Kaylie Carbine ande Michael Larson, Department of Psychology and Neuroscience
Introduction
Obesity is a prominent public health concern associated with increased risk of high blood pressure, chronic diseases, and mortality (National Institutes of Health, 2011). Understanding the neural underpinnings of obesity may prove beneficial for effective weight control interventions. Specifically, decreased ability to inhibit responses toward food-related cues may be associated with impulsive eating and subsequent weight gain (Batterink, Yokum, & Strice, 2010). Response inhibition is defined as one’s ability to withhold a prominent response in order to correctly respond to environmental or taskrelevant information (Ko & Miller, 2013). Weight and inhibitory control are negatively correlated, indicating that individuals with reduced inhibitory control may have a harder time restricting themselves from eating foods high in fat and sugar, leading to weight gain (Vainik, Dagher, Dube, & Fellows, 2013). However, the brain’s pattern of response to food-related stimuli is unclear and food-related response inhibition patterns that distinguish obese and lean individuals are unknown. Furthermore, no studies have examined if neural markers associated with response inhibition predict eating behaviors.
One way to examine neural responses to food is via event-related potentials (ERPs). Event related potentials are changes in the brain’s electrical waveforms due to responses toward stimuli. One ERP that is a neural indicator of response inhibition is the N2. The N2 is a negative deflection in the ERP that peaks 200 to 350 ms after the onset of a stimulus and is elicited when an individual must withhold an overt response tendency (Folstein & Van Petten, 2008).
We aimed to examine the relationship between food-related response inhibition to high and low calorie foods, as measured by the N2, and obesity. Increasing our knowledge of how the brain processes food stimuli has important implications for obesity treatment, such as focusing interventions on strengthening inhibitory control. We aimed to test two competing hypotheses: 1) obese individuals will have decreased response inhibition towards foods high in calories, sugar, and fat compared to normal weight individuals, or 2) obese individuals will require greater inhibitory control in order to achieve the same withholding response towards food as normal weight individuals.
Methodology
To elicit the N2, we used two separate Go/No-Go (GNG) tasks that used pictures of high and low calorie foods (Killgore et al., 2003) Pictures were presented for 100 ms with 300 to 800 ms varied randomly between trials to control for expectancy effects. For both GNG tasks, participants were told to push a button in response to a certain stimulus (go trial) but withhold the same response when a different stimulus was presented (no-go trial). One GNG task used high calorie foods as the no-go stimuli, while the other GNG task used low calorie foods as the no-go stimuli. Task order was counterbalanced and each task had five blocks of 50 trials, 40 trials being go trials and ten being no-go trials. During the tasks, the N2 was recorded using electroencephalogram (EEG) technology via an EEG net of 128 electrode sensors. EEG data was re-referenced to an average reference off-line, cleaned for eye movement and extremely high voltages (>100μV) and digitally low-pass filtered at 30Hz.
Body mass index (BMI) was used to classify individuals as obese (BMI > 30kg/m2) or lean (BMI < 25kg/m2). We had 37 participants (29 lean; 8 obese), aged 18-49 (M=20.52, SD=2.01) recruited via flyers on BYU’s campus. Food intake was measured using the Automated Self-Administered 24-hour Dietary Recall, where participants recorded their food consumption for five days, including a weekend. Participants were free of any neurological diseases, psychiatric illnesses, learning disabilities, and were required to get at least 7 hours of sleep the night before, arrive in a fasted state, and restrain from participating in vigorous physical activity or consuming caffeine 24 hours before. Data were analyzed using a two-weight (lean, obese) x two-food (low calorie, high calorie) ANOVA to compare response inhibition between weight-groups. Pearson’s correlations were used to examine the relationship between N2 amplitude and food intake.
Results
No-go trials for both high and low calorie foods were successful at eliciting a strong inhibitory response, as indicated by greater N2 amplitudes to no-go than go stimuli (ps< .05). A two-weight (lean, obese) x two-food (low, high) ANOVA showed no main effect for food type on N2 amplitude (p=.17), nor interaction between weight and food (p=.11). No Pearson’s correlations between the N2 and food intake were significant (all ps>.19).
Discussion
Although our tasks were successful at eliciting food-related response inhibition as evidenced by greater N2 amplitudes during no-go than go trials, there were no significant differences in response inhibition depending on food type or weight status. However, due to our low number of obese individuals (n=8), our study was underpowered. Taking a look at mean N2 amplitudes, data was trending in the direction that lean individuals have a greater inhibitory response to high calorie foods compared to low calorie foods, but obese individuals may not exhibit those differences. Therefore, we plan to continue data collection in order to increase our sample size and power to determine if these differences between lean and obese individuals are significant. In regards to food intake, N2 amplitude did not relate to food intake, regardless of weight status, although BMI may wish to be considered as a control variable in future analyses.
Conclusion
We aimed to examine if obese individuals would exhibit decreased response inhibition towards high calorie foods compared to lean individuals or require greater response inhibition to achieve the same withholding behavior. In addition, we examined if these neural responses related to food intake. Due to an underpowered sample, our results did not adequately answer our research question and more data needs to be collected. Although N2 amplitude did not correlate with food intake, more powerful analyses that control for BMI and demographics should be considered in future analyses.
References
Batterink, L., Yokum, S., & Strice, E. (2010). Body mass correlates inversely with inhibitory control in response to food among adolescent girls: An fMRI study. Neuroimage, 52, 1696-703. doi:10.1016/j.neuroimage.2010.05.059
Folstein, J.R., & Van Petten, C. (2008). Influence of cognitive control and mismatch on the N2 component of the ERP: A review. Psychophysiology, 45, 152-170. doi:10.1111/j.1469-8986.2007.00602.x
Killgore, W.D.S., Young, A.D., Femia, L.A., Bogorodzki, P., Rogowska, J. & Yurgelun- Todd, D.A. (2003). Cortical and limbic activation during viewing of high- versus low-calorie foods. Neuroimage, 19, 1381-94. doi:10.1016/S1053-8119(03)00191- 5
Ko, Y., & Miller, J. (2013). Signal-related contributions to stopping-interference effects in selective response inhibition. Experimental Brain Research, 228, 205-212. doi:10.1007/s00221-013-3552-y
National Institutes of Health. (2011). Strategic plan for NIH obesity research: A report of the NIH obesity research task force (NIH Publication No. 11-5493). Washington, DC: U.S.
Vainik, U., Dagherm A., Dube, L., & Fellows, L.K. (2013). Neurobehavioural correlated of body mass index and eating behaviours in adults: A systematic review. Neuroscience and Biobehavioral Reviews, 37, 279-299. doi:10.1016/j.neubiorev.2012.11.008