Altered Hypothalamic functional connectivity in adolescents with severe obesity

Altered Hypothalamic functional connectivity in adolescents with severe obesity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Altered Hypothalamic functional connectivity in adolescents with severe obesity Allison Shapiro, Meghan Pauley, Jaime M. Moore, Lucy Hall, Nicholas Stence, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8694593/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background/Objectives: Neurobiological frameworks of obesity in youth have focused largely on non-homeostatic systems (reward, salience, executive control), while the homeostatic system—particularly the hypothalamus—is comparatively understudied. A clearer picture of how these systems interact in adolescents with severe obesity is needed to inform treatment. This study sought to test whether adolescents with severe obesity exhibit altered hypothalamic functional connectivity, relative to healthy-weight peers, across fasting and fed states. Subjects/Methods: We analyzed data from the Food and Adolescent Brain Study, a single-blinded randomized cross-over trial (NCT04208256) of 13–18-year-old adolescents with severe obesity (SO; body mass index [BMI] >99 th %ile; n=30; mean [SD] age 14.6 years [1.5]) and with healthy weight (HW; BMI <85 th %ile; n=26; 15.5 years [1.6]). Interventions/Methods: Participants completed resting-state functional magnetic resonance scans during fasting and after ingesting a 75-gram glucose drink (1 255.8 kJ [300 kcal]) to induce a fed state. Multivariate general linear models were run in seed-to-voxel analyses to estimate functional connectivity, setting the hypothalamus as the seed region. All models were adjusted for age and sex, with significance determined via cluster-forming voxel-level p<0.005 and false discovery rate-corrected cluster-level p<0.05. Results : In adolescents with SO, during fasting, hypothalamic connectivity in adolescents with SO was weaker to the bilateral cerebellum and left(L) middle occipital gyrus, and stronger to the right(R) postcentral/supramarginal gyri, compared to the HW group. During the fed state, hypothalamic connectivity increased to the bilateral middle frontal gyri and R putamen and decreased to the R caudate and L superior frontal gyrus in adolescents with SO, relative to the HW group. Conclusion: Severe obesity in adolescence is associated with altered communication between homeostatic (hypothalamus) and non-homeostatic brain structures, evident across fasting and fed states. Findings underscore the need to incorporate homeostatic circuitry into pediatric obesity frameworks. Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Through substantial preclinical work and a growing number of studies in humans, the brain has been increasingly implicated in the etiology of obesity( 1 ). Indeed, enough evidence from human neuroimaging studies has amassed for neurobiological frameworks of obesity to emerge. Multiple frameworks have been proposed, with the most predominant being: 1) the reward surfeit model of obesity, which posits that people with obesity have a heightened reward response to appetizing foods, thus leading to overconsumption of such foods in an effort to achieve the desired reward; and 2) the inhibitory control deficit theory of obesity, proposing that people with obesity experience dysregulation of inhibitory control related to food stimuli, and therefore demonstrate eating disinhibition (reviewed in ( 2 )). Taken together, these theories of human obesity – and by extension, much of the evidence underlying them (reviewed in ( 3 )) – center on dysregulated non-homeostatic neural systems that contribute to overconsumption. A large body of work in animals has demonstrated that eating behavior within the context of obesity is driven by both non-homeostatic and homeostatic (e.g. hypothalamic appetite/satiety signaling, etc.) neural systems. The non-homeostatic and homeostatic systems underlying eating behavior do not function as isolated units but instead are integrated circuits that comprise multiple communicating brain regions. The hypothalamus is the key homeostatic regulator of appetite control,( 4 , 5 ) and together with non-homeostatic systems that underly cognitive control,( 6 – 8 ) salience,( 9 ) and reward( 10 – 12 ) comprises a circuit of functionally connected structures that influence eating behaviors (reviewed in ( 3 )). Adults and youth with obesity show altered neuronal activity in non-homeostatic brain regions in response to viewing and tasting highly palatable foods,( 13 – 17 ) with the degree of altered neuronal activity related to overeating.( 18 – 20 ) Hypothalamic dysfunction has been observed in adults( 21 ) and youth( 22 ) with obesity, where the hypothalamic response to a caloric stimulus – glucose or a liquid meal – is attenuated, relative to healthy weight counterparts (reviewed in ( 23 )). In adults with obesity, functional connectivity between the hypothalamus and non-homeostatic brain networks has been shown to also be altered,( 24 , 25 ) suggesting dual-system disruption of the non-homeostatic and homeostatic eating behavior circuitry. In adolescents with obesity, however, altered hypothalamic communication, or connectivity, with non-homeostatic brain regions and networks is understudied, leaving a critical gap in our understanding of the neural contributors to obesity development and persistence in youth, now affects approximately 20% of American adolescents( 26 ), with severe obesity (BMI ≥ 99th percentile for age and sex) being the most rapidly growing weight category in youth( 27 ). In the modern landscape of obesity treatment, powerful, neuromodulating pharmacotherapies such as glucagon-like peptide-1 receptor agonists (GLP-1RA) – shown to alter both non-homeostatic response to food (e.g., reward) and homeostatic control of eating behaviors via appetite suppression in adults, and metabolic bariatric surgery, also shown to reverse hypothalamic dysfunction( 28 ) – are now available for use in pediatrics, and recommended for adolescents with severe obesity.( 29 ) Importantly, adolescence is a uniquely dynamic developmental period during which both homeostatic ( 30 ) and non-homeostatic (e.g., reward and cognitive control( 31 , 32 )) neural systems are undergoing rapid change, making them potentially sensitive to neuromodulating interventions. Studies investigating the underlying non-homeostatic and homeostatic neurobiology of severe obesity in adolescents are, therefore, critically needed to build a comprehensive understanding of the baseline neural circuitry within which these available interventions may exert a neuromodulating effect. The primary objective of the current study was to examine functional connectivity of the hypothalamus, as a primary homeostatic structure, in adolescents with severe obesity, relative to their peers with healthy weight. We hypothesized that connectivity between the hypothalamus and non-homeostatic brain regions underlying reward, salience, and executive control would be weaker in adolescents with severe obesity, compared to adolescents with healthy weight. We further explored the effect of severe obesity in adolescence on whole brain functional connectivity, setting no a priori hypotheses, but expecting that adolescents with severe obesity would demonstrate different whole brain connectivity, again compared to their peers with healthy weight. MATERIALS/SUBJECTS AND METHODS Design and Participants Data for the current analysis were collected as part of the Food and ADO lescent B rain (ADOB) Study, a single-blinded randomized cross-over study (NCT04208256) investigating the response of the adolescent brain to energy loads and their relationship to eating behaviors. The ADOB study recruited male and female adolescents, 13–18 years-old, with severe obesity (SO; body mass index [BMI] > 99th %ile for age and sex) or with healthy weight (HW; BMI < 85th and ≥ 10th %ile for age and sex) from the Children’s Hospital Colorado Lifestyle Medicine Clinic and the surrounding communities and high schools. Prospective participants were excluded from enrollment if they met any of the following criteria: pre-diabetes defined as a hemoglobin A1c ≥ 39 mmol/mol (5.7%) or physician’s diagnosis of diabetes; diagnosis of anorexia nervosa or bulimia nervosa; prescribed anti-psychotic medications (not including anti-depressant or anti-anxiety medications) or medications for weight loss or appetite suppression (e.g., phentermine, GLP-1RA); diagnosis of Chron’s or celiac disease or a serious food allergy (e.g., nuts); diagnosis of autism or Down’s syndrome, or any severe developmental disorder (excluding ADHD, dyslexia, or having an individual development plan in school); diagnosis of genetically-linked obesity or glioma in the hypothalamus or pituitary; a non-MRI safe device or metal in the body; claustrophobia. The ADOB study enrolled a total of 64 adolescents (SO = 34, HW = 30) between 2020 and 2024. See Fig. 1 for study consort diagram. The ADOB protocol was approved by the Colorado Multiple Institutional Review Board (#19-1171). All participants 18 years of age provided written informed consent, with parents or guardians and participants providing written consent and assent, respectively, for those younger than 18 years of age. Data Collection Eligible participants were invited to complete three research visits at the University of Colorado Anschutz. Research visits included two imaging visits and one visit during which participants completed observed, laboratory-based eating behavior assessments. Participants completed demographic and health questionnaires. Participants also completed the Pubertal Development Scale (PDS), a validated self-report questionnaire for pubertal staging in male and female adolescents( 33 ). Five pubertal stage categories were derived from the PDS scores including pre-pubertal (male PDS score ≤ 3; female PDS score ≤ 2), early pubertal (male PDS score 4–5; female PDS score 2–3), mid-pubertal (male PDS score 6–8; female PDS score 4–6), late pubertal (male PDS score 9–11; female PDS score 7), and post-pubertal (male PDS score ≥ 12; female PDS score ≥ 8). At enrollment, participants were randomized to receive an energy stimulus, equivalent to the fed state, in the form of an uncarbonated 300 ml orange-flavored 75-gram glucose drink (1 255.8 kJ [300 kcal]; Glucola™) at their first imaging visit, or a 300 ml energy neutral stimulus in the form of an uncarbonated orange-flavored aspartame sweetened drink mixture (Diet Orange Crush™). Whichever stimulus was not received at the first imaging visit was received at the second imaging visit (cross-over). In female participants, menstrual cycle was documented from self-report of date of last period and duration, and all visits completed during the luteal phase of their menstrual cycle to reduce potential variability in outcomes due to cycle-dependent fluctuations in sex hormones. All scanning activities were conducted between 7 a.m. and 12 p.m. after an overnight fast at the University of Colorado Brain Imaging Center. Images were acquired on a Siemens 3T Skyra scanner (Siemens, Erlangen, Germany) with a 32-channel head coil and multi-slice acquisition (acceleration factor [SMS] = 3). Resting-state whole brain functional magnetic resonance imaging (rs-fMRI) data were acquired using the Blood Oxygen Level Dependent (BOLD) signal (TR = 1 s, TE = 35 ms, matrix 96 x 96, voxel size 3mm 3 ). rs-fMRI scans were acquired in a restricted field of view (slices = 27) and by angling the field of view 15 degrees cephalic from the corpus callosum line with the most inferior slice placed just above the pituitary to reduce signal dropout in the hypothalamus from the sinus cavities. A T1-weighted anatomical scan (3D-MPRAGE, 192 slices, TR = 2 s, TE = 2.06 ms, matrix 256 x 256, voxel size 0.9mm 3 ) was also collected. The participant’s head was stabilized within the coil using foam padding, and participants were instructed to keep their eyes closed and allow their mind to wander without falling asleep. During each imaging visit, a fasting rs-fMRI scan (600 volumes [~ 10 minutes]) was completed first, after which participants were slid out from the scanner bore and, with minimal body shifting, instructed to drink the entire volume of the given stimulus within a 5-minute period. The post-stimulus rs-fMRI scan (1 200 volumes [~ 20 minutes]) immediately followed the participant finishing the stimulus drink. All enrolled participants (N = 64) completed at least one of the imaging visits. Per the primary aim of the study, as described in the protocol (NCT04208256), only data from the fed state imaging visit (fasting + post-stimulus with 75g glucose during) were used in the current analysis. Thus, participants were included if they completed the imaging visit with the 75-gram glucose stimulus (n = 59). From the 59 participants with fed state imaging data, 1 SO participant was excluded due to an anatomical anomaly, and 2 participants (1 SO, 1 HW) were excluded due to imaging quality control issues (> 10% of volumes displaying translation >1mm or rotation > 3 degrees). Thus, the analytic sample size for the current analysis was 56 adolescents (SO = 30, HW = 26; Fig. 1 ). fMRI Preprocessing and Analyses All BOLD images were preprocessed in FSL using a standard pipeline including initial brain extraction via the Brain Extraction Tool (BET) and motion correction and 6-parameter estimation via the Flexible Linear Registration Tool (FLIRT). Spatial normalization was completed with the Flexible Non-Linear Registration Tool (FNIRT) to register BOLD and T1-weighted images to age-specific templates in Montreal Neurological Institute (MNI) standard space. Specifically, each participant was registered to the age-specific template closest to their age at the imaging visits (e.g., participant age = 15.4 y/o, registered to the 15.0 y/o template). Spatial smoothing with an 8mm full width at half max smoothing kernel was applied to the final registered image. Connectivity analyses were completed using the CONN Toolbox in Matlab vR2024a. Preprocessed scans were denoised using a standard denoising pipeline including regressing out the WM timeseries (5 CompCor noise components), CSF timeseries (5 CompCor noise components), movement regressors and their first order derivatives (12 components), and linear trends within each functional run, followed by bandpass frequency filtering of the BOLD timeseries (0.008 Hz and 0.09 Hz). Hypothalamic Functional Connectivity The hypothalamus ROI was defined by manual segmentation of the age-specific adolescent template( 34 ) representing the median age of the full study sample (15.0 y/o). A seed-to-voxel analysis was run, in which the hypothalamus was set as the seed, and all voxels in the brain included as targets. First-level, within-participant, seed-based and ROI-based connectivity maps (SBC) were estimated to characterize the patterns of functional connectivity between the hypothalamus and each voxel of the brain in the baseline/fasting and corresponding post-glucose load scan (2 conditions). Functional connectivity strength was estimated via bivariate regression coefficients from a weighted general linear model (GLM), defined separately for each seed(hypothalamus)-voxel pair, modeling the association between their BOLD signal timeseries. A multivariate GLM was used in second-level (group) analyses to test, 1) the association between obesity status (SO vs. HW) and fasting hypothalamic connectivity, and 2) the association between obesity status and change in hypothalamic functional connectivity from fasting to fed states. All models were adjusted for sex (female vs. male) and household income (< $ 100 000 vs. ≥ $ 100 000), as the SO and HW groups differed by these characteristics. Although previous studies have used self-identified race and ethnicity as covariates in models of brain-related outcomes, there is no biological reason to suspect different brain function outcomes by self-identified race and ethnicity. Thus, following the guidance of the American Academy of Pediatrics,( 35 ) we did not adjust models for self-identified race and ethnicity. In seed-to-voxel models, a separate GLM was estimated for each individual voxel, setting first-level connectivity measures at this voxel as the dependent variable (one independent sample per subject and one measurement per condition), and obesity status as the independent variable. Multivariate parametric statistics with random-effects across subjects and sample covariance estimation across multiple measurements were applied. Cluster-level inferences were based on parametric statistics from Gaussian Random Field theory. Results were thresholded using a combination of a cluster-forming p < 0.005 voxel-level threshold, and a familywise error corrected p-FDR < 0.05 cluster-size threshold. Untargeted Whole Brain Functional Connectivity To explore the association between severe obesity in adolescence and whole brain connectivity, a voxel-to-voxel analysis was run with no a priori ROIs or seeds defined. First-level voxel-based connectivity maps were estimated to characterize the patterns of functional connectivity between each voxel in the brain. This was completed for both the baseline/fasting and post-glucose load scan. Following the same analytic approach as the above SBC analysis, in each participant, functional connectivity strength was estimated via bivariate regression coefficients from a weighted GLM, defined separately for each voxel-voxel pair, modeling the association between their BOLD signal timeseries. Multivariate GLM was again run, adjusted for sex (female vs. male) and household income (< $ 100 000 vs. ≥ $ 100 000) to test, 1) the association between obesity status (SO vs. HW) and baseline/fasting whole brain connectivity, and 2) the association between obesity status and change in whole brain functional connectivity from baseline/fasting to post-glucose load. Given the exploratory nature of this analysis, we applied a two-sided test, and results were again thresholded using a combination of a cluster-forming p < 0.005 voxel-level threshold, and a familywise corrected p-FDR < 0.05 cluster-size threshold. RESULTS Participants were on average 15 y/o [SD, 1.5], 55% had severe obesity, 52% were female sex at birth, and predominantly non-Hispanic White (65%). The SO and HW groups differed significantly by sex, race and ethnicity, and household income (p < 0.05 for all, respectively). Specifically, the SO group had a larger proportion of adolescents with male sex at birth (63%), adolescents who self-identified as Hispanic (27%) or non-Hispanic Black (10%), and adolescents from households with an annual income less than $ 100 000 (50%). By design, adolescents with SO had significantly higher BMI (p < 0.001), compared to adolescents with healthy weight. See Table 1 for participant characteristics reported by obesity status. Table 1 Characteristics of ADOB analytic sample by obesity status (N = 56). SO (n = 30) HW (n = 26) p-value 1 Age (years), mean (SD) 14.6 (1.5) 15.5 (1.6) 0.07 Body mass index, mean (SD) 32.9 (5.3) 21.7 (2.4) < 0.001 Sex, n (%) 0.02 Female 11 (36.7) 18 (69.2) Male 19 (63.3) 8 (30.8) Pubertal Stage – Female 2 0.50 Pre-pubertal, no menarche 1 (9.1) 0 Early pubertal, no menarche 3 (27.3) 5 (27.8) Mid-pubertal, no menarche 7 (63.6) 13 (72.2) Late pubertal, menarche 0 0 Post-pubertal, menarche 0 0 Pubertal Stage – Male 2 0.20 Pre-pubertal 18 (94.7) 6 (75.0) Early pubertal 1 (5.3) 2 (25.0) Mid-pubertal 0 0 Late pubertal 0 0 Post-pubertal 0 0 Race or ethnicity, n (%) 0.02 Non-Hispanic White 18 (60.0) 18 (69.2) Non-Hispanic Black 3 (10.0) 0 Non-Hispanic multiple race 1 (3.3) 6 (23.1) Hispanic 8 (26.7) 2 (7.7) Parent education, n (%) 0.06 High school degree/GED and less 4 (13.8) 0 Some college or technical/vocational school 7 (24.1) 2 (8.0) College or graduate degree 18 (62.1) 23 (92.0) Not reported 1 1 Household income, n (%) 0.04 $ 100,000 15 (51.8) 22 (84.6) Not reported 1 1 ADOB = Food and ADO lescent B rain (ADOB) Study 1: p-values for comparison between SO and HW derived by Fisher’s exact test for categorical variables and Student's t-test for continuous variables. 2: Pubertal staging was self-reported via the Pubertal Development Scale Hypothalamic Functional Connectivity Overall, independent of sex and age, hypothalamic functional connectivity during both the fasted and fed states was significantly different in adolescents with severe obesity, compared to their healthy weight counterparts (Table 2 ). Specifically, in the fasted state, hypothalamic connectivity to bilateral voxel clusters in the cerebellum and left middle occipital gyrus was significantly weaker among adolescents with severe obesity, compared to those with healthy weight (Fig. 2 A). Compared to the healthy weight adolescents, those with severe obesity also showed significantly stronger hypothalamic connectivity to clusters in the right postcentral and supramarginal gyri during fasting. Table 2 Region names of significant voxel clusters found in seed-to-voxel hypothalamic functional connectivity and whole brain functional connectivity under fasting and fed (75g glucose) states in adolescents with SO vs. HW. Condition Region(s) Name 1 Peak Voxel (MNI: x, y, z) KE 2 p-Uncorrected pFDR-Corrected Fasting l. cerebellum (-) -42,-68,-28 246 < 0.0001 0.0009 r. cerebellum (-) 42,-68,-20 146 0.0002 0.0151 r. postcentral gyrus, r. supramarginal gyrus (+) 60,-14,28 121 0.0005 0.0277 l. middle occipital gyrus (-) -28,-102,-12 106 0.0010 0.0394 Fed vs. fasting l. middle frontal gyrus (+) -18,36,14 672 < 0.0001 < 0.0001 r. middle frontal gyrus (+) 26,26,10 584 < 0.0001 < 0.0001 r. caudate (-) 16,-6,28 123 0.0005 0.0223 l. superior frontal gyrus (-) -10,62,22 109 0.0009 0.0302 r. putamen (+) 12,12,-8 98 0.0016 0.0391 1: l. indicates the cluster was found in the left hemisphere; r. indicates the cluster was found in the right hemisphere; (+) indicates stronger connectivity within the cluster region, (-) indicates weaker connectivity within the cluster region. 