# Structural alterations of individual hypothalamic nuclei in young females with obesity and anorexia nervosa: an *in vivo* 7-T MRI study

Coleen Roger, Adele Lasbleiz, Hugo Dary, Lauriane Pini, Patricia Ancel, Anezka Kovarova, Marie-Pierre Ranjeva, Patrice Darmon, Benedicte Gaborit, Yu Fu, et al.

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## ► To cite this version:

Coleen Roger, Adele Lasbleiz, Hugo Dary, Lauriane Pini, Patricia Ancel, et al. Structural alterations of individual hypothalamic nuclei in young females with obesity and anorexia nervosa: an *in vivo* 7-T MRI study. The American Journal of Clinical Nutrition, 2025, 121 (5), pp.1186-1198. 10.1016/j.ajcnut.2025.02.003. hal-05049926

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Original Research Article

# Structural alterations of individual hypothalamic nuclei in young females with obesity and anorexia nervosa: an in vivo 7-T MRI study

Coleen Roger<sup>1,2,3,*</sup>, Adele Lasbleiz<sup>4,5</sup>, Hugo Dary<sup>1,2</sup>, Lauriane Pini<sup>1,2</sup>, Patricia Ancel<sup>4</sup>, Anezka Kovarova<sup>1,2,6,7</sup>, Marie-Pierre Ranjeva<sup>1,2</sup>, Patrice Darmon<sup>4,5</sup>, Benedicte Gaborit<sup>4,5</sup>, Yu Fu<sup>3</sup>, Patrick J Cozzone<sup>1,2</sup>, Maxime Guye<sup>1,2</sup>, Anne Dutour<sup>4,5</sup>, Jean-Philippe Ranjeva<sup>1,2</sup>

## ABSTRACT

**Background:** The hypothalamus (HT) plays a crucial role in regulating eating behaviors. Disruptions in its function have been linked to the development of weight-related disorders. Nevertheless, its characterization remains a challenge.

**Objectives:** We assessed the structural alterations of individual HT nuclei related to eating behaviors in patients with weight-related disorders, and their association with body mass index (BMI) and severity of eating disorders.

**Methods:** Forty-four young females with normal weight (HC, *n* = 21), restrictive anorexia nervosa (AN, *n* = 13), and living with obesity (OB, *n* = 10) were explored in vivo using 7-T high-resolution (0.6 mm isotropic voxel) *T*₁ quantitative magnetic resonance imaging (MRI). Volumes and quantitative *T*₁ values of individual HT nuclei were compared after whole-brain normalization using nonparametric tests (corrected for multiple comparisons for groups and regions). We investigated the parameters associated with BMI and eating disorders, such as MRI parameters of HT nuclei, ghrelin and leptin levels, depression, and anxiety using multivariate nonlinear partial least square (NIPALS).

**Results:** Both AN and OB showed higher volumes of HT relative to HC (Zscores: 0.78 ± 1.06; 1.43 ± 1.51). AN showed significantly higher volumes and *T*₁ values of the right paraventricular nucleus (PaVN) (volume Zscore: 1.82 ± 1.45; *T*₁ Zscore: 3.76 ± 4.67), and higher *T*₁ values of the left PaVN (Zscore: 2.25 ± 2.37) and right periventricular nuclei (Zscore: 3.73 ± 4.81). NIPALS models showed that lower BMI in AN was associated with structural alterations of the bilateral PaVN, right anterior commissure, and left fornix (FX). Higher BMI in OB was associated with structural alterations within the right PaVN, bilateral FX, left posterior hypothalamic nucleus, right lateral HT, and right anterior hypothalamic area. Finally, the severity of eating disorders was associated with larger structural alterations within the bilateral PaVN, bilateral arcuate hypothalamic nuclei, right bed nucleus of stria terminalis, left medial preoptic nucleus, and right tubero-mammillary hypothalamic nucleus.

**Conclusions:** Weight-related disorders are associated with significant micro and macrostructural alterations in HT nuclei involved in eating behaviors.

**Keywords:** hypothalamus, obesity, restrictive anorexia nervosa, 7T, quantitative *T*₁ MRI, hypothalamic nuclei, volume, leptin, ghrelin, eating disorders

<footnote n="1">¹ CNRS, Centre de Résonance Magnétique Biologique et Médicale, Aix Marseille University, Marseille, France; ² Assistance Publique-Hôpitaux de Marseille, Centre d'Exploration Métabolique par Résonance Magnétique, Hôpital Universitaire de la Timone, Aix Marseille University, Marseille, France; ³ Institute of Molecular and Cell Biology, A*Star, Singapore; ⁴ INSERM, INRAE, Centre de recherche en CardioVasculaire et Nutrition, Aix Marseille University, Marseille, France; ⁵ Assistance Publique-Hôpitaux de Marseille, Département d'Endocrinologie, Hôpital de la Conception, Aix Marseille University, Marseille, France; ⁶ CEITEC—Central European Institute of Technology, Masaryk University, Brno, Czech Republic; ⁷ Faculty of Medicine of the Masaryk University, First Department of Neurology, Brno, Czech Republic</footnote>

<footnote>**Abbreviations:** AC, anterior commissure; AHA, anterior hypothalamic area; AN, young females with anorexia nervosa; ARH, arcuate hypothalamic nucleus; BECK, Beck Depression Inventory; BNST, bed nucleus of the stria terminalis; DM, dorsomedial hypothalamic nucleus; DPEH, dorsal periventricular hypothalamic nucleus; EAT-26, Eating Attitudes Test-26; FX, fornix; GM, grey matter; HADS, Hospital Anxiety and Depression Scale; HC, young females with normal weight; HFD, high-fat diet; HT, hypothalamus; LH, lateral hypothalamus; MM, mammillary bodies; MPO, medial preoptic nucleus; MT, mammillothalamic tracts; NIPALS, nonlinear partial least square; OB, young females with obesity; PaVN, paraventricular hypothalamic nucleus; PeVN, periventricular hypothalamic nucleus; PH, posterior hypothalamic nucleus; SC, suprachiasmatic hypothalamic nucleus; SCOFF, Sick Control One Fat Food; TM, tuberomammillary hypothalamic nucleus; VM, ventromedial hypothalamic nucleus; WM, white matter; YFAS, Yale Food Addiction Scale; ZI, zona incerta.</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: coleen.roger@etu.univ-amu.fr (C. Roger).</footnote>
# Introduction

The prevalence of weight-related disorders is in constant augmentation and represents a real public health problem: obesity is now considered an epidemic [1], and anorexia nervosa has the highest mortality rate of all psychiatric disorders [2]. The pathophysiology of weight-related disorders is complex, affecting both energy regulation systems (lipostat/energy balance) and nonhomeostatic functions involving emotion, memory, reward, palatability value, and self-regulation [3,4].

