Cognitive and Brain Aging in the Baltimore Longitudinal Study of Aging

1. Cognitive and Brain Aging in the Baltimore Longitudinal Study of Aging Susan M. Resnick, Ph.D. Laboratory of Personality and Cognition National Institute on Aging

2. Cognitive and Brain Aging in Older Adults • What is the background upon which drug abuse is superimposed? • Which aspects of cognition show age-related decline in individuals without dementia? • How does the brain change with age? • Structural changes • Functional changes • fMR probes of specific regions

3. Baltimore Longitudinal Study of Aging BLSA • Study initiated in 1958 • Women studied since 1978 • Highly educated community-dwelling sample • GRC visits every 2 years for 2 1/2 days • Behavioral and physical assessments • Prospective diagnoses of dementia • Information on alcohol and smoking but no systematic information on other substance abuse

4. Age Effects Vary Across Specific Cognitive Functions • Some abilities are preserved throughout the lifespan, e.g. over-learned skills such as Vocabulary Vocabulary

5. Age Effects Vary Across Specific Cognitive Functions • Other specific functions show declines • Different abilities may begin declining at different ages • Different abilities may decline at different rates Benton Visual Retention Test BVRT

6. Age Differences and Longitudinal Changes on the BVRT Cross-sectional Longitudinal

7. California Verbal Learning Test

8. Longitudinal Change in Delayed Verbal Memory N = 266 N = 345 Neurology 2003

9. CVLT Long Delay Free Recall: Rates of Change are Variable Across Individuals Age > 60 Mean 72.3

10. LPC Neuroimaging Study Early Markers of Alzheimer’s Disease and Cognitive Decline Prior Cognitive Testing Annual Evaluation Men and Women (Age 55-85) Without Prior Neurologic or Severe Cardiovascular Disease MRI PET-CBF Neuropsychological Testing Brain Structure Rest Ischemic Change Verbal Memory Figural Memory

11. LPC Neuroimaging Assessments: 2/10/94 through 6/03/04 ASSESSMENT MEN WOMEN TOTAL 1 94* 69* 163 2 88 63 151 3 82 59 141 4 77 59 136 5 73 57 130 6 71 55 126 7 68 53 121 8 61 45 106 9 48 38 86 10 16 13 29 TOTAL 678 511 1189

12. Goals • To determine the rates of structural and functional brain changes as a prerequisite for identification of disease. • To determine whether some regions are more vulnerable to tissue loss and functional changes. • To identify brain changes that predict cognitive impairment and dementia. • To identify factors that modify brain-behavior associations in aging.

13. Variability in BLSA Brain Morphology (N = 18)

14. MR Image Processing Using RAVENS Original Automated Skull-Stripping Manual Editing 1 2 3 Segmentation 158 Average Model 4

15. Cross-sectional: Both Gray and White Matter Volumes Are Negatively Correlated with Age White Volume Gray Volume 700 700 r = -.29 r = -.23 600 600 cm3 cm3 500 500 400 400 300 300 50 60 70 80 90 50 60 70 80 90 AGE AGE WOMEN MEN N = 116; Mean Age 70.4 (7.5) Resnick et al. Cerebral Cortex2000;10:464

16. Longitudinal: Brain and CSF Volumes are Measured with High Reliability over Four Years Brain (G+W) Ventricles (n = 92, Mean Age= 70.4)

17. Annual Changes in Brain Volumes over 4 Years Resnick et al. J Neuroscience 2003

18. Longitudinal Brain Changes are Evident in Younger and Older Individuals *** Annual Rate of Change (cm3) *** p < .001

19. 4-year Gray Matter Loss in Specific Regions

20. Longitudinal Decreases and Increases in GM

21. Qualitative Changes in Tissue Composition Measured by Signal Intensity

22. Age Effects on Tissue Composition: Decreased Gray-White Signal Contrast Cross-sectional Longitudinal Tissue Contrast ***p < .001

23. Age Differences and Age Changes in Regional Cerebral Blood Flow (rCBF)

24. Men Women N46 37 Age (yrs)70.9 ± 7.3 70.6 ± 7.9 ApoE e4 (No. -/+) 34/12 24/13 Mild memory loss* (No. -/+) 41/5 31/6 *Clinical Dementia Rating (CDR) > 0.5 PET Sample Characteristics

25. L R CG L R INS IT PET Results: Cross-sectional Effects ofAge on Resting rCBF Older compared with younger individuals show selective decreases in rCBF in insular (INS), cingulate (CG), and inferior temporal (IT) regions.

26. L R L R Longitudinal Age Changes in Resting rCBF Longitudinal declines in rCBF over 4 years are observed in bilateral superior temporal, right middle temporal, inferior parietal and midline occipital regions.

27. L R L R Age Influences the RATE of rCBF Decline in the Mesial Temporal Lobe Older individuals show faster rates of decline in mesial temporal rCBF.