2: KE (cluster size) for identified cluster After glucose ingestion in the fed state, adolescents with severe obesity showed a significant increase in hypothalamic connectivity to voxel clusters in the bilateral middle frontal gyri and right putamen (Fig. 2 B), relative to the fasted state, and compared to their healthy weight counterparts. Further, in adolescents with severe obesity, hypothalamic connectivity to clusters in the right caudate and left superior frontal gyrus significantly weakened in the fed state, compared to the adolescents with healthy weight. Untargeted Whole Brain Functional Connectivity Across all voxels in the brain during fasting, adolescents with severe obesity had significantly stronger whole brain functional connectivity with large voxel clusters exclusively within the right hemisphere that included the superior temporal and fusiform gyri, parahippocampal gyrus, and hippocampus, and in the right inferior frontal gyrus and lingual gyrus (Table 3 ; Fig. 3 A), compared to those with healthy weight. In response to the glucose drink, relative to fasting, adolescents with severe obesity recruited left hemisphere structures such that, compared to those with healthy weight, they showed significantly increased whole brain functional connectivity with the bilateral parahippocampal gyri and hippocampus (Fig. 3 B), as well as the right putamen, and fusiform and superior temporal gyri. Table 3 Region names of significant voxel clusters found in whole brain voxel-to-voxel functional connectivity under fasting and fed (75g glucose) states in adolescents with SO vs. HW. Condition Region(s) Name 1 Peak Voxel (MNI: x, y, z) KE 2 p-Uncorrected pFDR-Corrected Fasting r. superior temporal gyrus, r. fusiform gyrus, r. hippocampus (+) 44,-14,8 244 0.0001 0.0138 r. fusiform gyrus, r. parahippocampal gyrus, r. hippocampus (+) 40,-48,-6 200 0.0003 0.0146 r. inferior frontal gyrus (+) 32,22,16 201 0.0003 0.0146 r. lingual gyrus (+) 18,-64,8 150 0.0012 0.0455 Fed vs. fasting r. parahippocampal gyrus, r. fusiform gyrus, r. hippocampus (+) 26,-60,6 551 < 0.0001 0.0003 l. hippocampus, l. parahippocampal gyrus (+) -30,-30,-4 310 0.0002 0.018 r. putamen (+) 18,0,6 190 0.0021 0.0498 r. superior temporal gyrus (+) 52,-12,2 184 0.0024 0.0498 1: l. indicates the cluster was found in the left hemisphere; r. indicates the cluster was found in the right hemisphere; (+) indicates stronger connectivity within the cluster region, (-) indicates weaker connectivity within the cluster region. 2: KE (cluster size) for identified cluster. DISCUSSION We found that adolescents with severe obesity had significantly altered functional communication between the hypothalamus, a central homeostatic driver of appetite control, and other key homeostatic and non-homeostatic regions of the brain regions that are known to contribute to eating behaviors. To date, few published studies have investigated hypothalamic function or connectivity in children or adolescents with obesity, with most studies involving adults.( 36 , 37 ) Compared to the limited studies in children and youth, however, our results are broadly consistent. In the fasted state, we observed hypoconnectivity between the hypothalamus and cerebellum among adolescents with severe obesity, relative to their healthy weight peers. Work over the past decade has revealed that the cerebellum, while traditionally thought to be primarily a motor control area, is also involved in eating behavior, where it contributes as an integration hub for food-related homeostatic and non-homeostatic (e.g., reward, affect) processes.( 38 ) For example, in children without obesity, two recent studies have shown that loss of control eating, a form of eating disinhibition, is associated with stronger neuronal activation in the bilateral cerebellum in response to visual food cues that varied in portion size and energy density.( 39 , 40 ) While these studies were cross-sectional in design and did not include functional connectivity analyses, their results suggest that altered cerebellar responsiveness to food stimuli in late childhood may contribute to heightened risk for obesity via eating disinhibition. Among adolescents with overweight or obesity (BMI ≥ 85th % ile), relative to adolescents with healthy weight, Martín-Pérez et al. (2018) found weaker resting state functional connectivity between the lateral hypothalamus and the right cerebellum, which is consistent with our fasted results.( 41 ) Importantly, however, Martín-Pérez et al. conducted scanning after participants had consumed the main meal of the day, and thus, participants were assumed to be in a sated state during scanning. Despite clear methodological differences, the consistency between our results and those of Martín-Pérez et al. across energy states suggests that obesity in adolescence is related to energy state-independent disruption in signal integration between the brain’s primary homeostatic structures. During the fed state, induced by oral glucose, a variable pattern of hypothalamic-corticostriatal connectivity emerged. Specifically, among adolescents with severe obesity, we observed stronger hypothalamic connectivity with regions implicated in reward-seeking behaviors (putamen( 42 )), and weaker hypothalamic connectivity with regions broadly involved in cognitive control and goal-directed behavior (superior frontal gyrus( 43 ) and caudate( 44 )). These observed connectivity patterns partially align with the theory of heightened incentive salience of food cues among people with obesity, whereby cognitive control processes are disrupted or weakened and reward responsiveness and salience valuation are enhanced in response to visual, olfactory, or taste food cues (reviewed in( 45 )). We also observed stronger hypothalamic connectivity with the middle frontal gyrus (MFG) – a structure implicated in inhibitory control( 46 – 48 ). While these results may appear to relate to the inhibitory control deficit theory of obesity and its supporting evidence, which shows lower activation of inhibitory control brain regions in response to food in people with obesity (reviewed in ( 49 )), it remains unclear whether increased hypothalamus-to-MFG connectivity is indicative of an attenuated or compensatory inhibitory response to the glucose stimulus in the current study. Further work using response inhibition tasks, such as the go/no-go task, is needed to make such a distinction. The middle frontal gyrus is also implicated in working memory, thus, alternatively, the observed increased connectivity with the hypothalamus may represent attention allocation to glucose and/or sweet taste. ( 50 ) Altogether, our hypothalamic connectivity results provide evidence of possible aberrant communication in, or integration of signaling across major homeostatic hubs and cognitive control and reward processes in adolescents with severe obesity, relative to their peers with healthy weight. In highlighting the potential significant role of disrupted homeostatic signaling in the neural circuitry present in obesity, our results suggest that in addition to reward processes that receive the majority of attention in models of pediatric obesity and obesogenic behaviors, it is likely important to also consider effects of homeostatic mechanisms that have been examined extensively in animal models of obesity (see ( 51 ) for an historical review). For example, preclinical models generated the roadmap of hypothalamic involvement in the pathogenesis of diet-induced obesity, specifically characterizing lateral hypothalamic nuclei and their functions in appetite regulation and energy homeostasis (reviewed in ( 52 )), and demonstrating the mechanisms of neuropeptide signaling in the control of eating behaviors (reviewed in ( 53 )). Additional research is needed to replicate our results and continue to understand involvement of homeostatic neural systems involvement in adolescent obesity. Moreover, it is critical for future research in the neural underpinnings of adolescent obesity to include investigation of homeostatic brain structures in addition to non-homeostatic circuitry. In untargeted, whole brain analyses, functional connectivity differences across non-homeostatic structures predominated. We found state-independent increases in connectivity across regions underlying response inhibition (e.g., inferior frontal gyrus( 54 )), reward (e.g., putamen( 42 )), episodic and semantic memory (e.g., hippocampus( 55 )), and food perception (e.g., lingual gyrus( 56 )). These results may indicate sweet taste-elicited attentional bias( 57 ) (“liking” response) and heightened incentive salience( 58 ) (“wanting” response) in the adolescents with severe obesity. Thus, aligning with the current non-homeostatic theoretical models of obesity and confirming, in part, the presence of disrupted non-homeostatic neural processes in adolescents with severe obesity. Strengths and Limitations Our results add novel insight into the potential homeostatic neural underpinnings of severe obesity in adolescents with a robust, physiologically informed study design that adds strength to our findings. Specifically, the imaging protocol included glucose ingestion as a stimulus for targeted engagement of the hypothalamus and an extended post-stimulus scan time (20 minutes) to improve BOLD signal detection and reduce imaging noise.( 59 ) Of note, however, several design limitations remain. First and foremost, this study was cross-sectional and focused in adolescents who already had severe obesity, thus, we cannot infer whether or how the observed hypothalamic connectivity patterns contribute to development of severe obesity. Second, the hypothalamus responds to dietary macronutrients other than glucose, such as free fatty acids (FFA), which may elicit a different hypothalamic connectivity pattern when orally ingested. Thus, interpretation of our results is limited to hypothalamic response to a large glucose bolus, equivalent to drinking two 12 oz cans of regular soda. Third, given the oral route for glucose ingestion in our design, we cannot decipher whether the hypothalamic connectivity patterns observed were due to direct fuel sensing in the hypothalamus via circulating blood glucose levels or peripheral signaling to the hypothalamus via vagal nerve afferent pathways from the gut. Finally, we tested undirected synchronizations of the BOLD signal in our functional connectivity analyses, which do not provide information about the direction of connectivity between brain regions. This limits the interpretation of our results, such that we do not know whether, in response to a fed state, the hypothalamus was driving the signal to the observed functionally connected brain regions, or vice versa. Future work is therefore needed to investigate the directionality of functional communication between homeostatic and non-homeostatic neural systems. Conclusion Our results broadly suggest possible disruption of the communication between homeostatic and non-homeostatic neural systems in adolescents with severe obesity. Considering dysfunction in both homeostatic and non-homeostatic systems as a component of the severe obesity phenotype may help to better inform our approach to obesity treatment in youth. Indeed, the American Academy of Pediatrics recently revised their clinical recommendations for pediatric obesity treatment to include use of anti-obesity medications and surgical options as first-line therapies alongside lifestyle interventions.