Physiological noise correction using ECG-derived ... · enhanced mapping of spontaneous neuronal...

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Physiological noise correction using ECG-derived respiratory signals forenhanced mapping of spontaneous neuronal activity withsimultaneous EEG-fMRI

Rodolfo Abreu a,⁎, Sandro Nunes a, Alberto Leal b, Patrícia Figueiredo a

a ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Portugalb Department of Neurophysiology, Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal

⁎ Corresponding author.E-mail address: [email protected] (R. Abreu).

http://dx.doi.org/10.1016/j.neuroimage.2016.08.0081053-8119/© 2016 Elsevier Inc. All rights reserved.

Please cite this article as: Abreu, R., et al.,spontaneous neuronal activity with si..., Neu

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Article history:Received 12 April 2016Revised 4 July 2016Accepted 5 August 2016Available online xxxx

The study of spontaneous brain activity based on BOLD-fMRI may be seriously compromised by the presence ofsignal fluctuations of non-neuronal origin, most prominently due to cardiac and respiratory mechanisms.Methods used for modeling and correction of the so-called physiological noise usually rely on the concurrentmeasurement of cardiac and respiratory signals. In simultaneous EEG-fMRI recordings, which are primarilyaimed at the study of spontaneous brain activity, the electrocardiogram (ECG) is typically measured as part ofthe EEG setup but respiratory data are not generally available. Here, we propose to use the ECG-derived respira-tory (EDR) signal estimated by EmpiricalMode Decomposition (EMD) as a surrogate of the respiratory signal, forretrospective physiological noise correction of typical simultaneous EEG-fMRI data. A physiological noise modelbased on these physiological signals (P-PNM) complementedwith fMRI-derived noise regressorswas generated,and evaluated, for 17 simultaneous EEG-fMRI datasets acquired from a group of seven epilepsy patients imagedat 3 T. The respiratory components of P-PNMwere found to explain BOLD variance significantly in addition to thecardiac components, suggesting that the EDR signal was successfully extracted from the ECG, and P-PNMoutperformed an image-based model (I-PNM) in terms of total BOLD variance explained. Further, the impactof the correction using P-PNM on fMRI mapping of patient-specific epileptic networks and the resting-statedefault mode network (DMN) was assessed in terms of sensitivity and specificity and, when compared withan ICA-based procedure and a standard pre-processing pipeline, P-PNM achieved the best performance. Overall,our results support the feasibility and utility of extracting physiological noisemodels of the BOLD signal resortingto ECG data exclusively, with substantial impact on the simultaneous EEG-fMRI mapping of resting-state net-works, and, most importantly, epileptic networks where sensitivity and specificity are still limited.

© 2016 Elsevier Inc. All rights reserved.

Keywords:Simultaneous EEG-fMRIPhysiological noiseEpilepsyResting-stateDefault mode networkECG-derived respiratory signal

Introduction

It is well known that changes in the blood oxygen level dependent(BOLD) signal measured with functional magnetic resonance imaging(fMRI) result from contributions of both neuronal and non-neuronalorigins. The latter include cardiac and respiratory sources commonlyreferred to as physiological noise (Brooks et al., 2013; Hutton et al.,2011), which, if left uncorrected, may compromise the analysis offMRI data, especially with regard to the study of spontaneous brain ac-tivity (Birn, 2012; Murphy et al., 2013). The importance of correctingBOLD-fMRI data for physiological noise has been brought to light withthe increasing interest in the study of the intrinsic functional connectiv-ity underlying resting-state networks (RSNs) (Biswal et al., 1995;

Physiological noise correctioroImage (2016), http://dx.do

Cordes et al., 2001). Because this is based on the correlation of slow(b0.1 Hz) BOLD signal fluctuations across different brain regions, with-out physiological noise correction it is impossible to ensure that func-tional connectivity measures reflect networks of neuronal activityexclusively. Simultaneous recordings of the electroencephalogram(EEG) with fMRI are particularly important in the study of spontaneousbrain activity, in which case it is not possible to correlate the two typesof signals if acquired separately (Jorge et al., 2013b; Murta et al., 2015).Besides applications in resting-state research, simultaneous EEG-fMRIhas been most useful in the investigation of epilepsy patients mainlythrough the identification of brain networks underlying the spontane-ous occurrence of epileptic discharges (Gotman and Pittau, 2011;Gotman et al., 2006; Lemieux et al., 2001; LeVan and Gotman, 2009).Again, the prediction of the epilepsy-related BOLD changes may becompromised by the presence of signal fluctuations of non-neuronalorigin (Liston et al., 2006; van Houdt et al., 2009).

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In fact, cardiac function leads to arterial pulsatility and results inbrain tissue movement as well as changes in cerebral blood volume(CBV) and cerebral blood flow (CBF) across the cardiac cycle (Greitzet al., 2010; Krüger and Glover, 2001; Purdon and Weisskoff, 1998). Asfor respiration, the thoracic modulation within each respiratory cycleproduces bulk head motion as well as changes in the static magneticfield B0 (Raj et al., 2001), and in the arterial CO2 partial pressure (Wiseet al., 2004). These changes associated with the cardiac and respiratorycycles induce correlated, quasi-periodic BOLD fluctuations, near andwithin large blood vessels or more generally across the brain, respec-tively (Birn, 2012). The cerebrospinal fluid (CSF) flow is also modulatedby both cardiac and respiratory cycles, resulting in additional signalchanges in CSF-filled regions (Klose et al., 2000). Even with the use ofhead restraints, bulkmotion related to the cardiac and respiration cycleswill lead to similar confounds as head motion itself (Murphy et al.,2013). Typically, these are correlated signal changes at the edges ofthe brain and in regions with large spatial variations in image contrast.Finally, non-periodic BOLD signal fluctuations are also produced due tochanges in cardiac rate (de Munck et al., 2008; Shmueli et al., 2007), aswell as in breathing depth and rate leading to changes in the CO2 arterialpartial pressure (Birn et al., 2006).

Several methods for physiological noise correction have been pro-posed, which are in most cases extensions of the retrospective image-based correction (RETROICOR) method (Glover et al., 2000), resortingto the simultaneous measurement of both cardiac and respiratorydata. The contribution of physiological processes is described by alow-order Fourier expansion in terms of the phases of the cardiac andrespiratory signals in relation to the fMRI acquisition time, which mayalso include their interactions (Harvey et al., 2008). All RETROICORterms (cardiac, respiratory and interactions) are then regressed outfrom the fMRI data using a general linear model (GLM) framework.Non-periodic physiological noise contributions can be additionallymodeled through the heart rate (HR) (de Munck et al., 2008; Shmueliet al., 2007), and respiratory volume per unit time (RVT) or respiratoryvolume (RV), surrogates of arterial CO2 changes (Birn et al., 2006; Changet al., 2009). Their contributions to the BOLD signal can be moreaccurately obtained by convolving the HR and RVT/RV time-courseswith appropriate cardiac and respiratory response functions (Birnet al., 2008; Chang et al., 2009; Falahpour et al., 2013), and/or shiftingthem by an optimized time lag (Bianciardi et al., 2009a; Jorge et al.,2013a).

