On the Optimal Strategy for Tackling Head Motion in fMRI Data

Júlia F. Soares

1 a

, Rodolfo Abreu

1 b

Ana Cláudia Lima

, Sónia Batista

2,3

, Lívia Sousa

2,3

Miguel Castelo-Branco

1,3 c

and João Valente Duarte

1,3 d

Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT),

Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal

Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal

Faculty of Medicine, University of Coimbra, Coimbra, Portugal

Keywords: Head Motion, Task-fMRI, Modelling, Interpolation.

Abstract: Head motion critically hampers the quality of functional magnetic resonance imaging (fMRI) data, with

several methods for its correction being already available in the literature. Head shifts are usually corrected

by realigning all functional volumes with relation to a reference volume using affine transformations, from

which the estimated motion parameters (MPs) can be additionally regressed out from fMRI data. However, a

consensus regarding the number of MPs to regress has not been achieved yet. More critically, abrupt head

motion induces the so-called motion outliers in the data, which cannot be accounted for by affine

transformations. Two common approaches are widely used to tackle this type of motion, namely modelling

strategies such as censoring, and volume interpolation. However, a direct comparison between strategies to

tackle motion outliers has not been performed so far. Importantly, to our knowledge no study has focused on

determining the extent at which the effects of different head motion correction methods differ between groups

in clinical studies. This is particularly relevant in task-related functional connectivity fMRI studies, which are

rapidly increasing in clinical research. In this study, we started by determining the optimal number of MPs

(between 6 and 24) to be regressed out from fMRI data collected from 8 participants (4 patients with Multiple

Sclerosis and 4 healthy controls) performing a perceptual decision-making task. Then we tested motion

censoring and volume interpolation for correcting motion outliers, using FD and DVARS metrics to detect

the outlier volumes. We found that task-specific activated brain regions were detected with higher sensitivity

when using 6 MPs relatively to using 24 MPs. As for the correction of motion outliers, our results suggest

that volume interpolation is the best method to use, however more data and external validation is needed to

achieve a definite conclusion. Importantly, the performance of motion correction algorithms was irrespective

of the subject group (patients and healthy participants). Our results pave the way towards finding an optimal

motion correction strategy, which is required to improve the accuracy of fMRI analyses in healthy and patient

populations and are an encouragement to test comprehensively different approaches.

1 INTRODUCTION

The blood oxygen-level-dependent (BOLD) signal

measured with functional magnetic resonance

imaging (fMRI) in the brain is a mixture of

fluctuations from both neuronal and non-neuronal

origins, the latter being responsible for inducing

BOLD signal changes that account for a substantial

amount of its variance (Caballero-Gaudes and

Reynolds 2017).

https://orcid.org/0000-0002-8960-7794

https://orcid.org/0000-0001-9496-3534

https://orcid.org/0000-0003-4364-6373

https://orcid.org/0000-0001-8586-9554

One of the most problematic sources of noise is head

motion. Because fMRI volumes are acquired over

multiple slices, the movement of the head causes

excitation of different slices at subsequent time points

relative to previous ones. These so-called ‘spin

history’ effects lead to motion-related changes in

BOLD signal intensity that obfuscate the

measurement of localized haemodynamic responses

(Parkes et al. 2018). This will cause distortions and

signal dropouts in brain regions prone to these effects.

306

Soares, J., Abreu, R., Lima, A., Batista, S., Sousa, L., Castelo-Branco, M. and Duarte, J.

On the Optimal Strategy for Tackling Head Motion in fMRI Data.

DOI: 10.5220/0010327803060313

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS, pages 306-313

ISBN: 978-989-758-490-9

In general, the effect of head motion is predominantly

seen in voxels at the edges of the brain and in voxels

lying close to tissue boundaries due to the differences

in proton density and relaxation parameters across

brain tissues (Liu 2016)

. Nonetheless, any brain

region might suffer from detrimental effects of head

motion.

There are two main types of head motion: gradual

head shifts and sudden movements of the head known

as motion outliers (Liu 2016). To compensate for

head shifts, it is common practice to realign the data

(as part of typical fMRI preprocessing) by estimating

the position of the head in space at each volume

relatively to a reference volume using rigid body

transformations. In a rigid body transformation, head

position is described at each timepoint by six motion

parameters (MPs): translational displacements along

X, Y, and Z axes; and rotational displacements of

pitch, yaw, and roll. In order to exclude the variance

of the BOLD signal associated with head shifts, these

6 MPs are commonly included as nuisance regressors

in a General Linear Model (GLM) analysis of the

fMRI data. Because residual BOLD variance

associated with head shifts can still be present,

additional MP-derived regressors have been

suggested, namely the temporal derivatives of the

MPs (Power et al. 2013). Motion outliers induce the

most critical BOLD signal changes. These can be

identified as spikes in the data and cause large

variations in image intensity. Such spikes are not

accurately estimated using rigid body

transformations, and thus the motion correction step

or the regression of the MPs fail to account for them.

