Voxel-based morphometry of comorbid schizophrenia and learning disability: analyses in normalized...

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NeuroImage 22 (2004) 188–202

Voxel-based morphometry of comorbid schizophrenia and learning

disability: analyses in normalized and native spaces using

parametric and nonparametric statistical methods

T. William J. Moorhead,* Dominic E. Job, Heather C. Whalley, Tracy L. Sanderson,Eve C. Johnstone, and Stephen M. Lawrie

Image Analysis Laboratory, Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh EH10 5HF, UK

Received 22 July 2003; revised 15 October 2003; accepted 8 December 2003

We employed voxel-based morphometry (VBM) to compare the

distributions of grey matter found in structural magnetic resonance

imaging (MRI) brain scans of patients with comorbid learning

disability with schizophrenia, schizophrenia alone, learning disability

alone, and normal controls. Our primary aim was to replicate a

previous region of interest (ROI) finding that comorbids and

schizophrenics belong to the same population. Nonparametric analysis

in normalized space showed no significant differences in grey matter

distribution between the comorbid and schizophrenia groups. Fur-

thermore, this analysis showed significant grey matter reductions in the

comorbid and schizophrenia groups when compared to the learning-

disabled or the normal controls. Parametric analysis localized the

significant grey matter reductions between the normal controls and the

comorbid and schizophrenia groups to the prefrontal and temporal

lobes. It also identified an area of increased grey matter, on the inferior

aspect of the postcentral gyrus, in the learning-disabled alone

compared to the other groups. Native space parametric and

nonparametric analyses, based on modulation of the normalized scans,

confirmed the similarity in grey matter distribution of the comorbid

and schizophrenia groups. Results confirm the ROI finding that in

native space the learning-disabled group possesses the least and normal

controls the most grey matter for the cohort. An increase in the basal

ganglia of patients with schizophrenia vs. the learning-disabled,

probably attributable to antipsychotic medication, was identified in

the native space analysis. The native space results did not however

register statistically significant temporal lobe reductions found under

normalized analysis between schizophrenics and normal controls. This

may be attributable to minor physical anomalies (MPA) in the

schizophrenic cranium. Overall, these VBM results replicate previous

ROI findings and are compatible with the view that comorbid learning

disability with schizophrenia is a severe form of schizophrenia, rather

than a consequence of learning disability. VBM has the facility to

compare grey matter distributions in this structurally diverse cohort.

D 2004 Elsevier Inc. All rights reserved.

Keywords: Schizophrenia; Learning disability; Voxel-based morphometry

1053-8119/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.neuroimage.2003.12.012

* Corresponding author. Image Analysis Laboratory, Division of

Psychiatry, Royal Edinburgh Hospital, The University of Edinburgh,

Morningside Park, Edinburgh EH10 5HF, UK. Fax: +44-131-537-6531.

E-mail address: [email protected] (T.W.J. Moorhead).

Available online on ScienceDirect (www.sciencedirect.com.)

Introduction

Systematic reviews of the literature reveal consistent evidence

that schizophrenia is associated with reductions in the volumes of

the whole brain, temporal lobes, and amygdalo-hippocamal com-

plexes, while the lateral ventricles are increased in size (Lawrie and

Abukmeil, 1998; Wright et al., 2000). A neuropathology review by

Harrison (1999) concluded that the pattern of brain abnormalities

in schizophrenia is suggestive of disturbance in connectivity within

and between affected regions that most likely occurs during brain

development. Marenco and Weinberger (2000) reviewed literature

relating to the neurodevelopmental hypothesis of schizophrenia

and concluded that it is likely that a brain insult before or at birth

plays a role in the expression of schizophrenia, but environmental

variables after birth cannot be ruled out. While it is generally

accepted that there is a biological basis for many cases of learning

disability and those structural studies that have been conducted

demonstrate abnormalities (Deb, 1997), the research base in this

area is very much weaker than it is for schizophrenia.

Sanderson et al. (1999, 2001) examined a group of comorbid

schizophrenia with learning disability that had been identified from

the records of inpatient care and discharged between 1970 and

1993. This was part of a wide-ranging study designed to examine

the relationship between comorbid learning disability and schizo-

phrenia and the separate conditions alone (Doody et al., 1998). The

neuroanatomy of these patients was compared to that of age- and

sex-matched schizophrenia, learning-disabled, and normal controls.

The correlations of Sanderson et al. (2001) between clinical and

historical variables, and cerebral structural variables were indicative

of a strong neurodevelopmental component in the presentation of

comorbid learning disability with schizophrenia. Our prime aim in

the work described in this paper was to replicate, through voxel-

based morphometry (VBM) grey matter analysis, the region of

interest (ROI) finding by Sanderson et al. (1999) that the comorbid

and schizophrenia groups belong to the same population. We also

wished to assess the feasibility of applying automated VBM

analysis to the disparate set of brains presented by the study cohort.

VBM provides for the grouped analysis of tissue distribution

(Ashburner and Friston, 2000; Friston et al., 1995a; Wright et al.,

1995). This is an automated methodology that avoids the scrutiny

T.W.J. Moorhead et al. / NeuroImage 22 (2004) 188–202 189

bias to which region of interest (ROI) studies are subject. Addi-

tionally, VBM facilitates the analysis of diffuse structures such as

those presented by cortical grey matter. The measurement of

diffuse tissue structures is extremely labor-intensive under the

ROI method. VBM parametric analysis has been used to locate

regional grey matter deficits in schizophrenic brains and in

particular does so to medial prefrontal and temporal lobe structures

(Ananth et al., 2002; Chua et al., 1997; Gaser et al., 1999; Job et

al., 2002; Kubicki et al., 2002; Wilke et al., 2001; Wright et al.,

1995, 1999).

The typical VBM parametric analysis is dependent upon an

assumption of distribution normality in the voxel-mapped grey

matter intensities, but in the relatively small group sizes employed

in most studies, the validity of normality testing is questionable.

Thus, we employed nonparametric tests to establish the validity of

our experimental groupings. The application of nonparametric

testing to the analysis of structural MR images was introduced

by Bullmore et al. (1999). An implementation of this method was

developed by Nichols and Holmes (2001). The nonparametric

method is not dependent upon distribution shape and thus provides

a useful means for verifying parametric findings. Use of nonpara-

metric methods to verify parametric results was reported by

Kubicki et al. (2002).

We report analyses in normalized and native spaces. For the

normalized space analysis, the subject brains are transformed into a

common stereotactic space defined through a default Montreal

Neurological Institute (MNI) template supplied in the Statistical

Parametric Mapping package (SPM99) (Ashburner and Friston,

2000). In this normalized analysis, we covary for whole brain

volumes to examine group differences in grey matter distribution

with the confounding effect of variation in subject brain size

removed. For the native space analysis, a modulation procedure

is applied to the normalized scans to recover the original brain grey

matter volumes and these volumes are viewed from the common

MNI stereotactic space. Thus, in the native space analysis, we

examine voxel-wise differences in absolute grey matter volume of

the cohort brains.

