequate detail to allow other researchers to replicate the experimental procedure, particularly during the fmri session where virtually all stimuli and their timing may affect the brain activations given the idiosyncrasies of the fmri environment (see the section Formulating Research Questions for an fmri Study ). In the Dimoka study, subjects entered the fmri scanner lying on their back, and visual stimuli were presented to them through fiber-optic goggles connected to a computer. After obtaining anatomical images, subjects undertook the experimental tasks following the trial design shown in Figure 5. Brain activations for comparison across conditions were obtained during the 5-second period while the subjects were reading and processing each measurement item (before posting their response). This 5-second period was selected to both assure temporal separation between brain activity between reading the measurement item and posting the behavioral response and to avoid brain activity due to hand movement when responding to the Likert-type scales. Another detailed example of fmri procedures is offered by Decety et al. (2004). Specifying Procedures After fmri Scan After the fmri scanning session, subjects may be asked to perform additional tasks to complement or check the manipulations of the fmri study. For example, subjects may be asked to engage in a task that is not constrained by the fmri scanner, but may be needed to complete the required set of experimental procedures, such as making a selection, coming to an economic decision, or performing an actual behavior. Specifically, a dependent variable that entails an actual physical behavior may be captured after the fmri session. Moreover, some experimental tasks may be performed ex post because they require temporal separation from the experimental tasks during the fmri session. Finally, as noted earlier, it is strongly recommended that researchers replicate the fmri protocol in a traditional lab to be able to compare the corresponding behavioral data within and outside the fmri scanner (e.g., Yoon et al. 2009), either before or after the fmri session, and either with the same or different subjects, depending on the study procedures. In the Dimoka study, after the fmri session, subjects were asked to give the price they would give each seller as part of the measurement of behavioral responses later to be predicted by fmri data. This was used to create temporal separation between the fmri data and the endogenous variable (price) of the study. Because the replication of the fmri procedures in a traditional lab setting was performed before the fmri study with a different set of subjects (which showed that all subjects had similar behavioral responses within and outside the fmri scanner), subjects did not repeat any of the fmri procedures after the fmri session. Finally, subjects were debriefed, offered answers to their questions, thanked, and dismissed. Acquiring fmri Images In fmri studies, it is necessary to describe the MRI scanner in terms of fabricator (e.g., Siemens, GE) and field strength (in Tesla). The higher the magnetic field strength, the better the resolution of the image. To scan (or acquire fmri images from) the whole brain, about 30 slices are needed, which are typically collected every 2 to 3 seconds (termed repetition time or TR), depending on the study s needs for temporal resolution. Depending on the format and length of the fmri protocol, 15,000 45,000 functional brain images are usually collected, 14 each one being subdivided into small cubes called voxels (volumetric or 3D pixels). Voxels are typically 3 5mm, with 25,000 50,000 voxels required to cover the whole brain. Data from a single voxel over the course of an fmri study constitute a time series of BOLD signals that amount to brain images. The fmri scanner captures two types of images with distinct acquisition parameters: structural images for the brain s anatomical structure and functional images that show changes in the BOLD signal (Figure 6). 15 Usually in a form of an appendix, it is necessary to report the key parameters of data acquisition, including whole or partial brain data acquisition, number of volumes acquired per session, pulse sequence type (e.g., gradient, spin echo), slice thickness, TE/TR/flip angle, sequential or inter- 14 Most fmri scanners collect dummy volumes while the scanner is warming up, which are automatically removed by the scanner data acquisition software. If not, they should be manually excluded from subsequent statistical analysis. 15 These include the pulse sequence type (gradient/spin echo, EPI/spiral), matrix size, field of view (FOV), acquisitions (NEX), flip angle, echo time (TE), slice thickness, acquisition orientation (axial, sagittal, coronal, oblique), and order of acquisition of slices (sequential or interleaved). See Huettel et al. (2004) for more details. 824 MIS Quarterly Vol. 36 No. 3/September 2012

15 + = Structural Image Functional Image Superimposed Image Figure 6. Superimposing the Functional Images on the Structural Brain Images leaved order of acquisition, and the orientation of acquisition (e.g., axial, sagittal). 16 The Dimoka study reported that whole-brain fmri data were acquired in a time-series of about 20 minutes with a Siemens 3Tesla whole-body fmri scanner with a standard head coil. Voxel size was mm. Other studies that reported the fmri acquisition procedures and fmri data analysis parameters in detail are Decety et al. (2004), Delgado et al. (2005), Deppe et al. (2005), and Phan et al. (2002). Obtaining IRB Approval fmri studies require institutional review board (IRB) approval, but similar to most traditional lab experiments and surveys, they most often qualify for an expedited review. A trained lab technician is often present during fmri studies, and institutional IRB guidelines specify whether a radiologist should review all fmri scans for potential brain abnormalities. It is useful for researchers to state the IRB protocol number and authorizing body, usually a university IRB board, in all publications resulting from the IRB protocol Subjects were scanned with a contiguous (no gap) 5mm axial highresolution T1-weighted anatomical images (matrix = ; TR = 600; TE = 15 ms; FOV = 21cm; NEX = 1; slice thickness = 5 mm). Functional images were acquired with a gradient-echo echo planar free induction decay (EPI-FID) sequence (T2*weighted: matrix = ; FOV = 21cm; slice thickness = 5mm; TR = 2s; TE = 30ms, number of slices = 28) in the same plane as the structural images. Depending on the fmri scanner and the type of the anatomical images (T1 or T2), the duration of acquiring anatomical images may vary, but it is typically a few minutes at the beginning of the scanning session. 17 While most institutions clearly distinguish fmri as a nonintrusive technique that qualifies for expedited review, a few may incorrectly classify fmri studies as an intrusive technique that uses radioactive isotopes, such as PET scans and x-rays. It is useful for researchers to clarify to IRB committees that fmri does not emit any harmful rays, and it is a noninvasive technique that simply records changes in the magnetic properties emitted by the body s natural brain activity. In the Dimoka study, the fmri protocol was reviewed and approved by the university s local IRB committee via an expedited review. Subjects were given a written informed consent to sign before the study. There may be some rare occasions in fmri studies when a researcher may observe brain abnormalities, either at the structural (e.g., tumor) or the functional (e.g., connectivity/integrity) level. Many IRBs have a standard procedure that should be followed in these rare instances, such as consulting a trained radiologist or notifying the subject. However, fmri is not a diagnostic tool, and social scientists should refrain from making medical diagnoses or draw any medical inferences based on fmri data. The key steps for designing an fmri protocol are summarized in Table 3. For more details, see Amaro and Barker (2006), Henson (2005), Logothetis (2008), and Poldrack (2006, 2008). Analyzing fmri Data The analysis of fmri data aims mainly at identifying the location and level of functional activation of the brain areas activated by the experimental stimuli specified in the fmri protocol. The functional images are then analyzed to identify brain areas that are significantly more active during the experiment relative to the baseline stimuli. The following steps should be followed when analyzing fmri data: (1) preprocessing of fmri data and (2) statistically analyzing fmri data. Because fmri data are massive by their very nature and also having a complex spatial and temporal noise structure, several assumptions and approximations need to be made for model efficiency, as presented below. Statistical Tools Statistical tools for analyzing fmri data include SPM ( FSL ( MIS Quarterly Vol. 36 No. 3/September

