- 77 Views
- Uploaded on
- Presentation posted in: General

Lee Friedman, Ph.D. And Gary Glover, Ph.D.

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Controlling for Scanner-to-Scanner Variance in Multicenter FMRI Studies: Covariation with Signal-to-Fluctuation Noise Ratio (SFNR)

Lee Friedman, Ph.D.

And

Gary Glover, Ph.D.

- To evaluate scanner effects on brain activation patterns in multisite fMRI studies
- To determine the cause of these scanner effects
- To develop methods to reduce such scanner effects

- 5 young healthy males traveled to 10 sites
- Underwent identical fMRI studies on 2 separate days
- Studied on multiple tasks – will emphasize the Sensorimotor (SM) Task and the Resting-State Scans
- Why I keep analyzing the SM Task?
- Same 5 subjects all sites
- Reduces subject variance
- Allows for estimation of scanner effects with no between-site effects due to different subjects.

- Very robust task – substantially more robust than SIRP or Oddball
- Well understood task

- Same 5 subjects all sites

- Pulse sequence = EPI or Spiral GRE
- Scan plane = oblique axial, AC-PC, copy T2
- FOV = 220 mm
- 35 slices, contiguous 4mm
- TR = 3000ms TE = 30ms (3T/4T), 40ms (1.5T)
- FA = 90 degrees
- BW >= ±100 kHz
- 64x64 matrix, 1 shot

Block trials, 15s “on”, 15s “off”, 8 blocks.

On Block:

• Alternating contrast checkerboard.

• Binaural tones generated. This is an auditorily annoying, mistonal scale.

• Subject performs bilateral finger apposition at 3Hz, in time with visual and auditory cues.

Off Block:

Fixation cross, silence, no motion.

4 runs, 4.25 min each

- Stare at fixation cross.
- Two runs, 85 TRs (4.25 min)

- SFNR – GM – REST
- Based on Resting-State Data
- (Mean GM intensity)/(SD Gray Matter Intensity)

- SFNR – GM – RESID
- Based on the SM Task, not Resting-State
- GLM model intercept is used as Signal
- SD of GLM Model Residuals used as Fluctuation Noise.

- AFNI – despike (conservative threshold), motion correction, slice time correction, detrend (2nd order polynomial)
- SPM – Spatial normalization to MNI Space
- Data presented in radiological convention, Image Left is Patient Right
- Level 1 analysis using FMRISTAT – Worsley’s fMRI analysis package.
- http://www.math.mcgill.ca/keith/fmristat
- Prewhitens – removes temporally autocorrelated residuals
- Regressors:
- (1) blocks convolved HRF
- (2) derivative of #1

- Statistic carried to Level II:
- Multiple r2 for both regressors, i.e.,
- Percent of Total Temporal Variance accounted for by both regressors, i.e,:
- Percent of Variance Accounted For or PVAF

- FMRI researchers are primarily interested in evaluating brain activation patterns.
- Brain activation patterns are typically produced by thresholding statistical maps (t-maps, F-maps, etc …) and looking only at voxels activated above a statistical threshold.
- Efforts to homogenize brain activation effect size (like PVAF) will result in homogenization of patterns of activation across sites.
- Since beta-weights alone carry no information about the statistical significance or CNR of an effect, they alone are poor candidates for use in homogenizing activations patterns across sites. They are useful in homogenizing signal amplitude across sites, which is important.

- Data were available for 5 subjects at 10 scanners.
- Each subject was scanned on 2 visits, and there were 4 runs per visit.
- The scanners were divided into low-field strength scanners (five scanners at 1.5T) and high-field strength scanners (four 3T and one 4T scanner).
- All analyses were performed in SAS PROC MIXED (ReML).
- To assess the effect of scanner (within field) without correction, a mixed model ANOVA was employed. PVAF was the dependent variable. Scanner was modeled as a fixed effect. Subject, visit, subject-by-visit, site-by-subject, site-by-visit, site-by-subject-by-visit were modeled as random effects.
- To assess the effectiveness of various SFNR estimates, each SFNR estimate was entered singly as a covariate into the above model.

- Analyses were conducted for separately for low-field scanners and high-field scanners, and for 8 ROIs (LA, RA, LC, RC, LM, RM, SM and the average, AV). Did not analyze visual cortex (why?)

-For each visit, each subject had only a single SFNR-GM-REST estimate from the resting-state data. So all four runs of the SM task were corrected with the same SFNR covariate.

-However, for SFNR-GM-RESID estimates based on the SM task rather than the resting state scan, each individual run of the SM task was corrected by an SFNR estimate from that same run.

Criteria for Ranking the SFNR Types:

(1) The number of statistically significant F-tests for the scanner effect in each model was calculated.

(2) The coefficient of variation (CV) was calculated for the estimated scanner means for each model.

“Anatomical Model of Normal Brain” described at:

http://www.bic.mni.mcgill.ca/brainweb/l

A communality is the proportion

of variance in a variable accounted

for collectively by all the other

variables.

Low Field: F(scanner) = 103.2, p <.0001

High Field: F(scanner) = 15.6 , p <.0001

Low Field : F = 28.8, df = 1,4, p = 0.0058

high field: F = 17.8, df = 1,4, p = 0.0135

High Field

Low Field

Low Field Scanners

High Field Scanners

- Subjects may vary on the signal (?,bigger heads closer to the coil ?)
- Most likely due to physiological noise:
- Respiration
- Cardiac aliased signals
- Brain pulsations
- Background metabolic and neuronal activity
- Components of physiological noise are a function of
- Tissue type (GM vs WM)
- Field Strength
- Voxel Size
- Flip Angle

- The intrinsic signal-to-noise ratio in NMR imaging.
- Edelstein WA, Glover GH, Hardy CJ, Redington RW.
- Magn Reson Med. 1986 Aug;3(4):604-18.

- Physiological noise in oxygenation-sensitive magnetic resonance imaging
- Kruger G, Glover GH..
- Magn Reson Med. 2001 Oct;46(4):631-7.

- Neuroimaging at 1.5 T and 3.0 T: comparison of oxygenation-sensitive magnetic resonance imaging
- Kruger G, Kastrup A, Glover GH.
- Magn Reson Med. 2001 Apr;45(4):595-604.

- Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters
- Triantafyllou C, Hoge RD, Krueger G, Wiggins CJ, Potthast A, Wiggins GC, Wald LL.
- Neuroimage. 2005 May 15;26(1):243-50.

- Using SFNR as a covariate is effective in reducing the effect of scanner on activation effect size
- Using an SFNR estimate based on the SM task itself is an effective approach – no resting state data is needed.
- Subject variance due to physiological noise is also controlled with this method.

Physiological Noise at 3T

At 3.0 T, the physiological noise is of increasing influence at higher excitation flip angles (larger signals) and results in the divergence of the SNR-to-SNR0 ratio from the line-of identity (dotted line). Kruger, Kastrup, and Glover, 2001