# Food addiction severity is associated with decreased functional connectivity and responses to palatable food pictures in brain areas involved in emotion and cognitive control

Nicolas Coquery, Mickael Som, Yann Serrand, Marie-Océane Guittière, Elise Bannier, Fabrice Lainé, Aymery Constant, Romain Moirand, Eric Stice, David Val-Laillet, et al.

► To cite this version:

Nicolas Coquery, Mickael Som, Yann Serrand, Marie-Océane Guittière, Elise Bannier, et al. Food addiction severity is associated with decreased functional connectivity and responses to palatable food pictures in brain areas involved in emotion and cognitive control. Clinical Nutrition, 2025, 51, pp.115-125. 10.1016/j.clnu.2025.06.001. hal-05110837

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Original article

# Food addiction severity is associated with decreased functional connectivity and responses to palatable food pictures in brain areas involved in emotion and cognitive control

<figure><img src="image_2.png" /></figure>

Nicolas Coquery<sup>a,1</sup>, Mickael Som<sup>a,b,1</sup>, Yann Serrand<sup>a</sup>, Marie-Océane Guittière<sup>a,b</sup>, Elise Bannier<sup>c,d</sup>, Fabrice Lainé<sup>e</sup>, Aymery Constant<sup>a,f</sup>, Romain Moirand<sup>a,g</sup>, Eric Stice<sup>h</sup>, David Val-Laillet<sup>a,*,1</sup>, Ronan Thibault<sup>a,b,1</sup>

## ARTICLE INFO

**Article history:**
Received 28 October 2024
Accepted 2 June 2025

**Keywords:**
Neuroimaging
Functional magnetic resonance imaging
Resting state magnetic resonance imaging
Eating disorders

## SUMMARY

**Background & aims:** Patients with obesity frequently report impulsivity or loss control of food intake, reflecting the dysfunction of brain regions involved in reward and cognitive control processing related to food addiction (FA). Functional magnetic resonance imaging (fMRI) may help to identify the cognitive processes underlying FA. In patients with obesity, we aimed at identifying (i) fMRI specific responses in brain regions involved in hedonic and motivational processes (striatum), and cognitive control (e.g. prefrontal cortex) according to FA severity; (ii) whether FA severity is related to anxiety and/or depression symptoms.

**Methods:** In this prospective, observational, monocentric study, forty women with moderate or severe obesity were recruited. FA was diagnosed with the Yale Food Addiction Scale (YFAS) 2.0. Binge Eating Scale (BES), and Hospital Anxiety and Depression Scale (HADS) was also completed. One fMRI session that included fMRI and resting-state fMRI (rsfMRI) assessed brain responses to palatable food images and functional connectivity. Statistical analysis included general linear model analysis and multivariate parametric statistics.

**Results:** Twenty-three (57.5 %) of the 40 included patients suffered from FA; Mean (± standard deviation) age, 36.6 ± 8.6 yr; Body mass index, 39.2 ± 2.9. In the food task condition, both low and high appetizing images activated similar brain regions, and FA severity was associated with reduced activation in the dorsal striatum and pre-central gyrus for high appetizing images (cluster: pFWE<0.05 family-wise error correction; peak: p < 0.001 uncorrected). In the resting-state condition, FA severity correlated negatively with functional connectivity (FC) between several brain regions, such as the anterior cingulate cortex and left accumbens, and positively with FC between the left amygdala and hippocampus (cluster connection: pFDR<0.05, false discovery rate correction). FA severity was associated to higher binge eating, but not anxiety and depression symptoms.

**Conclusion:** Brain profiles of patients with obesity are related to FA severity. FA severity is associated with impaired cortico-striatal functional connectivity between brain regions involved in motivation,

<footnote><sup>a</sup>Inrae, Inserm, Univ Rennes, Nutrition Metabolisms and Cancer, NuMeCan, Rennes, St Gilles, France</footnote>

<footnote><sup>b</sup>Department of Endocrinology-Diabetology-Nutrition, CHU Rennes, Rennes, France</footnote>

<footnote><sup>c</sup>Inria, CRNS, Inserm, IRISA UMR 6074, Empenn U1228, Univ Rennes, Rennes, France</footnote>

<footnote><sup>d</sup>Department of Radiology, CHU Rennes, Rennes, France</footnote>

<footnote><sup>e</sup>Univ Rennes, CHU Rennes, INSERM CIC1414, F-35000 Rennes, France</footnote>

<footnote><sup>f</sup>EHESP, School of Public Health, Rennes, France</footnote>

<footnote><sup>g</sup>Addictology Unit, CHU Rennes, Rennes, France</footnote>

<footnote><sup>h</sup>Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA</footnote>

<footnote>* Corresponding author.
E-mail address: david.val-laillet@inrae.fr (D. Val-Laillet).</footnote>

<footnote n="1">¹ These authors have contributed equally to this work.</footnote>
decision-making and inhibitory control. Interindividual variability in brain responses suggests that dedicated therapeutic strategies might be helpful for obese patients with FA.

© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

## Abbreviations

<table><tr><td>ACC</td><td>Anterior Cingulate Cortex</td></tr><tr><td>BES</td><td>Binge Eating Scale</td></tr><tr><td>BOLD</td><td>Blood Level Oxygen-Dependent</td></tr><tr><td>CAU</td><td>Caudate Nucleus</td></tr><tr><td>ED</td><td>Eating Disorders</td></tr><tr><td>DSM5</td><td>Diagnostic and Statistical Manual, fifth edition</td></tr><tr><td>FA</td><td>Food Addiction</td></tr><tr><td>fMRI</td><td>functional Magnetic Resonance Imaging</td></tr><tr><td>FWHM</td><td>Full Width at Half Maximum Kernel</td></tr><tr><td>FC</td><td>Functional connectivity</td></tr><tr><td>HA</td><td>High Appetizing images</td></tr><tr><td>HADS</td><td>Hospital Anxiety and Depression Scale</td></tr><tr><td>LA</td><td>Low Appetizing images</td></tr><tr><td>MCC</td><td>Middle Cingulate Cortex</td></tr><tr><td>MNI</td><td>Montréal Neurological Institute</td></tr><tr><td>PUT</td><td>Putamen</td></tr><tr><td>REE</td><td>Resting Energy Expenditure</td></tr><tr><td>rsfMRI</td><td>Resting State fMRI</td></tr><tr><td>SUD</td><td>Substance Use Disorder</td></tr><tr><td>YFAS</td><td>Yale Food Addiction Scale</td></tr></table>

# 1. Introduction

Obesity is growing worldwide, has important medical and socio-economic consequences, and affects the quality of life [1]. The plethora of easily accessible energy-dense foods that provide pleasure when eaten (i.e. palatable foods) is a major risk factor in the emergence of eating disorders (ED) and obesity. Indeed, these palatable foods (e.g. sweet, fatty, salty and processed) might induce craving and addictive symptoms resembling substance use disorder (SUD) [2-4]. In this context, the concept of food addiction (FA) has emerged [5]. This theory is particularly supported by animal studies showing for example, in a rodent model of chronic intermittent exposure to sugar, the development of sugar tolerance and withdrawal symptoms when administration is stopped [6,7]. Animal [8,9] and human studies [10] suggest that common neurochemical circuits are involved in all these forms of substance abuse, including for instance a lower availability of dopamine and opioids receptors [10,11]. Experimental studies also show the positive effect of particular foods on mood and the development of tolerance [12].

