Functional MRI of satiety, the interaction
between gastric stimulation and glucose & insulin
levels in healthy men
Title: Functional MRI of satiety: the interaction between gastric
stimulation and glucose & insulin levels in healthy men
Period: 21-03-2011 until 11-12-2011
ECTS: 38 and Final Date: 21-01-2012
Name: Lizet van Knippenberg, student number 0608866
Supervisor: M. S. Spetter
Co-assessor and UvA Representative: dr. H.S. Scholte
MSc in Brain and Cognitive Sciences, University of Amsterdam:
Cognitive science track
Abstract
To evaluate the interaction between food administration, hormone responses and brain responses, we have investigated the acute effect of food intake on brain processes by measuring hemodynamic changes following intra-gastric administration of a liquid. While administering this load, brain functional magnetic resonance images were collected and glucose and insulin levels were measured by taking blood samples. Seven male volunteers received either a caloric intra-gastric load (chocolate milk) or a non-caloric intra-gastric load (water with a thickening agent) in a single-blind randomized cross-over design intervention study. When functional magnetic resonance imaging data was analyzed, it appeared that food ingestion significantly increased activation in a wide range of brain areas, which partially show an overlap with previously found brain regions involved in food administration. Intake of calories induced signal change mainly in the thalamus, anterior cingulate cortex, orbitofrontal cortex and the frontal cortex. Our results contributed to the idea that the brain may respond differently to caloric and non-caloric food intra-gastrically. Further analysis is needed to investigate the precise role of the caudate in reward value and the hippocampus in calorie recognition. Ultimately, food ingestion induced fMRI of gastric distention in healthy subjects could be of use in understanding the mechanisms of satiety and behavior of overeating.
Introduction
Obesity is one of the most serious public health problems of the 21st century and is predominantly due to increased food intake and decreased physical activity (Barness et al., 2007).
In the regulation of food intake and food choice, satiety and satiation play an important role. Satiation refers to the inhibition of hunger and appetite, which ultimately stops food intake (Blundell et al., 1996) and is also known as intra-meal satiety (Blundell et al., 2010) Satiety is the state of being full and arises as a consequence of food ingestion, which is known as post-ingestive or inter-meal satiety (Blundell et al., 1996; 2010). Satiation plays an essential part in adjusting food consumption; it determines meal size, while the ensuing state of satiety determines meal frequency, i.e. the time until the next eating occasion.
One can distinguish two types of satiation, namely metabolic and sensory-specific satiation. Metabolic refers to the body’s need for macronutrients and is
strongly affected by stomach distention and hormone release. Food volume, weight and gastric emptying rate determine metabolic satiation. Sensory-specific satiation has been defined as the decrease in pleasantness of an eaten food relative to an uneaten food (Rolls et al., 1982) and is associated with the sensory properties of food. In daily life, food choice and intake are influenced by a combination of metabolic processes and sensory signals.
One of the key mechanisms during the regulation of food consumption is gastric distention. When the food reaches the gut, hormones and neural signals are released. Neural signals from the gastrointestinal tract travel via the vagus nerve to the brainstem and thalamus, which projects to the rest of the brain, in particular the hypothalamus, amygdala and primary sensory cortices. Positron emission tomography (PET) with water [O-15] (Ladabaum et al., 2001; Stephan et al., 2003) or functional magnetic resonance imaging (fMRI) with blood-oxygen-level-dependent (BOLD) contrast (Ladabaum et al., 2007; Lu et al., 2004) methods have been used to examine brain activation during gastric distention. In these neuroimaging studies, metabolic satiation has been imitated by inflating an orally placed gastric balloon in the stomach. This activated the right insula, left posterior amygdala, left posterior insula, left inferior frontal gyrus and anterior cingulate cortex (Stephan et al., 2003b; Wang et al., 2008b).
Food selection is determined by sensory properties of food like texture, taste, smell and sight. These food-specific properties induce preabsorptive physiological responses, which are collectively referred to as cephalic phase responses (Smeets et al., 2010). Neuroimaging studies on sensory signals mainly focus on the effect of being satiated (pre compared to post ingestion) on taste responses instead of studying the process of satiation. The insula and orbitofrontal cortex play an important role in tasting and taste validation (Rolls, 1989; Rolls et al., 2003; Kringelbach et al., 2003).
So far, no study has examined the effects of the process of ingestion or gastric infusion of food on the brain. Though the effect of satiation on the brain has been studied, the process of satiation in the brain, i.e. brain responses during food ingestion, has not been studied before (Small et al., 2001; O'Doherty et al., 2000). In addition to neural signals, hormonal signals are important for meal termination. Insulin and glucose play an important role in meal termination by interacting with gastric as well as with sensory signals during satiation.
The aim of this study was to investigate the interaction between food administration, hormone responses and brain responses. To evaluate this interaction we have investigated the acute effect of food intake on brain processes by measuring hemodynamic changes following intra-gastric administration of either a caloric load (chocolate milk) or a non-caloric load (water with a thickening agent). While administering this load, brain fMRI images were collected and glucose and insulin levels were measured by taking blood samples.
The first major goal of this project was to define which brains areas would give a food ingestion induced signal change compared to the baseline condition (before ingestion of either chocolate milk or water with a thickening agent). Based on previous literature, it is expected that food intake would stimulate activation in the bilateral insula, the amygdala and the precuneus (Wang et al., 2008b), but also in the dorsal brain stem, the frontal gyrus, the subgenual and anterior cingulate cortex (Stephan et al., 2003b).
Our second goal was to determine which brain areas respond differentially to a caloric or a non-caloric intra-gastric load. Based on previous literature, we predict that a caloric load relative to a non-caloric load would be associated with increased BOLD response in the hypothalamus and the ventral striatum (Van der laan et al., 2011), while decreased activity in expected in the amygdala (Smeets et al., 2011). Also taste pathways areas, such as the striatum, anterior cingulate and prefrontal cortex and the FO/AI (frontal operculum and anterior insula), a primary taste cortex, were expected to be affected stronger during the caloric condition compared to the non-caloric condition (Frank et al. 2008)
A third and final goal of this project was to look at glucose and insulin concentrations and subjective responses (i.e. fullness, desire to eat and anxiety ratings) as a result of either intra-gastric administration of a caloric or a non-caloric load. A change in glucose and insulin levels is expected during the non-caloric condition, while no such change is expected to be found after the intake of a non-caloric load intra-gastrically. Besides, it is expected that fullness ratings increase after ingesting an intra-gastric load in the same way during both conditions, while a decline is expected for desire ratings. Finally, more food is expected to be consumed during the ad libitum breakfast after the intake of a non-caloric intra-gastric load, compared to the intake of a caloric intra-intra-gastric load.
