Early Diagnosis of Alzheimer s Disease and MCI via Imaging and Pattern Analysis Methods. Christos Davatzikos, Ph.D.

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1 Early Diagnosis of Alzheimer s Disease and MCI via Imaging and Pattern Analysis Methods Christos Davatzikos, Ph.D. Director, Section of Biomedical Image Analysis Professor of Radiology Early detection of AD will be critical as treatments are developed A number of promising surrogate markers for pathology (MRI, PET-FDG, amyloid imaging, DTI, CSF biomarkers) Diagnosis of an individual? Early stages before MCI? Challenge: Sensitive and Specific Biomarkers for Individuals rather than groups Data from the ADNI study (Davatzikos et.al., NeuroImage, 27, 28)

2 Detecting spatially complex and subtle patterns of brain tissue loss? Healthy MCI Individual Diagnosis High-dimensional Pattern Classification (Machine learning) Evaluate spatial patterns of GM, WM, CSF, PET signal distribution Combine with other biomarkers, such as CSf t-tau, Aβ42, and p-tau181p Combine with genetic information (e.g. APOE genotype) Use these patterns to construct a classifier, using support vector machines w Region 2/ Biomarker 2 T-tau PET-post cingulate Anterior L-hipp P H Region1 / Biomarker 1 Spatial Patterns of Atrophy in AD GM WM Ventricles Data from ADNI (Fan, Davatzikos et.al., 27, Neuroimage)

3 Regions of most significant atrophy in MCI GM WM Ventricles Data from ADNI (Fan, Davatzikos et.al., 27, Neuroimage) Distinguishing pathologic from normal changes Regions of significant brain atrophy in a 4-year period High-Dimensional Pattern Classification Using Support Vectors Pattern Classification SPARE-AD Index - +

4 ADNI Study: Conversion from MCI to AD 239 MCI patients (75.2±7.3 y.o.) average12 months follow-up with a standard deviation of 6 months 69 MCI-C (76.86±6.88y.o) and 17 MCI-NC (74.47±7.35) mean baseline MMSE: MCI-C: 25.75±2.18; MCI-NC: 27.9±1.82. SPARE-AD Score distribution MCI Converters (MCI-C) MCI Non-Converters (MCI-NC) Trajectories of SPARE-AD Scores MCI-C MCI-NC part1 MCI-NC part2 MCI-NC part

5 Average baseline MMSE scores Average rate of change of MMSE per year MCI-C MCI-NC part 1 MCI-NC part MCI-NC part 3 Average baseline MMSE and rate of change of MMSE -.25 for MCI-C and three sub-groups of MCI-NC. GM differences between MCI-C and MCI-NC (Baseline) WM differences between MCI-C and MCI-NC (baseline) Differences in Rates of Change (MCI-C vs. MCI-NC) Leukoareosis GM atrophy

6 SPARE-AD and CSF Biomarkers SPARE-AD MCI-NC MCI-C ABeta42 (subset of 12 MCI patients) Sensitivity Specificity Classification rate AUC (%) (%) (%) SPARE-AD 94.73(89.86) 37.8(37.) 55.83(52.3).734(.66) SPARE-AD & t-tau SPARE-AD & Aβ SPARE-AD & t-tau & Aβ SPARE-AD & t-tau/ Aβ SPARE-AD & p-tau SPARE-AD & p-tau 181P / Aβ t-tau LR-TAA model t-tau /Aβ p-tau 181p p-tau 181P / Aβ t-tau & Aβ Penn Center of Excellence for Research on Neurodegenerative Diseases (CERND) AD, MCI, PD, PDD Multiple biomarkers: MRI, T1rho, PET- FDG, Evoked potentials, CSF, cognitive Emphasis on integration and relative merit evaluation of biomarkers