Sanderson et al. (1999) analyzed the extent to which the

neuroanatomy of the comorbids resembled the learning-disabled

and the schizophrenia subjects using ROI measures. The analysis

showed statistically significant differences between the learning-

disabled and comorbids that were repeated in comparisons between

the learning-disabled and the schizophrenia groups. In contrast, the

schizophrenia and comorbid groups did not differ, in particular in

volumes of the amygdalo-hippocampal complex. This ROI finding

by Sanderson provided our prime investigation hypothesis. In our

grey matter analysis, we tested whether at whole brain it would be

most appropriate to class the comorbids as schizophrenics, as

learning-disabled, or as belonging to a separate population. We

also applied small volume corrections (SVC) for the temporal lobes

and amygdalo-hippocampal complexes to our VBM parametric

results.

Methods

Participants

The magnetic resonance imaging (MRI) scans for this report

were obtained from patients who took part in a clinical comparison

of four matched subject groups. These subject groups were

comorbid learning disability with schizophrenia, schizophrenia

alone, learning disability alone, and a normal control group

(Sanderson et al., 1999). The comorbid patients had been identified

from the database held by the Information and Statistics Division

of the Scottish Health Service. Individuals were selected from

those who had at least one episode of inpatient care between 1970

and 1993, with discharge diagnosis of mild learning disability and

schizophrenia. For inclusion in the study, these comorbid patients

were required to be between 16 and 65 years of age to fulfill

research diagnostic criteria for schizophrenia (Spitzer et al., 1975)

and to have a premorbid IQ of between 50 and 70 and thus to have

attended remedial education.

Details of the clinical methodology and results are described in

Doody et al. (1998). Briefly, each comorbid patient was matched

for age (to within 5 years) and sex to both a schizophrenic patient

and a patient with learning disability. The group with learning

disability comprised individuals with a history of mild learning

disability of unknown aetiology, a documented IQ of between 50

and 70, and no known history of psychosis or current antipsychotic

use. They were identified from the community-nursing case lists of

patients with active learning disability. The schizophrenic controls

were identified from the Lothian Psychiatric Case Register (a

computerized admission-tracking database for the Lothian region

of Scotland); all fulfilled research diagnostic criteria for schizo-

phrenia and had no evidence of premorbid cognitive impairment.

The relevant local ethics committees approved the study. The

medication details of the comorbid and schizophrenics are de-

scribed by Doody et al. (1998) and show equivalent mean doses of

antipsychotic medication for both groups.

Image acquisition

Scans were carried out on 23 comorbid patients, 20 learning-

disabled patients, 25 schizophrenic patients, and 29 normal

controls. Each patient underwent magnetic resonance imaging

(MRI) scanning on a 1.0-T Siemens (Erlangen, Germany) Mag-

netom scanner. Midline sagittal localization was followed by two

sequences to image the whole brain. The first was a turbo spin-

echo sequence, which gave simultaneous proton density and T2-

weighted images (TR = 3500 ms; TE = 19 and 93 ms; 20

contiguous 5-mm sliced axial images aligned to the AC–PC line;

field of view: 230 mm; matrix size 256 � 192), that was used to

exclude any gross brain lesions. The second scan, for the

volumetric analysis, was a 3D turbo FLASH (MPRAGE) se-

quence consisting of an inversion pulse followed by a FLASH

collection (flip angle 12j; TR = 10 ms; TE = 4 ms; TI = 200 ms;

relaxation delay time: 500 ms; field of view: 250 mm), giving

128 contiguous 188 mm thick sliced coronal images orthogonal to

the AC–PC line.

In the Sanderson study, it was noted that one patient in the

learning-disabled group showed substantial disturbance of cerebral

anatomy, probably as a result of brain surgery in the postnatal

period, and this scan was excluded from the study. In preliminary

normalization steps, it was noted that the scan from one patient in

the learning disability group, who had suffered childhood menin-

gitis, could not be successfully preprocessed using the same

protocol as the other scans. This scan was therefore excluded from

our study. A total of 95 scans (18 learning-disabled, 23 comorbid,

25 schizophrenia, and 29 normal controls) were therefore used in

the VBM analysis. Table 1 gives the age and sex profiles of the

analysis study groups.

Table 1

Study groups, age, and gender

Patient group Number

in group

Mean age

(standard

deviation)

Percentage

male

Learning-disabled 18 45.2 (10.7) 44

Comorbid 23 49.1 (8.5) 65

Schizophrenic 25 50.9 (8.9) 56

Normal controls 29 42.8 (9.9) 45

T.W.J. Moorhead et al. / NeuroImage 22 (2004) 188–202190

Image processing

The VBM analysis was implemented on a Linux workstation

operating under Redhat 8.0. The workstation ran Matlab 6.0 (R13)

to host the Statistical Parametric Mapping package (SPM99)

(Ashburner and Friston, 2000). The SPM99 package employs a

normalization procedure that spatially transforms MRI brain scans

into a common stereotactic image space. A segmentation procedure

that uses partial volumes converts the normalized brain images into

grey, white, and CSF tissue images (Ashburner and Friston, 1997).

In our study, we use the optimized methodology introduced by

Good et al. (2001). We have modified the terminology used by

Good. We have substituted the term normalized space analysis for

Good’s statistics of concentrations (unmodulated analysis) and the

term native space analysis for Good’s statistics of volumes (modu-

lated analysis). We use the term normalized space analysis because

this describes our analysis when all scans have been translated,

rotated, and expanded so that they occupy the same volume. In this

normalization, variations in native space brain size have been

removed and an analysis is applied on the basis that all subject

brains are of equal size. In implementing the native space analysis,

we follow the Good et al. (2001) optimizedmethodology and apply a

modulation process to the normalized scans to recover the native

space tissue volumes into the SPM analysis space. Our analysis of

these absolute grey matter volumes is described as native space

analysis.

Optimized template generation

An optimized study-specific template set consisting of a T1

image and a priori grey, white, and CSF images was created for the

VBM analysis (Good et al., 2001; Ashburner and Friston, 2000).

This template set was constructed from 93 brain scans taken from

the study group of 95 scans. Two scans were excluded from the

template generation process. Sanderson et al. (1999) noted that one

patient in the comorbid group had high-intensity signal lesions

over the lentiform nuclei. This patient was excluded from the

study-specific template generation to avoid segmentation bias to

these unusual artifacts. The second exclusion was of a learning-

disabled scan that was found to exhibit smeared segmentation

results when preprocessed to the default MNI templates.

In construction of the study specific templates (Good et al.,

2001; Ashburner and Friston, 2000) the subject images were

normalized through linear affine transformation, based on grey

matter distribution, to the ICBM 152 template (Montreal Neuro-

logical Institute). The reslicing of the normalized images used the

nearest neighbor interpolation. The study-specific T1 template was

generated by averaging the 93 affine normalized images and then

applying an 8-mm 3D full width half maximum (FWHM) Gaussian

smoothing to this averaged image.

Separate grey white and CSF tissue maps were extracted from

each of the 93 affine normalized images using the SPM99

segmentation function. The SPM99 Xtract brain function was used

to remove extracerebral voxels from the grey and white profiles.

Extracerebral voxels were removed from the CSF images through

comparisons with the SPM99 default brain mask. The grey white

and CSF profiles were averaged and then smoothed by an 8 mm

3D FWHM Gaussian to provide study-specific a priori templates.

The unsmoothed a priori templates were summed to provide a

study-specific whole brain mask. The study-specific T1, a priori

templates and brain mask were formed with a voxel resolution of 1� 1 � 1 mm.