As a solution, several metrics have been proposed for

the detection of motion outliers, the most common

being the Framewise Displacement (FD) and the

Derivative or root mean square VARiance over

voxelS (DVARS) (Power et al. 2013). Then, motion

outliers can be corrected through motion censoring

(or scrubbing), whereby additional scan nulling

regressors (with 1s at the volumes where motion

spikes are detected and thus to be censored, and 0s

elsewhere) are regressed out from the fMRI data

(Siegel et al. 2014). Alternatively, volumes associated

with motion outliers can be interpolated based on

non-corrupted volumes (Rudas et al. 2020;

Mckechanie et al. 2019; Mazaika et al. 2009;

Caballero-Gaudes and Reynolds 2017).

Both task-related activation maps and measures of

functional connectivity depending on both short- and

long-range connections might be affected by both

types of head motion (Power et al. 2014; Seto et al.

2001). Resting-state fMRI studies have demonstrated

that head motion can introduce systematic bias to

connectivity estimates by creating spurious but

spatially structured patterns in functional

connectivity (Maknojia et al. 2019; Parkes et al. 2018;

Power et al. 2014). In

task-based fMRI studies, head

motion is particularly problematic when it correlates

with the experimental tasks leading to false brain

activations. If not properly accounted for, head

motion will bias the statistical results, reducing the

sensitivity and specificity for detecting task-specific

BOLD responses (Caballero-Gaudes and Reynolds

2017; Power et al. 2014; Seto et al. 2001).

Despite all the known effects of head motion on

the quality of fMRI data, and several correction

options, there is still no consensus regarding the

optimal number of MP-related regressors to consider

for tackling head shifts, nor the most appropriate

approach to mitigate motion outliers.

Also, there is a lack of studies in determining the

extent at which the effects of head motion differ

between groups in clinical studies. This is particularly

relevant in task-related functional connectivity fMRI

studies, which are rapidly increasing in clinical

research.

In this study, we started by testing the number of

MPs (between 6 and 24) that would improve the

ability to accurately detect task-specific BOLD

responses on fMRI data collected from 8 participants

performing a perceptual decision-making task. Then,

we tested motion censoring and volume interpolation

approaches for tackling motion outliers, with the

volumes to be censored detected with the FD and

DVARS metrics, whereas volumes to be interpolated

were identified with FD. The best approach (and

metric) was also determined based on the quality of

the data analyses. The effect of different approaches

for correction of head motion on task-related

activation maps was also compared between a control

group of healthy participants and a clinical group of

patients with Multiple Sclerosis.

2 METHODS

2.1 Participants

This study includes 4 patients with Multiple Sclerosis

(MS) and 4 healthy controls. Patients were recruited

at the Coimbra Hospital and Universitary Centre

(CHUC) and met the criteria for MS diagnosis

according to McDonald Criteria (Thompson et al.

2018). All participants gave written informed

consent. Local ethics committee approved the study.

On the Optimal Strategy for Tackling Head Motion in fMRI Data

307

2.2 Data Acquisition

Imaging was performed on a 3T Siemens

MAGNETOM Prisma Fit MRI scanner (Siemens,

Erlangen) using a 64-channel RF receive coil, at the

Portuguese Brain Imaging Network (Coimbra,

Portugal). fMRI data was acquired using a 2D

simultaneous multi-slice (SMS) gradient-echo echo-

planar imaging (GE-EPI) sequence (6× SMS and 2×

in-plane GRAPPA accelerations), with the following

parameters: TR/TE = 1000/37 ms, voxel size =

2.0×2.0×2.0 mm

, 72 axial slices (whole-brain

coverage), FOV = 200×200 mm

, FA = 68°, and

phase encoding in the anterior-posterior direction. A

short EPI acquisition (10 volumes) with reversed

phase encoding direction (posterior-anterior) was

also performed prior to each fMRI run, for image

distortion correction. A 3D anatomical T1-weighted

MP2RAGE (TR = 5000 ms, TE = 3.11 ms; 192

interleaved slices with isotropic voxel size of 1 mm)

was also collected for subsequent image registration.

