Relative Local Signal Strength: The Impact of Normalization on the Analysis of Neuroimaging Data with Deep Learning

Functional magnetic resonance imaging (fMRI) has revolutionized neuroscience, providing insights into brain activity through the analysis of variations in blood oxygen level dependent (BOLD) contrast. Deep learning is considered a promising approach to discover intricate patterns within such high-dimensional signals, but at the same time its application poses novel methodological issues, particularly regarding the normalization of time series associated with different brain areas. This paper addresses the impact of different normalization methods on the predictive performance of deep learning models, providing the first comprehensive comparison in the neuroimaging domain. Through experiments with two predictive models on two fMRI classification tasks, we demonstrate the significant influence of data normalization on the accuracy and interpretability of deep learning models in fMRI data analysis.