Image normalization and segmentation

The 95 analysis images were normalized to the study-specific

T1 template through a transformation process that included both

linear and nonlinear spatial warps (Good et al., 2001). The linear

normalization employed a 12-point linear affine transformation to

translate, rotate, and scale the images to minimize their squared

difference from the study-specific template based on grey matter

distribution. Residual low frequency discrepancies between the

images and the template were removed through nonlinear trans-

formations. The SPM99 basis settings used for this nonlinear

transformation were set to the 7 � 8 � 7. The regularization level

for the SPM99 package was set to medium. Bilinear interpolation

was used to write the transformed images into the stereotactic

space at a 1 � 1 � 1 mm voxel resolution.

The normalized images were segmented into grey matter, white

matter, and CSF images (Ashburner and Friston, 1997). The

study-specific a priori templates were used for this segmentation.

The SPM99 ‘Xtract’ brain function was used to exclude extrac-

erebral tissue from the grey matter images. The grey matter

segmentation of the two scans noted for exclusion from the

template generation process was visually inspected to assess

whether it was appropriate to include these in our statistical

analyses. The comorbid scan excluded because of bright lesions

over the lentiform nuclei was found to register reduced grey matter

distributions in these nuclei, but the rest of the brain had

segmented normally. As the lentiform nuclei are not included

within our a priori hypothesis, we elected to retain this image

within our analysis. The learning-disabled scan that had exhibited

tissue smearing when preprocessed to the default MNI templates

was found to exhibit much less smearing under study-specific

template preprocessing. Qualitative assessment of the grey matter

tissue in this scan led to its inclusion in our analysis. The decisions

to accept these preprocessing results were jointly arrived at by

experts in neuroanatomy and brain imaging (HCW, DEJ).

The 95 normalized grey matter images were smoothed using a

12-mm 3D FWHM Gaussian filter. Ashburner and Friston (2001)

report that the VBM normalization procedure is limited through

processing power requirements to providing low frequency cor-

egistration between brain images. The 12-mm smoothing imple-

ments a spatial low pass of the brain images to smooth the grey

matter surface and reduce the number of local maxima. The

smoothing process also improves the normality at voxel level of

the grey matter distributions (Ashburner and Friston, 2000).

The normalization of brain images is designed to exclude the

confounding effect of brain size from comparisons of grey matter

distribution. Summing extracted grey, white, and CSF images gave

the normalized brain volume for each patient. This measure was


found to have a peak deviation from its mean of 4.3%, indicating

that residuals of the brain size confound remained after normali-

zation. The wide variations in native space brain size of our study

contributed to this post-normalization residual. To compensate for

this residual in the statistical analysis, a brain volume nuisance

covariate was constructed from sums of the grey, white, and CSF-

normalized images for each patient.

Native space grey matter volumes

The modulation process described by Good et al. (2001) is

used to recover the native space grey matter volumes so that they

can be observed from the analysis matrix employed in SPM. In

our study, this recovery process was implemented through the

‘spm_preserve_quantity’ function (Ashburner and Friston, 2000).

This function takes as its inputs the normalized grey matter image

from a study brain and a mapping of the Jacobean determinates

that warped this brain from native space to normalized space.

Voxel-wise multiplication of the normalized image intensity by

the corresponding Jacobean determinant allows the grey matter

volume present in the native space to be observed from the

stereotactic space. Thus, the effective expansion of the subject’s

brain into stereotactic space is removed, but the facility to

observe all brains on the same voxel matrix is retained. The

‘spm_preserve_quantity’ function was applied to the unsmoothed

normalized grey matter images. These native space-representative

images were then smoothed using a 12-mm 3D (FWHM)

Gaussian filter.

Statistical analysis

The primary object of this study was to consider whether VBM

would replicate the ROI finding of Sanderson et al. (1999) that the

schizophrenia and comorbid groups in this cohort have similar

brains. We employ normalized and native space analyses to

examine both the relative distribution and absolute volumes of

grey matter. The SPM99 package used in this evaluation provides

for parametric and nonparametric statistical analyses of grouped

MR images. In the nonparametric analysis, randomized permuta-

tions of the group contrast labelling are used to generate distribu-

tions of the maximum t statistic (Bullmore et al., 1999; Nichols and

Holmes, 2001). The ranking of the experimental contrast within

these distributions is used to determine the appropriateness of the

patient groupings. In parametric analysis, the incidence of grey

matter for each voxel in the stereotactic space is used to create t

statistic image maps (Ashburner and Friston, 2000, 2001). These

maps characterize the localized grey matter contrasts among study

groups.

The design matrices in our analysis were set to examine grey

matter contrasts between the schizophrenics and comorbids, to

examine contrasts between the normal controls and the other

three groups, and to examine the contrasts between the learning-

disabled and both the schizophrenics and the comorbids. The

design matrices used in the nonparametric analysis were created

using the SnPM Plugin for two groups with one scan per subject.

The parametric design matrices were created from the single

subject conditions and covariates supplied under the SPM99 PET

toolbox.

It was important to maintain consistency across our analyses in

normalized and native space using parametric and nonparametric

statistical methods. To maintain this consistency, we employed the

same confounding covariates in all design matrices. This ensured

that equivalent GLM processing is performed for the examined

contrasts. Two confounding covariates were identified as appro-

priate for the analysis that we wished to implement. The age of the

patient at the time of scan was used as a confound because this has

been identified (Good et al., 2001) as a variable that can mask the

global and regional specificity of grey matter reductions. The

normalized brain volume, constructed from sums of the grey,

white, and CSF-normalized images for each patient, was used as

a confound to ensure that the variability of native space brain size

was removed from our mapping into stereotactic space.

The SnPM toolbox generates a distribution histogram of the

maximum voxel statistic for the experimental contrast. The histo-

gram is formed by randomly chosen relabellings of the data, each

relabelling producing a statistical image for each permutation. Due

to computational power constraints, we limited the number of

permutations to 1000 for each maximum voxel statistic histogram.

The ranked position of the experimental contrast labelling within

this distribution allows an assessment of the rejection of the null

hypothesis. Under the null hypothesis, all relabellings have the

same probability of occurrence; hence, the chance that the exper-

imental labelling falls above the 95th percentile is 5%. A P value

for the experimental contrast is calculated from its ranking within

the maximum t statistic histogram. For the group contrasts that we

were assessing, the number of possible permutations was of the

order of 1012. Thus, the P values for the experimental labelling that

we extract from the 1000 permutations are approximate. However,

these approximate P values are more conservative than those that

would be given if all possible permutations were used (Nichols and

Holmes, 2001).

The parametric processing in the SPM99 package produces

voxel-wise t statistics for each group contrast. An uncorrected

threshold of P < 0.001 was applied to the SPM images where due

to spatial cohesion suprathreshold clusters are formed (Friston et

al., 1995b; Wright et al., 1995, 1999). The SPM99 correction for

multiple comparisons is based on the theory of Gaussian random

fields signified by the existence of resolution elements (resels)

within the suprathreshold clusters. The resel size is determined

from the smoothness of the processed brain images. Resel P-

corrected results are reported by SPM99 at the points of maximal

change within the suprathreshold clusters. We employ the SPM99

small volume corrections (SVC) to examine contrast significance

in the temporal lobes. Small volume correction is also applied to

the amygdalo-hippocampal complex. These SVC regions were

identified by hand drawing on the template by an experienced

ROI tracer (HCW).

