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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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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



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



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



~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


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


- Alzheimer’s and vascular dementia

- Alzheimer’s and DLB


New Drugs: Hypotheses

(Summers Therapy Sept. 2011)

  • Amyloid
  • Tau protein
  • Inflammation
  • Oxidative Stress
  • Vascular
  • Disordered glucose metabolism

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

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

Experimental Design

Groups: Normal, early MCI, early AD

Goal for N: 20/group


ORNL simple 30

Working memory 15

Total 45


Why Working Memory Task?

Changes take place earliest in brain areas of short-term memory and progress


Actual Numbers Acquired and Analyzed

Groups SimpleWM

Normal 21 (15) 17

Early MCI 21 (16) 18

Early AD 18 (17) 11


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)

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



  • 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

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


  • 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


  • 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


  • 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)

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


Prevention: Summary

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

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.


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).


Sensitivity and Specificity

  • Sensitivity = ability to identify positive results; = TP


  • Specificity = ability to identify negative results; = TN



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.


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