Methods
Subjects
In total, seven healthy normal-weight right-handed men participated (age 25 SD ±4.5 years) with a mean Body Mass Index (BMI; in kg/m²) of 22.5 SD ±1.5. Male volunteers were recruited from students and the general public, through advertisements at the University Medical Centre Utrecht (UMCU), the University of Utrecht (UU) and at the world wide web: www.proefbunny.nl
Subjects received an information brochure about the study procedure and purpose and were screened by a short screening questionnaire to check if they met inclusion criteria. These inclusion criteria were as follows: male, right-handed, healthy volunteers within age 18-35 and currently having an BMI between 20 – 25 kg/m². Not having a BMI in this range could possibly result in other outcomes on both brain responses and blood values.
Exclusion criteria included any serious general medical condition, presence of metal in the body or other contraindications to MR imaging, claustrophobia, current or past known brain disease, psychiatric or neurological disorders, any medical diseases (including taste or smell disorders), presently taking any medication that affects the blood composition or the gastro-intestinal tract, illicit drug use, excessive consumption of alcohol (>28 u/week), smoking, dieting for weight loss or having a medically prescribed diet, restrained eating, having an eating disorder, and involvement in concurrent research or in research involving taking an experimental drug or blood in the previous months. Absence of these exclusion criteria has been checked with a short screening intake questionnaire which deals with eating habits and restrained eating, which are part of the Dutch Eating Behavior Questionnaire (Van Strien et al., 1986). All subjects met inclusion and exclusion criteria.
All experimental procedures were approved by the Medical Ethical Committee of the UMCU and written informed consent was obtained from each volunteer before the experiment.
Procedures
Study design
The study design was a single-blind randomized cross-over design intervention study. All subjects participated in a training session and two fMRI scan sessions. The first condition consisted of gastric distension by a non-caloric intra-gastric
load (water with a thickening agent) 1. During the second condition a caloric load
(chocolate milk) 2 was administered intra-gastrically. Subjects were randomly
assigned to the order in which the scans took place and were blind to treatment. Every MRI session consisted of two scans comprising an anatomical scan followed by an fMRI scan. During every scan session a naso-gastric tube and i.v. canula were placed. During the fMRI run 500 ml liquid load was administered through the naso-gastric tube in 5 min; blood samples were drawn at 6 time points and at the same time appetite questionnaires were filled in (figure 1). The two fMRI sessions have been performed with a minimal interval of one week and a maximal interval of three weeks. The training session took ±0.5 hour; each of the two visits ±1.5 hours.
Figure 1: Study design
Training session
1 Dutch tap water with a thickening agent (guargum) added to ensure that water and chocolate milk have a similar viscosity. 5 gram (1%) guargum is added to 500ml water. 2 ‘Nutricia Chocolade melk’ is regular full chocolate milk. (www.chocomel.nl).
During a training session lasting 30 minutes, a naso-gastric tube was inserted by a qualified nurse (according to the ‘’maagsonde-protocol’’ UMC Utrecht) in order to see if a subject can handle the tube and is eligible as a possible participant. While placing the tube, participants lie down on a doctors table to simulate the position in the MRI scanner. Subjects rate fullness, desire to eat and anxiety on a Visual Analogue Scale (VAS) scale before and after the placement of the tube.
MRI scan sessions
All subjects fast overnight for at least ten hours preceding the scan in the morning. The evening prior to the scan session, subjects consumed a standardized dinner given by the researchers (Albert Heijn kant & klaar).
Every scan condition consisted of a 35 min fMRI scan and an ad libitum breakfast intake; one total experimental session took ±1.5h. In each condition subjects received either a 500 ml caloric load or a non-caloric load intra-gastrically.
When subjects arrived they filled in a subjective appetite questionnaire (VAS) (fullness, desire to eat and anxiety). In total six appetite questionnaires were filled in; one after placing of the naso-gastric tube (baseline) and five inside the scanner at t=2.5, t=5, t=10, t=15 and t=30 min after intake of a liquid load. Subjective ratings were given on a 10 cm scale, anchored ‘not at all’ at the left end (score 0) and ‘extremely’ at the right end (score 10). Inside the scanner VAS appetite questionnaires were displayed onto a rear-projection screen placed in front of the MRI bed and were viewed via a mirror attached to the MRI head coil. Ratings were made by use of an MR-compatible response box placed on the left side of the participants, on account of the right side placed i.v. canula. Instructions were run by the computer program Presentation (Neurobehavioral Systems Inc.)
Besides the placement of the naso-gastric tube (the same procedure as in the training session), an i.v. canula was placed, both by a qualified nurse. After placing the subject in the scanner, the first blood sample was drawn (t=0 min). In total blood was drawn at six time points at t=0, t=2.5, t=5, t=10, t=15 and t=30 min after ingestion of either a caloric or non-caloric load. First a reference and anatomical scan were made. Subsequently the fMRI scan starts of which the first 5 min serve as baseline to compare the fMRI scans with. Thereafter food administration started for 5 min at t=0, which could consist of food intake
intra-gastrically through the naso-gastric tube (caloric or non-caloric). Next, the post ingestion fMRI run continued from t=5 until t=30.
During the whole experiment a researcher stood next to the subjects in order to draw blood, but also to take care of the participant in case aspiration occurs. Subjects were given instructions how to get out of the scanner in case they did not feel good. After 30 min subjects were taken out of the scanner and both the naso-gastric tube as the i.v. canula were removed. After being scanned, subjects were provided with an ad libitum breakfast buffet at t= 60 min. Food weight and macronutrient intake were recorded.
Stimuli
Intra-gastric liquid load consisted either of a caloric load, i.e. Nutricia Chocolate Milk, which is full chocolate milk
(
www.chocomel.nl
). The non-caloric load
consisted of Dutch tap water with a thickening agent (guargum) added to
ensure that water and chocolate milk were matched for viscosity. 5 gram
(1%) guargum is added to 500ml water.
Both liquids were consumed through a silicon naso-gastric tube (length 110 cm; diameter 2.67 mm) that was connected to a peristaltic pump (HR flow inducer, MHRE 200; Watson-Marlow Ltd, Falmouth, Cornwall, UK) via a tube (diameter 4.8 mm), whereby sip size, delivery rate and interval between sips were controlled. Sips (12.5ml/sip) were delivered in a rate of 100 ml/min with an inter sip interval of 2 s.
