Obesity is associated with reduced orbitofrontal cortex volume: A coordinate-based meta-analysis

Early life factors and structural brain network in children with overweight/obesity: The ActiveBrains project

BACKGROUND

The aims of this study were to investigate the association of early life factors, including birth weight, birth length, and breastfeeding practices, with structural brain networks; and to test whether structural brain networks associated with early life factors were also associated with academic performance in children with overweight/obesity (OW/OB).

METHOD

96 children with OW/OB aged 8-11 years (10.03 ± 1.16) from the ActiveBrains project were included. Early life factors were collected from birth records and reported by parents as weight, height, and months of breastfeeding. T1-weighted images were used to identify structural networks using a non-negative matrix factorization (NNMF) approach. Academic performance was evaluated by the Woodcock-Muñoz standardized test battery.

RESULTS

Birth weight and birth length were associated with seven networks involving the cerebellum, cingulate gyrus, occipital pole, and subcortical structures including hippocampus, caudate nucleus, putamen, pallidum, nucleus accumbens, and amygdala. No associations were found for breastfeeding practices. None of the networks linked to birth weight and birth length were linked to academic performance.

CONCLUSIONS

Birth weight and birth length, but not breastfeeding, were associated with brain structural networks in children with OW/OB. Thus, early life factors are related to brain networks, yet a link with academic performance was not observed.

IMPACT

Birth weight and birth length, but not breastfeeding, were associated with several structural brain networks involving the cerebellum, cingulate gyrus, occipital pole, and subcortical structures including hippocampus, caudate, putamen, pallidum, accumbens and amygdala in children with overweight/obesity, playing a role for a normal brain development. Despite no academic consequences, other behavioral consequences should be investigated. Interventions aimed at improving optimal intrauterine growth and development may be of importance to achieve a healthy brain later in life.

Neural correlates of obesity across the lifespan

Associations between brain and obesity are bidirectional: changes in brain structure and function underpin over-eating, while chronic adiposity leads to brain atrophy. Investigating brain-obesity interactions across the lifespan can help better understand these relationships. This study explores the interaction between obesity and cortical morphometry in children, young adults, adults, and older adults. We also investigate the genetic, neurochemical, and cognitive correlates of the brain-obesity associations. Our findings reveal a pattern of lower cortical thickness in fronto-temporal brain regions associated with obesity across all age cohorts and varying age-dependent patterns in the remaining brain regions. In adults and older adults, obesity correlates with neurochemical changes and expression of inflammatory and mitochondrial genes. In children and older adults, adiposity is associated with modifications in brain regions involved in emotional and attentional processes. Thus, obesity might originate from cognitive changes during early adolescence, leading to neurodegeneration in later life through mitochondrial and inflammatory mechanisms.

Childhood maltreatment influences adult brain structure through its effects on immune, metabolic, and psychosocial factors

Childhood maltreatment (CM) leads to a lifelong susceptibility to mental ill-health which might be reflected by its effects on adult brain structure, perhaps indirectly mediated by its effects on adult metabolic, immune, and psychosocial systems. Indexing these systemic factors via body mass index (BMI), C-reactive protein (CRP), and rates of adult trauma (AT), respectively, we tested three hypotheses: (H1) CM has direct or indirect effects on adult trauma, BMI, and CRP; (H2) adult trauma, BMI, and CRP are all independently related to adult brain structure; and (H3) childhood maltreatment has indirect effects on adult brain structure mediated in parallel by BMI, CRP, and AT. Using path analysis and data from N = 116,887 participants in UK Biobank, we find that CM is related to greater BMI and AT levels, and that these two variables mediate CM's effects on CRP [H1]. Regression analyses on the UKB MRI subsample (N = 21,738) revealed that greater CRP and BMI were both independently related to a spatially convergent pattern of cortical effects (Spearman's ρ = 0.87) characterized by fronto-occipital increases and temporo-parietal reductions in thickness. Subcortically, BMI was associated with greater volume, AT with lower volume and CPR with effects in both directions [H2]. Finally, path models indicated that CM has indirect effects in a subset of brain regions mediated through its direct effects on BMI and AT and indirect effects on CRP [H3]. Results provide evidence that childhood maltreatment can influence brain structure decades after exposure by increasing individual risk toward adult trauma, obesity, and inflammation.

