17. Deep Learning for Functional Brain Connectivity: Are We There Yet?

The detection of behavioral disorders rooted in neurological structure and function is an important research goal for neuroimaging communities. Recently, deep learning has been used successfully in diagnosis and segmentation applications using anatomical magnetic resonance imaging (MRI). One of the reasons for its popularity is that with repeated nonlinear transformations, the algorithm is capable of learning complex patterns in the data. Another advantage is that the feature selection step commonly used with machine learning algorithms in neuroimaging applications is eliminated which could lead to less bias in the result. However, there has been little progress in the application of these black-box approaches to functional MRI (fMRI). In this study, we explore the use of deep learning methods in comparison with conventional machine learning classifiers as well as their ensembles to analyze fMRI scans. We compare the benefits of deep learning against an ensemble of classical machine learning classifiers with a suitable feature selection strategy. Specifically, we focus on a clinically important problem of Attention Deficit Hyperactivity Disorder (ADHD). Functional connectivity information is extracted from fMRI scans of ADHD and control patients (ADHD-200), and analysis is performed by applying a decision fusion of various classifiers—the support vector machine, support vector regression, elastic net, and random forest. We selectively include features by a nonparametric ranking method for feature selection. After initial classification is performed, the decisions are summed in various permutations for an ensemble classifier, and the final results are compared with the deep learning-based results. We achieved a maximum accuracy of 93.93% on the KKI dataset (a subset of the ADHD-200) and also identified significantly different connections in the brain between ADHD and control subjects. In the blind testing with different subsets of the target data (Peking-1), we achieved a maximum accuracy of 72.9%. In contrast, the deep learning-based approaches yielded a maximum accuracy of 70.5% on the Peking-1 dataset and 67.74% on the complete ADHD-200 dataset, significantly inferior to the classifier ensemble approach. With more data being made publicly available, deep learning in fMRI may show a strong potential but as of now deep learning does not provide a magical solution for fMRI-based diagnosis.