Examining the Impact of fMRI Preprocessing Steps on Machine Learning-Based Classification of Autism Spectrum Disorder

The use of functional magnetic resonance imaging (fMRI) data in machine learning (ML)-based classification of autism spectrum disorder (ASD) has been a topic of increasing research interest in past years due to the noninvasiveness of the fMRI technique and its potential for providing valuable biomarkers. However, there are still controversies surrounding some fMRI data preprocessing steps, such as bandpass filtering and global signal regression (GSR). It still needs to be determined whether or how these preprocessing steps impact the classification accuracy of ML algorithms. This paper uses fMRI signals from the ABIDE-I dataset to train a long short-term memory (LSTM) network to classify subjects into ASD or healthy controls (HC). We considered 18 preprocessing pipelines comprising all combinations of with and without filtering, with and without global signal regression, and three different segment lengths of 1 min, 2 min, and 3 min. The best model was obtained when using a segment length of 2 min. Our results suggest that not filtering produces significantly higher classification accuracies than filtering, whereas there were no significant differences in classification accuracies when removing or not the global signal.