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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

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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.

Keywords:

ACC, anterior cingulate cortex ANOVA, analysis of variance Anatomical white-matter connectivity BMI, body mass index CT, cortical thickness Classification algorithm DTI, diffusion tensor imaging DWI, diffusion-weighted MRIs FA, flip angle FACT, fiber assignment by continuous tracking FDR, false-discovery rate FOV, field of view GLM, general linear model GMV, gray matter volume HAD, hospital anxiety and Depression Scale HC, healthy control MC, mean curvature Morphological gray-matter Multivariate analysis NPV, negative predictive value OFG, orbitofrontal gyrus Obesity Overweight PPC, posterior parietal cortex PPV, positive predictive value Reward network SA, surface area SPSS, statistical package for the social sciences TE, echo time TR, repetition time VIP, variable importance in projection VTA, ventral tegmental area aMCC, anterior mid cingulate cortex dlPFC, dorsolateral prefrontal cortex sPLS-DA, sparse partial least squares for discrimination Analysis sgACC, subgenual anterior cingulate cortex vmPFC, ventromedial prefrontal cortex

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Last Updated: Apr 16, 2026

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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.