( 29 ) With the introduction of GLP-1RAs and bariatric surgery, both of which are shown to change hypothalamic function and salience and reward circuitry in adults with obesity( 28 , 60 ), the treatment landscape has itself expanded to include potent homeostatic and non-homeostatic neuromodulating approaches in obesity treatment. Thus, a dual systems approach to treating disrupted non-homeostatic and homeostatic neural connections alongside lifestyle therapy in adolescent obesity is now possible. However, further work in adolescents with severe obesity is needed to replicate our findings and to inform future clinical trials of neuromodulating treatments and brain function in youth with obesity to better understand the effect of such therapies have on modulating non-homeostatic and homeostatic brain function. Declarations COMPETING INTERESTS The authors declare no conflicts of interest. FUNDING This work was funded primarily by the National Institute of Diabetes and Digestive and Kidney Diseases (K01DK120562; PI, Shapiro). Additional financial support was provided by pilot grant funding through the Colorado Clinical and Translational Research Institute (NIH/NCATS Colorado CTSA Grant Number UM1 TR004399) and the U.S. Department of Veterans Affairs (IK6CX002178; Tregellas). Contents are the authors’ sole responsibility and do not necessarily represent official NIH views. AUTHOR CONTRIBUTIONS ALBS conceived and designed the work that led to the submission, acquired data and completed all analyses and drafted the manuscript; MEP, JM, LH, NS, SLJ, KJN, KK, BR, MAC, and JRT all played an important role in interpreting the results, revising the manuscript, and approved the final version All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. References Markus A. Neurobiology of obesity. Nat Neurosci. 2005;8(5):551. Stice E, Burger K. Neural vulnerability factors for obesity. Clin Psychol Rev. 2019;68:38–53. Donofry SD, Stillman CM, Erickson KI. A review of the relationship between eating behavior, obesity and functional brain network organization. Social Cognitive and Affective Neuroscience. 2020;15(10):1157–81. 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Nutrients. 2024;16(5). Martín-Pérez C, Contreras-Rodríguez O, Vilar-López R, Verdejo-García A. Hypothalamic Networks in Adolescents With Excess Weight: Stress-Related Connectivity and Associations With Emotional Eating. J Am Acad Child Adolesc Psychiatry. 2019;58(2):211 – 20.e5. Leenaerts N, Jongen D, Ceccarini J, Van Oudenhove L, Vrieze E. The neurobiological reward system and binge eating: A critical systematic review of neuroimaging studies. International Journal of Eating Disorders. 2022;55(11):1421–58. du Boisgueheneuc F, Levy R, Volle E, Seassau M, Duffau H, Kinkingnehun S, et al. Functions of the left superior frontal gyrus in humans: a lesion study. Brain. 2006;129(Pt 12):3315–28. Pauli WM, O'Reilly RC, Yarkoni T, Wager TD. Regional specialization within the human striatum for diverse psychological functions. Proc Natl Acad Sci U S A. 2016;113(7):1907–12. Morys F, García-García I, Dagher A. Is obesity related to enhanced neural reactivity to visual food cues? A review and meta-analysis. Social Cognitive and Affective Neuroscience. 2023;18(1):nsaa113. Heitzeg MM, Nigg JT, Hardee JE, Soules M, Steinberg D, Zubieta JK, et al. Left middle frontal gyrus response to inhibitory errors in children prospectively predicts early problem substance use. Drug Alcohol Depend. 2014;141:51–7. Fonken YM, Rieger JW, Tzvi E, Crone NE, Chang E, Parvizi J, et al. Frontal and motor cortex contributions to response inhibition: evidence from electrocorticography. J Neurophysiol. 2016;115(4):2224–36. Tamm L, Menon V, Reiss AL. Maturation of Brain Function Associated With Response Inhibition. Journal of the American Academy of Child & Adolescent Psychiatry. 2002;41(10):1231–8. de Klerk MT, Smeets PAM, la Fleur SE. Inhibitory control as a potential treatment target for obesity. Nutritional Neuroscience. 2023;26(5):429–44. Xu P, Wang M, Zhang T, Zhang J, Jin Z, Li L. The role of middle frontal gyrus in working memory retrieval by the effect of target detection tasks: a simultaneous EEG-fMRI study. Brain Struct Funct. 2024;229(9):2493–508. Velloso LA, Schwartz MW. Altered hypothalamic function in diet-induced obesity. Int J Obes (Lond). 2011;35(12):1455–65. Cheon D-H, Park S, Park J, Koo M, Kim H-H, Han S, et al. Lateral hypothalamus and eating: cell types, molecular identity, anatomy, temporal dynamics and functional roles. Experimental & Molecular Medicine. 2025;57(5):925–37. Neves LdS, Oliveira RKG, dos Santos LS, Ribeiro IO, Barreto-Medeiros JMB, Matos RJB. Modulation of hypothalamic AMPK and hypothalamic neuropeptides in the control of eating behavior: A systematic review. Life Sciences. 2022;309:120947. Hampshire A, Chamberlain SR, Monti MM, Duncan J, Owen AM. The role of the right inferior frontal gyrus: inhibition and attentional control. Neuroimage. 2010;50(3):1313–9. Squire LR. Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans. Psychol Rev. 1992;99(2):195–231. Huerta CI, Sarkar PR, Duong TQ, Laird AR, Fox PT. Neural bases of food perception: coordinate-based meta-analyses of neuroimaging studies in multiple modalities. Obesity (Silver Spring). 2014;22(6):1439–46. Field M, Werthmann J, Franken I, Hofmann W, Hogarth L, Roefs A. The role of attentional bias in obesity and addiction. Health Psychology. 2016;35(8):767–80. Robinson MJF, Robinson TE, Berridge KC. Chapter 39 - Incentive Salience and the Transition to Addiction. In: Miller PM, editor. Biological Research on Addiction. San Diego: Academic Press; 2013. p. 391–9. Ooi LQR, Orban C, Zhang S, Nichols TE, Tan TWK, Kong R, et al. Longer scans boost prediction and cut costs in brain-wide association studies. Nature. 2025;644(8077):731–40. Park JS, Kim KS, Choi HJ. Glucagon-Like Peptide-1 and Hypothalamic Regulation of Satiation: Cognitive and Neural Insights from Human and Animal Studies. Diabetes Metab J. 2025;49(3):333–47. Additional Declarations There is NO conflict of interest to disclose Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 25 Feb, 2026 Review # 2 received at journal 17 Feb, 2026 Review # 1 received at journal 16 Feb, 2026 Reviewer # 2 agreed at journal 03 Feb, 2026 Reviewer # 1 agreed at journal 30 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 26 Jan, 2026 Unknown event 26 Jan, 2026 Editor assigned by journal 25 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8694593","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":582616676,"identity":"d2b78f9d-1dac-47fa-a4b4-06906baaf9b1","order_by":0,"name":"Allison Shapiro","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8334-974X","institution":"University of Colorado Anschutz","correspondingAuthor":true,"prefix":"","firstName":"Allison","middleName":"","lastName":"Shapiro","suffix":""},{"id":582616677,"identity":"86082158-6858-4bde-b816-5ec1a03d6ff2","order_by":1,"name":"Meghan Pauley","email":"","orcid":"","institution":"University of Colorado Anschutz","correspondingAuthor":false,"prefix":"","firstName":"Meghan","middleName":"","lastName":"Pauley","suffix":""},{"id":582616678,"identity":"0b7297bd-5bf0-4e88-96e3-20f63a09125b","order_by":2,"name":"Jaime M. 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Solid lines represent participant data included in the current analysis. Dotted lines represent participant data not included in the current analysis.\u003c/p\u003e","description":"","filename":"Fig1consort.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8694593/v1/3d7f75563dfd40c7c3c82f7e.jpg"},{"id":101660294,"identity":"6902207b-8ddf-40b5-a221-50fe12a94784","added_by":"auto","created_at":"2026-02-02 10:42:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1610855,"visible":true,"origin":"","legend":"\u003cp\u003eHypothalamic connectivity in adolescents with severe obesity compared to adolescents with health weight during fasting (\u003cstrong\u003eA\u003c/strong\u003e) and during a fed state (post 75-gram glucose oral bolus) (\u003cstrong\u003eB\u003c/strong\u003e). Blue clusters indicate lower functional connectivity with the hypothalamus and red clusters indicate higher functional connectivity with the hypothalamus. R=right; L=left; MNI=Montreal Neurologic Institute standard coordinates.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8694593/v1/6a82a6723269c3a6d6aa9aff.jpg"},{"id":101660299,"identity":"2c9de718-a79d-4b72-91aa-3b6694252058","added_by":"auto","created_at":"2026-02-02 10:43:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1411463,"visible":true,"origin":"","legend":"\u003cp\u003eWhole brain connectivity in adolescents with severe obesity compared to adolescents with health weight during fasting (\u003cstrong\u003eA\u003c/strong\u003e) and during a fed state (post 75-gram glucose oral bolus) (\u003cstrong\u003eB\u003c/strong\u003e). Blue clusters indicate lower functional connectivity with the hypothalamus and red clusters indicate higher functional connectivity with the hypothalamus. R=right; L=left; MNI=Montreal Neurologic Institute standard coordinates.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8694593/v1/b131ba1713cadbc144c7b5e6.jpg"},{"id":101753045,"identity":"1c102c88-5b1d-49ad-9e52-1c03bedbab1b","added_by":"auto","created_at":"2026-02-03 10:39:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4084568,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8694593/v1/a76091d3-26d3-44bd-a8de-e2fb1d530ee7.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Altered Hypothalamic functional connectivity in adolescents with severe obesity","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThrough substantial preclinical work and a growing number of studies in humans, the brain has been increasingly implicated in the etiology of obesity(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Indeed, enough evidence from human neuroimaging studies has amassed for neurobiological frameworks of obesity to emerge. Multiple frameworks have been proposed, with the most predominant being: 1) the reward surfeit model of obesity, which posits that people with obesity have a heightened reward response to appetizing foods, thus leading to overconsumption of such foods in an effort to achieve the desired reward; and 2) the inhibitory control deficit theory of obesity, proposing that people with obesity experience dysregulation of inhibitory control related to food stimuli, and therefore demonstrate eating disinhibition (reviewed in (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)). Taken together, these theories of human obesity \u0026ndash; and by extension, much of the evidence underlying them (reviewed in (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)) \u0026ndash; center on dysregulated non-homeostatic neural systems that contribute to overconsumption.