Resting-state connectivity within the brain's reward system predicts weight loss and correlates with leptin

Functional alterations in overweight/obesity: focusing on the reward and executive control network

Overweight (OW) and obesity (OB) have become prevalent issues in the global public health arena. Serving as a prominent risk factor for various chronic diseases, overweight/obesity not only poses serious threats to people's physical and mental health but also imposes significant medical and economic burdens on society as a whole. In recent years, there has been a growing focus on basic scientific research dedicated to seeking the neural evidence underlying overweight/obesity, aiming to elucidate its causes and effects by revealing functional alterations in brain networks. Among them, dysfunction in the reward network (RN) and executive control network (ECN) during both resting state and task conditions is considered pivotal in neuroscience research on overweight/obesity. Their aberrations contribute to explaining why persons with overweight/obesity exhibit heightened sensitivity to food rewards and eating disinhibition. This review centers on the reward and executive control network by analyzing and organizing the resting-state and task-based fMRI studies of functional brain network alterations in overweight/obesity. Building upon this foundation, the authors further summarize a reward-inhibition dual-system model, with a view to establishing a theoretical framework for future exploration in this field.

Body mass index-dependent shifts along large-scale gradients in human cortical organization explain dietary regulatory success

Making healthy dietary choices is essential for keeping weight within a normal range. Yet many people struggle with dietary self-control despite good intentions. What distinguishes neural processing in those who succeed or fail to implement healthy eating goals? Does this vary by weight status? To examine these questions, we utilized an analytical framework of gradients that characterize systematic spatial patterns of large-scale neural activity, which have the advantage of considering the entire suite of processes subserving self-control and potential regulatory tactics at the whole-brain level. Using an established laboratory food task capturing brain responses in natural and regulatory conditions (N = 123), we demonstrate that regulatory changes of dietary brain states in the gradient space predict individual differences in dietary success. Better regulators required smaller shifts in brain states to achieve larger goal-consistent changes in dietary behaviors, pointing toward efficient network organization. This pattern was most pronounced in individuals with lower weight status (low-BMI, body mass index) but absent in high-BMI individuals. Consistent with prior work, regulatory goals increased activity in frontoparietal brain circuits. However, this shift in brain states alone did not predict variance in dietary success. Instead, regulatory success emerged from combined changes along multiple gradients, showcasing the interplay of different large-scale brain networks subserving dietary control and possible regulatory strategies. Our results provide insights into how the brain might solve the problem of dietary control: Dietary success may be easier for people who adopt modes of large-scale brain activation that do not require significant reconfigurations across contexts and goals.

Dynamic Changes in Local Brain Connectivity and Activity: A Longitudinal Study in Adolescent Anorexia Nervosa

BACKGROUND

Resting-state functional connectivity analysis has been used to study disruptions in neural circuitries underlying eating disorder symptoms. Research has shown resting-state functional connectivity to be altered during the acute phase of anorexia nervosa (AN), but little is known about the biological mechanisms underlying neural changes associated with weight restoration. The goal of the current study was to investigate longitudinal changes in regional homogeneity (ReHo) among neighboring voxels, degree centrality (DC) (a voxelwise whole brain correlation coefficient), voxel-mirrored homotopic connectivity (VMHC) (measuring the synchronization between hemispheres), and the fractional amplitude of low-frequency fluctuations associated with weight gain during AN treatment.

METHODS

Resting-state functional connectivity data were acquired and analyzed from a sample of 174 female volunteers: 87 underweight patients with AN that were scanned before treatment and again after at least 12% body mass index increase, as well as 87 age-matched healthy control participants.

RESULTS

Longitudinal changes in ReHo, DC, VMHC, and the fractional amplitude of low-frequency fluctuations were observed in most regions identified to differ between patients with AN before treatment and healthy control participants. However, the degree of normalization varied for each parameter, ranging from 9% of all clusters in DC to 66% in VMHC. Longitudinal changes in ReHo and VMHC showed a linear association weight gain.

CONCLUSIONS

Resting-state functional magnetic resonance imaging measures, including ReHo, DC, VMHC, and the fractional amplitude of low-frequency fluctuations, show varying degrees of recovery after short-term weight restoration. Although only some of these changes were related to weight gain, our results provide an overall positive message, suggesting that weight restoration is associated with changes in functional brain measures that point toward normalization.

Novel Insights into the Physiology of Nutrient Sensing and Gut-Brain Communication in Surgical and Experimental Obesity Therapy

Despite standardized surgical technique and peri-operative care, metabolic outcomes of bariatric surgery are not uniform. Adaptive changes in brain function may play a crucial role in achieving optimal postbariatric weight loss. This review follows the anatomic-physiologic structure of the postbariatric nutrient-gut-brain communication chain through its key stations and provides a concise summary of recent findings in bariatric physiology, with a special focus on the composition of the intestinal milieu, intestinal nutrient sensing, vagal nerve-mediated gastrointestinal satiation signals, circulating hormones and nutrients, as well as descending neural signals from the forebrain. The results of interventional studies using brain or vagal nerve stimulation to induce weight loss are also summarized. Ultimately, suggestions are made for future diagnostic and therapeutic research for the treatment of obesity.