Additionally to extended RETROICORmethods, or even alternativelyto these, physiological noise can also be reduced by extracting confoundregressors from the fMRI data itself (Murphy et al., 2013). The rationaleunderlying this approach is that BOLD signalfluctuations of neuronal or-igin should be mainly located in gray matter (GM), with fluctuationsfound in the CSF and white matter (WM) likely reflecting physiologicalnoise contributions alone. Typically, the average BOLD time-courseswithin CSF and/or WM masks are therefore computed and regressedout from the fMRI data in a GLM framework. Furthermore, headmotionparameters are also commonly regressed out from the BOLD signal.These can be extracted from the fMRI time-series by estimating therotation and translation of the subject's head in the x, y and z directions,assuming rigid body motion, and thus yielding a total of 6 motion re-gressors (Friston et al., 1996; Murphy et al., 2013). Because large headmovements are not accurately estimated using affine transformations,metrics identifying fMRI volumes subjected to large head motion havealso been proposed; those volumes deemed to be affected should thenbe ignored from subsequent analyses (Power et al., 2012; Tierneyet al., 2015).

In a completely fMRI data-driven approach, Independent Compo-nent Analysis (ICA) can also be used for fMRI de-noising by separatingsources of scanner artefact, physiological noise and brain activation(Beckmann and Smith, 2004; Brooks et al., 2008). If the independentcomponents (ICs) related to non-neuronal BOLD fluctuations can beidentified, then a noise-free BOLD signal is obtained by removing

Please cite this article as: Abreu, R., et al., Physiological noise correctiospontaneous neuronal activity with si..., NeuroImage (2016), http://dx.do

them from the back-reconstruction of the data. Such identification pro-cedure can be done manually or resorting to automatic classificationtools (Churchill et al., 2012b; De Martino et al., 2007; Salimi-Khorshidiet al., 2014; Smith et al., 2013; Tohka et al., 2008).

Although some fMRI data-driven approaches have been reported,most physiological noise correction methods still rely on measuringboth cardiac and respiratory signals. The cardiac trace is typically re-corded by means of electrocardiography (ECG) or pulse-oximetry(PO) using a photoplethysmograph, while the respiratory trace is com-monly recorded using a pneumatic or a piezoelectric belt strappedaround the upper abdomen (Birn, 2012; Murphy et al., 2013). In typicalsimultaneous EEG-fMRI recordings, the ECG is measured as part of theEEG setup but respiratory sensors are not generally used. Interestingly,several methods have been proposed to estimate an ECG-derived respi-ratory (EDR) signal from a single-lead ECG signal (O'Brien andHeneghan, 2007). These are based on the rationale that respirationmodulates the ECG signal in various ways, inducing changes in beatmorphology due tomovement of the electrodeswithin each respiratorycycle (Sobron et al., 2010; Zhao et al., 1994), and short-term changes inthe electrical impedance of the thoracic cavity reflected by the fillingand emptying of the lungs (Clifford et al., 2006; Pallás-Areny et al.,1989). Additionally, respiration also modulates the HR, with increasedHR during inhalation (Eckberg, 2003). The use of ECG-derivedregressors has previously been reported for fMRI data correction ofcardiac-related noise (Liston et al., 2006; van Houdt et al., 2009), butno study has so far investigated whether respiration-related noiseregressors can also be extracted from the ECG.

Here, we aimed to investigate the feasibility and impact of fMRIphysiological noise correction based on cardiac- and respiratory-related BOLD signal fluctuations extracted from the single-lead ECG re-corded in simultaneous EEG-fMRI studies. Several methods were firsttested for the estimation of a surrogate of the respiratory signal fromthe ECG, from which the associated confounding fluctuations were in-cluded in a subject-specific extended RETROICOR physiological noisemodel (P-PNM). The BOLD variance explained by the various P-PNMcomponents was assessed and compared with an image-basedphysiological noise model (I-PNM), and the impact of the subsequentdata correction on the sensitivity and specificity for mapping bothepileptic networks and the resting-state default mode network (DMN)was evaluated. The performance of the proposed methodology wascompared with the FMRIB's ICA-based X-noiseifier algorithm (FIX,Salimi-Khorshidi et al., 2014), as well as with a standard pre-processing pipeline, on simultaneously acquired EEG-fMRI data from agroup of epilepsy patients imaged at 3 T.

Materials and methods

The main steps of the processing pipeline proposed in this work forthe fMRI physiological noise correction and subsequent evaluation, interms of changes in sensitivity and specificity of epileptic network andDMN analyses, are depicted in Fig. 1. A list of all abbreviations used inthis manuscript is provided in Table 1.

EEG-fMRI data acquisition

A group of seven patients (17± 9 years old, 4males/3 females)withdrug-refractory focal epilepsy undergoing pre-surgical evaluation wasselected from the Programof Surgery for Epilepsy of the Hospital Centerof West Lisbon, by the clinical team, at suggestion of the physicianresponsible for the neurophysiological studies. These patients werestudied at the Imaging Center of Hospital da Luz in Lisbon, Portugal.All patients or their legal representatives gavewritten informed consentand the study was approved by the local ethics committee.

The imaging was performed on a 3 T Siemens Verio scanner(Siemens, Erlangen) using a 12-channel RF receive coil. Functionalimages were acquired using a 2D multi-slice gradient-echo echoplanar



Step #1

Extraction of physiological regressors

Cardiac signalEDR estimationRespiratory signalRaw fMRI data

Brain extraction

RETROICORSlicewise regression

Motion and slicetiming correction

Lag-optimizedRV and HR models

Correlation analysis

Epileptic network maps

DMN functionalconnectivity maps

Sensitivity and specificitymetrics

Step

#2

Step

#3

Step

#4

fMRI #1

fMRI #2

Motion parameters

Image-based regressors(CSF, WM)

Volume regressionfMRI #3

Highpass temporal filtering and spatial smoothing

fMRI #4

Fig. 1. Schematic diagram of the processing pipeline. The fourmain steps are highlighted by the colored rectangles including: 1) estimation of the respiratory signal from the ECG recordings,2) regression of theRETROICOR cardiac and respiratory terms, 3) regression of the lag-optimized RRF- and CRF-convolvedRV andHRmodels, respectively, and image-based regressors and4) epileptic network and DMN functional connectivity mapping, and quantitative impact assessment on the analysis quality of the latter.

