Initial body fat gain is related to brain volume changes in adolescents: A repeated-measures voxel-based morphometry study

Biobehavioral susceptibility for obesity in childhood: Behavioral, genetic and neuroimaging studies of appetite

Modern food environments are conducive to overeating and weight gain, but not everyone develops obesity. One reason for this may be that individuals differ in appetitive characteristics, or traits, that manifest early in life and go on to influence their behavioral susceptibility to gain and maintain excess weight. Classic studies showing that eating behavior in children can be measured by behavioral paradigms such as tests of caloric compensation and eating in the absence of hunger inspired the development and validation of psychometric instruments to assess appetitive characteristics in children and infants. A large body of evidence now suggests that food approach traits increase obesity risk, while food avoidant traits, such as satiety responsiveness, decrease obesity risk. Twin studies and genetic association studies have demonstrated that appetitive characteristics are heritable, consistent with a biological etiology. However, family environment factors are also influential, with mounting evidence suggesting that genetic and environmental risk factors interact and correlate with consequences for child eating behavior and weight. Further, neuroimaging studies are revealing that individual differences in responses to visual food cues, as well as to small tastes and larger amounts of food, across a number of brain regions involved in reward/motivation, cognitive control and other functions, may contribute to individual variation in appetitive behavior. Growing evidence also suggests that variation on psychometric measures of appetite is associated with regional differences in brain structure, and differential patterns of resting state functional connectivity. Large prospective studies beginning in infancy promise to enrich our understanding of neural and other biological underpinnings of appetite and obesity development in early life, and how the interplay between genetic and environmental factors affects appetitive systems. The biobehavioral susceptibility model of obesity development and maintenance outlined in this narrative review has implications for prevention and treatment of obesity in childhood.

Brain functional and structural magnetic resonance imaging of obesity and weight loss interventions

Obesity has tripled over the past 40 years to become a major public health issue, as it is linked with increased mortality and elevated risk for various physical and neuropsychiatric illnesses. Accumulating evidence from neuroimaging studies suggests that obesity negatively affects brain function and structure, especially within fronto-mesolimbic circuitry. Obese individuals show abnormal neural responses to food cues, taste and smell, resting-state activity and functional connectivity, and cognitive tasks including decision-making, inhibitory-control, learning/memory, and attention. In addition, obesity is associated with altered cortical morphometry, a lowered gray/white matter volume, and impaired white matter integrity. Various interventions and treatments including bariatric surgery, the most effective treatment for obesity in clinical practice, as well as dietary, exercise, pharmacological, and neuromodulation interventions such as transcranial direct current stimulation, transcranial magnetic stimulation and neurofeedback have been employed and achieved promising outcomes. These interventions and treatments appear to normalize hyper- and hypoactivations of brain regions involved with reward processing, food-intake control, and cognitive function, and also promote recovery of brain structural abnormalities. This paper provides a comprehensive literature review of the recent neuroimaging advances on the underlying neural mechanisms of both obesity and interventions, in the hope of guiding development of novel and effective treatments.

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.

Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study

BACKGROUND

Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learning and structural magnetic resonance imaging (MRI) to study the impact of psychotic illness and obesity on brain ageing/neuroprogression shortly after illness onset.

METHODS

We acquired 2 prospective MRI scans on average 1.61 years apart in 183 FEP and 155 control individuals. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants and calculated BrainAGE by subtracting chronological from the estimated brain age.

RESULTS

Individuals with FEP had a higher initial BrainAGE than controls (3.39 ± 6.36 vs 1.72 ± 5.56 years; β = 1.68, t(336) = 2.59, P = .01), but similar annual rates of brain ageing over time (1.28 ± 2.40 vs 1.07±1.74 estimated years/actual year; t(333) = 0.93, P = .18). Across both cohorts, greater baseline body mass index (BMI) predicted faster brain ageing (β = 0.08, t(333) = 2.59, P = .01). For each additional BMI point, the brain aged by an additional month per year. Worsening of functioning over time (Global Assessment of Functioning; β = -0.04, t(164) = -2.48, P = .01) and increases especially in negative symptoms on the Positive and Negative Syndrome Scale (β = 0.11, t(175) = 3.11, P = .002) were associated with faster brain ageing in FEP.