Results

Our VBM investigation of the Sanderson et al. (1999) ROI

finding of similarity between the comorbid and schizophrenia

groups proceeded by using nonparametric permutation results,

given in Table 2, to establish the validity of the experimental

groupings in normalized space. Based on these permutation results,

contrasts were selected for statistical parametric analysis. Tables

3–6 tabulate the P-corrected results for the points of maximal

change in these analyses and include t values, Talairach coordi-

nates, and Brodmann area designation. The spatial extent of the

deficits in these contrasts is illustrated in the P < 0.001 (uncor-

rected) statistical parametric maps of Figs. 1–4, with the study-

Table 2

Normalized space nonparametric analysis

Contrast P value max

t statistic 1000

permutations

Degrees-

of-freedom

Normal controls > schizophrenia 0.012 50

Schizophrenia > normal controls 0.909

Normal controls > comorbid 0.002 48

Comorbid > normal controls 0.920

Normal controls > learning-disabled 0.001 43

Learning-disabled > normal controls 0.010

Comorbids > schizophrenia 0.083 44

Schizophrenia > comorbids 0.750

Learning-disabled > schizophrenia 0.049 39

Schizophrenia > learning-disabled 0.141

Learning-disabled > comorbids 0.021 37

Comorbids > learning-disabled 0.614


specific T1 template providing the spatial reference. The native

space analysis was performed to mirror the Sanderson ROI results

uncorrected for brain volume. We use nonparametric permutation

results, given in Table 7, to establish the validity of the experi-

mental groupings in native space and identify contrasts for statis-

tical parametric analysis. Tables 8–11 tabulate the P-corrected

points of maximal change for these native space parametric

analyses. Our choice of covariates ensures that all results are given

after discounting for age and variation of brain expansion into the

analysis space.

Table 3

Normalized space: normal controls > comorbids (degrees-of-freedom = 89)

Voxel, P-corrected t value Talairach (mm)

x y z

Whole brain corrected (resel count = 648) (resel size = 3204 voxels)

>0.001 6.66 � 53 17 0

0.006 5.48 55 9 12

0.039 4.98 55 20 5

0.007 5.45 45 6 � 4

0.008 5.44 54 14 � 7

0.049 4.91 39 � 69 � 11

Temporal lobe corrected (resel count = 70.1) (resel size = 3204 voxels)

>0.001 6.04 � 55 15 � 2

0.010 4.78 � 58 2 � 4

0.001 5.44 54 14 � 7

0.001 5.37 45 5 � 7

0.002 5.32 47 5 � 4

0.004 5.09 45 2 � 5

0.007 4.88 50 4 2

0.010 4.78 66 � 28 18

0.025 4.51 43 4 � 12

0.025 4.51 47 17 � 9

0.026 4.5 49 � 2 2

0.032 4.43 � 24 � 21 � 8

0.014 4.7 � 66 � 4 1

Amygdalo-hippocampal complex corrected (resel count = 4.8) (resel size = 3204

0.004 4.43 � 24 � 21 � 8

0.008 4.2 � 30 � 34 � 4

0.030 3.75 22 � 20 � 10

0.035 3.69 � 30 1 � 18

Nonparametric analysis in normalized space

The nonparametric whole brain results in normalized space are

given in Table 2 for the maximum t statistics. The contrasts for

normal controls greater than the schizophrenia and comorbid

groups returned ranked results of 12th and 2nd, respectively, for

the experimental labelling. Tested on 1000 permutations, these

rankings give P values <0.012 and thus rejection of the null

hypothesis. The reverse contrasts for the schizophrenia and comor-

bid groups greater than the normal controls return ranked results of

909 and 920, respectively. These maximum t statistic results

indicate that in normalized space the normal control group has a

more extensive grey matter distribution than the schizophrenia and

comorbid groups.

The contrasts between the normal controls and the learning-

disabled register a rejection of the null hypothesis in both

directions. In the 1000 permutation tests of Table 2, the normal

controls greater than the learning-disabled returned a ranking of

one and in reverse the learning-disabled greater than normal

controls returned a ranking of 10th. This indicates that both

contrasts exhibit significant localized grey matter deficits in

normalized space.

Nonparametric analysis for learning-disabled greater than the

comorbid in Table 2 gives maximum t statistic rejection of the null

hypothesis. A ranked result of 21st in 1000 permutations gave the

P value of 0.021 and indicates a more extensive grey matter

volume for the learning-disabled in normalized space. The reverse

contrast returned a ranked result of 614 in 1000 permutations to

Location of point of maximal change Side Brodmann

Inferior frontal gyrus L 47

Inferior frontal gyrus R 44

Inferior frontal gyrus R 45

Superior temporal gyrus R 22


Fusiform gyrus R 19

Superior temporal gyrus L 38











Hippocampus L

Middle temporal gyrus L 21

voxels)

Hippocampus L

Hippocampus L

Hippocampus or parahippocampal gyrus R 28

Amygdala L

Table 4

Normalized space: normal controls > schizophrenics (degrees-of-freedom = 89)

Voxel, P-corrected t value Talairach (mm) Location of point of maximal change Side Brodmann

x y z


0.011 5.33 � 62 � 3 0 Superior temporal gyrus L 22

0.045 4.93 60 � 4 6 Superior temporal gyrus R 22

0.036 5 8 � 81 14 Cuneus R 17

0.047 4.92 � 13 � 100 14 Cuneus L 18

0.041 4.96 17 41 53 Superior frontal gyrus R 8




0.050 4.29 45 4 � 9 Superior temporal gyrus R 38


0.033 4.42 50 � 38 � 18 Inferior temporal gyrus R 20

Amygdalo-hippocampal complex corrected (resel count = 4.8) (resel size = 3204 voxels)

0.001 4.76 � 27 2 � 17 Amygdala L

0.041 3.64 � 21 � 24 � 9 Hippocampus or parahippocampal gyrus L 28


indicate that there are no significant deficits in the learning-

disabled when compared to the comorbid group.

Nonparametric analysis for the learning-disabled greater than

schizophrenia in Table 2 gives maximum t statistic rejection of the

null hypothesis. A ranked result of 49th gave the P value of 0.049

for the learning-disabled greater than schizophrenia contrast. Al-

though the P value of 0.049 is marginal, the conservative nature of

the nonparametric results allows rejection of the null hypothesis.

Space. The schizophrenia greater than learning-disabled returned a

ranked result of 141 in 1000 permutations, and for this contrast the

null hypothesis is not rejected.

In Table 2, the two-way nonparametric comparisons between

the schizophrenia and comorbid groups gave P values of 0.083 and

0.750. Thus, the null hypothesis for these groups is not rejected in

the maximum t statistic test. This provides replication of the

Sanderson et al. (1999) structural findings by an alternative

method.

Parametric analysis in normalized space

Statistical parametric analysis was applied to group contrasts in

normalized space to localize grey matter deficits identified through

nonparametric permutation tests. The analyses were volume-cor-

rected for the whole brain, the temporal lobes, and the amygdalo-

hippocampal complex.

Table 5

Normalized space: normal controls > learning-disabled (degrees-of-freedom = 89


x y z


0.011 5.35 � 63 � 1 �0.026 5.1 � 11 10 1


0.001 5.35 � 63 � 1 �

Amygdalo-hippocampal complex corrected (resel count = 4.8) (resel size = 3204

0.01 4.1 25 � 19 �

Schizophrenia vs. comorbid

Contrasts between the schizophrenia and comorbid groups

show no significant differences between these populations at

whole brain, temporal lobe, or amygdalo-hippocampal complex.

