Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype for Schizophrenia and Autism Spectrum Disorders

Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype for Schizophrenia and Autism Spectrum Disorders

  • Aristotle N. Voineskos, 
  • Tristram A. P. Lett, 
  • Jason P. Lerch, 
  • Arun K. Tiwari, 
  • Stephanie H. Ameis, 
  • Tarek K. Rajji, 
  • Daniel J. Müller, 
  • Benoit H. Mulsant, 
  • James L. Kennedy
PLOS
x

Abstract

Background

Structural variation in the neurexin-1 (NRXN1) gene increases risk for both autism spectrum disorders (ASD) and schizophrenia. However, the manner in which NRXN1 gene variation may be related to brain morphology to confer risk for ASD or schizophrenia is unknown.

Method/Principal Findings

53 healthy individuals between 18–59 years of age were genotyped at 11 single nucleotide polymorphisms of the NRXN1 gene. All subjects received structural MRI scans, which were processed to determine cortical gray and white matter lobar volumes, and volumes of striatal and thalamic structures. Each subject's sensorimotor function was also assessed. The general linear model was used to calculate the influence of genetic variation on neural and cognitive phenotypes. Finally, in silico analysis was conducted to assess potential functional relevance of any polymorphisms associated with brain measures. A polymorphism located in the 3′ untranslated region of NRXN1 significantly influenced white matter volumes in whole brain and frontal lobes after correcting for total brain volume, age and multiple comparisons. Follow-up in silico analysis revealed that this SNP is a putative microRNA binding site that may be of functional significance in regulating NRXN1 expression. This variant also influenced sensorimotor performance, a neurocognitive function impaired in both ASD and schizophrenia.

Conclusions

Our findings demonstrate that the NRXN1 gene, a vulnerability gene for SCZ and ASD, influences brain structure and cognitive function susceptible in both disorders. In conjunction with our in silico results, our findings provide evidence for a neural and cognitive susceptibility mechanism by which the NRXN1 gene confers risk for both schizophrenia and ASD.

Introduction

Autism Spectrum Disorders (ASDs) and schizophrenia are highly heritable disorders with genetic factors comprising the majority of the known risk [1]. Currently, the gene with the best evidence for shared susceptibility for schizophrenia and ASD is the Neurexin-1 (NRXN1) gene, one of the largest known human genes (1.1 Mb) with 24 exons, located on chromosome 2p16.3 [2]. The NRXN1 gene encodes the neurexin-1α and neurexin-1β proteins that function as pre-synaptic neural adhesion molecules. Neurexin-1α is reported to interact with postsynaptic neuroligins (NLGNs) mediating GABAergic and glutamatergic synapse function [2]. It also has been reported to bind to leucine-rich repeat transmembrane protein (LRRTM2) [3], instructing presynaptic and mediating postsynaptic differentiation of glutamatergic synapses. Substantial evidence implicates deletions in the NRXN1 gene in ASD [4][10] and schizophrenia [11][17]. NRXN1 has also been associated with mental retardation [18], [19], nicotine dependence [20][22], alcoholism [23] and vertebral anomalies [24]. Therefore, it is apparent that disruptions of the NRXN1 gene, especially deletions, confer risk to a range of neurodevelopmental phenotypes, including ASDs, schizophrenia, and mental retardation.

The results of neuroimaging studies suggest that schizophrenia and ASD patients also share neural vulnerability, most notably in the frontal lobe and in frontal lobe circuitry [25], [26]. Therefore, genes that confer susceptibility to both schizophrenia and ASD might contribute to altered brain structure and/or function common to both disorders. Although few studies have included both ASD and schizophrenia patients, overlapping findings between these illnesses occur most prominently in the frontal lobe and in fronto-striatal circuitry [25], [26]. Grey and white matter in ASD has been associated with increased cortical grey to white matter ratio and decreased volumes beyond childhood [27], [28]. Although both increases and decreases in grey and white matter volumes in ASD have been reported, white matter abnormalities in the frontal lobe remain some of the most consistent neuroimaging findings in ASD [29][36]. Thus, developmental abnormalities in white matter growth seems important in the etioneuropathology of ASD [37]. Structural MRI findings in schizophrenia populations are typically characterized by decreases in temporal and frontal lobe volumes, and some reductions in total brain volume and parietal volumes [38], [39]. Although findings have not always been consistent, a recent meta-analysis of 17 studies confirmed a frontal lobe white matter deficit in patients with schizophrenia [40]. Furthermore, cytoarchitectural alterations of the prefrontal cortex have been found in schizophrenia, and decreased thalamic volume and altered prefrontal-thalamic circuitry are common findings in this disorder [41][47]. Altogether, these findings suggest abnormalities of frontal, thalamic, and striatal structure that may be shared in the neuropathology of schizophrenia and ASD. Neurocognitively, sensorimotor deficits are shared by both disorders. Such deficits are typically apparent in ASD patients [48]. Cognitive assessment [49] and birth cohort studies [50] also identify impaired sensorimotor function in schizophrenia.

The intermediate phenotype approach permits us to examine how shared genetic underpinnings of these two disorders may confer risk in the brain [51][53]. Therefore, we used this approach to investigate 11 single nucleotide polymorphisms (SNPs) of the NRXN1 gene lying within regions overlapped by numerous deletions implicated in ASD and schizophrenia, and their effects on brain morphometry in healthy individuals. Given that such deletions confer susceptibility to both schizophrenia and ASD, we hypothesized that NRXN1 polymorphisms would confer an intermediate phenotype related to schizophrenia and ASD, via effects on neural structures and cognitive function altered in both disorders.

