Spatially guided functional correlation tensor: A new method to associate body mass index and white matter neuroimaging
Introduction
Obesity is a worldwide health problem, which is linked to many diseases including cardiovascular disease, diabetes, and stroke [1]. Body mass index (BMI) is a simple measure of obesity [2], and it is a representative measurement to assess the accumulation of body fat [3]. Previous studies found a positive correlation between BMI and morbidity suggesting that BMI might be an indicator for the physical condition of people with obesity [2,3].
Many studies found that obesity is associated with brain based on neuroimaging results of magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography (SPECT) [[4], [5], [6], [7], [8], [9]]. MRI is an especially useful tool as it provides both structural and functional information of the brain. Diffusion tensor imaging (DTI) is one of the MRI modalities that measures the degree of water diffusion in white matter (WM). The directionality of diffusion is measured using fractional anisotropy (FA), and the magnitude of diffusion is measured using the mean diffusivity (MD). FA and the MD are the representative scalar measurements computed from DTI, and many studies adopt them to quantify the WM properties [10]. Previous neuroimaging studies found significant relationships between the WM structure alterations and obesity using DTI [[11], [12], [13]]. Karlsson et al. found that WM atrophy was associated with the percentage of body fat [11]. Bolzenius et al. adopted tractography analysis and found a negative correlation between BMI and the length of fibers in the temporal lobe [12]. Stanek et al. found that an increased BMI was related to decreased FA in the corpus callosum and fornix [13]. In a previous study, we predicted BMI using the structural connectivity of DTI [14]. These studies collectively suggest that obesity is related to the altered structure of WM.
Functional MRI (fMRI) that measures the blood-oxygen-level dependent (BOLD) signal from gray matter (GM) is a widely adopted neuroimaging modality in obesity-related studies [15,16]. Existing fMRI studies focused on analyzing the BOLD signal from GM [[17], [18], [19], [20]]. Still, a few studies reported that significant BOLD signal existed in WM in smaller magnitude [21]. Especially, the robust signal was observed in the corpus callosum and internal capsule, parts of WM [[21], [22], [23], [24]]. These studies collectively provided a basis for exploring the BOLD signal in the WM. A method to explore BOLD signals from WM using fMRI was proposed by Ding et al. [25]. They developed a method known as the functional correlation tensor (FCT) to mimic DTI [25,26]. The FCT was constructed by calculating the correlations of time courses between a given voxel and its adjacent voxels, and then the correlation values were further modified with a dyadic tensor. Ding et al. reconstructed the neuronal fibers in the corpus callosum and optic radiation using FCT suggesting the possibility of developing pseudo-DTI from fMRI [25]. This might allow us to extract DTI like information from fMRI data and thus might save the scanning time as there is less need to perform real DTI data acquisition. The decrease in the scanning time can be an important factor for children and elderly subjects who might have difficulties with staying in the scanner for an extended period of time, which is required for multimodal acquisition studies.
In this study, we aimed to achieve two goals. First, we aimed to enhance the FCT by incorporating spatial information of T1-weighted structural data. We hypothesized that the spatial information might provide complementary information for constructing the FCT. Second, we aimed to find biomarkers of obesity using a spatially guided enhanced FCT (s-eFCT). We predicted BMI using the s-eFCT. We hypothesized that the s-eFCT might be used as imaging biomarkers to distinguish among subclasses of obesity.
Section snippets
Subjects and imaging data
The institutional review broad (IRB) of Sungkyunkwan University approved our study. Consent was waived for this retrospective study. Our study was performed in full accordance with local IRB guidelines. We obtained DTI, resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted data from enhanced Nathan Kline Institute-Rockland sample (NKI-RS) database Release 1–8 [27]. All NKI-RS participants were scanned on a Siemens Magnetom Trio Tim scanner with the following imaging
Study participants
We considered 264 subjects in this study. The subjects were classified into three subgroups of HW, OW and OB groups. Detailed demographic information is described in Table 1 for all three subgroups.
Quality of the s-eFCT
We constructed the s-eFCT using the fMRI with T1-weighted data and calculated the FA and MD values for the WM regions. The tensors were compared with those of the real diffusion tensor and the s-eFCT using Pearson's correlation in the 10-fold cross-validation. The mean correlation of the tensors over
Discussion
In the current study, we aimed to develop a new variant of the FCT, the s-eFCT, which contains both functional and structural brain information using fMRI and T1-weighted data. The functional information was measured by correlating temporal fluctuations in fMRI between all pair of voxels in the WM. The structural information was voxel-wise T1-weighted data. We used our s-eFCT to predict the BMIs of people with a wide range of BMI values, and it led to fair prediction results (mean r = 0.57).
Conclusions
In this study, we combined the FCT and T1-weighted anatomical information to construct our s-eFCT in a regression framework to model the diffusion tensor. The s-eFCT model was used to compute the FA and MD values of the WM regions. We identified FA and MD of the WM regions related to BMI, which were used as input of a BMI score prediction model. The correlation between the real and predicted BMIs was 0.57. We divided the predicted BMI to distinguish between three subgroups of obesity. The
Acknowledgments
This study was supported by the Institute for Basic Science (grant number IBS-R015-D1), the National Research Foundation of Korea (grant numbers NRF-2016R1A2B4008545 and NRF-2016H1A2A1907833), and the Ministry of Science and ICT of Korea under the ITRC program (IITP-2018-2018-0-01798). Imaging data were obtained from the NKI-RS database.
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These authors contributed equally to this work.