Hoeksma Et Al Clin Neurophys (2005) 116(5) 1188-1194

8/2/2019 Hoeksma Et Al Clin Neurophys (2005) 116(5) 1188-1194

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Variability in spatial normalization of pediatric and adult brain images

Marco R. Hoeksmaa,*, J. Leon Kenemansb, Chantal Kemnerc, Herman van Engelandc

a Department of Psychopharmacology, Faculty of Pharmaceutical Sciences, Utrecht University, Sorbonnelaan 16, 3584CA Utrecht, The Netherlandsb Department of Psychonomics, Faculty of Social Sciences, Utrecht University, Utrecht, The Netherlands

c Department of Child- and Adolescent Psychiatry, Utrecht University Medical Center, Utrecht, The Netherlands

Accepted 21 December 2004

Abstract

Objective: Normalization of brain images is a necessity for group comparisons of source analyses based on realistic head models. In thispaper we compared the outcome of a linear registration method for brain images of psychiatric and control groups of different ages in order to

assess the relative adequacy of normalization in such diverse groups.

Methods: Magnetic Resonance images (MRI) of the brains of pediatric and adolescent subjects (mean ages 19 and 10.5 years) with a

pervasive developmental disorder (PDD) and their healthy controls were included. A simple voxel-wise test of the group variances in image

intensities was performed to evaluate regional differences in registration quality. Dipole analysis of visual P1 was performed to establish

whether source locations were comparable across groups.

Results: Significant differences between pediatric groups were found in white matter and thalamic regions of the brain. For all other group-

wise comparisons, differences were confined to skull and neck regions. Dipole locations were found to be more anteriorly located in the

adolescent groups.

Conclusions: The normalization procedure used in this paper is based on a brain template of normal adult brains from a restricted age

group, and the results show that the use of this method in pediatric groups is less adequate. The method seems suitable for use in psychiatric

groups. Also, the generators of visual P1 in PDD patients were found to be comparable to controls.

Significance: The results suggest that this existing normalization method can be used in diverse populations, but is less suitable forpediatric images.

q 2005 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Keywords: Head model; Magnetic resonance imaging; Source localization; Spatial normalization; Pervasive developmental disorder; P1

1. Introduction

In order to increase the accuracy of head models for

electrical and magnetic source analyses, structural brain

images have entered the field of electrophysiology (Fuchs

et al., 2001; Wagner, 1998). Traditionally, spherical models

were used to model the head (Scherg, 1990). The assumedspherical dimensions in these models were similar for all

subjects and a direct link to the anatomy was missing.

Structural images of the head have the advantage of

enabling the researcher to construct a head model that is

tailor made for each subject under study, thus improving

localization accuracy and opening the way for inferences

about the brain structures involved in the modeled

electrophysiological phenomena.

Although individual head models improve the quality

of single-subject localizations, they introduce a new

source of variance in group-wise comparisons of localiz-

ation results. In order to facilitate group comparisons and

the generalization of findings, individual variability inbrain images has to be minimized. This minimization, or

spatial normalization, is achieved by transforming the

individual image to a common template that is aligned in

standardized stereotaxic space. However, it may be

that the use of such a common standardized template

results in errors that differ in size or nature across age- or

patient groups. The present work addresses potential

differences in error patterns between typical age and

clinical groups.

Clinical Neurophysiology 116 (2005) 1188–1194

www.elsevier.com/locate/clinph

1388-2457/$30.00 q 2005 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/j.clinph.2004.12.021

* Corresponding author. Tel.:C31 30 2533845; fax: C31 30 2537387.

E-mail address: [email protected] (M.R. Hoeksma).

http://www.elsevier.com/locate/clinph

http://www.elsevier.com/locate/clinph



Spatial normalization procedures broadly fall into two

categories: intrinsic and extrinsic registration approaches

(Maintz and Viergever, 1998). Extrinsic techniques rely on

external landmarks or fiducial markers that are identified in

both the template and the image to be transformed. The

reliability of such techniques is completely dependent upon

the reproducibility of the markers, which often have to bedefined by means of an interactive procedure. Intrinsic

techniques on the other hand use information that is already

present in the image, either from segmented objects or from

the entire image. Image registration then entails the

optimization of some index of the difference between the

source and the template image, such as the minimization of

the distance between homologous external markers or the

maximization of cross-correlations between voxel values.

