Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, approaches commonly used are biased by the covariance between variables. Here, we propose computing the voxel-wise Mahalanobis distance (MhD), as a measure of deviation from normality that accounts for covariance between metrics. We show that this measure can be linked to behavior and to potential physiological underpinnings by extracting metrics contributing most to the MhD. Integrative multivariate models are crucial to expand our understanding of the multiple factors underlying disease development and progression.
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