Transcript Presentation

Methodological issues for
scanning geriatric populations
Andy James
fMRI Journal Club
October 12, 2004
Topics
1) Participant selection criteria
2) Participants’ ability to perform task
3) Our ability to measure functional data
Relevance of Aging Research
Data from the US Bureau of the Census, 2000
Statistics for Participant Selection Criteria
Older Americans 2000: Key Indicators of Well-Being
Federal Interagency Forum on Aging-Related Statistics (Forum)
Conditions Affecting
Participant Selection Criteria
Neurological conditions
• depression
• strokes / infarcts
• memory impairment
Physical conditions
• cardiac pacemaker
• artificial joints
• dental fixtures
• aneurysm clips
• arthritis
• spine curvature
• tattoos
D’Esposito MD, Deouell L, and Gazzaley
A. (2003). Nature Reviews, 4, 1-11
Participants’ Ability to Perform Functional Task
Performance influenced by:
• Eyesight
• Hearing
• Arthritis
• Memory
• Attention and working
memory
300-600 ms
Rest of trial
response
Total trial time: 1500 ms
Example: Serial Reaction Time task
• Participants make motor responses
to viewed stimuli
• Young RT: m (sd) = 323 (17) ms
• Older RT: m (sd) = 524 (88) ms
Howard JH and Howard DV. (1997)
Psychology and Aging, 12, 634-656
300-600 ms
Rest of trial
response
Total trial time: 1500 ms
Introducing a sequence to stimulus location results in decreased RTs (learning).
Should paradigm be adjusted to accommodate longer RTs?
Is a 100 ms learning gain in RT equivalent across groups?
Ability to compare functional data
How do rigid / nonrigid transformations
used to convert brains to Talairach or
MNI space account for age-related
morphology? (i.e. cortical shrinkage,
ventricular enlargement)
How can we compare sizes/shapes of
ROIs across age groups?
Head motion:
stroke; age: mean 58 (range: 22-78)
nonstroke; age: mean 59 (range 25-71)
young; age: mean 28 (range 25-38)
Seto E, Sela G, McIlroy WE et al.. 2001. Neuroimage, 14, 284-297
Functional signal detection
Huettel SA, Singerman JD and McCarthy G. (2001). The effects of aging upon the
hemodynamic response measured by functional MRI. Neuroimage, 13, 161-175.
Claim 1: The hemodynamic response function (HRF) changes with age:
Calcarine
Fusiform
Functional signal detection
Claim 1: The hemodynamic response function (HRF) changes with age:
Functional signal detection
Claim 1: The hemodynamic response function (HRF) changes with age:
“Nonparametric comparison of
relative standard deviation across all
epoch time points revealed that
elderly subjects had a higher standard
deviation than had the young in 15 of
19 time points (p<.01).”
Functional signal detection
Claim 2: Older participants have greater signal to noise ratios (SNRs) in
activated voxels than younger participants
Calcarine
SD (ROI)
Intersubject group variability
Calcarine
SD (voxel)
Functional signal detection
Claim 2: Older participants have greater signal to noise ratios (SNRs) in
activated voxels than younger participants
SNR not due to head motion
SNR differences largest when
considering only single best
voxel from ROI
Functional signal detection
Claim 2: Older participants have greater SNRs in activated voxels than young
Younger participants have
significantly more active
voxels (p<.001, both ROIs)
Difference above is not
an artifact from selected
t-value (3.5)
(note divergence at t=2.5)
Conclusions
Claim 1: The hemodynamic response function (HRF) changes with age.
HRF appears to peak earlier and return to baseline faster for older
Results could be skewed by increased variability and a potential outlier
in the older adult group
D’Esposito (1999) found no age difference for motor cortex.
Aizenstein (2003, 2004) and Richter and Richter (2003) found no age
difference in when HRFs peaked, but a delayed return to baseline
among older adults (~12+ s for older vs ~10 for younger participants)
Aizenstein et al., 2004. The BOLD Hemodynamic response to aging.
Journal of Cognitive Neuroscience, 16, 789-793.
Conclusions
Claim 2: SNR decreases with age
Older brains exhibit greater HRF variability
Older brains are activated to a lesser spatial extent (smaller ROI areas)
and to a lesser magnitude (t-value thresholds)
SNR improves with the square root of trials performed
Possibly due to attenuated return to baseline? (Aizenstein)
~1.5 SNR between groups means 2.25x as many trials for older adults
How feasible is this for paradigms?
Discussion: Your experiences with geriatric fMRI research.