16 Table 3. Key Guidelines when Designing fmri Protocol Step Selecting appropriate subjects Calculating statistical power Selecting trial design Specifying experimental tasks Designing contrasts and controls Repetition and duration Pretesting fmri protocol Conducting a small-scale fmri study Procedures before fmri session Procedures during fmri session Procedures after fmri session Acquiring fmri images Obtaining IRB approval Guidelines Select subjects who satisfy fmri compatibility criteria Specify number of subjects to provide adequate statistical power Select blocked versus event related trial design, trial duration, and ISI Determine the tasks that subjects will perform during the experiment Design contrasts with appropriate controls to cancel spurious activity Select repetitions based on fmri procedures relative to study duration Conduct a behavioral study with the same fmri protocol for pretesting Conduct a small-scale fmri study with a small number of subjects Specify pre-fmri instructions and what subjects are asked to do Obtain anatomical images and specify what subjects do in fmri scanner Collect behavioral data, conduct manipulation checks, debrief subjects Specify focal brain areas and level of detail for acquiring fmri images Propose protocol to conform to institution s IRB guidelines and rules AFNI (afni.nimh.nih.gov/afni) and Brain Voyager (www. brainvoyager.com). SPM is freeware (Wellcome Department of Cognitive Neurology, University College of London, UK), run under Matlab (The Mathworks, Inc., Natick, MA). A primer on SPM is offered by Flandin and Friston (2008). FSL is also freeware, written mainly by members of the Analysis Group (FMRIB, Oxford, UK); it runs on both Apples and PCs (Linux and Windows). AFNI is also freeware (NIMH, Bethesda, MD, USA); it is based on the C language. Brain Voyager is commercial (paid) software and a tutorial is available at documentation.html. The software used for analysis should be reported in the study, as often required by software licenses. In the Dimoka study, data analysis was performed with SPM8, a software package that offers an affordable, simple, one-stop solution for processing, analyzing, and visualizing fmri data; it also has good technical support. Preprocessing of fmri Data Before fmri data are ready for statistical analysis, they should be preprocessed to remove noise, increase the signalto-noise ratio, and allow comparisons across subjects anatomically different brains. Preprocessing of fmri data includes six major steps slice timing correction, realignment, spatial co-registration, segmentation, normalization, and smoothing as described in greater detail below. Slice timing correction: Since images (slices) within a single brain volume are obtained sequentially and not at exactly at the same time, a slice timing correction should be performed to compensate for delays associated with acquisition time differences among images during the sequential collections of functional fmri images (Smith 2001). For example, if TR is set at 2 seconds, then there is a certain time lag between the first and the final images. All of the fmri data analysis software (e.g., SPM, FSL, AFNI, and Brain Voyager) offer tools that correct for slice timing, and they should be used to correct any time lags in image acquisitions. In Dimoka, slice timing correction was performed using the standard procedure embedded in SPM8. Realignment: During an fmri study, subjects spend 20 to 40 minutes inside the fmri scanner, and some head movement is likely to occur, resulting in spatial changes in terms of where specific voxels correspond (Brammer 2001). Head motion can result in noticeable changes in signal intensity across voxels over time (Friston 2002). Accurate movement corrections are very important in fmri studies because even small movements may result in systematic effects, effects that could spawn false positive brain activations if accumulated over many images (Hajnal et al. 1994). Without proper motion realignment, head movement may result as erroneous brain activity (Forman et al. 1995). During the realignment process, the time-series of functional images acquired from the same subject should be realigned by specifying a set of realignment parameters to be used as statistical controls during the statistical analysis of fmri data (Figure 7). Realignment is usually conducted by estimating the parameters of an affine rigid-body transformation that minimizes the sum of squared differences among each scan and a reference scan using the transformation by resampling the data with a certain interpolation (e.g., trilinear, sinc, or cubic spline) (Friston 2002). 826 MIS Quarterly Vol. 36 No. 3/September 2012

17 Realignment Movement Movement Figure 7. Realignment of the Brain Images to Remove Movement In the Dimoka study, images were realigned with SPM8 within each run to correct for motion artifacts. Realignment was conducted by estimating six parameters of an affine rigidbody transformation that minimized the sum of squared difference between each scan and the mean of all scans in the time series from each subject and applying the transformation by resampling the data using a spline interpolation. Spatial Co-Registration: During the spatial co-registration process, the whole-brain images should be realigned to each other because there may be systematic differences in the images across whole-brain scans (Jenkinson 2001). Also images that are in different modalities (functional and anatomical images) should be aligned to each other (Figure 8). First, the images should be aligned to each other by aligning all subsequent images of each brain volume to the first image of the brain volume (Ashburner and Friston 2003). Second, all images in each brain volume of images should be aligned to the first image of the brain volume (Collignon et al. 1995). Parameter estimation for spatial co-registration should be accomplished with a transformation matrix that specifies the higher resolution anatomical images and applies the transformation parameters to all fmri images (e.g., Ashburner and Friston 2003). The spatial normalization method and the transformation type selected should be clearly reported. In the Dimoka study, SPM8 was used to create a six-parameter rigid body affine transformation (three translations in x, y, z direction and three rotations in x, y, z axes) to correct for head movement, and all co-registered functional images were aligned to the first image on a voxel-by-voxel basis. Segmentation: Healthy brain tissue can generally be classified into three broad types using MRI images: grey matter, white matter, and cerebrospinal fluid. Segmentation should be used to assign the probability that each voxel belongs to each tissue type based on combining the likelihood for belonging to that tissue type and the prior probability that is derived from maps from a large number of subjects (Ashburner and Friston 2005). In Dimoka, segmentation was performed using the SPM8 procedure. Normalization: Since the brains of various people differ in size and shape, to compare brain activations across subjects, their brains should be spatially normalized to a template (average) brain to account for structural differences in eacj subject s brain. 18 Normalization refers to scaling the data to a standard template brain to allow intersubject comparison. Normalization is a key step because it allows group analysis and generalizations of fmri results, and fmri studies should specify the template to which images are matched. There are two common standard stereotaxic coordinate spaces, Talairach and Tournoux (1988) 19 and MNI. 20 While these differ from one another, but they can be easily converted to the other (Brett et al. 2002). There are online resources (www. 18 Since group-level analyses of fmri data require normalization and spatial smoothing that reduce spatial resolution (Price and Friston 2005), there are guidelines for accounting for intersubject variability (e.g., Mechelli et al. 2002) and enhancing the statistical validity of group-level analysis (e.g., Roche et al. 2007; Thirion et al. 2007). 19 Talairach and Tournoux introduced the Talairach coordinates where any point in the brain can be specified relative to a three-dimensional (X, Y, Z) space, as shown in Table C1 in Appendix C. 20 The Montreal Neurological Institute (MNI) coordinates are based on the average of many normal brains. MIS Quarterly Vol. 36 No. 3/September

18 Co-Registration Figure 8. Co-Registration of the Functional and Anatomical Brain Images Figure 9. Normalization of the Brain Images to a Template Brain talairach.org) that automatically convert fmri data into various coordinates and provide a functional explanation of the identified brain areas. The statistical tools used for brain imaging analysis use one or the other stereotaxic coordinate space. Therefore, it should be reported which has been used. Moreover, it is also possible to compute a template (average) brain model for a studyspecific set of subjects and accordingly normalize the images on this custom-created template brain of each study subject (Figure 9). In the Dimoka study, using the normalization procedure embedded in SPM8, all functional and anatomical images were normalized into a standard stereotaxic space with MNI coordinates (Ashburner and Friston 1999). Smoothing: Since functional anatomy (the location of brain functionality) may differ across subjects, the functional images should be smoothed to overcome spatial variance (e.g., Poldrack et al. 2008). Smoothing also increases the signal-to-noise ratio by removing high spatial frequencies (Smith 2001). Smoothing is typically performed by replacing the value of each voxel with a weighted value of its own value and those of its neighboring voxels by averaging each voxel with its neighbors, weighted by a Gaussian function that falls with distance (Smith 2001). Smoothing is a standard procedure embedded in most statistical tools for brain imaging analysis. In the Dimoka study, the fmri images were smoothed by convolving them with a Gaussian kernel of 8 cubic mm full width at half maximum (FWHM). For data preprocessing, as reported Dimoka, the fmri data were realigned, co-registered, and normalized to a standard stereotaxic space based on Talairach and Tournoux. All functional and anatomical volumes were then transformed into a standard anatomical space using the T2 EPI template and the SPM8 normalization procedure. The data were spatially 828 MIS Quarterly Vol. 36 No. 3/September 2012