One trend result with P = 0.061 (corrected) was found for the

comorbid greater than schizophrenia contrast in the medial frontal

gyrus.

Normal controls vs. comorbid

Fig. 1 plots the P < 0.001 (uncorrected) parametric results taken

from normalized space for the normal controls greater than

comorbid contrast. Grey matter deficits for the comorbid group

are noted in temporal, prefrontal, and occipital lobes as well as the

cerebellum. The P-corrected points of maximal change for the

normal control greater than comorbid contrast are recorded in

Table 3. At whole brain, significant comorbid deficits occur in

the inferior frontal gyrus on the left and right sides, and the right

superior temporal gyrus and fusiform gyrus. The small volume

correction for the temporal lobes provides further localization of

the superior temporal lobe deficits in the Brodmann areas 22 and

38 on the left side, Brodmann areas 22, 38, and 42 on the right

side, the hippocampus on the left side, and the left middle temporal

gyrus. Localization of the hippocampus deficit was implemented

through further refinement of the small volume correction to an

amygdalo-hippocampal mask. This revealed deficits in the left

)

Location of point of Side Brodmann

maximal change

2 Superior temporal gyrus L 22

6 Caudate nucleus L

2 Superior temporal gyrus L 22

voxels)

8 Hippocampus R

Table 6

Normalized space: whole brain corrected (resel count = 648) (resel size =

3204 voxels) (degrees-of-freedom = 89)

Voxel, t Talairach (mm) Location of voxel Side

P-corrected valuex y z

of maximal change

Learning-disabled > normal controls

0.002 5.84 40 � 23 30 Postcentral gyrus R

Learning-disabled > comorbids


Learning-disabled > schizophrenics



hippocampus, the left amygdala, and at Brodmann area 28 on the

boundary of the left hippocampus and parahippocampal gyrus.

Parametric analysis of the comorbids greater than normal controls

Fig. 1. Normalized analysis; areas of grey matter deficit in comorbid learning disa

shows the corresponding t values. The numbers beside each slice show the heigh

contrast revealed no significant normal control differences with

correction for whole brain, temporal lobes, or amygdalo-hippo-

campal complex.

Normal control vs. schizophrenia

Fig. 2 plots the P < 0.001 (uncorrected) parametric results

taken from normalized space for the normal controls greater than

schizophrenia contrast. Grey matter deficits in the schizophrenia

group are noted in the temporal, prefrontal, and occipital lobes.

The P-corrected results for the normal control greater than

schizophrenic contrast are recorded in Table 4. At whole brain,

significant schizophrenia deficits occur in the superior temporal

gyrus in both left and right sides, in the right side cuneus, and

the superior frontal gyrus. The use of a small volume correction

analysis for the temporal lobes provides further localization of

the superior temporal lobe deficits to Brodmann area 22 on the

left side and Brodmann areas 22 and 38 on the right side.

bility with schizophrenia when compared to normal controls. The color bar

t on the z-axis (MNI) in millimeters.

Table 7

Native space: nonparametric analysis

Contrast P value max t

statistic 1000

permutations

Degrees-

of-freedom

Normal controls > schizophrenia 0.002 50

Schizophrenia > normal controls 0.887

Normal controls > comorbid 0.002 48

Comorbid > normal controls 0.979

Normal controls > learning-disabled 0.001 43

Learning-disabled > normal controls 0.657

Comorbids > schizophrenia 0.820 44

Schizophrenia > comorbids 0.430

Learning-disabled > schizophrenia 0.848 39

Schizophrenia > learning-disabled 0.010

Learning-disabled > comorbids 0.401 37

Comorbids > learning-disabled 0.149


Refinement through an amygdalo-hippocampal small volume

mask revealed deficits in the left amygdala and at Brodmann

area 28 on the boundary of the left hippocampus and para-

hippocampal gyrus. Parametric analysis of the schizophrenia

greater than normal control contrast revealed no significant

Table 8

Native space: normal controls > learning-disabled (degrees-of-freedom = 89)


x y z


0.000 6.88 6 40 30

0.000 6.68 3 54 29

0.002 5.56 0 0 64

0.003 5.4 0 2 54

0.003 5.4 52 � 15 57

0.000 6.74 15 � 86 34

0.000 6.1 � 14 � 90 28

0.000 6.46 � 51 � 55 48

0.000 5.9 � 18 � 101 1

0.003 5.44 � 9 � 76 18

0.001 5.66 26 � 78 � 7

0.001 5.61 30 � 90 28

0.002 5.49 60 � 63 20


0.004 4.78 � 24 � 34 13

0.004 4.77 � 27 � 36 10

0.005 4.7 � 19 � 19 � 11

0.001 5.11 � 50 19 � 20

0.008 4.56 � 59 � 28 17

0.009 4.55 � 53 17 � 4

0.010 4.52 � 53 � 26 14

0.011 4.49 � 57 � 26 14

0.013 4.42 � 26 14 � 26

0.035 4.11 � 49 � 29 4

0.017 4.34 � 49 � 25 14

0.019 4.3 � 64 � 40 � 13

0.002 4.95 23 � 18 � 8

0.003 4.87 25 � 36 11

0.021 4.28 49 � 4 4

0.026 4.2 50 2 2

0.033 4.12 47 � 7 8

0.047 4 47 2 � 2

differences with correction for whole brain, temporal lobes, or

amygdalo-hippocampal complex.

Normal control vs. learning-disabled

Fig. 3 plots the P < 0.001 (uncorrected) parametric results taken

from normalized space for the normal controls greater than

learning-disabled. In this comparison, learning disability deficits

are noted in the temporal, prefrontal, and lateral ventricles. The

temporal and prefrontal deficits are within the grey matter regions

defined by the study-specific template. The deficits noted in the

lateral ventricle spaces are probably a result of the gross native

space differences in ventricle architecture between the normal

control and the learning-disabled groups. The parametric results

for the normal control greater than learning-disabled contrast are

recorded in Table 5. At whole brain, significant learning-disabled

deficits occur in the superior temporal gyrus on the left sides and

the caudate nucleus. The use of a small volume correction analysis

for the temporal lobes provides further localization of the superior

temporal lobe deficits to Brodmann area 22 on the left side.

Refinement through an amygdalo-hippocampal small volume mask

revealed deficits in the right hippocampus.