\u003c/p\u003e \u003cp\u003eA large body of work in animals has demonstrated that eating behavior within the context of obesity is driven by both non-homeostatic and homeostatic (e.g. hypothalamic appetite/satiety signaling, etc.) neural systems. The non-homeostatic and homeostatic systems underlying eating behavior do not function as isolated units but instead are integrated circuits that comprise multiple communicating brain regions. The hypothalamus is the key homeostatic regulator of appetite control,(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and together with non-homeostatic systems that underly cognitive control,(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) salience,(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and reward(\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) comprises a circuit of functionally connected structures that influence eating behaviors (reviewed in (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)).\u003c/p\u003e \u003cp\u003eAdults and youth with obesity show altered neuronal activity in non-homeostatic brain regions in response to viewing and tasting highly palatable foods,(\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) with the degree of altered neuronal activity related to overeating.(\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Hypothalamic dysfunction has been observed in adults(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and youth(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) with obesity, where the hypothalamic response to a caloric stimulus \u0026ndash; glucose or a liquid meal \u0026ndash; is attenuated, relative to healthy weight counterparts (reviewed in (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)). In adults with obesity, functional connectivity between the hypothalamus and non-homeostatic brain networks has been shown to also be altered,(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) suggesting dual-system disruption of the non-homeostatic and homeostatic eating behavior circuitry. In adolescents with obesity, however, altered hypothalamic communication, or connectivity, with non-homeostatic brain regions and networks is understudied, leaving a critical gap in our understanding of the neural contributors to obesity development and persistence in youth, now affects approximately 20% of American adolescents(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), with severe obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;99th percentile for age and sex) being the most rapidly growing weight category in youth(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the modern landscape of obesity treatment, powerful, neuromodulating pharmacotherapies such as glucagon-like peptide-1 receptor agonists (GLP-1RA) \u0026ndash; shown to alter both non-homeostatic response to food (e.g., reward) and homeostatic control of eating behaviors via appetite suppression in adults, and metabolic bariatric surgery, also shown to reverse hypothalamic dysfunction(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) \u0026ndash; are now available for use in pediatrics, and recommended for adolescents with severe obesity.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) Importantly, adolescence is a uniquely dynamic developmental period during which both homeostatic (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and non-homeostatic (e.g., reward and cognitive control(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)) neural systems are undergoing rapid change, making them potentially sensitive to neuromodulating interventions. Studies investigating the underlying non-homeostatic and homeostatic neurobiology of severe obesity in adolescents are, therefore, critically needed to build a comprehensive understanding of the baseline neural circuitry within which these available interventions may exert a neuromodulating effect.\u003c/p\u003e \u003cp\u003eThe primary objective of the current study was to examine functional connectivity of the hypothalamus, as a primary homeostatic structure, in adolescents with severe obesity, relative to their peers with healthy weight. We hypothesized that connectivity between the hypothalamus and non-homeostatic brain regions underlying reward, salience, and executive control would be weaker in adolescents with severe obesity, compared to adolescents with healthy weight. We further explored the effect of severe obesity in adolescence on whole brain functional connectivity, setting no \u003cem\u003ea priori\u003c/em\u003e hypotheses, but expecting that adolescents with severe obesity would demonstrate different whole brain connectivity, again compared to their peers with healthy weight.\u003c/p\u003e"},{"header":"MATERIALS/SUBJECTS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and Participants\u003c/h2\u003e \u003cp\u003eData for the current analysis were collected as part of the Food and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eADO\u003c/span\u003elescent \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eB\u003c/span\u003erain (ADOB) Study, a single-blinded randomized cross-over study (NCT04208256) investigating the response of the adolescent brain to energy loads and their relationship to eating behaviors. The ADOB study recruited male and female adolescents, 13\u0026ndash;18 years-old, with severe obesity (SO; body mass index [BMI] \u0026gt; 99th %ile for age and sex) or with healthy weight (HW; BMI\u0026thinsp;\u0026lt;\u0026thinsp;85th and \u0026ge;\u0026thinsp;10th %ile for age and sex) from the Children\u0026rsquo;s Hospital Colorado Lifestyle Medicine Clinic and the surrounding communities and high schools.\u003c/p\u003e \u003cp\u003eProspective participants were excluded from enrollment if they met any of the following criteria: pre-diabetes defined as a hemoglobin A1c\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;39 mmol/mol (5.7%) or physician\u0026rsquo;s diagnosis of diabetes; diagnosis of anorexia nervosa or bulimia nervosa; prescribed anti-psychotic medications (not including anti-depressant or anti-anxiety medications) or medications for weight loss or appetite suppression (e.g., phentermine, GLP-1RA); diagnosis of Chron\u0026rsquo;s or celiac disease or a serious food allergy (e.g., nuts); diagnosis of autism or Down\u0026rsquo;s syndrome, or any severe developmental disorder (excluding ADHD, dyslexia, or having an individual development plan in school); diagnosis of genetically-linked obesity or glioma in the hypothalamus or pituitary; a non-MRI safe device or metal in the body; claustrophobia. The ADOB study enrolled a total of 64 adolescents (SO\u0026thinsp;=\u0026thinsp;34, HW\u0026thinsp;=\u0026thinsp;30) between 2020 and 2024. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for study consort diagram.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ADOB protocol was approved by the Colorado Multiple Institutional Review Board (#19-1171). All participants 18 years of age provided written informed consent, with parents or guardians and participants providing written consent and assent, respectively, for those younger than 18 years of age.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eEligible participants were invited to complete three research visits at the University of Colorado Anschutz. Research visits included two imaging visits and one visit during which participants completed observed, laboratory-based eating behavior assessments. Participants completed demographic and health questionnaires. Participants also completed the Pubertal Development Scale (PDS), a validated self-report questionnaire for pubertal staging in male and female adolescents(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Five pubertal stage categories were derived from the PDS scores including pre-pubertal (male PDS score\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;3; female PDS score\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;2), early pubertal (male PDS score 4\u0026ndash;5; female PDS score 2\u0026ndash;3), mid-pubertal (male PDS score 6\u0026ndash;8; female PDS score 4\u0026ndash;6), late pubertal (male PDS score 9\u0026ndash;11; female PDS score 7), and post-pubertal (male PDS score\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;12; female PDS score\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;8).\u003c/p\u003e \u003cp\u003eAt enrollment, participants were randomized to receive an energy stimulus, equivalent to the fed state, in the form of an uncarbonated 300 ml orange-flavored 75-gram glucose drink (1 255.8 kJ [300 kcal]; Glucola\u0026trade;) at their first imaging visit, or a 300 ml energy neutral stimulus in the form of an uncarbonated orange-flavored aspartame sweetened drink mixture (Diet Orange Crush\u0026trade;). Whichever stimulus was not received at the first imaging visit was received at the second imaging visit (cross-over). In female participants, menstrual cycle was documented from self-report of date of last period and duration, and all visits completed during the luteal phase of their menstrual cycle to reduce potential variability in outcomes due to cycle-dependent fluctuations in sex hormones.\u003c/p\u003e \u003cp\u003eAll scanning activities were conducted between 7 a.m. and 12 p.m. after an overnight fast at the University of Colorado Brain Imaging Center. Images were acquired on a Siemens 3T Skyra scanner (Siemens, Erlangen, Germany) with a 32-channel head coil and multi-slice acquisition (acceleration factor [SMS]\u0026thinsp;=\u0026thinsp;3). Resting-state whole brain functional magnetic resonance imaging (rs-fMRI) data were acquired using the Blood Oxygen Level Dependent (BOLD) signal (TR\u0026thinsp;=\u0026thinsp;1 s, TE\u0026thinsp;=\u0026thinsp;35 ms, matrix 96 x 96, voxel size 3mm\u003csup\u003e3\u003c/sup\u003e). rs-fMRI scans were acquired in a restricted field of view (slices\u0026thinsp;=\u0026thinsp;27) and by angling the field of view 15 degrees cephalic from the corpus callosum line with the most inferior slice placed just above the pituitary to reduce signal dropout in the hypothalamus from the sinus cavities. A T1-weighted anatomical scan (3D-MPRAGE, 192 slices, TR\u0026thinsp;=\u0026thinsp;2 s, TE\u0026thinsp;=\u0026thinsp;2.06 ms, matrix 256 x 256, voxel size 0.9mm\u003csup\u003e3\u003c/sup\u003e) was also collected. The participant\u0026rsquo;s head was stabilized within the coil using foam padding, and participants were instructed to keep their eyes closed and allow their mind to wander without falling asleep. During each imaging visit, a fasting rs-fMRI scan (600 volumes [~\u0026thinsp;10 minutes]) was completed first, after which participants were slid out from the scanner bore and, with minimal body shifting, instructed to drink the entire volume of the given stimulus within a 5-minute period. The post-stimulus rs-fMRI scan (1 200 volumes [~\u0026thinsp;20 minutes]) immediately followed the participant finishing the stimulus drink.