CONCLUSIONS

Brain alterations in psychosis are manifest already during the first episode and over time get worse in those with worsening clinical outcomes or higher baseline BMI. As baseline BMI predicted faster brain ageing, obesity may represent a modifiable risk factor in FEP that is linked with psychiatric outcomes via effects on brain structure.

Structural Brain Changes Associated with Overweight and Obesity

Obesity is a global health problem with a broad set of comorbidities, such as malnutrition, metabolic syndrome, diabetes, systemic hypertension, heart failure, and kidney failure. This review describes recent findings of neuroimaging and two studies of cell density regarding the roles of overnutrition-induced hypothalamic inflammation in neurodegeneration. These studies provided consistent evidence of smaller cortical thickness or reduction in the gray matter volume in people with overweight and obesity; however, the investigated brain regions varied across the studies. In general, bilateral frontal and temporal areas, basal nuclei, and cerebellum are more commonly involved. Mechanisms of volume reduction are unknown, and neuroinflammation caused by obesity is likely to induce neuronal loss. Adipocytes, macrophages of the adipose tissue, and gut dysbiosis in overweight and obese individuals result in the secretion of the cytokines and chemokines that cross the blood-brain barrier and may stimulate microglia, which in turn also release proinflammatory cytokines. This leads to chronic low-grade neuroinflammation and may be an important factor for apoptotic signaling and neuronal death. Additionally, significant microangiopathy observed in rat models may be another important mechanism of induction of apoptosis. Neuroinflammation in neurodegenerative diseases (such as Alzheimer's and Parkinson's diseases) may be similar to that in metabolic diseases induced by malnutrition. Poor cognitive performance, mainly in executive functions, in individuals with obesity is also discussed. This review highlights the neuroinflammatory and neurodegenerative mechanisms linked to obesity and emphasizes the importance of developing effective prevention and treatment intervention strategies for overweight and obese individuals.

Interaction Effect of Sex and Body Mass Index on Gray Matter Volume

Objective: Few studies have investigated sex differences in brain structure associated with body mass index (BMI), and the related findings are inconsistent. In this study, we aimed to investigate the effect of sex × BMI interactions on gray matter volume (GMV), and to determine the implications of any structural differences.

Methods: The final sample comprised 653 participants (449 women) who were assessed using voxel-based morphology analysis of T1-weighted magnetic resonance images. We used the voxel-based morphometry (VBM) to build a multiple regression model to explore the association between BMI and GMV, and used analysis of variance (ANOVA) to explore the BMI × sex interaction on GMV. A subset of 410 participants (291 women) underwent whole brain resting-state functional connectivity (rsFC) analysis to investigate sex differences in the seed (interaction) region. The cluster with a significant effect in the previous ANOVA analysis was used as a seed.

Results: A significant BMI × sex interaction was observed in the left anterior cingulate cortex (ACC), while GMV was negatively correlated with BMI in men but not in women. The rsFC between the left ACC and the caudate was lower in men than in women. Within the entire sample, the insula, caudate, and medial frontal cortex activities were negatively correlated with BMI while the cerebellum and postcentral gyrus activities were positively correlated with BMI.

Conclusions: Our findings address the interaction effect of BMI and sex on GM alterations. We found that the GMV in men seemed to be more likely to change with BMI than women, and the left ACC may be the reason for the increase in BMI of men, but not women.