Results

Genotypes

Concordance for the 10% of re-genotyping of all 11 SNPs (Figure 1) was 100%. No SNP deviated significantly from Hardy-Weinberg equilibrium. Four SNPs (rs10208208, rs12623467, rs10490162, 10490227) were not included in further analysis since their minor allele frequency (MAF) was below 15% (Table S1). Furthermore, none of the SNPs was in linkage disequilibrium (LD) (not shown), and their MAF was similar to the Hapmap CEU population [54]. For rs1045881 since only one TT homozygote was in the sample, we combined T-allele carriers (T/T and T/C) and collectively analyzed in one cell. Post hoc independent t-tests of rs1045881 genotype (T-Carriers vs. C/C) revealed no significant differences in any demographics measured (Table S2).

thumbnail
Figure 1. Reported Deletions in the Neurexin-1α gene.

Figure contains the location of gene, markers, and reported deletion in: developmental disorders (green; Ching et al. [18]), schizophrenia (red; Rujescu et al. [13], Vrijenhoek et al. [14], Magri et al. [96], Ikeda et al. [15], Need et al. [12]), and autism spectrum disorders (blue; Pinto et al. [97], Glessner et al. [17]. The Autism Chromosome Rearrangement Database [6]). Figure adapted from the UCSC genome browser (GRCh37/hg19 assembly) [98].

https://doi.org/10.1371/journal.pone.0020982.g001

For lobar gray matter volumes, no genotype by brain region interactions or main effects of genotype were found following repeated measure ANCOVAs conducted for each of the seven SNPs with MAF>15%, with age and total brain volume as covariates. Therefore, no follow-up analysis was performed. When examining white matter volumes, we found that for each lobe, a minimum of 85% of the variance in one hemisphere was explained by the white matter volume of the other hemisphere (P<0.001, R2(Pearson)>0.85); therefore, we combined lobar white matter volumes across hemispheres. For lobar white matter volumes, a genotype by white matter lobe volume interaction was found following repeated measures ANCOVA, at the rs1045881 (F2.25 = 5.498, p = 0.004) and rs858932 (F4.56 = 3.802, p = 0.004) polymorphisms (Bonferroni corrected alpha of 0.0071). We did not observe significant white matter region volume by genotype interactions in any other NRXN1 variants examined. The results for the rs1045881 and rs858932 SNPs were followed up using separate ANCOVAs for white matter volume at each lobe. The rs1045881 variant was significantly associated with frontal lobe white matter volume (Bonferroni corrected alpha = 0.0125 for four brain regions): F1,49 = 8.231, p = 0.006; (Figure 2), where ‘CC’ homozygotes demonstrated reduced frontal white matter volumes compared to ‘T’ allele carriers. Consistent with the direction of effect in frontal lobe, the rs1045881 was nominally associated (as it did not survive Bonferroni correction) with change in parietal lobe white matter volume (F1,49 = 4.089, p = 0.049). No association of this SNP with temporal or occipital lobe white matter volume was observed.

thumbnail
Figure 2. The effect of rs1045881 on combined hemispheric volume of brain regions with total brain volume (TBV) and age as covariates.

Brain regions: (A) Frontal Lobe, (B) Temporal Lobe, (C) Occipital Lobe, and (D) Parietal Lobe. Frontal lobe white matter volume was significantly greater in T allele carriers (T/T +T/C) (ANCOVA F1,52 = 8.197, p = 0.006), while other regions are non-significant after correcting for multiple comparisons. Covariates appearing in the model are evaluated at the following values: TBV = 1364768.17, Age = 39.04, (*) denotes significance of P<0.0125. Error bars represent +/− standard error of the marginal means and percentages reflect the percent change in each brain region.

https://doi.org/10.1371/journal.pone.0020982.g002

The follow-up ANCOVA examining rs858932 genotype also predicted frontal lobe white matter volume (F2,51 = 5.472, p = 0.007), where ‘GG’ individuals had lower frontal lobe white matter volume and nominal association in the parietal lobe also occurred in the same direction, but did not survive Bonferroni correction (F48,2 = 3.719, p = 0.032; Figure S1). Frontal lobe white matter volumes were also associated at the allelic level: both the ‘C’ allele of rs1045881 (χ2 = 7.184, p = 0.0074) and the ‘G’ allele of rs858932 (χ2 = 4.121, p = 0.0432) predicted lower frontal white matter volume (Table S3). Similar results were shown in the haplotype analysis (p(Global)<0.001; Table S4).

Repeated measures analysis for striatal and thalamic structures revealed a significant volume by region interaction for the rs858932 SNP only (F14,336 = 3.4, p<0.001; Greenhouse-Geiser correction: F4,99 = 3.4, p = 0.01). Follow-up ANCOVAs at left and right caudate, putamen, globus pallidus, and thalamus revealed that this interaction was driven by the influence of the rs858932 SNP on thalamic volumes only: for left thalamus (F2,48 = 8.9, p = 0.001), and for right thalamus (F2,48 = 7.3, p = 0.002), significant at the Bonferroni corrected alpha for eight comparisons (alpha = 0.0063, Figure 3). Here, ‘GG’ individuals had significantly lower thalamic volumes compared to ‘T’ allele carriers. No significant effects were observed at caudate, putamen, or globus pallidus.

thumbnail
Figure 3. The effect of rs858932 on right and left thalamic volume with TBV and age as covariates.

