Deep Learning Applications in Structural and Functional MRI

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Structural magnetic resonance imaging (sMRI) allows for non-invasive imaging of brain structures. Functional MRI (fMRI) measures certain aspects of human brain activity. sMRI and fMRI data (collectively, MRI data) are incredibly important in mind and brain research. Deep learning has been spectacularly successful in other fields like 2D image recognition and natural language processing. It has potential to greatly amplify the utility of MRI in neuroscience in multiple ways: automated quality control and preprocessing of the large MRI datasets now becoming available; improved decoding of fMRI brain signals; improved extraction of useful information from individual subjects' MRI data such as age, gender, and clinical health status. Important challenges must be overcome though.******Deep learning methods have been applied to MRI data, for example in segmenting brain regions in 2D MRI images, identifying brain activity patterns in fMRI, and differentiating patients with a clinical diagnosis (eg: schizophrenia) vs. healthy controls. Most previous studies used relatively small datasets (40 or fewer subjects). Objective 1 of this research program is to replicate some of the previous results with larger datasets including 100s or 1000s of subjects.******Deep learning requires large training data sets with 10,000s to millions of training examples. MRI datasets are comparatively small with only 1,000-2,000 examples in the largest MRI datasets currently available. To get around this, many deep learning applications in MRI extract many small 2D patches from the MRI data or else start with deep learning models trained on large datasets of everyday 2D images (eg: Imagenet). Objective 2a is to investigate incorporation of prior domain knowledge and assumptions (eg: fMRI blood oxygenation level dependent (BOLD) signal properties) into deep learning models as a means of reducing training dataset size requirements, as well as how this may affect model performance. Objective 2b is to extend deep learning methods to take advantage of 3D structure in sMRI and 4D structure in fMRI, currently ignored in most deep learning MRI studies.******Deep learning has a "black box" or non-interpretability problem in that it is typically not possible for a human to follow the deep learning model's "decision making" process. Objective 3 is to investigate methods for interpreting deep learning models as applied to MRI data. Success would improve neuroscientists' ability to extract knowledge from complex MRI datasets.**