1. NA-MIC National Alliance for Medical Image Computing http://na-mic.org fMRI in NAMIC Facilitator: Polina Golland Presenters: Jim Fallon and Andy Saykin

2. National Alliance for Medical Image Computing http://na-mic.org fMRI and NAMIC NAMIC Core 1 projects focus on structure –Anatomical –DTI Many of us are interested in fMRI –Core 1: analysis –Core 3: tool for study of the disease Potential for new collaborations

3. National Alliance for Medical Image Computing http://na-mic.org fMRI Status Update Basic analysis functions in ITK (GE/Kitware) User Interface in Slicer (BWH) Advanced detection/analysis –MIT/BWH – anatomically guided fMRI detection –UC Irvine – localization of activation peaks –Other Core 1 groups Integrated visualization of anatomy & function

4. National Alliance for Medical Image Computing http://na-mic.org Our goals Not to replicate existing analysis tools To identify problems that are –important to Core 3 –interesting to Core 1 Use NAMIC to create new collaborations

5. National Alliance for Medical Image Computing http://na-mic.org Our findings Some of the “problems” have already been “solved” Many items on the “wish list” are in reach –Especially with help of Core 2 There are some really hard and interesting problems

6. National Alliance for Medical Image Computing http://na-mic.org No Smoothing Gaussian MRF Anatomically guided fMRI detection With anatomy Wanmei Ou, MIT

7. National Alliance for Medical Image Computing http://na-mic.org Quality control for fMRI Spatiotemporal browser designed for quality control during preprocessing of single subject time series data or contrasts –Easy loading of raw scan formats –Easy navigation through time & space –Quantify signal to noise –Identify temporal spikes  optional smoothing –Identify spatial distortion B0 field map and phantom  optional adjustment Also feature to identify outliers in group data Tom Nichols at U. Michigan has something like this tool in Matlab. Core 2?

8. National Alliance for Medical Image Computing http://na-mic.org Managing fMRI findings fMRI activation cluster utility –Need to create functional ROI (fROI) label maps for use in subsequent analyses –Assuming user has created a thresholded activation map Ability to choose activation clusters to include in the label map User should be able to choose label values and provide a name in a text field for each cluster –Extract data from these clusters Individual time series or for group data Core 2?

9. National Alliance for Medical Image Computing http://na-mic.org Outline of this discussion Presentations (15-20min) –Jim Fallon –Andy Saykin Questions (15-20min) –Ask the speakers more detailed questions Discussion/brainstorming on how we might solve these problems

10. National Alliance for Medical Image Computing http://na-mic.org Major Themes Integration with anatomical and DTI: –Anatomically accurate and precise integration of all modalities, including fMRI, DTI, into a single analysis framework Characterizing fMRI activation areas: –Invent new ways to describe active areas and how they change from an experiment to an experiment. This ties into population analysis of activation.

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

12. National Alliance for Medical Image Computing http://na-mic.org Fallon OccipOccip Heschl’s Frontal poleFrontal pole 7 ITG STG CB DMPFC DLPFC VMPFC LOF IFG Critical samples in BOLD

13. National Alliance for Medical Image Computing http://na-mic.org Anterior ViewAnterior-Inferior View Variability in population

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

15. DLPFC BA 46 BA 7 SLF-2

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

17. McCarthy, 2004

18. NA-MIC National Alliance for Medical Image Computing http://na-mic.org beta map fBIRN phantom sensorimotor task Activation patterns mixture model (Kim, et al, 2005) Thresholded voxels (p<0.05) Add 20% “gutter region” around each strictly defined area (e.g., DLPFC) to capture “rogue” functional activations in different subject and patient populations…”DLPC PLUS”

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

20. National Alliance for Medical Image Computing http://na-mic.org fMRI Specific Applications Tool for assessment of test-retest reproducibility of fMRI experiments –Simple approach would be calculating intraclass correlation coefficients for voxels and ROIs Useful but limited value because of fluctuations in exact peak and distribution of activation foci –A more sophisticated approach would include identification and extraction of key spatiotemporal features Prior knowledge could be used to inform regarding importance Reproducible features could be quantified A related tool would provide an analysis of longitudinal stability and change –Consider reliable change index approach applied to activation maps

21. National Alliance for Medical Image Computing http://na-mic.org Multimodality Integration Registration of fMRI, DTI and anatomic MR –individual and group data Easy mapping between atlas space and native scan space –Permit warping from native space to atlas space or vice versa Automated parcellation of cortical surface and subcortical gray matter structures –Generate label maps –Extract quantitative data from labeled ROIs or fROIs e.g. examine atrophy within functionally derived ROI

22. National Alliance for Medical Image Computing http://na-mic.org Multimodality Integration - II Integrate measures of connectivity –Voxel by voxel and labeled ROI measures of connectivity within single subject time series Resting & Task-induced connectivity Changes in strength of connectivity over time –important for learning and habituation experiments –Relation to existing work PLS, SEM, DCM, POI, other? –Visualization tool to display strength of connectivity including functional and neuroanatomic (tractography)

23. National Alliance for Medical Image Computing http://na-mic.org Questions and Discussion

24. National Alliance for Medical Image Computing http://na-mic.org Major Themes Integration with anatomical and DTI: –Anatomically accurate and precise integration of all modalities, including fMRI, DTI, into a single analysis framework Characterizing fMRI activation areas: –Invent new ways to describe active areas and how they change from an experiment to an experiment. This ties into population analysis of activation.