To facilitate an easy and standardized diagnosis of FA in medicine, the Yale Food Addiction Scale (YFAS) questionnaire has been validated, especially in overweighed and obese individuals [13-15]. The new version, YFAS 2.0, based on the substance use disorder (SUD) criteria described in the Diagnostic and Statistical Manual, fifth edition (DSM5), is validated in several languages, including French [16]. Some studies report a prevalence of FA of 25 % in obese patients, 26 % in the subgroup of women [14], even rising to 57 % in cases of binge eating disorder [17], notably

because some symptoms are overlapping. Recently, we found a prevalence of FA of 37 % in a French obese clinical population, with an increased level of 41 % among unemployed patients [18].

The YFAS questionnaire remains subjected to the constraints of declarative methods. It does not allow the identification of neurological characteristics associated with the pathological FA behavior. Neuroimaging studies represent a unique promising approach to support the existence of FA. It is now well established that behavioral and cerebral characteristics related to hedonic functions (in particular the dopaminergic system) and inhibitory cognitive control are different in obese or overweight subjects compared to lean individuals [19-23]. Modern advances in functional brain imaging have demonstrated that obesity and ED are accompanied by functional brain abnormalities likely to promote or maintain risky eating behaviors [24-26].

Even though the neurobiology of EDs received much attention [25,27-29], studies that targeted FA in relation to brain imaging are scarce. Gearhardt et al. (2011) first showed that women with a high YFAS score showed greater activation in the dorsolateral prefrontal cortex and the caudate nucleus in response to the anticipation of a food reward [30]. Nevertheless, there was less activation during obtention of this reward, which consisted in the ingestion of 0.5 mL of chocolate milkshake. Other brain structures of interest also responded differently depending on the YFAS score, such as the anterior cingulate cortex, orbitofrontal cortex, and amygdala, which hold major roles in attention, hedonic appraisal, and associative learning processes. More recently, Ravichandran et al. (2024) demonstrated that FA is also associated with brain morphometry patterns in motivation and reward processing regions indicative of dopaminergic dysregulation and inhibition of cognitive control regions [31]. Using brain anatomical and functional imaging to demonstrate that there is a neurobiological substrate underlying the clinical and metabolic phenotype of FA, as well as identifying potential neuromarkers of FA (possibly correlated with biomarkers resulting from physiological analyses or metabolomic exploration) would make possible: i) the validation of classical questionnaire screening methods by providing an objective and measurable biological criterion in functional imaging, ii) the identification of neurocognitive processes and brain areas to be targeted within the framework of cognitive therapies, neuromodulation or other interventional approaches, and, iii) the follow-up of patients by means of longitudinal examinations, in order to validate the effectiveness of interventions or adapt them. Therefore, it is imperative for research efforts to characterize these biomarkers, which would represent an important innovation to describe and better treat FA associated with obesity, and optimize the medical and surgical management of obesity. The objective of our study was to identify the brain phenotype by fMRI of obese patients presenting with FA in the context of a food consumption imagination task and in resting state. In patients with obesity with or without FA, we aim at identifying (i) fMRI specific responses in brain regions involved in hedonic and motivational processes (striatum), and cognitive control (prefrontal cortex) according to FA severity; (ii) whether FA severity is related to binge eating, anxiety and/or depression symptoms.
## 2. Patients and methods

### 2.1. Ethics statement

This study was approved by the Institutional Review Board (Comité de Protection des Personnes "2017-44 Ouest II Angers", project N°35RC17_8949_AddictO; N°ID-RCB/EUDRACT 2017-A01269-44; National Clinical Trial number: NCT03454711) and was performed in accordance with the French and European General Data Protection Regulation 2016/679.

### 2.2. Patient recruitment

Forty female patients with obesity consulting for the first time at the department of Endocrinology-Diabetology-Nutrition, University Hospital of Rennes (CHU Rennes), an obesity referral center, were recruited between September 2018 and September 2021, with a break of more than a year in-between due to the recruitment interruption during the Covid-19 pandemic. All patients filled in the YFAS 2.0 questionnaire as part of the routine systematic assessment for all obese patients consulting for the first time in our department. Based on this systematic assessment, patients meeting the other inclusion criteria were invited to participate into the study. Participants met the following inclusion criteria: between 18 and 55 years of age, body mass index (BMI) ≥30, and an abdominal circumference (including arms) lower than 200 cm (due to the limited space within the MRI scanner). All participants were native French speakers, right-handed and provided voluntary informed and written consent to participate in the study. Non-inclusion criteria (i.e. criteria preventing inclusion) were having contra-indications to MRI (e.g. having braces, metallic implant, or a cardiac pacemaker), psychoactive substance consumption/addiction (alcohol, drug and tobacco), mental disorders (schizophrenia or bipolar disorder), any history of obesity surgery, pregnancy, no French health insurance and/or insufficient knowledge of French language. Potential exclusion criteria (i.e. criteria justifying the exclusion of a patient previously included) were for example too many movements during the MRI session, pregnancy during the course of the study, or presence of a brain anatomical anomaly in a region of interest.

A stratified randomization was initially planned to recruit 20 patients with FA and 20 without FA. At the end, due to recruitment constraints, 23 patients with FA and 17 patients without FA were included. To ensure a better interpretability of the results and to take into account the different levels of FA severity in the 'FA' group, analyses were performed in the 40 patients according to FA intensity.