2.3 Behavioral Task

The imaging session contained two functional runs

for collection of BOLD signals during the

performance of a perceptual decision-making task on

biological motion (BM), each consisting of 507

volumes (approximately 8.37 minutes). This task

comprised three categories of visual motion stimuli:

global biological motion; local biological motion; and

scrambled motion. Biological motion stimuli were

built based on human motion capture data collected at

60 Hz, comprising 12 point-lights placed at the main

joints of a male walker. Each BM perception run

consisted of 12 blocks of 40 seconds: 4 or 5 blocks

(depending on the starting block) of the point-light

walker facing rightwards or leftwards (global

biological motion), 4 or 5 blocks showing only the

point-light located at the right ankle and moving

rightwards of leftwards (local biological motion), and

3 blocks of point lights randomly positioned across

the y axis, while maintaining their true trajectory

across the x axis (scrambled motion). After each

stimulus presentation, the participants reported the

direction of motion of the dots (left or right) by

pressing one of two buttons. Figure 1 is a schematic

representation of the visual stimuli.

Figure 1: Schematic representation of the visual stimuli.

2.4 Data Processing

2.4.1 Pre-processing Steps

fMRI data were preprocessed using the MATLAB®

software, with SPM12 and the PhysIO toolbox

(Kasper et al. 2017), except for image distortion

correction which was performed using FMRIB

Software Library (FSL). The first part of the pre-

processing pipeline included: 1) slice timing

correction; 2) realignment of all fMRI volumes

relative to the first volume; 3) correction of geometric

distortions caused by magnetic field inhomogeneity;

4) bias field correction. The second part of the

preprocessing was related to regression of non-

neuronal fluctuations such as cardiac and respiratory

signals, WM and ventricular CSF average BOLD

fluctuations and head motion spikes. First, image

coregistration (anatomical to functional) and

segmentation of the structural image were done to

extract WM and ventricular CSF masks. Noise

fluctuations (cardiac and respiratory signals, WM and

CSF average BOLD fluctuations) including 6 and 24

MPs were computed with PhysIO toolbox and then

regressed out of the BOLD signal. After determining

the optimal set of MPs, motion outliers were either

regressed out of the BOLD signal (by adding scan

nulling regressors consisting of 1 at the volume to be

censored and 0s elsewhere) or interpolated. These

were identified with the FD or DVARS metrics. The

pre-processing was completed with spatial smoothing

with a 3 mm full-width-at-half-maximum (FWHM)

isotropic Gaussian kernel and high-pass temporal

filtering with a cut-off period of 108 s.

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2.4.2 Motion Processing

The first step is to study the number of MPs (between

6 and 24) needed to correct the effects caused by head

shifts and consequently which one would improve the

ability of our models to accurately detect task-specific

BOLD. Here, we tested 6 and 24 MPs because they

represent the two extreme cases complexity-wise

(Maknojia et al. 2019). The 6 MPs were computed as

part of the volume realignment step and the 24 MPs

which correspond to squares of the 6 MPs and

temporal derivatives were computed with PhysIO

toolbox. Then we regressed out these MPs from the

BOLD signal.

To test which strategy is best to correct the motion

outliers’ effects (modelling or interpolation), we start

by detecting the outlier’s volumes with the two most

used metrics: FD and DVARS.

FD is a scalar quantity to express instantaneous

head motion and it is computed through the time

series of the six MPs obtained during the motion

correction step (Power et al. 2013).

DVARS is a measure computed from the data

itself and does not depend on the MPs. It represents

how much the intensity of a volume changes in

comparison to the previous one (Power et al. 2013).

After identification of motion outliers, we tested

modelling strategies through motion censoring (with

1s at the volumes where motion spikes are detected

and thus to be censored, and 0s elsewhere) and

regression and volume interpolation to correct motion

outliers’ effects in data.

Modelling strategies were firstly implemented

with motion outliers being identified with FD and

secondly with motion outliers being detected by

DVARS. We used PhysIO toolbox to apply FD metric

with a threshold of 0.5mm and FSL utility

fsl_motion_outliers to compute the DVARS for all

volumes; motion outliers were identified by

thresholding the DVARS at the 75th percentile plus

1.5 times the inter-quartile range.

