We developed a predicting algorithm based on brain connectivity to quantify the altered brain regions in schizophrenia, bipolar, and attention deficit hyperactivity disorders, to help diagnose them using neuroimaging biomarkers. Functional connectivity was utilized to construct brain graphs, on which the node2vec framework was applied to produce the node embeddings. The concatenation of embeddings was used to derive the region feature vectors to feed support vector machine (SVM) classifiers. Also, we build a model to assist the diagnosis of disorders using a weighted voting ensemble. The achieved accuracy proved to outperform to the state-of-the-art models.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords