PowerPoint Presentation
Tracking dynamic networks in real time.R. Cameron Craddock, PhDDirector, Computational Neuroimaging LabNathan S. Kline Institute for Psychiatric ResearchDirector of Imaging, Center for the Developing BrainChild Mind Institute
March 8, 2016
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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