Elsevier

NeuroImage

Volume 44, Issue 3, 1 February 2009, Pages 967-974
NeuroImage

The brain tracks the energetic value in food images

https://doi.org/10.1016/j.neuroimage.2008.10.005Get rights and content

Abstract

Do our brains implicitly track the energetic content of the foods we see? Using electrical neuroimaging of visual evoked potentials (VEPs) we show that the human brain can rapidly discern food's energetic value, vis à vis its fat content, solely from its visual presentation. Responses to images of high-energy and low-energy food differed over two distinct time periods. The first period, starting at ∼ 165 ms post-stimulus onset, followed from modulations in VEP topography and by extension in the configuration of the underlying brain network. Statistical comparison of source estimations identified differences distributed across a wide network including both posterior occipital regions and temporo-parietal cortices typically associated with object processing, and also inferior frontal cortices typically associated with decision-making. During a successive processing stage (starting at ∼ 300 ms), responses differed both topographically and in terms of strength, with source estimations differing predominantly within prefrontal cortical regions implicated in reward assessment and decision-making. These effects occur orthogonally to the task that is actually being performed and suggest that reward properties such as a food's energetic content are treated rapidly and in parallel by a distributed network of brain regions involved in object categorization, reward assessment, and decision-making.

Introduction

Food is a highly salient biological stimulus category due to its relevance for survival and its inherently rewarding and hedonic nature (LaBar et al., 2001). Yet, foods differ with respect to their rewarding properties as a function of such features as palatability and energetic content relevant for nutritional homeostasis. The fat component of foods is a factor that strongly influences texture and palatability, and the impact of a food on the body's energetic balance and supply with essential fatty acids (Drewnowski, 1998, Rolls, 2007). Perceived energetic value and palatability are critical influences on eating behavior that can sometimes have detrimental consequences. High-fat foods are often consumed with more pleasure, and in larger quantities than healthier foods like vegetables. Such hedonic drives can produce inappropriate eating behaviors, obesity, diabetes, and hypertension. Understanding the brain mechanisms for appraising foods is likely to prove essential for learning how to affect inappropriate eating behavior across the lifespan. Perceived fat content after food ingestion is unreliable (Abdallah et al., 1998, Geiselman et al., 1998), so that food evaluation based on visual information before ingestion might present the perceptual stage at which nutritional choices are made by the individual. The current study addressed the implicit tracking of the energetic and reward value of seen foods by applying electrical neuroimaging analyses (Murray et al., 2008) to VEPs from normal-weighted humans when viewing photographs of foods divided into high-fat and low-fat categories but performing an orthogonal food vs. non-food discrimination task.

Several hemodynamic imaging studies (i.e. functional magnetic resonance imaging [fMRI] and positron emission tomography [PET]) in humans and animals have explored the network underlying food categorization and reward evaluation. A prefrontal cortical and subcortical network has been identified as being reactive to several food presentation modes including not only oral administration (de Araujo et al., 2003, O' et al., 2001b, Small et al., 2001), viewing of actual foods (Wang et al., 2004), and multisensory combinations (Gottfried et al., 2003, Small et al., 2004, Geliebter et al., 2006), but also to solely viewing food images (Killgore et al., 2003, Simmons et al., 2005, Beaver et al., 2006, Santel et al., 2006, Rothemund et al., 2007, Stoeckel et al., 2008).

Moreover, hemodynamic imaging studies in humans employing visual stimulation have identified additional temporo-parietal brain regions during food perception (Karhunen et al., 1997, Killgore et al., 2003, Santel et al., 2006, Uher et al., 2006, Rothemund et al., 2007). It has furthermore been found that visual cortex activity is modulated more strongly by the presentation of food than non-food images (LaBar et al., 2001, Killgore et al., 2003, Santel et al., 2006) possibly suggestive of a greater attentional and possibly motivational salience of food than non-food objects.

The spatio-temporal dynamics mediating the discrimination of food images remain unknown and were the focus of the present study. We were particularly interested in situating effects related to food evaluation with respect to the timecourse of the discrimination of other object categories (either visual or auditory), which has been demonstrated to occur within 100–200 ms post-stimulus presentation and to involve distributed brain networks in both sensory-related cortices and higher-order (pre)frontal regions (Michel et al., 2004a, Murray et al., 2006) involved in reward assessment (Thut et al., 1997, Critchley et al., 2001; O' et al., 2001a, Kringelbach and Rolls, 2004). Faces, another biologically salient and ethologically important object class, are also discriminated from other objects within this timeframe (Bentin et al., 2007, Rossion and Jacques, 2008). Moreover, electrophysiological studies on the categorization of images of living vs. non-living entities suggest the existence of two perceptual stages during image perception (∼ 150 ms and ∼ 300 ms post-stimulus onset). Differential processing of objects during these periods is likely due to an initial categorization based on functional and perceptual features followed by a more elaborate analysis of object concepts and semantic properties (Antal et al., 2000, Kiefer, 2001, Proverbio et al., 2007). Given the fundamental importance of discriminating the reward value of foods for an organism's survival, we hypothesized that discrimination of food images by their energetic content would occur within a similar timeframe as that of other object categories and involving brain regions previously identified by hemodynamic imaging studies.

Section snippets

Participants

Nineteen volunteers (3 males), aged 24–39 years (mean ± s.e.m. = 28.8 ± 0.9 yrs), rated each of the food images used in the EEG portion of the study, which are described below, according to their perceived fat content with a 7-point Likert scale. Their body mass indices (BMI) were within the normal range (mean ± s.e.m. = 22.2 ± 0.7 kg/m2). None of the participants had current or prior neurological or psychiatric illnesses or self-reported eating disorders. All participants had normal or corrected-to-normal

Results

A group of 19 observers, who did not partake in the VEP study, viewed each image and rated its fat content on a 7-point Likert scale. These subjective ratings significantly correlated with the actual fat content of the foods (Spearman's rho; ρ = 0.822; p < 0.001; Fig. 1b), indicating that the energetic value of foods could be reliably conveyed by and accurately perceived from their visual properties.

The 24 participants in the VEP study readily performed the discrimination between images of foods

Discussion

This study provides the first insights into the timecourse of the implicit discrimination of subclasses of food images that differ in their energetic (fat) content and reward properties. Two distinct temporal stages of discrimination were identified. The first stage onset ∼ 165 ms post-stimulus, while a later stage began at ∼ 300 ms post-stimulus. The latency of this first stage would suggest that within-category differentiation of biologically highly salient food objects can be accomplished

Acknowledgments

Cartool software (http://brainmapping.unige.ch/Cartool.htm) has been programmed by Denis Brunet, from the Functional Brain Mapping Laboratory, Geneva, Switzerland, and is supported by the EEG Brain Mapping Core of the Center for Biomedical Imaging (www.cibm.ch) of Geneva and Lausanne. We thank Christoph Michel for providing additional analysis tools. This work was supported by the Leenaards Foundation (2005 Prize for the Promotion of Scientific Research to MMM).

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