Department of Psychological Medicine, Institute of Psychiatry, London
Correspondence: Therese van Amelsvoort, Department of Psychological Medicine, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UK. Tel: 020 7848 0785; Fax: 020 7848 0650; e-mail: spjutva{at}iop.kcl.ac.uk
Declaration of interest This study was funded partially by the Theodore and Vada Stanley Foundation and the Medical Research Council.
* Preliminary data were presented at the Annual Meeting of the Society for
Biological Psychiatry,Washington,DC, 13-15 May 1999.
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Aims To investigate brain anatomy in adults with VCFS.
Method Magnetic resonance imaging was used to study 10 patients with VCFS and 13 matched controls. We carried out three analyses: qualitative; traced regional brain volume; and measurement of grey and white matter volume.
Results The subjects with VCFS had: a high prevalence of white matter hyperintensities and abnormalities of the septum pellucidum; a significantly smaller volume of cerebellum; and widespread differences in white matter bilaterally and regional specific differences in grey matter in the left cerebellum, insula, and frontal and right temporal lobes.
Conclusions Deletion at chromosome 22q11 is associated with brain abnormalities that are most likely neurodevelopmental and may partially explain the high prevalence of learning disability and psychiatric disorder in VCFS.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
METHOD |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Thirteen control subjects (eight females and five males) were recruited from local community centres for people with mild or borderline learning disabilities. The mean age of the control sample was 37 years (s.d.=10) with a mean FSIQ of 72 (s.d.=12) and a mean SES of 2.5 (s.d.=0.5). A deletion at chromosome 22q11 was excluded by FISH. Seven control subjects were free of mental illness, two met criteria for schizophrenia, two for major depression and two for dysthymia.
Magnetic resonance imaging
Magnetic resonance imaging of the brain was performed on a GE Signa 1.5
Tesla system (General Electric, Milwaukee, Wisconsin, USA) at the Maudsley
Hospital, London. A coronal three-dimensional spoiled grass (SPGR) data-set
covering the whole head was acquired with repetition time TR=13.8 ms,
echotime TE=2.8 ms, flip angle=20°, 1.5-mm slice thickness, one
acquisition per phase encode step and flow compensation of 10 min. The matrix
was 256 x 192 and the field of view was 22 cm, giving an in-plane
resolution of 0.859 mm. This data-set was used to measure whole and regional
brain volumes. In addition, we acquired a whole-brain near-axial dual-echo
fast-spin-echo (FSE) data-set aligned with the AC-PC line, with
TR=4000 ms, effective TE=20 and 85 ms, 3-mm slice thickness,
interleaved slices, flow compensation and echo train length=8 (8 min). The
matrix was 256 x 192 and the field of view was 22 cm, giving an in-plane
resolution of 0.859 mm. This data-set was used to estimate between-group grey
and white matter differences using a previously published methodology
(Suckling et al,
1999).
Three types of analysis were performed, all blind to subject group status. First, both MRI data-sets were analysed qualitatively by a neuroradiologist. Using a four-point rating scale adopted from Kozachuk et al (1990), the presence and extent of ventricular WHMIs was assessed as follows: grade 0=ventricular WMHIs absent; grade 1=frontal or occipital caps or pencil-thin lining of the lateral ventricle; grade 2=smooth halo surrounding the lateral ventricles; and grade 3=irregular ventricular WMHIs extending into the deep white matter. Deep WMHIs were graded as follows: grade 0=absent; grade 1=punctuate foci, either focal or symmetrical; grade 2=mild confluence of foci; and grade 3=large confluence of foci. Peripheral WMHIs were graded similarly to deep WMHIs. Structural abnormalities in cerebellum and septum pellucidum were noted.
Secondly, volumetric analysis of total and regional brain areas was performed. The reformatted SPGR data-set was analysed using Measure software (Barta et al, 1997). Total, right and left caudates, putamen, hippocampus, amygdala, frontal, occipito-parietal and temporal lobes, cerebral hemispheres and ventricular and peripheral cerebrospinal fluid (CSF) volumes were traced using a previously described method (Murphy et al, 1992, 1993a,b). The volume of each region was calculated by multiplying the summed pixel cross-sectional areas by slice thickness. Intrarater and interrater reliabilities were determined for all brain regions of interest (ROIs) traced by the operators as part of this analysis. Highly significant interrater and intrarater reliabilities were obtained in all cases. The interrater correlation coefficients were F>4.0 and P<0.01 (Bartko & Carpenter, 1986).