Disturbance of energy homeostasis both in anorexia nervosa and obesity primarily stems from hormonal imbalance [5,6]. The anorexigenic hormone leptin is reduced in anorexia nervosa and increased in obesity [7,8]. The orexigenic hormone ghrelin is increased in anorexia nervosa and reduced in obesity [8–10]. However, this physiological adaptation is undeniably not associated with normal homeostatic food consumption in weight-related disorders, because both patients with obesity and anorexia nervosa show a blunted responsivity to peripheral appetite regulation [11].

The key structure orchestrating the balance between these different functions is the hypothalamus (HT), a central small brain area organized in multiple nuclei exhibiting distinct physiological and anatomical characteristics. Rodents' studies have revealed that each HT nucleus possesses a specific role in the homeostatic regulation of energy, food intake, and thirst, as well as body temperature, sexual dimorphism, sleep, and circadian rhythm [12]. Therefore, studying the microstructural alterations in each HT nucleus is crucial to monitor their respective roles in the behavioral shifts associated with weight-related disorders in vivo in humans. However, the translation of the cellular mechanisms observed in animal models to human in vivo studies is not straightforward. In that context, neuroimaging has appeared as a game changer in exploring eating behavior networks and the consequences of their dysregulation from an anatomical, functional, and metabolic point of view [4,13,14]. BMI has been shown to correlate with HT volume [15,16]. However, a volume increase was only found in the left anterior-inferior and the right posterior parts of HT in adults living with obesity relative to healthy controls [16].

As of now, only 1 study has examined structural alterations in hypothalamic subregions in young females living with either anorexia nervosa or obesity [17]. This study found a larger HT volume in young females living with obesity (OB). It also found an atrophy of the inferior tubular subregion in young females with anorexia nervosa. Additionally, regional structural HT alterations were associated with variations in appetite-regulating hormone levels [17]. Indeed, a larger volume of posterior HT subregions was associated with an increase of leptin levels in the OB. Meanwhile, ghrelin levels were negatively associated with the global HT volumes in the whole cohort (anorexia, obesity, and control) [17].

Overall, previous studies using magnetic fields of 3 T or lower were unable to characterize individual HT nuclei because of challenging HT segmentation. Difficulties to segment HT nuclei emerge from a lack of spatial resolution and poor MRI contrast [18].

In this exploratory study, we combined the use of high-resolution quantitative 3D-T₁ MRI sequence to a super-resolute atlas of HT nuclei [19] to uncover morphostructural variations of HT nuclei, and their association with BMI and severity of eating disorders, in well-characterized cohorts of young females: with normal weight (HC), OB, or with restrictive anorexia nervosa (AN).

High spatial resolution multiparametric quantitative MRI [20] opens a new perspective to characterize individual HT nuclei in

humans, and steadily narrows the gap in our understanding from rodent studies.

# Methods

## Questionnaires

The morning of the scan, all participants completed questionnaires regarding their eating behavior and the presence of anxiety or depression.

* French version of the “Sick, Control, One, Fat, Food” (SCOFF) with 5 questions used to screen for eating disorders (presence of eating disorders score ≥2).

* Yale Food Addiction Scale 2.0 (YFAS) measures food addiction with 35 items which assess 11 indicators of addictive behaviors, distress, and related clinical impairment (presence of food addiction if ≥2 symptoms with clinical impairment).

* Eating Attitudes Test-26 (EAT-26) is a self-reported measure with 26 items to identify the presence of eating disorder risk (presence of eating disorders score ≥20).

* Hospital Anxiety and Depression Scale (HADS) is an instrument used to screen for anxiety and depressive disorders. It consists of 14 items rated from 0 to 3 (7 relate to anxiety and 7 to depressive dimension, presence of anxiety or depression if ≥11 in either item).

* Beck Depression Inventory (BECK) measures the severity of clinical depression through 13 items (a score between 0 and 3 suggests an absence of depression, 4 and 7 a mild depression, 8 and 15 a moderate to moderately severe depression, and above 16 a severe depression).

## Participants

A total of 44 adult young females (age 18–35 y old) were recruited in this protocol between July 2020 and May 2024 (participant flowchart in Supplemental Figure 1): 1) 13 young females on the “ill phase” of acute severe restrictive AN (BMI <17 kg/m², positive to the criteria defined by the Diagnostic and Statistical Manual of Mental Disorders 5), 2) 10 OB (BMI ≥ 30 kg/m² and no life time or current medical condition that could potentially affect appetite or weight (for example, diabetes, bariatric surgery history, glucagon-like peptide 1 (GLP-1) analog, corticoids or psychotropics treatment), and 3) 21 HC (18.5< BMI <25 kg/m², no life time or current medical condition that could potentially affect appetite or weight, no eating disorder defined by the EAT-26, SCOFF, and YFAS as well as no anxiety or depression evaluated by HADS and BECK. AN and OB were recruited from the Department of Endocrinology (CHU Nord and Hôpital de la Conception, AP-HM), a national reference center for anorexia nervosa and obesity.

For this exploratory study, the sample size of *n* = 44 allowed to detect differences among the 3 groups with a SD of 1, an effect of 1.2 with a statistical power of 0.861.

All participants completed a dietary survey analyzed by a dietitian to assess total energy intake. Bioelectrical impedance analysis was performed on AN and OB to measure body composition.

Participants with contraindication to 7 T MRI, at risk of noncompliance with the examination, unable to give their consent, with a history of head injury, neurosurgery, neurological pathology, psychosis or bipolar disorder or with a current addiction were not included in the study. A pregnancy test on urine sample was performed and pregnant and lactating females were excluded from the study. All participants were registered to a health insurance plan and gave their written informed consent after receiving detailed, understandable, and honest information. This protocol was in accordance with the Declaration of Helsinki and obtained ethics approval by the French Ethic Committee
"CPP EST I" (#20.02.13.47002-IDRCB: 2020-A00340-39; Clinical trial Hypothalnet: NCT04451759).