Blood sampling and storage
Every scan session an 8 ml blood sample was drawn at six time points via an i.v. canula and was collected in EDTA tubes. Blood samples were centrifuged at 1730 × g for 10 min at 4 ˚C. Glucose an insulin levels were determined.
fMRI data acquisition
All MR imaging was performed using a 3.0 Tesla Philips Achieva MRI scanner with an SENSE 8-channel head coil and body coil transmission (Philips Intera; Philips Medical Systems, Best, The Netherlands) at the University Medical Center Utrecht. A session protocol consisted of a high resolution 3DT1 weighted structural scan for registration and segmentation purposes (±4 min) and a functional BOLD-based scan (±35 min). For the BOLD-based scan the following
imaging parameters were used: TR/TE 2300/30 ms; FOV 220×220×120 mm2;
voxel size 4x4x4 mm, matrix size 96×95; 40 contiguous transversal slices; scanned ascending, thickness 3 mm; gradient echo single shot echo-planar EPI pulse sequence. The number of dynamics was 1490 for each functional scan with a total scanning time of 35 min. For the 3DT1 weighted structural scan, the same imaging parameters were used, except the voxel size 0.88x0.88x1.2, FOV 224×224×144 mm2 and 120 contiguous transversal slices.
Data Analysis
fMRI data was preprocessed and analyzed using the software package SPM 8 (Wellcome Department of Imaging Neuroscience, London, UK) run with MATLAB 7 (The Mathworks Inc., Natick, MA) and the WFU Pickatlas tool (Maldjian et al. 2003).
First, the functional volumes of every subject were realigned to the first volume of the first run. Next, the anatomical image was coregistered with the mean functional image. Then, images were spatially normalized (resampling to 4x4x4 mm) into the Montreal Neurological Institute space (MNI space)(Evans et al. 1993) and spatially smoothed with a Gaussian kernel of 8 mm full-width at half-maximum. After this spatial preprocessing, a statistical parametric map was generated for each subject using the general linear model (GLM) with a delayed boxcar waveform to model BOLD signal changes to generate a single mean image corresponding to each experiment. Data were high-pass filtered without a cut off.
First-level analysis was performed on each participant for each condition in the following ways. Preingestion, treatment and postingestion volumes were split into time bins; the preingestion volumes were divided into two bins: one bin formed the baseline time bin (T1) and the other a ratings and instruction bin (T2), which which is left out of account in the data analysis. The treatment bin (T3) consisted of volumes during which food administration took place. Postingestion volumes were divided into five consecutive ±4.5-minute time bins (T4-T8). For each participant, average neuronal responses in the postingestion bins were subtracted from those measured during the baseline bin (subtraction logic) 3, to
reveal areas of increased signal associated with food administration. For every subject, parameters were estimated for six comparisons per subject, yielding six contrast images; contrast images were calculated for the treatment bin versus
3 fMRI is a subtractive technique—that is, an increase in activity can only be measured as an increase relative to another condition
the baseline bin and all five postingestion bins (T3-T1, T4-T1,T5-T1, T6-T1, T7-T1 and T8-T1: contrast 1-6 respectively).
Subsequently, these images were combined in a second-level group analysis to investigate the main effect of ingestion. The modulation contrast images of all subjects were entered into a flexible-factorial repeated measures ANOVA, with condition and subject as repeated measures non-independent factors and time as a repeated measure dependent factor. In total nine contrasts were looked at. First of all, the main effect of food ingestion was calculated by adding all activity during the treatment contrast (1) from the caloric and non-caloric condition (resulting in first 2nd level contrast). Secondly, a difference in brain activity
between conditions was calculated by subtracting the treatment contrast (1) in the caloric condition from the treatment contrast (1) in the non-caloric condition and vice versa (resulting in 2nd level contrast 2 and 3). Thirdly, postingestion
contrasts were looked at by, a: comparing same postingestion contrasts (2-6) between conditions (resulting in 2nd level contrast 4 and 5), b: comparing
postingestion contrasts (2-6) with the treatment contrast (1) in both conditions (resulting in 2nd level contrast 6 and 7) and c: comparing the treatment contrast
(1) with postingestion contrasts (2-6) in both conditions (resulting in 2nd level
contrast 8 and 9).
These second-level effect analyses were thresholded at a family wise error rate-corrected (FWE-rate-corrected for multiple comparisons) for the first contrast using cluster sizes higher than 10 at P < .05 (Z-score > 5.7). Statistical maps of the second and third contrast were uncorrected for multiple comparisons and thresholded using cluster sizes higher than 5 at P < .001 (Z-score > 3.2). For the fourth and fifth contrast statistical maps were uncorrected for multiple comparisons and thresholded using cluster sizes higher than 10 at P < .001 (Z-score > 3.2). The sixth, seventh, eighth and ninth contrast were again thresholded as the first contrast (FWE-corrected for multiple comparisons; cluster sizes > 10 at P < .05 (Z-score > 4.5).
Analyses of subjective ratings were performed using SPSS (version 16; SPSS, Inc., Chicago, IL, USA). Effects were considered statistically significant at a value of P < .05. Mean subjective ratings of fullness, desire to eat and anxiety were calculated for each condition (caloric and non-caloric) and each time point (t=0, t=2.5, t=5, t=15, t=30). Subsequently, these average ratings were compared between conditions and differences in time were tested for with a repeated
measures ANOVA for the two conditions. The analysis was corrected for multiple comparisons by a Bonferroni’s test (P < .05).
Mean insulin (mIU/L) and glucose (mmol/L) concentrations were calculated for each condition in time. These average concentrations were compared between conditions and differences in time tested for with a repeated measures ANOVA for both the caloric and non-caloric condition. The analysis was corrected for multiple comparisons by a Bonferroni’s test (P < .05).
Mean amount of consumed energy (Kcal) during the ad libitum breakfast was calculated for both conditions with a paired t-test.
Results
fMRI Results
In total 14 scans were analyzed which all consisted of 1490 volumes. The main effect of ingestion of liquid food is represented in table 1 and figure 2. Overall, areas of activation for the main effect of gastric distention included the right thalamus, left and right fusiform gyrus, left and right parahippocampal area, right hippocampus, right middle and inferior orbitofrontal cortex (OFC), right precuneus, right insula, left cerebellum, left and right temporal cortices, right and left frontal areas, right middle cingulum and the right oper rolandic.