There are approximately 10% and 9% percent differences between the G/G to G/C and G/G and C/C genotypes for both thalamic hemispheres, respectively. Covariates appearing in the model are evaluated at the following values: TBV = 1364768.17, Age = 39.04, (*) denotes significance of P<0.0063. Error bars represent +/− standard error of the marginal means.

https://doi.org/10.1371/journal.pone.0020982.g003

Cognitive

Repeated measures ANCOVA showed a main effect of the rs1045881 SNP on sensorimotor function (F1,49 = 4.8, p = 0.03). The ‘C/C’ homozygotes had reduced finger tapping scores compared to ‘T’ allele carriers, consistent with the directional effect on white matter volumes. No association was observed for the rs858932 SNP (F1,48 = 0.4, p = 0.67). No task by genotype interaction was observed for either polymorphism.

Frontal lobe white matter volume was highly correlated with finger tapping (FT) score even after accounting for age effects (Dominant Hand: R2 = 0.404, p = 0.003; Non-Dominant Hand: R2 = 0.469, p = 0.001).

In silico analysis

The rs1045881 SNP is located in the 3′UTR of Neurexin-1. In silico prediction by miRBase analysis revealed the presence of the C-allele creates a binding site for the microRNA hsa-miR-1274a and hsa-miR-339-5p. Furthermore, alteration in exon splicing enhancer and other motifs were observed. The rs858932 SNP was not sufficiently near any splice site (i.e. intron/exon border) for in silico prediction.

Discussion

We found that genetic variation in the 3′untranslated region of the NRXN1 gene predicted an intermediate risk phenotype in healthy individuals relevant to schizophrenia and ASD. Our primary finding at the rs1045881 SNP in the 3′UTR of Neurexin1 demonstrated that the ‘C’ allele predicts reduced frontal white matter volume and sensorimotor function. Furthermore, our in silico analysis suggested presence of the same ‘C’ allele predicted microRNA binding, thus providing a potential mechanism for this allele's effects on brain structure and cognitive function. The gene variants that influenced brain morphology in our study are located in the regions of NRXN1 susceptible to deletion in schizophrenia and ASD. The effects of these genetic variants localized to brain structure and cognitive function that demonstrate overlapping susceptibility in both schizophrenia and ASD, namely frontal lobe white matter abnormalities, as shown in recent meta-analyses [40], [55] and sensorimotor function [56][59]. To our knowledge, this work provides the first evidence in vivo of how variation in the NRXN1 gene may confer a potential neural risk mechanism for schizophrenia and ASD.

Schizophrenia and ASD patients share sensorimotor deficits and soft neurological signs [60]. Such shared deficits are almost certainly neurodevelopmental in nature, as in ASD they present at a very early age, and when present in schizophrenia, they are often present before illness onset. White matter, likely through myelination, plays a key role in ensuring appropriate sensorimotor development, and motor tasks and motor speed are tightly correlated with white matter indices on MRI [61], [62]. Our finding correlating white matter volumes with sensorimotor performance is consistent with previous investigations [33], [63], [64]. Moreover, the same NRXN1 allele that predicted microRNA binding (and thus presumably increased enzymatic breakdown of NRXN1 mRNA and reduced NRXN1 translation) also correlates with reduced white matter volumes and altered sensorimotor function.

Our second finding was that the intronic rs858932 SNP, also located in a deletion site [13], [14], [18], similarly influenced frontal lobe white matter volume, but also prominently influenced left and right thalamic volumes. We consider this finding more preliminary due to the lower minor allele frequency at this variant in our sample. Nevertheless, association of this variant with thalamic volumes is consistent with overlapping neural vulnerability for ASD and schizophrenia as well [39], [65], and suggests that the NRXN1 gene may influence thalamocortical circuitry that is vulnerable in both disorders.

Little is known about how specific types of deletions within the NRXN1 gene may relate to a given neuropsychiatric phenotype. Our in silico analysis demonstrated the 3′UTR SNP as a putative microRNA binding site for hsa-miR-339 and hsa-miR-1274, thus suggesting a functional role for this region of the gene that may relate to mRNA expression of NRXN1. This is interesting since expression of miR-339 microRNA has been reported to be dysregulated in the cortex of psychotic patients [66]. Reduced NRXN1 mRNA may influence white matter alterations by concomitant reductions in binding to the NRXN1 binding partner, LRRTM2, which mediates postsynaptic differentiation of glutamatergic synapses [3], [67], [68]. Glutamatergic dysfunction is well established in schizophrenia [69]; further, NXRN1 expression is induced by AMPA receptors, and mediates recruitment of NMDA receptors, a hallmark of synapse maturation [70]. Glutamatergic dysfunction can also lead to white matter abnormalities. Oligodendrocytes possess glutamatergic receptors (both AMPA and NMDA), and are highly sensitive to any form of stress or toxicity [71]. Therefore, NRXN1 may influence frontal white matter in schizophrenia and ASD through disrupted interaction with its glutamatergically-related binding partners, or possibly via direct glutamatergic involvement as the NRXN1 knock out mouse demonstrates decreased excitatory synaptic strength and decreased prepulse inhibition [72].

