PARKINSON S PROGRESSION MARKERS INITIATIVE. PPMI 2018 Annual Investigators Meeting PPMI Data Blitz

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1 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2018 Annual Investigators Meeting PPMI Data Blitz

2 OVERVIEW Longitudinal Change of Clinical and Biological Measures in Early PD Presented by Andrew Siderowf (on behalf of Tanya Simuni) Cognitive-Psychiatric Disorders: Progression and Predictors Presented by Dan Weintraub Longitudinal alpha-synuclein measurements in CSF Presented by Brit Mollenhauer Two and Four Year Longitudinal Assessment of DAT Imaging Biomarkers Presented by John Seibyl Innovative Statistical Design and Analysis in PD Presented by Chris Coffey The Future of Clinical Trials Presented by Karl Kieburtz Baseline Predictors of Disease Progression Presented by Andrew Siderowf 2

3 How can PPMI study inform the design of Parkinson s disease clinical trials PPMI 5 year longitudinal data THANK YOU Tanya Simuni, MD on behalf of PPMI Consortium Parkinson s Disease and Movement Disorders Center Northwestern University Parkinson Foundation Center of Excellence

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6 CHANGE IN MDS-UPDRS TOTAL SCORE FROM BL TO YEAR 1 IN PD SUBJECTS Subjects that completed BL and Year 1 Visits p-value Variable All PD Untreated Treated with Treated with Untreated vs. Untreated vs. Levodopa/DA Subjects at Year 1 Levodopa/DA at Year 1 Other PD Med at Year 1 (N = 393) (N = 162) (N = 165) (N = 66) Levodopa/DA Other vs. Other Change in MDS-UPDRS Total Score N Completed Mean (SD) 7.45 (11.6) (10.7) 2.41 (11.4) 7.48 (11.2) (Min, Max) (-31.0, 60.0) (-17.0, 60.0) (-31.0, 32.0) (-19.0, 41.0) < *Time to evaluation was not collected at the 6 month visit* Year 1 Change in MDS-UPDRS Total Scores by Time to OFF Evaluation in Treated PD Subjects Variable Time to OFF Score at Year Hours >12 Hours p-value Change in MDS-UPDRS Total Score N Completed Mean (SD) 0.96 (13.6) 2.85 (10.7) (Min, Max) (-18.0, 31.0) (-31.0, 32.0) *Treatment status for this analysis is defined as treatment with either levodopa or dopamine agonists* 6

7 MDS-UPDRS OFF vs. ON in Treated PD Subjects Total MDS Scores Total MDS Scores OFF vs. ON Totals in Treated PD Subjects Year 1 Year 2 Axis Title Part III MDS Scores Part III MDS Scores OFF vs. ON in Treated PD Subjects Year 1 Year 2 Axis Title Levodopa/DA OFF Levodopa Only OFF Levodopa/DA OFF Levodopa Only OFF Levodopa/DA ON Levodopa ON Levodopa/DA ON Levodopa ON N Participants Treated with Levodopa/DA Treated with Levodopa Only OFF ON Off ON Year Year P Values Treated with Levodopa/DA Treated with Levodopa only Year 1 Total Year 1 Part III Year 2 Total Year 2 Part III <

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9 SUMMARY OF THE RESULTS SO WHAT???»Unique dataset that provides data on the natural Hx of PD progression using clinical scales, imaging and biologics»essential data for design of future disease modification trials»data completeness is essential for the utility of the dataset»thank you!!!! Coordinators /Investigators Participants 10