Table 1. Maximally activated voxels in areas in which significant evoked activity was related to food ingestion.
Main effect of food ingestion Talairach Coordinates Region Brodman n area Left/ Right x y z z-value Thalamus - R 14 -20 -6 Inf
Fusiform gyrus 30 R 26 -28 -18 Inf
30 L -18 -44 -10 Inf Parahippocampal area 20 R 30 -24 -14 Inf 30 L -22 -24 -22 Inf Hippocampus 37 R 38 -32 -6 Inf OFC 38 R 46 16 -10 Inf Precuneus 30 R 14 -52 14 Inf Insula 48 R 34 -20 6 Inf Cerebellum 18 L -6 -48 -22 Inf
Inferior Temporal 20 L -46 -48 -14 Inf
37 R 42 -52 -14 Inf
Mid Temporal 37 L -58 -60 2 5.7
Mid Temporal pole 38 R 50 12 -22 Inf
Mid Cingulum 23 R 10 -20 30 Inf
Inferior tri Frontal 45 R 54 28 2 Inf
Mid Frontal 46 R 34 44 30 Inf
Sub Frontal 10 R 30 52 10 Inf
Sub med Frontal 32 R 6 48 30 Inf
32 L -14 44 22 Inf
Oper rolandic 48 R 42 -32 22 Inf
For the main effect of food ingestion, only clusters where reported which were larger than 30 voxels, with a minimal Z-value of 5.7 and thresholded at P < .05 (FWE-corrected for multiple comparisons)
Figure 2: Main effect of food ingestion. Statistical parametric map of a flexible-factorial repeated measures ANOVA, thresholded at P < .05 (Z-score > 5.7) (FWE-corrected for multiple comparisons). Activation can be seen in the thalamus (green circle in the upper left picture), precuneus (green circle in the lower left picture) and prefrontal areas (upper right picture). Crosshair is situated at x= -2, y= 44, z=23.
A difference in brain activation was found between conditions (table 2). The caloric condition compared to the non-caloric condition activated the right middle cingulum, left parahippocampal area, right medial frontal cortex, left thalamus, and the left precuneus. In contrast, no brain areas were significantly increased in activity by the non-caloric condition, compared to the caloric condition.
Table 2. Maximally activated voxels in areas in which significant evoked activity was related to food ingestion between conditions.
Effect of food ingestion between conditions Talairach Coordinates Region Brodman n area Left/ Right x y z z-value Caloric condition
> non-caloric condition Mid Cingulum - R 14 -16 50 3.8 Parahippocampal area 20 L -30 -24 -18 3.6 Mid Frontal 8 R 30 20 50 3.6 Thalamus - L -22 -20 10 3.4 Precuneus 23 L -18 -56 26 3.2 Non-caloric condition > caloric condition X X X X X
For the effect of food ingestion between conditions, only clusters where reported which were larger than 5 voxels, with a minimal Z-value of 3.2 and thresholded at P < .001 (uncorrected for multiple comparisons).
An effect of condition and time, while comparing same postingestion contrasts (2-6) between conditions, can be found in table 3. Significant neuronal responses were observed in the left precuneus (in contrast 2, 3,4, 5 and 6), left thalamus (in contrast 3,4,5,6), left hippocampus (in contrast 2, 3, 4 en 6), right middle cingulum (in contrast 3 and 5), left posterior cingulum (in contrast 5 only), and in the left parahippocampal area (in contrast 2 only) during the caloric condition compared to the non-caloric condition. Vice versa, the right putamen (in contrast 3, 4, 5 and 6), left putamen (in contrast 5 and 6) left inferior parietal cortex (in contrast 3, 4, 5 and 6), and the left insula (in contrast 6 only) were increased in activation during the non-caloric condition compared to the caloric condition.
Table 3. Maximally activated voxels in areas in which significant evoked activity was related to 2nd level contrasts 4 and 5 (comparing same postingestion contrasts between
conditions).
Effect of postingestion contrasts Talairach Coordinates Region Brodman n area Left/ Right x y z z-value Caloric condition > non-caloric condition Precuneus (contrast 2, 3, 4, 5 and 6) 23 L -14 -56 30 3.9 Thalamus (contrast 3, 4, 5 and 6) - L -22 -16 6 3.8 Hippocampus (contrast 2, 3, 4, and 6) 37 L -26 -36 -2 3.4 Mid Cingulum 23 R 10 -8 38 3.3
(contrast 3 and 5) Post Cingulum (contrast 5) 23 L -6 -48 26 3.9 Parahippocampal area (contrast 2) 20 L -30 -24 -18 3.5 Non-caloric condition > caloric condition Putamen (contrast 3, 4, 5 and 6) 48 R 22 12 -6 4.7 Putamen (contrast 5 and 6) 48 L -22 0 6 3.3 Inferior Parietal (contrast 3, 4, 5 and 6) 40 L -26 -48 38 5.0 Insula (contrast 6) 48 L -34 0 -10 3.2
Only clusters where reported which were larger than 10 voxels, with a minimal Z-value of 3.2 and thresholded at P < .001 (uncorrected for multiple comparisons).
When comparing postingestion contrasts (2-6) with the time of ingestion itself, i.e. the treatment contrast, many brain areas were increased in activation during the caloric condition compared to the non-caloric condition (table 4 and figure 3). The most important of these areas to mention are the right anterior cingulum (ACC) as well as the right middle OFC, the right superior frontal cortex (in contrast 2, 3,4,5 and 6) and the left superior frontal cortex (contrast 4 and 5 only). Vice versa, a wide range of brain areas were also significantly activated after the intake of the non-caloric load compared to the caloric load intra-gastrically, among which the right caudate (in contrast 3,4,5,6), inferior temporal cortex (in contrast 2,3,4,5 and 6), right superior frontal cortex (in contrast 2, 3,4), and the right putamen (in contrast 5 and 6).
Table 4. Maximally activated voxels in areas in which significant evoked activity was related to 2nd level contrasts 6 and 7 (postingestion contrasts compared to ingestion
itself).
