Patterns of brain structural connectivity differentiate normal weight from overweight subjects
Open accessAbstract
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.
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The overweight and obese brain shows similar alterations in reward networks of the brain.
“Previous work has shown that the overweight and obese brain shows similar alterations in reward networks of the brain 41.”
From History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity (2019) · cites this paper as [41]
Individuals who are overweight demonstrate increased centrality between reward network regions and regions of the executive control, emotional regulation and somatosensory networks.
“Previous work has shown that individuals who are overweight demonstrate increased centrality between reward network regions and regions of the executive control, emotional regulation and somatosensory networks 41;”
From History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity (2019) · cites this paper as [41]
Neuroimaging studies have identified some of the brain mechanisms associated with obesity and food addiction, demonstrating alterations in the core reward network (e.g. nucleus accumbens) and the extended reward network (i.e. emotion regulation, executive control, salience and somatosensory networks).
“Neuroimaging studies have identified some of the brain mechanisms associated with obesity and food addiction 39, 40, demonstrating alterations in the core reward network (e.g. nucleus accumbens) and the extended reward network (i.e. emotion regulation, executive control, salience and somatosensory networks) 41, 42, 43.”
From History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity (2019) · cites this paper as [41]
Alterations in reward and emotion regulation regions have been previously demonstrated in individuals with obesity.
“Alterations in reward and emotion regulation regions have been previously demonstrated in individuals with obesity 41, 56, 57.”
From History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity (2019) · cites this paper as [41]
These study results are consistent with previous studies that demonstrated decreased functional activation and anatomical connectivity of somatosensory regions in obesity.
“These study results are consistent with previous studies, which have demonstrated decreased functional activation and anatomical connectivity of somatosensory regions in obesity 40, 41, 53, 56, 57, 98, 99.”
From History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity (2019) · cites this paper as [41]
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Tillisch, K., Mayer, E. A., Gupta, A., Gill, Z., Brazeilles, R., Le Nevé, B., van Hylckama Vlieg, J. E. T., Guyonnet, D., Derrien, M., & Labus, J. S. (2017) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000493 · Cited by 185 · see citing works
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Self-citationSex differences in the influence of body mass index on anatomical architecture of brain networks
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- Authors
- Gupta, A., Mayer, E. A., Sanmiguel, C. P., Van Horn, J. D., Woodworth, D., Ellingson, B. M., Fling, C., Love, A., Tillisch, K., & Labus, J. S.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Labus, Jennifer S.
- ORCID
- Venue
- NeuroImage: Clinical
- Publisher
- Elsevier BV
- Published
- 2015
- Volume
- 7
- Pages
- 506-517
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC4338207
- OpenAlex
- W2090738496
- Cited by
- 79
- References
- 65
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 6 · 2022: 9 · 2021: 7 · 2020: 11 · 2019: 5 · 2018: 7 · 2017: 11 · 2016: 5 · 2015: 5
- FWCI
- 7.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health (R01 DK048351, P50DK64539, R01 AT007137, P30 DK041301, K08 DK071626, R03 DK084169)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Clinical Psychology › Eating Disorders and Behaviors
- Keywords
- overweight, obesity, structural brain connectivity, gray matter morphology, reward network, frontoparietal network, salience network, somatosensory network, ventromedial prefrontal cortex, anterior insula, thalamus, body mass index, hedonic eating, neuroimaging biomarkers, multivariate pattern classification, deterministic tractography
- MeSH terms
- Adult, Algorithms, Brain, Brain Mapping, Feeding Behavior, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Overweight, Diffusion Tensor Imaging
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