19 Table 4. Key Guidelines for Preprocessing fmri Data Step Guidelines Slice-timing correction Compensate for delays from time differences among fmri images Realignment Specifying realignment parameters to be used as confounds in analysis Spatial co-registration Aligning all fmri images to first image to account for subject movement Segmentation Enhancing probability that voxels belong to appropriate brain tissue Normalization Normalizing all subjects to a template or average brain for group analysis Smoothing Smoothing images to account for spatial and temporal variation smoothed with a Gaussian kernel, and they were realigned to correct for motion artifacts, increase signal-to-noise ratio, and account for intersession variation. All brain volumes then underwent spatial smoothing by convolving a Gaussian kernel to increase the signal-to-noise ratio and account for residual intersession differences. Last, the brain images created a single image of mean activation per trial type and subject (Worsley 2001), and they were deemed ready for data analysis. For a detailed analysis on preprocessing fmri data, see Culham et al. (2006) and Knutson et al. (2007). The key steps required for preprocessing fmri data are summarized in Table 4. Data Analysis Data Analysis Philosophy: fmri results are usually reported in terms of activation maps, images that are obtained by conducting statistical tests at each individual voxel level for each condition and for each subject. This tests whether the time-series activation of each voxel corresponds to the experimental condition relative to the control (or baseline) condition. Spatial maps are then created using statistical tools. The most common fmri data analysis approach is a parametric univariate analysis implemented using the general linear model (GLM) (Poldrack et al. 2008; Worsley and Friston 1995), and most fmri studies employ GLM. However, other fmri data analysis approaches, such as independent components analysis (ICA) and its variants (probabilistic and temporal ICA), are available; these use entropy maximization to iteratively specify independent spatial modes (McKeown et al. 1998). Such approaches sometimes uncover more brain activations than GLM approaches (Calhoun and Pekar 2000), and researchers could replicate the analysis of their fmri data with several approaches to ensure the robustness of fmri results. GLM fits a reference hemodynamic response function (hrf) to the brain data. The hrf describes how the BOLD signal evolves over time in response to changes in brain activity. GLM models the BOLD response, and it determines whether an increase in the BOLD response occurs in response to the experimental versus the control condition by testing the probability that the relative BOLD effect significantly differs from zero (Kiebel and Holmes 2003). The significance of any given voxel and its level of activation reveal the strength of the experimental condition. After the analysis is conducted at each individual voxel to minimize error, the value of each voxel is a statistical quantity (often expressed in T-values or Z-values), formally displayed in a statistical parametric map (SPM). The T-statistic [SPM(t)] is transformed to a normal distribution [SPM(z)]. SPM is a voxel-based approach that makes area-specific statistical inferences to experimental (versus control) conditions (Friston 2002). Accordingly, SPMs should be reported relative to a selected threshold at each individual voxel represented by a desired alpha protection level (Worsley 2001). The resulting SPM is a functional brain image with all T-values of each individual voxel, a process that must account for standard errors through repetition (Lange 2001). Finally, the reported SPMs should be over-laid on the structural brain images to graphically display the specific areas of brain activity. This extensive procedure was undertaken in Dimoka through the facility of the SPM8 software. Design Matrix: In fmri implementations of GLM, a matrix is needed to define the experimental design and the nature of the hypothesis testing. This matrix, called the design matrix, should be created by the researcher who selects and organizes the images acquired in each run and groups them as a function of the condition from which they were taken. Creating a correct design matrix is one of the most important aspects of fmri data analysis and all statistical packages have specific guidelines on how to create a design matrix. In most fmri data analyses such as Dimoka, the design matrix contains the indicator variables (parametric covariates or regressors) that encode the experimental tasks. These are formally identical to classical analysis of (co)variance (i.e., ANCOVA) models. MIS Quarterly Vol. 36 No. 3/September

20 Specifically, each column of the design matrix should correspond to an effect that is built into the fmri protocol to detect confounds. These are referred to as explanatory variables (or covariates or regressors). Their effects on the response variable should be modeled in terms of functions of the presence of these conditions (i.e., events smoothed with a hemodynamic response function), and they should constitute the first columns of the design matrix. Then, there should be a series of terms designed to remove or model low frequency variations in signal due to noise artifacts. The final column is whole-brain activity. The relative contribution of each of these columns should be assessed using standard least squares, and inferences about these contributions should be made using standard T or F statistics, depending on whether the researcher is looking at a particular linear combination (e.g., a subtraction), or all combinations simultaneously. Finally, the rows of the design matrix should correspond to each of the scans. This procedure was followed by Dimoka. Another detailed implementation of the design matrix is provided by Dale (1999). Individual and Group Analysis: Data analysis should first be conducted separately for each subject to obtain individual brain activation maps (Genovese et al. 2002) (termed firstlevel analysis). The first-order analysis results in SPMs for each subject. The individual activations are then aggregated by combining the normalized brain activations of all subjects in the sample (e.g., Ashburner and Friston 1999; Smith 2001) (termed second-level analysis) to generalize the results to the entire population (e.g., Frackowiak et al. 2004). The secondlevel analysis aggregates the effects of the first-level analysis to infer whether the results are stable and common across the population. The second-level analysis across subjects is undertaken using the identified T-levels from the first-level analysis. This is because the absolute comparative levels are much different across subjects because of intersubject physiological variations, and thus not readily comparable. The firstlevel analysis models the sources of individual variation and data noise as confounds for the second-level analysis (Holmes and Friston 1998). Such intersubject physiological differences, albeit nonsystematic, are difficult to overcome due to the relatively small sample size of fmri studies. Therefore, individual-level data are often analyzed with fixed-effects models (Friston, Holmes et al. 1995), and group data with random-effects models to account for intersubject variability (Holmes and Friston 1998); mixed-effects models are also common. The first-level analysis uses a large number of autocorrelated data, while the second-level analysis assumes a small number of independent and identically distributed (IID) data. The level of brain activation should be obtained by the value of the T-test at each individual voxel, which should reflect the intensity of the BOLD signal. The aggregate of all brain activity is then captured with z-tests, tests that correspond to that unit normal distribution that renders the same p-values as the T-statistic, and quantified by continuous measures (termed z-scores) (Thirion et al. 2007). This analysis results in a thresholded map with a continuous intensity level (measured with z-scores) at each voxel (Genovese et al. 2002). This map can be used subsequently in correlation or regression-type models, either to compare the level of brain activations or to compare the level of brain activations with non-fmri data, such as psychometric or behavioral data. Finally, the peak activation (measured using a z-value) in a given brain area is considered to be a good proxy for the overall strength of all adjacent voxels in that brain area (e.g., Delgado et al. 2005; Rilling et al. 2002). In the Dimoka study, the analysis aimed at identifying the neural correlates of trust and distrust, first at the individual level for each subject separately and then at the group level by aggregating the individual data from all subjects to obtain group results. The fmri images were first analyzed with a first-level analysis, obtaining significant activations for each subject. Then, second-level analyses were performed on the aggregate data for the group (second-level) analyses. For the second-level analysis, ROI analysis was used, as explained below. Region of Interest (ROI) Analysis: ROI analysis offers a more precise analysis by focusing on certain brain areas, thus not being affected by spurious activations in unrelated brain areas (e.g., Poldrack 2007). The ROI should be defined in terms of either anatomical or functional criteria (or both) (Poldrack et al. 2008). Anatomical criteria should be used to represent the specific brain area studied based on standard anatomical coordinates (e.g., MNI or Talairach), while functional criteria should select voxels based on the observed brain activation patterns. ROI analysis is used to limit corrections for multiple tests to a smaller set of voxels in a known brain area as opposed the whole brain, thus enabling small volume correction (Culham 2006). ROI can help test hypotheses about the existence of significant activations in expected brain areas (Calhoun 2006). In the Dimoka study, SPM8 was used to create the design matrix using GLM and to identify statistically significant voxels by setting a threshold based on spatial extent using t $ 1.96 (Worsley 2001), a cluster probability of an uncorrected alpha of 0.05 (Forman et al. 1995), and eight voxels as a minimum size (Friston, Holmes et al. 1995). For the ROI analysis, Dimoka used the WFU_PickAtlas freeware, which specifies anatomical criteria using standard MNI coordinates 830 MIS Quarterly Vol. 36 No. 3/September 2012

