cognitive and brain aging in the baltimore longitudinal study of aging susan m. resnick, ph.d....
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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
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 changesFunctional changesfMR probes of specific regions
Baltimore Longitudinal Study of AgingBLSA
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
Age Effects Vary Across Specific Cognitive Functions
Some abilities are preserved throughout the lifespan, e.g. over-learned skills such as Vocabulary
Vocabulary
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
Age Effects Vary Across Specific Cognitive Functions
60 65 70 75 80 85
Age
5
7
9
11
13
15FemaleMale
Longitudinal Change in Delayed Verbal Memory
N = 266N = 345
Neurology 2003
Prior Cognitive Testing
Annual EvaluationMen and Women (Age 55-85)
MRIBrain StructureIschemic Change
PET-CBFRest
Verbal MemoryFigural Memory
NeuropsychologicalTesting
LPC Neuroimaging StudyEarly Markers of Alzheimer’s Disease and Cognitive Decline
Without Prior Neurologic or Severe Cardiovascular Disease
LPC Neuroimaging Assessments: 2/10/94 through 6/03/04
ASSESSMENT MEN WOMEN TOTAL1 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
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.
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4
OriginalOriginal Automated Skull-StrippingAutomated Skull-Stripping Manual EditingManual Editing
SegmentationSegmentation
MR Image Processing Using RAVENS
158 Average Model
Cross-sectional: Both Gray and White Matter Volumes Are Negatively Correlated with Age
Gray Volume White Volume
300
400
500
600
700
50 60 70 80 90
cm3
AGE
r = -.23
300
400
500
600
700
50 60 70 80 90cm
3
AGE
r = -.29
WOMEN MENResnick et al. Cerebral Cortex
2000;10:464N = 116; Mean Age 70.4 (7.5)
Longitudinal: Brain and CSF Volumes are Measured with High Reliability over Four Years
Brain (G+W) Ventricles
(n = 92, Mean Age= 70.4)
Brain Gray White Ventricles-8
-6
-4
-2
0
2
4
An
nu
al
Ra
te o
f C
han
ge (
cm
3)
1.221.451.39
-2.4-3.29
-3.06
-1.32
-2.76-2.39
-3.72
-6.05
-5.45
All (n = 92)
Some Medical (n = 68)
Healthy (n = 24)
Annual Changes in Brain Volumes over 4 Years Resnick et al. J Neuroscience 2003
BRAIN GRAY WHITE VENTRICLES-8
-6
-4
-2
0
2
4
-5.28-5.79
-2.40 -2.49-2.89
-3.30
1.141.75
<70 (N = 55)
70+ (N = 39)
An
nu
al R
ate
of
Ch
ang
e (c
m3)
Longitudinal Brain Changes are Evident in Younger and Older Individuals
***
*** p < .001
Age Effects on Tissue Composition:Decreased Gray-White Signal Contrast
0.22
0.24
0.26
0.28
0.30
0.32
50 60 70 80 90
AGE
W
M
r = -.49
Tis
su
e C
on
tra
st
Year 1 Year 3 Year 50.260
0.265
0.270
0.275
0.280
***p < .001
Cross-sectional Longitudinal
PET Sample Characteristics
MenMen WomenWomen
N 4646 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 Results: Cross-sectional Effects ofAge on Resting rCBF
L
L
R
R
Older compared with younger individuals show selective decreases in rCBF in insular (INS), cingulate (CG), and inferior temporal (IT) regions.
INS
CG
IT
Longitudinal Age Changes in Resting rCBF
L
L
R
R
Longitudinal declines in rCBF over 4 years are observed in bilateral superior temporal, right middle temporal, inferior parietal and midline occipital regions.
Age Influences the RATE of rCBF Decline in the Mesial Temporal Lobe
L
L
R
R
Older individuals show faster rates of decline in mesial temporal rCBF.
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.
fMR OFC Sample
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)
Younger Adults Activate Predicted OFC RegionsLamar, Yousem, Resnick NeuroImage 2004
Medial OFC (p<.01) Lateral OFC (p=.01)
Match - NonMatch NonMatch - Match
Older Adults Activate Posterior RegionsLamar, Yousem, Resnick NeuroImage 2004
Match - NonMatch NonMatch - MatchAssociation Cortices (ns) DLPFC (p=.006)
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.
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.
Collaborators: Neuroimaging Project
NIA JHU
Alberto Goldszal, PhD Christos Davatzikos, PhD Dzung Pham, PhD Michael Kraut, MD, PhDMelissa Lamar, PhD R. Nick Bryan, MD, PhDScott Moffat, PhD Jerry Prince, PhDStephanie Golski, PhD
JHU PET Facility
Robert Dannals, PhDHayden Ravert, PhD
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
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
4- 4+
An
nu
al R
ate
of
Ch
an
ge
(%
)Risk Factor: APOE 4 genotype is associated
with accelerated hippocampal volume loss
Neurology 2000;55:134-136.
N = 13 N = 13
Temporal Horn Brain
N = 82 N = 12 N = 82 N = 12
***
Increases in Temporal Horn Volumes Predict Mild Cognitive Impairment by CDR Score
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).
Automated analysis of specific regions with HAMMER and brain atlas*
Template brain
(*regional outlines provided by Noor Kabani)
BLSA brain
Age Effects on Regional White Matter Signal Intensities
L
L
L R
R
L
L
L RR
Age differences 4-year decline
Davatzikos and Resnick. Cerebral Cortex 2002;12:767-771
Analysis of 4-Year Change in MRI Volumes:Sample Characteristics
MEN WOMEN TOTAL
N 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 ± 2.8
Handedness (R:L) 47:3 40:2 87:5 Race (White:
Nonwhite) 48:2 36:6 84:8
PET Analysis
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
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.