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.


OBJECTIVE
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.
Gail 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.
Arpana
A
Gupta
Gail 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 ; Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, USA.
Emeran A
EA
Mayer
Gail 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.
Claudia P
CP
Sanmiguel
The Institute for Neuroimaging and Informatics, Keck School of Medicine, USC, Los Angeles, CA, USA.
John D
JD
Van Horn
Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress, Ingestive Behavior and Obesity Program (IBOP), UCLA, Los Angeles, CA, USA ; Radiology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
Davis
D
Woodworth
Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress, Ingestive Behavior and Obesity Program (IBOP), UCLA, Los Angeles, CA, USA ; Radiology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
Benjamin M
BM
Ellingson
Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress, Ingestive Behavior and Obesity Program (IBOP), UCLA, Los Angeles, CA, USA.
Connor
C
Fling
Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress, Ingestive Behavior and Obesity Program (IBOP), UCLA, Los Angeles, CA, USA.
Aubrey
A
Love
Gail 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 ; Integrative Medicine, GLA VHA, UCLA, Los Angeles, CA, USA.
Kirsten
K
Tillisch
Gail 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.
Jennifer S
JS
Labus
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D056324:Diffusion Tensor Imaging; D005247:Feeding Behavior / Q000502:physiology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D009434:Neural Pathways / Q000473:pathology*; D050177:Overweight / Q000503:physiopathology*
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