21 Table 5. Key Guidelines for Analyzing fmri Data Step Data analysis approach Design matrix Individual and group analysis Error correction Region of interest analysis Guidelines Select data analysis method, usually general linear model (GLM) Select and group fmri images as function of the experimental condition Perform analysis for each subject, then proceed with aggregate analysis Account for Type I error by adjusting for multiple statistical tests Focus on particular brain area to identify significant brain activations ( However, no functional (i.e., based on brain activity) criteria were used, given the exploratory nature of the study. Other notable examples that have used ROI analysis are King-Casas et al. (2005) and McClure et al. (2003). Error Correction: Since there are thousands of voxels in the whole brain, there is a risk of Type I error due to the many statistical tests for each voxel. Correcting for multiple comparisons is important because a standard fmri analysis includes individual statistical tests at each voxel, which may amount to approximately 10,000 tests for the whole-brain volume. Therefore, the significance threshold should go through a correction procedure (such as Bonferroni correction) to mitigate the number of false-positive voxels (Ramsey et al. 2002). An alpha threshold of p <.05 is deemed to be quite stringent, especially when coupled with a threshold of clusters of activated voxels (cluster-size thresholding). There are other ways in which a correction can be applied, such as using experiment-wise error rate or false discovery rate methods and non-parametric procedures (Poldrack et al. 2008). Besides tests at the voxel level, a cluster criterion of adjacent voxels that exceed a certain threshold is useful, and a cluster of about 10 voxels is generally deemed adequate to reduce spurious false positive activations. Authors should report how the error covariance is modeled (Poldrack et al. 2008); this could be temporal auto-correlation in the fmri time-series, for example. In the Dimoka study, a Bonferroni correction at an alpha protection level of p <.05 was used because it was deemed conservative. For other examples on applying error correction, see Dulebohn et al. (2009). Much of fmri research has focused on reducing Type I errors and the false discovery rate, thereby increasing Type II errors (Lieberman and Cunningham 2009). This makes it difficult to detect smaller but still important effects. The p-value threshold of brain activity and cluster size is an unresolved issue in the literature, typically specified at the p < 0.05 level and 20 voxels. However, to achieve a balance between Type I and Type II errors, Lieberman and Cunningham recommend other parameters (i.e., p <.005 with 10 voxels), while proposing a greater emphasis on meta-analysis and replication to balance between these two types of errors. The key steps required for the data analysis of fmri data are summarized in Table 5. For more details on analyzing fmri data, see Frackowiak et al. (2004) and Friston (2004); for a detailed example of fmri data analysis, see Knutson et al. (2007) and Kuhnen and Knutson (2007). Interpreting and Reporting fmri Results Interpreting fmri Results In terms of interpreting fmri results, given that the neuroscience literature has already identified the neural correlates of many social processes (Appendix B), the first step would be to interpret fmri results in view of related studies in the literature. Given the wealth of knowledge on the functionality of brain areas associated with many processes, researchers are advised to compare their results with related fmri studies to examine differences and similarities with the literature. By comparing findings with functional and neurological findings from the literature, researchers can enrich their interpretation of the fmri results. Therefore, the interpretation of fmri results should first be performed relative to the neuroscience literature and the similarities and differences from the literature should be a prominent issue in the interpretation. Second, it is important to avoid over-interpretation of brain activations, particularly the phenomenon of reverse inference discussed earlier. While activation in a certain brain area can be linked to the particular process used to trigger the brain activation in the focal study (Poldrack 2006), a brain activation is not necessary for a particular task or associated construct to be present (Van Horn and Poldrack 2009). Accordingly, causally backward inferences in the form of brain area X is activated, thereby prior process Y must be involved should be avoided, particularly when drawing inferences in a MIS Quarterly Vol. 36 No. 3/September

22 confirmatory manner. For example, in Dimoka, activation in the caudate nucleus does not necessarily suggest the existence of trust; there is evidence that this reward area of the brain can be activated by many other processes and constructs. Simply put, activation in the caudate nucleus is associated with the particular trust-building stimulus in the Dimoka study; still, one cannot infer that every time we observe activation in the caudate nucleus, this is necessarily trust. Therefore, as noted several times earlier, it is important for social scientists to appreciate that there is a many-to-many relationship among brain areas and human processes (e.g., Miller 2008; Poldrack 2006; Price and Friston 2005). Reverse inference should be used very selectively and with extreme care to interpret some findings ex post, such as brain area X is activated in our study, perhaps implying that process Y may be involved given the frequent involvement of brain area X into process Y in the literature. Such reverse inference should be backed up by similar findings in the literature that link similar human processes to the focal brain areas (Cacioppo et al. 2008). Third, despite the high spatial resolution of fmri, it is important to realize that voxel-wise analysis is still statistical in nature and so too-definitive statements about which specific brain areas are activated should be avoided. Besides, given the multiple statistical steps required to ensure proper comparisons across subjects, such as realignment, normalization, and co-registration (see Analyzing fmri Data ), it is not possible to make strict inferences about the exact localization of brain activity. The observed brain activations should be interpreted relative to similar studies in the literature before specifying the exact brain areas implicated in a process. Finally, it is important to refrain from strong causal interpretation of fmri results. Brain activations are statistical in nature (as described in the section Analyzing fmri Data ), and causality should be inferred judiciously when all criteria are satisfied (covariation, temporal precedence, ruling out rival explanations, and active manipulation of the independent variable the experimental stimulus) (Cook and Campbell 1979). Similar to behavioral lab experiments, the actively manipulated experimental stimuli are the likely covariates of the observed brain activations preceded temporally by the stimuli. Nonetheless, given our relatively weak knowledge of how the brain works and the nature of the BOLD effect upon which the fmri data are based, we cannot rule out all alternative possible explanations and threats to validity (Cook and Campbell 1979). Thus, researchers should refrain from making strong causal interpretations with fmri data. In summary, researchers should view fmri results as complementary findings to tentatively support theoretical conjectures rather than unequivocal and indisputable. Reporting fmri Results As described earlier (see Figure 6), the reported output of fmri studies consists of two types of images: (1) the anatomical images that show the brain s anatomical structure and (2) the functional images that show the level of BOLD signal in the form of voxels in color-coded SPMs, superimposed on the anatomical image for better visualization. Reporting of functional images typically involves presentation of figures with thresholded color-coded SPMs (as shown in Appendix C), and tables that show the locations where significant activations are observed, as shown in Appendix C (for a review, see Poldrack et al. 2008). In general, fmri results should be presented in figures and tables, as described in more detail below. Figures: fmri results are usually reported in the form of figures that graphically show the activated brain areas using a thresholded color-coded statistical map that specifies the intensity of brain activations and illustrates the number of activated voxels. Additional details include the view of the anatomical image (e.g., axial, coronal, saggital) and any details needed to precisely localize the areas of brain activation, both being provided in a figure caption or in the text. It is important to note that the thresholded color-coded images are statistical maps that imply a direct statistical comparison of the baseline and experimental conditions and that brain activation is present (statistically) in one area and absent (statistically) from another (Henson 2005). Researchers should explicitly state that there is a statistically significant effect across two contrasts (experimental versus control, for example), which produces significant brain activations to overcome concerns that fmri figures are somehow real world pictures of brain activity. Examples of representative information to include in figures with fmri results are shown in Appendix C (Figure C1), a similar figure was presented in Dimoka. Other representative examples include Rilling et al. (2004). Tables: fmri results should be reported in tables to precisely specify the location of brain activations in stereotactic space (showing the exact coordinates of all brain activations in either MNI or Talairach and Tournoux (1989) space), number of voxels in each activated area, activation statistics of each brain area (e.g., peak activation), along with anatomical labels. Examples of useful information to include in tables are shown in Appendix C (Table C1), while a detailed example is offered by Decety et al. (2004) and Rilling et al. (2004). In the Dimoka study, the functional brain activations (ROI analysis) associated with trust and distrust were presented in 832 MIS Quarterly Vol. 36 No. 3/September 2012