The normalized space parametric analysis of the learning-

disabled greater than normal control contrast revealed learning-

Location of point of maximal change Side Brodmann

Medial frontal gyrus R 8




Postcentral gyrus R 1

Superior occipital gyrus R 19

Superior occipital gyrus L 19

Inferior parietal lobe L 40

Cuneus L 17

Cuneus L 17

Fusiform gyrus R 19

Middle occipital gyrus R 18


Caudate nucleus L

Caudate nucleus L

Uncus L 34








Transverse temporal gyrus L 41

Inferior temporal gyrus L 20

Hippocampus or parahippocampal gyrus R 27

Caudate nucleus R





Table 9

Native space: normal controls > comorbid (degrees-of-freedom = 89)

Voxel, P-corrected t value Talairach (mm) Location of point of maximal change Side Brodmann

x y z


< 0.001 6.03 30 � 91 29 Middle occipital gyrus R 18

< 0.001 5.98 9 � 85 30 Cuneus R 19

0.017 4.93 � 8 � 82 34 Cuneus L 19

0.032 4.75 8 � 79 18 Cuneus R 17

0.001 5.71 � 54 16 � 2 Inferior frontal gyrus L 47

0.000 5.98 6 39 30 Medial frontal gyrus R 8

0.045 4.63 2 55 26 Medial frontal gyrus R 9

0.012 5.04 17 40 52 Superior frontal gyrus R 8

0.032 4.74 12 � 67 25 Precuneus R 7

0.015 4.96 � 50 � 32 23 Inferior parietal lobe L 40


0.020 4.88 53 14 � 5 Superior temporal gyrus L 38


0.013 5.01 51 � 70 � 27 Cerebellum R

0.017 4.94 � 10 � 47 � 40 Cerebellum L

0.031 4.75 70 � 30 23 Cuneus R

0.023 4.84 � 31 � 41 � 2 Fusiform gyrus L 36


< 0.001 5.58 � 55 15 � 2 Superior temporal gyrus L 38







0.021 4.27 � 47 � 15 10 Transverse temporal gyrus L 41





0.023 4.24 45 � 9 5 Transverse temporal gyrus R 41


0.003 4.83 � 31 � 40 � 2 Hippocampus L


disabled increased grey matter at the most medial point on the

inferior aspect of the postcentral gyrus. The statistical parametric

map of Fig. 4 illustrates this increase and Table 6 tabulates its point

of maximal change. No additional differences were recorded for

Table 10

Native space: normal controls > schizophrenia (degrees-of-freedom = 89)

Voxel, t value Talairach (mm) Location of Side Brodmann

P-correctedx y z

point of

maximal

change


0.000 5.91 8 � 83 33 Cuneus R 19

0.014 4.98 � 12 � 88 28 Cuneus L 19

0.033 4.74 7 � 72 21 Cuneus R 17

0.003 5.41 15 � 65 28 Precuneus R 7

0.002 5.6 5 39 30 Medial

frontal

gyrus

R 9

0.002 5.5 1 55 27 Superior

frontal

gyrus

R 9

this contrast with correction for whole brain, temporal lobes, or

amygdalo-hippocampal complex.

Learning-disabled vs. (comorbid and schizophrenia)

The normalized space contrast for the learning-disabled greater

than comorbid records a significant learning-disabled grey increase

at the most medial point on the inferior aspect of the postcentral

gyrus. A similarly positioned learning-disabled grey matter in-

crease was also recorded for the learning-disabled greater than

schizophrenia. The coordinates given in Table 6 show coregistra-

tion of this feature for the learning-disabled contrasts with the

schizophrenia, comorbid, and normal control groups. The Sander-

son et al. (1999) ROI analysis did not include this region. A visual

inspection of the normalized MRI scans revealed that the learning-

disabled appeared to have a relatively high concentration of grey

matter in this region. Reverse contrasts for the comorbid greater

than the learning-disabled and schizophrenia greater than learning-

disabled show no significant results.

Nonparametric analysis in native space

The native space nonparametric whole brain results are

given in Table 7 for maximum voxel t statistics. The contrasts

Table 11

Native space: schizophrenia > learning-disabled (degrees-of-freedom = 89)

Voxel, t value Talairach (mm) Location of point Side

P-correctedx y z

of maximal change


0.011 5.05 � 16 � 17 20 Thalamus L

0.026 4.81 15 � 25 18 Thalamus R

0.040 4.67 � 16 � 29 16 Thalamus L

0.031 4.75 15 � 21 19 Caudate nucleus R

0.034 4.73 17 � 28 16 Caudate nucleus R

0.036 4.7 � 16 � 23 19 Caudate nucleus L

0.047 4.62 � 13 � 6 22 Caudate nucleus L


for the normal controls greater than the other three groups

returned ranked results of 1st or 2nd for the experimental

labelling. On 1000 permutations, these rankings give a P value

Fig. 2. Normalized analysis; areas of grey matter deficit in schizophrenics when co

The numbers beside each slice show the height on the z-axis (MNI) in millimete

<0.002 and thus rejection of the null hypothesis. The reverse

contrasts for the other three groups greater than the normal

controls return ranked results of 657 or greater. The results

from this set of contrasts indicate that in native space the

normal controls have more extensive grey matter than the other

three study groups.

The contrast between the learning disability and schizophre-

nia groups shows rejection of the null hypothesis and indicates

that in native space the schizophrenia group has more grey

matter than the learning-disabled. The contrasts between the

learning disability and the comorbid groups do not give rejection

of the null hypothesis. This is indicative of a similarity in grey

matter distribution for the learning-disabled and comorbids in

native space. The contrasts between the schizophrenia and the

comorbid groups do not give rejection of the null hypothesis.

This is also indicative of a similarity in native space grey matter

distribution for comorbid and schizophrenia groups.

mpared to normal controls. The color bar shows the corresponding t values.

rs.

Fig. 3. Normalized analysis; areas of grey matter deficit in learning-disabled when compared to normal controls. The color bar shows the corresponding t values.

The numbers beside each slice show the height on the z-axis (MNI) in millimeters.


Parametric analysis in native space

Parametric analysis was performed with corrections for the

whole brain, temporal lobes, and amygdalo-hippocampal com-

plex using SVCs (Lawrie and Abukmeil, 1998; Wright et al.,

2000).

Schizophrenia vs. comorbid

Contrasts between the schizophrenia and comorbid groups

show no significant differences between these populations with

correction for the whole brain, temporal lobe, or amygdalo-hippo-

campal complex.

Comorbid vs. learning-disabled

Contrasts between the comorbid and learning-disabled show a

learning-disabled deficit with a point of maximal change in the

medial frontal gyrus at Brodmann area 6. The P value for this

deficit is 0.023, the t value is 4.84, and the Talairach coordinates

are x = 4, y = �2, z = 68. The reverse contrast of the learning-

disabled greater than the comorbids shows that there are no

significant differences.

Normal control vs. learning-disabled

In Sanderson et al. (1999), the learning-disabled are recorded as

having the smallest brains of the four study groups. Table 8 gives

our native space points of maximal change for the normal control

greater than learning-disabled contrast. At whole brain, we find

extensive deficits in the learning-disabled group. These deficits

occur in the occipital lobes, the frontal lobes, the right temporal

lobe, and the left and right parietal lobes. The temporal lobe SVC

was applied to this contrast and revealed further deficits within the

temporal lobes. In raw volume ROI results, Sanderson recorded

significant deficits in the learning-disabled when compared to the

normal controls in the left and right prefrontal lobes and left and

Fig. 4. Normalized analysis; areas of grey matter increase in the learning-

disabled when compared to normal controls. The color bar shows the

corresponding t values. The numbers beside each slice show the height on

the z-axis (MNI) in millimeters.


right temporal lobes. The occipital and parietal lobes were not

measured by Sanderson et al. (1999). The reverse contrast of the

learning-disabled greater than the normal controls shows no

significant grey matter deficits.

Normal control vs. comorbid

The parametric results in native space for the normal control

greater than comorbid are recorded in Table 9. Correction for

whole brain analysis shows extensive grey matter deficits in the

comorbid group. The deficits occur in the occipital lobes, frontal

lobes, temporal lobes, and cerebellum. These indicate that the

comorbid group has significantly less cortical grey matter than

the normal controls. In the Sanderson et al. (1999) study, the

ROI results for these group contrasts show a significant deficit

for the comorbid group when corrected for whole brain analysis.

