Tracking Dynamic Networks in Real Time

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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


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


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


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


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