Overview

1. Longitudinal Changes In White Matter Disease and Cognition in the First Year of the Alzheimer’s Disease Neuroimaging Initiative Owen Carmichael1, Christopher Schwarz1, David Drucker1, Evan Fletcher1, Danielle Harvey1, Laurel Beckett1, Clifford R. Jack Jr.2, Michael Weiner3, Charles DeCarli1, and the Alzheimer’s Disease Neuroimaging Initiative. University of California, Davis 1; Mayo Clinic 2; University of California, San Francisco In press, Archives of Neurology

2. Overview • Investigate WMH • In association with diagnosis and cognition at baseline • As a predictor of subsequent cognitive trajectory • Examine relation between WMH • Markers of AD pathology • Vascular risk

3. Demographics

4. Methods: WMH Detection from MRIBayesian Inference Model Use two key sources of information to determine whether there is a white matter hyperintensity at each voxel: ? ? Prior knowledge Do WMHs tend to occur at this voxel in general? The image signal Does it look like a WMH on PD, T1, and T2 MRI? Combine these two sources of information in a Bayesian inference framework.

5. Example UCD ADC Result PD T1 T2 Likelihood of WMH from PD, T1, T2 Spatial Prior: Prior Probability of WMH Posterior Probability of WMH From PD, T1, T2 and Spatial Prior Gold Standard WMH Map from FLAIR

6. ADNI subjects had WMH burden at baseline that is comparable to that of population-based studies • Normal and MCI had similar WMH distributions; increased WMH burden in AD with suggestions of anterior-posterior progression (Agrees with Yoshita et al. Neurology 2006) Results: Baseline WMH Burden Normal MCI AD

7. Prediction of MMSE

8. Baseline Cognition and WMH

9. Baseline WMH and Longitudinal Differences in Cognition

10. Change in WMH and Change in Cognition

11. Summary • AD was associated with significantly greater baseline WMH and rate of WMH accretion was lowest for normal and highest for AD • Vascular risk was significantly associated with baseline WMH and accretion in WMH • Baseline WMH volume was significantly associated with change in MMSE and ADAS-Cog including adjustment for brain and hippocampal volume

12. Summary II • Change is WMH volume was significantly associated with worsening scores in MMSE and ADAS-Cog independent of: • Age • ApoE4 • Vascular risk • Diagnosis

13. Conclusion I • ADNI subjects have substantial CVD burden that is increasing over time and negatively impacting cognitive function • The effects of CVD on cognition should be accounted for during biomarker evaluation in ADNI

14. Periventricular WMH are related to vascular risk—even in ADNI Charles DeCarli, Danielle Harvey, Laurel Beckett, Christopher Schwarz, David Drucker, Evan Fletcherand Owen Carmichael Imaging of Dementia and Aging laboratory, Department of Neurology and Center for Neuroscience, University of California at Davis. Davis, California, USA

15. Goal • Assess whether regional WMH is a viable biological marker for vascular brain injury in ADNI, a clinical trial cohort with an extremely mild profile of vascular risk

16. Study Design • ADNI • 409 subjects • MRI, Hachinski, CSF Measures • 75.6 + 7 years, • 40% Female • Diagnosis • 25% AD, • 48% MCI • 28% normal controls

17. Cubic Spline Warping Affine Registration Target T1 WMH Replacement WMH Mapping Segmentation DSE WMH MRI WMH Mapping Method

18. Pm Pp Pp Pm Pa Po Po Pa Cs Cg Cg B A Cs Periventricular Regions

19. Periventricular WMH and HSS

20. Predictors of Anterior Periventricular WMH

21. Predictors of Middle Periventricular WMH

22. Conclusions II • Vascular disease, even when crudely measured by the HSS, is associated with WMH even in ADNI • Periventricular WMH are associated with HSS independent of typical AD markers such as ApoE4 genotype, CSF Tau and amyloid beta, and diagnosis

23. Supported by NIH: U01 AG024904, P30 AG10129, R01 AG021028, R01 NS 29993, P01 AG12435, P01 AG0027232, R01 AG111101, R01 AG08122, R01 AG16495, R01 AG09029, ocarmichael@ucdavis.edu http://rope.ucdavis.edu/~owenc http://neuroscience.ucdavis.edu/idealab/