10 QUESTIONS? 11

11 Cognitive-Psychiatric Disorders: Progression and Predictors Daniel Weintraub, MD

12 PD Cognition Over Time PD Subjects Variable Baseline Year 1 Year 2 Year 3 Year 4 Year 5 (N = 423) (N = 395) (N = 378) (N = 366) (N = 346) (N = 289) MOCA N Mean (SD) (2.3) (2.8) (3.2) (3.0) (3.6) (3.6) (Min, Max) (17.0, 30.0) (15.0, 30.0) (9.0, 30.0) (13.0, 30.0) (11.0, 30.0) (2.0, 30.0) MOCA <26 N No 327 (77.9%) 257 (65.6%) 253 (67.6%) 246 (67.8%) 238 (69.8%) 205 (72.4%) Yes 93 (22.1%) 135 (34.4%) 121 (32.4%) 117 (32.2%) 103 (30.2%) 78 (27.6%) MOCA <21 N No 416 (99.0%) 379 (96.7%) 354 (94.7%) 349 (96.1%) 320 (93.8%) 266 (94.0%) Yes 4 (1.0%) 13 (3.3%) 20 (5.3%) 14 (3.9%) 21 (6.2%) 17 (6.0%) 2 scores >1.5 SD below mean N Yes 46 (11.0%) 56 (14.2%) 49 (13.1%) 55 (15.2%) 49 (14.5%) 37 (13.4%) Site Investigator Diagnosis N Normal 97 (91.5%) 231 (85.2%) 309 (83.5%) 284 (78.0%) 265 (77.9%) 222 (79.9%) Mild Cognitive Impairment 9 (8.5%) 38 (14.0%) 58 (15.7%) 74 (20.3%) 63 (18.5%) 46 (16.5%) Dementia 0 (0.0%) 2 (0.7%) 3 (0.8%) 6 (1.6%) 12 (3.5%) 10 (3.6%) Data courtesy Chelsea Caspell-Garcia.

13 Comments Highly educated, motivated and relatively young cohort Raises possibility of practice effects and temporary positive impact of introduction of PD meds on cognition Good cohort for studying resiliency to cognitive decline Hopefully dropouts aren t differentially cognitively impaired For dementia Apply more stringent cut-off for MoCA (<21) Subset of patients developing significant cognitive impairment relatively quickly For MCI Apply site investigator diagnosis Consider using HC norms instead of published norms Years 5-10 data will be more informative Mean age by year 10 will be 72, likely an inflection point

14 Supporting Evidence Wyman-Chick. Movement Disorders 2018; /mds Pigott et al. Neurology 2015;85:

15 PD Psychiatric Symptoms Over Time Variable Baseline Year 1 Year 2 Year 3 Year 4 Year 5 PD Subjects (N = 423) (N = 398) (N = 378) (N = 366) (N = 348) (N = 293) GDS-15 N Not Depressed (<5) 364 (86.1%) 330 (83.5%) 309 (82.2%) 304 (83.1%) 283 (82.5%) 228 (79.7%) Depressed ( 5) 59 (13.9%) 65 (16.5%) 67 (17.8%) 62 (16.9%) 60 (17.5%) 58 (20.3%) STAI-State N Score < (84.8%) 343 (86.8%) 332 (88.1%) 321 (88.2%) 302 (88.0%) 252 (88.1%) Anxious ( 45) 64 (15.2%) 52 (13.2%) 45 (11.9%) 43 (11.8%) 41 (12.0%) 34 (11.9%) QUIP N No Disorders 335 (79.4%) 342 (86.6%) 301 (79.8%) 281 (76.8%) 257 (74.9%) 210 (73.7%) ICD behaviors ( 1) 87 (20.6%) 53 (13.4%) 76 (20.2%) 85 (23.2%) 86 (25.1%) 75 (26.3%) MDS-UPDRS Part I Apathy N (97.4%) 366 (92.7%) 342 (90.7%) 338 (92.3%) 309 (90.1%) 251 (87.2%) >1 (Apathy) 11 (2.6%) 29 (7.3%) 35 (9.3%) 28 (7.7%) 34 (9.9%) 37 (12.8%) MDS-UPDRS Part I Psychosis N (100.0%) 395 (100.0%) 373 (98.9%) 361 (98.6%) 336 (98.0%) 281 (97.6%) >1 (Psychosis) 0 (0.0%) 0 (0.0%) 4 (1.1%) 5 (1.4%) 7 (2.0%) 7 (2.4%) Healthy Controls (N = 196) (N = 185) (N = 174) (N = 167) (N = 162) (N = 140) GDS-15 Depressed ( 5) 13 (6.6%) 10 (5.4%) 8 (4.6%) 7 (4.2%) 5 (3.1%) 9 (6.6%) STAI-State Anxious ( 45) 9 (4.6%) 11 (5.9%) 6 (3.4%) 5 (3.0%) 7 (4.3%) 6 (4.4%) QUIP ICD behaviors ( 1) 37 (18.9%) 37 (20.0%) 29 (16.7%) 27 (16.2%) 24 (14.8%) 21 (15.3%) MDS-UPDRS Part I Apathy >1 1 (0.5%) 1 (0.5%) 1 (0.6%) 2 (1.2%) 1 (0.6%) 1 (0.7%) MDS-UPDRS Part I Psychosis >1 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)