28. Functional Brain Changes with PET • Regional decreases in resting rCBF are observed in older individuals, with the greatest differences apparent in insular, cingulate and temporal regions, including hippocampus. • These changes reflect a combination of structural and functional brain changes. • With increasing longitudinal interval, we are investigating associations between specific brain and cognitive changes.

29. fMR Probes for Regions Vulnerable to Aging

30. Younger Adults Older Adults n 20 (10M/10F) 20 (10M/10F) Age (yrs) 28.7 (6.4) 69.3 (5.2) (range) 20-40 60-80 Education (yrs) 15.2 (2.4) 15.0 (3.6) MMSE 29.4 (1.0) 28.9 (1.6) CESD 7.5 (4.1) 4.7 (5.2) fMR OFC Sample

31. Younger Adults Activate Predicted OFC RegionsLamar, Yousem, Resnick NeuroImage 2004 Match - NonMatch NonMatch - Match Medial OFC (p<.01) Lateral OFC (p=.01)

32. Older Adults Activate Posterior RegionsLamar, Yousem, Resnick NeuroImage 2004 Match - NonMatch NonMatch - Match Association Cortices (ns) DLPFC (p=.006)

33. Conclusions • Cognitive and brain changes associated with drug abuse in the elderly will be superimposed upon a changing brain. • Some but not all cognitive functions show age changes. • Many but not all individuals show age changes in cognition and brain structure and function.

34. BLSA Cognition and Neuroimaging Study: Possibilities for Studies of Drug Abuse • Assessments of older adults continue and neuroimaging studies will be expanded with the NIA IRP MRI facility. • Potential to include additional assessments. • Abuse of prescription medications, including pain-killers will be most informative in this sample.

35. Collaborators: Neuroimaging Project NIAJHU Alberto Goldszal, PhD Christos Davatzikos, PhD Dzung Pham, PhD Michael Kraut, MD, PhD Melissa Lamar, PhD R. Nick Bryan, MD, PhD Scott Moffat, PhD Jerry Prince, PhD Stephanie Golski, PhD JHU PET Facility Robert Dannals, PhD Hayden Ravert, PhD

36. Neuroimaging Participant Selection • Inclusion: • Age 55-85 • Prior cognitive and memory assessment • Exclusion: • Existing neurologic disease, including dementia • (mild cognitive decline is not exclusionary) • Severe cardiovascular disease • (hypertension alone is not exclusionary) • Metastatic cancer • Weight greater than 300 lbs or other factors precluding neuroimaging assessment

37. Risk Factor: APOE 4 genotype is associated with accelerated hippocampal volume loss N = 13 N = 13 1.0 4- 4+ 0.5 0.0 -0.5 -1.0 Annual Rate of Change (%) -1.5 -2.0 -2.5 -3.0 -3.5 -4.0 Neurology 2000;55:134-136.

38. Increases in Temporal Horn Volumes Predict Mild Cognitive Impairment by CDR Score Temporal Horn Brain N = 82 N = 12 N = 82 N = 12 ***

39. Annual percentage changes from baseline, respectively for brain, gray, white, and ventricular volumes: entire sample -0.55 (0.5), -0.42 (0.9), -0.67 (1.5), 4.82 (2.5); subgroup with some medical problems -0.62 (0.5), -0.50 (0.9), -0.73 (1.6), 4.97 (2.7); very healthy -0.36 (0.4), -0.19 (0.8), -0.48 (1.4), 4.39 (1.5).

40. Automated analysis of specific regions with HAMMER and brain atlas* Template brain (*regional outlines provided by Noor Kabani) BLSA brain

41. Gray White

42. Age Differences and Age Changes in Spatial Rotation: Card Rotations Test

43. Age Effects on Regional White Matter Signal Intensities L R L L R L R R L L Age differences 4-year decline Davatzikos and Resnick. Cerebral Cortex 2002;12:767-771

44. Analysis of 4-Year Change in MRI Volumes: Sample Characteristics MEN WOMEN TOTALN 50 42 92 Age (yrs) 70.5 ± 6.4 70.4 ± 7.7 70.4 ± 7.0 Education (yrs) 16.0 ± 3.2 16.2 ± 2.4 16.1 ± 2.8Handedness (R:L) 47:3 40:2 87:5 Race (White: Nonwhite) 48:2 36:6 84:8

45. IMAGE PROCESSING AND STATISTICAL ANALYSIS: Preprocessing using SPM99 and the STAR algorithm for elastic stereotaxic normalization Voxel-based statistical analysis using customized SPM99 software Cross-sectional analysis: mean CBF across Years 1, 3, and 5 Longitudinal analysis: rates of change over time Significance threshold: p < .01 and cluster size > 35 PET Analysis

46. Summary: Age-Related Structural Changes • Regional brain structure can be measured reliably over time. • Both gray and white matter volumes show longitudinal declines even in the healthy elderly. • Increases in ventricular volumes are greater in older than younger individuals. • There are regional patterns to both tissue loss and qualitative changes in tissue composition.