Prediction Analysis in Clinical and Basic Neuroscience
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Talk given at the Resting State and Brain Connectivity 2016 conference symposium "The Emerging Field of Predictive Analytics in Neuroimaging: Applications, Challenges and Perspectives"
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Prediction Analysis in Clinical and Basic Neuroscience
- 1. Prediction Analysis in Clinical and Basic Neuroscience R. Cameron Craddock, PhD Director, Computational Neuroimaging Lab Nathan S. Kline Institute for Psychiatric Research Director of Imaging, Center for the Developing Brain Child Mind Institute September 24, 2016
- 2. Prediction for clinical applications • Neuroimaging biomarkers are not particularly needed for diagnosis, but might provide information about the brain areas affected by a disorder – Neuroscientific interpretability requires feature selection – Typically requires linear classifiers • Key areas for prediction include prognosis and treatment response (regression) – Few are doing this – Need to deal with heterogeneity (unsupervised learning) • A clinically useful biomarker must be valid, reliable, and have good positive prediction, and negative prediction values – Report sensitivity (SS) and specificity (SP)! • Must generalize to data collected regardless of parameters or vendor – Otherwise quantitative MRI has no clinical value!
- 3. Positive and Negative Prediction Value
- 5. Learning from heterogeneity 539 Pts w/ ASD, 573 Typical Controls 16 International sites 16 Preprocessing pipelines
- 6. Processing and data-size are crucial for cross-site prediction
- 7. Inter-site Multi-modal Age Prediction NeuroImage, in press
- 8. Standardization to address inter-site differences
- 9. Optimizing for discriminability, not “generalizability” Joshua Vogelstein et al. in preparation
- 11. Deep Phenotyping
- 12. Reproducibility and Reliability in Connectomics • 2 participants scanned 5 times a day for 3 days • 1 participant scanned 100 times • Time between scans varies from minutes, days, months 1,629 Healthy Controls 3,357 MRI scans 5,093 rs-fMRI scans 1,629 Diffusion scans 300 CBF scans
- 13. Quality Assessment Protocol • Spatial Measures – Contrast to Noise Ratio – Entropy Focus Criterion – Foreground to Background Energy Ratio – Smoothness (FWHM) – % Artifact Voxels – Signal-to-Noise Ratio • Temporal Measures – Standardized DVARS – Median distance index – Mean Functional Displacement – # Voxels with FD > 0.2m – % Voxels with FD > 0.2m http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/
- 14. Predicting Intrinsic Brain Activity Multivariate model of brain activity xn = b0 + bv v¹n å xv +x Underdetermined problem: solved using support vector regression or other regularized regression / dimensionality reduction method Craddock et al. NeuroImage 2013.
- 15. Data Driven ROI Atlas Craddock et al. Human Brain Mapping 2012.
- 16. Nonparametric prediction, activation, influence and reproducibility resampling Predicted Time Course Observed Time Course Features Dataset 1 Observed Time Course Features Dataset 2 Model Estimation Model Estimation wixi+b i Prediction Prediction Accuracy Reproducibility Prediction wixi+b i Predicted Time Course Prediction Accuracy Network Model Network Model B A
- 17. Prediction Accuracy • Measure of the generalization ability of a model • Can be interpreted as a measure of the information content in the model about the region being modeled p(xn x1...xv ) » I(xn x1...xv )
- 18. Reproducibility • Measures the Signal-to-Noise ratio of the model SNR @ 1+r 1-rStrother, S. C. et al. NeuroImage 2003
- 19. Predicting Intrinsic Brain Function
- 21. Intra-individual variation Reproducibility PredictionAccuracy 0.840.860.880.900.92 0.30 0.35 0.40 0.45 0.50 Lobe Frontal Occipital Parietal Subcortical Temporal A Reproducibility PredictionAccuracy 0.840.860.880.900.92 0.35 0.40 0.45 Functional Hierarchy Heteromodal Limbic Paralimbic Sensory−Motor Subcortical Unimodal B
- 22. Effect of Scan Length
- 23. Inter-subject prediction • 480 subjects – 69 DZ twin pairs – 80 MZ twin pairs – 200 Non-siblings • Train on one individual, test with another – Intra individual – Between siblings (MZ, DZ) – Age and sex matched non-siblings
- 26. SVR Training
- 30. Exp. Design Class Training Labels Training run Time-Labeled Scans Image Recon and SVM Classification Image DataData Acquisition Stimulus Presentation Stimulus Conventional FMRI Test Data Classifier Output Testing Run Real-Time Tracking RSNs LaConte, et al. (2007) Hum Brain Mapp. 28: 1033-1044 Stephen LaConte August 19, 2009
- 33. Software for prediction analysis
- 34. Acknowledgments • CMI/NKI – Michael Milham, MD, PHD – Zarrar Shehzad – Stan Colcombe, PhD • Virginia Tech Carilion Research Institute – Stephen LaConte, PhD – Jonathan Lisinski, MS • Siemens Medical – Keith Heberlein, PhD – Chris Glielmi, PhD • Research Funded in part by a NARSAD Young Investigator Award and NIMH R01MH101555 Thank You!