The third analysis that we employed was a voxel-based method for the statistical analysis of grey and white matter differences. The FSE data-set was analysed using an automated software procedure. Voxels representing intracerebral tissue were identified using a set of linear scale-space features obtained from derivatives of the Gaussian kernel (Suckling et al, 1998). The probability of each intracerebral voxel belonging to each of four possible tissue classes (grey matter, white matter, CSF or dura/vasculature) was then estimated by a modified fuzzy clustering algorithm (Suckling et al, 1999). On the basis of prior results, we equated these probabilities to the proportional volumes of each tissue class in the often heterogeneous volume of tissue represented by each voxel (Bullmore et al, 1995). Thus, for example, if the probability of grey matter class membership was 0.8 for a given voxel, it was assumed that 80% of the tissue represented by that voxel was grey matter. Because the voxel size was predetermined (2.2 mm3), we then estimated the volume in millilitres of grey matter, white matter and CSF in each voxel. Summing these voxel tissue class volumes over all intracerebral voxels yielded global tissue class volumes.
To allow estimation of between-group differences at each intracerebral voxel, the short echo (proton-density-weighted) FSE images were co-registered using an affine transformation (Press et al, 1992; Brammer et al, 1997) with a template image in the coordinate system of standard space as defined by Talairach & Tournoux (1988). This individually estimated transformation was then applied to each of that subject's grey and white tissue probability maps.
Statistics
Analysis I (qualitative data)
Group differences in frequencies of structural abnormalities were assessed
using a 2-test, whereas between-group differences in extent of
WMHIs were assessed using a two-tailed independent sample t-test,
with level of significance for both tests at P<0.05.
Analysis 2 (SPGR data)
Between-group differences in total and regional brain volumes were
calculated by analysis of covariance (ANCOVA) using age, IQ and total
intracranial volume as covariates, with a significance level at
P<0.05.
Analysis 3 (FSE data)
Inference of between-group differences of grey and white matter probability
maps was made with ANCOVA implemented by voxelwise linear regression. The
model included covariates for age and gender. A test statistic was derived
from clusters of spatially contiguous voxels significant at a probability
threshold of P<0.05. The probability of significance of the
clusters was determined using a randomisation procedure
(Bullmore et al,
1999). The level of significance was set at P<0.001,
giving an estimated number of false-positive or type I error clusters of <
1 across the three-dimensional image volume.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
Brain volumes
There were no significant group differences in total brain volume
(F=0.4, d.f.=1, 22, P=0.6), although total intracranial
volume tended to be smaller in people with VCFS (F=3.8, d.f.=1, 22,
P=0.07). However, compared with controls, people with VCFS had a
significantly smaller volume of cerebellum (F=16.0, d.f.=1, 22,
P=0.001) (Table 2). No
significant between-group differences were found in the volume of any other
brain region or CSF.
|
Tissue class volumes
Total brain grey and white matter and CSF volumes were non-significantly
smaller in people with VCFS by 5, 9 and 4%, respectively
(Table 3).
|
Regional differences in grey matter volume
There was a significant difference between control and VCFS groups in grey
matter volume at four spatially extensive three-dimensional voxel clusters.
Two voxel clusters had a significantly reduced grey matter volume in the VCFS
group: a cerebellar cluster (left side) and a temporal cluster (extending from
the right uncus to the superior temporal gyrus and parahippocampal gyrus). In
contrast, two voxel clusters showed relatively increased grey matter volume in
the VCFS group: a temporal cluster (extending from the left insula to the
superior and trans temporal gyrus) and a frontal cluster (extending from the
left median gyrus to the medial and superior frontal gyrus)
(Table 4 and
Fig. 1).
|
|
The mean between-group difference in grey matter volume for the combined deficit regions was 27% (t=-4.2, d.f.=21, P=0.0001), and for the combined excess regions it was 30% (t=10.9, d.f.=21, P=0.0001).