An fMRI-Based Brain Marker of Individual Differences in Delay Discounting

Individual differences in delay discounting-how much we discount future compared to immediate rewards-are associated with general life outcomes, psychopathology, and obesity. Here, we use machine learning on fMRI activity during an intertemporal choice task to develop a functional brain marker of these individual differences in human adults. Training and cross-validating the marker in one dataset (Study 1, N = 110 male adults) resulted in a significant prediction-outcome correlation (r = 0.49), generalized to predict individual differences in a completely independent dataset (Study 2: N = 145 male and female adults, r = 0.45), and predicted discounting several weeks later. Out-of-sample responses of the functional brain marker, but not discounting behavior itself, differed significantly between overweight and lean individuals in both studies, and predicted fasting-state blood levels of insulin, c-peptide, and leptin in Study 1. Significant predictive weights of the marker were found in cingulate, insula, and frontoparietal areas, among others, suggesting an interplay among regions associated with valuation, conflict processing, and cognitive control. This new functional brain marker is a step toward a generalizable brain model of individual differences in delay discounting. Future studies can evaluate it as a potential transdiagnostic marker of altered decision-making in different clinical and developmental populations.SIGNIFICANCE STATEMENT People differ substantially in how much they prefer smaller sooner rewards or larger later rewards such as spending money now versus saving it for retirement. These individual differences are generally stable over time and have been related to differences in mental and bodily health. What is their neurobiological basis? We applied machine learning to brain-imaging data to identify a novel brain activity pattern that accurately predicts how much people prefer sooner versus later rewards, and which can be used as a new brain-based measure of intertemporal decision-making in future studies. The resulting functional brain marker also predicts overweight and metabolism-related blood markers, providing new insight into the possible links between metabolism and the cognitive and brain processes involved in intertemporal decision-making.

Perigenual anterior cingulate cortex and its structural covariance as predictors for future body fat gain in young adults

OBJECTIVE

This study aimed to examine whether baseline gray matter (GM) volume and structural covariance patterns could predict body fat gain over 1 to 2 years in a relatively large sample.

METHODS

Voxel-based morphometry (VBM) analysis was applied to examine the association between baseline GM volume and body fat gain in 502 participants over 1 to 2 years. Furthermore, this study tested whether the structural covariances between the regions identified as seeds from VBM analysis and the rest of the brain were associated with future body fat gain.

RESULTS

A significant positive association was observed between baseline GM volume in the perigenual anterior cingulate cortex (pgACC) and body fat gain over 1 to 2 years. Furthermore, relative to those with lower future body fat gain, pgACC covaried more extensively with the middle frontal gyrus, middle temporal gyrus, inferior temporal gyrus, and cerebellum in participants with higher future body fat gain.

CONCLUSIONS

Using VBM and structural covariance network analysis, the current study revealed that higher GM volume of pgACC and its increased structural covariances with specific brain regions were associated with future weight gain, which may guide the development of more effective prevention and treatment interventions for obesity.

A neuromarker for drug and food craving distinguishes drug users from non-users

Craving is a core feature of substance use disorders. It is a strong predictor of substance use and relapse and is linked to overeating, gambling, and other maladaptive behaviors. Craving is measured via self-report, which is limited by introspective access and sociocultural contexts. Neurobiological markers of craving are both needed and lacking, and it remains unclear whether craving for drugs and food involve similar mechanisms. Across three functional magnetic resonance imaging studies (n = 99), we used machine learning to identify a cross-validated neuromarker that predicts self-reported intensity of cue-induced drug and food craving (P < 0.0002). This pattern, which we term the Neurobiological Craving Signature (NCS), includes ventromedial prefrontal and cingulate cortices, ventral striatum, temporal/parietal association areas, mediodorsal thalamus and cerebellum. Importantly, NCS responses to drug versus food cues discriminate drug users versus non-users with 82% accuracy. The NCS is also modulated by a self-regulation strategy. Transfer between separate neuromarkers for drug and food craving suggests shared neurobiological mechanisms. Future studies can assess the discriminant and convergent validity of the NCS and test whether it responds to clinical interventions and predicts long-term clinical outcomes.

Brain functional connectivity differences between responders and non-responders to sleeve gastrectomy

PURPOSE

To compare resting-state functional connectivity (RSFC) of obese patients responders or non-responders to sleeve gastrectomy (SG) with a group of obese patients with no past medical history of metabolic or bariatric surgery.

METHODS

MR images were acquired at 1.5 Tesla. Resting-state fMRI data were analyzed with statistical significance threshold set at p < 0.05, family-wise error (FWE) corrected.