Recent imaging-genetics studies [73], [74] have implicated a neurexin superfamily member, the contactin-associated protein-like 2 (CNTNAP2) gene in brain structure and function providing evidence for neural susceptibility patterns relevant to ASD. These studies demonstrated volumetric reductions for CNTNAP2 risk allele carriers particularly in frontal lobe [73], [74] and also showed altered frontal connectivity. One of these two studies [74] demonstrated strong effects with sample sizes smaller than ours. Our findings, in conjunction with the recent imaging-genetics findings of CNTNAP2 demonstrate the value of examining common variants within known ASD risk genes to understand neural susceptibility mechanisms conferred by these risk genes. The ‘added-value’ of this approach lies in the neural localization of gene effects, providing information regarding how the genes may confer brain risk patterns for these disorders.

There are several limitations in our study that should be considered. First, we imposed a dominant model by combining genotypic groups C/T and T/T at rs1045881; however concern regarding this model can be mitigated by our findings that allelic association analysis supported such a model. Second, one could argue that our finding may constitute a ‘winner's curse’, and therefore we would encourage replication efforts. A third limitation of our study is that while there was a clear effect of this putative risk variant on frontal lobe white matter volume, in a direction consistent with cognitive function findings and in silico prediction, various MRI studies have reported either reductions or increases in frontal lobe white matter for both populations. Finally, given that we measured gray and white matter volumes for cortical lobar structures, we were somewhat limited in obtaining more localized regional specificity for effects of NRXN1 variation. More detailed parcellation, white matter voxel-based morphometry, or other white matter imaging techniques such as diffusion tensor imaging, magnetization transfer imaging, or T2 techniques should help clarify further the manner in which NRXN1 influences frontal white matter.

In summary, we found that variants within the NRXN1 gene influence brain morphometry with a susceptibility pattern relevant to both schizophrenia and ASD. This finding is consistent with the fact that NRXN1 is a vulnerability gene for both disorders. In addition to reporting that the rs1045881 gene variant is associated with frontal white matter volume and sensorimotor performance, we provide a putative mechanistic explanation for its effects in the brain. Taken together, our findings provide evidence that genetic variation in NRXN1, a risk gene for schizophrenia and ASD, may confer neural and cognitive susceptibility common to both disorders.

Materials and Methods

Participants

Fifty-three healthy volunteers (15 women, 38 men) (Table 1) met the following eligibility criteria: age between 18 and 59; right handedness; absence of any history of a mental disorder, current substance abuse or a history of substance dependence, positive urine toxicology, history of head trauma with loss of consciousness, seizure, or another neurological disorder; no first degree relative with a history of psychotic mental disorder. All participants were assessed with the Edinburgh handedness inventory [75] for handedness, Wechsler Test for Adult Reading (WTAR) for IQ, and Hollingshead index for socio-economic status [76]. They were interviewed by a psychiatrist, and completed the Structured Clinical Interview for DSM-IV Disorders [77]. They also completed a urine toxicology screen. The study was approved by the Research Ethics Board of the Centre for Addiction and Mental Health (Toronto, Canada) and all participants provided informed, written consent.

Neuroimaging

High resolution magnetic resonance images were acquired as part of a multi-modal imaging protocol using an eight-channel head coil on a 1.5 Tesla GE Echospeed system (General Electric Medical Systems, Milwaukee, WI), which permits maximum gradient amplitudes of 40 mT/m. Axial inversion recovery prepared spoiled gradient recall images were acquired: echo time (TE) = 5.3, repetition time (TR) = 12.3, time to inversion (TI) = 300, flip angle = 20, number of excitations (NEX) = 1 (124 contiguous images, 1.5 mm thickness).

Image Processing

Each subject's T1 image was submitted to the CIVET pipeline (version 1.1.7) developed at the Montreal Neurological Institute [78]. The processing steps included registration to the symmetric ICBM 152 template [79] with a 12-parameter linear transformation [80], correction for inhomogeneity artifact [81], skull stripping [82], tissue classification into white and grey matter, cerebrospinal fluid and background [83], [84] and neuroanatomical segmentation using ANIMAL [85]. Total volumes for each cortical lobe and subcortical structures were estimated for each individuals by non-linearly warping each T1 image towards a segmented atlas [86]. Volume (mL) was extracted from each of these regions using the RMINC package (version 0.4) for reading and analyzing MINC2 output files. Total gray matter, white matter, and CSF volumes were calculated, along with lobar cortical gray and white matter volumes (i.e., left and right frontal, temporal, parietal, occipital), along with volumes of subcortical structures related to the fronto-striato-thalamic loop implicated in both schizophrenia and ASD including left and right caudate, putamen, globus pallidus, and thalamus.

Genetics

Genomic data was extracted from ethylenediametetraaecidic acid (EDTA) anticoagulated venous blood according to standard procedures. Eleven SNPs were genotyped on an Applied Biosystems ABI 7500 Real-Time PCR system, using Taqman 5′ nuclease assay. Genotyping accuracy was assessed by running 10% of the sample in duplicate. Eleven SNPs were selected across the NRXN1 gene (NC_000002.11). Each marker is located in reported regions within which multiple rare deletions associated with ASD and schizophrenia (Figure 1, Table S5).

The program Haploview 4.2 [87] was used to determine pair-wise LD between all SNPs with blocks determined by the Gabriel et al. method [88]. Haploview 4.2 was also used to determine whether SNPs were in Hardy Weinberg equilibrium.

Cognitive assessment

Fifty-two of the study participants completed cognitive testing that included the finger-tapping test [89][91]. Although cognitive deficits in ASD are not as well-characterized as those in schizophrenia, sensorimotor function is disrupted in both disorders [92][95]. Therefore, we used the finger-tapping test to assess sensorimotor function.