### 2.3. Study design

This was an observational, single-site study. After inclusion, patients completed two visits. The first occurred at the Clinical Investigation Unit (ICU, CHU Rennes) to measure weight, height, body mass index, waist and hip circumferences, body composition, and resting energy expenditure (REE) assessments, and allow participants to complete questionnaires (see below). The second visit was occurred one month later at the magnetic resonance imaging (MRI) Neurinfo platform (Rennes University-Hospital, University of Rennes) to perform brain MRI acquisitions including functional MRI (fMRI) and resting state fMRI (rsfMRI).

### 2.4. Questionnaire-based characterization of the participants

As noted, all participants completed the YFAS 2.0 [32] adapted and validated in French language [16]. The self-administered

questionnaire YFAS 2.0 consists of 35 items evaluating the 11 addiction symptoms. Answers from the YFAS 2.0 allow the computation of a composite score that determines the FA intensity, i.e. no addiction (FA score = 0), low (FA score = 1), moderate (FA score = 2) and severe (FA score = 3).

Participants were also asked to answer two other questionnaires: the Binge Eating Scale (BES), a 16-item questionnaire used to assess the presence of binge eating disorder [33,34]; the Hospital, Anxiety and Depression Scale (HADS), a 14-item questionnaire commonly used to assess anxiety and depression symptoms [35,36].

### 2.5. Body composition, resting energy expenditure (REE) and metabolic plasma parameters

#### 2.5.1. Body composition

Body composition was assessed by bioimpedance analysis using a monofrequency (50 kHz) bioimpedance analyzer (Bodystat 1500 MDD, Bodystat Ltd, Man Island, UK). The measurement procedure complied with the standard procedure and the manufacturer's instructions. More information can be found in published papers [37,38].

#### 2.5.2. REE measurement by indirect calorimetry

The indirect calorimeter used was the Quark RMR (Cosmed, Italy). The canopy was used to measure REE in spontaneously breathing subjects. The procedure recommended by Oshima et al. was followed [39]. The subject was placed under a clear canopy with a plastic drape to avoid air leakage. The calorimeter possesses a constant flow generator creating an outward flow through the canopy. The exhaled breath by the subject is diluted by the constant flow Q (L/min), and collected by the calorimeter for gas analysis, enabling calculations of oxygen and carbon dioxide consumptions. These values are used to calculate the EE using the Weir's equation.

#### 2.5.3. Metabolic plasma parameters

During the first visit, a systematic biological blood testing included: Fasting glucose (g/l), total cholesterol, triglycerides, high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol (g/l).

### 2.6. Brain responses to palatable food images during a fMRI task

The palatable food image fMRI task was adapted from Stice et al. [40] with French catering food images from a food picture database that we have previously created, validated and published in open access [41]. Within one week prior to imaging, obese patients completed at home a hedonic evaluation answering the question "*Indicate how much you like this starter/main course/dessert using the numeric scale from 1 to 10 at the bottom of the screen*", ranging from 1: "I really don't like it" to 10: "I really like it", for each of the 160 food pictures from our home-made food picture database already validated for fMRI tasks [41,42]. The responses were used to select for each patient, a group of Low Appetizing images (LA, n = 40) and High Appetizing images (HA, n = 40), as followed: The first 40 lowest appetizing images for a given person were chosen as LA for this person, and the first 40 highest appetizing images were chosen as HA similarly. Each task was consequently individually tailored according to the patient's declared preferences. As "neutral" image, a glass of water was used. In the entire included population, the mean "hedonic" values were 2.8 ± 1.3 for LA and 8.7 ± 1.1 for HA with no difference on these values between FA intensity level.
## 2.7. fMRI procedure

### 2.7.1. Brain activation analysis

2.7.1.1. *Preparation of the subjects.* The participants were asked to fast at least 3 h before undergoing the MRI session, which was performed between 11:00 am and 12:30 pm (before lunch). In the MRI scanner, the participants were placed in a recumbent head-first supine position. A mirror was fixed on the head coil above the participants at eye level to allow the visualization of a LCD screen at the rear of the scanner, on which the visual task was presented. Before the task, participants received orally the corresponding task instructions.

2.7.1.2. *MRI acquisition.* Acquisitions were performed on a 3T Siemens MRI (Magnetom Prisma, VE11C, Siemens Healthineers, Erlangen, Germany) with a 64-channel head coil. The Nordic Neurolab solution (Bergen, Norway) and EPrime 2.0.8 Professional (PST, Sharpsburg, USA) were used to display the visual task *via* a rear-facing mirror placed on the head coil and an LCD screen at the back of the MRI bore. An anatomical sequence (3D T1 MPAGE, FOV = 256 × 256 mm², 176 slabs, TR = 1900 ms, TE = 2.26 ms, TI = 900 ms, flip angle = 9°, voxel size = 1x1x1mm3) was acquired as well as an axial T2 FLAIR sequence in order to detect a potential brain abnormality, in which case the subject was excluded from the analysis and referred to a specialized physician. Functional MRI was acquired during the 11-min task (323 volumes). The acquisition parameters were FOV = 192 × 192 mm², 36 slices, voxels size = 3 × 3 × 3 mm³, TR = 2000 ms, TE = 30 ms, and flip angle = 90°. Resting-state (rsMRI) was acquired during 12 min (514 volumes). The acquisition parameters were FOV = 192 × 192 mm², 20 slices, EPI factor 64, voxel size = 2.3 × 2.3 × 4 mm³, TR = 1400 ms, TE = 30 ms, and flip angle = 70°.

2.7.1.3. *fMRI analysis.* Analyses were performed as previously described [41,42] using the Statistical Parametric Toolbox 12 (Wellcome Department of Imaging Neuroscience, London, England) under Matlab (Mathworks, Inc., Sherborn, MA). Functional images were realigned to the mean to correct for motion during the acquisition and coregistered on 3D T1 anatomical images. The anatomical and functional images were then normalized to the Montréal Neurological Institute (MNI) space. Spatial smoothing of functional images was performed with a 6-mm full width at half maximum kernel (FWHM). Vector onsets were compiled for the water (reference), low appetizing (LA) and high appetizing (HA) conditions using Matlab. A general linear model analysis was performed on brain masked data (i.e. after having removed extracerebral matter from the data analysis), using the canonical hemodynamic response function (HRF) without time and dispersion derivatives. The model was built with Water, LA and HA conditions, and the FA intensity values were used as covariate. Statistical analysis was performed either with the following contrasts: (i) Any choice, as defined from any responses in the Water, LA and HA conditions, (ii), LA vs. Water, (iii) HA vs. Water, and (iv) LA vs. HA conditions. Statistical significance was assessed for non-corrected p-value at a cluster peak level of p < 0.001 and family-wise error correction at the cluster level of pFWE-corrected<0.05. False Discovery Rate (FDR) correction was also indicated in addition to FWE (pFDRcorrected<0.05), as both correction methods could have different meanings.

