Introduction

Methods

References

Effect of Motion vs. Effect of Pipeline

 The Uncertain Relationship between BOLD Variability and Age Brian A. Lopez, Benjamin O. Turner, Tyler Santander, Misty Schubert & Michael B. Miller

Department of Psychological & Brain Sciences, University of California, Santa Barbara This project was supported by the Institute for Collaborative Biotechnologies through contract W911NF-09-D-0001 from the U.S. Army Research Office.  

Analysis Pipelines BOLD Variability

•  Variability of the BOLD signal has begun to receive greater attention as the field of fMRI research moves beyond simply characterizing mean activity.

•  For instance, Grady et al. have investigated the relationship between the variability of the BOLD timeseries and various individual difference measures. Specifically, they have found that younger, faster, and more consistent performers exhibited significantly higher signal variability (Garrett et al., 2011).

•  We demonstrate the impact of a variety of analysis choices on the qualitative picture of how BOLD variability relates to age, with a focus on controlling for two potential confounds:

Potential Confounds with Age

1.  Motion artifacts: The issue of motion-related artifacts has gained attention as a methodological problem (e.g., Satterthwaite et al., 2013). Motion artifacts will increase most measures of variability.

2.  BOLD grand mean: Differences in BOLD grand mean will result in differences in the grand mean intensity normalization (GMIN) factor, which is inversely proportional to the BOLD grand mean (10,000/μBOLD). Its multiplicative nature will affect both the mean and the variability of the BOLD data.

fMRI Data Analysis (Six pipelines; see table)

•  All pipelines shared the same initial preprocessing steps in FEAT, including brain extraction, spatial smoothing (5mm-FWHM Gaussian kernel), motion correction, and grand-mean intensity normalization.

•  Pipelines varied in terms of (1) which confounds were partialed out when correlating BOLD variability and age, (2) whether or not preprocessed data were regressed on a task-related and/or nuisance model, and (3) whether or not preprocessed data were denoised using a wavelet-based method.

Correlation Maps

•  The standard deviation of the BOLD timeseries was calculated in a voxelwise manner for each scan for each participant, which was then averaged to generate a single variability map per participant.

•  Participant variability maps were then transformed to standard MNI space using FLIRT.

•  Finally, a group-level correlation map was generated for each pipeline by computing the voxelwise Spearman correlation between participant age and variability, transformed to z-values. All maps were thresholded with a z threshold of ± 2.3 and a cluster p threshold of 0.05.

•  Our results indicate that specific regions, when exceeding threshold, always evince either a positive or negative correlation between variability and age.

•  However, the overall relationship between variability and age depends critically on the exact pipeline being used.

•  For variability analyses in particular, there is the additional complicating issue of the GMIN factor (a proxy for mean BOLD), which varies reliably with age and should be considered in analyses of BOLD variability.

•  Our focus here was on the temporal standard deviation of the BOLD signal. However, other variance-based measures (e.g., ALFF/fALFF) will presumably be similarly affected.

•  There are theoretical ramifications to drawing incorrect conclusions—for instance, whether variability increases with age and reflects compensatory mechanisms (e.g., Cabeza et al.,2002) or rather decreases with age and reflects loss of flexibility (Garrett et al., 2011).

Cabeza, R., Anderson, N. D., Locantore, J. K., & McIntosh, A. R. (2002). Aging gracefully: compensatory brain activity in high-performing older adults. NeuroImage, 17(3), 1394–1402.

Cohen, A.L., Fair, D.A., Dosenbach, N.U., Miezin, F.M., Dierker, D., Van Essen, D.C., Schlaggar, B.L., Petersen, S.E. (2008). Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage, 41, 45–57.

Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2011). The importance of being variable. The Journal of Neuroscience, 31(12), 4496–4503.

Patel, A. X., Kundu, P., Rubinov, M., Jones, P. S., Vértes, P. E., Ersche, K. D., Suckling, J., & Bullmore, E. T. (2014). A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. NeuroImage, 95, 287–304.

Satterthwaite, T.D., Elliott, M.A., Gerraty, R.T., Ruparel, K., Loughead, J., Calkins, M.E., Eickhoff, S.B., Hakonarson, H., Gur, R.C., Gur, R.E., et al (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage, 64, 240–256.

Pipeline Wavelet Denoising Nuisance Regression Confounds Partialed Out

1a No Task Only None

1b No Task Only GMIN Factor

1c No Task Only GMIN Factor & Mean Motion

2a No Task & Nuisance GMIN Factor

2b No Task & Nuisance GMIN Factor & Mean Motion

3 Yes Task & Nuisance GMIN Factor

Discussion Nuisance Regression Pipelines

Minimal Processing Pipelines Task-related Regression •  Regressors for each probability block (and

their temporal derivatives) were convolved with FEAT’s default gamma-function HRF.