Statistical Analysis

Statistical analysis was performed using SPSS for Windows 15.0. To test for effects of NRXN1 genotype on brain morphometry, three separate repeated measures ANCOVA (for cortical lobar gray matter, cortical lobar white matter, and subcortical structures) were performed with genotype as the between group factor, brain region volume as the within group factor, and age and total brain volume (TBV) as covariates. To ensure adequate power, only markers with a minor allele frequency (MAF) greater than 15% were tested. We used a Bonferroni correction based on multiple comparisons of 7 SNPs to determine significance (alpha = 0.0071). Where the repeated measures ANCOVA revealed a significant volume by genotype interaction, follow-up ANCOVAs were performed and Bonferroni correction applied. When significant effect of a genotype on brain volume was found, UNPHASED 3.1 was then used to examine allelic association with brain phenotypes. Haplotype quantitative analysis of frontal lobe white matter volume and the rs1045881 and rs858932 NRXN1 variants were calculated using haplotype score (Methods S1). Finally, for those genotypes that significantly predicted brain measures, repeated measures ANCOVA for sensorimotor function was performed (dominant and nondominant finger-tapping scores as within group measures) with age as covariate. For any gene variant that predicted both brain measures and cognitive performance, the relationship between that brain measure and cognitive performance was examined using a linear regression model, accounting for age effects.

In Silico Analysis

In order to enhance the understanding of the biological meaningfulness of the genetic associations, we used in silico methods to predict potential function of the SNPs investigated in this study. Depending on their location, SNPs were assessed for alteration in transcription factor binding using MatInspector (Genomatix; promoter and intron 1). Presence of splicing enhancers, repressors or intronic regulatory elements (intronic and exonic, synonymous and nonsynonymous SNPs) were determined using F-SNP (http://compbio.cs.queensu.ca/F-SNP) and Human Splicing Finder (http://www.umd.be/HSF/). 3′UTR SNPs were also assessed for alteration in microRNA binding sites (http://www.targetscan.org/).

Supporting Information

Figure S1.

The effect of rs858932 on combined hemispheric volume of brain regions with TBV and age as covariates. Brain regions: (A) Frontal Lobe, (B) Temporal Lobe, (C) Occipital Lobe, and (D) Parietal Lobe. Frontal and parietal lobe white matter volumes were significantly greater in G allele carriers (T/T +T/C) (ANCOVA F2,52 = 7.074, p = 0.002; ANCOVA F2,52 = 5.724, p = 0.006). Other region are non-significant after correcting for multiple comparisons. Covariates appearing in the model are evaluated at the following values: TBV = 1364768.17, Age = 39.04, (*) denotes significance of P<0.0125. Error bars represent +/− standard error of the marginal means and percentages reflect the percent change in each brain region.

https://doi.org/10.1371/journal.pone.0020982.s001

(TIFF)

Table S1.

Locations and Minor Allele Frequency in Toronto and Hapmap (CEU) Samples.

https://doi.org/10.1371/journal.pone.0020982.s002

(DOC)

Table S2.

T-test between rs1045881 T-Carriers Vs C/C and Demographics.

https://doi.org/10.1371/journal.pone.0020982.s003

(DOC)

Table S3.

Chi-squared Tests of Region by Genotype or Allele Interactions of rs1045881 and rs858932. Analysis was performed by Unphased 3.1 with total brain volume and age as confounding factors.

https://doi.org/10.1371/journal.pone.0020982.s004

(DOC)

Table S4.

Haplotype Association between Frontal Lobe White Matter and rs1045881 (T/C) and rs858932 (G/C).

https://doi.org/10.1371/journal.pone.0020982.s005

(DOC)

Table S5.

Reported deletions within NRXN1 in Developmental Disorders, Schizophrenia and Autism Spectrum Disorders.

https://doi.org/10.1371/journal.pone.0020982.s006

(DOC)

Acknowledgments

The authors would like to thank Dielle Miranda for her help with this study. Dr. A. Voineskos and Mr. T. Lett had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Author Contributions

Conceived and designed the experiments: ANV TAPL AKT BHM JLK. Performed the experiments: ANV TAPL JPL. Analyzed the data: ANV TAPL JPL AKT. Contributed reagents/materials/analysis tools: BHM JLK. Wrote the paper: ANV TAPL JPL AKT SHA TKR BHM JLK.