3R. Abreu et al. / NeuroImage xxx (2016) xxx–xxx

imaging (EPI) sequence, with TR/TE= 2500/30ms, 37 or 40 contiguousaxial slices with interleaved acquisition, and 3.5 × 3.5 × 3.0 mm3 voxelsize, yielding whole-brain coverage in all cases.

EEG data were recorded using an MR-compatible 32-channelBrainAmp MR plus amplifier (Brain Products, Germany). A standardBrainCap MR model (EasyCap, Herrsching, Germany) was used, con-taining 31 Ag/AgCl ring-type electrodes arranged according to the10–20 system, a dedicated electrode for the referencing, and one

Table 1List of all abbreviations used throughout the manuscript.

BCG Ballistocardiogram IEDBET Brain extraction tool I-PNBOLD Blood oxygen level dependent IMFCBF Cerebral blood flow kPCCBV Cerebral blood volume MCCRF Cardiac response function MCCSF Cerebrospinal fluid MNDMN Default mode network PCAECG Electrocardiogram PCCEDR ECG-derived respiratory POEEG Electroencephalogram P-PEMD Empirical mode decomposition RBFEPI Echoplanar imaging RETFAST FSL's automated segmentation tool RMFFT Fast Fourier transform RRFFLIRT FSL's linear image registration tool RSNfMRI Functional magnetic resonance imaging RVFWHM Full width at half maximum RVTGLM General linear model tSTDGM Gray matter VEHR Heart rate VIPICA Independent component analysis WM


electrode placed on the back for ECG recording. Sampling was per-formed at 5000 Hz, synchronized with the scanner's 10 MHz clock.

For each patient, two or three simultaneous EEG-fMRI runs of 10 or20 min each were then performed inside the MR scanner during rest,yielding a total of 30 min in all cases. For the 7 patients, a total of 17EEG datasets concurrently acquired with fMRI were collected. Inter-ictal epileptiform discharges (IEDs) were captured on the EEG on 4out of the 7 patients. A reference 10 min EEG recording was performed

Inter-ictal epileptiform dischargesM Image-based physiological noise model

Intrinsic mode functionA Kernel principal component analysis

Motion correctionFLIRT FSL's motion correction toolI Montreal neurological institute

Principal component analysisPrecuneusPulse-oximetry

MN Physiological recordings based physiological noise modelRadial basis function

ROICOR Retrospective image-based correctionSF Root mean square frequency

Respiratory response functionResting-state networkRespiratory volumeRespiratory volume per unit timeTemporal standard deviationVariance explained

H Variability of the cardiac pulse heightWhite matter




outside the MR scanner in order to allow for the cross-validation of theintra-MR EEG traces.

EEG data pre-processing

All processing steps were implemented inMATLAB® using in-housesoftware, except for the pre-processing and ICA decomposition of EEGdata, which were performed using the EEGLAB toolbox (Delorme andMakeig, 2004). All EEG datasets were volume-wise gradient (Allenet al., 2000) and ballistocardiogram (BCG) (Abreu et al., 2016a) artefactcorrected, downsampled to 250 Hz and band-pass filtered to 1–45Hz. Anarrower frequency band (4–45Hz)was applied to the ECG signal, as toincrease QRS complex detection accuracy.

The detection of QRS complexes was performed using a modifiedversion of the Pan-Tompkins algorithm (Pan and Tompkins, 1985),which consists of submitting the annotations resulting from the originalimplementation to the following additional routines. Additional steps tocorrect for both false negative and positive QRS detection were applied.False negatives/positives were first detected by probing the timedifferences between two consecutive annotations for values higher/lower than 1.5/0.4 times the median time difference across all annota-tions. In the case of a false negative, an R peak was manually added inthe middle of the two adjacent R peak occurrences; regarding the falsepositives, the annotation yielding the lowest ECG amplitude betweenthe two adjacent occurrences was deemed spurious and removed.Next, the corrected annotations were subjected to an alignment step,by shifting them to match the occurrence of the R peak. Finally, themodified algorithm was applied to the time-inverted ECG signal,performing a back-search for the R peaks, in order to overcome thepoor detection at both ends of the ECG signal. The final set is obtainedby taking the union of the two sets of detected R peaks. An almostflawless QRS detector is of the utmost importance, as some of the ECGfeatures that are modulated by the respiratory cycle, and thus used tocompute a surrogate of the respiratory signal, are intimately relatedwith changes within each heartbeat.

Severalmethodswere then tested for the extraction of the respirato-ry signal from a single-lead ECG recording, following the review byO'Brien and Heneghan (2007). The procedures used are comprehen-sively described in the ECG-derived respiratory signal estimation sectionof the Supplementary material, as well as the results obtained (Figs. S2and S3). Additionally, the associated in-house MATLAB® code is avail-able at: https://github.com/rmabreu/Respiratory_Signal_Estimation. Insummary, the empirical mode decomposition (EMD) (Huang et al.,1998; Labate et al., 2013)method yielded themost accurate EDR signalson average, which were thus used in subsequent analyses.

MRI data pre-processing

The MPRAGE structural images were brain-extracted using the FSL'sbrain extraction tool (BET, Smith, 2002) and segmented using the FSL'sautomated segmentation tool (FAST) (Zhang et al., 2001) into GM,WMand CSF. The functional images were co-registered into both structuraland Montreal Neurological Institute (MNI) (Collins et al., 1994) spacesby means of the FSL's linear image registration tool (FLIRT) (Jenkinsonand Smith, 2001; Jenkinson et al., 2002). The registration matriceswere used to transform the WM and CSF masks from the structuralinto the functional space. Both masks were then eroded using a 3 mmspherical kernel, as recommended in Jo et al. (2010), in order to mini-mize partial volume effects. Additionally, the eroded CSF mask wasintersected with a mask of the large ventricles obtained in the MNIspace using the MNI structural atlas, following the rationale in Changand Glover (2009), and subsequently transformed into each subject'sfunctional space. Finally, the following regions-of-interest (ROIs) wereidentified in the MNI space using the MNI structural atlas, and subse-quently transformed into each subject's functional space: gray matter,


brainstem, caudate nucleus, cerebellum, cortical gray matter, insula,putamen and thalamus.