A widely used template for such normalization pro-

cedures is the Talairach template (Talairach and Tournoux,

1988). This system relies on aligning the anterior and

posterior commissures (AC PC) and the interhemispheric

plane of the source image with a template brain. Although

widely accepted in the field of neuroimaging, the original

template brain has a number of shortcomings, compromis-

ing its generalizability to a wider population. Probably the

most important limitation is that the Talairach system is

based on post-mortem sections of a single brain: that of a

60-year-old female. Furthermore, complete brain symmetry

has to be assumed since only one hemisphere is given in

detail. This clearly does not represent the majority of

subjects in in vivo imaging studies (Toga and Thompson,

2001).

Another widely used template from the Montreal

Neurological Institute (MNI) was constructed from 305MRI scans of healthy subjects (239 male, 66 female; mean

age 23.4G4.1 years) that were individually registered to

Talairach space and subsequently averaged (Evans et al.,

1992). The use of this template in automated registration

methods is now widespread. Although this template is

intuitively better suited to serve as a template for normal-

ization, it still remains to be established whether it is

applicable to data from subjects that do not fit in the specific

age-range or from subjects with neuropsychiatric conditions

that may affect brain anatomy.

In the present study, it was assessed whether the

adequacy of an automatic spatial normalization

procedure (Collins et al., 1994; http://www.bic.mni.mcgill.

ca/software) when used in children and psychiatric patients

differs from that in healthy subjects. The linear registration

procedure studied in this paper aligns the source brains with

the MNI template, and it may well be that this template is

less suited to the normalization of pediatric scans.

Furthermore, there may be structural abnormalities in the

brains of (psychiatric-) patients that may impede successful

registration. Therefore we included patients diagnosed with

a pervasive developmental disorder (PDD), a psychiatric

condition known to have effects on regional brain anatomy

(Courchesne et al., 1993; Piven et al., 1990, 1996) that are

possibly age dependent (Carper et al., 2002; Courchesne

et al., 2001).

It is important to know whether there are any diagnosis-

or age-related differences in the outcome of the registration

procedure; it may be even more important to know where

such deviations occur. The normalization method used in

this paper (Collins et al., 1994) uses cross correlationsbetween source and template brain as an objective function

that is to be maximized by the normalization process; higher

final objective values indicate increased similarity between

source and template. However, a difference in objective

values can be the result of a structural bias in normalization

or of random normalization differences. Two questions can

be raised regarding the success of normalization: (1) are the

individual brains well matched to the template (as is

expressed by the cross correlations) and (2) are the

individual brains well matched to each other. Here, we

propose a measure of voxel variance to answer the second

question for comparisons between different groups of

subjects. Successful normalization should maximize the

similarity between images, and thus corresponding voxels

should have similar values across individual images.

Consequently, the voxelwise variance within a group

reflects the success of normalization. The extent to which

groups differ in the voxelwise or average (across voxels)

variance can be tested statistically. A relatively low

objective value, paired with low voxel variances within a

group thus points in the direction of a structural bias in

normalization, whereas a low objective value paired with

high variances would indicate random differences. The

spatial distribution of significant differences in voxel-

variance can be represented graphically, and indicateswhere significant differences arise between groups. Such a

representation may identify areas with significant differ-

ences in voxel variance when the overall variance (across

voxels) does not differ.

To demonstrate the application of the spatial normal-

ization procedure in a source localization procedure in

diverse groups, source localizations were performed on

visual P1. This, however, cannot be seen as a litmus test for

the success of the normalization procedure, since age-

related changes in P1 generators cannot be ruled out

(Allison et al., 1984; Levine et al., 2000; Onofrj et al.,

2001). Moreover, although no abnormalities have been

reported in visual P1 in patients with PDD, this does not

imply that none exist. In the present study, we expected

more anteriorly located dipoles in the older age group due to

the increased contributions of frontocentral generators

(Dustman et al., 1982), and no effects of diagnosis.

2. Materials and methods

MRI scans were available for four groups of subjects:

adolescent patients with a pervasive developmental disorder

(PDD, nZ13, mean age 19.9G2.9 years) and adolescent

M.R. Hoeksma et al. / Clinical Neurophysiology 116 (2005) 1188–1194 1189

http://www.bic.mni.mcgill.ca/software






healthy subjects (nZ13, mean age 18.2G0.74 years), PDD

children (nZ11, mean age 10.6G1.56 years) and healthy

control children (nZ14, mean age 10.4G1.13 years).