References

  1. 1. Carroll LS, Owen MJ (2009) Genetic overlap between autism, schizophrenia and bipolar disorder. Genome Medicine 1: 102.LS CarrollMJ Owen2009Genetic overlap between autism, schizophrenia and bipolar disorder.Genome Medicine1102
  2. 2. Südhof TC (2008) Neuroligins and neurexins link synaptic function to cognitive disease. Nature 455: 903–911.TC Südhof2008Neuroligins and neurexins link synaptic function to cognitive disease.Nature455903911
  3. 3. de Wit J, Sylwestrak E, O'Sullivan ML, Otto S, Tiglio K, et al. (2009) LRRTM2 interacts with Neurexin1 and regulates excitatory synapse formation. Neuron 64: 799–806.J. de WitE. SylwestrakML O'SullivanS. OttoK. Tiglio2009LRRTM2 interacts with Neurexin1 and regulates excitatory synapse formation.Neuron64799806
  4. 4. Kim H-G, Kishikawa S, Higgins AW, Seong I-S, Donovan DJ, et al. (2008) Disruption of neurexin 1 associated with autism spectrum disorder. American Journal of Human Genetics 82: 199–207.H-G KimS. KishikawaAW HigginsI-S SeongDJ Donovan2008Disruption of neurexin 1 associated with autism spectrum disorder.American Journal of Human Genetics82199207
  5. 5. Glessner JT, Hakonarson H (2009) Common variants in polygenic schizophrenia. Genome Biology 10: 236.JT GlessnerH. Hakonarson2009Common variants in polygenic schizophrenia.Genome Biology10236
  6. 6. Marshall CR, Noor A, Vincent JB, Lionel AC, Feuk L, et al. (2008) Structural variation of chromosomes in autism spectrum disorder. American Journal of Human Genetics 82: 477–488.CR MarshallA. NoorJB VincentAC LionelL. Feuk2008Structural variation of chromosomes in autism spectrum disorder.American Journal of Human Genetics82477488
  7. 7. Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, Brian J, et al. (2007) Mapping autism risk loci using genetic linkage and chromosomal rearrangements. Nature Genetics 39: 319–328.P. SzatmariAD PatersonL. ZwaigenbaumW. RobertsJ. Brian2007Mapping autism risk loci using genetic linkage and chromosomal rearrangements.Nature Genetics39319328
  8. 8. Morrow EM, Yoo S-Y, Flavell SW, Kim T-K, Lin Y, et al. (2008) Identifying autism loci and genes by tracing recent shared ancestry. Science (New York, NY) 321: 218–223.EM MorrowS-Y YooSW FlavellT-K KimY. Lin2008Identifying autism loci and genes by tracing recent shared ancestry.Science (New York, NY)321218223
  9. 9. Feng J, Schroer R, Yan J, Song W, Yang C, et al. (2006) High frequency of neurexin 1beta signal peptide structural variants in patients with autism. Neuroscience Letters 409: 10–13.J. FengR. SchroerJ. YanW. SongC. Yang2006High frequency of neurexin 1beta signal peptide structural variants in patients with autism.Neuroscience Letters4091013
  10. 10. Yan J, Noltner K, Feng J, Li W, Schroer R, et al. (2008) Neurexin 1alpha structural variants associated with autism. Neuroscience Letters 438: 368–370.J. YanK. NoltnerJ. FengW. LiR. Schroer2008Neurexin 1alpha structural variants associated with autism.Neuroscience Letters438368370
  11. 11. Kirov G, Rujescu D, Ingason A, Collier DA, O'Donovan MC, et al. (2009) Neurexin 1 (NRXN1) deletions in schizophrenia. Schizophrenia Bulletin 35: 851–854.G. KirovD. RujescuA. IngasonDA CollierMC O'Donovan2009Neurexin 1 (NRXN1) deletions in schizophrenia.Schizophrenia Bulletin35851854
  12. 12. Need AC, Ge D, Weale ME, Maia J, Feng S, et al. (2009) A genome-wide investigation of SNPs and CNVs in schizophrenia. PLoS Genetics 5: e1000373.AC NeedD. GeME WealeJ. MaiaS. Feng2009A genome-wide investigation of SNPs and CNVs in schizophrenia.PLoS Genetics5e1000373
  13. 13. Rujescu D, Ingason A, Cichon S, Pietiläinen OPH, Barnes MR, et al. (2009) Disruption of the neurexin 1 gene is associated with schizophrenia. Human Molecular Genetics 18: 988–996.D. RujescuA. IngasonS. CichonOPH PietiläinenMR Barnes2009Disruption of the neurexin 1 gene is associated with schizophrenia.Human Molecular Genetics18988996
  14. 14. Vrijenhoek T, Buizer-Voskamp JE, van der Stelt I, Strengman E, Sabatti C, et al. (2008) Recurrent CNVs disrupt three candidate genes in schizophrenia patients. American Journal of Human Genetics 83: 504–510.T. VrijenhoekJE Buizer-VoskampI. van der SteltE. StrengmanC. Sabatti2008Recurrent CNVs disrupt three candidate genes in schizophrenia patients.American Journal of Human Genetics83504510
  15. 15. Ikeda M, Aleksic B, Kirov G, Kinoshita Y, Yamanouchi Y, et al. (2010) Copy number variation in schizophrenia in the Japanese population. Biological Psychiatry 67: 283–286.M. IkedaB. AleksicG. KirovY. KinoshitaY. Yamanouchi2010Copy number variation in schizophrenia in the Japanese population.Biological Psychiatry67283286
  16. 16. Shah AK, Tioleco NM, Nolan K, Locker J, Groh K, et al. (2010) Rare NRXN1 promoter variants in patients with schizophrenia. Neuroscience Letters 475: 80–84.AK ShahNM TiolecoK. NolanJ. LockerK. Groh2010Rare NRXN1 promoter variants in patients with schizophrenia.Neuroscience Letters4758084
  17. 17. Glessner JT, Wang K, Cai G, Korvatska O, Kim CE, et al. (2009) Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459: 569–573.JT GlessnerK. WangG. CaiO. KorvatskaCE Kim2009Autism genome-wide copy number variation reveals ubiquitin and neuronal genes.Nature459569573