Opposite changes in morphometric similarity of medial reward and lateral non-reward orbitofrontal cortex circuits in obesity

Obesity has a profound impact on metabolic health thereby adversely affecting brain structure and function. However, the majority of previous studies used a single structural index to investigate the link between brain structure and body mass index (BMI), which hinders our understanding of structural covariance between regions in obesity. This study aimed to examine the relationship between macroscale cortical organization and BMI using novel morphometric similarity networks (MSNs). The individual MSNs were first constructed from individual eight multimodal cortical morphometric features between brain regions. Then the relationship between BMI and MSNs within the discovery sample of 434 participants was assessed. The key findings were further validated in an independent sample of 192 participants. We observed that the lateral non-reward orbitofrontal cortex (lOFC) exhibited decoupling (i.e., reduction in integration) in obesity, which was mainly manifested by its decoupling with the cognitive systems (i.e., DMN and FPN) while the medial reward orbitofrontal cortex (mOFC) showed de-differentiation (i.e., decrease in distinctiveness) in obesity, which was mainly represented by its de-differentiation with the cognitive and attention systems (i.e., DMN and VAN). Additionally, the lOFC showed de-differentiation with the visual system in obesity, while the mOFC showed decoupling with the visual system and hyper-coupling with the sensory-motor system in obesity. As an important first step in revealing the role of underlying structural covariance in body mass variability, the present study presents a novel mechanism that underlies the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches.

White adipose tissue distribution and amount are associated with increased white matter connectivity

Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a deeper characterization of body composition is required, especially considering the links between abdominal obesity and metabolic dysfunction. This study aims to enhance our understanding of the relationship between obesity and WM connectivity by directly assessing the amount and distribution of fat tissue. Whole-body magnetic resonance imaging (MRI) was employed to evaluate total adipose tissue (TAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT), while MR liver spectroscopy measured liver fat content in 63 normal-weight, overweight, and obese males. WM connectivity was quantified using microstructure-informed tractography. Connectome-based predictive modeling was used to predict body composition metrics based on WM connectomes. Our analysis revealed a positive dependency between BMI, TAT, SAT, and WM connectivity in brain regions involved in reward processing and appetite regulation, such as the insula, nucleus accumbens, and orbitofrontal cortex. Increased connectivity was also observed in cognitive control and inhibition networks, including the middle frontal gyrus and anterior cingulate cortex. No significant associations were found between WM connectivity and VAT or liver fat. Our findings suggest that altered neural communication between these brain regions may affect cognitive processes, emotional regulation, and reward perception in individuals with obesity, potentially contributing to weight gain. While our study did not identify a link between WM connectivity and VAT or liver fat, further investigation of the role of various fat depots and metabolic factors in brain networks is required to advance obesity prevention and treatment approaches.

Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention

Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.

Neuroprotection in metabolic syndrome by environmental enrichment. A lifespan perspective

Metabolic syndrome (MetS) is defined by the concurrence of different metabolic conditions: obesity, hypertension, dyslipidemia, and hyperglycemia. Its incidence has been increasingly rising over the past decades and has become a global health problem. MetS has deleterious consequences on the central nervous system (CNS) and neurological development. MetS can last several years or be lifelong, affecting the CNS in different ways and treatments can help manage condition, though there is no known cure. The early childhood years are extremely important in neurodevelopment, which extends beyond, encompassing a lifetime. Neuroplastic changes take place all life through - childhood, adolescence, adulthood, and old age - are highly sensitive to environmental input. Environmental factors have an important role in the etiopathogenesis and treatment of MetS, so environmental enrichment (EE) stands as a promising non-invasive therapeutic approach. While the EE paradigm has been designed for animal housing, its principles can be and actually are applied in cognitive, sensory, social, and physical stimulation programs for humans. Here, we briefly review the central milestones in neurodevelopment at each life stage, along with the research studies carried out on how MetS affects neurodevelopment at each life stage and the contributions that EE models can provide to improve health over the lifespan.

