prediction analysis in clinical and basic neuroscience
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Prediction Analysis in Clinical and Basic NeuroscienceR. Cameron Craddock, PhDDirector, Computational Neuroimaging LabNathan S. Kline Institute for Psychiatric ResearchDirector of Imaging, Center for the Developing BrainChild Mind Institute
September 24, 2016
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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 selectionTypically requires linear classifiersKey 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 valuesReport sensitivity (SS) and specificity (SP)!Must generalize to data collected regardless of parameters or vendorOtherwise quantitative MRI has no clinical value!
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Positive and Negative Prediction Value
Parsing inter-subject variability
Learning from heterogeneity
539 Pts w/ ASD, 573 Typical Controls16 International sites16 Preprocessing pipelines
Processing and data-size are crucial for cross-site prediction
Inter-site Multi-modal Age Prediction
NeuroImage, in press
Standardization to address inter-site differences
Optimizing for discriminability, not generalizability
Joshua Vogelstein et al. in preparation
Data is key!
http://fcon_1000.projects.nitrc.org/http://preprocessed-connectomes-project.org/
Deep Phenotyping
Reproducibility and Reliability in Connectomics2 participants scanned 5 times a day for 3 days1 participant scanned 100 timesTime between scans varies from minutes, days, months
1,629 Healthy Controls3,357 MRI scans5,093 rs-fMRI scans1,629 Diffusion scans300 CBF scans
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Quality Assessment ProtocolSpatial MeasuresContrast to Noise RatioEntropy Focus CriterionForeground to Background Energy RatioSmoothness (FWHM)% Artifact VoxelsSignal-to-Noise RatioTemporal MeasuresStandardized DVARSMedian distance indexMean Functional Displacement# Voxels with FD > 0.2m% Voxels with FD > 0.2m
http://preprocessed-connectomes-project.github.io/quality-assessment-protocol/
Predicting Intrinsic Brain ActivityMultivariate model of brain activity
Underdetermined problem: solved using support vector regression or other regularized regression / dimensionality reduction methodCraddock et al. NeuroImage 2013.
Data Driven ROI AtlasCraddock et al. Human Brain Mapping 2012.
Nonparametric prediction, activation, influence and reproducibility resampling
Prediction AccuracyMeasure of the generalization ability of a modelCan be interpreted as a measure of the information content in the model about the region being modeled
ReproducibilityMeasures the Signal-to-Noise ratio of the model
Strother, S. C. et al. NeuroImage 2003
Predicting Intrinsic Brain Function
Intra-individual variation
Intra-individual variation
Effect of Scan Length
Inter-subject prediction 480 subjects69 DZ twin pairs80 MZ twin pairs200 Non-siblings
Train on one individual, test with anotherIntra individualBetween siblings (MZ, DZ)Age and sex matched non-siblings
Global prediction accuracy
Regional Differences
SVR Training
Tracking Intrinsic Connectivity Networks
Amount of Training
Predicting the Future
RT Neurofeedback of the Default Mode Network (DMN)
Exp. Design
Class Training Labels
Training run
Time-LabeledScans
Image Recon and SVM Classification
Image Data
Data AcquisitionStimulus Presentation
StimulusConventional FMRI
Test Data Classifier OutputTesting Run
Real-Time Tracking RSNsLaConte, et al. (2007) Hum Brain Mapp. 28: 1033-1044Stephen LaConte August 19, 2009
Stimulus seen by volunteerUpdated fMRI resultsMotion tracking and correctionIntensity (brightness) of a single voxel, changing during stimulus conditionsController interface for display parameters
RT Neurofeedback of DMNTest hypothesis of DMN dysregulation in depression, ADHD, aging, etc
Preprocessing
Online preprocessing can be performed in ~ 5 minutes, most of which can occur in parallel with acquisition
Online DenoisingfMRI activity is confounded by intensity modulations induced by head motion, physiological noise, scanner drift, Implemented RT denoising in AFNI to remove contributions of confoundsNth order polynomialGlobal meanMask average time series (i.e. WM, CSF)Motion parameters (6 or 24 regressor models)Spatial smoothingAdds ~ 5 ms of delay
DMN Modulation Task
Modulating the DMN
Results
Accuracy was measured from Pearsons correlation between task paradigm and DMN activity extracted after post-processing.
Behavioral Correlates
Measures that were significantly associated with DN regulation include (p