•  This removes the contribution of task-driven variability to our estimates of BOLD variability.

•  Standard deviation was computed on the residuals.

Pipeline 1a: Uncorrected correlation map between age and variability using the minimal processing pipeline and without partialing out any possible confounds. Note the extensive regions of positive correlation.

Pipeline 1b: Partial correlation map between age and variability, controlling for GMIN factor, using the minimal processing pipeline. Now, many of the regions that were above threshold in Pipeline 1a are sub-threshold, yielding a substantially sparser map.

Pipeline 1c: Partial correlation map between age and variability, controlling for GMIN factor AND mean relative motion, using the minimal processing pipeline. Now, the qualitative picture is one of extensive negative correlation.

Pipeline 1a

Pipeline 1b

Pipeline 1c

Task-related and Nuisance Regression •  In addition to the task-related regressors,

pre-processed data were regressed on the six motion parameters returned by FEAT, their temporal derivatives, and the mean signal associated with CSF.

•  Standard deviation was computed on the residuals.

Pipeline 2a: Partial correlation map between age and variability, controlling for GMIN factor, using the nuisance regression pipeline. This pipeline reveals regions of positive and negative correlation.

Pipeline 2b: Partial correlation map between age and variability, controlling for GMIN factor AND mean relative motion, using the nuisance regression pipeline. Like pipeline 1c, there are extensive areas of negative correlation.

Pipeline 2a

Pipeline 2b

Wavelet-based Denoising •  The wavelet-based method of Patel et al.

(2014) was used to denoise the data. •  The preprocessed data were entered

directly into the WaveletDespike function. •  The signal component was regressed on

the nuisance model including the motion parameters, their temporal derivatives, CSF signal, and task-related regressors.

•  Standard deviation was computed on the residuals.

Pipeline 3

Pipeline 3: Partial correlation map between age and variability of the signal component, controlling for GMIN factor, using the wavelet despiking pipeline. Like pipeline 2a, regions of positive and negative correlation are observed.

Pipeline 1b Pipeline 1c Pipeline 2a Pipeline 2b Pipeline 3

Pipeline 1a .0285 .6771 .6863 .6735 .0270

Pipeline 1b .1634 .1603 .1598 .0080

Pipeline 1c -.0001 .0015 .2068

Pipeline 2a .0027 .2090

Pipeline 2b .2004

r = .92***

DIFFERENCE IN MEAN RELATIVE MOTION (log-scaled)

DIS

SIM

IMLA

RITY

(1

– η2

, lo

g-s

ca

led

) WITHIN-PARTICIPANT EFFECT OF MOTION

Wavelet Despiking Pipeline

To quantify the influence of motion and pipeline on variability maps, we computed the dissimilarity (1 – η2) between maps (Cohen et al., 2008).

Effect of Motion on Within-Participant Variability Maps The larger the difference in an individual’s head motion between scans, the larger the dissimilarity of the variance maps (e.g., r = .92, p < .001 for the nuisance regression pipeline; see figure on right).

Effect of Motion on Age-Variability Maps •  To isolate the influence of motion, the dissimilarity of age-variability maps was computed

between low- and high-motion participants (mean relative motion of .054 vs. .134).

•  The computed dissimilarity value of .27 can be compared to the values we obtain for the effect of pipeline.

Effect of Pipeline on Age-Variability Maps •  To isolate the influence of pipeline, the dissimilarity of age-variability maps was computed

between pipelines for motion-matched participants (mean relative motion of .092 vs. .096).

•  Across pairs of pipelines, dissimilarities range from 0 to .69 (see table below).

•  Therefore, the choice of pipeline may exert a larger impact on the age-variability maps than motion itself.

Note: Effect of Motion Benchmark = 0.2731

Between Pipeline Dissimilarities

Confounds with Age

Motion

Me

an

Rela

tive

Mo

tion

Age

Mean BOLD

GM

IN F

ac

tor

Age

Motion (left): Scatterplot demonstrating the relationship between age and mean relative motion (Spearman’s ρ = 0.51). Motion increases with age.

Mean BOLD (right): Scatterplot demonstrating relationship between age and GMIN factor (Spearman’s ρ = 0.43). This factor is inversely proportional to the BOLD grand mean; therefore, mean BOLD decreases with age.

P(old) ≈ 70% P(new) ≈ 30%

P(old) ≈ 30% P(new) ≈ 70%

HIGH

LOW

HIGH

LOW

Participants Three age groups assessed (N = 109): 37 Late Adolescents (age 18), 36 Young Adults (ages 25-33), and 36 Older Adults (ages 60-75)

Recognition Memory Test •  3 recognition tests, each with 51 old words and 51 new

words. A separate fMRI scan was acquired during each test.

•  Words were presented in alternating high (70% old) and low (30% old) target probability blocks presented in BLUE or ORANGE font.

HIGH

LOW