Real-time Independent Component Analysis of functional MRI time-series

1. Real-time Independent Component Analysis of functional MRI time-series A new TBV (3.0) Plugin for Real-Time ICA during fMRI

2. Real-time ICA of fMRI data: Outline Data model and analysis tools in real-time fMRI: Sliding-window vs Cumulative approaches Data-driven analysis tools in fMRI: Component-based generative models for fMRI Spatial independent component analysis (s-ICA) Real-time (spatial) Independent Component Analysis Data model and implementation The “Sliding-window” FastICA algorithm Perfomances, operation and user interface Examples of applications Motor activity, Auditory and emotional activity during music listening A New “plug-in” for Turbo BrainVoyager 3.0 Example of application for visual activity monitoring

3. Data Analysis Tools for Real-time fMRI (1) Real-time fMRI enables one to monitor a subject’s brain activities during an ongoing session: Results are to be delivered (and used) in/near real-time, i. e. within times in the order of one (or a few) TR(s) ... Trade-off between accuracy VS computational times: > Minimum batch of temporal observations [# time points] to generate a reliable activation map (statistical power) > Minimum time window size [s] to cover the essential dynamics of the activaiton (hemodynamics, stimulus changes, ...)

4. Real-time fMRI enables one to monitor a subject’s brain activities during an ongoing session: Results are to be delivered (and used) in/near real-time, i. e. within times in the order of one (or a few) TR(s) ... Trade-off between accuracy VS computational times: < Maximum batch of temporal observation to generate the activation map in real-time (bottleneck: computational load) < Maximum time window size [s] to promptly detect transient (or temporally nonstationary) dynamic effects before these become “irrelevant” and sacrificed in favor of more repetitive and temporally stationary effects (Mitra and Pesaran, 1999). Data Analysis Tools for Real-time fMRI (2)

5. Real-time fMRI utilizes two different approaches: cumulative window (Cox et al., 1995) sliding window (Gembris et al., 2000; Posse et al., 2001) In the cumulative approach: the entire partially measured fMRI time-series is analyzed in one step. One edge of the time window is fixed, whereas the other moves during the acquisition of new data. the specificity (wrt repetitive/stationary effects) increases over time (more data become available for “averaging”). The sensitivity (wrt transient/non-stationary effects) is reduced (more fluctuations become relevant) The computational load increases over time (unless spatial or temporal resolution is sacrificed!) Data Analysis Tools for Real-time fMRI (3)

6. Real-time fMRI utilizes two different approaches: cumulative window (Cox et al., 1995) sliding window (Gembris et al., 2000; Posse et al., 2001) In the sliding-window approach: The analysis is restricted to the most recently acquired data. Both edges of the window move during the acquisition. The accuracy is constant over time and the sensitivity to dynamic changes in brain activity can be maximized. The specificity is limited and critically dependent on SNR The computational load is constant Data Analysis Tools for Real-time fMRI (4)

7. Sliding Window ON OFF Time [Units of TR] ti-L+1 ti t1 (a) Cumulative Read-out (b) Dynamic Read-out Target Area Target Signal Esposito et al., Neuroimage 2003

8. Data-driven tools in Real-time fMRI (1) Off-line, data-driven tools nicely and usefully complement by hypethesis-driven analysis tools E. g., independent component analysis (ICA) can identify brain activity without a priori “temporal” assumptions on brain activity: No info about experimental paradigm (stimulus) No detailed information about hemodynamics “Rough” knowledge of potentially relevant areas ...

9. Data-driven tools in Real-time fMRI (2) Real-time fMRI data analysis is traditionally based solely on hypothesis-driven tools (e. g. GLM) because data-driven tools (such as ICA) are: computationally demanding (time consuming) difficult settings (options, contrains and constants) e. g. convergence problems (no result delivering) difficult selection of the results “post-hoc” (complex) interpretation ...

10. Component-based Generative Models (1) Measured fMRI time-series C#1 C#3 Time (scans) C#2 C#n

11. Component-based Generative Models (2) High statistical dependencies Low statistical dependencies Mixing Unmixing voxels voxels C1j C2j ... Cnj COMPONENTS (C) time time W-1(A) DATA (Y) Yj Ai Al

12. Principal Component Analysis • Orthogonality Principle (simple linear decorrelation): • Maximum variance principle (VARIMAX): • (1): time-courses must be also orthogonal (uncorrelated) • (2): components ordered by relative contribution to variance

