  Spatial and intensity normalization are nowadays a prerequisite for
neuroimaging analysis. Influenced by voxel-wise and other univariate
comparisons, where these corrections are key, they are commonly applied to any
type of analysis and imaging modalities. Nuclear imaging modalities such as
PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's Disease
diagnosis, are especially dependent on intensity normalization. However, these
steps are computationally expensive and furthermore, they may introduce
deformations in the images, altering the information contained in them.
Convolutional Neural Networks (CNNs), for their part, introduce position
invariance to pattern recognition, and have been proven to classify objects
regardless of their orientation, size, angle, etc. Therefore, a question
arises: how well can CNNs account for spatial and intensity differences when
analysing nuclear brain imaging? Are spatial and intensity normalization still
needed? To answer this question, we have trained four different CNN models
based on well-established architectures, using or not different spatial and
intensity normalization preprocessing. The results show that a sufficiently
complex model such as our three-dimensional version of the ALEXNET can
effectively account for spatial differences, achieving a diagnosis accuracy of
94.1% with an area under the ROC curve of 0.984. The visualization of the
differences via saliency maps shows that these models are correctly finding
patterns that match those found in the literature, without the need of applying
any complex spatial normalization procedure. However, the intensity
normalization -- and its type -- is revealed as very influential in the results
and accuracy of the trained model, and therefore must be well accounted.