2.7.1.4. *rsMRI analysis.* Functional connectivity (FC) was performed using CONN Toolbox version 21.a on Matlab 2023b environment (The MathWorks, Inc., Natick, Massachusetts). After all default preprocessing steps, including normalization to MNI atlas space, slice timing, spatial smoothing with an 8-mm FWHM,

motion artefact detection and correction, denoising and first-level analysis, a ROI-to-ROI second level analysis was performed using the default setting for alternative setting for ROI-based inferences, parametric multivariate statistics [43]. This multivariate parametric statistic approach can group a set of connections that arise from the same ROI. We predefined 16 ROIs in the CONN toolbox as having a role in food perception and eating [44]: Bilateral caudate and bilateral putamen for the dorsal striatum (motivation), bilateral nucleus accumbens for the ventral striatum (reward), bilateral thalamus (sensory gateway), bilateral amygdala (emotion processing), bilateral hippocampus (learning and memory), anterior cingulate cortex (attention and conflict monitoring) and bilateral insular cortex (gustatory cortex, multisensory integration and body representation) as key nodes of the Salience Network and the posterior cingulate cortex as a key node of the Default Mode Network [45]. Due to a limited number of patients, the statistical power in our study was not sufficient to integrate too many brain areas in the analysis, and given that we could not detect statistical difference promoted by FA in the frontal brain responses in the palatable food fMRI task, frontal brain regions were not included in the analysis. Statistical significance was set, as predefined, at the ROI-level, pFDR-corrected<0.05 (Multi-voxel pattern analysis (MVPA) bus test), and at the connection level, p-value<0.01 uncorrected.

### 2.7.2. Statistical analyses

2.7.2.1. *Sample size calculation.* Power calculations in activation fMRI paradigms are not the subject of consensus in the literature [46–48]. However, several simulations for event-type paradigms suggest that 20 subjects per group makes it possible to achieve the required power at the 5 % threshold. In addition, with such a patient number, several brain imaging studies related to diet have already demonstrated significant variations in activity in the regions of interest chosen for our project [30,49]. Gearhardt et al. [30], which comes closest to our scientific question, reports significant variations in brain activity in the dorsolateral prefrontal cortex and the caudate nucleus (dorsal striatum) when comparing subjects with high addiction scores (N = 15) vs. subjects with low scores (N = 11).

The primary judgment criterion was the significant modification of brain activity (expressed as a significant variation in the BOLD hemodynamic response under the condition of food consumption imagination task, i.e. HA vs. LA) in areas involved in the inhibitory control of food intake (dorsolateral prefrontal cortex, superior frontal gyrus). The secondary judgment criterion was the significant modification of brain activity in areas involved in the control of reward and motivation phenomena (dorsal striatum, insula). With forty subjects, we estimated that we will obtain sufficient statistical power to establish differences in brain activity between groups and look for correlations between brain responses and metabolic and/or behavioral covariates.

2.7.2.2. *Statistical analysis.* Qualitative variables are reported as n (%) and compared with Chi² (K) or Fisher (F) tests. Quantitative variables are reported as mean ± standard deviation, and compared with Student or Mann-Whitney Wilcoxon tests. Except for brain imaging analysis described above, all statistical analyses were performed with general linear model (SPSS software), with a threshold at p < 0.05 for statistical significance. For multiple comparison between the different FA intensity group, LSD posthoc tests were performed.
# 3. Results

## 3.1. Patient characteristics

Forty female patients with moderate or severe obesity were included in this study. Demographic and anthropometric characteristics, as well as body composition, REE, and behavioral questionnaires data, according to FA intensity, are reported in Table 1. Anthropometry, body composition, REE and metabolic biological parameters did not differ significantly according to FA intensity, except for dry fat-free mass. BES scores were positively correlated with FA intensity, whereas HAD anxiety and depression scores did not differ significantly. The correlation matrix between all patients' measures is presented in Supplementary Table 1.

## 3.2. Brain responses to food consumption imagination task during fMRI

We first investigated the brain responses to the low and high appetizing images for the entire population independently of FA score (Fig. 1A). For both low and high appetizing images, similar patterns of activated clusters in various brain regions were detected as seen in the brain activation maps (voxel p < 0.001, correction at the cluster level p(FWE) < 0.05) (Fig. 1): In the gustatory and multisensory integration cortex (insula: INS), the motivation centers (caudate: CAU, putamen: PUT) but also in areas involved in decision making (middle cingulate cortex: MCC) as well as in relaying sensory information, regulation of consciousness and alertness (thalamus). We did not detect any statistical difference (at the uncorrected voxel level, p < 0.001 or corrected cluster level p(FWE) < 0.05) in the brain activation map

for the high (HA) vs. low (LA) appetizing images (data not shown).

The effect of FA intensity on brain response to different level of appetizing images is presented in Fig. 1B and Table 2A. Higher FA intensity was associated with lower brain response in the bilateral dorsal striatum (caudate and putamen) for high appetizing images but not for low appetizing images. Additionally, higher FA intensity correlated with lower brain responses in the pre-central gyrus for an increasing hedonic level, i.e. high appetizing images vs. low appetizing images.

## 3.3. rsMRI based brain functional connectivity (FC)

As seen with the two-sided statistical analysis (Fig. 2A and Table 2B), higher levels of FA intensity were associated with a less connectivity between i), the anterior cingulate cortex and two other ROIs: The left caudate and left accumbens; ii), the left accumbens and three other ROIs: Anterior cingulate cortex, left insular cortex and left amygdala. Higher levels of FA intensity level were also associated with increased functional connectivity between the left amygdala and the right hippocampus. One-sided statistics investigation provided additional results: higher levels of FA intensity level was associated with greater functional connectivity between the left amygdala and the left hippocampus (Fig. 2B), and with lower functional connectivity between the anterior cingulate cortex and the left thalamus (Fig. 2C).

## 3.4. Mean caloric density of low and high appetizing images in relationship with food addiction

There was no significant difference between the caloric density of LA and HA foods in patients with no food addiction (No FA),

<caption>Table 1</caption>

Anthropometric, body composition, resting energy expenditure, and behavioral questionnaires data, according to FA intensity, in the 40 female obese patients included in the study. BES, binge eating scale; HADS, hospital anxiety and depression scale; REE, resting energy expenditure; YFAS, Yale food addiction scale. Data are presented as mean ± standard deviation, and compared with Student or Mann-Whitney Wilcoxon tests.