16 Comments Disorders Depression and Apathy: increasing over time Anxiety: puzzling, but little non-motor fluctuations in early PD ICDs and Psychosis: dopamine agonists less commonly used as initial therapy, long lag time after starting medications (ICDs), cognitively healthy group (psychosis) Comparing change in rates over time in PD relative to healthy controls of value Depression goes from 2x-3x as common in PD as HCs

17 Biomarker MoCA<26 or Change in MoCA score Definition of cognitive impairment Cognitive test deficits Site investigator diagnosis ( 2 tests >1.5 SD below (PD-MCI or PDD) mean) DAT imaging - - ipsilateral caudate * contralateral caudate contralateral putamen CSF - - CSF Aβ 1-42 * Structural MRI (volume) entorhinal * superior temporal * caudal middle frontal lateral orbitofronal - lateral occipital * lateral orbitofrontal * fusiform * superior temporal superior parietal Structural MRI (thickness) precentral - caudal anterior cingulate fusiform DTI (FA) DTI (MD) - - inferior cerebellar peduncle * Genetics - - COMT val158met (val/val) BDNF val66met (val/val) # Significant in univariate analysis, survived FDR correction, and significant in multivariable analysis * Baseline biomarker value Longitudinal biomarker value Caspell-Garcia et al. PLoS ONE 2017;12(5): e

18 Predicting Future Psychosis in Early PD Reports of Psychotic Symptoms, n Characteristic 0 (n=285) 1 (n=54) 2 (n=84) P value Scales for Outcomes in Parkinson s Disease Autonomic, mean (SD) 8.5 (5.5) 10.2 (6.3) 12.2 (6.5) < REM Sleep Behavior Disorder Screening Questionnaire Median Score (IQR) 3 (2-5) * 3.5 (2-6) 5 (3-7) ** Score >5, n (%) 91 (32.2) * 21 (38.9) 46 (55.4) ** Epworth Sleepiness Scale Median Score (IQR) 5 (3-8) 5 (3-7) 6 (3-10) 0.03 Score 0, n (%) 34 (11.9) 6 (11.1) 26 (31.0) <0.001 * n=283. ** n=83. Adjusted for age and sex in multivariate regression, the following baseline symptoms were associated with increased risk of reporting psychotic symptoms on 2 occasions: Greater autonomic symptoms (OR 1.07, P=0.002) Presence of REM sleep behavior disorder (OR 1.9, P=0.021) Excessive daytime sleepiness (OR 2.5, P=0.003) IQR, interquartile range; OR, odds ratio; REM, rapid eye movement; SD, standard deviation Cholinergic nucleus 4 Cholinergic nuclei 1, 2, and 3 P=0.51 P=0.02 P=0.04 P=0.67 P=0.31 P=0.17 Barrett et al. Neurology. 2018; /WNL

19 SSRI Use and Cognitive Decline Hypothesis: Serotonin may modulate Aβ metabolism through upregulation of alpha-secretase Kotagal et al. Annals Neurology. 2018; /ana

20 QUESTIONS? 21

21 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI Data BLITZ Longitudinal alpha-synuclein measurements in CSF Brit Mollenhauer and Douglas Galasko, Biologics Working Group

22 TRACKING PROGRESS IN TRACKING PARKINSON S Parkinson s disease is a progressing disorder by motor and non-motor symptoms and dopamine transporter imaging MDS-UPDRS Total Score over time 3.0 Mean Striatum over time MDS-UPDRS Total Score Mean Striatum Report generated on data submitted as of: 04Apr Month Mean for PD Subjects OFF Mean for PD Subjects ON Mean for Healthy Controls Mean for SWEDD Subjects Report generated on data submitted as of: 04Apr Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects But NOTE: there Points are isonly noplotted PD if 5 or specific more subjects have progression data at that visit. in biochemicalnote: markers Points are only in plotted the if 5 or cerebrospinal more subjects have data at that fluid visit. in 12 months 2300 Alpha-Synuclein over time 60 Tau over time 450 A-beta over time Alpha-Synuclein Tau A-beta Report generated on data submitted as of: 13Apr Report generated on data submitted as of: 13Apr Report generated on data submitted as of: 13Apr Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects 23 NOTE: Points are only plotted if 5 or more subjects have data at that visit. NOTE: Points are only plotted if 5 or more subjects have data at that visit. NOTE: Points are only plotted if 5 or more subjects have data at that visit.