## Hormone assays

On the day of the MRI exploration and after an overnight fasting period (no food or caloric beverages later than 8 pm), blood samples were obtained from each participant for leptin and ghrelin dosages. Blood samples were centrifuged at 3000 g for 10 min and stored at -80°C for subsequent analysis. Plasma ghrelin (total: acylated + nonacylated) and leptin concentrations were measured using commercially available kits from R&D Systems [Human Ghrelin DuoSet ELISA 5 plate, lot: P394826, assay range (31.3-2000 pg/mL), coefficient variation: 8.5%; Quantikine ELISA Human Leptin, lot: P387308, assay range (15.6-1000 pg/mL), coefficient variation: 5.2%]. Different dilution ranges were used to obtain all measures within the detection limits. For leptin, we initially used a 1/100 dilution, as recommended by the supplier, followed by a 1/10 dilution for all patients with anorexia. Ghrelin was first tested without dilution, followed by a second test with a 1/2 dilution for all patients. No specific bias was introduced, as a standard range was included in each test, and only samples within that range were analyzed. The only correction applied was the subtraction of the initial blank to obtain the standard curve.

## Image acquisition

Participants were scanned once after an overnight fasting period using a 7-T MR scanner with 40 participants ($n_{AN} = 12, n_{OB} = 9, n_{HC} = 19$) explored on a 7-T Terra MR system (Siemens Healthineers); and 4 participants ($n_{AN} = 1, n_{OB} = 1, n_{HC} = 2$) explored on a 7-T Siemens Magnetom step 2 system (Siemens Healthineers). Both scanners were equipped with the same (1Tx/32Rx) head coil (NOVA Medical). After an automatic 3D $B_0$ shimming procedure, a whole-brain $B_1^+$ map was acquired using a spin-echo-based sequence by assessing the ratio of consecutive spin and stimulated echoes (WIP#658, Siemens Healthineers). Anatomical images were obtained using a 3D-MP2RAGE sequence (TR = 5 ms, TE = 3 ms, $T_1$ = 900 ms/$T_2$ = 2.750 ms, 256 slices, 0.6 mm isotropic voxel, TA = 10 min 12 s), already proven to be reliable and sensitive to characterize structural alterations in early multiple sclerosis [21], Parkinson disease [22], or partial epilepsy [20].

Unbiased $T_1$ maps and $T_1$-w-UNIDEN images were obtained after $B_1^+$ inhomogeneity correction [23]. Qualitative high-resolution $T_2$-weighted images (turbo spin echo) and 3D fluid-attenuated inversion recovery (FLAIR) were also acquired for radiological assessment of the HT in all participants. Diffusion-weighted imaging, resting-state, and task-related fMRI acquisitions were also performed in the context of a larger research protocol, but at lower spatial resolution (1.2 mm isotropic voxel) limiting the possible use of these data for the current objective aiming to obtain a precise morphostructural characterization of individual HT nuclei (the whole study protocol in Supplemental Table 1).

## Image processing

Image quality and registration were evaluated independently by 2 experienced raters (CR and J-PR) based on a continuous scale (0-5) obtained for 8 items (maximum global score of 40) evaluating 1) the quality of $T_1$-w-Uniform Denoised (UNIDEN) images, 2) quality of $qT_1$ maps, 3) quality of registration of the minimum deformation averaging (MDA) template onto $T_1$-w-UNIDEN images and 4) quality of parcellation of 5 anatomically identifiable hypothalamic regions: fornix, mammillary bodies, mammillary-thalamic tracts, paraventricular nuclei, and arcuate nuclei. The inter-rater reliability between the 2 raters was evaluated using the intra-class correlation

coefficient (ICC) (Supplemental Figure 2). The $T_1$ values of grey matter (GM) and white matter (WM) masks for each participant were obtained by segmenting the $T_1$-weighted images using SPM12 (threshold >0.5) and applying the segmentation to the whole-brain unbiased $T_1$ maps. Whole-brain volume (WBV) of each participant was obtained using fslbet [24]. Automated segmentation of HT nuclei was performed using a similar procedure to that described by Brun et al. [25] for segmenting various nuclei of the basal ganglia using precise coregistration of high-resolution 7-T MRI template and atlas (Figure 1 and see the method in Supplemental Figure 3). The pre-defined open source "MDA_990HCP_t1_MNI152b_0.5" MRI high-resolution template (0.5 mm isotropic voxel) associated with the Neudorfer atlas obtained by manual segmentation of 2 experienced raters were employed [19]. This high-resolution template was derived from datasets of 990 participants extracted from the HCP1200 data release, normalized to the "MNI152b" space using the MDA pipeline [19]. The reliability of the atlas, reported in the seminal paper as a measure for voxel-wise volume overlap among 2 different raters, was described in [19]. They reported acceptable margins of error in Dice similarity coefficients (mean ± SD = 0.63 ± 0.19) and Hausdorff distances (mean distance=1.44 ± 0.67 mm) for hypothalamic nuclei [19]. In addition, to improve spatial registration centered on the HT, the $T_1$-weighted template was cropped using the following limits in the z-axis: up-down = corpus callosum-pons; left-right = left insula-right insula; anterior-posterior = genu-splenium to obtain the "cropped-MDA_990HCP_t1_MNI152b_0.5" template and the "cropped-Neudorfer-Atlas." Similar cropping was applied to the $B_1^+$ corrected $T_1$-w-UNIDEN MP2RAGE images and $T_1$ maps for each participant. The "cropped-MDA_990HCP_t1_MNI152b_0.5" template was then coregistered to the $B_1^+$ corrected "cropped-T1w-UNIDEN MP2RAGE" of each participant by estimating nonlinear warp (https://github.com/ANTsX/ANTs/blob/master/Scripts/antsRegistrationSynQuick.sh) (Supplemental code). Mutual information was used for rigid and affine steps, and cross-correlation for the last BSplineSynN transformation (gradientStep = 0.1, Correlation window radius=4). The corresponding spatial transform was applied to the "cropped-MDA_990HCP_t1_MNI152b_0.5" and the "cropped-Neudorfer-Atlas" (supplemental data for the script).the

Finally, we used the function "Segmentation/Volumes and Statistics" of the ITK-SNAP toolbox to extract volumes and $T_1$ values from HT nucleus masks derived from the coregistered atlases applied to cropped-$T_1$ maps of individual participants and the WBV derived from the brain mask extracted using fslbet. The global hypothalamic volume included the inner nuclei segmented with Neudorfer atlas.

For each subject, volumes of HT nuclei were normalized by their WBV. Then, Zscores of normalized volumes and $T_1$ values of HT nuclei were computed using the control's mean and SD (Zscore done on 19 controls for the Terra MR scanner and 11 controls for the Magnetom MR scanner).