RESULTS

Sixty-two subjects were enrolled: 20 controls (age range 25-64; 14 females), 24 responders (excess weight loss > 50%; age range 23-68; 17 females), and 18 non-responders to sleeve gastrectomy (SG) (excess weight loss < 50%; age range 23-67; 13 females). About within-network RSFC, responders showed significantly lower RSFC with respect to both controls and non-responders in the default mode and frontoparietal networks, positively correlating with psychological scores. Non-responders showed significantly higher (p < 0.05, family-wise error (few) corrected) RSFC in regions of the lateral visual network as compared to controls. Regarding between-network RSFC, responders showed significantly higher anti-correlation between executive control and salience networks (p < 0.05, FWE corrected) with respect to both controls and non-responders. Significant positive correlation (Spearman rho = 0.48, p = 0.0012) was found between % of excess weight loss and executive control-salience network RSFC.

CONCLUSION

There are differences in brain functional connectivity in either responders or non-responders patients to SG. The present results offer new insights into the neural correlates of outcome in patients who undergo SG and expand knowledge about neural mechanisms which may be related to surgical response.

Associations of leptin and corticostriatal connectivity in bipolar disorder

Bipolar disorder (BD) and metabolic disturbance represent a chronic state of low-grade inflammation and corticostriatal circuitry alterations. Herein, we aimed to investigate whether plasma leptin, an adipokine that plays a key role in the interplay of metabolism and inflammation, is associated with corticostriatal connectivity in patients with BD. Twenty-eight BD I patients, 36 BD II patients and 66 healthy controls were enrolled and completed the Hamilton Depression Rating Scale, the Young Mania Rating Scale, and the Recent Life Change Questionnaire. Fasting plasma leptin and C-reactive protein (CRP) levels were measured, and corticostriatal connectivity was examined using functional magnetic resonance imaging (fMRI). The relationships between leptin, CRP and body mass index (BMI) identified in the controls and BD II patients were absent in the BD I patients. We did not find a significant group difference in the leptin level; nevertheless, the negative correlation between leptin level and corticostriatal connectivity (ventrolateral prefrontal cortex and inferior temporal gyrus) observed in the healthy controls was absent in the BD patients. The disproportionate increase in leptin level with increasing BMI in BD indicated a potential inflammatory role of white adipose tissue in BD. Furthermore, higher CRP levels in BD I patients might induce leptin resistance. Collectively, our results implied vulnerability to inflammatory and metabolic diseases in patients with BD, especially BD I.

Computational approaches to predicting treatment response to obesity using neuroimaging

Obesity is a worldwide disease associated with multiple severe adverse consequences and comorbid conditions. While an increased body weight is the defining feature in obesity, etiologies, clinical phenotypes and treatment responses vary between patients. These variations can be observed within individual treatment options which comprise lifestyle interventions, pharmacological treatment, and bariatric surgery. Bariatric surgery can be regarded as the most effective treatment method. However, long-term weight regain is comparably frequent even for this treatment and its application is not without risk. A prognostic tool that would help predict the effectivity of the individual treatment methods in the long term would be essential in a personalized medicine approach. In line with this objective, an increasing number of studies have combined neuroimaging and computational modeling to predict treatment outcome in obesity. In our review, we begin by outlining the central nervous mechanisms measured with neuroimaging in these studies. The mechanisms are primarily related to reward-processing and include "incentive salience" and psychobehavioral control. We then present the diverse neuroimaging methods and computational prediction techniques applied. The studies included in this review provide consistent support for the importance of incentive salience and psychobehavioral control for treatment outcome in obesity. Nevertheless, further studies comprising larger sample sizes and rigorous validation processes are necessary to answer the question of whether or not the approach is sufficiently accurate for clinical real-world application.

Effects of bariatric surgery on functional connectivity of the reward and default mode network: A pre-registered analysis

Obesity imposes serious health risks and involves alterations in resting‐state functional connectivity of brain networks involved in eating behavior. Bariatric surgery is an effective treatment, but its effects on functional connectivity are still under debate. In this pre‐registered study, we aimed to determine the effects of bariatric surgery on major resting‐state brain networks (reward and default mode network) in a longitudinal controlled design. Thirty‐three bariatric surgery patients and 15 obese waiting‐list control patients underwent magnetic resonance imaging at baseline, after 6 and 12 months. We conducted a pre‐registered whole‐brain time‐by‐group interaction analysis, and a time‐by‐group interaction analysis on within‐network connectivity. In exploratory analyses, we investigated the effects of weight loss and head motion. Bariatric surgery compared to waiting did not significantly affect functional connectivity of the reward network and the default mode network (FWE‐corrected p > .05), neither whole‐brain nor within‐network. In exploratory analyses, surgery‐related BMI decrease (FWE‐corrected p = .041) and higher average head motion (FWE‐corrected p = .021) resulted in significantly stronger connectivity of the reward network with medial posterior frontal regions. This pre‐registered well‐controlled study did not support a strong effect of bariatric surgery, compared to waiting, on major resting‐state brain networks after 6 months. Exploratory analyses indicated that head motion might have confounded the effects. Data pooling and more rigorous control of within‐scanner head motion during data acquisition are needed to substantiate effects of bariatric surgery on brain organization.