Central Regulation of Eating Behaviors in Humans: Evidence from Functional Neuroimaging Studies

Neuroimaging has great potential to provide insight into the neural response to food stimuli. Remarkable advances have been made in understanding the neural activity underlying food perception, not only in normal eating but also in obesity, eating disorders, and disorders of gut-brain interaction in recent decades. In addition to the abnormal brain function in patients with eating disorders compared to healthy controls, new therapies, such as neurofeedback and neurostimulation techniques, have been developed that target the malfunctioning brain regions in patients with eating disorders based on the results of neuroimaging studies. In this review, we present an overview of early and more recent research on the central processing and regulation of eating behavior in healthy and patient populations. In order to better understand the relationship between the gut and the brain as well as the neural mechanisms underlying abnormal ingestive behaviors, we also provide suggestions for future directions to enhance our current methods used in food-related neuroimaging studies.

Brain alterations associated with overweight evaluated by body mass index or body fat index in an elderly population: the PROOF study

Background/objectives

Obesity is a complex health issue in which the brain plays a role yet to be determined, especially in the elderly. Indeed, in the ageing population, the balance between fat and lean mass is different; thus, the co-influence between the brain and obesity may differ between the elderly and younger subjects. Our main goal is thus to explore the relationship between the brain and obesity using two different approaches to measure obesity: body mass index (BMI) and an index centred on fat mass, the body fat index (BFI).

Subjects/methods

Among the 1,011 subjects of the PROOF population, 273 subjects aged 75 years underwent 3D magnetic resonance imaging as well as dual-energy X-ray absorptiometry to assess fat mass. Voxel-based morphometry was used to explore the local differences in brain volume with obesity.

Results

Higher BMI and BFI were associated with higher grey matter (GM) volume in the left cerebellum. Higher BMI and BFI were mainly associated with higher white matter volume in the left and right cerebellum and near the right medial orbital gyrus. Higher BMI was also associated with higher GM volume in the brainstem, whereas higher BFI was associated with higher GM volume in the left middle temporal gyrus. No decrease in white matter was associated with BMI or BFI.

Conclusion

In the elderly, the relationship between the brain and obesity does not depend on the marker of obesity. Supra-tentorial brain structures seem to be slightly associated with obesity, whereas the cerebellum seems to be one of the key structures related to obesity.

Neural structural abnormalities behind altered brain activation in obesity: Evidence from meta-analyses of brain activation and morphometric data

Obesity represents a risk factor for disability with a major bearing on life expectancy. Neuroimaging techniques are contributing to clarify its neurobiological underpinnings. Here, we explored whether structural brain abnormalities might accompany altered brain activations in obesity. We combined and compared data from brain activation studies for food stimuli and the data reported in structural voxel-based morphometry studies. We found that obese individuals have reduced grey matter density and functional activations in the thalamus and midbrain. A functional connectivity analysis based on these two clusters and its quantitative decoding showed that these regions are part of the reward system functional brain network. Moreover, we found specific grey matter hypo-densities in prefrontal cortex for the obese subjects, regions involved in controlled behaviour. These results support theories of obesity that point to reduced bottom-up reward processes (i.e., the Reward Deficit Theory), but also top-down theories postulating a deficit in cognitive control (i.e., the Inhibitory Control Deficit Theory). The same results also warrant a more systematic exploration of obesity whereby the reward of food and the intentional control over consummatory behaviour is manipulated.