In Table 9, the temporal lobe SVC shows comorbid deficits in

the left superior temporal gyrus at Brodmann 22, 38, and 42

and in the right superior temporal gyrus at Brodmann 22 and

38. The left and right transverse temporal gyri also record

comorbid deficits at Brodmann 41. The reverse contrast of the

comorbid greater than the normal controls shows no significant

differences.

Normal control vs. schizophrenia

The native space parametric results for the normal control

greater than schizophrenia are recorded in Table 10. Correction

for whole brain analysis shows grey matter deficits in the schizo-

phrenia group. These deficits occur in the occipital lobes and right

frontal lobe. Under modulation at whole brain level, we did not

record any deficits in the temporal lobes of the schizophrenia group

when they were compared to the normal controls. The application of

temporal lobe and amygdalo-hippocampal SVCs to this contrast

revealed no significant deficits. These results are at variance from

the normalized space analysis that shows deficits at whole brain

level in both the left and right superior temporal gyri and multiple

deficits for these regions for the temporal lobe SVC. An examina-

tion of the uncorrected temporal lobe results reveals clusters with

focal points that match the whole brain normalized results, although

these focal points are not statistically significant under multiple

comparison corrected analysis. The reverse contrast of the schizo-

phrenics greater than the normal controls shows no significant

deficits.

Schizophrenia vs. learning-disabled

The native space parametric results for the schizophrenia

greater than learning disability results are recorded in Table 11.

Correction for whole brain analysis shows significant grey matter

deficits in the learning-disability group. These deficits occur in the

pulvinar and caudate nucleus. In the Sanderson et al. (1999) study,

the whole brain volume contrast between these two groups shows a

significant deficit for the comorbid group. Additionally, for this

contrast Sanderson showed learning-disabled deficits in the left

and right caudate nuclei, lentiform and thalamic nuclei, and the left

and right temporal lobes. The reverse contrast of the learning-

disabled greater than the schizophrenics shows no significant grey

matter differences.

Discussion

The prevalence of schizophrenia within the mild learning-

disabled population is approximately three times that of the general

population. This higher frequency of schizophrenia among the

learning-disabled prompted a ROI study (Sanderson et al., 1999) to

investigate the neuroanatomy of comorbid schizophrenia and

learning disability. This study and the accompanying clinical

investigation (Sanderson et al., 2001) support the view that

schizophrenia and comorbid learning disability with schizophrenia

have a common gross neuropathology. This may result from

common genetic and environmental factors such as obstetric

complications (Doody et al., 1998). Comorbid learning disability

and schizophrenia may therefore represent an early onset severe

form of schizophrenia, rather than learning disability complicated

by subsequent psychosis.

In our VBM analysis of the Sanderson et al. (1999) cohort, we

employed normalized and native space analyses using both non-

parametric and parametric methods to consider the grey matter

evidence for Sanderson’s finding. This use of VBM on a diverse

set of brains presented a significant departure from the normal

usage of this technique. The diversity of the four groups used in

our study is exemplified by the variation in brain and ventricular

volumes (Sanderson et al., 1999). The mean ventricular volumes

range from 11 cm3 in the normal controls to 26 cm3 in the

learning-disabled. Additionally, the mean whole brain volumes

range from 1327 cm3 in the normal controls to 1137 cm3 in the

learning-disabled. This variation in brain structures necessitated

the creation of a study-specific template for the VBM normaliza-

tion procedure.

We adopted the optimized methodology of Good et al. (2001)

for the use of VBM on a study group with diverse brain

structures. This methodology incorporates the creation of a

study-specific template. In the creation of our study-specific

template, we used the optimized template creation procedure

(Good et al., 2001; Ashburner and Friston, 2000). By using this

method we ensured that our template reflected the distribution of

grey matter found within the study cohort. Normalization of the

cohort MR images to a stereotactic space given through use of

the optimized template ensured a minimal degree of warping was

required for coregistration.

euroImage 22 (2004) 188–202

Normalized space

We used a standard nonparametric multiple comparisons proce-

dure based on permutation testing (Bullmore et al., 1999; Nichols

and Holmes, 2001) to assess whole brain evidence for the Sander-

son finding. The nonparametric results in Table 2 employ permu-

tation testing based on maximum t statistics. The measures show

that there is sufficient evidence to reject the null hypothesis that

there is no difference between the comorbid and learning-disabled

groups and insufficient evidence to reject it between the schizo-

phrenia and comorbid groups. In addition, the nonparametric

analysis demonstrates rejection of the null hypothesis between the

normal controls and the other three study groups, indicating that

normal controls have more extensive volumes of grey matter at

whole brain level. These results are in keeping with the Sanderson

et al. (1999) finding.

The locations where the grey matter volume is significantly

greater in normal controls than other groups are given in Tables 3–5.

Results for the whole brain and small volume correction of the

temporal lobes and the amygdalo-hippocampal complex are

recorded. The results from Tables 3 and 4 are in keeping with the

reported bilateral deficits in the superior temporal gyrus (Menon et

al., 1995; Wright et al., 1999; Zipursky et al., 1994) and prefrontal

lobe deficits (Buchanan et al., 1998) in schizophrenia. Deficits in the

left hippocampus are also in keeping with the reported pathology of

schizophrenia (Lawrie and Abukmeil, 1998; Wright et al., 2000).

The deficits noted in the learning-disabled group in Table 5 are less

extensive than those noted for the comorbid and schizophrenia

groups. These parametric results taken from the normalized space

analysis provide further confirmation of the Sanderson et al. (1999)

finding.

In Fig. 3, the uncorrected parametric results for the normal

control vs. learning-disabled contrast show deficits in the lateral

ventricle spaces. These deficits are probably a result of the gross

native space differences in ventricle architecture between the

normal control and the learning-disabled groups. The study-spe-

cific template reflects the enlarged ventricles found in the comor-

bid, schizophrenia, and the learning-disabled patients (Sanderson et

al., 1999). The normal controls possess grey matter that falls within

this enlarged CSF space. The SPM99 normalization settings that

we used had insufficient warp capability to coregister these

ventricle spaces. Attempts to increase the SPM99 warp capability

to provide this ventricle coregistration were found to distort the

cortical grey matter that was our prime interest.

The learning-disabled exhibited increased grey matter in nor-

malized space at the most medial point on the inferior aspect of the

postcentral gyrus. This increase was registered against the comorbid,

schizophrenia, and normal control groups. It is illustrated in Fig. 4

for the learning-disabled to normal control contrast, and tabulated

for contrasts between the learning-disabled and the other three

groups in Table 6. Visual inspection of the MRI scans suggests that

this increase results from unusual appearances in the right parietal

lobe of the learning-disabled. Qualitative studies of this novel

finding continue, and at present, its frequency and significance are

unknown. No prior knowledge of this appearance existed and

without the analysis provided by VBM we would not have it now.

Native space analysis

In the native space analysis, we compared the native space grey

matter distributions of the four study groups. Under nonparametric

T.W.J. Moorhead et al. / N200

analysis, it was again found that the schizophrenia and comorbid

groups had similar grey matter distributions and we were again

unable to reject the null hypothesis for this comparison. Parametric

analysis of the schizophrenia to comorbid contrast also found that

there were no significant regional differences between these two

groups with correction for whole brain volume and SVCs for the

temporal lobes and the amygdalo-hippocampal complexes. These

native space distribution results are in keeping with our VBM

normalized space results and with the ROI results reported by

Sanderson et al. (1999) for the comorbid to schizophrenia con-

trasts. This provides additional support that the comorbid and

schizophrenia groups belong to the same population.