  18. 18. Ching MSL, Shen Y, Tan W-H, Jeste SS, Morrow EM, et al. (2010) Deletions of NRXN1 (neurexin-1) predispose to a wide spectrum of developmental disorders. American Journal of Medical Genetics Part B, Neuropsychiatric Genetics: The Official Publication of the International Society of Psychiatric Genetics 153B: 937–947.MSL ChingY. ShenW-H TanSS JesteEM Morrow2010Deletions of NRXN1 (neurexin-1) predispose to a wide spectrum of developmental disorders.American Journal of Medical Genetics Part B, Neuropsychiatric Genetics: The Official Publication of the International Society of Psychiatric Genetics153B937947
  19. 19. Zweier C, de Jong EK, Zweier M, Orrico A, Ousager LB, et al. (2009) CNTNAP2 and NRXN1 are mutated in autosomal-recessive Pitt-Hopkins-like mental retardation and determine the level of a common synaptic protein in Drosophila. American Journal of Human Genetics 85: 655–666.C. ZweierEK de JongM. ZweierA. OrricoLB Ousager2009CNTNAP2 and NRXN1 are mutated in autosomal-recessive Pitt-Hopkins-like mental retardation and determine the level of a common synaptic protein in Drosophila.American Journal of Human Genetics85655666
  20. 20. Nussbaum J, Xu Q, Payne TJ, Ma JZ, Huang W, et al. (2008) Significant association of the neurexin-1 gene (NRXN1) with nicotine dependence in European- and African-American smokers. Human Molecular Genetics 17: 1569–1577.J. NussbaumQ. XuTJ PayneJZ MaW. Huang2008Significant association of the neurexin-1 gene (NRXN1) with nicotine dependence in European- and African-American smokers.Human Molecular Genetics1715691577
  21. 21. Bierut LJ, Madden PAF, Breslau N, Johnson EO, Hatsukami D, et al. (2007) Novel genes identified in a high-density genome wide association study for nicotine dependence. Human Molecular Genetics 16: 24–35.LJ BierutPAF MaddenN. BreslauEO JohnsonD. Hatsukami2007Novel genes identified in a high-density genome wide association study for nicotine dependence.Human Molecular Genetics162435
  22. 22. Novak G, Boukhadra J, Shaikh SA, Kennedy JL, Le Foll B (2009) Association of a polymorphism in the NRXN3 gene with the degree of smoking in schizophrenia: a preliminary study. The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry 10: 929–935.G. NovakJ. BoukhadraSA ShaikhJL KennedyB. Le Foll2009Association of a polymorphism in the NRXN3 gene with the degree of smoking in schizophrenia: a preliminary study.The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry10929935
  23. 23. Yang H-C, Chang C-C, Lin C-Y, Chen C-L, Fann CSJ (2005) A genome-wide scanning and fine mapping study of COGA data. BMC Genetics 6: Suppl 1S30.H-C YangC-C ChangC-Y LinC-L ChenCSJ Fann2005A genome-wide scanning and fine mapping study of COGA data.BMC Genetics6Suppl 1S30
  24. 24. Zahir FR, Baross A, Delaney AD, Eydoux P, Fernandes ND, et al. (2008) A patient with vertebral, cognitive and behavioural abnormalities and a de novo deletion of NRXN1alpha. Journal of Medical Genetics 45: 239–243.FR ZahirA. BarossAD DelaneyP. EydouxND Fernandes2008A patient with vertebral, cognitive and behavioural abnormalities and a de novo deletion of NRXN1alpha.Journal of Medical Genetics45239243
  25. 25. Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A (2010) Dysconnectivity in schizophrenia: Where are we now? Neuroscience and Biobehavioral Reviews. W. Pettersson-YeoP. AllenS. BenettiP. McGuireA. Mechelli2010Dysconnectivity in schizophrenia: Where are we now?Neuroscience and Biobehavioral Reviews
  26. 26. Minshew NJ, Keller TA (2010) The nature of brain dysfunction in autism: functional brain imaging studies. Current Opinion in Neurology 23: 124–130.NJ MinshewTA Keller2010The nature of brain dysfunction in autism: functional brain imaging studies.Current Opinion in Neurology23124130
  27. 27. Acosta MT, Pearl PL (2004) Imaging data in autism: from structure to malfunction. Seminars in Pediatric Neurology 11: 205–213.MT AcostaPL Pearl2004Imaging data in autism: from structure to malfunction.Seminars in Pediatric Neurology11205213
  28. 28. Courchesne E, Karns CM, Davis HR, Ziccardi R, Carper RA, et al. (2001) Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 57: 245–254.E. CourchesneCM KarnsHR DavisR. ZiccardiRA Carper2001Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study.Neurology57245254
  29. 29. Sundaram SK, Kumar A, Makki MI, Behen ME, Chugani HT, et al. (2008) Diffusion tensor imaging of frontal lobe in autism spectrum disorder. Cerebral Cortex (New York, NY: 1991) 18: 2659–2665.SK SundaramA. KumarMI MakkiME BehenHT Chugani2008Diffusion tensor imaging of frontal lobe in autism spectrum disorder.Cerebral Cortex (New York, NY: 1991)1826592665
  30. 30. McAlonan GM, Daly E, Kumari V, Critchley HD, van Amelsvoort T, et al. (2002) Brain anatomy and sensorimotor gating in Asperger's syndrome. Brain: A Journal of Neurology 125: 1594–1606.GM McAlonanE. DalyV. KumariHD CritchleyT. van Amelsvoort2002Brain anatomy and sensorimotor gating in Asperger's syndrome.Brain: A Journal of Neurology12515941606
