Older adult scanning methodology II: How to decide between conflicting literature? Andy James fMRI Journal Club October 26, 2004

Conflicting articles: who should we believe? How much do the articles conflict each other? Assess methodology for differences in –subject samples –experimental paradigm –data collection and processing –statistical analyses Do results support claims? Note: Should be doing this already!

Example:  BOLD HDR Function with Age Delayed return to baseline in older (red) vs younger (black) adults in visual (A) and motor (B) cortices. Aizenstein et al., The BOLD Hemodynamic response to aging. Journal of Cognitive Neuroscience, 16, Calcarine Fusiform No difference in HDR between older and younger adults for visual cortex. Huettel et al., The effects of aging upon the hemodynamic response measured by functional fMRI. Neuroimage, 13,

Methodology issue? Subjects Younger: 8 (3 male); 24.2 (4.37) years old; no medication Older: 10 (6 male); 67.2 (4.83) years old; no psychotropic medicines Paradigm Keypress with both index fingers whenever stimulus tap appeared. (1 s stimulus with 12 s ITI) Subjects Younger: 11 (7 male); 23 (range: 8-32) years old; Older: 11 (7 male); 66 (range ) years old; Medications not addressed Paradigm Passive viewing of flashing checkerboard (500 ms stimulus with 16.5s ITI) Huettel 2001 Aizenstein 2004

Methodology issue? Data Collection/ Processing TR 2s, 1.5T GE, 3.8x3.8 mm 2 Statistical Analysis HDRs generated from positive sig. voxels only Data Collection/Processing TR 1s, 1.5T GE, 3.8x3.8 mm 2 Statistical Analysis HDRs generated from positive sig. voxels (?) or most sig. voxel Huettel 2001 Aizenstein 2004

Do results support claim? Delayed return to baseline in older (red) vs younger (black) adults in visual (A) and motor (B) cortices. But both Huettel and Aizenstein excluded negative voxels! (Aizenstein compares Huettel and Buckner, but never addresses this study’s discrepancy.)

How much do the articles conflict each other? Alternate explanation: older adults have more negative voxels that affect HRF when averaged in.

Alternate explanation: older adults have more negative voxels that affect HRF when averaged in.

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.

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 Example: Serial Reaction Time task Participants make motor responses to viewed stimuli Young RT:  (sd) = 323 (17) ms Older RT:  (sd) = 524 (88) ms Howard JH and Howard DV. (1997) Psychology and Aging, 12, 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? ms Rest of trial response Total trial time: 1500 ms ms Rest of trial response Total trial time: 1500 ms

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 Neuroimage, 14,

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, Claim 1: The hemodynamic response function (HRF) changes with age: CalcarineFusiform

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) Calcarine SD (voxel) Intersubject group variability

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)