## Statistical analysis

Differences of age, BMI, behavioral scores, leptin and ghrelin concentrations, WBV, GM and WM $T_1$ values, HT volume, right and left HT volumes as well as structural HT nuclei parameters between AN, OB, and HC were assessed by nonparametric tests with post-hoc corrections using the Steel-Dwass procedure (JMP 16). Additionally, we applied a false discovery rate ($p_{FDR} < 0.05$) to correct for the number of HT nuclei.

The inter-rater reliability was evaluated using the ICC (EMP Measurement System Analysis platform, JMP16 pro) with global
<figure><img src="image_5.png" /></figure>

(caption on next page)
scores as responses, subjects as samples, and raters as grouping variables.

To deal with data characterized by multicollinearity, we used multivariate nonlinear partial least square (NIPALS) analyses to determine the leading parameters associated with weight disorders including appetite-regulating hormone levels, behavior scores, and MRI parameters of HT nuclei in the (AN+HC) and (OB+HC) cohorts.

A second NIPALS analysis was conducted in the whole cohort of participants (AN+OB+HC) to determine the potential association of MRI parameters of HT nuclei with the severity of eating disorders (EAT-26 scores) (Supplemental Figure 4). Both models were constructed in 2 steps. First, all parameters were integrated in the model and a first selection of variables was conducted according to variables' effect threshold of 1.2. The NIPALS algorithm was reapplied using these selected variables using a threshold of effect of 1. Explained variance of the model, importance (effect), and coefficients of variables were reported.

# Results

## General features

We did not find any significant difference between groups for age, total plasmatic ghrelin levels, WBV and $T_1$ values in whole-brain GM and WM (Table 1 and Supplemental Figure 5). In line with the inclusion criteria, BMI was significantly different between groups, with $BMI_{AN} < BMI_{HC}$ ($P < 0.0003$) and $BMI_{OB} > BMI_{HC}$ ($P < 0.0002$). Plasmatic leptin levels were significantly higher in OB compared with HC ($P < 0.0003$) and lower in AN compared with HC ($P < 0.0001$). OB showed higher percentages of fat mass compared with AN ($P < 0.0004$), whereas percentages of lean mass were higher in AN relative to OB ($P < 0.0004$). Relative to HC, AN showed a higher percentage of carbohydrates ($P = 0.0022$) and lower percentages of lipids ($P = 0.0046$) intake, higher eating disorders scores (EAT26 $P < 0.0001$, SCOFF $P < 0.0001$, YFAS symptom counts $P < 0.0009$, YFAS diagnosis $P < 0.0002$), anxiety (HADS anxiety $P = 0.0022$), depression scales (BECK $P < 0.0001$; HADS depression $P = 0.003$). Relative to HC, OB also showed higher eating disorders (EAT26 $P = 0.0335$), anxiety (HADS anxiety $P = 0.0087$), and depression (HADS depression $P = 0.0178$, BECK $P = 0.0175$) scores (Table 1). The medians and ranges of global quality scores of imaging data for raters 1 and 2 were 39 [26–33] and 38 [26–35] respectively, with an ICC (including bias) of 0.9742.

## Altered morphometric and microstructure patterns of HT nuclei in restrictive anorexia nervosa

The volume of the global HT (Zscore: $0.780 \pm 1.059$; $P = 0.0246$) (Figure 2A) and right HT (Zscore: $0.916 \pm 1.150$; $P = 0.0099$) (Figure 2B) was higher in AN. At the nucleus level, AN showed higher volumes of the right paraventricular nucleus (PaVN) (Zscore: $1.824 \pm 1.455$; $p_{FDR} = 0.0009$) and left Fx (Zscore: $1.451 \pm 1.248$; $p_{FDR} = 0.0022$) (Figure 3A); and higher $T_1$ values within right PaVN (Zscore:

$3.761 \pm 4.477$; $p_{FDR} = 0.0017$), left PaVN (Zscore: $2.249 \pm 2.367$; $p_{FDR} = 0.0036$), and right periventricular hypothalamic nucleus (PeVN) (Zscore: $3.734 \pm 4.811$; $p_{FDR} = 0.0041$) relative to HC (Figure 3B) (Supplemental Figure 6).

Partial least square analysis conducted in the (AN+HC) cohort (explained variance of 74.1% using 13 variables) showed that the severity of weight disorders in AN (lower BMI scores) was associated with 1) lower $T_1$ values of the right PaVN ($\beta = 0.0129$, effect size = 0.8968) and the right AC ($\beta = 0.2683$, effect size = 1.0624), higher $T_1$ values of the left PaVN ($\beta = -0.0219$, effect size = 0.8782), higher volumes of the left FX ($\beta = -0.1308$, effect size = 0.8537), and the right PaVN ($\beta = -0.0085$, effect size = 0.9089), 2) eating behavior scores [SCOFF ($\beta = -0.2435$, effect size = 1.4043); EAT-26 ($\beta = -0.1365$, effect size = 1.1647); YFAS diagnosis scores ($\beta = -0.0386$, effect size = 0.9778); YFAS symptom counts ($\beta = -0.0495$, effect size = 0.8782)], 3) more severe depression [BECK scores ($\beta = -0.0710$, effect size = 1.0614)] and anxiety [HADS anxiety scores ($\beta = -0.1245$, effect size = 0.9770)], 4) lower plasmatic leptin levels ($\beta = 0.1139$, effect size = 0.9002) and higher plasmatic ghrelin levels ($\beta = -0.2191$, effect size = 0.8881) (Figure 4A).

## Altered morphometric and microstructure patterns of HT nuclei in obesity

Relative to HC, OB showed a higher volume of the global HT (Zscore: $1.434 \pm 1.515$; $P = 0.0039$) (Figure 2A), right HT (Zscore: $1.530 \pm 1.468$; $P = 0.0032$) (Figure 2B), and left HT (Zscore: $1.187 \pm 1.486$; $P = 0.0083$) (Figure 2C). However, no observed differences in the macrostructure of HT nuclei in OB relative to HC survived FDR multiple comparison corrections (Figure 3A) (Supplemental Figure 6).