The last method we used to repair the volumes

most affected by movement was a linear interpolation

(INTERP) with the ArtRepair software (Mazaika et

al. 2009). Motion outliers were identified with FD

metric with a threshold of 0.5mm.

2.5 Statistical Analysis

For the purpose of mapping the regions involved in

our perceptual task, the GLM framework was used.

GLM is a common way to analyse fMRI and it is

basically a linear regression represented by:

 =  +  (1)

with y the time series from one voxel, X the design

matrix, β the model parameters, ε, the normally

distributed error (or residuals) with zero mean (Pernet

2014).Onsets and durations of each experimental

condition were included in the model of the BOLD

signal as regressors of interest representative of our

task. We ended up with three regressors representing

periods showing global biological motion, local

biological motion, and scrambled motion. These

regressors were built based on unit boxcar functions

with ones during the respective periods, and zeros

elsewhere and convolved with a canonical, double-

gamma hemodynamic response function (HRF). The

HRF-convolved regressors were then included in a

GLM (X, the design matrix) that was subsequently

fitted to the fMRI data. After the fitting, the β’s are

estimated, weighting the relevance of each regressor

in explaining the variance of the data. Here, we set to

study brain regions that are activated when global

biological motion stimuli are present more than when

scrambled motion versions appears. Thus, the areas

associated with this condition were localized

according to the following contrast: [global biological

motion – scrambled motion].

Because many voxels are tested simultaneously,

the chance of observing false positives (i.e., the

Family Wise Error (FWE) rate) is very high in the

absence of any correction. To address this issue, we

used a FEW correction method based on Random

Field Theory (RFT), and we only considered

activations as significant those with a threshold of p

< 0.05 (the probability that we will observe a false

positive is only 5%).

GLMs were estimated for each participant

containing the two runs of the behavioral task and

statistical maps with voxels exhibiting significant

changes specified by the contrast [global biological

motion – scrambled motion] were identified with a

cluster threshold of p < 0.05 (FWE corrected).

After determining the optimal set of MPs (6, see

Results below), the subsequent analyses regarding

motion outliers were performed only considering 6

MPs. In this way, each participant ended up with 5

GLMs consisting of: 1) only 6 MPs, 2) only 24 MPs,

3) 6 MPs and motion outliers regressors detected with

FD, 4) 6 MPs and motion outliers regressors detected

with DVARS, 5) interpolated volumes.

From the resulting activation maps, the maximum

(Z-max) and mean (Z-mean) Z-score values were

extracted. Also, we quantified the amount of variance

of the average BOLD signal

On the Optimal Strategy for Tackling Head Motion in fMRI Data

309

across each activation map that was explained by the

MPs 

( BOLD/Motion). Despite the MPs are

regressed out from the data and motion outliers

corrected, head motion may not be fully corrected,

thus leaving residual contributions in the BOLD

signal (Abreu 2017). The 

( BOLD/Motion)

measure was estimated by the coefficient of

determination adjusted for the degrees of freedom,





which is defined according to (Montgomery,

Peck, and Vining 2012):





= 1 −

 − 1

 −  − 1

∑





2

=1

∑ (





− �

)



=1

(2)

where � is the average BOLD signal, N is the number

of volumes and P the number of motion regressors; ε

denotes the residual of the model under analysis,

which is described by  =  −  , where  is the

matrix containing the MPs, and β the associated

weights estimated using a GLM framework.

These were the metrics we compared to assess the

quality of each method. The Z values indicate the

sensitivity of the model in detecting brain regions that

are associated with our behavioral task. The higher

the values of Z the higher is the quality of the method.

The lower the values of 

(BOLD/Motion) the less

variance of the BOLD signal is explained by motion

so the better is the method.

In order to statistically compare the performance

of the methods tested here, a two-way mixed

ANOVA (one between-subjects and one within-

subjects factors) was performed. Prior to these

analyses, the requirements of the statistical tests

described next were verified. For the two

comparisons the between-subjects factor is “Group”,

which has two nominal unrelated or independent

categories: Multiple Sclerosis (MSC) and control

(CNT) participants. The within-subjects factors are

the “MPs” (number of motion parameters) and

“Correction Method” for the first and second

comparison respectively.