23 both figures and tables. Specifically, Appendix C shows the fmri results for trust and distrust for the high trust/low distrust (HL) and low trust/high distrust (LH) sellers using a simplified version of Dimoka s results. Appendix C depicts the exact localization of the neural correlates of trust and distrust. 21 Besides Dimoka, other examples of presenting fmri results in figures are King-Casas et al. (2005) and Sanfey et al. (2005). A more detailed illustration of fmri results is given by Bhatt and Camerer (2005). Drawing Theoretical Implications: As in all studies in the social sciences, fmri results should be used to draw theoretical implications about existing theories in the social sciences. As elaborated earlier in the section Formulating Research Questions for an fmri Study, stating the research question to be answered through fmri data ensures that some meaningful implications for theory are derived, creating novel insights that may guide social science theories to be more consistent with the functioning of the brain. Indeed, many more social scientists can certainly contribute to the emerging neuroscience literature by adding knowledge about the neural correlates of new, interesting constructs. In the Dimoka study, whether trust and distrust are distinct constructs or the ends of the same continuum has been a key question for several disciplines in the social sciences. Therefore, offering brain evidence that trust and distrust are neurologically distinct constructs with clearly distinct neural correlates may be viewed as having useful theoretical implications for the social sciences. Thus, the results may guide future research on trust and distrust to treat these two constructs as distinct variables with different antecedents and effects. Dimoka s findings contributed to the neuroscience literature by specifying the neural correlates of these two dimensions of trust (credibility and benevolence) and distrust (discredibility and malevolence). In doing so, the study showed that there is no single brain area dedicated to trust or distrust, but rather a network of brain areas comprised of dedicated areas related to the dimensions of trust and distrust jointly form the constructs of trust and distrust. Similar to trust and distrust, most complex theoretical constructs are 21 In Dimoka, the neural correlates of trust (specifically the caudate nucleus and putamen) were believed to capture the trustor s confident expectations about anticipated rewards (e.g., King-Casas et al. 2005); the anterior paracingulate cortex was shown to capture the trustor s prediction of how the trustee will act (McCabe et al. 2001); the orbitofrontal cortex was explained to capture the uncertainty due to the trustor s willingness to be vulnerable (e.g., Krain et al. 2006). In terms of the neural correlates of distrust, the insular cortex were interpreted to capture the trustor s fear of loss (Todorov 2008), while the observed amygdala activation was thought to capture the trustor s intense negative emotions of vigilance and betrayal (consistent with Winston et al. 2002). likely to be associated with several brain areas. Accordingly, researchers should not focus on simplistic one-to-one correlations between brain areas and constructs but rather the network of brain areas that underlie these constructs and explain their subprocesses. In terms of interpreting and reporting fmri results, it is important to avoid over-interpretation by closely reflecting on the neuroscience literature, avoiding liberal reverse or forward inferences (Poldrack 2006). fmri data also can be used for statistical analysis by comparing with other types of data, such as behavioral or secondary data. Moreover, fmri results should be used to draw managerial implications, as noted below. Drawing Managerial Implications: fmri findings could have broader implications for managerial practice. Hence, social scientists, particularly in applied sciences, should consider reporting the managerial implications of fmri results. Implications could be in the form of deep knowledge about the nature of human processes that could guide the design of specific interventions for particular segments of the population to selectively manipulate these processes. Or they could be in more specific terms, such as designing firm web sites in such a manner that customer trust is maximized and distrust is minimized. Dimoka offered several managerial implications from the distinction between trust and distrust. As trust and distrust reside in distinct brain areas with distrust being more affective, abrupt, and impulsive, and trust being more cognitive, calculative, and long-lived, managers could design distinct strategies for building trust through deliberate and long-term efforts that rely on cognitive calculation while preventing distrust by carefully avoiding situations that could trigger the abrupt and affective nature of distrust. Potential Comparisons between fmri Data and Other Data: Given the continuous nature of fmri data, it is possible to use them in statistical analyses with other data, such as psychometric and behavioral data. Specific guidelines for integrating fmri with other sources of data are offered by Huettel and Payne (2009) and Yoon et al. (2009); a note of caution is offered by Vul et al. (2009). Potential statistical analyses include correlation and regression. fmri can be also combined with other neurophysiological measures (Dimoka, Banker et al. 2012), such as EEG for enhancing the temporal resolution of fmri (e.g., Menon and Crottaz-Herbette 2005) and TMS (transcranial magnetic stimulation ), a technique that temporarily and noninvasively suppresses certain brain areas (e.g., Knoch et al. 2006; Miller 2008). Such approaches can strengthen causal inferences. MIS Quarterly Vol. 36 No. 3/September

24 In the Dimoka study, statistical comparisons, including correlation and regression analyses, contrasted fmri data on trust and distrust with the corresponding psychometric data on trust and distrust. Other examples of fmri studies that integrate fmri with either behavioral or psychometric data are Delgado et al. (2005), Hsu et al. (2005), and Linden et al. (2003). fmri data can also be used to predict behavioral responses (e.g., Paulus et al. 2003). Finally, examples of meta-analyses of fmri studies that aim to specify the neural correlates of well-studied processes are Krain et al. (2006) and Phan et al. (2002). In sum, general advice for reporting fmri results is to provide adequate detail so the fmri study can be initially reproduced and interpreted by others. For more details on interpreting and reporting fmri results, see Poldrack et al. (2008) and Ramsey et al. (2002). Concluding Remarks This study has implications for conducting high-quality fmri studies in social science research by providing a set of guidelines for fmri studies and specifying methodological details that could be included in academic papers. Coupled with earlier work on why to conduct fmri studies (Dimoka et al. 2011) and what to examine with fmri (Dimoka, Banker et al. 2012), this paper on how to conduct an fmri study aims to provide a detailed blueprint for why, what, and how to conduct fmri studies in the social sciences. fmri data are particularly valuable when existing sources of data cannot offer reliable data, and the goal of fmri data should be to create novel insights beyond existing sources of data. Simply conducting an fmri study with the sole aim to identify neural correlates may often not be enough. The author advises researchers to rely on the neuroscience literature to propose hypotheses that can be tested with fmri data, thus not flying blind and relying on existing knowledge, whenever possible. While fmri studies should have compelling motivations and be guided by the literature, exploratory work is a well-accepted practice in the neuroscience literature by allowing brain data to speak for itself. Given that formulating interesting research questions is a challenging task, especially for fmri studies, there are several studies that have attempted to offer guidance on formulating research questions for fmri. Dimoka et al. (2011) offered a set of seven opportunities that focus on (1) localizing the neural correlates of constructs, (2) capturing hidden mental processes, (3) complementing existing sources of data with brain data, (4) identifying antecedents of constructs, (5) testing consequences of constructs, (6) inferring the temporal ordering among constructs, and (7) challenging assumptions and enhancing existing theories. Dimoka, Banker et al. (2012) proposed several examples of research questions that could benefit from the use of neurophysiological tools in the context of Information Systems research, specifically in three major areas of IS research: (1) development and use of systems, (2) IS strategy and business outcomes, and (3) group work and decision support. Riedl et al. (2010) also reflected on the discussions of a retreat on NeuroIS to propose another set of suggestions for IS research. In sum, there are several sources that help guide research questions that could be potentially answered with fmri studies in the social sciences, and this paper focuses on how to appropriately undertake such fmri studies. It is important to maintain that these suggested guidelines should not be viewed as strict rules for how to conduct fmri studies but rather a flexible set of tenets that are likely to evolve as the discipline matures. Moreover, there are technical books that provide extensive details about how to conduct an fmri study (e.g., Buxton 2002; Faro and Mohamed 2005; Friston 2006; Huettel et al. 2005; Jezzard et al. 2001, Poldrack 2006). Social scientists should remain current to the rapid advancement of recent extensions of fmri methods (e.g., Bandettini 2009), such as diffusion spectrum imaging that examines interconnectivity and temporal sequencing among brain areas (e.g., Hagmann et al. 2008), pattern classification methods that can identify patterns of brain activations (e.g., Kriegeskorte et al. 2006), and clustering tools that help identify functionally connected brain areas (Stanberry et al. 2008). It is expected that major advances in neuroimaging tools will take place in the next few years, and social scientists who conduct fmri studies should stay attuned to these developments. Reasonable Expectations for Reviewers and Editors Journal reviewers and editors should consider two critical points before hastily dismissing fmri studies as not meeting traditional standards for traditional experiments. First, subject sample sizes, for the interim, will tend to be small. While this is currently a function of high cost, and the time involved to collect and analyze data per subject, fmri scholars can still make legitimate empirical inferences and test theories. While researchers typically have 10 to 20 subjects, there are tens of thousands of observations per subject. fmri studies will continue to report very small subject sample sizes, but with 834 MIS Quarterly Vol. 36 No. 3/September 2012

