Transcript
The Preprocessed Connectomes Project
Quality Assessment Protocol - a resource for measuring the
quality of MRI data
Cameron CraddockComputational Neuroimaging Lab
Center for Biomedical Imaging and NeuromodulationNathan S. Kline Institute for Psychiatric Research
Center for the Developing BrainChild Mind Institute
Neuroimaging meets big data
1,112 rs-fMRI and MRI datasets539 w/ASD, 573 Typical
1,629 Healthy Controls3,357 MRI scans
5,093 rs-fMRI scans
Sharing preprocessed data
• Make data available to a wider audience of researchers
• Evaluate reproducibility of analysis results
http://preprocessed-connectomes-project.github.io/
Which of the data do you include in an analysis?
• Use it all and hope that the introduced error will be swamped out by the large number of samples? – data quality vs. size trade-off
• Only use the best data as determined by visual inspection?– Limited by intra- and inter- rater reliability– Is the human eye good enough to identify data that is good
enough? (what is “good enough” for data?)• Choose data based on quantitative metrics?
– Which Metrics?– What thresholds?
Motion Crisis
Motion Crisis 2016
Manualized manual inspection
https://github.com/SIMEXP/niak_manual/blob/master/qc_manual_v1.0/qc_manual_niak.pdf
Quality Assessment Protocol
http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/
Quality Assessment Protocol (2)- Open source pipeline for calculating quality assessment measures
from raw MRI data- Implemented in Python using Nipype
- Fast execution on cluster and multi-core computers- Restart processing without having to redo everything
- Uses AFNI and custom Python functions- Runs on Mac OSX, Linux, BSD, and Unix
Spatial Measures• sMRI & fMRI• Foreground to Background Energy
Ratio• Contrast to Noise Ratio (sMRI only)• Entropy Focus Criterion• Smoothness (FWHM)• % Artifact Voxels• Signal-to-Noise Ratio• Ghost-to-Signal Ratio (fMRI only)
Mortamet et al 2009
Spatial Measures• sMRI & fMRI• Foreground to Background Energy
Ratio• Contrast to Noise Ratio (sMRI only)• Entropy Focus Criterion• Smoothness (FWHM)• % Artifact Voxels• Signal-to-Noise Ratio• Ghost-to-Signal Ratio (fMRI only)
Temporal Measures • Global Correlation (GCOR)• Standardized DVARS• Median distance index• Mean Functional
Displacement• # Voxels with FD > 0.2m• % Voxels with FD > 0.2m
Reports
Repository of data measures
• Normative datasets to help learn thresholds for quality control– ABIDE– CoRR
http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/
B
Correlation Between Measures
A
Correlation between measures for structural (A) and functional (B) data. ABIDE on lower triangle, CoRR on upper triangle. X values are insignificant at FDR corrected q < 0.05.
Most discriminative measures
Green is good data, red is poor data as determined by manual assessments from four expert raters (consensus).
Test-Retest Reliability
Acknowledgements• Steve Giavasis, MS @ Child Mind Institute – developer• Sang Han Lee, PhD @ Nathan Kline Institute – analysis• John Pellman, BA @ Child Mind Institute –
documentation and support• Zarrar Shehzad, MA @ Yale – initial implementation• Chris Gorgelewski, PD @ Stanford – contributor
06.26 - 06.30 OHBM Hackathon,
Lausanne09.18 - 09.20
Brainhack Vienna10.10 - 10.12
Brainhack LA
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