The functional images were first brain-extracted using BET (Smith,2002) and then (after RETROICOR, if applied) subjected to slice timingandmotion correction using the FSL's motion correction tool (MCFLIRT)(Jenkinson et al., 2002). After removal of all physiological noiseregressors, if applied, high-pass temporal filtering with a cut-off periodof 100 s and spatial smoothing with a Gaussian kernel of 8 mm fullwidth at half-maximum (FWHM) were performed.

Extraction of physiological noise regressors

Cardiac and respiratory phases were computed at each slice acquisi-tion time from the ECG and EDR signals, respectively, and a Fourier ex-pansion of these phases up to the fourth order was generated forRETROICOR correction as in Glover et al. (2000), yielding theRETROICOR set of regressors (rRETROICOR). The RV was computed as thestandard deviation of the EDR waveform on a sliding window of 3 TRs(2.5 × 3 = 7.5 s) centered at each desired TR (Chang et al., 2009),followed by the convolution with the respiratory response function(RRF) to yield theRV regressor (rRV) (Birn et al., 2008). TheHRwas com-puted as the inverse of the difference between two consecutive R peaks(deMunck et al., 2008; Shmueli et al., 2007). The HR time-courses werethen subjected to outlier removal, replacing samples located more than1.96 standard deviations away from the local mean by linearinterpolation. Additionally, a 2D 3 × 3 Gaussian filter was applied,followed by normalization in the range of [−1; 1], downsampling tomatch the fMRI acquisition rate and convolution with the cardiacresponse function (CRF), yielding the HR regressor (rHR) (Chang et al.,2009).

The BOLD time-courses averaged within the CSF and WM masks(rCSF and rWM) and the six parameters estimated by motion correction(MC) (rMC) were also used as nuisance regressors.

Physiological noise correction

The full physiological noise correction pipeline, including data pre-processing, is depicted in Fig. 1. While the RETROICOR variables are es-timated and regressed out from the data on a slice-wise basis immedi-ately following brain extraction, the remaining GLM variables areestimated and regressed out on a volume-wise basis, after slice timingand motion correction. To complete data pre-processing, high-passtemporal filtering and spatial smoothing were performed.

Physiological recordings based physiological noise model (P-PNM)A nested GLM approach was employed for the estimation of the

physiological recordings based physiological noise model (P-PNM)and the respective correction of the fMRI data (Bianciardi et al., 2009a;Jorge et al., 2013a), in which the regressors described above weresuccessively added in the following order: 1) slice-wise cardiac and re-spiratory RETROICOR 2) RRF-convolved RV; 3) CRF-convolved HR;4) average CSF time course; 5) average WM time course; and 6) MCtime courses. A total of six different GLM'swere built with the followingdesign matrices Xi:

X1 ¼ X0; rRETROICOR½ �X2 ¼ X1; rRV½ �X3 ¼ X2; rHR½ �X4 ¼ X3; rCSF½ �X5 ¼ X4; rWM½ �XP−PNM ¼ X5; rMC½ �

ð1Þ

where X0 is a N × 1 vector of ones. Each additional physiologicalregressorwas included based on its contribution relative to the previousmodel, as assessed by the associated variance explained (VE). The VEwas computed as the difference between the coefficients of


https://github.com/rmabreu/Respiratory_Signal_Estimation



determination adjusted for the degrees of freedom, R2adj, of two succes-sive models (Bianciardi et al., 2009a):

VE rið Þ ¼ 100� R2adj Xið Þ−R2

adj Xi−1ð Þ� �

ð2Þ

where ri denotes the set of regressors included in model Xi, orthogonal-ized in relation to all regressors in Xi−1. Each VE(ri) was averaged acrossGM and tested for significant differences from 0 (p b 0.05). Prior to sta-tistical inference, the normality of the sample distribution was firstassessed by the Lilliefors test (Lilliefors, 1967) and, if normally distribut-ed, a one-tailed t-test was used; otherwise, the non-parametricWilcoxon rank sum test was used.

Firstly, the nested model was applied to the slice-wise RETROICORterms, in order to ascertain which orders of the Fourier expansionwere significantly contributing to themodel. For this purpose, the ratio-nale described inHarvey et al. (2008)was followed, inwhich each orderof RETROICOR is considered as being composed of both its cardiac andrespiratory components. Therefore, although the cardiac and respirato-ry regressors are added separately for each order, the decision to includea higher order is based on the significance of the total VE including bothregressors. As a consequence, in order to keep a given cardiac orrespiratory RETROICOR term, all the lower order cardiac and respiratoryterms are also kept, including those terms that did not add significantVE individually. A lag-based optimization procedure was then appliedto both RRF- and CRF-convolved RV and HR time-courses, respectively,as described in Bianciardi et al. (2009a); Jorge et al. (2013a). The time-courseswere time-shifted by lags in the range of [−20; 20] s, in steps of1 s, and the lag yielding the maximum VE was selected on a dataset-specific basis. A second nested model was then applied using the re-maining GLM terms, in order to obtain the final P-PNM for each dataset.

Image-based physiological noise model (I-PNM)For comparison purposes, a commonly used image-based physiolog-

ical noise model (I-PNM) was also generated following a similarapproach, but using only the regressors extracted from the fMRI data:1) average CSF time course; 2) average WM time course; and 3) MCtime courses. The corresponding design matrix is then:

XI−PNM ¼ rCSF ; rWM ; rMC½ � ð3Þ

ICA-FIXThe fMRI data were first subjected to pre-processing steps similar to

the ones in the P-PNM and I-PNM pipelines, including: non-braintissues removal, motion correction, slice-timing correction, high-passtemporal filtering and spatial smoothing. Next, the pre-processed fMRIdata were submitted to a probabilistic spatial ICA (sICA) decompositionusing the FSL's tool MELODIC (Beckmann and Smith, 2004), with thedefault parameters, including the MELODIC's automatic dimensionalityestimation, as recommended in Salimi-Khorshidi et al. (2014). The toolFIX was then used to extract a large number of temporal and spatialfeatures to be fed into a core classifier previously trained using hand-labeled components. The standard training weights were used, as theimage acquisition parameters for our fMRI data roughly resembledthose described as standard in FIX's user guide page (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX). The non-neuronal related ICs were thenautomatically classified by FIX, and subsequently removed fromthe back-reconstruction step of the fMRI data, yielding ICA-basedphysiological noise corrected data.

Impact of physiological noise correction on fMRI data analyses

The physiological noise corrected fMRI data following the ap-proaches described above was then used for mapping epileptic net-works and the resting-state DMN. For comparison purposes,


uncorrected data were also used, being solely subjected to standardpre-processing steps.