25 family-wise adjustments that can correct for errors in statistical inference (as pointed out earlier in this paper), we can interpret the huge number of voxels in the data and draw reasonable scientific conclusions about brain activity responding to highly specific stimuli. Second, with particular regard to IS studies, the short history of fmri studies means that there will be much less prior literature using neurophysiological methods. While self-correcting over time, reviewers and editors will have to give degrees of freedom to authors in their referring to analogs to studies in other domains and disciplines. As fmri and other neurophysiological studies increasingly find their way into the IS literature, this problem will recede in significance, but, as with any other new areas, forward-looking journal editors and reviewers will see the long term value of opening up new channels of exploration and taking more risk in trusting results from scientific methods that are new to the discipline. Looking Toward the Future With the improvement of fmri scanners that permit faster and higher resolution images with less noise and at a lower cost, fmri is likely to continue becoming a method of choice for measuring brain activity in the social sciences. For example, human computer interaction research is already looking into fmri to evaluate system designs (e.g., Minnery and Fine 2009). As with all new measurement tools, they are most useful when they are complemented by existing methods and data collection tools (Cacioppo et al. 2008), such as field studies, field and lab experiments, research instruments, interviews, archival data, and simulation models. In fact, fmri can benefit from other brain imaging tools, such as EEG, near-infrared spectroscopy (NIRS), and transcranial magnetic stimulation (TMS), to enhance its temporal resolution and help infer causality (Dimoka et al. 2011; Riedl et al. 2010). Similar to other methodological paradigms in the social sciences, such as experiments (Jarvenpaa et al. 1985), case studies (Benbasat et al. 1987), and theory of measurement (e.g., Burton-Jones 2009; Burton-Jones and Straub 2006; Straub et al. 2004), it is necessary for new paradigms, such as fmri, to establish their own practical guidelines, quality controls, and best practices. 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28 Knoch, D., Treyer, D., Regard, M., Müri, R. M., Buck, A., and Weber, B Lateralized and Frequency-Dependent Effects of Prefrontal rtms on Regional Cerebral Blood Flow, NeuroImage (31:2), pp Knutson, B. Rick, S., Wimmer, G. E., Prelec, D., and Loewenstein, G Neural Predictors of Purchases, Neuron (53), pp Krain, A., Wilson, A. M., Arbuckle, R., Castellanos, F. X. Milham, M. P Distinct Neural Mechanisms of Risk and Ambiguity: A Meta-Analysis of Decision-Making, NeuroImage (32:1), pp Kriegeskorte, N., Goebel, R., and Bandettini, P Information-Based Functional Brain Mapping, Proceedings of the National Academy of Sciences of the United States of America (103), pp Kuhnen, C., and Knutson, B The Neural Basis of Financial Risk Taking, Neuron (47:5), pp Lange, N Statistical Procedures for Functional MRI, in Functional MRI, C. T. W. Moonen and P. A. 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29 Pavlou, P. A., and Dimoka, A The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation, Information Systems Research (17:4), pp Pessoa, L On the Relationship Between Emotion and Cognition, Nature (9), pp Phan, K. L., Taylor, S. F., Welsh, R. C., Ho, S., Britton, J. C., and Liberzon, I Neural Correlates of Individual Ratings of Emotional Salience: A Trial-Related fmri Study, NeuroImage (21), pp Phan, K. L., Wager, T., Taylor, S. F., and Liberzon, I Functional Neuroanatomy of Emotion: A Meta-Analysis of Emotion Activation Studies in PET and fmri, NeuroImage (16), pp Phelps, E. A Emotion and Cognition: Insights from Studies of the Human Amygdala, Annual Review of Psychology (57:1), pp Poldrack, R. A Can Cognitive Processes be Inferred from Neuroimaging Data?, Trends in Cognitive Sciences (10:2), pp Poldrack, R. 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30 Worsley, K. J Statistical Analysis of Activation Images, in Functional MRI: An Introduction to Methods, P. Jezzard, P. M. Matthews, and S. M. Smith (eds.), New York: Oxford University Press, pp Worsley, K. J., and Friston, K. J Analysis of fmri Time- Series Revisited Again, NeuroImage (2), pp Yoon, C., Gonzales, R., and Bettman, J. R Using fmri to Inform Marketing Research: Challenges and Opportunities, Journal of Marketing Research (46:1), pp Yoon, C., Gutchess, A. H., Feinberg, F., and Polk, T. A A Functional Magnetic Resonance Imaging Study of Neural Dissociations between Brand and Person Judgments, Journal of Consumer Research (33:1), pp Zheng, E., and Pavlou, P. A Toward a Causal Interpretation for Structural Models: A New Bayesian Networks Method for Observational Data with Latent Variables, Information Systems Research (21:2), pp About the Author Angelika Dimoka is an associate professor of Marketing and Management of Information Systems at the Fox School of Business, Temple University. She is also director of the Center for Neural Decision Making. She holds a Ph.D. from the Viterbi School of Engineering (specialization in Neuroscience and Brain Imaging) with a minor in MIS from the Marshall School of Business at the University of Southern California. Angelika s research interests are in the areas of cognitive neuroscience and functional neuroimaging in marketing and MIS (Neuromarketing and NeuroIS), quantitative analysis of uncertainty in online marketplaces, and modeling of information pathways in the brain. Her research has appeared in Information Systems Research, MIS Quarterly, NeuroImage, IEEE Transactions in Biomedical Engineering, Annals of Biomedical Engineering, IEEE in Biology and Medicine, and Neuroscience Methods. 840 MIS Quarterly Vol. 36 No. 3/September 2012

31 RESEARCH ESSAY HOW TO CONDUCT A FUNCTIONAL MAGNETIC RESONANCE (FMRI) STUDY IN SOCIAL SCIENCE RESEARCH 1 Angelika Dimoka Fox School of Business, Temple University, Philadelphia, PA U.S.A. {angelika@temple.edu) } Appendix A Glossary Arteries Axial B 0 Term Basis functions Between-subjects Block Blocked design Blood-oxygenation-leveldependent (BOLD) contrast Bonferroni correction Brodmann areas Central nervous system (CNS) Cerebrospinal fluid (CSF) Consent form Contrast Explanation Blood vessels that carry oxygenated blood from the heart to the rest of the body. A horizontal view of the brain (along the x y plane in MRI) The strong static magnetic field generated by an MRI scanner. A set of functions whose linear combination can take on a wide range of functional forms. In fmri analyses, researchers often replace a single hemodynamic response function with basis functions, to improve the flexibility of their design matrices. A manipulation in which different conditions are assigned to different subject groups. A time interval that contains trials from one condition. The separation of experimental conditions into distinct blocks, so that each condition is presented for an extended period of time. The difference in signal on T 2 *-weighted images as a function of the amount of deoxygenated hemoglobin. A stringent form of family-wise error rate correction for multiple comparisons in which the alpha value is decreased proportionally to the number of independent statistical tests. Divisions of the brain based on the influential cyto-architectonic criteria initially proposed by Korbinian Brodmann. The system composed by the brain and the spinal cord. A colorless liquid that surrounds the brain and spinal cord and fills the ventricles within the brain. An abstract concept that explains behavior but which itself is not directly observable. Attention is an example of a psychological construct. (1) The intensity difference between different quantities being measured by an imaging system. It also can refer to the physical quantity being measured (e.g., T 1 contrast). (2) A statistical comparison of the activation evoked by two (or more) experimental conditions, in order to test a research hypothesis. MIS Quarterly Vol. 36 No. 3 Appendices/September 2012 A1

32 Term Control block Control condition Converging operations Co-registration Coronal Correlation analysis Deactivations Deoxygenated Hemoglobin (dhb) Design Matrix Diamagnetic Dorsal Event Event-related design False Discovery Rate Field map Field of view (FOV) Field potentials Field strength Flip angle Frontal lobe Functional magnetic resonance imaging (fmri) General linear model (GLM) Gradient coils Hemodynamic response (HDR) Independent components analysis (ICA) Informed consent Institutional Review Board (IRB) Interleaved slice acquisition Explanation A time interval that contains trials of the control condition. A condition that provides a standard to which the experimental condition(s) can be compared. Also called the baseline condition or non-task condition. The use of two or more techniques to provide complementary evidence used to test an experimental hypothesis or scientific theory. The spatial alignment of two images or image volumes. A frontal view of the brain (along the /x z/ plane in MRI). A type of statistical test that evaluates the strength of the relation between two variables. For fmri studies, correlation analyses typically evaluate the correspondence between a predicted hemodynamic response and the observed data. Decreases in BOLD activation during task blocks compared with non-task blocks. Hemoglobin without attached oxygen; it is paramagnetic. In fmri implementations of the general linear model, the specification of how the model factors change over time. Having the property of a weak repulsion from a magnetic field. Toward the top of the brain. A single instance of the experimental manipulation. Also known as a trial. Presentation of discrete, short-duration events with randomized timing and order. Statistical method to correct for multiple comparisons in many hypotheses testing. An image of the intensity of the magnetic field across space. The total extent of an image along a spatial dimension. Changes in electrical potential over space associated with postsynaptic neuronal activity. The magnitude of the static magnetic field generated by a scanner, typically expressed in units of Tesla. The change in the precession angle of the net magnetization following excitation. The most anterior lobe of the cerebrum; it is important for executive processing, motor control, memory, and many other functions. A neuroimaging technique that uses standard MRI scanners to investigate changes in brain function over time. A class of statistical tests that assume that the experimental data are composed of the linear combination of different model factors, Electromagnetic coils that create controlled spatial variation in the strength of the magnetic field. The change in MR signal on T_2 * images following local neuronal activity. The hemodynamic response results from a decrease in the amount of deoxygenated hemoglobin present within a voxel. An important class of data-driven analyses that identify stationary sets of voxels whose activations vary together over time and are maximally distinguishable from other sets. The process in which a potential subject voluntarily agrees to participate in a research study, after learning about the procedures, risks, and benefits of the study. An independent group of individuals who evaluate the ethical practice of research conducted at their institution. Researchers who wish to conduct research with human subjects should generally receive the approval of their local IRB. The collection of data in an alternating order, so that data are first acquired from the oddnumbered slices and then from the even-numbered slices, to minimize the influence of excitation pulses upon adjacent slices. A2 MIS Quarterly Vol. 36 No. 3 Appendices/September 2012

