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

<div><p>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.</p></div

Structural and Functional Brain Connectivity of People with Obesity and Prediction of Body Mass Index Using Connectivity - Fig 1

<p>(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.</p

Correlation between structural and functional connectivity of various regions.

<p>Six connections with significant correlations between structural and functional connections are reported.</p

Identified regions and features and their contributions to BMI values.

<p>R-squared and p-values are reported. Features with two regions are associated with structural (fiber density) connections, and features with one region are associated with functional correlations. Each structural connection has two regions, and the first region is used as the seed region. Structural and functional features that best explained BMI are shown in italicized bold font.</p

The efficiency of sonophoresis at two ultrasound frequencies for four different concentrations of SonoVue^®.

<p>The efficiency of sonophoresis at two ultrasound frequencies for four different concentrations of SonoVue^®.</p

Optical properties measurement system.

<p>Optical properties measurement system.</p

The efficiency of sonophoresis at two ultrasound frequencies for four different concentrations of Definity^®.

<p>The efficiency of sonophoresis at two ultrasound frequencies for four different concentrations of Definity^®.</p

Experiment setup of ultrasound application.

<p>Experiment setup of ultrasound application.</p

Steady state flux values of diffusion and ultrasound with Definity^® by Franz diffusion cell (mean ± S.D).

<p>Steady state flux values of diffusion and ultrasound with Definity^® by Franz diffusion cell (mean ± S.D).</p