7 spare ad converters non converters MCI conversion: Sub-Study cohort : 41 patients from CERND (average age : 71.6, 18 males 23 females) Initial Diagnosis : MCI Clinical Follow-ups 12 converted to AD/AD probable (average age : 7.8) 29 remained as MCI (average age : 72) Biomarkers available CSF : t-tau and abeta42 MRI : SPARE AD score Histogram of SPARE-AD scores frequency 4 3 converters non converters spare ad + SPARE-AD score: MCI nonconverters MCI converters Importance of Precuneous and Adjacent WM frequency

8 MCI-Non-Converters: Positive vs. Negative SPARE-AD frequency 4 3 converters non converters spare ad Histogram of CSF t-tau scores (Luminex) frequency converters non converters ttau Histogram of CSF Aβ42 scores (Luminex) frequency 4 3 converters non converters abeta42

9 SPARE-AD vs. t-tau csf ttau 1 cni converters non converters spare ad SPARE-AD vs. abeta abeta converters non converters spare ad Biomarker Accuracy in Predicting Conversion MCI AD Sensitivity (%) Specificity (%) Classification rates (%) AUC SPARE-AD & CSF t-tau SPARE-ADc CSF t-tau/ Aβ CSF t-tau SPARE-AD & CSF t-tau & CSF Aβ42 SPARE-AD & t-tau/ Aβ p-tau 181P / Aβ SPARE-AD & CSF Aβ SPARE-AD & p-tau 181P / Aβ SPARE-AD CSF t-tau & CSF Aβ CSF Aβ

10 CORRELATION OF IMAGING PATERNS WITH COGNITIVE SCORES Word list memory learning score Cognitive scores vs SPARE AD 12 Word List Memory Learning Score SPARE - AD Very Early Markers: Normal Aging Data from the Baltimore Longitudinal Study of Aging (BLSA) NIA (Dr. Resnick), Johns Hopkins (Dr. Kraut), University of Pennsylvania (Dr. Davatzikos) now in the 15th year 15 initially healthy elderly participants Annual structural and PET O 15 scans + detailed neuropsychological examinations (now DTI and PET-PIB) Normal aging and very early markers of AD Age 5~59 6~69 7~79 8~89 >=9 Total Total Number of scans (subjects) 27 (3) 34 (42) 329 (44) 146 (2) 12 () 818 (19) % scans (subjects) with SPARE- AD > 1.32% (2.38%) 1.52% (%) 15.7% (15%) 25.% (N/A) 4.16% (3.67%)

11 This Sub-study Group MCI NORMAL No. of subjects Sex 1 1 No. of males No of left-handed 1 Years of education Mean (SD) Baseline Age Mean (SD) Age at Last Visit Mean (SD) MMSE at Last Visit Mean (SD) 15.8 (3.73) (3.22) (7.28) (6.85) 82.4 (6.59) (6.57) 25.8 (2.96) 29. (1.41) Average SPARE-AD scores of each of the 19 CN individuals plotted against average age over their follow-up period. T-statistic voxel-wise maps of GM and WM RAVENS between (a) CNs that had negative SPARE-AD (CNlike-CNs) minus CNs that had positive scores (AD-like-CNs); (b) 75% of CNs with the lowest SPARE-AD scores (CN-like CNs) minor those with the top 25% SPARE-AD (relatively more AD-like CNs).

12 Regions in which CNs with positive SPARE-AD had significantly higher GM RAVENS maps, indicating increased peri-ventricular abnormal WM tissue that appears gray in T1-weighted images and is segmented as GM. (Right) Similar results comparing the subjects with the top 25% of the SPARE-AD scores versus the bottom 75%. Rate of SPARE-AD change as a function of average age during follow-up period, for the 19 CN individuals Rate of SPARE-AD change as a function of age in 15 MCI participants

13 ROC curve: classification of MCI based on rate of SPS-score change MCI: SPARE-AD against MMSE Conclusions Imaging patterns are much more distinctive than conventional ROI measurements potential predictors of MCI AD conversion Combinations of imaging and CSF biomarkers might provide additional power Very early changes in brain structure are observed in cognitively normal elderly

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