Diagnoses of PDD were made by child psychiatrists and

were corroborated by scores on the Autism Diagnostic

Inventory, revised edition (Lord et al., 1994). All subjects

were free of medication and had no significant neurologicalhistory. IQ was matched within age groups. PDD children

came form the department of Child- and Adolescent

Psychiatry of the Utrecht Academic Hospital. Adolescent

PDD subjects were patients from the Dr Leo Kanner house,

a residential institution for persons with autism. Controls

were recruited from schools in and around Utrecht. All

participants or their parents gave informed consent and the

study adhered to the guidelines of the hospital’s medical

ethical committee. Before entering the MRI scanning

procedure, children were extensively informed and famil-

iarized with the scanning apparatus by means of photo-

graphs, sound recordings of scanner noise and a mock-up of

the head coil. While in the scanner, participants listened to

music of their own choice and were able to see the MRI

operator through a mirror. To limit head movements during

scanning, the head was fixated with foam cushions and a

head band.

Coronal T1 weighted spoiled gradient echo (3D-FFE)

MR images of the whole head were acquired on a 1.5

Tesla Philips Gyroscan NT scanner (1!1!1.5 mm slices

for children, 1!1!1.2 mm voxels for adolescents, 256!

256 matrix, TEZ4.6 ms, TRZ30 ms, flip angle 308,

contiguous slices). Total acquisition time was about

12 min. Volumes were converted to MINC format and

resliced into 1!1!1 mm voxels. Subsequently, anintensity non-uniformity correction was applied to reduce

artifacts due to field inhomogeneities (Sled et al., 1998).

Also, an overall intensity normalization was performed

over all scans. All volumes were then non-interactively

registered with the MNI template brain, using an

automatic linear procedure provided by the MNI (Collins

et al., 1994). The procedure optimizes the image intensity

cross-correlations between template and target images.

Since spatial detail in the MNI template is degraded due

to averaging, the registration procedure includes a number

of intermediate blurring steps for the source image. These

blurring steps use a Gaussian kernel and filter the higher

spatial details out of the source image, thus increasing

similarity with the target template. The individual images

were then transformed with the resulting parameters for

rotation, translation and scaling. After this spatial normal-

ization, the images were averaged per age- and diagnostic

group and the variance for each average was computed,

thus resulting in four average volumes with four

corresponding variance-images. The variance images

express the deviations of voxel intensity values from the

group mean, distributed throughout the entire volume.

The Hartley F -max test (Levy, 1975) was then applied to

the respective groupwise comparisons of voxel values

(adolescent controls vs. young controls, adolescent PDD

vs. young PDD, young PDD vs. young controls and

adolescent PDD vs. adolescent controls). The F -max

statistic is the ratio of the largest variance divided by the

smallest variance. Voxels that differed significantly with

P!0.01 (F -maxZ23.2) were colour-coded for each

comparison. The colour-coded images were then plottedas superimposed coronal sections over the background of

the grand average brain volume for display (spatial extent

K106 through 102 mm, D 2 mm, Fig. 1). Additionally,

the products of the objective function used in the

registration procedure (i.e. cross-correlations, Fig. 2)

were entered in an analysis of variance, as a means of

cross-validation of the voxel-based analysis of variances.

2.1. EEG procedure

While subjects were performing a visual selective

attention task (for a detailed descripition, see Jonkman

et al., 1997), EEG activity was measured from 62 tin

electrodes placed on the scalp by means of an electrocap. A

reference electrode was attached to the left mastoid, and a

ground electrode was placed in the middle of the forehead.

Impedances of ground and reference electrodes were kept

below 5 k O. Horizontal EOG was recorded from electrodes

attached to the outer canthus of each eye. Vertical EOG was

measured from infra- and supra-orbitally placed electrodes

at the left eye. All signals were amplified with a time constant

of 10 s by Sensorium EPA-5 amplifiers (Sensorium, Inc.,

Charlotte, VT), digitized on-line by a computer at 256 Hz and

stored as a continuous signal. After completion of the task,

electrode positions and five fiducial marker positions(nasion, left and right pre-auricular points and mastoids)

were digitized by means of a Polhemus Isotrak digitizer

(Polhemus, Inc., Colchester, VT) for coregistration with

individual MRI scans. Oil-filled capsules were placed on the

digitized marker positions to make them clearly visible in the

MR images.

Signals were epoched off-line starting 100 ms before

stimulus onset and lasting for 1 s. Epochs were filtered with

a 30 Hz, 24 dB/octave digital low pass filter and baseline

corrected on the basis of the 100 ms pre-stimulus interval.

