In contrast, in an indirect normalization procedure both sets use the same non-linear transformation, which is estimated using only the structural data (often with higher spatial resolution, better tissue contrast, and more anatomical detail). In a direct normalization procedure the functional and anatomical data are normalized separately/independently, each using its own non-linear transformation to project the data from its original space to a common MNI-space. 2017) that an indirect normalization procedure results in better performance compared to a direct normalization procedure. In those cases it can be argued (Calhoun et al. default preprocessing pipeline for volume-based analyses (indirect normalization to MNI-space) (also known as ' default_mnifield' in CONN's batch commands) is an alternative preprocessing pipeline designed for cases when high-quality fieldmaps are available for susceptibility distortion correction. Some popular alternatives either directly implemented or supported by CONN are:ДЎ. While the above preprocessing pipeline is designed to offer robust performance in most common scenarios, researchers are encouraged to explore different alternatives tailored to the specificities of their scanning protocol or analysis plans. Both functional and anatomical data are resampled to a default 180x216x180mm bounding box, with 2mm isotropic voxels for functional data and 1mm for anatomical data, using 4th order spline interpolationДЄlternatives/additions to the default preprocessing pipeline Direct normalization applies this unified segmentation and normalization procedure separately to the functional data, using the mean BOLD signal as reference image, and to the structural data, using the raw T1-weighted volume as reference image. This procedure iteratively performs tissue classification, estimating the posterior tissue probability maps (TPMs) from the intensity values of the reference functional/anatomical image, and registration, estimating the non-linear spatial transformation best approximating the posterior and prior TPMs, until convergence. Functional and anatomical data are normalized into standard MNI space and segmented into grey matter, white matter, and CSF tissue classes using SPM12 unified segmentation and normalization procedure (Ashburner and Friston, 2005).

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