<table><thead><tr><th rowspan="2">Variables</th><th>ALL</th><th colspan="3">FA intensity</th><th>FA intensity</th><th>FA intensity: Absence vs severe</th></tr><tr><th>N = 40</th><th>Absence<br>N = 17</th><th>Mild/Moderate<br>N = 10</th><th>Severe<br>N = 13</th><th>p-value</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years)</td><td>36.6 ± 8.6</td><td>36.6 ± 5.8</td><td>38.1 ± 9.9</td><td>34.8 ± 10.8</td><td>0.674</td><td>0.580</td></tr><tr><td><strong>Anthropometry and body composition</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Weight (kg)</td><td>103.4 ± 10.0</td><td>104.3 ± 12.3</td><td>100.2 ± 10.8</td><td>104.7 ± 5.1</td><td>0.515</td><td>0.928</td></tr><tr><td>Height (cm)</td><td>162.3 ± 6.6</td><td>161.3 ± 6.5</td><td>159.8 ± 6.9</td><td>165.5 ± 5.5</td><td>0.079</td><td>0.075</td></tr><tr><td>Body mass index (kg/m²)</td><td>39.2 ± 2.9</td><td>40.0 ± 3.3</td><td>39.2 ± 3.0</td><td>38.2 ± 2.1</td><td>0.260</td><td>0.443</td></tr><tr><td>Waist circumference (cm)</td><td>120.6 ± 8.4</td><td>120.6 ± 9.5</td><td>119.8 ± 8.2</td><td>120.5 ± 7.5</td><td>0.967</td><td>0.973</td></tr><tr><td>Hip circumference (cm)</td><td>126.4 ± 7.4</td><td>126.0 ± 9.8</td><td>129.1 ± 5.7</td><td>124.8 ± 4.0</td><td>0.376</td><td>0.655</td></tr><tr><td>Fat mass (kg)</td><td>47.9 ± 7.5</td><td>48.2 ± 9.4</td><td>47.6 ± 7.8</td><td>47.8 ± 4.2</td><td>0.982</td><td>0.909</td></tr><tr><td>Fat mass (%)</td><td>46.1 ± 4.0</td><td>45.8 ± 4.7</td><td>47.3 ± 3.2</td><td>45.6 ± 3.7</td><td>0.557</td><td>0.895</td></tr><tr><td>Fat-free mass (kg)</td><td>55.7 ± 5.6</td><td>56.4 ± 6.2</td><td>52.5 ± 4.0</td><td>57.1 ± 5.0</td><td>0.104</td><td>0.710</td></tr><tr><td>Fat-free mass (%)</td><td>53.9 ± 4.0</td><td>54.2 ± 4.7</td><td>52.7 ± 3.2</td><td>54.4 ± 3.7</td><td>0.557</td><td>0.895</td></tr><tr><td>Dry fat-free mass (kg)</td><td>14.4 ± 2.2</td><td>14.1 ± 2.2</td><td>13.3 ± 1.5</td><td>15.5 ± 2.2</td><td>0.043</td><td>0.074</td></tr><tr><td>Body water (%)</td><td>40.0 ± 3.4</td><td>40.7 ± 3.6</td><td>39.6 ± 2.7</td><td>39.6 ± 3.7</td><td>0.642</td><td>0.428</td></tr><tr><td>Fat-free mass index (kg/m²)</td><td>21.1 ± 1.4</td><td>21.6 ± 1.3</td><td>20.6 ± 1.1</td><td>20.9 ± 1.5</td><td>0.114</td><td>0.127</td></tr><tr><td>Fat mass index (kg/m²)</td><td>18.2 ± 2.8</td><td>18.5 ± 3.5</td><td>18.6 ± 2.6</td><td>17.5 ± 1.9</td><td>0.568</td><td>0.351</td></tr><tr><td><strong>REE (kcal/day)</strong></td><td>1834.3 ± 266.5</td><td>1861.8 ± 308.8</td><td>1814.0 ± 246.6</td><td>1813.8 ± 237.1</td><td>0.860</td><td>0.636</td></tr><tr><td><strong>Metabolic plasma parameters</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Glucose (g/L)</td><td>1.01 ± 0.17</td><td>1.05 ± 0.20</td><td>1.04 ± 0.16</td><td>0.94 ± 0.12</td><td>0.163</td><td>0.079</td></tr><tr><td>Triglycerides (g/L)</td><td>1.20 ± 0.67</td><td>1.10 ± 0.53</td><td>1.33 ± 0.95</td><td>1.24 ± 0.60</td><td>0.660</td><td>0.559</td></tr><tr><td>Total cholesterol (g/L)</td><td>1.86 ± 0.30</td><td>1.85 ± 0.24</td><td>1.88 ± 0.29</td><td>1.85 ± 0.39</td><td>0.963</td><td>0.975</td></tr><tr><td>HDL Cholesterol (g/L)</td><td>0.49 ± 0.14</td><td>0.50 ± 0.19</td><td>0.45 ± 0.07</td><td>0.50 ± 0.09</td><td>0.603</td><td>0.983</td></tr><tr><td>LDL Cholesterol (g/L)</td><td>1.13 ± 0.26</td><td>1.14 ± 0.20</td><td>1.17 ± 0.24<sup>a</sup></td><td>1.10 ± 0.33</td><td>0.838</td><td>0.735</td></tr><tr><td><strong>Questionnaires</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>YFAS 2.0 symptoms (nb)</td><td>4.6 ± 2.8</td><td>2.9 ± 2.4</td><td>3.6 ± 1.3</td><td>7.7 ± 1.5</td><td><strong>&lt;0.001</strong></td><td><strong>&lt;0.001</strong></td></tr><tr><td>BES</td><td>19.2 ± 8.1</td><td>16.3 ± 8.3</td><td>17.3 ± 5.7</td><td>24.4 ± 7.4</td><td><strong>0.015</strong></td><td><strong>0.006</strong></td></tr><tr><td>HADS Anxiety</td><td>9.8 ± 3.5</td><td>8.9 ± 3.3</td><td>9.6 ± 3.5</td><td>11.0 ± 3.7</td><td>0.268</td><td>0.109</td></tr><tr><td>HADS Depression</td><td>8.1 ± 3.5</td><td>8.6 ± 3.8</td><td>7.4 ± 3.8</td><td>7.9 ± 3.2</td><td>0.689</td><td>0.611</td></tr></tbody></table>

Significant differences appear in **bold** while statistical trends (0.5≤p < 0.1) appear in *italic*.