Epileptic network mappingThe pre-processed EEG data was first decomposed using temporal

ICA (tICA) by means of the Infomax algorithm as implemented in theEEGLAB toolbox (Delorme and Makeig, 2004). The epilepsy-related ICswere then automatically identified (Abreu et al., 2016b) and subjectedto visual inspection for the identification of the selected IC with thehighest expression of epileptic activity. Finally, the root mean squarefrequency (RMSF) of the selected IC was computed by time-frequencyanalysis. This EEG metric is based on a biophysically inspired heuristicand has been previously found to yield superior results relatively toalternative metrics when attempting to map activity of interest (Rosaet al., 2010), including epilepsy applications (Leite et al., 2013), andwas thus used is this study. The EEG-RMSF time coursewas subsequent-ly convolved with a canonical double-gamma hemodynamic responsefunction (Friston et al., 1998), down-sampled to the fMRI acquisitionrate, and used to generate a GLM of epilepsy-related BOLD changes.The GLMwas fit to the pre-processed data using FILM, and the resultingstatistical parametric maps were subjected to cluster thresholding(voxel Z N 2.3, cluster p b 0.05) (Woolrich et al., 2001).

DMN mappingA seed-based GLManalysiswas performed tomap theDMN. Follow-

ing the procedures described in Chang and Glover (2009), an 8 mm-radius sphere centered at (−4, −58, 30) mm (Talairach coordinates(−4, −55, 30)) in the precuneus (PCC) was chosen as seed ROI, andco-registered to each subject's functional space. The average BOLD sig-nal within this seed was used to generate a GLM of PCC-related BOLDchanges, fitted to the pre-processed data using FILM, and the resultingstatistical parametric maps were subjected to cluster thresholding(voxel Z N 2.3, cluster p b 0.05) (Woolrich et al., 2001).

Physiological noise correction impact evaluationThe impact of physiological noise removal on the epileptic network

maps obtained for the patients from which epileptic activity wasrecorded during the simultaneous EEG-fMRI acquisitions, was assesseddirectly by visual inspection due to the patients' heterogeneous epilepsyprofiles.

For the DMN mapping, which was performed on 17 datasets of allpatients, sensitivity and specificity were evaluated using the followingmetrics:

1. Number of voxels. The ratio between the number of voxels in GMrelative to CSF and WM within the DMN map thresholded at Z =2.3 was computed.

2. Average Z-score. The ratio between the average Z-score in GM relativeto CSF and WM within the DMN map thresholded at Z = 2.3 wascomputed.

3. Dice coefficient. The spatial distribution of the DMN map was com-pared with the DMN template provided in Smith et al. (2009) bymeans of the dice coefficient. Both DMNmapswere first thresholdedat Z = 3.0.

Results

The results from the proposed physiological noise correction ap-proach are described here, including the optimization of the physiolog-ical noise model, and the impact of different correction approaches onboth epileptic network and DMN mapping. The findings related withthe choice of the EDR signal estimationmethod are provided in the Sup-plementary material (Figs. S2 and S3).


http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX



RETROICOR Terms

Fig. 2. Group average variance explained (VE) by each physiological noise regressor, averaged across different ROIs. (Top) Left stacked bars: physiological recordings-based physiological noisemodel (P-PNM). Right stacked bars: image-based physiological noise model (I-PNM). P-PNM yielded a total VE significantly higher than that of I-PNM, across all ROIs except for putamen.(Bottom) Group average VE of each RETROICOR term, averaged across GM.


Physiological noise modeling

The group average VE results obtained for each term in P-PNMand I-PNM, averaged across different ROIs, are presented in Fig. 2 (left andright bars, respectively), including the contribution of each individualRETROICOR term in P-PNM averaged across GM. All four orders of thecardiac terms yielded significant contributions to the RETROICORmodel, while only the second order of the respiratory terms significant-ly contributed to the model. Using our approach, this yields an optimalmodelwith four orders of the cardiac and three orders of the respiratoryRETROICOR terms. As expected, the cardiac terms explained the highestvariance when averaged across the brainstem, a brain structure locatedclose to major arteries and adjacent to pulsatile CSF-filled spaces(mainly ventricules III and IV) (Brooks et al., 2013). The remainingregressors in P-PNM also yielded significant contributions to the finalmodel, including the optimal RV model extracted from the EDR signal.The final model, XP−PNM in Eq. (1), explained a total VE of 33.5 ±4.6%, averaged across GM.

All physiological regressors in I-PNM (Fig. 2, right bars) also yieldedsignificant contributions to the final model, with a total VE of 31.2 ±6.3%, averaged across GM. Additionally, the total VE of I-PNM wasfound to be significantly lower than that of P-PNM, across all ROIsexcept for putamen. It is noteworthy that the additional VE by theregressors extracted from physiological recordings did not overlap withthat of the regressors extracted from the BOLD-fMRI data, as the differ-ence between the VE of P-PNM and I-PNM is approximately equal tothe total VE of the regressors extracted from physiological recordings.For these reasons, P-PNM was chosen for the subsequent assessment ofthe impact of physiological noise correction on fMRI network mapping.

The VE maps for the RETROICOR cardiac and respiratory compo-nents, as well as the RV and HR models, of a representative dataset,are showed in Fig. 3. RETROICOR cardiac terms up to the fourth order


yielded strong correlations with the BOLD signal in voxels near thebrainstem, large draining vessels and also within neighboring regionsof CSF-filled areas. The respiratory terms exhibited strong effects onposterior brain regions, particularly near the cerebellum, as well as inperipheral CSF areas. The RV and HR models yielded quite similar VEmaps, which is consistent with the results from (Birn et al., 2006) and(Shmueli et al., 2007). Both maps show mostly posterior brain regions,particularly near the cerebellum and the occipital lobe. When averagedacross subjects, RV and HR regressors yielded the optimal lags 2.6 ±9.7 s and −4.9 ± 8.6 s, respectively, across GM voxels. Despite thegreat inter-subject variability, these results are in agreement withthose reported in Bianciardi et al. (2009a), with RV and HR exhibitingsubstantially different optimal lags.