3 RESULTS

3.1 6 MPs vs 24 MPs

Group mean Z-max, Z-mean and 

(BOLD/Motion)

values of the models with 6 MPs and 24 MPs are

represented in Table 1. As evidenced, the Z-max and

Z-mean values are systematically higher when using

6 MPs. No differences were found in the



( BOLD/Motion) values. The two-way mixed

ANOVA showed that the comparison between these

values concerning the main effect “MPs” was

statistically significant (p<0.05). The “Group” main

effect proved to be non-significant (p>0.05) for this



( BOLD/Motion) values. The two-way mixed

ANOVA showed that the comparison between these

values concerning the main effect “MPs” was

statistically significant (p<0.05). The “Group” main

effect proved to be non-significant (p>0.05) for this

comparison. There was also no statistically

significant interaction between “Group” and “MPs”

(p>0.05). Figure 2 shows the activation maps of one

participant when using 6 vs 24 MPs.

Table 1: Metrics to assess the quality of the models using 6

MPs and 24 MPs. Values are presented as mean ± standard

deviation in each group of participants.

Metrics

MPs Group Z-max Z-mean



(BOLD/Motion)

MSC

8.015

0.500

5.974

0.317

-0.012

0.000

CNT

7.094

1.050

5.631

0.300

-0.011

0.001

MSC

7.648

0.766

5.814

0.283

-0.012

0.000

CNT

6.875

1.264

5.536

0.365

-0.011

0.001

3.2 FD vs DVARS vs INTERP

Table 2 depicts group mean Z-max, Z-mean and



(BOLD/Motion) values of the models used to test

the different methods for correction of motion

outliers. Despite Z-max and Z-mean values are higher

for the interpolation method, the two-way mixed

ANOVA showed that the comparison between these

values was not statistically significant (p>0.05)

considering both “Correction Method” and “Group”

main effects. No statistically significant differences

were found to the 

(BOLD/Motion) values. There

was also no statistically significant interaction

between “Group” and “Correction Method” (p>0.05).

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310

Table 2: Metrics to assess the quality of the models using

FD, DVARS and INTERP methods to correct motion

outliers’ effects. Values are present Values are presented as

mean ± standard deviation in each group of participants.

Metrics

Correction

Method

Group Z-max Z-mean



(

BOLD/

Motion)

FD MSC

8.015

± 0.496

5.971

± 0.311

-0.012

± 0.000

FD CNT

7.070

± 1.213

5.632

± 0.310

-0.011

± 0.001

DVARS MSC

8.015

± 0.500

5.974

± 0.317

-0.012

± 0.000

DVARS CNT

6.983

± 1.234

5.603

± 0.336

-0.005

± 0.017

INTERP MSC

8.035

± 0.505

6.022

± 0.321

-0.012

± 0.000

INTERP CNT

7.105

± 1.054

5.641

± 0.288

-0.006

± 0.011

Figure 2: Activation maps of one participant, resulting from

the contrast [global biological motion – scrambled motion].

On the left is represented an activation map resulting from

a model with 6 MPs. On the right is represented an

activation map resulting from a model with 24 MPs. The

model with 6 MPs (left side) shows that task-specific brain

regions are detected with higher sensitivity relatively to

using 24 MPs (right side). Results are presented at a voxel

p-value < 0.05, FWE corrected for multiple comparisons.

Color bar scale represents t-values. The t-value is the result

of the statistical test (t-test) in each voxel and measures the

size of the difference calculated between the BOLD signal

in the presence of biological motion stimulus and the

BOLD signal during the presence of scrambled motion

stimulus. The higher the t-value the most correlated is the

BOLD signal with the task condition or the specified

contrast in a given brain region, thus more sensitive is that

(group of) voxel(s).

4 DISCUSSION

Due to the lack of consensus on how to deal with the

head motion effects in fMRI data analysis, in this

study we compared different strategies to compensate

for head motion, in the context of a perceptual

decision task performance between MS patients and

controls. We started by testing if including temporal

derivatives of MPs would improve the results of our

analyses. Next, we compared three methods to correct

the effects of motion outliers. Two of them were

modelling approaches (censoring) based on two

different motion outliers detection algorithms: FD

and DVARS. The third strategy used was

interpolation of volumes affected by motion,

INTERP.

The first comparison, 6 vs 24 MPs, revealed that

higher Z-score values are obtained when considering

6 MPs, suggesting that task-specific brain regions are

detected with higher sensitivity relatively to using 24

MPs. This is further supported by the activation maps

resultant from both models. Head shifts are usually

corrected through regression of MPs, but there is still

no consensus regarding the optimal number of MPs

to include. Our results using just 6 MPs are consistent

with literature reporting that adding temporal

derivatives can result in loss of degrees of freedom

and therefore loss of valuable information. (Yang et

al. 2019).