Impulsivity and body fat accumulation are linked to cortical and subcortical brain volumes among adolescents and adults

Obesity is associated not only with metabolic and physical health conditions, but with individual variations in cognition and brain health. This study examined the association between body fat (an index of excess weight severity), impulsivity (a vulnerability factor for obesity), and brain structure among adolescents and adults across the body mass index (BMI) spectrum. We used 3D T1 weighted anatomic magnetic resonance imaging scans to map the association between body fat and volumes in regions associated with obesity and impulsivity. Participants were 127 individuals (BMI: 18-40 kg/m2; M = 25.69 ± 5.15), aged 14 to 45 years (M = 24.79 ± 9.60; female = 64). Body fat was measured with bioelectric impendence technology, while impulsivity was measured with the UPPS-P Impulsive Behaviour Scale. Results showed that higher body fat was associated with larger cerebellar white matter, medial orbitofrontal cortex (OFC), and nucleus accumbens volume, although the latter finding was specific to adolescents. The relationship between body fat and medial OFC volume was moderated by impulsivity. Elevated impulsivity was also associated with smaller amygdala and larger frontal pole volumes. Our findings link vulnerability and severity markers of obesity with neuroanatomical measures of frontal, limbic and cerebellar structures, and unravel specific links between body fat and striatal volume in adolescence.

Invasive and Non-invasive Stimulation of the Obese Human Brain

Accumulating evidence suggests that non-invasive and invasive brain stimulation may reduce food craving and calorie consumption rendering these techniques potential treatment options for obesity. Non-invasive transcranial direct current stimulation (tDCS) or repetitive transcranial magnet stimulation (rTMS) are used to modulate activity in superficially located executive control regions, such as the dorsolateral prefrontal cortex (DLPFC). Modulation of the DLPFC’s activity may alter executive functioning and food reward processing in interconnected dopamine-rich regions such as the striatum or orbitofrontal cortex. Modulation of reward processing can also be achieved by invasive deep brain stimulation (DBS) targeting the nucleus accumbens. Another target for DBS is the lateral hypothalamic area potentially leading to improved energy expenditure. To date, available evidence is, however, restricted to few exceptional cases of morbid obesity. The vagal nerve plays a crucial role in signaling the homeostatic demand to the brain. Invasive or non-invasive vagal nerve stimulation (VNS) is thus assumed to reduce appetite, rendering VNS another possible treatment option for obesity. Based on currently available evidence, the U.S. Food and Drug Administration recently approved VNS for the treatment of obesity. This review summarizes scientific evidence regarding these techniques’ efficacy in modulating food craving and calorie intake. It is time for large controlled clinical trials that are necessary to translate currently available research discoveries into patient care.

Volumetric gray matter measures of amygdala and accumbens in childhood overweight/obesity

Objectives

Neuroimaging data suggest that pediatric overweight and obesity are associated with morphological alterations in gray matter (GM) brain structures, but previous studies using mainly voxel-based morphometry (VBM) showed inconsistent results. Here, we aimed to examine the relationship between youth obesity and the volume of predefined reward system structures using magnetic resonance (MR) volumetry. We also aimed to complement volumetry with VBM-style analysis.

Methods

Fifty-one Caucasian young subjects (32 females; mean age: 13.8±1.9, range: 10.2–16.5 years) were included. Subjects were selected from a subsample of the I.Family study examined in the Hungarian center. A T₁-weighted 1 mm³ isotropic resolution image was acquired. Age- and sex-standardized body mass index (zBMI) was assessed at the day of MRI and ~1.89 years (mean±SD: 689±188 days) before the examination. Obesity related GM alterations were investigated using MR volumetry in five predefined brain structures presumed to play crucial roles in body weight regulation (hippocampus, amygdala, accumbens, caudate, putamen), as well as whole-brain and regional VBM.

Results

The volumes of accumbens and amygdala showed significant positive correlations with zBMI, while their GM densities were inversely related to zBMI. Voxel-based GM mass also showed significant negative correlation with zBMI when investigated in the predefined amygdala region, but this relationship was mediated by GM density.

