NA-MICNational Alliance for Medical Image Computing http://na-mic.org

fMRI within NAMIC

Sandy Wells, Polina Golland

Discussion moderator: Andy Saykin

National Alliance for Medical Image Computing http://na-mic.org

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

National Alliance for Medical Image Computing http://na-mic.org

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

National Alliance for Medical Image Computing http://na-mic.org

Surface vs. Volume Smoothing

Surface

Volume

• Four subjects (fixed-effects, single visit), 15mm FWHM:

• Demonstrated better detection power

National Alliance for Medical Image Computing http://na-mic.org

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

National Alliance for Medical Image Computing http://na-mic.org

Hierarchy in Single Subject

AuditoryMotor

High Visual

?

STS+

? ?

Visual Motor+Aud

Motor+AudRetinotopic

High Visual

Intrinsic Stimulus Dependent

STS?

National Alliance for Medical Image Computing http://na-mic.org

Group Analysis of 2 systemsIndividual Maps Group Average

NA-MICNational Alliance for Medical Image Computing http://na-mic.org

Enabling Methodologies

Core 1 / Core 2 / Core 3

National Alliance for Medical Image Computing http://na-mic.org

fMRI/DTI Connectivity• 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

National Alliance for Medical Image Computing http://na-mic.org

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

National Alliance for Medical Image Computing http://na-mic.org

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

Unaligned input

Aligned output

–Serdar Balci, Lilla Zollei, Mert Sabuncu, Sandy Wells, Polina Golland

National Alliance for Medical Image Computing http://na-mic.org

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

Acquired

Estimated