<footnote>a One missing value for this measurement (n = 9).</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Brain activations detected in the **palatable food images functional magnetic resonance imaging (fMRI) task** viewed in coronal slices **A.** for all participants (n = 40), and **B.** depending on the food addiction (FA) intensity. MNI y-coordinates are indicated for each coronal slice and brain regions abbreviations are indicated below.</figcaption></figure>

<table><caption>**Table 2**
Effect of FA in functional magnetic resonance imaging (fMRI) response to palatable food images and resting state MRI (rsMRI).</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">Anatomical area</th><th rowspan="2">P-value (Cluster)</th><th rowspan="2">MNI Peak Coordinates</th><th colspan="2">P-value (Peak)</th></tr><tr><th>x</th><th>y</th><th>z</th><th>T</th><th>Uncorr</th></tr></thead><tbody><tr><td rowspan="12">A. fMRI, response to palatable food images, n = 40</td><td rowspan="12">K</td><td rowspan="3">Structure</td><td rowspan="3">Hemisphere</td><td rowspan="3">FWE</td><td rowspan="3">FDR</td><td rowspan="3">Uncorr</td><td rowspan="3">x</td><td rowspan="3">y</td><td rowspan="3">z</td><td rowspan="3">T</td><td rowspan="3">Uncorr</td></tr><tr><td>FA: HA sup Water</td><td>183 Putamen, Caudate</td><td>L</td><td>&lt;0.001</td><td>&lt;0.001</td><td>-24</td><td>8</td><td>-4</td><td>5.61</td><td>&lt;0.001</td></tr><tr><td></td><td>L</td><td></td><td></td><td></td><td>-18</td><td>2</td><td>17</td><td>4.89</td><td>&lt;0.001</td></tr><tr><td></td><td>L</td><td></td><td></td><td></td><td>-21</td><td>17</td><td>2</td><td>4.70</td><td>&lt;0.001</td></tr><tr><td rowspan="4">59 Putamen, Caudate</td><td>R</td><td>0.016</td><td>0.021</td><td>0.001</td><td>24</td><td>8</td><td>-1</td><td>4.66</td><td>&lt;0.001</td></tr><tr><td>R</td><td></td><td></td><td></td><td>15</td><td>14</td><td>5</td><td>4.60</td><td>&lt;0.001</td></tr><tr><td>R</td><td></td><td></td><td></td><td>24</td><td>17</td><td>-10</td><td>3.77</td><td>&lt;0.001</td></tr><tr><td>L</td><td>0.015</td><td>0.035</td><td>&lt;0.001</td><td>-45</td><td>2</td><td>23</td><td>4.19</td><td>&lt;0.001</td></tr><tr><td rowspan="3">FA: HA sup LA</td><td>60 Pre Central Gyrus</td><td>L</td><td></td><td></td><td>-42</td><td>14</td><td>23</td><td>3.99</td><td>&lt;0.001</td></tr><tr><td></td><td>L</td><td></td><td></td><td>-48</td><td>8</td><td>11</td><td>3.85</td><td>&lt;0.001</td></tr><tr><td></td><td>L</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="12">B. rsMRI- FA effect on functional connectivity, n = 40</td><td rowspan="12">K</td><td rowspan="3"> connected ROIs</td><td rowspan="3">FA effect</td><td rowspan="3">F</td><td rowspan="3">FDR</td><td rowspan="3">Uncorr</td><td rowspan="3">P-value (Cluster)</td><td rowspan="3">P-value (Connection)</td><td rowspan="3">T</td><td rowspan="3">FDR</td><td rowspan="3">Uncorr</td></tr><tr><td>Anterior Cingulate Cortex</td><td>L Accumbens</td><td>↓</td><td>6.72</td><td>0.045</td><td>0.003</td><td>-2.81</td><td>0.039</td><td>0.008</td></tr><tr><td>L Accumbens</td><td>L Caudate</td><td>↓</td><td></td><td></td><td>-3.01</td><td>0.058</td><td>0.005</td></tr><tr><td rowspan="3">L Accumbens</td><td>Anterior Cingulate Cortex</td><td>↓</td><td>5.76</td><td>0.045</td><td>0.007</td><td>-2.81</td><td>0.058</td><td>0.008</td></tr><tr><td>L Insular Cortex</td><td>L Amygdala</td><td>↓</td><td></td><td></td><td>-2.92</td><td>0.039</td><td>0.006</td></tr><tr><td>L Accumbens</td><td>L Amygdala</td><td>↓</td><td>5.45</td><td>0.045</td><td>0.008</td><td>-3.51</td><td>0.018</td><td>0.001</td></tr><tr><td>L Amygdala</td><td>L Accumbens</td><td>↓</td><td></td><td></td><td>-3.51</td><td>0.018</td><td>0.001</td></tr><tr><td></td><td>L Hippocampus</td><td>↑</td><td></td><td></td><td>3.01</td><td>0.035</td><td>0.005</td></tr></tbody></table>

**A.** Height threshold T = 3.35, pFWE<0.05 and pUncorr<0.001 at peak level, df = 1, Extent threshold: k = 40 voxels, voxels size: 3.0 3.0 3.0 mm. **B.** Alternative settings for ROI-based inferences, parametric multivariate statistics pFDR<0.05 at cluster level, pUncorr<0.01 at connection level, Benjamini and Hochberg, 1995. L: left, R: right, K: cluster size (number of voxels), FWE: family-wise error correction, FDR: false discovery rate correction, Uncorr: uncorrected p-value. FA: Food addiction; LA: Low appetizing images; HA: High appetizing images.
while there was a highly significant difference in patients with FA, independently from the severity of FA, with HA foods higher in calories than LA foods (Fig. 3A). In FA patients compared to No FA patients, the LA foods were also significantly lower in calories, and HA foods were significantly higher in calories (Fig. 3A, B).

## 4. Discussion

The objective of our study was to identify by fMRI, in the context of a food consumption imagination task, the brain phenotype of women patients with obesity and FA, and their brain functional connectivity. We first demonstrated that severe FA was associated with lower brain responses in the dorsal striatum during the visualization of palatable food images, and that a higher FA score was associated with lower brain functional connectivity between several regions of the corticolimbic and striatal circuits. Secondly, we found that FA was not associated with a specific anthropometric, body composition or REE phenotype. FA was positively associated with binge-eating. Interestingly, low appetizing (LA) and high appetizing (HA) foods did not differ in caloric load in patients without FA, but did so in FA patients, independently from FA severity, with HA foods being more caloric than LA foods in FA patients. Significant differences were also observed between groups, meaning that in FA patients compared to No FA, preferred foods were higher in calories, and less preferred foods were lower in calories.