Regional differences in white matter volume
There was a significant difference in white matter volume between the VCFS
and control groups at seven extensive three-dimensional clusters. People with
VCFS had reduced white matter volume in six voxel clusters: two involved
median, superior and medial frontal regions bilaterally; two included
fasciculus longitudinalis superior (FLS) bilaterally, extending to
temporo-parietal regions; one involved left fasciculus longitudinalis inferior
(FLI) and optic radiation, and extended to left superior temporal regions; and
the largest cluster extended from the left optic radiation bilaterally to
occipital regions.
In contrast, one white matter voxel cluster had a relatively increased volume in people with VCFS; it extended from the splenium of the corpus callosum bilaterally to the optic radiation, posterior cingulate and parahippocampal regions (Table 4 and Fig. 2).
|
The mean between-group difference in white matter volume for the combined deficit regions was 33% (t=-15.5, d.f.=21, P=0.0001), and for the excess in white matter volume it was 29% (t=7.7, d.f.=21, P=0.0001).
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Qualitative analysis
In our qualitative analysis, we found that abnormalities of septum
pellucidum and (deep, ventricular and peripheral) WMHIs were
(non-significantly) more often present in people with VCFS, and this is in
agreement with observations from others
(Mitnick et al, 1994;
Vataja & Elomaa, 1998;
Chow et al, 1999).
Although their aetiology is not fully understood, WMHIs may reflect
abnormalities in myelination and increased white matter water content
(Kozachuk et al,
1990) and have been described in otherwise healthy people with
cardiovascular anomalies. Velo-cardio-facial syndrome is also associated with
cardiovascular malformations (Goldberg
et al, 1993;
Shprintzen et al,
1997; Chow et al,
1999) and therefore the development of white matter may be
particularly affected in VCFS, possibly secondary to cardiovascular
abnormalities affecting brain development in addition to genetically
determined neurodevelopmental abnormalities. The clinical significance of
septum pellucidum abnormalities is not clear but they are probably caused by a
disturbance of midline brain development or maturation
(Schaefer & Bodensteiner,
1999).
Quantitative analysis
In contrast to previous studies
(Mitnick et al, 1994;
Lynch et al, 1995), we did not find qualitative cerebellar abnormalities in people with VCFS,
although our quantitative analysis revealed a significantly smaller cerebellum
in people with VCFS compared with the controls. Traditionally the cerebellum
has been associated with coordination of movement but recently attention has
been drawn to cerebellar involvement in cognitive processing, possibly via
neural circuits that link prefrontal, posterior parietal and limbic cortices
(Schmahmann & Sherman,
1998). These brain regions are important for planning, problem
solving and visuospatial processing: all cognitive domains that we have
reported as being affected in people with VCFS
(Henry et al, 2000).
Thus, cerebellar abnormalities may contribute to the deficits in planning,
problem solving and visuospatial processing exhibited by people with VCFS.
In addition, we found widespread loss of white matter, extending bilaterally in frontal, temporal and occipito-parietal regions. This is in agreement with a recent study in children with VCFS (Kates & Burnette, 2000), although their findings were more pronounced in posterior brain regions. Taken together, these results suggest that the cognitive phenotype in VCFS (i.e. a generalised decrement in cognitive ability, but with particular deficits in visuoperceptualspatial function, mathematics, problem solving, planning and abstract thinking (Golding-Kushner et al, 1985; Goldberg et al, 1993; Swillen et al, 1997; Vataja & Elomaa, 1998; Henry et al, 2000)) may be associated with widespread abnormalities in white matter but with greater abnormalities in development and connectivity of brain regions implicated in these higher cognitive functions.
In contrast to the study by Eliez et al (2000) of children with VCFS, we did not find differences in total volume of frontal lobes in adults with VCFS. However, we did find differences in frontal grey and white matter volume. Thus, people with VCFS may have a relatively delayed frontal lobe maturation (Giedd et al, 1999; Sowell et al, 1999), which is detectable as differences in total frontal volume in childhood but subsequently normalises somewhat in adulthood, albeit with subtle differences in tissue composition remaining. In addition, others have noted a smaller total brain volume in children (Eliez et al, 2000) with VCFS, and cerebral atrophy in adults with VCFS (Chow et al, 1999). We found a trend towards smaller intracranial volume in adults with VCFS but no gross cortical atrophy.