Regarding the second comparison, the Z-max and

Z-mean values are higher for the interpolation

method. Although the two-way mixed ANOVA

showed that the comparison between the values

obtained with the different methods was not

statistically significant (p>0.05), we suggest the use

of the interpolation method. However, further studies

with more data are needed to reach a definite

conclusion about which method is best to correct the

effects of motion outliers. Furthermore, it is

important to discuss the impact of modelling motion

outliers and interpolation in the data.

Modelling motion outliers is a widely used

technique to correct sudden movements of the head,

however it creates temporal discontinuities.

Interpolation overcomes this problem and avoids side

effects in the high-pass filter (Michielsen et al. 2011).

However, volume interpolation induces synthetic

data, and the duration of the censored segment, as

well as the type of interpolation (linear, Fourier,

wavelets or splines), may produce different effects

that depend on the choice of these parameters

(Caballero-Gaudes and Reynolds 2017). To our

On the Optimal Strategy for Tackling Head Motion in fMRI Data

311

knowledge these effects and the negative impacts of

using interpolation are still largely unknown.

Although the two approaches are widely used, to our

knowledge there are no studies that contemplate the

question, with a direct comparison on the same data,

about which strategy is best to correct motion

outliers: modelling or interpolation. Because there are

no negative effects reported when using interpolation

and that it appears as an alternative to solve the data

loss caused by censoring, we further suggest that this

method may be the best one to adopt to mitigate the

effects of motion outliers. Nevertheless, we believe

further studies with a higher number of participants

will allow to derive conclusive results and to a greater

consensus on which strategy to use. Thus, our study

paves the way towards finding an optimal motion

correction strategy.

In both comparisons, the main effect of group and

also the interaction of correction approach with group

proved to be not significant, which means that there

are no differences provoked by head motion

correction effects between groups. So, the quality of

head motion correction is mainly due to the method.

This is an important issue to consider in fMRI studies

in clinical context, as previous reports show group

differences in head motion between control and

patient groups (Seto et al. 2001). This is particularly

relevant in task-related and resting-state (RS)

functional connectivity fMRI studies, which are

rapidly increasing in clinical research (Goto et al.

2016). Previous studies show that group differences

in head motion between control and patient groups

cause group differences in the resting-state network

with RS-fMRI (Lee, Smyser, and Shimony 2014;

Song et al. 2012; Maknojia et al. 2019). To our

knowledge there is a lack of this kind of studies in the

MS context. Furthermore, our study raises the

importance of this processing step in functional

connectivity studies, where one wants to study

functionally connected networks throughout the brain

that are correlated only due to the stimulation or

cognitive processing, in task-based fMRI, or due their

intrinsic functional organization, not because of head

motion.

We decided to compare these approaches,

however there are other techniques that can be

implemented. External optical tracking systems that

constantly measure the position of the head or the use

of dedicated sequences with navigators echoes or

active markers (Maknojia et al. 2019; Caballero-

Gaudes and Reynolds 2017) are such examples. Data

driven approaches can also be used, namely

algorithms such as Principal Component Analysis

(PCA) or Independent Components Analysis (ICA),

which first decompose the data into a set of

components, then the corrected fMRI data is obtained

by removing the contribution of motion-related

components (Caballero-Gaudes and Reynolds 2017;

Liu 2016). Yet, we focused on study the number of

MPs that would better characterize the head shifts to

be regressed out from fMRI data and on comparing

modelling vs interpolation methods to tackle the

motion outliers’ effects since these are the most used

in the literature, and as such, are of greater relevance.

5 CONCLUSIONS

In this paper, we aimed at applying different

techniques to tackle head motion in fMRI data in

order to reach a consensus on the best strategies to

use. We compared common approaches to correct

head motion effects such as motion regression,

motion censoring and data interpolation. Our results

pave the way towards finding an optimal motion

correction strategy, which is required to improve the

accuracy of fMRI analyses, crucially in clinical

studies with patient populations, and are an

encouragement to test comprehensively different

approaches.

ACKNOWLEDGEMENTS

This work was supported by Grants Funded by

Fundação para a Ciência e Tecnologia, CIBIT

strategic plan UIDP/04950/2020 and BIOMUSCLE

PTDC/MEC-NEU/31973/2017. FCT also funded an

individual grant to JVD (Individual Scientific

Employment Stimulus 2017 -

CEECIND/00581/2017).

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