Two aims: 1.Take stock of the dMRI literature on TBI. 2.Make a case for patient specific identification of dMRI abnormalities.

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?

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).

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.

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.

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

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.

Aim 2: The case for individualized measurement ** : 1.Necessary for clinical use. 2.Arguably the more appropriate approach for research. **dMRI measures are extracted using individualized techniques. Studies of efficacy, etc. employ these measures at the group level.

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

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

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

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

Presentation in the clinic

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

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

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

Group vs. Individual Approaches Group-level Identification FA/RD vs. PCS – FA: ρ =-0.138, p= – RD: ρ=-0.112, p=0.594 Discriminant Function – 73.1% correct classification – 62.5% sensitivity – 77.8% specificity – NOT significant 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)

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

Acknowledgements 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) Craig A. Branch, PhD Mimi Kim, ScD Namhee Kim, PhD, PhD Jennifer Provataris, MD Richard B Lipton MD Molly E Zimmerman, PhD

Absolute truth belongs to Thee alone. -Gotthold Ephraim Lessing ( )