13. Independent Component Analysis (1) • Independency Principle (non-linear decorrelation): • Information Theory: Minimization of mutual information • Maximize entropy flow of a neural network: H(C) -> max (Infomax) • Maximize Non-gaussianity of components: N(C) -> max (Fastica) • Statistical dependency is removed along one dimension (e.g. space): • (1): time-courses can be correlated (spatial ICA) • (2): components not ordered by relative contribution to variance

14. ICA vs PCA Formisano, et al., Magnetic Resonance Imaging 2004

15. (Like PCA) ICA requires the computation of the data covariance matrix of the voxels’ time courses included in the analysis (Unlike PCA) spatial ICA only models the spatial distributions of brain activities (and builds accordingly the output maps) What ICA “offers” in addition to PCA does not depend on the covariance but only the spatial statistics While the statistical power of covariance estimation depends on the temporal window of observation (and the number of time points), the power of the spatial distribution estimation only depends on the voxel space Independent Component Analysis (2)

16. The “power” of spatial statisistics (1) Signal Features Noise (pure)

17. The “power” of spatial statisistics (2) Z-score (activation parameter)

18. The computational load of spatial ICA algorithms grows much more with the temporal dimension than with the number of voxels included in the analysis If we “fix” the temporal window the power of spatial statistics is constant. If the temporal window is large enough to ensure enough accuracy of the maps, the computation load can be held constant in a sliding-window approach In order to deliver components as fast as possible a “deflation” scheme can be used to extract ICA components one by one (FastICA algorithm by Hivarinen 1999). This renders the ICA component maps immediately available even in the presence of convergence problems. Real-time ICA (1)

19. The FastICA algorithm “one-unit” function deflation “multi-unit” function symmetric

20. Real-time ICA (2) • Rt-ICA -> sliding-window approach + FastICA • The window is chosen to solve the trade-off between accuracy and computational load. • This approach works and can be useful if: • FastICA delivers useful and accurate components among the “first” extracted ICs in a relatively low number of iteration per run. If not, we cannot assume “no activity” • The selection can be aided and supported by (rough) prior knowledge about where activity of interest takes place but selectivity should be unambigous • Cumulative maps about a process of interest can be obtained by adequately tracking over time (and combining) subsequent sliding-window ICA components

21. Sliding Window ON OFF Time [Units of TR] ti-L+1 ti t1 (a) Cumulative Read-out (b) Dynamic Read-out Target Area Target Signal Esposito et al., Neuroimage 2003

22. Esposito et al., Neuroimage 2003

23. Esposito et al., Neuroimage 2003

24. ICA G5 ICA G3 Time of extracttion [s] unsmoothed images smoothed images unsmoothed images smoothed images unsmoothed images smoothed images Subject FE Subject AA Subject FDS Esposito et al., Neuroimage 2003

25. Dynamic Maps Cumulative Map and Time-course Frames #.. Normalized Signal Change #40 #41…… #42 scans 8 8 8 8 #43 #44 z z z z Normalized Signal Change #45 2 2 2 2 #46…... #47 scans #48 #49 Normalized Signal Change #50 #51.….. #52 scans #53 Normalized Signal Change 2 4 5 3 1 #54 #55.….. #.. scans Esposito et al., Neuroimage 2003

26. ICA in real-time fMRI during visual stimulation: A new plugin for Turbo Brain Voyager 3.0

27. ICA in real-time fMRI during visual stimulation: A new plugin for Turbo Brain Voyager 3.0

28. ICA in real-time fMRI during visual stimulation: A new plugin for Turbo Brain Voyager 3.0

29. ICA in real-time fMRI during visual stimulation: A new plugin for Turbo Brain Voyager 3.0 TBV LOG Incoming Data Real time ROI Selection Data Pointer ICA Component Rankings Spatial correlations and/or other relevant parameters RTICA PLUGIN MAP VIEWER NeuroFeedback (MAP ANALYZER) Ranked ICA Component Maps

30. ICA in real-time fMRI during visual stimulation: A new plugin for Turbo Brain Voyager 3.0

31. ICA in real-time fMRI during visual stimulation: A new plugin for Turbo Brain Voyager 3.0

32. Real-time ICA of fMRI data: Conclusions • Real-time ICA during fMRI is feasible in many circumanstances and has some potentials in monitoring brain activity under typical real-time fMRi settings • The Sliding-window fastICA algorithm has comparable performances to GLM under highly controlled situations but requires no timing information and no critical settings • This opens the possibility of monitoring non-triggered, non-repetitive and non-stationary neural activity with only mininal spatial prior on the networks involved • Integration of rt-ICA generated maps in neurofeedback experiments now possible with the new Plugin for TurboBrainVoyager 3.0

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