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) Brain is intrinsically organized into dynamic, anti- correlated functional networks (Fox et al., 2005) common assumption: correlated fluctuations in resting state networks are neuronal As you have understand by now, low frequency fluctuations have been analyzed to map restings state networks. Patrickj just told you that researchers claimed that brain is intrinsically organized into dynamic anti-correlaed functional networks. Disruptions in connection of parts of Resting state networks are related to disorders (Alzheimer, schizophrenia, autism),  large interest in this topic. The common assumption in those connectivity analysis is that correlated fluctuations are neuronal in origin.

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 Fluctuation in signal can also be caused by changes in cardiac pulsation, respiration. These noise can be controlled by measuring these parameters. It has also been shown that changes in arterial carbon dioxide cause very low frequency fluctuations. And then you can blame the hardware and subject for more noise. There are several methods to reduce noise. To account for several potential sources of noise Global signal regression is very often performed. That means you average the time series over all voxels and use it as a regressor in a general linear model.

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 But if you filter that signal out – the question is: Is this global signal really just uninteresting source of noise? One could say that if the global signal and the experimental conditions are uncorrelated. But a PET study by Andersson has shown that the time course was not uncorrelated to the task induced activation. So task related voxels were included in the global regressor which could lead to an underestimating of the true activation and can even introduce deactivations In a simple button-press study they did a covariation for the global signal and found out that the global signal reduced intensity and also introduced new negatively activated areas. The interesting thing is that those areas lie within the 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 Global signal regression can cause reductions in sensitivity and introduce false deactivations when experimental induced activations contaminate global signal. By definition the task is undefined in resting state data therefore the exact timing, the spatial extent and the relative phase between the areas are unknown and correlation between global signal and resting state fluctuations can‘t be calculated. That 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

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) The voxel-wise GLM is expressed as: Y =βX+ε (1) Y is a column vector of N rows (the number of collected time-points) representing the time-series BOLD signal associated to a single voxel. X represents the design matrix with N rows × p columns, each representing a regressor (i.e. an explanatory variable). Of interest are the columns representing manipulations or experimental conditions, although the matrix often may include regressors of non-interest, modelling the mean signal (i.e. the intercept), trends (typically linear and quadratic) and other design specific confounds. β is a column vector with p rows representing the unknown parameter associated to each regressor. Finally, ε is also a column vector, with N rows, representing the estimation error (or residuals) defined as Y − βˆX.

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 Results high SNR low SNR phase shifting – sine wave and gaussian noise is shifted

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

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

Breath holding & visual data While in the condition which only presented the checkerboard (VisOnly) global signal resgression doesn‘t change too much about the activation. In the other conditions global signal regression result in anticorrelated areas. The highest impact can be seen in the Synch+10 condition, where the „resting state“ fluctuations are similiar to the global signal.

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

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