Impact on DMN and epileptic network mapping

An illustrative example of the DMN obtained from the fMRI datasubjected to standard pre-processing, ICA-FIX and P-PNM, is providedin Fig. 4A, and the group average performance of each approach interms of the sensitivity/specificitymetrics is shown in Fig. 4B. A clear in-crease in specificity can be observed on the DMNmaps after using ICA-FIX relative to standard pre-processing, which is however surpassedwhen using P-PNM, as evidenced by the fact that voxels located nearbrain regions usually associated with physiological noise are no longersignificantly correlated with the seed. Moreover, the spatial mapsobtained using both ICA-FIX and P-PNM are more similar to that ofthe Smith's DMN template, as evidenced by a substantially higher dicecoefficient of 0.25 and 0.29, respectively, relative to the standard pre-processing (0.19). Increases in the GM-to-WM/CSF ratio of the numberof voxels and average Z-score were also achieved by P-PNM relative toICA-FIX, and by ICA-FIX relative to standard pre-processing. In contrastwith ICA-FIX, significant effects were found between P-PNM and



RETROICOR (Cardiac terms)

RETROICOR (Respiratory terms)

RV

HR

0.05 0.5

0.02 0.05

0.02 0.05

0.02 0.05

Fig. 3.Variance explained (VE)maps froma representative subject. (From top to bottom)RETROICOR cardiac terms up to the fourth order; RETROICOR respiratory terms up to the third order;optimized RV and HR models.


standard pre-processing; moreover, P-PNM also yielded significantimprovements when compared with ICA-FIX. The DMN maps obtainedfor two additional patients are provided in Figs. S4 and S5 (Supplemen-tary material).

The epileptic networks obtained from the four patients for whomepileptic activity was captured on the concurrently acquired EEG-fMRI data are shown in Fig. 5; information regarding these data isprovided in Table 2, including the clinical profile of each patientbased on which the corresponding epileptic networks were hypoth-esized. In all cases, it is clear that P-PNMwas themost efficient meth-od at recovering such hypothesized networks; it achieves the mostfavorable weighting between increases in sensitivity and specificity,by eliminating spurious correlations in noise-related regions relativeto standard pre-processing, while extending the cluster activationwithin the hypothesized network.

In particular, a clear lack of sensitivity was observed for the stan-dard pre-processing pipeline when mapping the epileptic activity ofPatient P1, with no voxels surviving the cluster thresholding. If ICA-FIX or P-PNM is applied, similar activations maps are obtained. ForPatient P2, the removal of physiological noise fluctuations by P-PNM eliminated noise-related regions but preserved the expectedepileptic network, while ICA-FIX was found to remove a substantialpart of this network together with the spurious correlations. The ep-ileptic network of Patient P3 was completely obscured when onlystandard pre-processing was performed, with noise-related regions


being exclusively correlated with EEG-RMSF; although ICA-FIXmanages to remove most of the spurious correlations, the epilepticnetwork was only partially recovered, while P-PNM fully recoveredit. As for Patient P4, the epileptic network was successfully obtainedwhen submitting the fMRI data to standard pre-processing. Whileincreases in sensitivity can be observed when applying P-PNM,ICA-FIX possibly removed fluctuations of interest in the back-reconstruction of the fMRI data, by wrongly classifying ICs of interestas related to noise, and consequently no brain regions were found tobe correlated with EEG-RMSF in this case.

Discussion

In this paper, we propose the use of a physiological noise model tocorrect BOLD data for fluctuations of non-neuronal origin based on si-multaneously acquired ECG data in the scope of simultaneous EEG-fMRI studies. The respiratory-induced fluctuations are modeled basedon a surrogate of the respiratory signal estimated by means of EMD,and the physiological noise models including both cardiac and respira-tory contributions were found to explain more BOLD variance thanthe respective image-based models. Furthermore, increases in termsof sensitivity and specificity were found when mapping resting-stateand epileptic networks, relatively to using a standard pre-processingpipeline or an ICA-based noise correction.



Standard Pre-processing (PreProc)

ICA-FIX

P-PNM

(A)R L

#Voxels Z-score0

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5

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io

**

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PreProcICA-FIXP-PNM

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** (B)

Template DMN

Fig. 4. DMN mapping results for the different correction approaches, quantified by the proposed metrics. (A) Illustrative example from a representative dataset of the DMN functionalconnectivity maps (red-yellow) for fMRI data subjected to standard pre-processing steps (PreProc), ICA-FIX and P-PNM. A substantially higher similarity between the DMN maps (red-yellow) and the DMN template (light blue) obtained from (Smith et al., 2009) is observed when using P-PNM relative to PreProc or ICA-FIX. (Bottom) Three proposed metrics used toquantify changes in specificity and sensitivity on the DMN functional connectivity analyses. Only P-PNM exhibited significantly higher performance values when compared withPreProc, for all metrics.


Estimation of the respiratory signal

We performed a comprehensive comparison between differentmethods for estimating a respiratory signal from a single-lead ECG sig-nal, which extends other comparisons already found in the literature(Labate et al., 2013; Madhav et al., 2011). One particular motivationfor such comparison was the unorthodox location of the ECG electrodeused in our study. In fact, while common ECG recordings are usuallyperformed by placing the electrode on the chest, in our study the elec-trodewas strategically placed on the subjects' back in order tominimizemotion and associated artifactswithin the fMRI environment. Neverthe-less, and as in agreement with the findings reported in Labate et al.(2013); Madhav et al. (2011), the EMD method was found to surpass


all the other methods with the spectral content and temporal dynamicsof the EDR signals being similar to those of typical respiratory signals.

Although we could not compare the EDR signal with a ground truth(for instance, by also monitoring respiration with a respiratory belt),plausible variance explained maps for the regressors extracted fromthe EMD-based EDR signals were obtained, with voxels on posteriorbrain regions, and near CSF-filled areas and image edges exhibitinghigher VEs (Birn et al., 2006); on the other hand, these regressors signif-icantly contributed to the physiological noise model in terms of VE.These findings suggest that the EMD-based EDR represents a good sur-rogate of the respiratory signal, based on which respiratory-relatedBOLD fluctuations can be accurately modeled and removed from thedata. Ultimately, an alternative physiological noise model where



Patient P1 Patient P2

Stan

dard

Pre-

proc

essi

ngIC

A-F

IXP-

PNM

Patient P3 Patient P4

2.3 8.2 2.3 6.7

2.3 5.0 2.3 7.2

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dard

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proc

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ngIC

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IXP

-PN

M

R L R L

R L R L

Fig. 5. Epileptic network mapping for all patients from whom epileptic activity was recorded, using the different correction approaches. By applying P-PNM, overall increases in sensitivity (inparticular, P1 and P4) and specificity (in particular, P2 and P3) were observed. The epileptic network from patient P3 was only recovered with P-PNM, with standard pre-processingrendering noise-related activated voxels.

Table 2Characterization of the EEG-fMRI datasets for all patients onwhich epileptic activity was recorded. The duration of the datasets, the number of IEDs identified in each case, and themean headmotion are reported, the latter estimated by the FSL'smotion correction tool (MCFLIRT, Jenkinson et al., 2002). A brief description of each patient's clinical picture at the time of the simul-taneous EEG-fMRI studies is also provided. The EEG datasets acquired outside the MR scanner are indicated by *.