Gray Matter Volume Abnormality in Chronic Pain Patients With Depressive Symptoms: A Systemic Review and Meta-Analysis of Voxel-Based Morphometry Studies

Background

Gray matter volume (GMV) alteration in specific brain regions has been widely regarded as one of the most important neuroplasticity features in chronic pain patients with depressive symptoms (CP-D). However, the consistent and significant results were still lacking. Thus, further exploration was suggested to be performed.

Objectives

This study aimed to comprehensively collect the voxel-based morphometry (VBM) studies on GMV alteration between CP-D and healthy controls (HCs). And a systemic review and meta-analysis were made to explore the characteristic brain regions in chronic pain and depression comorbidity.

Methods

Search of PubMed, MEDLINE, Web of Science, and Cochrane Library databases updated to July 13, 2021. The altered GMV between CP-D and HCs in VBM studies was included in this meta-analysis. In total, 18 studies (20 datasets) and 1320 participants (520 patients and 800 HCs) were included. The significant coordinate information (x, y, z) reported in standard space and the effect size (t-value or z-score) were extracted and analyzed by anisotropic effect size-signed differential mapping (AES-SDM) 5.15 software.

Results

According to the main analysis results, CP-D showed significant and consistent increased GMV in the left hippocampus (HIP. L) and decreased GMV in the medial part of the left superior frontal gyrus (SFG. L, BA 10) compared to HCs. Subgroup analysis showed significant decreased GMV in the medial orbital part of SFG.R (BA 10) in neuropathic pain, as well as significant increased GMV in the right parahippocampal gyrus (PHG.R, BA 35), left hippocampus (HIP.L, BA 20), and right middle frontal gyrus (MFG.R) in musculoskeletal pain. Furthermore, meta-regression showed a positive relationship between the decreased GMV in the medial part of SFG.L and the percentage of female patients.

Conclusion

GMV abnormality in specific brain areas (e.g., HIP.L and SFG) was robust and reproducible, which could be significantly involved in this comorbidity disease. The findings in this study may be a valuable reference for future research.

Systematic Review Registration

[www.crd.york.ac.uk/prospero/].

Effects of Obesity, Blood Pressure, and Blood Metabolic Biomarkers on Grey Matter Brain Healthcare Quotient: A Large Cohort Study of a Magnetic Resonance Imaging Brain Screening System in Japan

This study investigated the relationship between grey matter (GM) volume and blood biomarkers, blood pressure, and obesity. We aimed to elucidate lifestyle factors that promote GM volume loss. A total of 1799 participants underwent the brain dock as a medical checkup. Data regarding blood pressure, obesity measurements, and standard blood biomarkers were obtained. Further, brain magnetic resonance imaging (MRI), including high-resolution T1-weighted imaging, was performed. We calculated the grey matter brain healthcare quotient (GM-BHQ), which represents GM volume as a deviation value. After adjusting for confounding variables, multiple regression analysis revealed that body mass index (BMI) (b = −0.28, p < 0.001), gamma-glutamyltransferase (γ-GTP) (b = −0.01, p = 0.16), and fasting blood glucose (b = −0.02, p = 0.049) were significantly correlated with GM-BHQ. Although the current cross-sectional study cannot determine a cause-and-effect relationship, elevated BMI, γ-GTP, and fasting blood glucose could promote GM volume loss.

The Association of Obesity and Cardiorespiratory Fitness in Relation to Cognitive Flexibility: An Event-Related Potential Study

