Major depression is highly-prevalent disorder with frustratingly-high rates of treatment resistance. Ultrahigh field imaging may provide objective quantitative biomarkers for characterizing depression, generating insight into clinical phenotypes of this heterogeneous disease. Forty-two major depressive disorder patients currently off anti-depressant treatment were recruited for scanning at ultrahigh field, and given batteries of clinical symptom measures. Machine-learning clustering analysis was performed to group patients by clinical symptoms and differences in imaging features observed. A separate analysis was performed in the reverse direction clustering on quantified imaging features and identifying clinical differences between clusters, including differences in ruminative response between the patient clusters.
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