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The impact of global signal regression on resting state networks Are anti-correlated networks introduced? Kevin Murphy, Rasmus M. Birn, Danil A. Handwerker,

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Presentation on theme: "The impact of global signal regression on resting state networks Are anti-correlated networks introduced? Kevin Murphy, Rasmus M. Birn, Danil A. Handwerker,"— Presentation transcript:

1 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

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 General linear model  voxel-wise GLM is expressed by Y =βX+ε  Y … column vector of N rows  X … design matrix with N rows × p columns – regressors  β… column vector with p rows - unknown parameters associated to each regressor  ε … column vector, with N rows, estimation error (residuals)

9 Theory  S i (t)... voxel‘s time series  g(t)... global signal  β i... regression coefficient  x i (t) … time series after global signal regression

10 Theory  After Global Signal Regression, the sum of correlation value of a seed voxel across the entire brain is less than or equal to 0  For all voxels that correlate positively with the seed, negatively correlated voxels must exist to balance the equation.

11 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

12 Simulation Results high SNRlow SNR

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

14 Breath holding & visual data

15  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

16  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 Breath holding & visual data

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

19 Resting state data

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21 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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