Patterns of brain structural connectivity differentiate normal weight from overweight subjects.
Arpana Gupta1, Emeran A Mayer2, Claudia P Sanmiguel1
1Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress, Ingestive Behavior and Obesity Program (IBOP), UCLA, Los Angeles, CA, USA ; David Geffen School of Medicine, UCLA, Los Angeles, CA, USA ; Division of Digestive Diseases, UCLA, Los Angeles, CA, USA.
Neuroimage. Clinical
|March 5, 2015
Summary
Overweight individuals exhibit distinct brain gray and white matter changes, particularly in reward and executive control networks. This study accurately distinguishes overweight from normal weight individuals using brain imaging, offering targets for obesity interventions.
Area of Science:
- Neuroimaging
- Neuroscience
- Obesity Research
Background:
- Altered hedonic aspects of eating are linked to overweight and obesity.
- Brain imaging reveals structural, functional, and neurochemical changes in the reward network of individuals with higher BMI.
Purpose of the Study:
- To use multivariate pattern analysis to differentiate normal weight and overweight individuals based on brain gray and white matter measurements.
Main Methods:
- Structural and diffusion tensor imaging (DTI) data from 120 healthy subjects (63 overweight) were analyzed.
- Brain regions were segmented, and fiber density was measured using deterministic tractography.
- Multivariate pattern analysis was applied to distinguish between overweight and normal weight groups.
Main Results:
- White-matter analysis achieved 97% accuracy, identifying altered connectivity in reward, executive control, and emotional networks in overweight individuals.
- Gray-matter analysis achieved 69% accuracy, showing reduced gray matter in reward and executive control networks in overweight individuals.
- Specific patterns of increased and decreased fiber density were observed between different brain regions in overweight versus normal weight individuals.
Conclusions:
- Increased BMI (overweight) is associated with significant changes in brain gray matter and white-matter fiber density.
- White-matter connectivity patterns in reward and associated networks can accurately identify overweight individuals, suggesting targets for future research and drug development for abnormal eating behaviors.
Keywords:
ACC, anterior cingulate cortexANOVA, analysis of varianceAnatomical white-matter connectivityBMI, body mass indexCT, cortical thicknessClassification algorithmDTI, diffusion tensor imagingDWI, diffusion-weighted MRIsFA, flip angleFACT, fiber assignment by continuous trackingFDR, false-discovery rateFOV, field of viewGLM, general linear modelGMV, gray matter volumeHAD, hospital anxiety and Depression ScaleHC, healthy controlMC, mean curvatureMorphological gray-matterMultivariate analysisNPV, negative predictive valueOFG, orbitofrontal gyrusObesityOverweightPPC, posterior parietal cortexPPV, positive predictive valueReward networkSA, surface areaSPSS, statistical package for the social sciencesTE, echo timeTR, repetition timeVIP, variable importance in projectionVTA, ventral tegmental areaaMCC, anterior mid cingulate cortexdlPFC, dorsolateral prefrontal cortexsPLS-DA, sparse partial least squares for discrimination AnalysissgACC, subgenual anterior cingulate cortexvmPFC, ventromedial prefrontal cortex