Effect of postingestion contrasts Talairach Coordinates Region Brodman n area Left/ Right x y z z-value Caloric condition > non-caloric condition ACC (contrast 2,3,4,5 and 6) 25 R 2 32 2 5.5
OFC (contrast 2,3,4,5 and 6)
47 R 34 44 -6 5.4
Sub Frontal (contrast 2,3,4,5 and 6)
10 R 30 52 10 5.3
Sub Frontal (contrast 4 and 5)
10 L -18 52 18 4.9
Sub med Frontal (contrast 2)
10 R 10 56 18 7.2
Sub med Frontal (contrast 4) 10 L -2 56 30 4.5 Parahippocampal area (contrast 2) 30 R 18 -24 -18 Inf Sub Temporal (contrast 2, 3,4,5 and 6) 21 R 42 -36 2 Inf Inf Temporal (contrast 2,3,4,5 and 6) 20 L -46 -48 -14 6.9 Mid Temporal (contrast 3,4,5 and 6) 42 R 54 -40 10 Inf Fusiform (contrast 2 and 3) 30 R -26 -28 -18 Inf Fusiform (contrast 2,3,4 and 5) 19 L -26 -68 -6 5.6 Lingual (contrast 2 and 3) 19 L -18 -56 -6 6.7 Supramarginal (contrast 4,5 and 6) 41 L -50 -48 26 7.0 Non-caloric condition > caloric condition Caudate (contrast 3,4,5 and 6) 11 R 18 24 -6 5.0 Putamen (contrast 5 and 6) 48 R 22 12 -2 4.6
Sub Frontal (contrast 2, 3 and 4)
10 R 30 52 10 5.9
Sub Frontal (contrast 3 and 4)
32 L -18 48 18 4.8
Sub med Frontal (contrast 2 and 3) 32 L -14 44 22 6.1 Hippocampus (contrast 2 and 3) 37 R 38 -32 -10 Inf Fusiform gyrus (contrast 2) 37 R 42 -40 -10 7.6 Fusiform gyrus (contrast 2 and 5) 19 L -30 -68 -6 6.7 Inf Temporal 20 L -46 -48 -14 7.0
(contrast 2,3,4,5 and 6) Inf Temporal (contrast 3,4,5 and 6) 20 R 50 -52 -14 Inf Mid Temporal (contrast 2 and 3) 21 L -62 -44 -2 4.9 Sub Temporal (contrast 2,4 and 5) 21 R 42 -36 2 7.7 Cerebellum (contrast 2 and 3) 19 L -14 -56 -14 5.7 Inf Occipital (contrast 3 and 6) 19 L -34 -72 -6 Inf Parietal (contrast 3) - L -22 -44 38 5.4
Inf OFC (contrast 4) 47 L -38 40 -10 4.6
Rectus (contrast 6) 11 R 18 16 -14 5.0
Only clusters where reported which were larger than 10 voxels, with a minimal Z-value of 4.5 and thresholded at P < .05 (FWE-corrected for multiple comparisons).
Figure 3: Effect of postingestion contrasts against ingestion itself. Statistical parametric map of a flexible-factorial repeated measures ANOVA, thresholded at P < .05 (Z-score > 4.5) (FWE-corrected for multiple comparisons). Activation can be seen in the ACC (green circle in the upper left picture), the OFC (green circle in the lower left picture) and prefrontal areas (upper right picture). Crosshair is situated at x= 2, y= 52, z= 7.
Ingestion itself, that means the treatment contrast, compared to postingestion contrasts (2-6) significantly activated the left caudate (in contrast 3,4,5 and 6) in both conditions (table 5). The left insula was activated during ingestion (in contrast 5 and 6) in the caloric condition, as well as the left precentral (in contrast 3, 4, 5, and 6). Areas of increased activation included the left thalamus during the non-caloric condition (in contrast 3 and 4) and the left middle temporal cortex (in contrast 5 and 6).
Table 5. Maximally activated voxels in areas in which significant evoked activity was related to 2nd level contrasts 8 and 9 (ingestion itself compared to postingestion
contrasts).
Effect of postingestion contrasts Talairach Coordinates Region Brodman n area Left/ Right x y z z-value Caloric condition > non-caloric condition Caudate (contrast 3, - L -14 0 14 4.8
4, 5 and 6) Insula (contrast 5 and 6) 48 L -42 4 -10 5.0 Precentral (contrast 3, 4, 5 and 6) 6 L -46 2 38 4.6 Non-caloric condition > caloric condition Caudate (contrast 3, 4, 5 and 6) 48 L -18 24 22 5.5 Thalamus (contrast 3 and 4) - L -10 -4 10 5.0 Mid Temporal (contrast 5 and 6) 22 L -54 -8 -10 5.9
Only clusters where reported which were larger than 10 voxels, with a minimal Z-value of 4.6 and thresholded at P < .05 (FWE-corrected for multiple comparisons).
Subjective Appetite Ratings (VAS)
Fullness
The main effect of time was significant during the non-caloric condition (F = 5.88, P < .05), but was not during the caloric condition (F = 2.42, P = 0.06). Also the main effect of condition (F = 0.38, P = 0.56), and the time-by-condition interaction effect were not significant (F = 0.94, P = 0.47), as shown in figure 4. A similarity can be seen in fullness ratings between conditions in time (time-by-condition interaction effect).
Figure 4: Mean (± standard error) VAS ratings of fullness obtained during fMRI scans (N=7). Main effect of time in the non-caloric condition was significant.
Desire to eat
The main effect of condition (F = 0.10, P = 0.76) as well as the main effect of time (F = 1.73, P = 0.16) and the time-by-condition interaction effect (F = 1.12, P = 0.37) were not significant, as shown in figure 5.
Figure 5: Mean (± standard error) VAS ratings of desire to eat obtained during fMRI scans (N=7). No significant effects can be seen.
Anxiety
The main effect of condition (F = 0.49, P = 0.51) as well as the main effect of time (F = 1.55, P = 0.20) and the time-by-condition interaction effect (F = 0.67, P = 0.65) were not significant, as shown in figure 6.
Figure 6: Mean (± standard error) VAS ratings of anxiety to eat obtained during fMRI scans (N=7). No significant effects can be seen.
Blood Parameters (Insulin & Glucose)
Insulin
For insulin a main effect of condition (F = 25.27, P < .05) was found. Also a main effect of time was found in the caloric condition (F = 22.24, P < .05) between 0 and 30 min (P < .05), 2.5 and 30 min (P < .05), 5 and 15 min (P < .05) and 5 and 30 min (P < .05). For the non-caloric condition an effect of time was found (F = 3.92, P < .05), but disappeared after post-hoc testing. The time-by-condition interaction effect was significant (F = 21.51, P < .05) between 0 and 30
min (P < .05), 2.5 and 30 min (P < .05), 5 and 15 min (P < .05) and 5 and 30 (P < .05).
