## 2 Materials and Methods
### Human Brain Data
#### 2.1.2 Data Preprocessing and Feature Extraction  
Neuroimaging data are usually high-dimensional, with each modality having different properties and dimensions. For example, sMRI data contain spatial information with a high spatial resolution but contain no temporal information. FMRI data convey both temporal information and spatial information, but with a lower spatial resolution. To jointly analyze different datasets in a common subspace, it is important to reduce data of each modality down to a single feature for each subject, a multivariate lower-dimensional representation of the data, and collect the features from all subjects together in a single dataset [13; 15]. Such a reduction enables the datasets from all modalities to share a common dimension--i.e., the number of subjects--which provides a natural connection across different data types. The dMRI data we used were preprocessed using the FMRIB Software Library ([[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/)]([https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/)), accessed on 8 October 2018). We carried out the preprocessing steps as follows: (1) we identified and removed excessive motion or vibration artifacts through a quality check with any gradient directions; (2) we corrected motion and eddy currents; (3) we corrected gradient directions for any image rotation caused by the previous motion correction step; and (4) we calculated the diffusion tensor and then derived the scalar measure--fractional anisotropy (FA)--from the tensor. The FA, a measurement of WM integrity, was calculated as the fraction of total diffusion that can be attributed to anisotropic diffusion. A higher value of FA corresponds to more consistent diffusion orientation and thus more integrity of WM [38]. The FA maps were smoothed with an 8 mm full-width half-maximum (FWHM) Gaussian filter and then used as the feature for dMRI data. Using the unified segmentation method in Statistical Parametric Mapping (SPM8) ([[http://www.fil.ion.ucl.ac.uk/spm](http://www.fil.ion.ucl.ac.uk/spm)]([http://www.fil.ion.ucl.ac.uk/spm](http://www.fil.ion.ucl.ac.uk/spm)), accessed on 8 October 2018), the sMRI data were (1) normalized to the Montreal Neurological Institute (MNI) space; (2) resliced to \(3\times 3\times 3\) mm\({}^{3}\); and (3) segmented into GM, WM, and CSF images. The segmented GM imageswere then smoothed with an 8 mm FWHM Gaussian filter. We further detected subject outliers using the spatial Pearson correlation with a template image to ensure that all images of subjects were properly segmented [39]. The GM images were used as the feature for sMRI data. The fMRI data were preprocessed using an automated analysis pipeline [40] carried out in SPM8. The pipeline consisted of (1) aligning all the images with the first image as the reference using INRIalign approach [41] to correct minor motions of the subject; (2) correcting for time differences between the slices using the middle slice as the reference; and (3) spatial normalization to MNI space, including reslicing to \(3\times 3\times 3\) mm\({}^{3}\), resulting in \(53\times 63\times 46\) voxels. We then regressed out six motion parameters, WM, and CSF signals to further denoise the data. Data were then spatially smoothed with an 8 mm FWHM Gaussian filter. We estimated the fractional amplitude of low-frequency fluctuation (fALFF) [42], which is an effective approach to represent brain activity with high sensitivity and specificity that has been used in a large array of studies [43; 44; 45; 46; 47; 48]. The fALFF is calculated as the ratio of the power spectrum of low frequency (0.0-0.08 Hz) to that of the whole detectable frequency range (0-0.25 Hz) [49]. The fALFF maps were then used as the feature for fMRI in this study. For each modality, based on its own extracted feature, a two-dimensional matrix--feature dataset--was formed by concatenating the features across all subjects.