Michael L Lipton, MD, PhD, FACR

1. Tools for the delineation of regional MR diffusion abnormalities in individual patients:Where have we been?Where should we be going? Michael L Lipton, MD, PhD, FACR

2. Two aims: • Take stock of the dMRI literature on TBI. • Make a case for patient specific identification of dMRI abnormalities.

3. Aim 1: Where have we been? “The overwhelming consensus of these studies is that low white matter FA is characteristic of TBI.” Should we expect this convergence?

4. Many studies – Many variations • >115 Studies • Mild-Severe TBI • Acute-Chronic TBI • Varied technique • Acquisition • Analysis • Varied outcome measures • If they were included • Relatively few longitudinal studies (but growing).

5. What then do we know? • Low FA is typical of TBI – regardless of details. • Certain brain regions are susceptible to TBI. • Low FA is associated with typical adverse TBI outcomes. • The prognostic role of dMRI remains uncertain.

6. What is missing? • Efficacy for prognosis. • Will more of the same type of longitudinal studies be revealing? • Will more sophisticated dMRI measures help? • Turnkey techniques usable in the clinic. • Quantification in individual patients.

7. An untenable hypothesis? Both drivers in this head-on collision will have injury at the same brain locations!!

8. A potential missing link? • dMRI measurements are typically determined in an unreasonable manner: • a priori • large ROI • No accounting of interindividual variation. • Patient specific delineation of dMRI abnormalities is needed.

9. Aim 2: The case for individualized measurement**: • Necessary for clinical use. • Arguably the more appropriate approach for research. **dMRI measures are extractedusing individualized techniques. Studies of efficacy, etc. employ these measures at the grouplevel.

10. What has been done: • ≈ 12 published studies • histogram • a priori ROI analysis • tractography • Voxelwise 1 vs. many T-test • Voxelwise Z-score • Analysis of individual measures from group studies • ROI, tractography • >100 Case reports

11. Requirements for individualized detection of dMRI abnormalities • Stable dMRI measure • Comparable normative data • Excellent co-registration • Metric for quantification • Threshold for abnormality • These steps are ROI-agnostic

12. An approach to individualized detection (voxelwise) • Enhanced Z-score Microstructural Assessment for Pathology (EZ-MAP) • Regression adjustment of dMRI data • Voxelwise Z-score • Bootstrap resampling of reference variance • Thresholding and clustering **Kim, et al., PLoS ONE 2013 **Lipton, et al. Brain Imaging and Behavior 2012

13. EZ-MAP: Three mTBI Patients Patient A Patient B Patient C

14. Presentation in the clinic

15. Can you do this in real life? Courtesy: Roman Fleysher, PhD • Normative data • Data quality • Data consistency • Quantitative analysis • Validation • Quality assurance Not for the faint of heart. • Need for accessible approaches.

16. Might dMRI be a better test than the literature suggests? • Most studies are based on group-level identification. • It is highly unlikely that injury mechanism is uniform across patients. • If injury variability leads to varied spatial distribution of pathology, simple group-wise comparisons may be very insensitive and poor prognostic tools. • “…their anatomical location does not always converge. This lack of convergence is not, however, surprising, given the heterogeneity of brain injuries…”** **Shenton, et al. Brain Imaging and Behavior 2012

17. Individualized dMRI measures outperform group-level identification • 26 mTBI patients/40 controls • Normal CT; sMRI, SWI, etc. • 3T DTI <2 weeks post mTBI • Rivermead PCS assessment at 1 year • Dual track identification of DTI measures • groupwise T-test (SPM) • individual EZ-MAP • Assess FA, RD and outcomes • Spearman correlation • Discriminant function analysis

18. Group vs. Individual Approaches Group-level Identification Individual-level Identification FA/RD vs. PCS FA: ρ= -0.35, p=0.094 RD: ρ= 0.43, p=0.036 Discriminant Function 91.3% correct classification 100% sensitivity 88.9% specificity (p=.012; Wilks’ lambda=.402) • FA/RD vs. PCS • FA: ρ =-0.138, p= 0.512 • RD: ρ=-0.112, p=0.594 • Discriminant Function • 73.1% correct classification • 62.5% sensitivity • 77.8% specificity • NOT significant

19. Conclusion • “Data driven” delineation of dMRI abnormalities is achievable • Acknowledging technical “overhead” • Requires local norms at present • Clinical implications • Immediate clinical translation to ID dMRI changes • Potential for prognostic inferences • Clinical research implications • Predictors in line with likely inter-patient differences • Potential for advancing prediction based on existing metrics

20. Acknowledgements • Craig A. Branch, PhD • Mimi Kim, ScD • Namhee Kim, PhD, PhD • Jennifer Provataris, MD • Richard B Lipton MD • Molly E Zimmerman, PhD • Jacqueline A. Bello, MD, FACR • Margo Kahn, BA • Hannah Scholl, BA • Miriam B Hulkower, MD • Sara B. Rosenbaum, MD • The Gruss Magnetic Resonance Research Center • NIH/NINDS (R01 NS082432) • The Dana Foundation David Mahoney Neuroimaging Program • The Einstein Aging Study (NIH/NIA P01 AG003949) • The Rose F. Kennedy IDDRC (NIH/NICHD P30 HD071593)

21. Absolute truth belongs • to • Thee alone. • Gotthold Ephraim Lessing • (1729-1781)