The obese patients recruited in our study are mostly patients with severe obesity, and showed a higher prevalence of depression and anxiety symptoms, 12.5 % and 42.5 %, respectively, compared to a previous study reporting 7 % of depression (assessed with Beck Depression Inventory-II, BDI-II) and 25 % of anxiety (self-reported) in obese male and female patients wishing obesity surgery [50]. This higher level of depression and anxiety symptoms in our population might be explained by the fact that we recruited only women, who generally present a higher prevalence of depression and anxiety compared to men [51]. Moreover, this study was performed, in part, during the Covid-19 pandemic and shutdown, which is known to have promoted an increase in mental disorders [52] and emotional overeating [53]. This higher level of depression and anxiety symptoms might also be explained by the patients' selection. FA is indeed known to be associated with a higher level of depression and anxiety symptoms [54], and nearly 60 % of our selected patients endorsed FA, whereas FA in our general clinical obese population was reported to be 37 % [18]. As expected, higher FA scores were associated with a statistically higher binge eating [17]. Independently of FA condition, the brain circuits activated by

the view of palatable food images were comparable to what was previously described in lean and obese participants [40,55], i.e. high appetizing images promoted brain activation in sensory, motivational and hedonic brain regions. This supports using this fMRI task of imagined food consumption to investigate hedonic processes across levels of FA.

Interestingly in our study, the brain responses to palatable food images were modulated by the severity of FA. The dorsal striatum, including caudate and putamen, was less activated in response to HA images in patients with high FA scores. This brain region has a key role in motivational processes but is also involved in inhibitory control [56,57]. Conversely, Gearhardt et al. [30] have previously showed that FA was associated with higher fMRI response in the dorsal striatum during anticipation of palatable food distribution (milkshake), but these results were obtained in a sample of lean to obese participants. This discrepancy might be explained by differences in the experimental designs, an imaginary consumption task in our study vs. anticipation followed by food distribution in Gearhardt et al., but it could also be attributable to the different weight status of the participants in the two studies. Indeed, a higher anticipatory brain response in the dorsal striatum is well documented in obesity, for nearly all kind of experimental design (food cues, taste, cognitive task) [58]. An increased hedonic value in the presented food pictures (associated with an increased caloric density in FA patients only), i.e. high vs. low appetizing comparison, promoted a decreased brain response from FA in the precentral gyrus (PrCG). Activation in the PrCG, similarly to the insula, has been documented for gustatory imagery and perception as a top-down signaling for gustatory hallucination [59]. An impaired brain response in the PrCG during food reward has also been shown in bulimia nervosa [60]. In bulimia nervosa compared with anorexia nervosa, Brooks and al [61]. also detected an increased brain response in the PrCG, associated with an increased response in caudate and motor areas, and suggested that these higher responses reflect an increased appetitive response to food pictures. Altogether, these results suggest that FA might promote brain responses that are quite different and even opposite compared to the brain response phenotype usually described in obesity, especially in the context of BED. This bears some interest for further interventional investigations, since different clinical pictures between BED and FA might necessitate different therapeutic strategies.

We did not detect any statistical difference in the overall brain activation map for the high vs. low appetizing images. Given that the participants were all obese, this restriction in range surely affected the statistical power, leading to no differences in the brain

<figure><img src="image_4.png" /><figcaption>**Fig. 2. ROI-to-ROI analyses of functional connectivity depending on food addiction (FA) intensity.** Connectome ring of A. two-sided statistics, B. one-sided positive contrast and C. one-sided negative contrast. Colour map of the connectome ring: Red indicates increased FC for high FA and blue indicates decreased FC for high FA (Spatial Pairwise Clustering approach, all *p* < 0.05, corrected for multiple comparison with FDR approach and punc.<0.01) The regions included in the analysis are: Bilateral caudate (CAU), putamen (PUT), nucleus accumbens (Ac), thalamus (THL), amygdala (AMY), hippocampus (HIP), insular cortex (IC) and the anterior cingulate cortex (ACC). L = left, R = right.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Mean caloric load of low and high appetizing images in relationship with food addiction. A. Comparison between the three categories explored in brain imaging, i.e. No food addiction (FA), low and mild FA together, and high FA patients. B. Comparison between No FA and FA patients (whatever the severity of FA). Dishes considered for the calculation of the mean caloric load per dish included starters, main dishes and desserts. Two different letters indicate a significant difference (p < 0.01) between the mean caloric load of LA or HA images between groups (comparisons between same-color histograms only). The following symbols are used to indicate the statistical threshold for comparisons between LA and FA within groups: NS, non-significant p > 0.05; ** 0.01 < p ≤ 0.001; ****p < 0.0001.</figcaption></figure>

responses to high vs. low appetizing food pictures. Our statistical model is also taking into consideration FA level as a covariate, which could have also affected results. Further studies are required though to disentangle the respective effects of hedonic value and caloric density in the preferred foods, since HA foods were higher in calories than LA foods in FA patients only, this difference being absent in patients without FA.

Functional connectivity from rsMRI can provide crucial in-
formation about the cerebral modifications associated with
obesity [62,63]. In the resting condition, we observed some
differences in functional connectivity between several brain
region initially selected as ROIs for having a role in eating
behavior [44,58]. Overall, FA was characterized by lower func-
tional connectivity, mainly in the left hemisphere, between
brain regions involved in reward (accumbens), motivation
(caudate), executive function (anterior cingulate cortex),
emotional responses (amygdala) and gustatory or body