As shown in figure 7, insulin concentrations differed between the caloric condition and the non-caloric condition (main effect of condition). Furthermore, the difference in insulin concentrations in time was much bigger during the caloric versus the non-caloric condition (time-by-condition interaction effect).
Figure 7: Mean (± standard error) insulin concentrations obtained during fMRI scans (N=7).
Main effects of condition, of time in both conditions and time-by-interaction were significant.
Glucose
For glucose, a significant main effect of time was found during the caloric condition (F = 32.31, P < .05) between 0 and 10 min (P < .05), 0 and 15 min (P < .05), 0 and 30 min (P < .05), 2.5 and 15 min (P < .05), 2.5 and 30 min (P < .05), 5 and 30 min (P < .05), 10 and 30 min (P < .05) and 15 and 30 min (P < .05). During the non-caloric condition no such time effect was found (F = 1.57, P = 0.2).
The main effect of condition was non-significant (F = 3.36, P = 0.12), but the time-by-condition interaction effect was significant (F = 34.48, P <.05). The interaction effect was significant between 0 and 10 min (P < .05), 0 and 15 min (P < .05), 0 and 30 min (P < .05), 2.5 and 30 min (P < .05), 5 and 30 min (P < . 05), 10 and 30 min (P < .05) and 15 and 30 min (P < .05).
As shown in figure 8, the difference in glucose concentrations in time was much bigger during the caloric versus the non-caloric condition, i.e. glucose concentrations increased after the ingestion of chocolate milk and stayed constant during the non-caloric condition (time-by-condition interaction effect).
Figure 8: Mean (± standard error) glucose concentrations obtained during fMRI scans (N=7). Main effect of time in the caloric condition and the time-by-interaction effect were significant.
Ad libitum breakfast
Participants eat significantly more during breakfast after getting a non-caloric intra gastric load, compared to a caloric gastric load (P < .05). After the caloric
condition average food intake was 450 kcal SD ±109; after the non-caloric condition 565 kcal SD ±164 on average were consumed.
Discussion
In this study, fMRI is used to investigate the acute effect of food intake on brain processes. By evaluating the effect of intra-gastric administration of a caloric versus a non-caloric load we studied the neural pathways of short-term satiety. Consistent with previous research we found a significantly food ingestion signal change in the precuneus and insula as in Wang et al (2008b), who studied gastric distention by measuring blood-oxygen-level-dependent (BOLD) responses. Also frontal areas like the medial, superior, medial superior and inferior frontal cortex were significantly increased in activation after gastric distention, that is to say eating. This was also found in Stephan et al (2003b), which revealed with positron emission tomography (PET) that gastric distention was related to increased frontal activity, thus adding the finding that neurons in this area respond to a satiety signal. The frontal cortex thus seems to represent an area of convergence for food stimuli. Furthermore, the OFC was significantly increased in activity, which is in line with research on taste, food stimuli and satiety processes (Small et al., 2001; O'Doherty et al., 2000; Kringelbach et al., 2003; Van der Laan et al., 2011, Rolls et al., 1989; Rolls et al., 2003). However, additional activated areas, like the right thalamus, left and right fusiform gyrus, left and right parahippocampal area, right hippocampus, left cerebellum, left and right inferior temporal cortex, left middle temporal cortex, right middle temporal pole, right oper rolandic, and the right middle cingulum were also found. Notably, no effect was found in the amygdala, while the amygdala was previously found to be activated in neuroimaging studies on food related stimuli (Wang et al., 2008). Given the previously found effects of caloric and non-caloric food intake, it was of interest whether brain responses changed as a result of it. As expected, signal change was found in the thalamus in the caloric condition (Van der laan et al., 2011). Previous studies report increasing signal change in the ventral striatum and taste pathways such as the ACC, prefrontal areas and the FO/AI (Smeets et al., 2011; Frank et al., 2008). However, the intake of the caloric load in our study did not affect these areas (when comparing all significantly evoked activity between conditions). Yet, as time increased (i.e. comparing postingestion contrasts with ingestion itself) the right ACC, as well as the right OFC and the right superior frontal cortex were increased in activation in the caloric condition but not during the non-caloric condition, which could be seen as a main finding of this study. This finding is in line with the study of Frank et al (2008), in which the
intake of caloric sucrose activated the ACC and frontal areas more than a non-caloric artificial sweetener. The ACC on the other hand should also be active during the non-caloric condition (comparing postingestion contrasts with ingestion itself) as it is assumed to be active after gastric distention (Stephan et al., 2003), seeing its role in the medial network within the OFC and its connections to the hypothalamus and brain stem by which it provides frontal cortical influence over autonomic functions. The fact that we did not find the ACC to be significantly active also in the non-caloric condition could be due to a small group size in this comparison.
The left thalamus was significantly increased in activation during the caloric condition but not during the non-caloric condition (when comparing food ingestion between conditions and same postingestion contrasts with each other). This is in line with Frank et al (2008) and its general role in taste, because neural signals from the gastrointestinal tract travel via the vagus nerve to the brainstem and the thalamus, which projects to the rest of the brain.
The dorsal striatum, which includes the caudate, putamen and pallidum, encodes consummatory food reward (Small et al., 2001; 2003; Smeets et al., 2011). We found increased activation in the right caudate and right putamen in the non-caloric condition, while these areas where not activated by the caloric load (comparing post-ingestion contrasts with ingestion itself). This finding is in contradiction with Smeets et al (2011), who investigated the consummatory role of calories in modulating taste activation in the striatum by measuring
BOLD responses. Higher activation in the left caudate on the other hand, was found after the acute intake of both the caloric as the non-caloric load compared to post-ingestion contrasts. This could be explained by the role of the caudate in reward as participants are conscious about the intake of food. Striatal activation may reflect reward value and/or affective value (Small et al., 2003; Spetter et al., 2010).
The hippocampus
is predominantly involved in spatial learning and
memory processing
, but has also been implicated in sensing the metabolic and hormonal status of the body (the so-called ‘enteroception’) and the regulation of food consumption (Delparigi et al.,2004; Spetter et al., 2012). We found an effect in the left hippocampus and left hippocampal area during the caloric condition, while the right hippocampus appeared to be increased in activity during the non-caloric condition.Subjective Appetite Ratings (VAS)
As expected, a similarity in fullness ratings between conditions in time (time-by-condition effect interaction effect) was found. This indicates that subjective fullness ratings stay relatively stable in time, within a range of 30 min. Subjects thus, reported to be equally full after the intake of either chocolate milk or water, while gastric distention was similar in both conditions. This may demonstrate that the body cannot differentiate between a caloric and a non-caloric intra-gastric load, in case distention of the stomach is equal. The main effect of time appeared to be significant during the non-caloric condition, but disappeared after post-hoc testing. This could be a trend, possibly due to the small sample size or due to strong post hoc testing (Bonferroni’s test with P < .05).
