Increased Neurite Orientation-Dispersion and Density in the TgCRND8 Mouse Model of Amyloidosis: Inverse Relation with Functional Connectome Clustering and Modulation by Interleukin-6

Luis M. Colon-Perez; Kristen R. Ibanez; Mallory Suarez; Kristin Torroella; Kelly Acuna; Edward Ofori; Yona Levites; David E. Vaillancourt; Todd E. Golde; Paramita Chakrabarty; Marcelo Febo

As an additional step to supplement seed-based and network analyses, we conducted analyses using probabilistic independent component analysis (ICA) in FSL MELODIC (multivariate exploratory linear decomposition into independent components) for 48 resting state fMRI mouse brain scans . Prior to ICA, post-processed and atlasaligned functional scans (see above) were cropped using manually segmented masks. This provided optimal co-registration of fMRI scans with each subject anatomical and with the mouse brain template. In MELODIC, the following steps were carried out: voxel-wise de-meaning, variance normalization, pre-whitening, and data were then projected into a 30-dimensional subspace using PCA, and then ICA estimation was conducted in concatenated maps. Mixture model fitting, rescaling and thresholding was carried out on z-transformed ICA maps (alternative hypothesis test set at p > 0.5). ICA maps likely representing noise or not deemed to include welldefined regions were removed and the rest were considered of interest. The latter were assembled for visualization using slices summary and overlay scripts in FSL, using a lower z threshold of 2.3. Using the Glm tool in FSL a general linear model design matrix was generated to test for main effects of strain, treatment, and strain x treatment interactions, and used in dual regression analysis and randomize permutation testing in FSL to test for differences between groups (Nickerson et al., 2017) .