Nancy B. Munro ORNL, retired Lee M. Hively Computational Sciences and Information Divi sion

1. Early Alzheimer’s Detection via Advanced EEG Analysis Nancy B. MunroORNL, retired Lee M. Hively Computational Sciences and Information Division Yang Jiang, University of Kentucky College of Medicine Charles D. Smith, MD, UK College of Medicine Gregory A. Jicha, MD, UK College of Medicine Xiaopeng Zhao, University of Tennessee Oak Ridge, Tennessee October 26, 2011

2. Acknowledgements University of Kentucky David Wekstein, William Markesbery, MD, dec. Adam Lawson and several other doctoral students Juan Li, UK & Chinese Academy of Sciences, Institute of Psychology, Beijing, China Luke Broster, MD/PhD student University of Tennessee: Joseph McBride, PhD student Thibaut de Bock, Satyajit Das, Maruf Mohsin, BME students (2009-10 senior design project) Robert Sneddon W. Rodman Shankle, MD

3. Outline • Introduction • Experimental design • ERP analysis and results (UK) • SVM analysis and results (UT) • Graph theoretic analysis (ORNL) • Prevention through delay

4. Rationale Alzheimer’s ~5.4 million Americans today ~16 million expected by 2050 Current costs ~$183 billion annually ~70% cared for at home Diagnosis of exclusion; confirmation only at autopsy Value of early diagnosis - Early intervention - Tool for drug discovery

5. Alzheimer’s Disease Alzheimer’s: late onset - onset age 60 and up - 4-20 year course to death Alzheimer’s: early onset - onset in 40’s, 50’s - 4-8 year course to death Mixed: - Alzheimer’s and vascular dementia - Alzheimer’s and DLB

6. Diagnosis via Analysis of Scalp EEG • Non-invasive • Simple • Relatively inexpensive • Rapid results • Other current approaches costly, invasive • MRI • PET • Neuropsychological testing • Spinal tap for biomarkers: amyloid, tau

7. Experimental Design Groups: Normal, MCI, early AD Goal for N: 20/group ProtocolsMin ORNL simple 30 Working memory 15 Total 45

8. Why Working Memory Task? Changes take place earliest in brain areas of short-term memory and progress

10. Actual Numbers Acquired and Analyzed Groups SimpleWM Normal 21 17 MCI 21 18 Early AD 18 11

11. Intra-Individual Variability • Minimize by: • All EEGs at same time of day • All subjects at ease • Same mental activity during protocol • No APOE-e4 allele • No co-existing brain conditions • No psychoactive drugs • Well-matched: age (76) • education (17yr)

12. Simple ORNL EEG Protocol Attach electrodes in standard 19-channel montage, then record scalp EEG: - 5 minutes eyes open - 10 minutes eyes closed, counting silently backwards while tap finger on each count - 10 minutes eyes closed, awake - 5 minutes eyes open - 30 minutes total De-identify, convert data to ASCII format: UK Data quality check: ORNL

13. Hybrid Working Memory Task Subjects were asked to hold the sample target object in mind and indicate whether each test object was the same as or different from the sample object by pressing one of two buttons using their Right or Left hand.

14. Results: UK Event-Related Potential (ERP) Analysis The MCI group is similar in accuracy of memory (above) to normal (NC), but ERPs (on right) of MCIs were identical to those of ADs (blue arrows, L frontal).

15. Tsallis Entropy: UT A measure of information in EEG signal; non-extensive ST ~1- variance of selected bins of signal variance of entire signal

16. Regions for Tsallis Entropy Functionals; UT

17. Results: UT, WM Task Data Support Vector Machine (SVM) Analysis Features: 12 Tsallis entropies for each brain region Radial basis kernel function Accuracy: 82% Sensitivity: 88% Specificity: 76% SVM analysis. An example of SVM classification using a radial basis kernel function. The features are averaged Tsallis entropy values of the frontal sites (abscissa) and that of left temporal sites (ordinate); N = 0, MCI = 1.

18. Sensitivity and Specificity • Sensitivity = ability to identify positive results; = TP TP + FN • Specificity = ability to identify negative results; = TN TN + FP

19. Results: UT, ORNL Protocol Data SVM analysis, some features from Snaedal et al. (2010)

20. ORNL Advanced Analysis • Graph-theoretic analysis under development • Uses existing ORNL technology to filter data and construct phase-space diagram • From that, network (graph) constructed and analysis performed • Much work remains

21. Phase-Space Analysis • Reconstruction of phase space (PS) to unfold dynamics: • y(i) = [xi, xi+] in 2D • y(i) = [xi, xi+, xi+2] in 3D • y(i) = [xi, xi+, …, xi+(d-1)] in general • Distribution function (DF) of PS points to capture process dynamics: shape and occurrence frequency

22. Further Steps: Graph-Theoretic Analysis • From phase-space diagram, network (graph) is constructed and analysis performed • Initial work applied to seizure data sets • No optimization of parameters as yet

23. Conclusions • Can discriminate normal from MCI and AD • Both via ERP and SVM analyses • Nonlinear analysis of WM and simple protocols shows promise • Work ongoing on ORNL protocol data • Further work needed for clinical utility

24. Future Work • Acquire data for more participants • Continue to improve analyses: UT • Apply ORNL graph-theoretic method • Enhance accuracy with few electrodes • Implement on laptop or PDA

25. Vision • A device usable in • Primary care setting • Community hospitals • For drug discovery • Adapt for other neurodegenerative diseases • Diffuse Lewy Body Disease • Parkinson’s Disease • Fronto-temporal dementia

26. Prevention • Risk factors: Not Controllable • Age • Family history • Genetic makeup • Risk factors: Controllable • Smoking • High blood pressure • High cholesterol • Poorly-controlled diabetes • Poor lung function • Sleep apnea • Lack of exercise • Poor diet • Education

27. Prevention • Exercise • New neurons in hippocampus (memory area) • Vigorous exercise reduces AD incidence • Cognitive activity: new neuronal connections • Study foreign language • Learn to play musical instrument • Brain games (crosswords, Sudoku, etc.) • Mindfulness meditation • Diet rich in antioxidants, not pills; Mediterranean (combats inflammation)

28. Healthy Aging Good physical health = Great aging brain  Regular physical exercise Positive emotions  Positive relationships  Limiting chronic stress “Memory and the Aging Brain.” Steven W. Anderson, PhDThomas J. Grabowski, Jr. MD The University of Iowa. June 2003

29. Prevention: Summary • What’s good for your heart and lungs is good for your brain! • Prevention through delay

31. Questions?

32. Backup Slides

33. New Drugs: Hypotheses (Summers Therapy Sept. 2011) • Amyloid • Tau protein • Inflammation • Oxidative Stress • Vascular • Hibernation

34. EEG Analysis: Filter Eye Blink • Time-serial data: xi • Raw (EEG) data (3 s) • Artifact (eyeblink) removal • Artifact-filtered data