Neurodevelopmental or neurochemical
The neurobiological basis for the high rates of learning disability and
psychosis in people with VCFS is poorly understood but may include genetically
determined abnormalities in brain structure and function resulting from
hemizygosity for a gene or genes at chromosome 22q11. Many investigators
favour the view that schizophrenia is a neurodevelopmental disorder, because
individuals with schizophrenia have an increased frequency of minor physical
anomalies (including midfacial anomalies)
(Murphy & Owen, 1996) and
midline brain anomalies such as cavum septum pellucidum and hypoplastic vermis
(Lewis & Mezey, 1985;
Martin & Albers, 1995). These abnormalities are also present in people with VCFS
(Mitnick et al, 1994;
Lynch et al, 1995;
Vataja & Elomaa, 1998; Chow et al, 1999). In
addition, we found a disproportionate excess of white matter in the posterior
region of the corpus callosum, a midline brain structure reported to be
affected in psychosis (Pearlson &
Marsh, 1999) and in people with learning disability
(Schaefer & Bodensteiner,
1999) and possibly reflecting abnormal myelination and/or abnormal
interhemisphere connectivity with subsequent differences in brain structure
and function. Moreover, in VCFS, defective development and migration of neural
crest cells may play a significant role in the pathogenesis of midfacial,
cranial and cardiac abnormalities
(Scambler et al,
1992), and disruption in neural cell migration may therefore be a
common neuro-developmental mechanism in VCFS, its cognitive profile and
psychosis (Chow et al,
1994). Although the genetic basis for this is not yet understood,
there are several neurodevelopmental candidate genes that map to chromosome
22q11 (Demczuk et al,
1995,
1996;
Wilming et al, 1997;
Yamagishi et al,
1999). Consequently, haplo-insufficiency of one or more
neurodevelopmental candidate genes (possibly by disrupting neural cell
migration) may explain the high prevalence of learning disability and
psychosis seen in people with VCFS. Our findings provide evidence that people
with a 22q11 deletion have disrupted brain development (which may involve
abnormal neural crest cell migration), and this might explain the cognitive
profile, ocular abnormalities and cognitive phenotype of people with a
deletion at 22q11 (Mansour et al,
1987; Kerstjens-Frederikse
et al, 1999).
Disturbances in catecholamine neurotransmission also have been implicated in the aetiology of psychotic disorders, and offer an alternative explanation for the high rates of psychosis in people with VCFS. The gene for catechol-O-methyltransferase (COMT), an enzyme involved in the degradation of dopamine, maps to chromosome 22q11. An amino acid polymorphism (val-108-met) determines high and low activity of this enzyme (Lachman et al, 1996). It has been hypothesised that individuals with 22q11 deletion who are hemizygous for COMT, as well as carrying a low-activity allele on their non-deleted chromosome, may be predisposed to the development of psychosis in VCFS (Dunham et al, 1992). However, we recently found no association between the low-activity COMT allele and the presence of psychosis in people with VCFS (Murphy et al, 1999). Consequently, our findings suggest that the neurobiology for the psychosis observed in VCFS may be more neurodevelopmental than neurochemical in origin.
Limitations and advantages of the study
Our control group consisted of people with borderline intellectual
functioning, our study was relatively small and we carried out multiple
statistical comparisons (there-by increasing the risk of a type I error). It
is not generally agreed as to which is the best control group to
use when studying people with genetically determined neurodevelopmental
disorders. Disadvantages of using people with borderline learning disability
are the relative population heterogeneity, including people with genetically
and environmentally determined causes of cognitive impairment that we did not
detect using our screening techniques, and the fact that they are not
representative of the healthy population. Advantages of asking them to
volunteer as controls include ability to match on IQ (intellectual functioning
is related to brain volume) (Andreasen
et al, 1993; Reiss
et al, 1996; Schaefer
& Bodensteiner, 1999), and to attempt to control for
clinically undetected birth trauma (because people with genetically determined
learning disability also have an increased rate of birth trauma), which
affects brain anatomy and cognitive function
(Stewart et al,
1999). We included controls suffering from similar psychiatric
disorders and there were no significant between-group differences in age, IQ,
gender or SES, so the differences we found in brain anatomy are likely to be
associated with a deletion at chromosome 22q11.