Patient Age Dataset Duration[min]

#IEDs

Mean head motion[mm]

Clinical condition

P1 8 1* 10 387 N.A. Right neonatal thalamic hemorrhage and epilepsy, with a posteriorepileptic focus and frontal propagation.2 20 754 0.26

3 10 292 0.12P2 9 1* 10 164 N.A. Dysfunction over the Wernicke area with severe verbal agnosia and

ability to maintain a sustained attention.2 10 596 0.043 20 738 0.06

P3 27 1* 10 70 N.A. Refractory focal epilepsy with a right posterior occipital-temporalepileptic focus, with frontal propagation.2 10 15 0.19

3 20 7 0.21P4 33 1* 10 837 N.A. Large left-temporal cortical dysplasia with continuous partial epilepsy

accompanied by continuous myoclonias of the right hand.2 10 288 0.163 10 287 0.124 10 342 0.15


Please cite this article as: Abreu, R., et al., Physiological noise correction using ECG-derived respiratory signals for enhanced mapping ofspontaneous neuronal activity with si..., NeuroImage (2016), http://dx.doi.org/10.1016/j.neuroimage.2016.08.008



respiratory-related fluctuations extracted from the actual respiratorysignal could be ideally built, and its impact on fMRI data analyses com-paredwith that of our proposed P-PNM. This validationwasnot possiblein the current study because the EEG-fMRI setup used did not includethe hardware required to record respiratory data. In fact, in typicalEEG-fMRI studies respiratory data are not generally available, as theirrecording require additional hardware that is not usually included inthe EEG setup. This is mainly to avoid adding complexity to the alreadyquite intricate setup, and to minimize patient discomfort. According toour experience, this issue is particularly problematic in children,whose cooperation may be easily compromised.

Alternatively to ECG, cardiac monitoring can also be performedusing pulse-oximetry (PO), which provides a directmeasure of changesin blood oxygenation that is known to be affected by breathing(Mannheimer, 2007). Taking this into account, Verstynen andDeshpande (2011) proposed the extraction of physiological noisemodels for both cardiac- and respiratory-induced BOLD fluctuationssolely from the PO signal; the PO signal was simply band-pass filteredwithin typical cardiac and respiratory frequency bands, yielding twophysiological signals from which RETROICOR, RV and HR regressorswere extracted. If used directly as a physiological regressor (afterdownsampling tomatch the acquisition rate of fMRI data), the PO signalyielded similar results to those of both conventional and PO-basedphysiological noise models. Although it provides a quite interestingalternative, additional hardware would need to be included on thealready complex EEG-fMRI acquisition setup.

Contribution of ECG-derived regressors to physiological noise models

The statistical significance of the additional VE of each regressor wasused as an inclusion criterion of that regressor into the optimized phys-iological noisemodel (P-PNM). Naturally, the order inwhich the regres-sors are successively included in the model will affect the associatedamount of VE, because each additional regressor is orthogonalizedwith relation to all the other regressors already included in the model.Mainly to allow direct comparisons with other studies in the literature,a conventional inclusion order was used, although it would be useful tofurther investigate the impact of changing such order on which regres-sors to be included in the final model.

There is currently no consensus regarding the order by which thevarious steps in the processing pipeline of fMRI data should be takento include physiological noise correction. A preliminary study showedthat RETROICOR should be performed prior to slice timing correction(Jones et al., 2008). More generally, a recent study aimed to systemati-cally investigate the optimal processing step order to minimize the im-pact of physiological noise on task-based activation maps (Churchillet al., 2012a). For that purpose, each dataset was divided into twoparts and two metrics were computed: 1) reproducibility between theactivation maps obtained from the two parts, and 2) accuracy of aBayesian model obtained from one part to predict the results from theother. A total of 48 different combinations between the main steps ofthe processing pipeline were tested, with the combination of motioncorrection with a 2nd order polynomial detrending yielding the highestgroup-averaged performance. Most importantly, a high variabilityacross subjects was found for the optimal order of the processing pipe-line, with individual-level pipeline optimization providing significantincreases in performance (Churchill et al., 2012a). Interestingly, in thework by Churchill et al. (2012a) it was also recommended that physio-logical noise correction should be carefully optimized on an individuallevel, as highly heterogeneous performances in terms of both metricswere found across subjects. Inter-subject variability and the need forindividual-level optimization have been reported in other studies onphysiological noise modeling (Falahpour et al., 2013; Golestani et al.,2015; Nunes et al., 2015). Altogether, these reports further supportthe subject-level lag optimization performed in our study for the HRand RV regressors.


Regarding the statistics used to infer which regressor is included inthe model, we estimated one full model containing all the regressors(XP−PNM in Eq. (1)), once this was optimized for the order (numberof cardiac and respiratory terms) of RETROICOR and for the lag of theRV and HR time-courses. However, because a nested model approachwas used, the order by which the regressors are added is pre-specified, and each additional regressor is orthogonalized in relationto the preceding regressors. Thus, its variance explained (VE) is evaluat-ed in relation to the model comprising those preceding regressors. Inthis study, we opted to use a nested model approach and to computethe VE of the average BOLD signal by a given regressor, because this isthe methodology followed by a significant number of studies(Bianciardi et al., 2009a; Jo et al., 2010; Jorge et al., 2013a; Shmueliet al., 2007). Alternatively, a partial F-test for a certain regressor couldhave been performed instead (de Munck et al., 2007; van Houdt et al.,2009), which in this context would be qualitatively equivalent to com-puting the difference between Radj

2 of the models with and withoutthat regressor (i.e., VE). Furthermore, a number of other studies haveemployed a different methodology, whereby a standard F-test wasused to assert the number of voxels significantly correlated with agiven regressor (Chang et al., 2009; Harvey et al., 2008; Kong et al.,2012).

Alternative physiological noise correction approaches

According to the definition of RETROICOR, RV and HR regressors,only RETROICOR allows to capture cardiac- and respiratory-relatedBOLD fluctuations with the temporal resolution of the individual sliceacquisition times. In fact, while RETROICOR describes fast, periodic fluc-tuationswithin the cardiac and respiratory cycles, RV and HR regressorsrepresent slow, non-periodic fluctuations reflecting changes in cardiacrate and respiratory rate and depth, respectively. For these reasons,while it suffices to model RV and HR contributions on a volume-by-volume basis, it has been shown that RETROICOR benefits from beingapplied slice-wise, particularly if a high heart rate variability is observed(Jones et al., 2008). As for the CSF andWM average BOLD time-courses,the limitation for a slice-wise regression is that it would require that allslices comprise voxels segmented as CSF andWM. Additionally, motioncorrection is known to interfere with slice timing, which requires thatslice-wise regression steps need to be performed before motion correc-tion (and slice timing correction).

