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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, Tyler B. Jones, Peter A. Bandettini

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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

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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

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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

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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

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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

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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

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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)

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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

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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.

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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

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Simulation Results high SNRlow SNR

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

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Breath holding & visual data

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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

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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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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

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Resting state data

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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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