Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity - PubMed (original) (raw)

Comparative Study

Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity

Bo-yong Park et al. PLoS One. 2015.

Abstract

Obesity is a medical condition affecting billions of people. Various neuroimaging methods including magnetic resonance imaging (MRI) have been used to obtain information about obesity. We adopted a multi-modal approach combining diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) to incorporate complementary information and thus better investigate the brains of non-healthy weight subjects. The objective of this study was to explore multi-modal neuroimaging and use it to predict a practical clinical score, body mass index (BMI). Connectivity analysis was applied to DTI and rs-fMRI. Significant regions and associated imaging features were identified based on group-wise differences between healthy weight and non-healthy weight subjects. Six DTI-driven connections and 10 rs-fMRI-driven connectivities were identified. DTI-driven connections better reflected group-wise differences than did rs-fMRI-driven connectivity. We predicted BMI values using multi-modal imaging features in a partial least-square regression framework (percent error 15.0%). Our study identified brain regions and imaging features that can adequately explain BMI. We identified potentially good imaging biomarker candidates for obesity-related diseases.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1

Fig 1

(A) Structural (fiber density) connections with significant group-wise differences. (B) Structural (fiber density) connections that showed significant correlations with fMRI. Green dots and lines represent nodes and edges showing the most significant correlation between structural and functional connectivity, respectively.

Fig 2

Fig 2. Correlation between structural and functional connectivity of various regions.

Six connections with significant correlations between structural and functional connections are reported.

Fig 3

Fig 3. Linear regression results between structural and functional features of different regions that best explained BMI.

Fig 4

Fig 4. Comparison of actual and predicted BMI between HW and non-HW subjects.

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