23 DISCREPANCY OF CSF ALPHA-SYNUCLEIN MEASUREMENTS BETWEEN 2013 AND 2016 RE-ANALYSIS OF SAMPLES Possible reasons for this discrepancy: -preanalytical sample handling (e.g. gradient effect) -analytical variability (non-automated performance of >50 96 well plate ELISA) -other reasons 24

24 SECOND LONGITUDINAL ANALYSIS OF CSF ΑLPHA-SYNUCLEIN IN PPMI (MEASUREMENTS OF 2016) o PD participants who dropped out after baseline had significantly worse cognitive decline shown on HVLT (p=0.039), SDMT (p<0.001), LNS (p=0.031) and in BJLOT (p=0.002). o Prodromal hyposmic participants with baseline data only had greater cognitive decline (by HVLT; p=0.0007, by SDMT, p=0.045) and lower mean caudate striatal binding ration (SBR) values (p=0.011) on DaTscan. o irbd participants with milder irbd by RBDSQ (<6) were more likely to drop out after baseline assessment (p=0.029). 25

25 PPMI: CSF ΑLPHA-SYNUCLEIN RE-ANALYSIS OF 2016 BY ELISA IN (1) PD AND HEALTHY SUBJECTS UNTIL 36 MONTHS FOLLOW-UP (2) IN PRODROMAL SUBJECTS UNTIL 12 MONTHS FOLLOW-UP 1. CSF α-syn levels in PD were significantly lower in across all visits 2. CSF total α-syn levels in PD decreased slightly (p=0.031); levels showed a trend to increase in the control group (p=0.054). 3. Prodromals: The hyposmic participants showed the lowest mean CSF α-syn levels, while irbd participants had intermediate levels. CSF α-syn remained relatively stable over time. 3. Changes in CSF α-syn were not related to changes in MDS- UPDRS III, MoCA and DaTscan values (p>0.05) in PD 4. We again found a longitudinal relationship between CSF α- syn and LED based on dopamine replacement (p=0.023) (not just dopamine agonists, p=0.145) 5. No association with genetic variants and SNCA transcripts Mollenhauer et al., Neurology under review 26

26 QUESTIONS? 27

27 Two and Four Year Longitudinal Assessment of DAT Imaging Biomarkers in a Progressing Parkinson Disease Cohort: Implications for Clinical Trial Design John P Seibyl, MD on behalf of the PPMI Investigators Institute for Neurodegenerative Disorders, and Invicro, New Haven, United States PPMI Annual Meeting 2018 Data Blitz 411

28 Measuring DAT changes in de novo PD with 123-I Ioflupane SPECT over four years RATIONALE: Prior studies show loss of striatal signal Parkinson's patients studied longitudinally with 123-I Ioflupane SPECT. These studies demonstrate annual loss approximately 7 to 10% of SBR per year, but with significant between subject variance. The purpose of the present investigation was to evaluate different analytic approaches in a large PD cohort studied over four years with serial DAT SPECT. RESEARCH QUESTIONS: 1. Do analytic strategies incorporating curve fitting applied to serial within subject longitudinal SBR data increase the signal size and reduce the variance compared with standard baseline-follow-up SBR change measures? 2. What are the implications of scan analysis method on clinical therapeutic trial design and sample size estimates?

29 METHODS 343 PD patients in PPMI had serial ioflupane SPECT scans at baseiine, 1, and 2 years post enrollment, 271 PD patients had an additional 4 year scan Employed small region of interest template previously described for developing regional specific binding ratios (SBR). Strategies to measure SBR change follows two approaches.

30 ANALYSIS METHODS Method 2 Exponential fit Delta and % change from equation Method 1 Standard analysis Delta SBR= - SBR(y0)-SBR(y4) % change = delta SBR/SBR(y0) Years post Baseline Scan Compare variance and signal:noise for each measure as well as strength of correlation with motor symptoms and power analyses for detecting change in clinical trials.