\u003c/p\u003e \u003cp\u003eAll enrolled participants (N\u0026thinsp;=\u0026thinsp;64) completed at least one of the imaging visits. Per the primary aim of the study, as described in the protocol (NCT04208256), only data from the fed state imaging visit (fasting\u0026thinsp;+\u0026thinsp;post-stimulus with 75g glucose during) were used in the current analysis. Thus, participants were included if they completed the imaging visit with the 75-gram glucose stimulus (n\u0026thinsp;=\u0026thinsp;59). From the 59 participants with fed state imaging data, 1 SO participant was excluded due to an anatomical anomaly, and 2 participants (1 SO, 1 HW) were excluded due to imaging quality control issues (\u0026gt;\u0026thinsp;10% of volumes displaying translation \u0026gt;1mm or rotation\u0026thinsp;\u0026gt;\u0026thinsp;3 degrees). Thus, the analytic sample size for the current analysis was 56 adolescents (SO\u0026thinsp;=\u0026thinsp;30, HW\u0026thinsp;=\u0026thinsp;26; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003efMRI Preprocessing and Analyses\u003c/h3\u003e\n\u003cp\u003eAll BOLD images were preprocessed in FSL using a standard pipeline including initial brain extraction via the Brain Extraction Tool (BET) and motion correction and 6-parameter estimation via the Flexible Linear Registration Tool (FLIRT). Spatial normalization was completed with the Flexible Non-Linear Registration Tool (FNIRT) to register BOLD and T1-weighted images to age-specific templates in Montreal Neurological Institute (MNI) standard space. Specifically, each participant was registered to the age-specific template closest to their age at the imaging visits (e.g., participant age\u0026thinsp;=\u0026thinsp;15.4 y/o, registered to the 15.0 y/o template). Spatial smoothing with an 8mm full width at half max smoothing kernel was applied to the final registered image.\u003c/p\u003e \u003cp\u003eConnectivity analyses were completed using the CONN Toolbox in Matlab vR2024a. Preprocessed scans were denoised using a standard denoising pipeline including regressing out the WM timeseries (5 CompCor noise components), CSF timeseries (5 CompCor noise components), movement regressors and their first order derivatives (12 components), and linear trends within each functional run, followed by bandpass frequency filtering of the BOLD timeseries (0.008 Hz and 0.09 Hz).\u003c/p\u003e\n\u003ch3\u003eHypothalamic Functional Connectivity\u003c/h3\u003e\n\u003cp\u003eThe hypothalamus ROI was defined by manual segmentation of the age-specific adolescent template(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) representing the median age of the full study sample (15.0 y/o). A seed-to-voxel analysis was run, in which the hypothalamus was set as the seed, and all voxels in the brain included as targets.\u003c/p\u003e \u003cp\u003eFirst-level, within-participant, seed-based and ROI-based connectivity maps (SBC) were estimated to characterize the patterns of functional connectivity between the hypothalamus and each voxel of the brain in the baseline/fasting and corresponding post-glucose load scan (2 conditions). Functional connectivity strength was estimated via bivariate regression coefficients from a weighted general linear model (GLM), defined separately for each seed(hypothalamus)-voxel pair, modeling the association between their BOLD signal timeseries.\u003c/p\u003e \u003cp\u003eA multivariate GLM was used in second-level (group) analyses to test, 1) the association between obesity status (SO vs. HW) and fasting hypothalamic connectivity, and 2) the association between obesity status and change in hypothalamic functional connectivity from fasting to fed states. All models were adjusted for sex (female vs. male) and household income (\u0026lt;\u003cspan\u003e$\u003c/span\u003e100 000 vs. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u003cspan\u003e$\u003c/span\u003e100 000), as the SO and HW groups differed by these characteristics. Although previous studies have used self-identified race and ethnicity as covariates in models of brain-related outcomes, there is no biological reason to suspect different brain function outcomes by self-identified race and ethnicity. Thus, following the guidance of the American Academy of Pediatrics,(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) we did not adjust models for self-identified race and ethnicity.\u003c/p\u003e \u003cp\u003eIn seed-to-voxel models, a separate GLM was estimated for each individual voxel, setting first-level connectivity measures at this voxel as the dependent variable (one independent sample per subject and one measurement per condition), and obesity status as the independent variable. Multivariate parametric statistics with random-effects across subjects and sample covariance estimation across multiple measurements were applied. Cluster-level inferences were based on parametric statistics from Gaussian Random Field theory. Results were thresholded using a combination of a cluster-forming p\u0026thinsp;\u0026lt;\u0026thinsp;0.005 voxel-level threshold, and a familywise error corrected p-FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 cluster-size threshold.\u003c/p\u003e\n\u003ch3\u003eUntargeted Whole Brain Functional Connectivity \u003c/h3\u003e\n\u003cp\u003eTo explore the association between severe obesity in adolescence and whole brain connectivity, a voxel-to-voxel analysis was run with no \u003cem\u003ea priori\u003c/em\u003e ROIs or seeds defined. First-level voxel-based connectivity maps were estimated to characterize the patterns of functional connectivity between each voxel in the brain. This was completed for both the baseline/fasting and post-glucose load scan. Following the same analytic approach as the above SBC analysis, in each participant, functional connectivity strength was estimated via bivariate regression coefficients from a weighted GLM, defined separately for each voxel-voxel pair, modeling the association between their BOLD signal timeseries. Multivariate GLM was again run, adjusted for sex (female vs. male) and household income (\u0026lt;\u003cspan\u003e$\u003c/span\u003e100 000 vs. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u003cspan\u003e$\u003c/span\u003e100 000) to test, 1) the association between obesity status (SO vs. HW) and baseline/fasting whole brain connectivity, and 2) the association between obesity status and change in whole brain functional connectivity from baseline/fasting to post-glucose load. Given the exploratory nature of this analysis, we applied a two-sided test, and results were again thresholded using a combination of a cluster-forming p\u0026thinsp;\u0026lt;\u0026thinsp;0.005 voxel-level threshold, and a familywise corrected p-FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 cluster-size threshold.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eParticipants were on average 15 y/o [SD, 1.5], 55% had severe obesity, 52% were female sex at birth, and predominantly non-Hispanic White (65%). The SO and HW groups differed significantly by sex, race and ethnicity, and household income (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all, respectively). Specifically, the SO group had a larger proportion of adolescents with male sex at birth (63%), adolescents who self-identified as Hispanic (27%) or non-Hispanic Black (10%), and adolescents from households with an annual income less than \u003cspan\u003e$\u003c/span\u003e100 000 (50%). By design, adolescents with SO had significantly higher BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared to adolescents with healthy weight. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for participant characteristics reported by obesity status.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of ADOB analytic sample by obesity status (N\u0026thinsp;=\u0026thinsp;56).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHW\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.6 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.9 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.7 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePubertal Stage \u0026ndash; Female\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pubertal, no menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly pubertal, no menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMid-pubertal, no menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate pubertal, menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-pubertal, menarche\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePubertal Stage \u0026ndash; Male\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pubertal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly pubertal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMid-pubertal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate pubertal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-pubertal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace or ethnicity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic multiple race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent education, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school degree/GED and less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or technical/vocational school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or graduate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u003cspan\u003e$\u003c/span\u003e50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e50,000-100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eADOB\u0026thinsp;=\u0026thinsp;Food and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eADO\u003c/span\u003elescent \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eB\u003c/span\u003erain (ADOB) Study\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e1: p-values for comparison between SO and HW derived by Fisher\u0026rsquo;s exact test for categorical variables and Student's t-test for continuous variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e2: Pubertal staging was self-reported via the Pubertal Development Scale\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eHypothalamic Functional Connectivity\u003c/h3\u003e\n\u003cp\u003eOverall, independent of sex and age, hypothalamic functional connectivity during both the fasted and fed states was significantly different in adolescents with severe obesity, compared to their healthy weight counterparts (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, in the fasted state, hypothalamic connectivity to bilateral voxel clusters in the cerebellum and left middle occipital gyrus was significantly weaker among adolescents with severe obesity, compared to those with healthy weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Compared to the healthy weight adolescents, those with severe obesity also showed significantly stronger hypothalamic connectivity to clusters in the right postcentral and supramarginal gyri during fasting.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegion names of significant voxel clusters found in seed-to-voxel hypothalamic functional connectivity and whole brain functional connectivity under fasting and fed (75g glucose) states in adolescents with SO vs. HW.