33 Term Interstimulus interval (ISI) Intersubject correlations Jittering Magnetic resonance MNI space MR signal Neural Correlates Normalization Parameter matrix Peak Pixel Preprocessing Pulse sequence Random-effects analysis Reaction time Reference volume Region-of-interest (ROI) analyses Relaxation Repetition time (TR) Response time Reverse inference Right-hand rule Rigid-body transformation Rostral Sagittal Segmentation Explanation The separation in time between successive stimuli. Usually refers to the time between the end of one stimulus and the onset of the next, with the term stimulus-onset asynchrony (SOA) used to define the time between successive onsets. Functional MRI time courses that are shared by different individuals while performing the same experimental tasks or experiencing the same stimuli. Randomizing the intervals between successive stimulus events over some range. Absorption of energy from a magnetic field that oscillates at a particular frequency. A commonly used space for normalization of fmri data; its coordinates are derived from an average of MRI structural images from several hundred individuals. The current measured in a detector coil following excitation and reception. Brain areas activated by a certain construct The transformation of MRI data from an individual subject to match the spatial properties of a standardized image, such as an averaged brain derived from a sample of many individuals. A matrix that describes the relative contributions of each model to the observed data for each voxel. The maximal amplitude of the hemodynamic response, occurring typically about 4 to 6 s following a short-duration event. A two-dimensional picture element. Computational procedures that are applied to fmri data following image reconstruction but before statistical analysis. Preprocessing steps are intended to reduce the variability in the data that is not associated with the experimental task, and to prepare the data for statistical testing. A series of changing magnetic field gradients and oscillating electromagnetic fields that allows the MRI scanner to create images sensitive to a particular physical property. Intersubject analysis that treats the effect of the experimental manipulation as variable across subjects, so that it could have a different effect on different subjects. The time required for someone to make a simple motor response to the presentation of a visual stimulus. Note that this is distinct from response time, which applies to situations in which someone should choose between two or more possible responses. A target image volume to which other image volumes are to be aligned. Evaluations of hypotheses about the functional properties of brain regions (i.e., aggregated over a pre-determined set of voxels), often chosen to reflect a priori anatomical distinctions within the brain. A change in net magnetization over time. The time interval between successive excitation pulses, usually expressed in seconds. The time required for someone to execute a choice between two or more possible responses. Note that this is distinct from reaction time, which applies to situations when only one possible response is present. Reasoning from the outcome of a dependent variable to infer the state of an independent variable (or an intervening unobservable variable). A method used to determine the direction of a magnetic moment generated by a moving charge or electrical current. If the fingers of the right hand are curled around the direction of spin, then the magnetic moment will be in the direction indicated by the thumb. A spatial transformation that does not change the size or shape of an object; it has three translational parameters and three rotational parameters. Toward the front of the brain. A side view of the brain (along the /y/ /z/ plane in MRI). The process of partitioning an image into constituent parts, typically types of tissue (e.g., gray matter, white matter) or topographical divisions (e.g., different structural regions like Brodmann areas). MIS Quarterly Vol. 36 No. 3 Appendices/September 2012 A3

34 Signal Term Signal-to-noise ratio (SNR) Slice Slice selection Small-volume correction Smoothness Spatial smoothing Static magnetic field Statistical map (or Statistical parameter map) Stereotaxic space Subtraction Superposition Talairach space Time course Time series Translation Trial Ventral Volume (brain) Volumetric Voxel Explanation Meaningful changes in some quantity. For fmri, an important class of signals includes changes in intensity associated with the BOLD response. The relative strength of a signal compared with other sources of variability in the data. A single slab of an imaging volume. The thickness of the slice is defined by the strength of the gradient and the bandwidth of the electromagnetic pulse used to select it. The combined use of a spatial magnetic field gradient and an electromagnetic pulse to excite spins within a slice. The restriction of analyses to specific regions-of-interest, defined a priori, to reduce the total number of statistical tests and thus allow for a more liberal significance threshold. The degree to which the time courses of nearby voxels are temporally correlated. The blurring of fmri data across adjacent voxels to improve the validity of statistical testing and maximize functional SNR, at a cost of spatial resolution. The strong magnetic field at the center of the MRI scanner whose strength does not change over time. The strength of the static magnetic field is measured in Tesla. In fmri, the labeling of all voxels within the image according to the outcome of a statistical test. A precise mapping system (e.g., of the brain) using 3-D coordinates. In experimental design, the direct comparison of two conditions that are assumed to differ only in one property, the independent variable. A principle of linear systems that states that the total response to a set of inputs is equivalent to the summation of the independent responses to the inputs. A commonly used space for normalization of fmri data; its coordinates are based on measurements from a single post-mortem human brain, as published in an atlas by Talairach and Tournoux. The change in MR signal over a series of fmri images. A large number of fmri images collected at different points in time. The movement of an object along an axis in space (in the absence of rotation). A single instance of the experimental manipulation, such as stimulus presentation. Toward the bottom of the brain. The collection of all images of the brain, consisting of multiple slices and voxels. Relating to the measurement of volume. A three-dimensional volume element. Voxel-wise analysis Evaluation/testing of hypotheses about functional properties of individual voxels (or small clusters of voxels), often throughout the entire brain volume. A4 MIS Quarterly Vol. 36 No. 3 Appendices/September 2012

35 Appendix B Review of Neuroscience Literature Related to Constructs of Interest to the Social Sciences Construct/Process Sample Brain Areas* Key References Ambiguity Insular cortex, Parietal cortex Krain et al Anger Lateral Orbitofrontal cortex Murphy et al Anxiety Amygdala prefrontal circuitry, Inferior Frontal gyrus (Brodman Area 45), Ventromedial Prefrontal cortex Bishop 2007 Etkin and Wager 2007 Mujica-Parodi 2007 Attention Right Frontal and Parietal cortices and Thalamus Coull et al Automaticity Frontal and Striatal cortex, Parietal lobe (Deactivation) Kubler et al Poldrack et al Calculation Anterior Cingulate cortex, Prefrontal cortex limbic system (mainly anterior cingulate cortex and amygdala) Ernst and Paulus 2005 McClure et al Cognitive Effort Dorsolateral prefrontal cortex, Parietal cortex Linden et al Owen et el Competition Inferior parietal cortex, Medial Prefrontal cortex Decety et al Consciousness Parietal and Dorsal Prefrontal cortex, Striate cortex, Extrastriate cortex Rees, Kreiman, and Koch 2002 Rees, Wojciulik et al Cooperation Orbitofrontal cortex Rilling, Gutman et al Disgust Insular cortex Britton et al Lane et al Murphy et al Phan et al Displeasure Amygdala, Hippocampus, Insular cortex, Superior Temporal gyrus Britton et al Casacchia et al Distrust Amygdala, Insular cortex Dimoka 2010 Winston et al Emotion in Moral Judgment Medial Prefrontal cortex, Posterior Cingulate, and angular gyrus Greene et al Emotional Processing Anterior Cingulate cortex, Medial Prefrontal cortex (Emotional Information- dorsal frontomedial cortex) Damasio 1996 Ferstl et al Phan et al Envy Anterior Cingulate cortex Takahashi et al Fear Amygdala LeDoux 2003 Murphy et al Phan et al Flow Dorsomedial Prefrontal cortex, Medial Parietal cortex Iacobini et al Katayose 2006 Frustration Right Anterior Insula, Right Ventral Prefrontal cortex Abler et al Habit Basal Ganglia, Medial Prefrontal cortex, Medial Temporal lobe Graybiel 2008 Salat et al Happiness Basal Ganglia (Ventral Striatum and Putamen) Murphy et al Phan et al Hate Medial Frontal gyrus, Right Putamen, Bilaterally in Premotor cortex, Frontal Pole and bilateral Medial Insula, Right Insula, Right Premotor cortex, Right Fronto-Medial gyrus Zeki and Romaya 2008 MIS Quarterly Vol. 36 No. 3 Appendices/September 2012 A5