31 Mean Regional Percent Change SBR by Analysis Method % c h a n g e b y r e g i o n / a n a l y s i s S t d 1 Y S t d 2 Y i = ip s ila te r a l c = c o n t r a l a t e r a l f o l l o w e d b y c = c a u d a t e a p = a n t. p u t a m e n p = p u t a m e n s = s tria tu m m s = m e a n s t r i a t u m % C h a n g e S B R Means SD E x p 2 Y S t d 4 Y E x p 4 y S t d 1 Y S D S t d 2 Y S D E x p 2 Y S D S t d 4 y s d E x p 4 Y s d 4 0 i c c c i a p c a p i p c p i s c s m s n = y n = y

32 Sample Size Estimates by Striatal Region, Trial Duration, and Analysis Method S a m p l e s i z e b y r e g i o n S a m p l e s i z e S t d 1 y d S td 1 y % S t d 2 y d S td 2 y % S t d 4 y d S td 4 y % E x p 2 y d E x p 2 y % E x p 4 y d Observations: - Exp analysis better than std - % change better than delta SBR at 2,4 y, but not 1 y - Ipsi better than contra - Contra put worst E x p 4 y % I P S I C A U D C O N T R A C A U D IP S I A P C O N T R A A P IP S I P U T C O N T R A P U T IP S I S T R IA C O N T R A S T R I A M E A N S T R I A A S S U M E S : 5 0 % e f f e c t s i z e 8 0 % p o w e r, p < , 2 - t a i l e d

33 Best Sample Size Estimate by Method, Normalized to 2 Y Standard Analysis P e r c e n t 2 Y s a m p l e s i z e % 2 y S a m p l e Delta SBR are green % Change SBR are blue Best region used 0 S t d 1 y d S t d 1 y % S t d 2 y d S t d 2 y % S t d 4 y d S t d 4 y % E x p 2 y d E x p 2 y % E x p 4 y d E x p 4 y % V M A T 2 Y N=17

34 Regional striatal SBR based on based on monexponential curve fits of 4 year, 4 scan data extrapolating 6 y prior and 11 y post baseline shows similar curves apparently phase shifted M ean curves (n=271, 4 Y data) M ean curves (n=271, 4 Y data) 4 3 IP C A C CA 4 3 IP C A C CA IP A P S h ift (y ) IP A P C A P 6.0 SBR 2 C A P IP P SBR 2 IP P C P C P YEARS YEARS Shifting the curves back to right such that they overlap results in an estimate of the years that region is ahead in the neurodegenerative process relative to ipsilateral caudate.

35 Conclusions 1. Exponential curve fitting analysis of serial longitudinal DAT SPECT results in similar change in SBR signal as the standard analysis, but with much less noise. 2. This permits sample sizes for the exponential analysis to be roughly half that of the standard analysis for comparable power for detecting a 50% slowing in rate of decline in a treatment v placebo cohort. 3. Different striatal sub-regions show better (ipsi striatum, ipsi ant putamen, mean striatum) or worse (contra putamen) power based on their signal to noise characteristics. 4. The different outcome measures, delta SBR v % change SBR, provide comparable power for the standard analysis, but the % change outcome is more robust with the exponential method. 5. Preliminary VMAT PET in a limited n shows great promise. 6. Finally, speculative extrapolation of mean striatal subregion exponential curves suggest a 10.5 year head start in SBR signal change for contra putamen over ipsi caudate.

36 QUESTIONS? 37

37 INNOVATIVE STATISTICAL DESIGN & ANALYSIS IN PD Christopher S. Coffey Department of Biostatistics University of Iowa May 02, 2018

38 OVERVIEW The traditional approach to clinical trials tends to be large, costly, and time-consuming. There is a need for more efficient clinical trial design, which should lead to an increased chance of a successful trial that answers the question of interest. Hence, there is increasing interest in innovative trial designs. For example, adaptive designs allow reviewing accumulating information during an ongoing clinical trial to possibly modify trial characteristics. 39

39 ENRICHMENT DESIGNS An adaptive enrichment design fulfills the desire to target therapies to patients who can benefit the most from treatment. The first study period reveals participant groups most likely to benefit from treatment (discovery phase). Subgroup members are then randomized to treatment groups (validation phase). Hence, study power is increased (sample size decreased) by focusing only on subgroups most likely to show benefit. 40