Epochs containing artifacts like flat lines, saturation of the

A/D converter and amplitudes larger than G125 mV were

removed. EOG artifacts were removed from the EEG by

subtracting vertical and horizontal EOG from the EEG

epochs by a regression method in the time domain

(Kenemans et al., 1991). ERPs for unattended standard

and deviant stimuli were computed by averaging all

remaining trials with correct performance for each subject

per lead. For localizations of P1, the ERPs to unattended

stimuli were averaged for each subject.

All source localizations were done with the Curry

program (Neuroscan, Inc., El Paso, TX). Individual realistic

boundary element (BEM) models of the head were

constructed by an automated procedure (Wagner, 1998).

M.R. Hoeksma et al. / Clinical Neurophysiology 116 (2005) 1188–11941190



The unregistered (i.e. non-normalized) scans were used for

this procedure. Using the scaled scans for dipole locali-

zations would result in a localization error, since the scalp

potentials are not scaled accordingly. Therefore, we chose to

use the unregistered scans for localization purposes and to

convert the resulting dipole locations to standardized space

afterwards with the individual registration parameters

resulting from the normalization procedure. For each

subject, the digitized electrode cloud was fitted to the

head model through manual identification of the marker

positions visible in the MR images, which corresponded to

the digitized fiducial marker positions.

Localizations of P1 were based on the averaged ERPs for

unattended stimuli in order to have maximal signal to noise

ratios. All leads were re-referenced to a common average

reference, and a mirrored fixed dipole pair was chosen to

model P1. An initial dipole solution was produced based on

the grand average ERPs for each group on a normalized

averaged head model (nZ50). The time point for this

solution was chosen as a 25 ms window around the peak of

Fig. 1. Coronal sections showing areas of significant differences in voxel variance ( P!0.01) in group-wise comparisons.




the global field power in the P1 latency (approximately 100–

125 ms). This grand average solution was used to seed the

individual fits. First, a fit was performed with the fixed grand

average dipole locations to compute the explained variance

of this source configuration. For individual fits, the time

point for final reconstructions were chosen similar to the

grand average fit described above, but now the point where

the explained variance was maximal was taken instead of

the maximum global field power (Latency of best fit,

Kenemans et al., 2002). Finally, all source parameters were

optimized. The locations of the dipoles were transformed

with the parameters resulting from the MR normalization

procedure in order to register them into a standardized

coordinate system, enabling between subjects comparisons.

Separate ANOVAs for dipoles were done for each of the

three location parameters ( x, y, z) with Age and Diagnosis as

between factors. Dipole locations in three orthogonal slices

are depicted in Fig. 3.

3. Results

The analysis of objective function values showed

that there was a significant effect of age (F (1,42)Z16.134,

PZ0.000), indicating that the cross-correlations between

target and template brain were significantly lower in the

young groups than in the adolescent groups. There was no

effect of diagnosis.

Overall pair-wise comparisons of whole-volume pooled

variance did not reveal any significant differences between

groups (pooled variance for young PDD 2704.46; young

controls 3451.50; adolescent PDD 2937.57; adolescent

controls 3144.46, ns).The group-wise comparisons of voxel variances pre-

sented in Fig. 1 indicate that the largest differences in the

registration procedure were found in white matter and

thalamic regions in the comparison of the young PDD vs.

young controls. Similar differences were not encountered

when the young groups were compared to adolescent brains.

In all other comparisons, significant differences in variance

seemed to be mostly confined to scalp and neck regions and

to the larger cavities of the head.

The analyses of the dipole locations (Fig. 3) showed

that y-location differed with age, indicating more

Fig. 2. Mean objective function values (cross-correlations) per group. The

young groups show significantly lower cross-correlations than adolescents.

Fig. 3. Orthogonal views of dipole locations of P1 in all groups.




anterior locations in the older age group (left dipole:

F (1,32)Z16.201, PZ0.000, right dipole F Z13.848,

PZ0.001). No further effects were found.

4. Discussion

The results suggest that the automatic linear registration

procedure used in this study (Collins et al., 1994) performs

less adequate in pediatric groups. The data show that the

final objective values in the pediatric groups were

significantly lower than in the adolescent groups. This

difference could be the result of a structural bias or of

random differences in normalization. The overall group

variances did not differ between groups, but the fact that the

young PDD group showed the lowest overall variance,

paired with a relatively low objective value points in the

direction of a structural bias in normalization in this group.