The results of desire to eat ratings did not fulfill our expectations. We expected those ratings of desire to decline as participants became fuller, while we found no such difference between conditions, which suggests that wanting for food did not change in time during both conditions. This finding could be explained by the properties of food, i.e. we used a liquid load instead of real food. For that reason, participants could still feel a desire for food, while their stomach distended.
Anxiety ratings were expected to be stable in both conditions, which we found. Our data indicated that subjects did not become more or less anxious in time during both conditions.
Blood Parameters (Insulin and Glucose)
Consistent to our hypotheses, glucose and insulin level change was greater after the intake of a caloric load, compared to a non-caloric load intra-gastrically. The difference in insulin concentrations in time was much bigger during the caloric versus the non-caloric condition (time-by-condition interaction effect), as expected. Also, the main effect of time in the caloric condition was in line with our expectations. In contrast, the main effect of time appeared to be significant in the non-caloric condition, while it disappeared after post-hoc testing. This indicated a significant fall in insulin concentrations, which could be due to the small sample size.
Glucose concentrations increased after ingestion of chocolate milk and stayed constant during the non-caloric condition (time-by-condition interaction effect).
The main effect of time during both conditions in is agreement with our expectations.
Avenues for future research
We believe that our results start to build a proper picture of an intra-gastric load induced food ingestion response on the neuronal circuitry involved in neurological processes of satiety. There are nevertheless a few considerations to bear in mind.
First of all, it is important to dwell upon our size of the intra-gastric load. This size was chosen by considerations of satiety, i.e. subjects appeared to be quite satiated after such a load (Smeets et al., 2011), but results could have been different when using a higher dose of either chocolate milk or water.
Secondly, higher-level effect analyses were thresholded at either a family wise error rate-corrected (FWE-corrected for multiple comparisons, P < .05) or where uncorrected for multiple comparisons (P < .001). It would have been more decent to correct all second-level analyses in a consistent manner.
Also, one could stand still by the fact that postingestion contrasts were compared with ingestion itself in both conditions and vice versa (2nd level
contrast 6, 7, 8 and 9). Question is to what extend these comparisons contributed to answer our main questions, seeing the previously made comparison between the same postingestion contrasts (2nd level contrast 4 and 5). We assumed that
these comparisons contributed to reveal a possible effect of the timecourse so that one could see the same or different brain areas to be increased in activity in the different subsequent contrasts. Besides, in this way, activity during the ingestion contrast itself was subtracted, which may give a more accurate picture of significantly increased brain areas in the time course.
Finally, to generalize findings and to get more statistical power, this study should be replicated, including more participants.
Conclusions
In this study the acute effects of food ingestion on brain activations associated with gastric distension and short-term satiety, were investigated. One of our aims was to define which brains areas would give a food ingestion induced signal change compared to the baseline condition. A wide range of brain areas seemed to be increased in activity, among which the precuneus, insula, OFC and frontal brain areas were the most important. Secondly, it was of interest whether brain regions responded differentially to a caloric or a non-caloric intra-gastric load in time. A major conclusion from our study is that ACC, OFC and the frontal cortex were significantly active after the intake of calories, but were not when water was ingested. Besides, the role of the caudate in reward value is strengthened by this study. Our results thus, contributed to the idea that the brain may respond differently to caloric and non-caloric food intra-gastrically. However, further research is needed to clarify the precise involvement of the caudate in reward value and the hippocampus in calorie recognition. Ultimately, it is expected that food ingestion induced fMRI of gastric distention in healthy subjects will be of use in understanding the mechanisms of satiety and behavior of overeating.
Acknowledgements
This study was approved by the Medical Ethical Committee of the UMCU. We gratefully acknowledge the financial support of the Dutch Technology Foundation STW (grant nr. 07438). Also, I acknowledge Maartje S. Spetter for being the principal investigator of this study, creating fMRI tasks and being my supervisor. In addition, I want to thank Paul A.M. Smeets for his help and suggestions and Tineke A. Baten for being our nurse.
References
Barness LA, Opitz JM, Gilbert-Barness E (2007) Obesity: genetic, molecular, and environmental aspects". Am. J. Med. Genet. A 143A (24): 3016–34.)
Blundell, J.E. and Halford, J.C.G. (1994) Regulation of nutrient supply: the brain and appetite control. Proceedings of the Nutrition Society, 53, 407-418.
Blundell,J.E., Lawton,C.L., Cotton,J.R. and Macdiarmid,J.I. (1996) Control of human appetite: implications for the intake of dietary fat. Annu. Rev. Nutr., 16, 285-319. Blundell, J., de Graaf, C., Hulshof, T., Jebb, S., Livingstone, B., Lluch, A., Mela, D., Salah, S., Schuring, E., van der Knaap, H. and Westerterp, M. (2010) Appetite control: methodological aspects of the evaluation of foods. Obesity reviews, 11, 251-270.
Cecil, J.E., Francis, J. and Read, N.W. (1998) Relative contributions of Intestinal, Gastric, Oro-sensory influences and information to changes in appetite induced by the same liquid meal. Appetite, 31, 377-390.
Chaudhri, O., Small, C. and Bloom, S. (2006) Gastrointestinal hormones regulating appetite. Phil. Trans. R. Soc.B, published online.
Delzenne, D., Blundell, J., Brouns, F., Cunningham, K., de Graaf, C., Erkner, A., Lluch, A., Mars, A., Peters, H.P.F. and Westerterp-Plantenga, M. (2010)
Gastrointestinal targets of appetite regulation in humans. Obesity reviews, 11, 234-250.
DelParigi,A., Chen, K., Salbe, A.D., Hill, J.O., Wing, R.R., Reiman, E.M. and
Tataranni, P.A. (2004) Persistence of abnormal neuronal responses to a meal in postobese individuals. International Journal of obesity, 28, 370-377.
Frank, G.K.W., Oberndorfer, T.A., Simmons, A.N., Paulus, M.P., Fudge, J.L., Yang, T.T. and Kaye, W.H. (2008) Sucrose activates human pathways differently from artificial sweetener. Neuroimage, 39, 1559-1569.
