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The impact of global signal regression on resting state networks

The impact of global signal regression on resting state networks. Are anti- correlated networks introduced ? Kevin Murphy, Rasmus M. Birn , Danil A. Handwerker, Tyler B. Jones, Peter A. Bandettini. Introduction. Low frequency fluctuations ( ~ 0.1 Hz)

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The impact of global signal regression on resting state networks

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  1. The impactof global signalregressionon restingstatenetworks Are anti-correlatednetworksintroduced? Kevin Murphy, Rasmus M. Birn, Danil A. Handwerker, Tyler B. Jones, Peter A. Bandettini

  2. Introduction • Low frequency fluctuations (~0.1 Hz) • Brain is intrinsically organized into dynamic, anti-correlated functional networks (Fox et al., 2005) • common assumption: • correlated fluctuations in resting state networks are neuronal

  3. Introduction • non neuronal sources of fluctuation (noise): • cardiac pulsation, respiration  physiological measured • changes in CO2 (Wise et al., 2004) • magnetic noise, subjects head sinks… • Noise reduction: • Preprocessing: body, head correction... • Global signal regression (GLM) • filter out global signal

  4. Introduction • Is global signal just uninteresting source of noise? • only global signal and experimental conditions are orthogonal / uncorrelated • PET: resulting time course not orthogonal to task-induced activations (Andersson, 1997) • task-related voxels included in global regressor •  underestimating true activation •  introducing deactivations • covariation for global signal  reduce intensity and introduce new negatively activated areas  default mode network

  5. Introduction • Global signal regression can cause reductions in sensitivity and introduce false deactivations • in resting state data experimental condition is undefined • exact timing, spatial extent and relative phase between areas are unknown • correlation between global signal and resting state fluctuations cannot be determined • this could lead to wrong results in seed voxel correlation analyses

  6. Introduction • seed voxel analyses • 1 time series (hypothesized fluctuations of interest) correlate with every other voxel • Studies have used global signal regression • default mode network = task negative network • anti-correlated network = task positive network • If global signal is uncorrelated with resting state fluctuations then finding is correct • If not  brain may not be organized into anti-correlated networks

  7. Introduction • How does global signal regression affect seed voxel functional connectivity analyses? • different aspects of resting state fluctuations • theory  global signal regression in seed voxel analyses always results in negative mean correlation value (math) • simulation  empirical demonstration… • breath-holding and visual task • visual task – localisable connectivity maps • breath-holding as comparatively global fluctuation • resting state scans

  8. Theory • Si(t) ... voxel‘s time series • g(t) ... global signal • βi ... regression coefficient • xi(t) … time series after global signal regression

  9. Theory • After Global Signal Regression, thesumofcorrelationvalueof a seedvoxelacrosstheentirebrainislessthanorequalto 0 • For all voxelsthatcorrelatepositivelywiththeseed, negativelycorrelatedvoxels must existtobalancetheequation.

  10. Simulations Matlab • 1000 time series • 2 time courses • Resting state fluctuations generated by • sine wave, randomly choosen frequency • Gaussian noise added (global) • Each time serie‘s global signal regressed with GLM

  11. Simulation Results high SNR low SNR

  12. Breath holding & visual data • 8 adults scanned on 3T scanner (27 sagittal slices) • Pulse oximeter • Pneumatic belt

  13. Breath holding & visual data

  14. Breath holding & visual data • 5 conditions • VisOnly = 30s OFF (fixation) / 20s ON (flashing checkerboard) • Synch • 30s countdown – „breath in (2s)“, „breath out“ (2s) • then breath holding & checkerboard • Synch+10 = like above but 10s delayed checkerboard • Asynch = visual ON period ended when breath holding ON commenced??? • RandVis = event-related design • var. ISI, each second 50% probability of checkerboard

  15. Breath holding & visual data • Preprocessing • AFNI (Cox, 1996) • RETROICOR (remove cardiac and repiration effects) • Correction of timing for slices • bandpass filtering (0.01 Hz – 0.1 Hz) • 1 Dataset with GLM | 1 Dataset without GLM

  16. Breath holding & visual data

  17. Resting state data • 12 subjects – 2 resting state scans (5 min) • correlation maps from seed region in posterior cingulate/precuneus (PCC) • with global signal removed • without global signal removal • with RVT (respiration volume per time) correction • voxels correlating with PCC ROI  task-negative network

  18. Resting state data

  19. Resting state data

  20. Conclusions • Mathematically global signal regression forces half of the voxels to become anti-correlated • On data with known respiration confound (global signal) global signal regression not effective in removing noise & location of anti-correlated effect is dependent on relative phase of global and seed voxel time series • In resting state data, anti correlated networks are not evident until global signal regression

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