Researchers are working to harness a deep learning framework to learn more about how mental illness and other disorders affect the brain. The team is combining different types of brain imaging data to capture patterns that are indicative of brain disorders. Their work is funded by a $2.4 million, a four-year award from the National Institute of Biomedical Imaging and Bioengineering.
Advances in brain imaging mean researchers have access to significantly more data than in the past, but the relationships among modalities, or types of data captured, are complex and poorly understood. The researchers are focused on characterizing these relationships by merging and analyzing data from multiple sources.
Using the deep learning framework, the team will develop and train algorithms on thousands of existing datasets. By examining the data along with two spectra, mood, and psychosis, they can determine which modalities or brain regions are most relevant to specific disorders. The hope is to develop multi-modal biomarkers that healthcare experts could use for diagnosis of mental health disorders such as schizophrenia, depression, or bipolar disorder.