1. Jordan Hamm (BA, BSc) University of Georgia, Athens, Georgia Alexandra Reichenbach (MSc, Dipl-Ing) Max Planck Institute for Biological Cybernetics, Tuebingen, Germany

2.  Technical considerations  Mean ADC values, FA, and tract volume as measurements  Application  Rationale of the project  Approaches ▪ Automatized / manual ▪ Tract / ROI based  Results  Conclusions

3. What is b-value? -higher b-values may probe different diffusion -more sensitive to differences in restricted (Assaf, 2004) Do more angles provide any benefit beyond more SNR? -i.e. are more gradient directions just redundant? -6 dir(8 times) or 50 directions (1 time)? Is motion correction effective? - Leemans vector table rotation

4. What effect does b-value, angular resolution, and motion correction have on common diffusion metrics? - Scanned 2 subjects - Compared parameters in -tract reconstructions -5x5mm ROIs for maximum sensitivity

5. 6 dir, b1200 50 dir, b120050 dir, b2400 Raw Motion Corrected Qualitative analyses -Tracts produced with FACT algorithm (BF approach) using tensors in 6 direction data and using non-negativity constrained spherical de-convolution in 50 direction data.

6. First compared average FA of a tract to overall tract volume As volume of a tract increases, overall average FA of that tract decreases - so tract integrity is not necessarily revealed in a tract based analysis. Instead, tract volume and/or number of “tracts” are best used for tract based analyses

7. Initially, b-value didn’t appear to affect tractability…. But…. Assessed number of voxels involved in each reconstructed tract from each scan.

8. Motion correction (12 parameter) with vector table rotation reveals benefit of higher b-values (Leemans and Jones, 2009)

9. Motion correction appears to improve tracking, but differentially for different b-values. Why? - longer scans  more movement? - b2400 scan 10% longer (2 min) -higher b-values are more sensitive -scan artifacts

10. Manual selection of 3x3 voxel ROI Compared between b-values, ang. res., and raw/motion corrected data -Mean diffusivity (verified with known values) -FA estimate

11. Mean diffusivity variable between b=1200 and b=2400 before motion correction -Overall variance of ADC values reduced after motion correction -also closer to prescribed 7.0 X 10^-4 (Johansen-Berg and Behrmans, 2009) -B=2400 with motion correction is best -ROI close to CSF, to which lower b-values are more sensitive. -Again, differential effects of motion correction seen

12. -Higher b-values yield more consistent measure of fractional anisotropy across subjects -Some anisotropy captured by low b-values could be non-axonal which does not contribute to long range tractography -lower b-values have more “hindered” and less “restricted” Why does FA in a voxel cluster decrease with more resolution, but tract volume increase?

13. Learning aims  Learn different DTI analysis software and their strengths & weaknesses  Explore a real scientific question with different DTI approaches  Get to know pitfalls and possible difficulties on real data Haxby et al. (2000)

14. Avidan & Behrmann (2009)  Familiar vs. unknown faces elicit specific BOLD activation in healthy controls but not in CP patients in  left precuneus/posterior cingulate cortex  anterior paracingulate cortex  Outside the ‘core system’ for face processing  Hypothesis Structural changes in white matter tracts between these regions might underlie the functional differences  Target tract: Cingulum

15.  Measurements (for ROIs or tracts)  Fractional anisotrophy (FA)  Radial diffusivity (RD)  Transverse diffusitivity (TD)  Number of detected fibers (# fibers)  Number of voxels within detected tract (# voxels)  Approaches  Automatic fiber seeding based on fMRI group coordinates  Extraction of cingulum fibers based on anatomy (manual seeding)  ROI analysis of sup. cingulum with automatic seeding based on standard space coordinates  (probabilistic tracking from fMRI group coordinates, FSL)  Data: previously acquired from 17 controls & 6 patients  TR/TE = 4900/82ms; 6 directions; b = 850 s/mm 2 ; 1.6*1.6*3mm voxel size  Is this angular resolution sufficient for these regions (fiber crossing!)?

16.  Transformation of fMRI MNI coordinates in native space (FSL FLIRT)  Construction of spheric ROIs around these coordinates (MATLAB)  Extraction of tracts traversing both ROIs (ExploreDTI)  Only about 1/3 of the subjects had tractable fibers  Increasing the radius of the ROI did not solve the problem background: FA values precuneus / posterior cingulate cortex anterior paracingulate cortex ROIs: 18mm diameter

17.  Analysis with DTI Studio, manual seeding by 2 independent investigators  Comparison of left & right cingulum in healthy controls and DTI patients  Results (whole tracts as ROI)  Inter-rater reliability: >.8  No group differences in corpus callosum (CC) ▪ control tract  FA & TD larger in left than in right cingulum ▪ consistent with literature  Significant differences in # fibers total   in line with fMRI data: no activation of left precuneus / PCC in patients ( *) *

18.  Analysis with Explore DTI, MNI coord of ROI transformed in native space  Results (only ROI voxels included)  Larger FA value left than right in controls can be explained by a smaller RD  fibers more directed  TD left in CP patients smaller than in controls  fibers more directed in controls  in line with fMRI data: activation of left precuneus/PCC in controls but not in patients

19.  Automatic seeding based on fMRI data fails  Possibly due to large inter-individual differences – BUT no individual fMRI available  Possibly due to insufficient tractability with 6 direction data – higher angular resolution data is acquired at the moment  ExploreDTI can model multiple fibers in a voxel (CSD)  Analysis data-driven, no operator bias  Manual cingulum tracking  High inter-rater reliability due to ‘standardized’ method of ROI definition  DTI Studio: easy-to-use & user-friendly GUI, ideal for exploration and manual intervention BUT supports only tensor model  Results in controls are consistent with literature  Automatic seeded ROI analysis  No manual intervention, no operator bias  Besides ILF and IFOF the left cingulum is another tract involved in face processing that seems to be compromised in CP patients

20.  Seong-Gi Kim & Bill Eddy  Kwan-Jin Jung  Marlene Behrmann  John Migliozzi  Tomika Cohen  Rebecca Clark  NIH