In our study, an image-based correction (I-PNM) was also per-formed for comparison purposes; we found that the total VE of P-PNMwas significantly higher than that of I-PNM across the majority of ROIstested here. This is in agreement with the results in Jo et al. (2010),who reported that, although RETROICOR and RVT/RV regressors ex-plained little variance, they should nevertheless be included in thefinal model. In particular, the inclusion of RVT/RV fluctuations is of theutmost importance whenever subjects exhibit a high variability intheir breathing depth and rate during the acquisitions.

For the generation of I-PNM, we computed the average BOLD time-courses from co-registered, and then eroded, anatomical masks of CSFandWM,whichwere subsequently regressed out from the BOLD signal.Variations of this approach can be found in literature in terms of thedefinition of the noise-related ROIs, as well as the features to beextracted from them. As for the former, the temporal standard deviation(tSTD) can be computed for each voxel, and then thresholded so as toobtain the desired noise-related ROI. Such criterion is supported bythe observation of a positive correlation between the VE by therespective RETROICOR regressors and the tSTD value for a given voxel(Behzadi et al., 2007). Alternatively, a biophysically-inspired measureof robust temporal signal-to-noise ratio has also been reported as acriterion for the delineation of noise-related regions, whereby amixtureof Gaussians is fitted to this metric in each voxel using an expectation-maximization approach. The final selection of noise voxels is performed




by thresholding a given parameter from the estimated distribution(Tierney et al., 2015).

Regarding the extraction of features from the noise-related ROIs, inthis studywe opted for the conventional BOLD signal average. However,multiple regressors can be extracted, with PCA the most commonlyused approach for that purpose, where a given number of PCs are thenregressed out of the data (Behzadi et al., 2007; Bianciardi et al., 2009b;Jorge et al., 2013a; Tierney et al., 2015). With PCA, the variability ofthe physiological fluctuations within noise-related ROIs can be takeninto account, with the identification of the optimal number of PCs tokeep crucial, as to avoid under/over-estimation of the contribution ofphysiological fluctuations if an excessively low/high number of PCs areincluded in the model (Behzadi et al., 2007). In our proposed P-PNM,only the average BOLD time-course from CSF and WM masks wereincluded as to keep the loss in degrees of freedom below 10% (P-PNMcomprises a total of 24 regressors), which would be exacerbated if agiven number of PCs extracted from CSF and WM masks were to beincluded. For consistency, only the average BOLD time-courses wereincluded in I-PNM, although the loss in degrees of freedom is notproblematic is this case.

A purely ICA-basedmethod FIXwas also used and comparedwith P-PNM.We chose to use the default settings, because our data acquisitionparameters roughly resemble those described as standard in FIX's userguide (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX). It is expected thatchanging some of these settings would have a substantial impact onthe results. In particular, the threshold reflecting the proportion ofgood vs. bad componentswill determine howdemanding the algorithmis at classifying a component as noise; we used a conservative thresholdof 5, as it is deemed sensible in Salimi-Khorshidi et al. (2014). Further-more, different training weights could have been used in the classifica-tion step, using for instance our own data to train the core classifier.However, thiswould require a larger number of datasets, and ultimatelyto hand-label all the ICs obtained from the training set. Apart frombeinga time-consuming task, the manual classification of ICs may prove to bequite subjective.

Impact of physiological noise removal on functional connectivity analyses

In this work, the impact of P-PNM and FIX on DMN functionalconnectivity was assessed, as physiological noise correction is knownto be an essential step for the analysis of resting-state functionalconnectivity in general, with several studies reporting substantialimprovements on the specificity and/or reproducibility of DMNmapping (Birn et al., 2014; Chang and Glover, 2009; Chang et al.,2009; Falahpour et al., 2013; Jo et al., 2010). Our assessment wasbased on three differentmetrics, with the ratios of the number of voxelsand average Z-score between regions of interest (GM) and no interest(WM and CSF) showing increases in both sensitivity and specificity.Moreover, the dice coefficient between the DMN maps and the DMNresting-state template from Smith et al. (2009) was also increased.

To our knowledge, only two studies systematically assessed theimpact of physiological noise removal on epileptic network mapping(Liston et al., 2006; van Houdt et al., 2009). In Liston et al. (2006), theauthors found that approximately 25% of GM voxels significantlycorrelated with IED-related BOLD signal fluctuations were contaminat-ed by cardiac effects. This value was substantially reduced after model-ing such effects. However, respiratory-induced signal changes were notmodeled due to hardware constraints, which is acknowledged as alimitation by the authors, further supporting the importance ofmodeling both cardiac and respiratory physiological effects (Birn,2012; Murphy et al., 2013). In contrast, van Houdt et al. (2009) tookinto account not only cardiac- and respiratory-related fluctuationsusing standard physiological regressors, but also the variability of thecardiac pulse height (VIPH). However, substantial improvements on ep-ileptic network mapping were found in only one of the five patientsstudied, when including VIPH in a GLM as a confounding regressor. In


our study, heterogeneous epilepsy profiles of the patients renderedthe computation of generic performance metrics unsuitable. Neverthe-less, clear improvements on EEG-correlated fMRI epileptic maps couldbe found by visual inspection, when performing physiological noisecorrection, even in the absence of appropriate hardware to monitorrespiration.

Conclusion

We have proposed a new fMRI data pre-processing pipeline, inwhich a subject-level optimized physiological noise model is generatedbased solely on concurrent ECG recordings and used to remove bothcardiac- and respiratory-induced BOLDfluctuations of non-neuronal or-igin. We found that EMD estimated the respiratory signal with thehighest accuracy, and that respiratory-related signal changes could bemodeled by extracting nuisance regressors from the EDR. The impactof the associated physiological noise correction was found to outper-form ICA-based correction on subsequent resting-state and epilepticnetwork mapping. Our results support the feasibility of extractingrespiratory-related BOLD fluctuations resorting exclusively to the ECGsignal recorded in the scope of simultaneous EEG-fMRI acquisitions,with significant impact on the quality of resting-state functionalconnectivity analyses, known to be critically contaminated by physio-logical noise, and also in studies of epileptic activity in which the lackof sensitivity is still a major limitation.

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.neuroimage.2016.08.008.

Acknowledgements

We acknowledge the Portuguese Science Foundation (FCT) forfinancial support through Project PTDC/SAUENB/112294/2009, ProjectPTDC/EEIELC/3246/2012, Grant LARSyS UID/EEA/50009/2013, and theDoctoral Grant PD/BD/105777/2014.

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