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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

Quality Assurance

NITRC Enhancement Grantee Meeting

June 18, 2009

Susan Whitfield-Gabrieli & Satrajit Ghosh

RapidArt

MIT


Acknowledgements

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

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

QA in fMRI

Before Quality Assurance


Qa in fmri1

QA in fMRI

Before QA

After QA


Qa outline

QA: Outline

  • fMRI quality assurance protocol

  • QA (bottom up)

  • QA (top down)


Quality assurance preprocessing

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 post 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

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

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

Power Spectra: HPF Cutoff Selection

.01 .02


Artifact detection

Artifact Detection

Scan 79

Scan 95


Artifact detection rejection

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 up auditory rhyming rest

BOTTOM UPAUDITORY RHYMING > REST

Outlier Scans

T map

ResMS

Before ART

ResMS

T map

After ART


Top down 2 nd level rfx

“TOP DOWN” 2nd level, RFX


Group stats n 50

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

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


Find offending subjects 2 of 50 subjects

Find Offending Subjects: 2 of 50 subjects


Artifacts in outlier images

Artifacts in outlier images

Scan 86

Scan 79

Scan 95

Scan 83


Comparison of group stats working memory 2b x

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

ORIGINAL

FINAL


Comparison of group statistics default network

Comparison of Group Statistics: Default Network


Method validation experiment

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).


Quality assurance

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)


Quality assurance

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

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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