Keywords: Functional Connectivity, Functional Connectivity, optimal shrinkage denoising, precision functional mapping
Motivation: Resting-state functional MRI gradients are widely used to study functional connections’ hierarchies in the neocortex. Due to noise, these gradients are usually analyzed at population level, leaving individual gradients underexplored.
Goal(s): We develop a method that produces high-quality individual gradients robust to noise.
Approach: We applied block-based optimal shrinkage denoising to rs-fMRI data, then used diffusion embedding to derive functional gradients.
Results: Denoising improved temporal signal-to-noise ratio eightfold. Resulting individual gradients are smoother, less noisy, and consistent with established literature for both adult and infant fMRI. Our findings suggest that acquisition time can be reduced by 16 times without losing gradient quality.
Impact: Denoising enables high-quality individual gradients, even with temporally undersampled data, allowing precise assessment of individual gradient changes.
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