A direct comparison between ERP and fMRI measurements of food-related inhibitory control: Implications for BMI status and dietary intake
Introduction
Obesity is a prevalent health concern affecting over one-third of adults in the United States (Flegal et al., 2016). Consequently, it is important to understand what factors may influence the development and maintenance of obesity. Perhaps one the most widely studied factors related to obesity is dietary decision making (Davis et al., 2004). Possible factors that influence unhealthy dietary decisions include visibility and availability of high energy foods (Befort et al., 2006), exposure to food advertisements (Halford et al., 2004), hormones (Begg and Woods, 2013), and social influences, such as the presence of others when eating or noticing how much others are eating (Herman et al., 2003, Vartanian et al., 2015).
Another factor that may relate to dietary decision-making is inhibitory control towards food (Blundell and Gillett, 2001, Davis et al., 2007, Guerrieri et al., 2007b, Jasinska et al., 2012). Inhibitory control is defined as one's ability to withhold a dominant or automatic response to an external cue in order to correctly respond to goals or environmental demands (Ko and Miller, 2013). The ability to withhold an automatic response to eat in the presence of high-calorie or palatable foods may aid in weight loss and weight maintenance efforts (Pauli-Pott et al., 2010). Furthermore, individuals with greater inhibitory control may be less likely to engage in emotional eating or yield to food cravings (Davis et al., 2007, Guerrieri et al., 2007a, Guerrieri et al., 2007b, Jasinska et al., 2012). In fact, individuals with higher levels of impulsivity, as measured by self-reports or behavioral indices such as accuracy or reaction times, tend to eat more when presented with palatable foods compared to individuals with lower levels of impulsivity (Guerrieri et al., 2007b, Nederkoorn et al., 2009). BMI has also been negatively correlated with behavioral measures of inhibitory control (Appelhans et al., 2011, Nederkoorn et al., 2006, Vainik et al., 2013), and individuals with lower levels of inhibitory control also tend to have increased risk for future weight gain (Anzman and Birch, 2009, Nederkoorn et al., 2010). Together, these results suggest that inhibitory control not only influences eating habits, but may also predispose individuals to increased obesity risk.
Understanding the neural mechanisms associated with inhibitory control may help elucidate the relationship between food-related inhibitory control, dietary decisions, and obesity. One method that has been used to measure neural indices of food-related inhibitory control is ERPs. Specifically, an ERP component that commonly reflects inhibitory control processes is the N2-- a negative-going deflection in the ERP waveform that peaks approximately 200–350 ms after stimulus onset. Depending on the nature of the task and stimulus, N2 amplitude can reflect sensory processing, conflict monitoring, or response inhibition towards a stimulus (Folstein and Van Petten, 2008). Specifically for response inhibition, N2 amplitude is larger (i.e., more negative) when an individual is required to suppress a prepotent response to a frequent stimulus, supporting the notion that the N2 reflects recruitment of inhibitory control processes (Folstein and Van Petten, 2008). Indeed, one recent study demonstrated that females have larger N2 amplitudes when inhibiting responses to food versus non-food stimuli, suggesting participants recruit additional cognitive resources to inhibit in the presence of food items (Watson and Garvey, 2013). Females in this sample who had attenuated N2 amplitudes were also more likely to have a higher BMI. Furthermore, individuals have a larger inhibitory response, as evidence by a larger N2 amplitude, when inhibiting towards high-relative to low-calorie food stimuli, suggesting more palatable foods higher in calories may require greater inhibitory control processes (Carbine et al., 2017a).
An additional ERP that has been commonly used to examine inhibitory control processes is the P3-- a positive-going deflection in the ERP waveform that occurs between 300 and 600 ms after stimulus onset (Falkenstein et al., 1999, Albert et al., 2013). Similar to the N2, the functional significance of the P3 varies depending on the task at hand and can reflect various cognitive processes, such as working memory/context updating or attention allocation (Polich, 2007). P3 amplitude during a go/no-go task is larger (i.e., more positive) over a frontocentral location when individuals have to withhold a dominant response (Kaiser et al., 2003; Wessel and Aron, 2015). While research generally agrees that a larger No-Go P3 amplitude reflects inhibitory control processes, there is some debate if it specifically reflects suppressing attention to non-relevant task information (Polich, 2007), evaluating an inhibitory response (Huster et al., 2013), or withholding a dominant motor response (Gajewski and Falkenstein, 2013; Smith et al., 2008). Inhibiting to food compared to neutral images elicits a larger No-Go P3 amplitude, supporting N2 research that individuals need to recruit more inhibitory control resources when withholding dominant responses to food (Watson and Garvey, 2013).
Another method that can be utilized to study neural activation associated with food-related inhibitory control is fMRI. Using fMRI procedures, multiple frontal brain regions have been associated with general inhibitory control processes, such as the ventrolateral prefrontal cortex (Levy and Wagner, 2011), medial prefrontal cortex (Ridderinkhof et al., 2004a, Ridderinkhof et al., 2004b), ACC (Braver et al., 2001) and OFC (Bechara et al., 2000a). Studies utilizing fMRI procedures have shown that when adolescents passively view foods (Batterink et al., 2010) or inhibit responses to pictures of food (He et al., 2014), BMI is inversely related with neural activation in regions associated with inhibitory control. Together, results suggest that individuals with higher BMIs do not recruit the necessary neural resources to inhibit in the presence of food.