36 Construct/Process Sample Brain Areas* Key References Information Processing Anterior Frontal cortex, Lateral Prefrontal cortex, Medial Orbitofrontal cortex Hippocampus, Amygdala (Emotional Information- Dorsal Frontomedial cortex) Dimoka and Davis 2008 Elliot et al Ferstl et al Intentions Ventrolateral Prefrontal cortex, Brodmann Area 47 Dove et al Okuda et al Jealousy Left Prefrontal cortex Harmon-Jones et al Language function Broca s area McDermott et al Loss Insular cortex Paulus and Frank 2003 Love (maternal) Ventral part of Anterior Cingulate cortex Bartels and Zeki 2004 Love (overlap of maternal and romantic) Love (romantic) Moral Judgments Moral Sensitivity Striatum (Putamen, Globus Pallidus, Caudate Nucleus), Middle Insula and Dorsal Anterior Cingulate cortex Dentate gyrus/hippocampus, Hypothalamus, Ventral Tegmental area Frontopolar cortex (Brodmann Area 10), Posterior Superior Temporal Sulcus Amygdala, Thalamus, Upper Midbrain, Medial Orbitofrontal cortex, Medial Prefrontal cortex, Superior Temporal Sulcus Bartels and Zeki 2004 Bartels and Zeki 2004 Borg et al Moll et al Moll et al Motor Intentions Premotor and Parietal cortex Desmurget et al Lau et al Multi-Tasking Fronto-polar cortex (Brodman Area 10) Dreher et al Optimism Rostral Anterior Cingulate cortex, Amygdala Sharot et al Person Recognition Left Hippocampus, Left Middle Temporal gyrus, Left Insula, and Bilateral Cerebellum Paller et al Pleasure/Enjoyment Priming Rewards and Utility Anterior Cingulate cortex, Putamen, Medial Prefrontal cortex,nucleus Accumbens Parietal cortex, Middle Temporal cortex, Posterior Superior cortex Anterior Cingulate cortex, Caudate Nucleus, Nucleus Accumbens, Putamen Klasen et al McLean et al Sabatinelli et al Naccache and Dehaene 2001 Wible et al Bush et al Delgado et al McClure et al Risk Nucleus Accumbens Knutson et al Sadness Subcallosal Cingulate cortex Murphy et al Phan et al Self-reflection Medial Prefrontal cortex, Posterior Cingulate Johnson et al Self-regulation of emotion Amygdala, Dorsolateral Prefrontal cortex, Hypothalamus Beauregard et al Social Cognition Amygdala, Cingulate cortex, Temporal lobe, Orbitofrontal Adolphs 1999, 2001 cortex, Right Somatosensory cortex, Ventromedial Frontal cortex Amygdala, Orbitofrontal cortex, Dorsolateral Prefrontal Rilling, Glenn et al Social Cooperation cortex Spatial Cognition Hippocampus, Medial Temporal Lobe Moser et al Shrager et al Sympathy Anterior Superior Frontal gyrus, Inferior Frontal gyrus, Temporal pole, Amygdala, Left Central Sulcus, Right Dorsal Premotor cortex, Dorsomedial prefrontal cortex, pre-sma, and Inferior Parietal lobule Decety et al A6 MIS Quarterly Vol. 36 No. 3 Appendices/September 2012

37 Construct/Process Sample Brain Areas* Key References Task Intentions Anterior Cingulate cortex, Medial and Lateral Prefrontal Haynes et al Winterer et al Theory of Mind Anterior Paracingulate cortex, Medial Prefrontal cortex McCabe et al Trust Anterior Paracingulate cortex, Caudate Nucleus, Putamen Dimoka 2010 King-Casas et al Uncertainty Orbitofrontal and Parietal cortex Huettel et al Krain et al Unfairness Dorsolateral Prefrontal cortex, Ventrolateral Prefrontal cortex, Dulebohn et al Superior Temporal Sulcus Visual Perception Dorsal Lateral Geniculate nucleus to the temporal lobe Pollen 1999 Working Memory Anterior Cingulate, Dorsolateral Prefrontal cortex, Orbital Cortical cortex Braver et al Callicott et al Cohen et al *As discussed in the paper in more detail ( Basic Neuroscience Foundations), while it is possible to make a (forward) inference that a specific construct is associated with certain brain areas, caution should be paid when trying to infer a construct based on the existence of activation in certain brain areas (termed reverse inference). Given that the same brain areas are activated in response to several constructs (as clearly noted in Appendix B), such over-interpretation is problematic. For more details on forward and reverse inference, see Lieberman (2007) and Poldrack (2008). References Abler, B., Walter, H., and Erk, S Neural Correlates of Frustration, Neuroreport (16:7), pp Adolphs, R Social Cognition and the Brain, Trends in Cognitive Science (3:12), pp Adolphs, R The Neurobiology of Social Cognition, Current Opinion in Neurobiology (11), pp Bartels, A., and Zeki, S The Neural Correlates of Maternal and Romantic Move, NeuroImage (21:3), pp Beauregard, M., Le vesque, J., and Bourgouin, P Neural Correlates of Conscious Self-Regulation of Emotion, The Journal of Neuroscience (21), pp Bishop, S. J Neurocognitive Mechanisms of Anxiety: An Integrative Account, Trends in Cognitive Sciences (11:7), pp Borg, J. S., Hynes C., Van Horn, J., Grafton, S., and Sinnott-Armstrong, W Consequences, Action, and Intention as Factors in Moral Judgments: An fmri Investigation, Journal of Cognitive Neuroscience (18:5), pp Braver, T. S., Cohen, J. D., Jonides, J., Smith, E. E., and Noll, D. C A Parametric Study of Prefrontal Cortex Involvement in Human Working Memory, NeuroImage (5:1), pp Britton, J. C., Phan, K. L., Taylor, S. F., Welsh, R. C., Berridge, K. C., and Liberzon, I Neural Correlates of Social and Nonsocial Emotions: An fmri Study, NeuroImage (31), pp Bush, G., Vogt, B. A., Holmes, J., Dale, A. M., Greve, D., Jenike, M. A., and Rose, B. R Dorsal Anterior Cingulate Cortex: A Role in Reward-Based Decision Making, Proceedings of the National Academy of Sciences of the United States of America (99:1), pp Callicott, J. H., Mattay, V. S., Bertolino, A., Finn, K., Coppola, R., Frank, J. A., Goldberg, T. E., and Weinberger, D. R Physiological Characteristics of Capacity Constraints in Working Memory as Revealed by Functional MRI, Cerebral Cortex (9), 1999, pp Casacchia, M., Mazza, M., Catalucci, A., Pollice, R., Gallucci, M., and Roncone, R Abnormal Emotional Responses to Pleasant and Unpleasant Visual Stimuli in First Episode Schizophrenia: f-mri Investigation European Psychiatry (24:1), pp. S700. Cohen, J. D., Perlstein, W. M., Braver, T. S., Nystrom, L. E., Noll, D. C., Jonides, J. and Smith, E. E Temporal Dynamics of Brain Activation during a Working Memory Task, Nature (386), pp Coull, J. T Neural Correlates of Attention and Arousal: Insights from Electrophysiology, Functional Neuroimaging and Psychopharmacology, Progress in Neurobiology (55:4), pp Damasio, A. R., The Somatic Marker Hypothesis and the Possible Functions of the Prefrontal Cortex, Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences (351), pp Decety, J., and Chaminade, T Neural Correlates of Feeling Sympathy, Neuropsychologia (41:2), pp Decety, J., Jackson, P. L., Sommerville, J. A., Chaminade, T., and Meltzoff, A. N The Neural Bases of Cooperation and Competition: An fmri Investigation, NeuroImage (23:2), pp Delgado, M. R., Miller, M. M., Inati, S., and Phelps, E. A An fmri Study of Reward-related Probability Learning, NeuroImage (24), pp MIS Quarterly Vol. 36 No. 3 Appendices/September 2012 A7

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40 Wible, C. G., Han, S. D., Spencer, M. H., Kubicki, M., Niznikiewicz, M. H., Jolesz, F. A., McCarley, R. W., and Nestor, P Connectivity Among Semantic Associates: An fmri Study of Semantic Priming, Brain and Language (97), pp Winston, J. S., Strange, B. A., O Doherty, J., and Dolan, R. J Automatic and Intentional Brain Responses During Evaluation of Trustworthiness of Faces, Nature Neuroscience (5), pp Winterer, G., Adams, C. M., Jones, D. W., and Knutson, B Volition to Action An Event-Related fmri Study, NeuroImage (17), pp Zeki, S., and Romaya, J. P Neural Correlates of Hate, PLoS ONE (3:10), e3556 (doi: /journal.pone ). Appendix C Example Results HL TRUST LH HL DISTRUST LH DISTRUST TRUST Amygdala Insular Cortex Amygdala Insular Cortex Anterior PCC Putamen Anterior PCC Putamen TRUST Orbitofrontal Caudate Nucleus Orbitofrontal Caudate Nucleus DISTRUST Figure C1. The Neural Correlates of Trust and Distrust (Simplified Version of Figure 3 (p. 386) from Dimoka 2010) A10 MIS Quarterly Vol. 36 No. 3 Appendices/September 2012