This study investigates an association between obesity and cardiorespiratory fitness concerning their potential effects on cognitive flexibility in young adults from behavioral and neuroelectrical perspectives. Eligible young adults (N = 140, 18-25 years) were assigned into one of four groups, according to their status of obesity (i.e., body mass index) and cardiorespiratory fitness levels (i.e., estimated maximal oxygen uptake), namely, normal weight with high cardiorespiratory fitness (NH), obese with high cardiorespiratory fitness (OH), normal weight with low cardiorespiratory fitness (NL), and obese with low cardiorespiratory fitness (OL). The task-switching test was utilized, and its induced endogenous (P3) and exogenous (N1) event-related potential components were recorded. Concerning behavioral indices, the NH demonstrated superior behavioral performance across global switching and local switching of the task-switching test compared to individuals with lower cardiorespiratory fitness and obesity (i.e., NL, OH, and OL). Additionally, the OH demonstrated better performance than the OL during the heterogeneous condition. For neuroelectrical indices, the NH had larger mean P3 amplitudes during global and local switching than the other three groups. A larger N1 amplitude was also observed in the NH during local switching than in the OH group. The findings suggest that cardiorespiratory fitness has beneficial effects on cognitive flexibility, attentional resource allocation, and sensory evaluation in young adults. Furthermore, our research provided novel evidence showing that cardiorespiratory fitness might potentially alleviate the adverse effects of obesity on cognitive flexibility in young adults.

Altered Brain Structural Reorganization and Hierarchical Integrated Processing in Obesity

The brain receives sensory information about food, evaluates its desirability and value, and responds with approach or withdrawal. The evaluation process of food in the brain with obesity may involve a variety of neurocircuit abnormalities in the integration of internal and external information processing. There is a lack of consistency of the results extant reported for aberrant changes in the brain with obesity that prohibits key brain alterations to be identified. Moreover, most studies focus on the observation of neural plasticity of function or structure, and the evidence for functional and structural correlations in the neuronal plasticity process of obesity is still insufficient. The aims of this article are to explore the key neural structural regions and the hierarchical activity pattern of key structural nodes and evaluate the correlation between changes in functional modulation and eating behavior. Forty-two participants with obesity and 33 normal-weight volunteers were recruited. Gray matter volume (GMV) and Granger causality analysis (GCA) were performed using the DPARSF, CAT12, and DynamicBC toolbox. Compared with the normal weight group, the obesity group exhibited significantly increased GMV in the left parahippocampal gyrus (PG). The obesity group showed decreased causal inflow to the left PG from the left orbitofrontal cortex (OFC), right calcarine, and bilateral supplementary motor area (SMA). Decreased causal outflow to the left OFC, right precuneus, and right SMA from the left PG, as well as increased causal outflow to the left middle occipital gyrus (MOG) were observed in the obesity group. Negative correlations were found between DEBQ-External scores and causal outflow from the left PG to the left OFC, and DEBQ-Restraint scores and causal inflow from the left OFC to the left PG in the obesity group. Positive correlation was found between DEBQ-External scores and causal outflow from the left PG to the left MOG. These results show that the increased GMV in the PG may play an important role in obesity, which may be related to devalued reward system, altered behavioral inhibition, and the disengagement of attentional and visual function for external signals. These findings have important implications for understanding neural mechanisms in obesity and developing individual-tailored strategies for obesity prevention.

Obesity and Attention-Deficit Hyperactivity Disorder

An association between ADHD and obesity has been established throughout the past 20 years via animal model experiments and both correlational and longitudinal studies in humans. However, much remains to be determined regarding causality, developmental course, and effective treatments targeting both conditions. This chapter provides an overview and update on the current state of the science on the relationship between obesity and ADHD; expands the scope of the connection between obesity and ADHD to include behavioral components important to weight regulation - i.e., physical activity, eating behaviors, and sleep; and presents applications of these findings to treatment approaches and future directions.

Gray matter volume alterations in subjects with overweight and obesity: Evidence from a voxel-based meta-analysis

BACKGROUND

Obesity is a multi-systemic disease with complex etiology. And consistent evidence indicated obesity or overweight subjects render brain structure changes. Increasing evidence indicates these subjects have shown widespread structural brain gray matter volume (GMV) changes. However, results from other neuroimaging studies have been inconsistent. Consequently, the question remains whether body mass index (BMI), a gold standard to define obesity/overweight, is associated with brain structural changes.