representations (insular cortex). This pronounced effect in the
left hemisphere could be explained by the stronger reduction in
brain responses with increasing FA in the left dorsal striatum
.cluster size: 183 voxels) compared with the right dorsal stria-
tum (cluster size: 59 voxels) during the palatable food images
tasks. ACC is involved in executive functions, such as conflict
monitoring and behavioral inhibition [56], but also in emotions
regulation [64]. Previous studies have shown that prefrontal
structures, such as the ACC, are involved in obesity and FA
[65,66]. Indeed, lower brain activation in ACC, as well as in the
dlPFC, are linked with behavioral inhibition deficits that could
promote abnormal eating behaviors such as hyperphagia [67].
Additionally, executive functions, to which the ACC contributes,
are known to be affected by traumatic life events, which are
often reported by patients suffering from severe obesity [68,69].
The amygdala FC pattern was very different according to the FA
severity. Higher FA was associated with greater connectivity
with the hippocampus, whereas lower FA was associated with greater FC with the accumbens, and thus indirectly with insular cortex and ACC. As discussed by Bari et al. [70] when investigating impulsive choices, a stronger structural connectivity of the amygdala with hippocampus might reflect a tendency to prefer larger delayed reward, whereas a higher connectivity of the amygdala with ACC and insular cortex might reflect an inclination towards more immediate and smaller rewards. A decreased functional connectivity between the amygdala and hippocampus has also been reported in addiction studies [71]. The particular pattern of connectivity modifications with FA in our study, i.e. higher functional connectivity between amygdala and hippocampus associated with a lower FC between amygdala and accumbens, and a lower functional connectivity of the accumbens with the ACC and insular cortex, might reflect a specificity of FA in obese patients in contrast to other addictions. Patients with this kind of functional connectivity alteration might have more difficulties to make healthy decisions when hedonic motivation is high, perhaps because the inhibitory control system is unable to suppress or diminish these hedonic drives. Previous studies showed a lower connectivity between the hippocampus and caudate in subjects with high FA [72], or positive associations between YFAS score and amygdala functional connectivity with nucleus accumbens and anterior insula [73], but half of the participants were normal-weight in these studies, which complicates the comparison with our own experiment. Surprisingly, we did not find any significant modification of the functional connectivity in frontal and prefrontal areas apart from the ACC, contrary to Ding et al. [74] who showed in the left frontoparietal network a negative correlation between functional connectivity strength of left dlPFC and YFAS score.

Altogether, the functional connectivity results suggest that FA is linked with an impaired communication between brain regions supporting motivation, decision and inhibitory control processes. This impaired corticolimbic and corticostriatal connectivity has already been described in obese patients in comparison to lean subjects [75]. Interestingly, Gupta et al. [76] even showed a stronger relationship between increased BMI and decreased connectivity of core reward network components with cortical and emotion regulation regions in women, in line with reduced craving control. Given that we observed similar outcomes explained by FA in a cohort of women who were all obese, one might wonder whether the results of previous studies investigating the "obese brain characteristics" highlighted features that would have been explained by FA rather than excess body weight and adiposity. This comes in favor of taking into account FA for phenotyping obese patients in the scope of personalized nutrition and medicine, since an impaired corticostriatal/corticolimbic connectivity associated with a reduced cognitive control might be more related to FA status than obesity in itself, consequently requiring specific interventions that would be different from those proposed to obese patients without FA.

Our study suggests that FA diagnosis confirmed by brain responses profiles to imagination of food consumption should be part of obesity phenotyping. Indeed, these brain responses are the manifestation of a specific brain functioning that, in the daily life, may expose obese patients to greater risk of eating disorders and binge eating. Not implementing binge-eating disorder treatment in the health care pathway would expose the patient to a risk of obesity treatment failure. Eating disorders being also a contraindication to obesity surgery, identifying them early would allow to choose the adapted personalized therapy. More research is needed to identify the best therapy for FA in obese patients, and also how these therapies could be helpful to improve the pre- and post-

operative management of obese patients undergoing obesity surgery.

Our study is, to our knowledge, the first investigating FA among obese patients with an fMRI food task as well as resting state brain investigation. Due to limitations in patient sample, we could only investigate FA effect as a continuous process according to FA severity, thus making the hypothesis that FA severity might be related to linear and possibly progressive cerebral changes in terms of intensity. Further research might consider investigating the differences between FA severity values to refine the characterization of FA consequences on brain. Since we could not detect a strong difference between LA and HA food images in the food consumption imagination task, this could suggest that FA might have interfered with the task expected responses, and an increased sample size might also be needed to highlight how FA severity might interfere with increased food hedonic levels. Due to the limited space within the MRI scanner, we could not include obese patients with an abdominal circumference (including arms) higher than 200 cm, which limits our interpretation for high BMI.

In conclusion, brain profiles of patients with obesity are related to FA severity. FA severity is associated with impaired corticostriatal functional connectivity. Communication between brain regions involved in motivational processes, decision-making and inhibitory cognitive control was impaired. Interindividual variability in brain responses suggests that dedicated therapeutic strategies might be helpful for obese patients with FA.

## Credit author statement

**Nicolas Coquery:** Data acquisition, data collection, analysis and interpretation, writing original draft, review & editing. **Mickaël Som:** Funding acquisition, patient selection and inclusion, data acquisition, collection and analysis. **Yann Serrand:** Data acquisition, collection and analysis, review & editing. **Marie-Océane Guittière:** Patient selection and inclusion, data analysis. **Elise Bannier:** Data acquisition and analysis, review & editing. **Fabrice Lainé:** Data acquisition. **Aymery Constant:** Study design, data acquisition. **Romain Moirand:** Study design, data acquisition, review & editing. **Eric Stice:** Study design, data interpretation, review & editing. **David Val-Laillet:** Study design, funding acquisition, data acquisition, analysis and interpretation, writing original draft, review & editing, supervision. **Ronan Thibault:** Study design, funding acquisition, patient selection and inclusion, data collection, analysis and interpretation, writing original draft, review & editing, supervision.

## Funding statement

This study received financial support by CHU Rennes (CORECT) and INRAE.

## Conflict of interest

The authors declare that they have no conflict of interest.

## Acknowledgements

We thank all the nurse and medical staff from the Department of Endocrinology-Diabetology-Nutrition at Rennes University Hospital, Rennes, France. The authors gratefully acknowledge Christian Barillot, Jean-Christophe Ferré, Isabelle Corouge and Quentin Duché, as well as the technical and clinical staff of the Neurinfo imaging platform of Rennes University Hospital, where all the imaging sessions were performed. This includes all the MRI operators including Marithé Desclos, Catherine Guillemot,
Vanessa Even, Gwenola Morrisse, Lucille Patier, and Fabien Vigouroux. MRI data acquisition was supported by the Neurinfo MRI research facility from the University of Rennes, University Hospital of Rennes, Inria, CNRS and the Rennes Cancer Center. Neurinfo was also supported by the European Research Council (FEDER), the French State, the Brittany Council, Rennes Metropole, Inserm and GIS IBISA. We also thank Fabrice Lainé from the clinical investigation unit (UIC) of Rennes University Hospital, as well as the UIC nurses and the staff from DRCI (Direction de la Recherche Clinique et de l'Innovation), notably Loïc Fin and Kristell Coat. Finally, we acknowledge Restauration Collective Casino (R2C) for its partnership in preparing the dishes that were used for creating our food picture database.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.clnu.2025.06.001.

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