In nonparametric comparisons between the learning-disabled

and the schizophrenia groups, the schizophrenia group has more

grey matter than the learning disability group. This native space

finding is in keeping with the Sanderson et al. (1999) results that

show the learning-disabled to have significantly smaller brains than

the schizophrenia group. The native space parametric results

detailed in Table 11 indicate rejection of the null hypothesis for

the schizophrenia to learning disability contrast, in that patients

with schizophrenia have more grey matter in the right and left

thalamus and the caudate nuclei. The schizophrenia patients had all

been treated with dopamine blocking antipsychotic while the

learning-disabled had not (Doody et al., 1998). It is established

that such medication is associated with increased size of the

subcortical nuclei, particularly the caudate (Chakos et al., 1994;

Corson et al., 1999; Keshavan et al., 1994).

In nonparametric comparisons between the learning-disabled

and the comorbids, the two groups are shown to have similar grey

matter distributions. In parametric analysis for the native space

result, we register a deficit in the right medial frontal gyrus of the

learning-disabled for this contrast. This is in keeping with a right

side prefrontal deficit recorded by Sanderson et al. (1999). Sander-

son also records deficits in the learning-disabled for this contrast in

the caudate nuclei and lentiform nuclei. These native space deficits

are not registered in our VBM analysis by either the parametric or

nonparametric methods. This may reflect that the ROIs measured

by Sanderson et al. (1999) are inclusive of grey and white matter.

In the nonparametric comparisons between the normal controls

and the other three study groups, we find rejections of the null

hypothesis indicating that the normal controls have the largest

volumes of grey matter in native space. This is in agreement with

the Sanderson et al. (1999) whole brain results. The parametric

results given in Tables 8 and 9 show that the deficits in the learning-

disabled and comorbid groups for these contrasts are extensive. A

comparison at whole brain level between the normalized and native

space results shows the learning-disabled native space deficits occur

in the temporal, prefrontal, occipital, and parietal lobes, whereas the

normalized deficits are restricted to the left superior temporal gyrus

and the caudate nucleus. At whole brain level in the native space,

comorbid analysis deficits occur in the temporal, prefrontal, pari-

etal, and occipital lobes, whereas in normalized analysis deficits are

restricted to the right superior temporal and fusiform gyri and the

right and left inferior temporal gyri. The increased deficits in the

learning-disabled and comorbids when switching from normalized

to native space analysis are however to be expected as these two

groups have significantly smaller brains than the normal controls

(Sanderson et al., 1999). Thus, under modulation analysis they have

greater grey matter deficits.

The native space parametric analysis of normal controls to

comorbid contrast was extended to an examination of the temporal


lobes and amygdalo-hippocampal complexes through the use SVCs.

The SVC results show a similar degree of temporal lobe deficits in

the normalized and native space analyses. These grey matter deficits

are located in the left and right superior temporal gyri, the left

hippocampus, as well as the parahippocampal gyri. The brains of the

comorbids were significantly smaller than the normal controls. The

modulation analysis is based on the absolute volumes of grey matter.

Thus, we had expected that in comparison to the normalized

analysis, the degree of deficit would be increased under modulation,

but this was not the case for the comorbid’s temporal lobes.

The parametric results for the normal control to schizophrenia

contrast in Table 10 show deficits in the prefrontal and occipital

lobes. Temporal lobe SVCs showed no significant deficits in the

schizophrenia group. In this native space analysis, we had expected

to see more schizophrenia deficits in the temporal lobes than given

by the normalized results. The Sanderson ROI analysis found

smaller brain volumes for the schizophrenia group than the normal

controls. This loss of reported deficit in the schizophrenia temporal

lobes resembles the failure of the native space comorbid analysis to

show increased deficits in the temporal lobes. However, the native

space contrast between the normal controls and the learning-

disabled shows the expected increase of deficits in the temporal

lobes and throughout the brain. The reduction of grey matter

intensity in the learning-disabled under modulation probably

accounts for the loss of the normalized analysis deficit at the

anterior aspect of the postcentral gyrus when the learning-disabled

were compared to the other three groups.

We postulate that the anomalous loss of deficit for the schizo-

phrenia and comorbid groups under modulation is a result of these

groups having brains with structural aspects that differ from the

normal controls. Minor physical anomalies (MPA) including cra-

nial width increase have been identified in schizophrenia and

related populations (Boyes et al., 2001; Lane et al., 1997; McGrath

et al., 2002). It is considered that an increase of brain width in the

schizophrenia suffers in this study would account for our loss of

temporal lobe deficits under modulation. In the case of increased

brain width, the expansion of the schizophrenia and comorbid

temporal lobes under normalization would be less than that applied

to normal controls. In modulation this would result in a greater

reduction of grey matter intensity in the normal control temporal

lobes than that applied to the schizophrenia or comorbid groups.

Hence, the native space analysis could fail to register the expected

deficits in the schizophrenia and comorbid temporal lobes. We

think a systematic processing cause for this anomalous loss of

deficit in schizophrenia is unlikely because the modulation process

step applied to the learning-disabled produced the expected corre-

spondence between native space temporal lobe deficits and known

brain size.

Conclusion

The automated nature of the VBM approach provides for

considerable labor savings over an equivalent ROI analysis of

MRI scans. VBM is unbiased in its analysis of brain tissue

distributions. Thus, deficits that occur between test groupings

can be detected without prior knowledge. We have demonstrated

the use of nonparametric analysis and shown that this is a useful

means of assessing the appropriate population assignment of

subjects to contrast groups. The labor savings provided by VBM

coupled with facility to deal with different brains in an unbiased

way mean that large study cohorts can be analyzed and represen-

tative population samples reported. In the poorly researched field

of learning disability such studies would be valuable.

Under normalized analysis, we have shown that the VBM

optimized methodology is capable of comparing grouped grey

matter distributions when the subjects are drawn from populations

that differ significantly in gross neuroanatomy. The normalized

VBM results show that from grey matter analysis we were able to

detect deficits predicted from ROI analysis. The comorbid and

schizophrenia groups were found to have similar grey matter

distributions and these differ significantly from the learning-dis-

abled and normal controls. This confirmation of an existing ROI

result, that the comorbids and schizophrenics belong to the same

population, was the prime objective of our research.

Our native space analysis is also generally in keeping with the

results of the ROI study. We show the learning-disabled to have the

lowest volume of grey matter in native space and the normal

controls the largest. Intermediate to these extremes, we find that the

schizophrenia and comorbid groups exhibit similar distributions of

grey matter. This provides additional evidence in support of the

finding that the schizophrenics and comorbids belong to the same

population. Our VBM modulation results highlight the possible

effects of cranial anomalies in schizophrenia, which were not

evident under normalized analysis. These anomalies could mask

disease-related deficits in the temporal lobes and in schizophrenia

research, limit the usefulness of native space volumes analysis

derived from the modulation of normalized scans. The modulated

VBM methodology for the recovery of native space volumes may

be more applicable to the study of neurodegenerative rather than

neurodevelopmental disease.

Acknowledgment

This study was funded by a program grant from the Medical

Research Council of Great Britain.

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