METHODS

This study will apply an updated meta-analysis of voxel-based GMV studies to compare GMV changes in overweight and obese subjects. Online databases were used to build on relevant studies published before May 2022. The updated Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) explores GMV changes in individuals with overweight and obesity and further examines the correlation between GMV and obesity-related variables, specifically body mass index (BMI).

RESULTS

This research included fourteen studies and provided a whole-brain analysis of GMV distribution in overweight and obese individuals. It revealed lower GMV in brain regions, including the left putamen and right precentral gyrus, in individuals with overweight and obesity compared to lean controls. Further, meta-regression analyses revealed GMV in the left middle occipital gyrus was negatively correlated with the BMI of the whole sample.

CONCLUSION

GMV decreased was reported in reward circuit processing areas and sensorimotor processing areas of individuals with overweight and obesity diagnoses, suggesting an underlying structural basis for reward processing and sensorimotor processing dysregulation in overweight and obese subjects. Our results also suggest that GMV in occipital gyrus, a key region for food visual and gustatory encoding, is negatively associated with BMI. These results provide further evidence for the dysregulated reward circuit in individuals with overweight and obesity.

Body mass index but not genetic risk is longitudinally associated with altered structural brain parameters

Evidence from previous studies suggests that elevated body mass index (BMI) and genetic risk for obesity is associated with reduced brain volume, particularly in areas of reward-related cognition, e.g. the medial prefrontal cortex (AC-MPFC), the orbitofrontal cortex (OFC), the striatum and the thalamus. However, only few studies examined the interplay between these factors in a joint approach. Moreover, previous findings are based on cross-sectional data. We investigated the longitudinal relationship between increased BMI, brain structural magnetic resonance imaging (MRI) parameters and genetic risk scores in a cohort of n = 502 community-dwelling participants from the Study of Health in Pomerania (SHIP) with a mean follow-up-time of 4.9 years. We found that (1) increased BMI values at baseline were associated with decreased brain parameters at follow-up. These effects were particularly pronounced for the OFC and AC-MPFC. (2) The genetic predisposition for BMI had no effect on brain parameters at baseline or follow-up. (3) The interaction between the genetic score for BMI and brain parameters had no effect on BMI at baseline. Finding a significant impact of overweight, but not genetic predisposition for obesity on altered brain structure suggests that metabolic mechanisms may underlie the relationship between obesity and altered brain structure.

Novelty seeking is associated with increased body weight and orbitofrontal grey matter volume reduction

Novelty seeking (NS) has previously been identified as a personality trait that is associated with elevated body mass index (BMI) and obesity. Of note, both obesity and reduced impulse control - a core feature of NS - have previously been associated with grey matter volume (GMV) reductions in the orbitofrontal cortex (OFC). Yet, it remains unknown, if body weight-related grey matter decline in the OFC might be explained by higher levels of NS. To address this question, we studied associations between NS, BMI and brain structure in 355 healthy subjects. Brain images were pre-processed using voxel-based morphometry (VBM). BMI was calculated from self-reported height and weight. The Tridimensional Personality Questionnaire (TPQ) was used to assess NS. NS and BMI were associated positively (r = .137, p = .01) with NS being a significant predictor of BMI (B = 0.172; SE B = 0.05; ß = 0.184; p = 0.001). Significant associations between BMI and GMV specifically in the OFC (x = -44, y = 56, z = -2, t(350) = 4.34, k = 5, p_FWE = 0.011) did not uphold when correcting for NS in the model. In turn, a significant negative association between NS and OFC GMV was found independent of BMI (x = -2, y = 48, z = -10, t(349) = 4.42, k = 88, p_FWE = 0.008). Body mass-related grey matter decrease outside the OFC could not be attributed to NS. Our results suggest that body-weight-related orbitofrontal grey matter reduction can at least partly be linked to higher levels of NS. Given the pivotal role of the OFC in overweight as well as cognitive domains such as impulse inhibition, executive control and reward processing, its association with NS seems to provide a tenable neurobiological correlate for future research.