Careful consideration was required to find accurate corresponding histological slides for each MRI slice. Since the block face of the tissue was used to determine both the direction of MRI acquisition and the histology slide cutting, this information was used to simplify a transformation model between 3D MRI volume and a stack of 2D histology images. We modelled the z-direction transformation (perpendicular to the block face) as a global translation and the xy-plane transformation (parallel to the block face) as a slice-by-slice 2D rigid transformation to account for possible misalignment during staining and scanning. Based on this transformation model, an up-sampled 3D volume of T1WI (68.36 μm2 per pixel) was then registered to a stack of down-sampled 2D KB staining images (68.36 μm2 per pixel) to maximize mutual information (MI)22. In Supplementary Figure S1, the original T1WI (C-I) was registered to the KB stain image (A) to obtain the aligned T1WI (C-II). Up-sampling was done with a cubic B-spline interpolation and a non-negativity constraint23; this image registration code with the above transformation model was implemented using the MATLAB Image Processing Toolbox (The Mathworks, USA). The resulting registration information was used to align other MRI images (e.g., T2*WI) to the corresponding KB staining images since all MRI images with different contrasts were obtained without moving the target tissue. In Supplementary Figure S1, the aligned T2*WI (D-II) was obtained by registering the original T2*WI (D-I) to the KB stain image (A) using the same transformation information obtained from the T1WI to KB image registration. Lastly, TH, Perl, and calbindin staining images (10×, 6.836 μm2 per pixel) were registered to the closest KB staining image (10×, 6.836 μm2 per pixel) using a MI-based rigid image registration. Since image features of each stained image are quite different from one another, we used thresholded binary stained images of KB, TH, Perl, and calbindin to perform image alignments so that no strong internal correlation between images would affect the results of image alignment. In Supplementary Figure S1, the aligned binary Perl stained image (B-II) was obtained from the original Perl stained image (B) that was automatically thresholded to generate the binary image (B-I) using red channel information, which has the largest contrast between the Prussian blue-stained tissue and the white slide background. It was then registered to the binary image (A-I) generated from the KB stained image (A) using red channel information, which also has the highest contrast between the KB blue-stained tissue and the white background. Similarly, the aligned Perl stained image (B-III) was made using the same transformation, in which all three channels of information from the original Perl stained image (B) was aligned to the original KB stained image (A). For the TH and calbindin stained images, similar procedures were applied so that the registered stained images to the KB stained image were obtained. We visually confirmed that our co-registration method between the various MR images and histology images worked well for small tissues cases, like the SN. However, more sophisticated methods such as the work of Adler et al.24 may be required for imaging larger tissue samples.