Partial least square analysis conducted in the (OB+HC) cohort (explained variance of 68.5% using 14 variables) showed that the severity of weight disorders in OB (higher BMI scores) was associated with higher alterations of 1) higher $T_1$ values of the right PaVN ($\beta = 0.0611$, effect size = 0.8048) and higher volumes of left PH ($\beta = 0.0712$, effect size = 0.9380), left FX ($\beta = 0.0636$, effect size = 0.8380), right FX ($\beta = 0.0627$, effect size = 0.8261), right AHA ($\beta = 0.0627$, effect size = 0.8257), right LH ($\beta = 0.0611$, effect size = 0.8049), 2) eating behavior scores [EAT26 ($\beta = 0.0956$, effect size = 1.2601); SCOFF ($\beta = 0.0711$, effect size = 0.9362); YFAS symptom counts ($\beta = 0.0631$, effect size = 0.8317)], 3) more severe depression [BECK scores ($\beta = 0.0928$, effect size = 1.2232); HADS depression ($\beta = 0.0921$, effect size = 1.2139)] and anxiety [HADS anxiety ($\beta = 0.0993$, effect size = 1.3087)], and 4) higher plasmatic leptin levels ($\beta = 0.1088$, effect size = 1.4332) and plasmatic ghrelin levels ($\beta = 0.0620$, effect size = 0.8171) (Figure 4B).

## Association between severity of eating disorders (EAT-26) and MRI parameters of HT nuclei

Partial least square analysis conducted in the whole cohort (AN+OB+HC) using only MRI parameters of HT nuclei (explained

**FIGURE 1.** Projection of the Neudorfer atlas on individual $T_1$ weighted images and unbiased $T_1$ maps for young females living with (A) anorexia nervosa, (B) obesity, and (C) healthy control (resolution (0.6 mm)$^3$). The top images represent the delineated nuclei on T1 weighted images, and the bottom images display the delineated nuclei on unbiased $T_1$ maps. AC, anterior commissure; AHA, anterior hypothalamic area; AN, arcuate nucleus; BNST, bed nucleus of stria terminalis; dB, diagonal band of Broca; DM, dorsomedial hypothalamic nucleus; DPEH, dorsal periventricular hypothalamus; FX, fornix; ITP, inferior thalamic peduncle; LH, lateral hypothalamus; MM, mammillary bodies; MPO, medial preoptic nucleus of the hypothalamus; MT, mammillothalamic tract; NBM, nucleus basalis of Meynert; PaVN, paraventricular nucleus; PeVN, periventricular nucleus; PH, posterior hypothalamus; RN, red nucleus; SC, suprachiasmatic nucleus; SN, substantia nigra; SO, supraoptic nucleus; STN, subthalamic nucleus, TM, tuberomammillary nucleus; VM, ventromedial nucleus; ZI, zona incerta.
<table><caption>**TABLE 1**
General features of study participants (n = 44).</caption><thead><tr><th></th><th>AN (n = 13)</th><th>OB (n = 10)</th><th>HC (n = 21)</th></tr></thead><tbody><tr><td>Age (y)</td><td>21 ± 6.24 (18–34)</td><td>26.5 ± 3.53 (23–33)</td><td>25 ± 4.03 (19–34)</td></tr><tr><td>BMI (kg/m<sup>2</sup>)</td><td>15 ± 1.16 (13.10–16.98)</td><td>36.33 ± 4.52 (31.43–42.87)</td><td>20.94 ± 1.98 (18.56–24.91)</td></tr><tr><td>Leptin (pg/mL)</td><td>940 ± 866.15 (128–2702)</td><td>76 866 ± 24 479.39 (44 164–119 215)</td><td>14 957 ± 13 795.9 (4210–59 098)</td></tr><tr><td>Ghrelin (pg/mL)</td><td>240 ± 4256 (56–15 264)</td><td>221 ± 988.04 (56–3125)</td><td>222 ± 141.76 (45–653)</td></tr><tr><td>Calorie intake (kcal)</td><td>1850 ± 605.33 (717–2269)</td><td>1941 ± 521.04 (1380–3025)</td><td>1922 ± 300.76 (1380–2501)</td></tr><tr><td>Lipids (%)</td><td>28 ± 7.26 (15.6–40.2)</td><td>39 ± 5.38 (25–42.5)</td><td>42.05 ± 15.06 (31.4–52.7)</td></tr><tr><td>Proteins (%)</td><td>18 ± 3.58 (11.6–26)</td><td>14 ± 2.46 (12–19)</td><td>19 ± 1.91 (15.8–19.2)</td></tr><tr><td>Carbohydrates (%)</td><td>51.5 ± 7.79 (43–67.6)</td><td>46 ± 7.05 (39.9–63)</td><td>36.75 ± 12.94 (27.6–45.9)</td></tr><tr><td>Fat mass (%)</td><td>7 ± 6.17 (3–20)</td><td>49.5 ± 4.94 (38–51)</td><td>N/A</td></tr><tr><td>Lean mass (%)</td><td>87.5 ± 5.82 (75–91)</td><td>47.5 ± 4.37 (45–57)</td><td>N/A</td></tr><tr><td>SCOFF</td><td>4 ± 1.11 (1–5)</td><td>1 ± 0.7 (0–2)</td><td>0 ± 0.4 (0–1)</td></tr><tr><td>YFAS # symptoms</td><td>4 ± 1.44 (2–6)</td><td>2 ± 3.51 (0–11)</td><td>0 ± 1.34 (0–5)</td></tr><tr><td>YFAS diagnosis</td><td>1 ± 0.51 (0–1)</td><td>0 ± 0.32 (0–1)</td><td>0 ± 0 (0–0)</td></tr><tr><td>EAT-26</td><td>44 ± 15.87 (7–64)</td><td>13 ± 13.55 (4–47)</td><td>5 ± 4.87 (0–17)</td></tr><tr><td>HADS anxiety</td><td>10 ± 5.04 (2–20)</td><td>10.5 ± 4.85 (4–19)</td><td>5 ± 2.41 (0–9)</td></tr><tr><td>HADS depression</td><td>8 ± 4.78 (0–14)</td><td>6 ± 6.23 (0–18)</td><td>1 ± 2.86 (0–10)</td></tr><tr><td>BECK</td><td>14 ± 6.05 (4–23)</td><td>8.5 ± 7.49 (0–22)</td><td>2 ± 2.77 (0–8)</td></tr><tr><td>WM T<sub>1</sub></td><td>1200.1 ± 38.28 (1139.09–1284.02)</td><td>1188.7 ± 33.06 (1158.34–1276.94)</td><td>1175.6 ± 33.16 (1135.4–1254.7)</td></tr><tr><td>GM T<sub>1</sub></td><td>1776.78 ± 60.16 (1693.91–1939.46)</td><td>1755.93 ± 62.32 (1701.86–1915.56)</td><td>1761.15 ± 51.28 (1705.27–1916.01)</td></tr></tbody></table>