In conclusion, to our knowledge this is the first study to use quantitative MRI in adults with VCFS. Our results, although preliminary, demonstrate that people with a deletion at chromosome 22q11, when compared with people of a similar intellectual level, have differences in brain anatomy affecting: midline structures such as the septum pellucidum and corpus callosum; the cerebellum; widespread areas of white matter; and grey matter in temporal and left frontal regions. These abnormalities most likely reflect abnormal early brain development, and may partially explain the cognitive profile and neuropsychiatric problems seen in people with VCFS. Future, larger studies are planned to investigate how these brain abnormalities are associated with cognitive dysfunction and development of psychosis in VCFS.
![]() |
Clinical Implications and Limitations |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
LIMITATIONS
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Andreasen, N. C., Flaum, M., Swayze, II, V., et al (1993) Intelligence and brain structure in normal individuals. American Journal of Psychiatry, 150, 130-134.[Abstract]
Barta, P. E., Dhingra, L., Royall, R., et al (1997) Efficient estimates for the volume of structures identified in three-dimensional arrays of spatial data. Journal of Neuroscience Methods, 75, 111-118.[CrossRef][Medline]
Bartko, J. J. & Carpenter, W.T. (1986) On the methods and theory of reliability. Journal of Nervous and Mental Disease, 163, 307-317.
Bingham, P. M., Zimmerman, R. A., McDonald-McGinn, D., et al (1997) Enlarged Sylvian fissures in infants with interstitial deletion of chromosome 22q11. American Journal of Medical Genetics (Neuropsychiatric Genetics), 74, 538-543.[Medline]
Brammer, M., Bullmore, E. T., Simmons, A., et al (1997) Generic brain activation mapping in fMRI: a nonparametric approach. Magnetic Resonance Imaging, 15, 763-770.[CrossRef][Medline]
Bullmore, E. T., Brammer, M., Rouleau, G., et al (1995) Computerised brain tissue classification of magnetic resonance images: a new approach to the problem of partial volume artefact. Neuroimage, 2, 133-147.[CrossRef][Medline]
Bullmore, E. T., Suckling, J., Rabe-Hesketh, S., et al (1999) Global, voxel, and cluster tests, by theory and permutation for a difference between two groups of structural MR images of the brain. IEEE Transactions on Medical Imaging, 18, 32-42.[CrossRef][Medline]
Chow, E. W., Bassett, A. S. & Weksberg, R. (1994) Velo-cardio-facial syndrome and psychotic disorders: implications for psychiatric genetics. American Journal of Medical Genetics, 54, 107-112.[Medline]
Chow, E. W., Mikulis, D. J., Zipursky, R. B., et al (1999) Qualitative MRI findings in adults with 22q11 deletion syndrome and schizophrenia. Biological Psychiatry, 46, 1436-1442.[CrossRef][Medline]
Demczuk, S., Aledo, R., Zucman, J., et al (1995) Cloning of a balanced translocation breakpoint in the DiGeorge syndrome critical region and isolation of a novel potential adhesion receptor gene in its vicinity. Human Molecular Genetics, 4, 551-558.[Abstract]
Demczuk, S., Thomas, G. & Aurias, A. (1996)
Isolation of a novel gene from the DiGeorge syndrome critical region with
homology to Drosophila gdl and to human LAMCI genes. Human
Molecular Genetics, 5,
633-638.
Dunham, I., Collins, J., Wadey, R., et al (1992) Possible role for COMT in psychosis associated with velo-cardiofacial syndrome. Lancet, 340, 1361-1362.
Eliez, S., Schmitt, E. J., White, C. D., et al (2000) Children and adolescents with velo-cardio-facial syndrome: a volumetric MRI study. American Journal of Psychiatry, 57, 409-415.
Giedd, J. N., Blumenthal, J., Jeffries, N. O., et al (1999) Brain development during childhood and adolescence: a longitudinal MRI study. Nature Neuroscience, 2, 861-863.[CrossRef][Medline]
Goldberg, R., Motzkin, B., Marion, R., et al (1993) Velo-cardio-facial syndrome: a review of 120 patients. American Journal of Medical Genetics, 45, 313-319.[Medline]
Golding-Kushner, K. J., Weller, G. & Shprintzen, R. J. (1985) Velo-cardio-facial syndrome: language and psychological profiles. Journal of Craniofacial Genetics and Developmental Biology, 5, 259-266.[Medline]
Henry, J. C., van Amelsvoort, T., Morris, R. G., et al (2000) An investigation of the neuropsychological profile in velocardiofacial syndrome. Schizophrenia Research, 41, 95.
