1 / 12

fMRI within NAMIC

fMRI within NAMIC. Sandy Wells, Polina Golland Discussion moderator: Andy Saykin. fMRI Update. Algorithms for time-series analysis Regularization/smoothing Segmentation/clustering Enabling methodologies Joint analysis with other modalitites Group analysis

jakeem-mooney
Download Presentation

fMRI within NAMIC

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. fMRI within NAMIC Sandy Wells, Polina Golland Discussion moderator: Andy Saykin

  2. fMRI Update • Algorithms for time-series analysis • Regularization/smoothing • Segmentation/clustering • Enabling methodologies • Joint analysis with other modalitites • Group analysis • Core 1 / Core 3 projects to apply to clinical data • Core 1 / Core 2 projects to integrate into NAMIC-kit

  3. fMRI Detection/Regularization • Smarter strategies for smoothing • MRF priors (MIT/BWH) • Wanmei Ou, Polina Golland, Sandy Wells • Surface-based vs. volumetric smoothing (MGH) • Anastasia Yendiki, Doug Greve, Bruce Fischl • Example: MIND fMRI reliability study • Sensorimotor paradigm • 10 subjects on 2 visits at each of 4 sites • We thank Randy Gollub for providing the MIND data

  4. Surface Volume Surface vs. Volume Smoothing • Four subjects (fixed-effects, single visit), 15mm FWHM: • Demonstrated better detection power

  5. Functional Hierarchy/Segmentation • Hierarchical clustering of time series data (MIT) • Polina Golland, Bryce Kim, Danial Lashkari, • Simultaneously estimate • Representative “signatures” • Which signature best describes each voxel • Example: diverse set of visual and mental tasks • localizer, rest, movie, etc.; ~1 hour of fMRI data • 7 subjects

  6. Intrinsic Stimulus Dependent Visual Motor+Aud Motor+Aud STS+ Retinotopic High Visual ? ? High Visual ? ? STS Motor Auditory Hierarchy in Single Subject

  7. Group Analysis of 2 systems Individual Maps Group Average

  8. Enabling Methodologies Core 1 / Core 2 / Core 3

  9. DTI-based Connectivity Analysis Path of interest analysis (MGH) Probabilistic tractography (MT/BWH/Harvard) Strength of connection between ROIs Tri Ngo, C-F Westin, Marek Kubicki, Polina Golland ROIs from fMRI Color Stroop in Schizophrenia 15 subjects in each group Implementation in NAMIC-kit in progress fMRI/DTI Connectivity

  10. Anatomical Analysis • Cortical segmentation and flattening (MGH) • Freesurfer tools, now compatible with Slicer • Doug Greeve, Bruce Fischl, Steve Pieper • Conformal mapping of the cortex (Georgia Tech) • Yi Gao, John Melonakos, Allen Tannebaum • Filters in ITK

  11. Aligned output Unaligned input Population Registration • Information-theoretic group-wise alignment (MIT/MGH/BWH) • Integration into NAMIC-kit in progress • In the fututre: non-rigid deformations using B-splines • Serdar Balci, Lilla Zollei, Mert Sabuncu, Sandy Wells, Polina Golland

  12. Acquired Estimated EPI Registration/De-Warping • Combine segmentation and registration with Physics-based modeling of susceptibility (MIT/BWH/fMRIB) • Accurate registration of fMRI to anatomical MR • Retrospective correction of EPI distortions • Clare Poynton, Sandy Wells, Mark Jenkinson

More Related