No studies to date have compared ERP and fMRI neural indices of food-related inhibitory control in the same group of participants. A direct comparison of the two methods is empirically meaningful, as it would determine the extent to which ERP and fMRI measures are providing the same overall conclusions on the magnitude and characteristics of food-related inhibitory control. A direct comparison of ERP and fMRI methods would also clarify for past and future research if fMRI data from commonly examined food-related inhibitory control regions support findings obtained from commonly examined food-related inhibitory control ERPs, and how independently conducted fMRI and ERP food studies relate to each other. If the methods yield comparable results, researchers may choose to utilize a method that is more convenient or cost-effective (Huettel et al., 2004, Luck, 2014). Further, examining both ERP and fMRI data would provide insight on the immediate magnitude of food-related inhibitory control processes in addition to the location of those processes. As such, the current study fills a unique gap in the literature by examining inhibitory control processes in the same participants using both ERP and fMRI methods.
In addition to comparing ERP and fMRI methods, it is important to assess the real-world applicability of such methods by examining how neural indices of inhibitory control relate to dietary intake. Within the fMRI literature, increased activation in the DLPFC (a brain region associated with inhibitory control) when viewing high-calorie food images has been related to decreased caloric intake in a laboratory setting (Cornier et al., 2010). Additionally, adolescents with greater activation in the ACC when inhibiting towards food images consumed less high-calorie foods, as reported by an interviewer administered dietary recall (He et al., 2014). Results suggest that individuals with increased neural activation in inhibitory control regions were able to better regulate their food consumption than those who had attenuated indices of cognitive processing. Within the ERP literature, one study has suggested that individuals who increased their recruitment of inhibitory control processes when inhibiting to high-calorie food images consumed fewer calories, as assessed by self-reported dietary recalls (Carbine et al., 2017a). Additional research is needed to determine how neural indices of inhibitory control relate to dietary intake, measured both in laboratory settings and from self-reports of dietary intake. Establishing the ecological validity of such neural measures will allow future health interventions to not only use neural indices as a way to assess eating habits, but also to provide an additional outlet to improve eating habits and weight status by targeting cognitive mechanisms.
We had three main aims for our study. First, we aimed to characterize differences in neural indices of inhibitory control toward high- and low-calorie food images between normal-weight, overweight, and individuals with obesity. We hypothesized that overweight and individuals with obesity would have significantly reduced fMRI activation in regions associated with inhibitory control as well as smaller N2 (i.e., less negative) and P3 (i.e., less positive) amplitudes when inhibiting to high-compared to low-calorie food images. Second, we aimed to test if commonly examined ERPs and fMRI ROIs provide similar information on neural indices of food-related inhibitory control. We hypothesized that when directly comparing standardized values obtained from ERP and fMRI measurements, there would be no significant differences between the methodologies in measures of food-related inhibitory control across BMI groups and food types. Given that both methods measure the magnitude of an inhibitory response, we felt it was reasonable to hypothesize there would be no differences. However, given there is no research that directly compares food-related inhibitory control between ERP and fMRI data and the important implications for cognitive food research if the two methods do result in different conclusions, we felt our it was a meaningful, empirical question to compare the two methods, even if we believed there would be no differences. Finally, we aimed to assess how ERP and fMRI measures of inhibitory control predict dietary intake, both in a laboratory setting as well as daily self-reports of food intake. We hypothesized that decreased indicators of inhibitory control as measured by ERP and fMRI methodologies (i.e., smaller N2 and P3 amplitude and decreased neural activation in inhibitory ROIs) would be related to increased food intake from both an in-laboratory buffet and self-reports of dietary intake.
Section snippets
Participants
The local university's Institutional Review Board approved all study procedures and all participants provided written informed consent. Participants were recruited via flyers posted in the local community for payment compensation and from undergraduate classes for course credit compensation. All participants were right-handed, native English speakers, and free from current psychiatric conditions, learning disabilities, neurological disorders, head injuries that resulted in a loss of
ERP and fMRI testing environments
See Table 3 for means and standard deviations of behavioral data, separated by task and method. For RTs, the main effect of method was not significant (F[1, 51] = 0.07; p = 0.80; ηp2 = 0.001). The Method by Task interaction was also not significant (F[1, 51] = 0.45; p = 0.51; ηp2 = 0.01), suggesting RTs did not differ between ERP and fMRI testing environments. The main effect of method on accuracy (F[1, 53] = 0.05; p = 0.82; ηp2 = 0.01) and the Method by Trial interaction (F[1, 53] = 3.89; p
Discussion
The primary purpose of our study was to examine how neural measurements of food-related inhibitory control vary by food type, BMI status, assessment method, and predict dietary intake. Our behavioral results (RT and accuracy) support previous findings from our lab examined in a separate, independent sample (Carbine et al., 2017a). Specifically, longer RTs to high-calorie stimuli suggest that individuals, regardless of BMI status, allocate increased attention to high-calorie compared to
Conclusions
In summary, our findings suggest that while there is increased recruitment of inhibitory control resources in the presence of high-calorie food images, there was no significant effect of BMI on inhibitory control. ERP and fMRI assessments seem to provide similar information about food-related inhibitory control, particularly for the influence of BMI status. However, the N2 ERP component, but not P3 ERP component, compared to ROI fMRI analyses may be more sensitive in detecting differences in
Acknowledgements
We would like to thank BYU's MRI Research Facility for helping fund our study. Funding was also obtained from BYU's Mentoring Environment Grant and BYU's Graduate Research Fellowship. The authors have no conflicts of interest to disclose.
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