Abstract #2471

Brain functional fingerprinting predicts individual differences in cognition: An AI-based approach

Morteza Esmaeili^1,2 and Alireza Salami^3,4,5

¹Department of Diagnostic Imaging, Akershus University Hospital, Lørenskog, Norway, ²Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway, ³Wallenberg Center for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden, ⁴Department of Integrative Medical Biology, Umeå University, Umeå, Sweden, ⁵Aging Research Center, Karolinska Institute and Stockholm University, Solna, Sweden

Synopsis

Machine learning approaches provide convenient autonomous object classification in medical imaging domains. This study examines the utility of convolutional neural networks in predicting individual differences in cognition from the resting-state functional connectome. We observed significant contributions from the subcortical areas (including hippocampus) and their interactions with the cortical default mode network to the training progress. Our results demonstrate that an AI-based model can predict an individual's EM scores.

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.

Click here for more information on becoming a member.