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Quality Assurance. NITRC Enhancement Grantee Meeting June 18, 2009. Susan Whitfield-Gabrieli & Satrajit Ghosh RapidArt MIT. Acknowledgements. THANKS! Collaborators: Alfonso Nieto Castañón Shay Mozes Data:

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Quality Assurance

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Quality Assurance

NITRC Enhancement Grantee Meeting

June 18, 2009

Susan Whitfield-Gabrieli & Satrajit Ghosh

RapidArt

MIT


Acknowledgements

THANKS!

Collaborators:

  • Alfonso Nieto Castañón

  • Shay Mozes

    Data:

  • Stanford, Yale, MGH, CMU, MIT

    Funding:

  • R03 EB008673: PIs: SatrajitGhosh, Susan Whitfield-Gabrieli, MIT


fMRI QA

  • Data inspection as well as artifact detection and rejection routines are essential steps to ensure valid imaging results.

  • Apparent small differences in data processing may yield large differences in results


QA in fMRI

Before Quality Assurance


QA in fMRI

Before QA

After QA


QA: Outline

  • fMRI quality assurance protocol

  • QA (bottom up)

  • QA (top down)


Quality Assurance: Preprocessing

Bottom Up: review data

Raw

Images

Artifact

Detection

Preprocessing

Review Data

Check behavior

Create mean functional image

Review time series, movie

Interpolate prior to preprocessing


Quality Assurance: PostPreprocessing

Top Down: review stats

Bottom Up: review functional images

GLM

PreProc

Artifact

Check

RFX

Artifact

Check

Artifact

Check

- Check registration

- Check motion parameters

- Generate design matrix template

- Check for stimulus corr motion

- Check global signal corr with task

- Review power spectra

- Detect outliers in time series, motion:

determine scans to omit /interp or deweight

Data Review

- time series

- movie

Review Statisitcs

Mask/ResMS/RPV

Beta/Con/Tmap


Data Review

Global

mean

COMBINED

OUTLIERS

Thresholds

INTENSITY

OUTLIERS

Deviation

From mean

Over time

MOTION

OUTLIERS

Realign

Param

Outliers

Data Exploration


Including motion parameters as covariates

  • Eliminates (to first order) all motion related residual variance.

  • If motion is correlated with the task, this will remove your task activation.

  • Check SCM: If there exists between group differences in SCM, AnCova


Power Spectra: HPF Cutoff Selection

.01 .02


Artifact Detection

Scan 79

Scan 95


Artifact Detection/Rejection

Artifact Sources:

Head motion *

Physiological : respiration and cardiac effects

Scanner noise

Solutions:

Review data

Apply artifact detection routines

Omit*, interpolate or deweight outliers

*Include a single regressor for each scan you want to remove, with a 1

for the scan you want to remove, and zeros elsewhere.

*Note # of scan omissions per condition and between groups

Correct analysis for possible confounding effects:

AnCova : use # outliers as a within subject covariate


BOTTOM UPAUDITORY RHYMING > REST

Outlier Scans

T map

ResMS

Before ART

ResMS

T map

After ART


“TOP DOWN” 2nd level, RFX


Group Stats ( N = 50 )

Working Memory Task

Not an obvious problem:

Frontal and parietal

activation for a working

memory task.


Group Stats (N=50) 2B Working Memory Task


Find Offending Subjects: 2 of 50 subjects


Artifacts in outlier images

Scan 86

Scan 79

Scan 95

Scan 83


Comparison of Group Stats:Working Memory (2B>X)

ORIGINAL

FINAL


Comparison of Group Statistics: Default Network


Method Validation Experiment

  • Data analyzed: 312 subjects, 3 sessions per subject

  • Outlier detection based on global signal and movement

  • Normality: tests on the scan-to-scan change in global BOLD signal after regressing out the task and motion parameters. Normally-distributed residuals is a basic assumption of the general linear model. Departures from normality would affect the validity of our analyses (resulting p- values could not be trusted) If all is well, we should expect this global BOLD signal change to be normally distributed because: average of many sources (central limit theorem )

  • Power: the probability of finding a significant effect if one truly exists. Here it represents the probability of finding a significant (at a level of p<.001 uncorrected) activation at any given voxel if in fact the voxel is being modulated by the task (by an amount of 1% percent signal change).


Outlier Experiment

  • Global signal is not normally distributed

    In 48% of the sessions the scan-to-scan change in average BOLD signal is not normally distributed.

    This percentage drops to 4% when removing an average of 8 scans per session (those with z score threshold = 3)


Removing outliers improves the power

  • Plot shows the average power to detect a task effect (effect size = 1% percent signal change, alpha = .001)

  • Before outlier removal the power is .29 ( 29% chance of finding a significant effect at any of these voxels) After removing an average of 8 scans per session (based on global signal threshold z=3) power improves above .70


THANKS!

Dissemination (NITRC)

- International visiting fMRI fellowships @ MGH

- 2 week MMSC @ MGH

- SPM8 Courses (local/remote)

-Visiting programs at MIT

Documentation

  • Manuals, Demos, Tutorials

  • Scripts


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