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion(s) Name\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeak Voxel (MNI: x, y, z)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKE\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Uncorrected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epFDR-Corrected\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003el. cerebellum (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-42,-68,-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. cerebellum (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42,-68,-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. postcentral gyrus, r. supramarginal gyrus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60,-14,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003el. middle occipital gyrus (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-28,-102,-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eFed vs. fasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003el. middle frontal gyrus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-18,36,14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. middle frontal gyrus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,26,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. caudate (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,-6,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003el. superior frontal gyrus (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10,62,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. putamen (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,12,-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e1: l. indicates the cluster was found in the left hemisphere; r. indicates the cluster was found in the right hemisphere; (+) indicates stronger connectivity within the cluster region, (-) indicates weaker connectivity within the cluster region.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e2: KE (cluster size) for identified cluster\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter glucose ingestion in the fed state, adolescents with severe obesity showed a significant increase in hypothalamic connectivity to voxel clusters in the bilateral middle frontal gyri and right putamen (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), relative to the fasted state, and compared to their healthy weight counterparts. Further, in adolescents with severe obesity, hypothalamic connectivity to clusters in the right caudate and left superior frontal gyrus significantly weakened in the fed state, compared to the adolescents with healthy weight.\u003c/p\u003e\n\u003ch3\u003eUntargeted Whole Brain Functional Connectivity\u003c/h3\u003e\n\u003cp\u003eAcross all voxels in the brain during fasting, adolescents with severe obesity had significantly stronger whole brain functional connectivity with large voxel clusters exclusively within the right hemisphere that included the superior temporal and fusiform gyri, parahippocampal gyrus, and hippocampus, and in the right inferior frontal gyrus and lingual gyrus (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), compared to those with healthy weight. In response to the glucose drink, relative to fasting, adolescents with severe obesity recruited left hemisphere structures such that, compared to those with healthy weight, they showed significantly increased whole brain functional connectivity with the bilateral parahippocampal gyri and hippocampus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), as well as the right putamen, and fusiform and superior temporal gyri.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegion names of significant voxel clusters found in whole brain voxel-to-voxel functional connectivity under fasting and fed (75g glucose) states in adolescents with SO vs. HW.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion(s) Name\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeak Voxel (MNI: x, y, z)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKE\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Uncorrected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epFDR-Corrected\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. superior temporal gyrus, r. fusiform gyrus, r. hippocampus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44,-14,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. fusiform gyrus, r. parahippocampal gyrus, r. hippocampus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40,-48,-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. inferior frontal gyrus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32,22,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. lingual gyrus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,-64,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFed vs. fasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. parahippocampal gyrus, r. fusiform gyrus, r. hippocampus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,-60,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003el. hippocampus, l. parahippocampal gyrus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-30,-30,-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. putamen (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er. superior temporal gyrus (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52,-12,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e1: l. indicates the cluster was found in the left hemisphere; r. indicates the cluster was found in the right hemisphere; (+) indicates stronger connectivity within the cluster region, (-) indicates weaker connectivity within the cluster region. 2: KE (cluster size) for identified cluster.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe found that adolescents with severe obesity had significantly altered functional communication between the hypothalamus, a central homeostatic driver of appetite control, and other key homeostatic and non-homeostatic regions of the brain regions that are known to contribute to eating behaviors. To date, few published studies have investigated hypothalamic function or connectivity in children or adolescents with obesity, with most studies involving adults.(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) Compared to the limited studies in children and youth, however, our results are broadly consistent.\u003c/p\u003e \u003cp\u003eIn the fasted state, we observed hypoconnectivity between the hypothalamus and cerebellum among adolescents with severe obesity, relative to their healthy weight peers. Work over the past decade has revealed that the cerebellum, while traditionally thought to be primarily a motor control area, is also involved in eating behavior, where it contributes as an integration hub for food-related homeostatic and non-homeostatic (e.g., reward, affect) processes.(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) For example, in children without obesity, two recent studies have shown that loss of control eating, a form of eating disinhibition, is associated with stronger neuronal activation in the bilateral cerebellum in response to visual food cues that varied in portion size and energy density.(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) While these studies were cross-sectional in design and did not include functional connectivity analyses, their results suggest that altered cerebellar responsiveness to food stimuli in late childhood may contribute to heightened risk for obesity via eating disinhibition.\u003c/p\u003e \u003cp\u003eAmong adolescents with overweight or obesity (BMI \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;85th % ile), relative to adolescents with healthy weight, Mart\u0026iacute;n-P\u0026eacute;rez et al. (2018) found weaker resting state functional connectivity between the lateral hypothalamus and the right cerebellum, which is consistent with our fasted results.(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) Importantly, however, Mart\u0026iacute;n-P\u0026eacute;rez et al. conducted scanning after participants had consumed the main meal of the day, and thus, participants were assumed to be in a sated state during scanning. Despite clear methodological differences, the consistency between our results and those of Mart\u0026iacute;n-P\u0026eacute;rez et al. across energy states suggests that obesity in adolescence is related to energy state-independent disruption in signal integration between the brain\u0026rsquo;s primary homeostatic structures.\u003c/p\u003e \u003cp\u003eDuring the fed state, induced by oral glucose, a variable pattern of hypothalamic-corticostriatal connectivity emerged. Specifically, among adolescents with severe obesity, we observed stronger hypothalamic connectivity with regions implicated in reward-seeking behaviors (putamen(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)), and weaker hypothalamic connectivity with regions broadly involved in cognitive control and goal-directed behavior (superior frontal gyrus(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and caudate(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)). These observed connectivity patterns partially align with the theory of heightened incentive salience of food cues among people with obesity, whereby cognitive control processes are disrupted or weakened and reward responsiveness and salience valuation are enhanced in response to visual, olfactory, or taste food cues (reviewed in(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)). We also observed stronger hypothalamic connectivity with the middle frontal gyrus (MFG) \u0026ndash; a structure implicated in inhibitory control(\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). While these results may appear to relate to the inhibitory control deficit theory of obesity and its supporting evidence, which shows lower \u003cem\u003eactivation\u003c/em\u003e of inhibitory control brain regions in response to food in people with obesity (reviewed in (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e)), it remains unclear whether increased hypothalamus-to-MFG connectivity is indicative of an attenuated or compensatory inhibitory response to the glucose stimulus in the current study. Further work using response inhibition tasks, such as the go/no-go task, is needed to make such a distinction. The middle frontal gyrus is also implicated in working memory, thus, alternatively, the observed increased connectivity with the hypothalamus may represent attention allocation to glucose and/or sweet taste. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAltogether, our hypothalamic connectivity results provide evidence of possible aberrant communication in, or integration of signaling across major homeostatic hubs and cognitive control and reward processes in adolescents with severe obesity, relative to their peers with healthy weight. In highlighting the potential significant role of disrupted homeostatic signaling in the neural circuitry present in obesity, our results suggest that in addition to reward processes that receive the majority of attention in models of pediatric obesity and obesogenic behaviors, it is likely important to also consider effects of homeostatic mechanisms that have been examined extensively in animal models of obesity (see (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) for an historical review). For example, preclinical models generated the roadmap of hypothalamic involvement in the pathogenesis of diet-induced obesity, specifically characterizing lateral hypothalamic nuclei and their functions in appetite regulation and energy homeostasis (reviewed in (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)), and demonstrating the mechanisms of neuropeptide signaling in the control of eating behaviors (reviewed in (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)). Additional research is needed to replicate our results and continue to understand involvement of homeostatic neural systems involvement in adolescent obesity. Moreover, it is critical for future research in the neural underpinnings of adolescent obesity to include investigation of homeostatic brain structures in addition to non-homeostatic circuitry.