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Tracking Dynamic Networks in Real Time
The document discusses real-time tracking and modeling of intrinsic brain activity, utilizing methods such as support vector regression to enhance prediction accuracy and reproducibility in neuroimaging. It outlines the experimental design for tracking and modulating the default mode network (DMN) and the challenges faced in fMRI data acquisition and preprocessing. Additionally, it provides insights into the reliability of connectomics through extensive MRI scans and acknowledges contributions from various institutions and researchers.
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Tracking Dynamic Networks in Real Time
- 1. Tracking dynamic networksin real time. 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 March 8, 2016
- 2. Predicting Intrinsic BrainActivity 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.
- 3. Data Driven ROIAtlas Craddock et al. Human Brain Mapping 2012.
- 4. Nonparametric prediction, activation, influenceand 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
- 5. Prediction Accuracy • Measureof 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 )
- 6. Reproducibility • Measures theSignal-to-Noise ratio of the model SNR @ 1+r 1-rStrother, S. C. et al. NeuroImage 2003
- 7. Predicting Intrinsic BrainFunction
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- 9. Intra-individual variation Reproducibility PredictionAccuracy 0.840.860.880.900.92 0.30 0.350.40 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
- 10. Effect of ScanLength
- 11. Inter-subject prediction • 480subjects – 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
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- 18. RT Neurofeedback ofthe Default Mode Network (DMN)
- 19. Exp. Design Class Training Labels Trainingrun 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
- 20. Stimulus seen byvolunteer Updated fMRI results Motion tracking and correction Intensity (brightness) of a single voxel, changing during stimulus conditions Controller interface for display parameters
- 21. RT Neurofeedback ofDMN • Test hypothesis of DMN dysregulation in depression, ADHD, aging, etc …
- 22. Preprocessing Skullstripping (3dSkullStrip) Linear Registrationto MNI (flirt) Segmentation (fast) Anatomical Acquisition (T1 MPRAGE 4m 30s) 1 - 2.5min 12s 30s Coregister EPI to T1 (flirt+BBR) Write DMN template into EPI space Write WM+CSF mask into EPI space 30s 1s 1s Mask Acquisition (EPI 4m 30s) Calculate mean (3dTstat) Calculate mask (3dAutomask) 1s 1s Resting State (Training) Scan (EPI 6m) Motion correction to mean EPI (3dvolreg) Nuisance variable regression (3dDetrend) Spatial smoothing (3dmerge) Spatial regression to extract DMN time course (fsl_glm) Support vector regression training (3dsvm) 13s 2s 2s 32s 6-20s Indicates data dependency • Online preprocessing can be performed in ~ 5 minutes, most of which can occur in parallel with acquisition
- 23. Online Denoising • fMRIactivity is confounded by intensity modulations induced by head motion, physiological noise, scanner drift, … • Implemented RT denoising in AFNI to remove contributions of confounds – Nth order polynomial – Global mean – Mask average time series (i.e. WM, CSF) – Motion parameters (6 or 24 regressor models) – Spatial smoothing • Adds ~ 5 ms of delay
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- 25. Modulating the DMN−2−1012 0100 200 300 400 Best Subject Worst Subject TR Z−scoreDMNActivity −20246 0 100 200 300 400 TR Z−scoreDMNActivity
- 26. Results 0.00.10.20.30.40.50.6 3 1 713 6 9 5 10 11 8 4 2 12 Subject Accuracy Feedback No feedback FB NOFB 0.10.20.30.40.50.6 1 2 1 2 Scan Number Accuracy p = 0.055p = 0.68 Accuracy was measured from Pearson’s correlation between task paradigm and DMN activity extracted after post-processing.
- 27. Behavioral Correlates Measures thatwere significantly associated with DN regulation include (p<0.05, FDR corrected): the affect intensity measure (AIM), ruminative responses scale (RRS), and the imaginal processes inventory.
- 28. RT fMRI Neurofeedbackfor Children
- 29. All data isbeing prospectively shared
- 30. Reproducibility and Reliabilityin 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
- 32. Acknowledgments • CMI/NKI – MichaelMilham, 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!