Kates, W. R. & Burnette, C. P. (2000) Posterior cortical white matter anomalies in velocardiofacial syndrome. Biological Psychiatry, 47, 1025.[CrossRef][Medline]
Kerstjens-Frederikse, W. S., Hofstra, R. M. W., van Essen, A.
J., et al (1999) A Hirschsprung disease locus at
22q11? Journal of Medical Genetics,
36,
221-224.
Kozachuk, W. E., DeCarli, C., Schapiro, M. B., et al (1990) White matter hyperintensities in dementia of Alzheimer's type and in healthy subjects without cerebrovascular risk factors. A magnetic resonance study. Archives of Neurology, 47, 1306-1310.[Abstract]
Lachman, H. M., Papolos, D. F., Saito, T., et al (1996) Human catechol-O-methyltransferase pharmacogenetics: description of a functional polymorphism and its potential application to neuropsychiatric disorders. Pharmacogenetics, 6, 243-250.[Medline]
Lewis, S. W. & Mezey, G. C. (1985) Clinical correlates of septum pellucidum cavities: an unusual association with psychosis. Psychological Medicine, 15, 43-54.[Medline]
Lynch, D. R., McDonald-McGinn, D. M., Zackai, E. H., et al (1995) Cerebellar atrophy in a patient with velocardiofacial syndrome. Journal of Medical Genetics, 32, 561-563.[Abstract]
Mansour, A. M., Goldberg, R. B., Wang, F. M., et al (1987) Ocular findings in the velo-cardio-facial syndrome. Journal of Pediatric Ophthalmology and Strabismus, 24, 263-266.[Medline]
Martin, P. & Albers, M. (1995) Cerebellum and schizophrenia: a selective review. Schizophrenia Bulletin, 21, 241-250.[Medline]
Mitnick, R. J., Bello, J. A. & Shprintzen, R. J. (1994) Brain anomalies in velo-cardio-facial syndrome. American Journal of Medical Genetics, 54, 100-106.[Medline]
Murphy, D. G. M., DeCarli, C. D., Schapiro, M. B., et al (1992) Age related differences in volumes of subcortical nuclei, brain matter, and cerebrospinal fluid in healthy men as measured with MRI. Archives of Neurology, 49, 839-849.[Abstract]
Murphy, D. G. M., DeCarli, C. D., Daly, E., et al (1993a) x chromosome effects on female brain: a magnetic resonance imaging study of Turner's syndrome. Lancet, 342, 1197-1200.[Medline]
Murphy, D. G. M., DeCarli, C. D., Daly, E., et al (1993b) Volumetric magnetic resonance imaging in men with dementia of Alzheimer type: correlations with disease severity. Biological Psychiatry, 34, 612-621.[CrossRef][Medline]
Murphy, K. C. & Owen, M. J. (1996) Minor physical anomalies and their relationship to the aetiology of schizophrenia. British Journal of Psychiatry, 168, 139-142.[Abstract]
Murphy, K. C., Jones, L. A. & Owen, M. J.
(1999) High rates of schizophrenia in adults with
velo-cardio-facial syndrome. Archives of General
Psychiatry, 56,
940-945.
Papolos, D. F., Faedda, G. L., Veit, S., et al (1996) Bipolar spectrum disorders in patients diagnosed with velo-cardio-facial syndrome: does a hemizygous deletion of chromosome 22q11 result in bipolar affective disorder? American Journal of Psychiatry, 153, 1541-1547.[Abstract]
Pearlson, G. D. & Marsh, L. (1999) Structural brain imaging in schizophrenia: a selective review. Biological Psychiatry, 46, 627-649.[CrossRef][Medline]
Press, W. H., Taukolsky, S. A., Vetterling, W. T., et al (1992) Numerical Recipies in C: The Art of Scientific Computing (2nd edn). Cambridge: Cambridge University Press.
