Sex Commonalities and Differences in Obesity-Related Alterations in Intrinsic Brain Activity and Connectivity.

OBJECTIVE: This study aimed to characterize obesity-related sex differences in the intrinsic activity and connectivity of the brain's reward networks. METHODS: Eighty-six women (n = 43) and men (n = 43) completed a 10-minute resting functional magnetic resonance imaging scan. Sex differences and commonalities in BMI-related frequency power distribution and reward seed-based connectivity were investigated by using partial least squares analysis. RESULTS: For whole-brain activity in both men and women, increased BMI was associated with increased slow-5 activity in the left globus pallidus (GP) and substantia nigra. In women only, increased BMI was associated with increased slow-4 activity in the right GP and bilateral putamen. For seed-based connectivity in women, increased BMI was associated with reduced slow-5 connectivity between the left GP and putamen and the emotion and cortical regulation regions, but in men, increased BMI was associated with increased connectivity with the medial frontal cortex. In both men and women, increased BMI was associated with increased slow-4 connectivity between the right GP and bilateral putamen and the emotion regulation and sensorimotor-related regions. CONCLUSIONS: The stronger relationship between increased BMI and decreased connectivity of core reward network components with cortical and emotion regulation regions in women may be related to the greater prevalence of emotional eating. The present findings suggest the importance of personalized treatments for obesity that consider the sex of the affected individual.

Morphological brain measures of cortico-limbic inhibition related to resilience.

Resilience is the ability to adequately adapt and respond to homeostatic perturbations. Although resilience has been associated with positive health outcomes, the neuro-biological basis of resilience is poorly understood. The aim of the study was to identify associations between regional brain morphology and trait resilience with a focus on resilience-related morphological differences in brain regions involved in cortico-limbic inhibition. The relationship between resilience and measures of affect were also investigated. Forty-eight healthy subjects completed structural MRI scans. Self-reported resilience was measured using the Connor and Davidson Resilience Scale. Segmentation and regional parcellation of images was performed to yield a total of 165 regions. Gray matter volume (GMV), cortical thickness, surface area, and mean curvature were calculated for each region. Regression models were used to identify associations between morphology of regions belonging to executive control and emotional arousal brain networks and trait resilience (total and subscales) while controlling for age, sex, and total GMV. Correlations were also conducted between resilience scores and affect scores. Significant associations were found between GM changes in hypothesized brain regions (subparietal sulcus, intraparietal sulcus, amygdala, anterior mid cingulate cortex, and subgenual cingulate cortex) and resilience scores. There were significant positive correlations between resilience and positive affect and negative correlations with negative affect. Resilience was associated with brain morphology of regions involved in cognitive and affective processes related to cortico-limbic inhibition. Brain signatures associated with resilience may be a biomarker of vulnerability to disease. © 2016 Wiley Periodicals, Inc.

Patterns of brain structural connectivity differentiate normal weight from overweight subjects.

BACKGROUND: Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. AIM: To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. METHODS: Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. RESULTS: 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69% accuracy in discriminating overweight from normal weight. In both brain signatures regions of the reward, salience, executive control and emotional arousal networks were associated with lower morphological values in overweight individuals compared to normal weight individuals, while the opposite pattern was seen for regions of the somatosensory network. CONCLUSIONS: 1. An increased BMI (i.e., overweight subjects) is associated with distinct changes in gray-matter and fiber density of the brain. 2. Classification algorithms based on white-matter connectivity involving regions of the reward and associated networks can identify specific targets for mechanistic studies and future drug development aimed at abnormal ingestive behavior and in overweight/obesity.