Computational protocol: MRI-based Brain Healthcare Quotients: A bridge between neural and behavioral analyses for keeping the brain healthy

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Protocol publication

[…] T1-weighted images were preprocessed and analyzed using Statistical Parametric Mapping 12 (SPM12; Wellcome Trust Centre for Neuroimaging, London, UK) running on MATLAB R2015b (Mathworks Inc., Sherborn, MA, USA), where the preprocessing steps of segmentation, bias correction, and spatial normalization are incorporated into a single generative model. Each MPRAGE image was segmented into GM, WM, and cerebrospinal fluid (CSF) images using SPM12 prior probability templates. The intensity non-uniformity bias correction was applied to aid segmentation by correcting for scanner-induced smooth intensity differences that varied in space. Subsequently, the segmented GM images were spatially normalized using the diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) algorithm []. A modulation step was also incorporated into the preprocessing model to reflect regional volume and preserve the total GM volume from before the warp. As a final preprocessing step, all normalized, segmented, modulated images were smoothed with an 8-mm full width at half-maximum (FWHM) Gaussian kernel.Additionally, the global volumes of GM, WM, and CSF for each scan were calculated. The volume of each tissue class was estimated as the total number of voxels multiplied by the voxel size. Intracranial volume (ICV) was calculated by summing the GM, WM, and CSF images for each subject. Proportional GM images were generated by dividing smoothed GM images by ICV to control for differences in whole-brain volume across participants. Using these proportional GM images, mean and standard deviation (SD) images were generated from all participants. Next, we calculated the GM brain healthcare quotient (BHQ), which is similar to the intelligence quotient (IQ). The mean value was defined as BHQ 100 and SD was defined as 15 BHQ points. By this definition, approximately 68% of the population is between BHQ 85 and BHQ 115, and 95% of the population is between BHQ 70 and BHQ 130. Individual GM quotient images were calculated using the following formula: 100 + 15 × (individual proportional GM—mean) / SD. Regional GM quotients were then extracted using an automated anatomical labeling (AAL) atlas [] and averaged across regions to produce participant-specific GM-BHQs.DTI data were preprocessed using FMRIB Software Library (FSL) 5.0.9 []. First, all diffusion images were aligned with the initial b0 image, and motion correction and eddy current distortion correction was performed using eddy_correct. Following these corrections, FA images were calculated using dtifit. FA images were then spatially normalized into the standard Montreal Neurological Institute (MNI) space using FLIRT and FNIRT. FLIRT, a linear registration tool, was used to roughly align a set of brains to MNI space. Then FNIRT, a non-linear registration tool, was used to achieve better registration. After spatial normalization we smoothed the data with an 8-mm FWHM. Mean and SD images were generated from all the FA images, and both individual FA quotient images and GM-BHQ images were calculated. Individual FA quotient images were calculated using the following formula: 100 + 15 × (individual FA–mean) / SD. Regional FA quotients were extracted using Johns Hopkins University (JHU) DTI-based white-matter atlases [] and averaged across regions to produce participant-specific FA-BHQs. [...] First, the correlation coefficient between BHQ (GM-BHQ and FA-BHQ) and age were examined. Then, in order to investigate the relationships of physical factors on BHQ, general linear regression analyses were used. We employed three models for analysis: Model 1.1 assessed the relationship of BMI with BHQ, adjusting for age and sex; Model 1.2 then introduced blood pressure and pulse as an additional independent variable to Model 1.1. Model 1.3 introduced daily time use as an additional independent variable to Model 1.2. In model 1.3, independent variables were selected by the stepwise method because there were many variables of daily time use. We added these respective variables to the models based on the hypotheses that blood pressure is more closely related to BHQ than is BMI, and that daily time use is more closely related to BHQ than are the previous two variables. We also investigated the relationship between BHQ and social factors in a similar way using general linear regression analyses. For these analyses, we employed another set of models: Model 2.1 assessed the relationship of socioeconomic status with BHQ after adjusting for age and sex; Model 2.2 then introduced subjective well-being as an additional independent variable to Model 2.1; Model 2.3 introduced attitude (post-materialism and Epicureanism) as an additional independent variable to Model 2.2. Similarly to the physical factors of the first model series, we added these variables based on the hypotheses that subjective well-being is more closely related to BHQ than is socioeconomic status, and that attitude is more closely related to BHQ than are the previous two variables. The significance level was determined at p < .05. All statistical analyses were conducted using IBM SPSS Statistics Version 20 (IBM Corp., Armonk, NY, USA). […]

Pipeline specifications

Software tools SPM, AAL, FSL, SPSS
Applications Miscellaneous, Magnetic resonance imaging
Diseases Nervous System Diseases