Abbreviations: AN, young females with anorexia nervosa; BECK, Beck Depression Inventory (a score between 0 and 3 suggests an absence of depression, 4 and 7 a mild depression, 8 and 15 a moderate to moderately severe depression, and above 16 a severe depression); EAT-26, Eating Attitudes Test-26 (presence of ED score ≥20); GM, grey matter; HADS, Hospital Anxiety and Depression Scale (presence of anxiety or depression score ≥11 in either items); HC, young females with normal weight; N/A, Not Available; OB, young females with obesity; SCOFF, Sick, Control, One, Fat, Food (presence of ED score ≥2); WM, white matter; YFAS, Yale Food Addiction Scale 2.0 (presence of ED score if diagnosis ≥2). The values are displayed as the median ± SD (minimum–maximum).

variance of 51.08% using 10 variables) showed that the severity of eating disorders (EAT-26 score) was associated with larger micro and macrostructural alterations within the bilateral PaVN [Zscore_Vol_R_PaVN (β=0.1024, effect size = 1.2090); Zscore_T<sub>1</sub>_R_PaVN (β = 0.0854, effect size = 1.0080); Zscore_T<sub>1</sub>_L_PaVN (β = 0.0945, effect size = 1.1154)], the bilateral arcuate hypothalamic nucleus (ARH) [Zscore_Vol_L_ARH (β = 0.0783, effect size = 0.9246), Zscore_Vol_R_ARH (β = 0.0799, effect size = 0.9434); Zscore_T<sub>1</sub>_R_ARH (β = 0.0879, effect size = 1.0382)], the right BNST [Zscore_T<sub>1</sub>_R_BNST (β = 0.0748, effect size = 0.8827)], the left MPO [Zscore_Vol_L_MPO (β = 0.0869, effect size = 1.2060)], and the right TM [Zscore_T<sub>1</sub>_R_TM (β = 0.0730, effect size = 0.8616)] (Figure 4C).

Discussion

The current study shows that quantitative T<sub>1</sub> relaxometry derived from 7T MRI is sensitive enough to investigate in vivo morphological and microstructural alterations of individual HT nuclei in weight-related disorders. To our knowledge, this is the first study characterizing those alterations with such high granularity. It was achieved by means of the registration of high-resolution in vivo anatomical atlas of human hypothalamic nuclei on individual subjects [19]. This was applied to carefully selected young female participants according to age and sex, and constrained to a 15-h fasting period. No global microstructural brain effect was observed in whole-brain WM and GM of AN and OB relative to HC; however, we observed specific alterations in HT. This suggests that

<figure><img src="image_6.png" /><figcaption>**FIGURE 2.** Global, right and left hypothalamic volume in AN, HC, and OB. Nonparametric tests corrected for multiple comparisons using the Steel-Dwass all pairs procedure (JMP 16 pro) were used to compare between AN, HC, and OB groups the following parameters: (A) global hypothalamic volume, (B) right hypothalamic volume, and (C) left hypothalamic volume. AN, young females with anorexia nervosa; HC, young females with normal weight; HT, hypothalamus; OB, young females with obesity.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**FIGURE 3.** (A) Macrostructural and (B) microstructural modulation of hypothalamic nuclei in AN and OB relative to HC. (A) Relative to HC, AN showed a significant increase (*p*<sub>FDR</sub> < 0.05) in the volumes of right PaVN and left FX whereas the observed differences in left ARH, left MPO, left DPEH, and right FX did not survive multiple comparison corrections (*p*<sub>uncorr</sub> < 0.05). (B) AN showed a significant increase in *T*<sub>1</sub> values (*p*<sub>FDR</sub> < 0.05) in bilateral PaVN and right PeVN whereas differences observed in bilateral BNST, right ARH, bilateral MM, and right PH did not survive multiple comparison corrections (*p*<sub>uncorr</sub> < 0.05). Relative to HC, OB did not show a significant difference remaining after multiple comparison corrections but only trends of increases (*p*<sub>uncorr</sub> < 0.05) in the volumes of right FX, right LH, bilateral BNST, right NBM, right AHA and left PH. AHA, anterior hypothalamic area; AN, young females with anorexia nervosa; ARH, arcuate nucleus; BNST, bed nucleus of stria terminalis; DPEH, dorsal periventricular hypothalamus; FX, fornix; HC, young females with normal weight; LH, lateral hypothalamus; MM, mammillary bodies; MPO, medial preoptic nucleus of the hypothalamus; NBM, nucleus basalis of Meynert; OB, young females with obesity; PaVN, paraventricular nucleus; PeVN, periventricular nucleus; PH, posterior hypothalamus.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>FIGURE 3. (continued).</figcaption></figure>

the observed alterations in micro and macrostructural features are not associated with a widespread phenomenon, but that they are specific to the HT. Thus, in addition to higher HT volumes present in both AN and OB, we observed significant micro and macrostructural alterations in individual HT nuclei involved in eating behavior networks (PaVN and PeVN) in AN. Furthermore, lower BMI scores in AN were associated with larger micro and macrostructural alterations of bilateral PaVN, right AC and left FX, lower plasmatic leptin levels, higher plasmatic ghrelin levels, greater severity of eating behaviors, depression, and anxiety. In contrast, higher BMI scores in OB were associated with greater micro and macrostructural alterations of right PaVN, right LH, right AHA, left PH and bilateral FX, higher plasmatic leptin and ghrelin levels, greater severity of eating behaviors, depression, and anxiety. Finally, the severity of eating disorders was associated with enhanced alterations within bilateral PaVN, bilateral AN, right BNST, left MPO, and right TM.

## Macrostructural and microstructural fingerprinting of HT nuclei in restrictive anorexia nervosa

Previous works have shown that anorexia nervosa was associated with a pattern of lower HT and global brain volumes [36,37]. Nevertheless, most previous studies showed that these morphological

alterations in the HT did not remain after intracranial volume normalization [11,16]. In contrast, in this well-controlled cohort of AN, we observed higher volumes of the global HT after normalization by the whole-brain volume. High hypothalamic volume could be associated with hypothalamic inflammation, which is a well-known factor in the development of anorexia-cachexia syndrome [38]. Focusing on individual HT nuclei, AN showed a higher volume of the right PaVN, and higher $T_1$ values within bilateral PaVN, and right PeVN, possibly reflecting local inflammation. In addition, the partial least square analysis conducted on the (AN+HC) cohort showed the association of the severity of restrictive anorexia nervosa (reflected by the lower BMI scores) with PaVN and FX higher volumes, as well as lower plasmatic leptin concentrations. Structural and functional alterations of PaVN may have major impacts on energy homeostasis because PaVN stimulation was found to inhibit food intake [39]. Thus, the exchange of information between the PaVN and ARH is essential for translating nutritional signals received by the HT into actionable signals that induce metabolic alterations [40]. For example, stimulation of Pro-o-piomelanocortin (POMC)/ cocaine- and amphetamine-regulated transcript (CART) neurons in the ARH releases excitatory neurotransmitters alpha-melanostimulating hormone (MSH) in the
A Contributions of imaging, behavioral and hormone variables to model BMI in AN & HC

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

B Contributions of imaging, behavioral and hormone variables to model BMI in OB & HC

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

C Contributions of imaging variables of HT nuclei to model EAT-26 scores in AN, HC & OB

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

(caption on next page)
PaVN which activate PaVN Melanocortin-3 and -4 receptor, resulting in reduced food intake.