Kringelbach,M.L., O'Doherty,J., Rolls,E.T. and Andrews,C. (2003) Activation of the human orbitofrontal cortex to a liquid food stimulus is correlated with its
subjective pleasantness. Cereb. Cortex, 13, 1064-1071.
Ladabaum, U., Minoshima, S., Hasler, W.L., Cross, D., Chey, W.D. and Owyang, C. (2001) Gastric distention correlates with activation of multiple cortical and
subcortical regions. Gastroenterology, 120, 369-376.
Ladabaum, U., Roberts, T.P. and McGonigle, D.J. (2007) Gastric fundic distention activates fronto-limbic structures but not primary somatosensory cortex: A functional magnetic resonance imaging study. Neuroimage, 34, 724-732. Lassman,D.J., McKie,S., Gregory,L.J., Lal,S., D'Amato,M., Steele,I., Varro,A., Dockray,G.J., Williams,S.C. and Thompson,D.G. (2010) Defining the role of
cholecystokinin in the lipid-induced human brain activation matrix. Gastroenterology, 138, 1514-1524.
Lu, C.L., Wu, Y.T., Yeh, T.C., Chen, L.F., Chang, F.Y., Lee, S.D., Ho, L.T. and Hsieh, J.C. (2004) Neurocal correlates of gastric pain induced by fundus distention: a 3T-fMRI study. Neurogastroenterology & motility, 16, 575-587.
McKie,S., Del-Ben,C., Elliott,R., Williams,S., del,V.N., Anderson,I. and Deakin,J.F. (2005) Neuronal effects of acute citalopram detected by pharmacoMRI.
Psychopharmacology (Berl), 180, 680-686.
Min, D.K., Tuor, U.I., Koopmans, H.S. and Chelikani, P.K. (2011) Changes in
differential functional magnetic resonance signals in the rodent brain elicited by mixed-nutrient or protein-enriched meals. Gastroenterology, 141, 1832-1841. O'Doherty,J., Rolls,E.T., Francis,S., Bowtell,R., McGlone,F., Kobal,G., Renner,B. and Ahne,G. (2000) Sensory-specific satiety-related olfactory activation of the human orbitofrontal cortex. Neuroreport, 11, 893-897.
Ritter, R.C. (2004) Gastrointestinal mechanisms of satiation of food. Physiology & Behavior, 81, 249-273.
Rolls,B.J., Rowe,E.A. and Rolls,E.T. (1982) How sensory properties of foods affect human feeding behavior. Physiol Behav., 29, 409-417.
Rolls,E.T. (1989) Information processing in the taste system of primates. J. Exp. Biol., 146, 141-164.
Rolls,E.T., Kringelbach,M.L. and de,A., I (2003) Different representations of pleasant and unpleasant odours in the human brain. Eur. J. Neurosci., 18, 695-703.
Small,D.M., Gregory,M.D., Mak,Y.E., Gitelman,D., Mesulam,M.M. and Parrish,T. (2003) Dissociation of neural representation of intensity and affective valuation in human gustation. Neuron, 39, 701-711.
Small,D.M., Zatorre,R.J., Dagher,A., Evans,A.C. and Jones-Gotman,M. (2001) Changes in brain activity related to eating chocolate: from pleasure to aversion. Brain, 124, 1720-1733.
Smeets,P.A., Erkner,A. and de Graaf, C. (2010) Cephalic phase responses and appetite. Nutr. Rev., 68, 643-655.
Smeets,P.A., Weijzen,P., de Graaf, C. and Viergever,M.A. (2011) Consumption of caloric and non-caloric versions of a soft drink differentially affects brain
activation during tasting. Neuroimage., 54, 1367-1374.
Spetter,M.S., Smeets,P.A., de Graaf, C. and Viergever,M.A. (2010) Representation of sweet and salty taste intensity in the brain. Chem. Senses, 35, 831-840.
Spetter, M.S., de Graaf, C., Viergever, M.A. and Smeets, P.A.M. (2012) Anterior cingulate taste activation predicts ad libitum of sweet and savory drinks in healthy men. Submitted for publication in Journal of Nutrition.
Stephan,E., Pardo,J.V., Faris,P.L., Hartman,B.K., Kim,S.W., Ivanov,E.H.,
Daughters,R.S., Costello,P.A. and Goodale,R.L. (2003b) Functional neuroimaging of gastric distention. J. Gastrointest. Surg., 7, 740-749.
Stephan,E., Pardo,J.V., Faris,P.L., Hartman,B.K., Kim,S.W., Ivanov,E.H.,
Daughters,R.S., Costello,P.A. and Goodale,R.L. (2003a) Functional neuroimaging of gastric distention. J. Gastrointest. Surg., 7, 740-749.
Van der Laan, L.N., de Ridder, D.T.D., Viergever, M.A. and Smeets, P.A.M. (2011) The first taste is always with the eyes: A meta-analysis on the neural correlates of processing visual food cues. Neuroimage, 55, 296-303.
Van Oudenhove, L., (2011) Fatty acid-induced gut-brain signaling attenuates neural and behavioral effects of sad emotions in humans.
Van Strien,T., Frijters,J.E.R., Bergers,G.P.A. and Defares,P.B. (1986) Handleiding Nederlandse Vragenlijst voor Eetgedrag (NVE). Swets en Zeitlinger b.v., Lisse. Van, S.T. (1997) The concurrent validity of a classification of dieters with low versus high susceptibility toward failure of restraint. Addict. Behav., 22, 587-597. Wang,G.J., Tomasi,D., Backus,W., Wang,R., Telang,F., Geliebter,A., Korner,J., Bauman,A., Fowler,J.S., Thanos,P.K. and Volkow,N.D. (2008a) Gastric distention activates satiety circuitry in the human brain. Neuroimage., 39, 1824-1831. Wijlens,A., Erkner, A., Alexander, E., Mars, M., Smeets, P.A.M. and de
Graaf, C. (2011) Effects of oral exposure duration and gastric filling volume on satiety. Submitted for publication.
Weijzen, P.L.G., Smeets, P.A.M. and de Graaf, C. (2009) Sip size of orangeade: effects on intake and sensory-specific satiation. Britisch Journal of Nutrition, 102, 1091-1097.
Zijlstra, N., Mars, M., de Wijk, R.A., Westerterp-Plantenga, M.S., Holst, J.J. and de Graaf, C. (2009) Effects of viscosity on appetite and gastro-intestinal hormones. Physiology & Behavior, 97, 68-75.