\u003c/p\u003e \u003cp\u003eIn untargeted, whole brain analyses, functional connectivity differences across non-homeostatic structures predominated. We found state-independent increases in connectivity across regions underlying response inhibition (e.g., inferior frontal gyrus(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)), reward (e.g., putamen(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)), episodic and semantic memory (e.g., hippocampus(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e)), and food perception (e.g., lingual gyrus(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e)). These results may indicate sweet taste-elicited attentional bias(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) (\u0026ldquo;liking\u0026rdquo; response) and heightened incentive salience(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) (\u0026ldquo;wanting\u0026rdquo; response) in the adolescents with severe obesity. Thus, aligning with the current non-homeostatic theoretical models of obesity and confirming, in part, the presence of disrupted non-homeostatic neural processes in adolescents with severe obesity.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eOur results add novel insight into the potential homeostatic neural underpinnings of severe obesity in adolescents with a robust, physiologically informed study design that adds strength to our findings. Specifically, the imaging protocol included glucose ingestion as a stimulus for targeted engagement of the hypothalamus and an extended post-stimulus scan time (20 minutes) to improve BOLD signal detection and reduce imaging noise.(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) Of note, however, several design limitations remain. First and foremost, this study was cross-sectional and focused in adolescents who already had severe obesity, thus, we cannot infer whether or how the observed hypothalamic connectivity patterns contribute to development of severe obesity. Second, the hypothalamus responds to dietary macronutrients other than glucose, such as free fatty acids (FFA), which may elicit a different hypothalamic connectivity pattern when orally ingested. Thus, interpretation of our results is limited to hypothalamic response to a large glucose bolus, equivalent to drinking two 12 oz cans of regular soda. Third, given the oral route for glucose ingestion in our design, we cannot decipher whether the hypothalamic connectivity patterns observed were due to direct fuel sensing in the hypothalamus via circulating blood glucose levels or peripheral signaling to the hypothalamus via vagal nerve afferent pathways from the gut. Finally, we tested undirected synchronizations of the BOLD signal in our functional connectivity analyses, which do not provide information about the direction of connectivity between brain regions. This limits the interpretation of our results, such that we do not know whether, in response to a fed state, the hypothalamus was driving the signal to the observed functionally connected brain regions, or vice versa. Future work is therefore needed to investigate the directionality of functional communication between homeostatic and non-homeostatic neural systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results broadly suggest possible disruption of the communication between homeostatic and non-homeostatic neural systems in adolescents with severe obesity. Considering dysfunction in both homeostatic and non-homeostatic systems as a component of the severe obesity phenotype may help to better inform our approach to obesity treatment in youth. Indeed, the American Academy of Pediatrics recently revised their clinical recommendations for pediatric obesity treatment to include use of anti-obesity medications and surgical options as first-line therapies alongside lifestyle interventions.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) With the introduction of GLP-1RAs and bariatric surgery, both of which are shown to change hypothalamic function and salience and reward circuitry in adults with obesity(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), the treatment landscape has itself expanded to include potent homeostatic and non-homeostatic neuromodulating approaches in obesity treatment. Thus, a dual systems approach to treating disrupted non-homeostatic \u003cem\u003eand\u003c/em\u003e homeostatic neural connections alongside lifestyle therapy in adolescent obesity is now possible. However, further work in adolescents with severe obesity is needed to replicate our findings and to inform future clinical trials of neuromodulating treatments and brain function in youth with obesity to better understand the effect of such therapies have on modulating non-homeostatic and homeostatic brain function.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eThis work was funded primarily by the National Institute of Diabetes and Digestive and Kidney Diseases (K01DK120562; PI, Shapiro). Additional financial support was provided by pilot grant funding through the Colorado Clinical and Translational Research Institute (NIH/NCATS Colorado CTSA Grant Number UM1 TR004399) and the U.S. Department of Veterans Affairs (IK6CX002178; Tregellas). Contents are the authors\u0026rsquo; sole responsibility and do not necessarily represent official NIH views.\u003c/p\u003e\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e \u003cp\u003eALBS conceived and designed the work that led to the submission, acquired data and completed all analyses and drafted the manuscript; MEP, JM, LH, NS, SLJ, KJN, KK, BR, MAC, and JRT all played an important role in interpreting the results, revising the manuscript, and approved the final version All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarkus A. Neurobiology of obesity. Nat Neurosci. 2005;8(5):551.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStice E, Burger K. Neural vulnerability factors for obesity. 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Longer scans boost prediction and cut costs in brain-wide association studies. Nature. 2025;644(8077):731\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark JS, Kim KS, Choi HJ. Glucagon-Like Peptide-1 and Hypothalamic Regulation of Satiation: Cognitive and Neural Insights from Human and Animal Studies. Diabetes Metab J. 2025;49(3):333\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8694593/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8694593/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground/Objectives: \u003c/em\u003eNeurobiological frameworks of obesity in youth have focused largely on non-homeostatic systems (reward, salience, executive control), while the homeostatic system—particularly the hypothalamus—is comparatively understudied. A clearer picture of how these systems interact in adolescents with severe obesity is needed to inform treatment. This study sought to test whether adolescents with severe obesity exhibit altered hypothalamic functional connectivity, relative to healthy-weight peers, across fasting and fed states.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubjects/Methods: \u003c/em\u003eWe analyzed data from the Food and Adolescent Brain Study, a single-blinded randomized cross-over trial (NCT04208256) of 13–18-year-old adolescents with severe obesity (SO; body mass index [BMI] \u0026gt;99\u003csup\u003eth\u003c/sup\u003e %ile; n=30; mean [SD] age 14.6 years [1.5]) and with healthy weight (HW; BMI \u0026lt;85\u003csup\u003eth\u003c/sup\u003e %ile; n=26; 15.5 years [1.6]).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInterventions/Methods:\u003c/em\u003e Participants completed resting-state functional magnetic resonance scans during fasting and after ingesting a 75-gram glucose drink (1 255.8 kJ [300 kcal]) to induce a fed state. Multivariate general linear models were run in seed-to-voxel analyses to estimate functional connectivity, setting the hypothalamus as the seed region. All models were adjusted for age and sex, with significance determined via cluster-forming voxel-level p\u0026lt;0.005 and false discovery rate-corrected cluster-level p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e: In adolescents with SO,\u003cem\u003e \u003c/em\u003eduring fasting, hypothalamic connectivity in adolescents with SO was weaker to the bilateral cerebellum and left(L) middle occipital gyrus, and stronger to the right(R) postcentral/supramarginal gyri, compared to the HW group. During the fed state, hypothalamic connectivity increased to the bilateral middle frontal gyri and R putamen and decreased to the R caudate and L superior frontal gyrus in adolescents with SO, relative to the HW group.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion: \u003c/em\u003eSevere obesity in adolescence is associated with altered communication between homeostatic (hypothalamus) and non-homeostatic brain structures, evident across fasting and fed states. Findings underscore the need to incorporate homeostatic circuitry into pediatric obesity frameworks.\u003c/p\u003e","manuscriptTitle":"Altered Hypothalamic functional connectivity in adolescents with severe obesity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 10:40:35","doi":"10.21203/rs.3.rs-8694593/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-02-25T14:55:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-17T20:41:32+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-16T22:05:23+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-03T17:37:17+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-01-30T18:22:55+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-01-29T17:34:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-27T14:26:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Obesity","date":"2026-01-26T15:25:24+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2026-01-26T15:17:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-25T19:49:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e9038b0c-638f-479d-b86a-55d51d0c2f97","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61988159,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity"},{"id":61988160,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"}],"tags":[],"updatedAt":"2026-04-23T01:45:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 10:40:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8694593","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8694593","identity":"rs-8694593","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}