40 SAMPLE SIZE RE-ESTIMATION A sample size re-estimation (SSR) design refers to an adaptive design that allows for adjustment of sample size based on a review of the interim data. An internal pilot (IP) design refers to an SSR used to reassess nuisance parameters (only) mid-study. With moderate to large sample sizes, IP designs can be used to make appropriate modifications with minimal (if any) inflation of type I error rate. Thus, there is little reason (statistically) not to do this for most clinical trials! 41

41 MDS-UPDRS TOTAL SAMPLE SIZE Hypothetical Early PD Clinical Trial: Required sample size for detecting change in MDS- UPDRS OFF scores over 1 year (using PPMI Data) TIME Mean (SD) Change From Baseline Power All 7.5 (11.6) 80% 90% Treated 5.0 (12.0) 80% 90% Untreated 10.0 (10.6) 80% 90% Reduce by 50% Reduce by 25%

42 MDS-UPDRS TOTAL SAMPLE SIZE Hypothetical Clinical Trial: Power greatly impacted by frequency of subjects who start therapy within the 1 year period Can base sample size calculation on best guess Two adaptive options: Enrichment Design: Identify those unlikely to require treatment during first year Internal Pilot Design: After some fraction of subjects have completed one year of follow-up, estimate the percentage starting therapy If different from original assumption, re-estimate sample size based on revised estimate 43

43 QUESTIONS? 44

44 THE FUTURE OF CLINICAL TRIALS CENTER FOR HEALTH + TECHNOLOGY DO THE UNPRECEDENTED Working to enable anyone, anywhere to receive care, participate in research, and benefit from the resulting advances. Karl Kieburtz

45 PPMI PD DRUG VISUALIZATION Predictive Disease Modeling + Simulation

46 PPMI PD DRUG VISUALIZATION (PD-RX) Work by Monica Javidnia, Chris Snyder, Charles Venuto 47

47 PPMI PD DRUG VISUALIZATION (PD-RX) Levodopa/Carbidopa (28%) Levodopa/Carbidopa dose adjustment (15%) Rasagiline (1.5%) Work by Monica Javidnia, Chris Snyder, Charles Venuto 48

48 PREDICTING DISEASE PROGRESSION Random Forest models constructed from PPMI to predict future MDS- UPDRS Part III scores Work by Sam Lerman, Charles Venuto 49

49 EMERALD PD Leveraging Technology to Advance Care + Research

50 IN COLLABORATION WITH MIT, WE ARE NOW USING CELLULAR WAVES TO MONITOR GAIT AND MOVEMENT Source: Emerald-PD Overview: We enrolled 7 participants with PD Participants homes were fitted with the Emerald device and observed for 8 weeks Aims of the study: Assess ability of device to measure disease features in the home Develop algorithms to characterize abnormal movements Assess response to medication Detect and quantify previously unmeasured symptoms Measure gait, sleep, and bathroom usage 51

51 THE EMERALD DEVICE CAPTURES NOVEL DISEASE FEATURES IN REAL TIME Confidential Source: Tarolli et al., in development (confidential) 52

52 WE ARE DISCOVERING TRENDS IN SLEEP IN PD THAT WERE NOT PREVIOUSLY KNOWN Midnight Blue = time in bed Red = large movements Noon 53

53 QUESTIONS? 54

54 Baseline Predictors of Disease Progression by Andrew Siderowf

55 LIST OF BASELINE PREDICTORS AND OUTCOMES

56 SIGNIFICANT CLINICAL PREDICTORS ACROSS MODELS Outcome Baseline predictor Gender (male) UPSIT SCOPA- AUT Clinical site ESS RBDSQ Total on NS NS NS NS Total off NS NS NS NS Part 3 on NS NS NS NS Part 3 off NS NS NS NS NS Mean striatum NS NS NS NS NS < Mean putamen NS NS NS NS NS

57 SIGNIFICANT BIOMARKER PREDICTORS ACROSS MODELS Outcome Baseline predictor abeta mean striatum Total on NS Total off Part 3 on NS Part 3 off Mean striatum NA Mean putamen

58 CHANGE IN STRIATAL DAT AND OFF UPDRS AS A FUNCTION OF BASELINE ABETA perchg_striatum bl_abeta chg_updrs_totscore bl_abeta

59 QUESTIONS? 60

60 THANK YOU