Conclusions

Overweight/obesity related morphometric brain differences already seem to be present in children/adolescents. Our work highlights the disparity between volume and VBM-derived measures and that GM mass (combination of volume and density) is not informative in the context of obesity related volumetric changes. To better characterize the association between childhood obesity and GM morphometry, a combination of volumetric segmentation and VBM methods, as well as future longitudinal studies are necessary. Our results suggest that childhood obesity is associated with enlarged structural volumes, but decreased GM density in the reward system.

Contribution of structural brain phenotypes to the variance in resting energy expenditure in healthy Caucasian subjects

Brain gray (GM) and white matter (WM) volumes are related to weight changes. The impact of structural variations in GM and WM on the variance in resting energy expenditure (REE) and the REE-on-fat-free mass (FFM) association is unknown. The aim of this study was to address this in healthy Caucasian subjects. Cross-sectional data analysis of 493 healthy Caucasian subjects (age range 6–80 years; 3 age groups) was conducted with comprehensive information on FFM, organ and tissue masses, and detailed brain composition as assessed by whole body magnetic resonance imaging and REE (assessed by indirect calorimetry). REE was calculated (REEc) using organ and tissue masses times their specific metabolic rates. FFM was the major determinant of REE (70.6%); individual masses of liver, total brain, and heart explained a further 2.1% of the variance in REE. Replacing total brain with GM and WM did not change the total R2. Nevertheless, GM added more to the variance in REE (5.6%) and corresponding residuals (12.5%) than did total brain. Additionally, up to 12% was explained by age and sex (<2%). There was a systematic bias between REE and REEc with positive values in younger subjects but negative values in older ones. This bias remained after substituting the specific metabolic rate of brain with the specific metabolic rates of GM and WM. In healthy Caucasian subjects, GM and WM contributed to the variance in REE. Detailed brain structures do not explain the bias between REE and REEc.

NEW & NOTEWORTHY Detailed brain composition (gray and white matter) contributed to the variances of resting energy expenditure (REE) and REE-on-fat-free mass residuals. Gray matter explained most of the variances, and for future studies on energy expenditure, brain compartments should be analyzed separately with regard to their different energy needs.

Obesity, dyslipidemia and brain age in first-episode psychosis

INTRODUCTION

Obesity and dyslipidemia may negatively affect brain health and are frequent medical comorbidities of schizophrenia and related disorders. Despite the high burden of metabolic disorders, little is known about their effects on brain structure in psychosis. We investigated, whether obesity or dyslipidemia contributed to brain alterations in first-episode psychosis (FEP).

METHODS

120 participants with FEP, who were undergoing their first psychiatric hospitalization, had <24 months of untreated psychosis and were 18-35 years old and 114 controls within the same age range participated in the study. We acquired 3T brain structural MRI, fasting lipids and body mass index. We used machine learning trained on an independent sample of 504 controls to estimate the individual brain age of study participants and calculated the BrainAGE score by subtracting the chronological from the estimated brain age.

RESULTS

In a multiple regression model, the diagnosis of FEP (B = 1.15, SE B = 0.31, p < 0.001) and obesity/overweight (B = 0.92, SE B = 0.35, p = 0.008) were each additively associated with BrainAGE scores (R² = 0.22, F(3, 230) = 21.92, p < 0.001). BrainAGE scores were highest in participants with FEP and obesity/overweight (3.83 years, 95%CI = 2.35-5.31) and lowest in normal weight controls (-0.27 years, 95%CI = -1.22-0.69). LDL-cholesterol, HDL-cholesterol or triglycerides were not associated with BrainAGE scores.

CONCLUSIONS

Overweight/obesity may be an independent risk factor for diffuse brain alterations manifesting as advanced brain age already early in the course of psychosis. These findings raise the possibility that targeting metabolic health and intervening already at the level of overweight/obesity could slow brain ageing in FEP.