In the young controls, the highest overall variance was

found. Together with the low objective value, this can be

interpreted as this group showing more random differences

in normalization.

In the regional analysis of voxel-variance, differences

became evident in white matter areas and regions around the

thalamus in the comparison of the young control and PDD

groups. However, no differences were found in cortical grey

matter areas. Therefore, we conclude that in the present

samples differences in the quality of normalization do not

result in differences in the quality of the corresponding head

models for EEG source localization.

A likely explanation for the larger registration differ-

ences in young groups is found in the template brain used inthe normalization procedure. The MNI-template is an

average brain constructed from 305 young adult brains,

and thus shows less similarity with the pediatric scans than

with the adult scans in our study.

The differences found in the scalp and neck regions in the

other comparisons are of little consequence for functional

imaging studies. It is clear that the largest deviations are

most likely to be found in the regions where the change of

contrast in the image is largest, e.g. at the borders of the

head and of the skull cavities. In brains where the linear

registration is less successful, variances will be large at

these borders as opposed to, e.g. white matter areas where

the distribution of grey-values is more homegeneous. In

fMRI analyses, the skull will likely be removed from the

image since it is unnecessary for the functional analysis. For

electrophysiological source localisation studies, the skull is

needed for the construction of a model of the head. We

chose to build the head model and to do the source analyses

on the unregistered scans, and to align the resulting sources

with the registered volume afterwards. If the source

reconstructions are done on scans of which the physical

dimensions have changed due to the registration procedure,

it is necessary to scale the scalp-recorded raw data as well.

Using unregistered scans for the actual source modeling

solves this problem and also does not introduce the

registration errors of the skull region in the head model.

Still, the resulting source locations have to be registered to

standardized space in order to facilitate group comparisons

and statistical analyses, and thus proper spatial normal-

ization remains of pivotal importance.

Voxelwise F -tests were used to test for differencesbetween group variances. Concluding from the results that

there are no such differences in variances incorporates the

problem of confirming the null hypothesis. However, in the

current comparisons voxels with a significance level of 1%

and lower were marked. This is very liberal; considering

that for each contrast 2563 comparisons were made, errors in

registration had every chance to show up throughout the

entire volume. It is important to note that the head only

comprises about 30% of the entire volume. Given the

statistical threshold of 1% used in this study, averaged over

volumes 3.97% of all voxels was labeled as significantly

different. On average 81% of these significant voxels were

located in the head. Given the large variation in grey values

within the head, we would expect the majority of significant

voxels in this area. Furthermore, 3.97% voxels identified as

significant is indeed more than would be expected by

chance, based on a 1% threshold. Given the figures noted

above, there is almost a 10% positive rate within the head,

yet significant differences were only encountered in certain

head regions and not in gyral patterns or throughout the

brain.

The larger variance differences and lower cross-corre-

lation values found in the present study may indicate that the

registration of pediatric data was less successful, and that

one should be cautious especially when using the procedurein comparisons of pediatric images. Also, dipole analyses of

P1 did not reveal an effect of diagnosis on dipole location,

but dipoles in the older age group were located more

anteriorly. This effect may be the result of further

maturation of the visual areas (Allison et al., 1984; Dustman

et al., 1982; Ossenblok et al., 1994) and of larger

contributions of frontocentral areas to the scalp distribution

(Dustman et al., 1982). To what extent the different spatial

normalization in the pediatric groups has contributed to the

age effect remains inconclusive. Differences in spatial

normalization may be a confound in the interpretation of

source localizations, and the present paper provides a simple

method to check for such differences. A limitation of the use

of the cross correlations to assess whether the individual

brains were adequately matched to the template is that this

same measure was used as a criterion in the normalization

procedure. Therefore, it should be supplemented with

additional measures of normalization accuracy and var-

iance, like the variance measures as presently proposed. In

turn, a possible complication with the use of such variance

measures may lie in the way they depend on aspects of data

processing, most notably global normalization of image

intensities. For example, although the presently applied

intesity normalization serves in general to increase




the comparability among individual datasets, its precise

effects on variance measures should be further investigated.

In addition, a systematic bias in normalization to a template

is not revealed in voxelwise group variance, and therefore

the latter measure should be supplemented by other

measures like the objective function.

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Hoeksma Et Al Clin Neurophys (2005) 116(5) 1188-1194