An alternative hypothesis is that inflammation and mitochondrial dysfunction may alter structural connectivity between PaVN and ARH in anorexia nervosa. This altered connectivity associated with fewer fibers could trigger an aberrant glutamatergic response to food intake [14,34, 35]. Our results are in line with disturbances of the signaling pathway between PaVN and ARH in restrictive anorexia nervosa. Interestingly, PaVN alterations and severity of depression and anxiety scores were associated with weight-disorder severities of both anorexia nervosa and obesity. As the primary driver of hypothalamo–pituitary–adrenocortical responses, PaVN seems to be involved in handling the demands of chronic stress exposure [26]. Therefore, dysfunctions of PaVN due to inflammatory processes may induce dysfunctions of the emotional-stress systems with altered expression in PaVN of arginine vasopressin, oxytocin, neuropeptide S or orexin receptors [27].

Concurrently, PeVN belongs to the eating behavior control system and is regulated by the neocortex and the limbic system. Its activity is influenced by hippocampal outflow, which is balanced by low-affinity and high-affinity glucocorticoid receptors [28]. Thus, inflammation of PeVN observed by 7-T MRI might influence the allocation of energy and may play a central role in the pathogenesis of anorexia nervosa [28]. Moreover, the association between lower BMI and higher left FX volume observed in the present study suggests the presence of inflammation in specific WM tracts of AN. As FX is particularly involved in reward processing, damage of these WM tracts may reflect the reduced capacity for reward (anhedonia) reported in severe restrictive anorexia nervosa [29]. Numerous studies using diffusion-weighted MRI have demonstrated the presence of microstructural alterations (that is, increased mean diffusivity and reduced fractional anisotropy) within the FX in anorexia nervosa [30–32] which can be reversed by brief weight restoration therapy [33]. Furthermore, we found that leptin, which stimulates POMC/CART neurons [41], shows lower levels in AN compared with HC. The role of leptin is to signal to the brain a reduction in stored energy to initiate metabolic responses and conserve energy to allow survival [42]. This finding is in line with the decrease in body fat mass and very low leptin blood levels reported in anorexia nervosa patients compared with healthy controls [43].

## Macrostructural and microstructural fingerprinting of HT nuclei in obesity

In this limited sample of OB patients (n = 10), we were only able to observe higher global HT volume relative to HC, which is in line with other studies using much larger cohorts [17,44]. Higher macrostructural HT volume can be a symptom of more intricate alterations in cellular processes. Indeed, studies on brain samples from rodents and postmortem humans have linked higher HT T₂-w signal intensity in obesity to the proliferation and activation of microglia and astrocytes [45,46]. Modification of T₂-weighted signal was strongly associated with systemic low-grade inflammation [47]. Indeed, during

inflammation, astrocytes accumulate water through aquaporin-4 which increases T₂-weighted signal intensity [48]. Furthermore, numerous studies on rodents reported that high-fat diet (HFD) exposure triggers chronic low-grade HT inflammation with insulin and leptin resistance [49,50]. Over time, this localized inflammation impairs the sensing of anorexigenic signals, potentially disrupting brain circuits regulating the balance between energy intake and energy expenditure. This disruption promotes weight gain and leads to obesity [51].

Nevertheless, we did not observe any significant differences in micro and macrostructural characteristics at the level of HT nuclei. This may be because of the limited statistical power from the sample size of OB participants (n = 10). However, in the (OB+HC) cohort, the partial least square analysis demonstrated an association between the morbid augmentation of BMI scores and higher plasmatic leptin and ghrelin levels, greater severity of eating behaviors, depression, and anxiety, as well as larger volumes and/or T₁ value alterations of the right LH, right PaVN, right AHA, left PH, and bilateral FX. Indeed, the LH is known as the “hunger center” because its stimulation leads to an increase in food intake [39]. Numerous studies have demonstrated that a fat-rich diet is associated with inflammation and resistance to anorexigenic signals in the HT [49]. Therefore, dependence between increased volumes of the LH and BMI scores in OB may indicate a progressive, inflammation-mediated, disruption of the integration of anorexigenic signals in the LH. This disruption could induce the observed altered eating behavior. Moreover, LH neurons are integrative sites for signals involved in the motivation to eat [52]. Indeed, experiments on the LH revealed its role in adjusting self-stimulation behavior based on feeding, suggesting its involvement in reward-seeking behavior usually found in obesity [53]. Additionally, the LH seems to contribute to the processing of food palatability because it is involved in forming conditioned preferences and aversions to tastes based on gustatory stimuli [54–56]. Furthermore, the LH has recently been involved in nonhomeostatic feeding. The latter is linked to overconsumption of food independent of satiety levels, biased toward palatable foods [57]. These findings suggest the involvement of LH in the overconsumption of food, the leading factor for obesity in our modern world [58]. In addition, although the LH is one of the main targets of ghrelin projections and is believed to mediate ghrelin-induced hyperphagia [59]. The impact of higher ghrelin concentrations on BMI scores in the present study could illustrate enhancement by ghrelin of the reward value of a HFD through an orexin pathway connecting the LH and ventral tegmental areas [60]. As stated before, the PaVN plays a critical role in energy homeostasis as its stimulation was found to inhibit food intake [39]. Its dysfunction could promote food overconsumption, as shown by the observed higher EAT-26, SCOFF, and YFAS scores, associated with higher BMI scores in OB. The observed high plasmatic leptin concentrations reflect the resistance to leptin that fails to promote satiety in obesity [8].