Pulver, A. E., Nestadt, G., Goldberg, R., et al (1994) Psychotic illness in patients diagnosed with velo-cardio-facial syndrome and their relatives. Journal of Nervous and Mental Disease, 182, 476-478.[Medline]
Reiss, A. L., Abrams, M. T., Singer, H. S., et al (1996) Brain development, gender and IQ in children. A volumetric imaging study. Brain, 119, 1763-1774.[Abstract]
Scambler, P. J., Kelly, D., Lindsay, E., et al (1992) Velo-cardio-facial syndrome associated with chromosome 22 deletion encompassing the DiGeorge locus. Lancet, 339, 1138-1139.[CrossRef][Medline]
Schaefer, G. B. & Bodensteiner, J. B. (1999) Developmental anomalies of the brain in mental retardation. International Review of Psychiatry, 11, 47-55.[CrossRef]
Schmahmann, J. D. & Sherman, J. C. (1998) The cerebellar cognitive affective syndrome. Brain, 121, 561-579.[Abstract]
Shprintzen, R. J., Goldberg, R. B., Lewin, M. L., et al (1978) A new syndrome involving cleft palate, cardiac anomalies, typical facies, and learning disabilities: velo-cardio-facial syndrome. Cleft Palate Journal, 15, 56-62.[Medline]
Shprintzen, R. J., Goldberg, R. B., & Golding-Kushner, K. J. (1992) Letter to the editor: late-onset psychosis in the velo-cardio-facial syndrome. American Journal of Medical Genetics, 42, 141-142.[Medline]
Shprintzen, R. J., Morrow, B. & Kucherlapati, R. (1997) Vascular anomalies may explain many of the features in velo-cardio-facial syndrome. American Journal of Human Genetics, 61, 16.[Medline]
Sowell, E. R., Thompson, P. M., Holmes, C. J., et al (1999) In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nature Neuroscience, 2, 859-861.[CrossRef][Medline]
Stewart, A. L., Rifkin, L., Amess, P. N., et al (1999) Brain structure and neurocognitive and behavioural function in adolescents who were born very preterm. Lancet, 353, 1653-1657.[CrossRef][Medline]
Suckling, J., Brammer, M. J., Lingford-Hughes, A., et al (1998) Removal of extracerebral tissues in dual-echo magnetic resonance images via linear scalesspace features. Magnetic Resonance Imaging, 17, 247-256.[CrossRef]
Suckling, J., Sigmundsson, T., Greenwood, K., et al (1999) A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images. Magnetic Resonance Imaging, 17, 1065-1076.[CrossRef][Medline]
Swillen, A., Devriendt, K., Legius, E., et al (1997) Intelligence and psychosocial adjustments in velocardiofacial syndrome: a study of 37 children and adolescents with VCFS. Journal of Medical Genetics, 34, 453-458.[Abstract]
Talairach, J. & Tournoux, P. (1988) Co-planar Stereotaxic Atlas of the Human Brain. New York: Thieme Medical.
Usiskin, S., Nicolson, R., Krasnewich, D., et al (1999) Velocardiofacial syndrome and childhood-onset schizophrenia. Biological Psychiatry, 45, 143S.
Vataja, R. & Elomaa, E. (1998) Midline brain anomalies and schizophrenia in people with CATCH 22 syndrome. British Journal of Psychiatry, 172, 518-520.[Abstract]
Wechsler, D. (1987) Wechsler Adult Intelligence Scale Revised. New York: Psychological Corporation & Harcourt Brace.
Wilming, L. C., Snoeren, C. A. S., van Rijswijk, A., et
al (1997) The murine homologue of HIRA, a DiGeorge
syndrome candidate gene, is expressed in embryonic structures affected in
human CATCH 22 patients. Human Molecular Genetics,
6, 247-258.
Wing, J. K., Babor, T., Brugha, T., et al (1990) Schedule for Clinical Assessment in Neuropsychiatry. Archives of General Psychiatry, 47, 589-593.[Abstract]
Yamagishi, H., Garg, V., Matsuoka, R., et al
(1999) A molecular pathway revealing a genetic basis for
human cardiac and craniofacial defects. Science,
283,
1158-1161.
Received for publication August 7, 2000. Accepted for publication October 25, 2000.
Related articles in BJP: