The impact of global signal regression on resting state networksAre 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)

Brain is intrinsically organized into dynamic, anti-correlated functional networks (Fox et al., 2005)

common assumption: correlated fluctuations in resting state networks are

neuronal

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

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

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

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

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

Theory

Si(t) ... voxel‘s time series

g(t) ... global signal

βi ... regression coefficient

xi(t) … time series after global signal regression

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.

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

Simulation Resultshigh SNR low SNR

Breath holding & visual data

8 adults scanned on 3T scanner (27 sagittal slices)

Pulse oximeter

Pneumatic belt

Breath holding & visual data

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

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

Breath holding & visual data

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

Resting state data

Resting state data

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