Nevertheless, individual HT nuclei present significant functional overlap and extensive coordination. Furthermore, the diverse range of functions attributed to a single HT nucleus can be attributed to the

**FIGURE 4.** Relative contributions of variables to predict the severity of eating disorders and weight disorders. The size effects (left) and coefficients (right) of imaging, behavioral, and hormone parameters to model: (A) the severity of eating disorders (EAT-26 scores) observed in the whole subjects [explained variance = 51.08%, 10 variables used (effect >0.8), 7 cross-validations using fast Single Value Decomposition (SVD) nonlinear partial least square (NIPALS), Van de Voet $T^2 = 0, 1$ factor], (B) BMI scores in AN(+HC) [explained variance = 74.1%, 13 variables used (effect >0.8), 7 cross-validations using fast SVD NIPALS, Van de Voet $T^2 = 0, 2$ factors] and (C) BMI scores in OB(+HC) [explained variance = 68.5%, 14 variables used (effect >0.8), 7 cross-validations using fast SVD NIPALS, Van de Voet $T^2 = 0, 1$ factor] using NIPALS modeling (JMP 16 Pro). AN, young females with anorexia nervosa; EAT-26, Eating Attitudes Test-26; HC, young females with normal weight; HT, hypothalamus; OB, young females with obesity; SVD.
heterogeneous population of neurons it contains. For example, sub-
types of orexigenic agouti-related peptide neurons in the arcuate nu-
cleus (ARHAgRP) show distinct transcriptional responses to fasting.
This suggests they may represent separate populations, which may
respond specifically to different energy states [61]. Similarly, the
diverse populations of neurons in the LH enable this nucleus to coor-
dinate a range of essential behaviors, including sleep, wakefulness,
feeding, stress, and motivated actions [62].

Association of MRI HT parameters with the severity of
eating disorders

Presently, the severity of eating disorders (EAT-26 score) was
associated with higher volumes and/or higher T₁ values not only of
PaVN, ARH, and right BNST, but also of the MPO and TM. As dis-
cussed, the first 3 HT nuclei (PaVN, ARH, and right BNST) are the
main hypothalamic centers responsible for regulating food-related
homeostasis [63]. They were reported to exhibit the largest structural
and connectivity differences in adults with anorexia nervosa and
obesity, with respect to healthy controls [14,16,17]. In contrast, MPO
and TM have been reported to be primarily involved in arousal,
wakefulness, and sleep [64]. The latter is known to be altered in eating
disorders [65].

Limitations

There are several limitations to this study. First, we recruited un-
equal cohort sample sizes, with a particularly small sample size of OB
(n = 10), because of very strict inclusion criteria, with great care for
age, sex, and conditions (15-h fasting, blood sample collection,
morning scan) control. Therefore, longitudinal studies and larger
sample sizes are warranted to determine the sensitivity of variation in
repeated measurements relative to the differences between cohorts and/
or treatment effects. The absence of significant differences in ghrelin
levels could be explained by the fact that at the time of 7-T MRI
assessment, there were no significant differences in total energy intake
between cohorts. Indeed, we conducted scans on AN during their
hospitalization in which they complied with a diet rich in carbohydrate,
lipid, and protein, relative to the patients' weight and energy re-
quirements. Furthermore, the inclusion of patients with very high BMI
was not possible according to the mechanical constraints related to the
60 cm diameter of the 7 T bore.

Although we found some altered morphostructural common to both
AN and OB, it is interesting to observe that HT alterations are
pathology-specific and closely related to HT nuclei function in feeding.
Ultimately, we showed that conclusive results can be obtained with
fewer participants than previously required before the advent of 7-T
MRI. This opens new perspectives for MRI studies, specifically
those involving integrative multimodal protocols which are common in
appetite-related research.

Among the 44 scans, 4 were performed on a different 7-T MR
scanner. However, the same head coil and similar MRI sequences were
used. Furthermore, B<sub>1</sub><sup>†</sup> corrections of quantitative maps minimized
interscanner and intersubject biases, revealing subtle microstructural
differences in HT nuclei. However, it is important to note that the
granularity of our investigation cannot be directly applied using a
magnetic field lower than 7 T, which makes it currently challenging to
apply this as a diagnostic tool in a clinical setting.

Although numerous studies have linked higher volume and T₁
values to inflammation, the exact cellular processes responsible for
such alterations are still speculative. Furthermore, the cross-sectional
design of the protocol limits its ability to determine whether HT

alterations in AN and OB are causal or consequential. However, animal
experiments have shown that HT inflammation precedes body weight
gain [66].

In conclusion, we assessed specific micro and macrostructural al-
terations of individual HT nuclei involved in eating behavior networks
and found relationships between these alterations with leptin levels,
eating disorders, and anxiety and depression scores, in AN and OB.
Future prospective studies will look into larger cohorts and integrate
these morphostructural findings with structural and functional con-
nectivity, as well as activation patterns related to food cues in AN and
OB.

We believe that this present study is a first step toward a more
comprehensive characterization of HT nuclei. This is crucial for un-
derstanding how eating behavior circuits become maladaptive and
recover after therapy both in AN and OB. More specifically, a better
characterization of eating behavior circuits could help decipher how
GLP-1 agonists modulate these networks in females living with
obesity. Ultimately, this could enable early prognosis of treatment ef-
ficacy and promote a better understanding of partial or complete non-
responders.

Acknowledgments

We thank Timothy Anderson for his contribution to proofreading
this manuscript.

Author contributions

The authors’ responsibilities were as follows – CR, BG, AD, MG,
PD, YF, M-PR, PJC, J-PR: designed research; CR, AL, LP, PA, J-PR:
conducted research; CR, J-PR: analyzed data, wrote the paper, had
primary responsibility for final content; HD, AK: helped with pre-
processing; and all authors: read and approved the final manuscript.

Conflict of interest

J-PR reports that financial support was provided by French National Research Agency, Excellence Initiative of Aix-Marseille University, and PHC MERLION 2019. CR reports that financial support was provided by EUR Neuroschool. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

CR has received a PhD grant from EUR Neuroschool (ANR-17-EURE-0029), and Agency for Science, Technology and Research, Singapore (A\*STAR). This work received the support of the Merlion Program Campus France (PHC MERLION 2019 # 43654ZK) and the A\*STAR. The project leading to this publication has received funding from the French government under the “France 2030” investment plan managed by the French National Research Agency (reference: ANR-16-CONV000X/ANR-17-EURE-0029) and from Excellence Initiative of Aix-Marseille University—A\*MIDEX (AMX-19-IET-004, 7TEAMS Chair). This work was performed by a laboratory member of France Life Imaging network (grant ANR-11-INBS-0006).

Data availability

Data described in the manuscript, code book, and analytic code will
be made available on request pending (application and approval).
# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ajcnut.2025.02.003.

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