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

Healthy Heart, Healthy Brain: Early Alzheimer’s Detection and Prevention. Nancy B. Munro, ORNL, 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

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

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  1. Healthy Heart, Healthy Brain: Early Alzheimer’s Detection and Prevention Nancy B. Munro,ORNL, 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 March19, 2013

  2. Acknowledgements University of Kentucky David Wekstein, PhD, William Markesbery, MD (dec.) Adam Lawson and several other doctoral students Juan Li, PhD, 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, PhD W. Rodman Shankle, MD

  3. Outline • Alzheimer’s disease • Alzheimer’s early detection • Gold standard methods • EEG analysis approach, results • Vision, future work • Prevention through delay

  4. Rationale Alzheimer’s ~5.4 million Americans today ~16 million expected by 2050 Current costs ~$200 billion in 2012; $1.5 trillion estimated by 2050 ~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. New Drugs: Hypotheses (Summers Therapy Sept. 2011) • Amyloid • Tau protein • Inflammation • Oxidative Stress • Vascular • Disordered glucose metabolism

  7. New Drugs • Amyloid-blocking (immunization; other): failure to improve late-stage AD in trials • Insulin via injection or nasal powder inhalation: improved memory • FDA requirement: efficacy for function and cognition

  8. Diagnosis via Analysis of Scalp EEG • Current approaches costly, invasive • MRI • PET • Neuropsychological testing • Spinal tap for biomarkers: amyloid, tau • EEG • Non-invasive • Simple • Inexpensive • Rapid results

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

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

  11. Actual Numbers Acquired and Analyzed Groups SimpleWM Normal 21 (15) 17 Early MCI 21 (16) 18 Early AD 18 (17) 11

  12. Intra-Individual Variability • Minimize by: • All EEGs at same time of day • All subjects at ease • Same mental activity during protocol • No ApoE4 allele • No co-existing brain conditions • No psychoactive drugs • Well-matched: age (76) • education (17yr)

  13. 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

  14. Results: UT, Resting EEG Data

  15. Conclusions • Can discriminate normal from MCI and AD • Both via ERP and advanced EEG analyses • Nonlinear analysis both of WM and resting EEG data show promise • Work ongoing on resting EEG data • Further work needed for clinical utility

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

  17. 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

  18. Prevention • Risk factors: Not Controllable • Age • Family history • Genetic makeup • Risk factors: Controllable • Smoking • High blood pressure • High cholesterol • Poorly-controlled diabetes • Depression • Sleep apnea • Lack of exercise • Poor diet/obesity • Education/cognitive inactivity

  19. Prevention • Eliminate preventable risk factors, e.g., smoking • Exercise • New neurons in hippocampus (memory area) • Regular 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)

  20. 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

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

  22. Questions?

  23. Backup Slides

  24. 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.

  25. 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).

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

  27. 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.

  28. 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 • Performs extremely well for seizure FW • Must be adapted to group comparisons

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