  31. 31. McAlonan GM, Cheung V, Cheung C, Suckling J, Lam GY, et al. (2005) Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism. Brain: A Journal of Neurology 128: 268–276.GM McAlonanV. CheungC. CheungJ. SucklingGY Lam2005Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism.Brain: A Journal of Neurology128268276
  32. 32. McAlonan GM, Cheung C, Cheung V, Wong N, Suckling J, et al. (2009) Differential effects on white-matter systems in high-functioning autism and Asperger's syndrome. Psychological Medicine 39: 1885–1893.GM McAlonanC. CheungV. CheungN. WongJ. Suckling2009Differential effects on white-matter systems in high-functioning autism and Asperger's syndrome.Psychological Medicine3918851893
  33. 33. Herbert MR, Ziegler DA, Makris N, Filipek PA, Kemper TL, et al. (2004) Localization of white matter volume increase in autism and developmental language disorder. Annals of Neurology 55: 530–540.MR HerbertDA ZieglerN. MakrisPA FilipekTL Kemper2004Localization of white matter volume increase in autism and developmental language disorder.Annals of Neurology55530540
  34. 34. Herbert MR, Ziegler DA, Deutsch CK, O'Brien LM, Lange N, et al. (2003) Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain: A Journal of Neurology 126: 1182–1192.MR HerbertDA ZieglerCK DeutschLM O'BrienN. Lange2003Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys.Brain: A Journal of Neurology12611821192
  35. 35. Barnea-Goraly N, Kwon H, Menon V, Eliez S, Lotspeich L, et al. (2004) White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biological Psychiatry 55: 323–326.N. Barnea-GoralyH. KwonV. MenonS. EliezL. Lotspeich2004White matter structure in autism: preliminary evidence from diffusion tensor imaging.Biological Psychiatry55323326
  36. 36. Mengotti P, D'Agostini S, Terlevic R, De Colle C, Biasizzo E, et al. (2010) Altered white matter integrity and development in children with autism: A combined voxel-based morphometry and diffusion imaging study. Brain Research Bulletin. P. MengottiS. D'AgostiniR. TerlevicC. De ColleE. Biasizzo2010Altered white matter integrity and development in children with autism: A combined voxel-based morphometry and diffusion imaging study.Brain Research Bulletin
  37. 37. Williams DL, Minshew NJ (2007) Understanding autism and related disorders: what has imaging taught us? Neuroimaging Clinics of North America 17: 495–509.ixDL WilliamsNJ Minshew2007Understanding autism and related disorders: what has imaging taught us?Neuroimaging Clinics of North America17495509ix
  38. 38. McCarley RW, Wible CG, Frumin M, Hirayasu Y, Levitt JJ, et al. (1999) MRI anatomy of schizophrenia. Biological Psychiatry 45: 1099–1119.RW McCarleyCG WibleM. FruminY. HirayasuJJ Levitt1999MRI anatomy of schizophrenia.Biological Psychiatry4510991119
  39. 39. Shenton ME, Dickey CC, Frumin M, McCarley RW (2001) A review of MRI findings in schizophrenia. Schizophrenia Research 49: 1–52.ME ShentonCC DickeyM. FruminRW McCarley2001A review of MRI findings in schizophrenia.Schizophrenia Research49152
  40. 40. Di X, Chan RCK, Gong Q-y (2009) White matter reduction in patients with schizophrenia as revealed by voxel-based morphometry: An activation likelihood estimation meta-analysis. Progress in Neuro-Psychopharmacology and Biological Psychiatry 33: 1390–1394.X. DiRCK ChanQ-y Gong2009White matter reduction in patients with schizophrenia as revealed by voxel-based morphometry: An activation likelihood estimation meta-analysis.Progress in Neuro-Psychopharmacology and Biological Psychiatry3313901394
  41. 41. Brickman AM, Buchsbaum MS, Shihabuddin L, Byne W, Newmark RE, et al. (2004) Thalamus size and outcome in schizophrenia. Schizophrenia Research 71: 473–484.AM BrickmanMS BuchsbaumL. ShihabuddinW. ByneRE Newmark2004Thalamus size and outcome in schizophrenia.Schizophrenia Research71473484
  42. 42. Danos P, Baumann B, Krämer A, Bernstein H-G, Stauch R, et al. (2003) Volumes of association thalamic nuclei in schizophrenia: a postmortem study. Schizophrenia Research 60: 141–155.P. DanosB. BaumannA. KrämerH-G BernsteinR. Stauch2003Volumes of association thalamic nuclei in schizophrenia: a postmortem study.Schizophrenia Research60141155
  43. 43. Goldman-Rakic PS, Selemon LD (1997) Functional and Anatomical Aspects of Prefrontal Pathology in Schizophrenia. Schizophrenia Bulletin 23: 437–458.PS Goldman-RakicLD Selemon1997Functional and Anatomical Aspects of Prefrontal Pathology in Schizophrenia.Schizophrenia Bulletin23437458
  44. 44. James AC, James S, Smith DM, Javaloyes A (2004) Cerebellar, Prefrontal Cortex, and Thalamic Volumes Over Two Time Points in Adolescent-Onset Schizophrenia. Am J Psychiatry 161: 1023–1029.AC JamesS. JamesDM SmithA. Javaloyes2004Cerebellar, Prefrontal Cortex, and Thalamic Volumes Over Two Time Points in Adolescent-Onset Schizophrenia.Am J Psychiatry16110231029
  45. 45. Jones EG (1997) Cortical Development and Thalamic Pathology in Schizophrenia. Schizophrenia Bulletin 23: 483–501.EG Jones1997Cortical Development and Thalamic Pathology in Schizophrenia.Schizophrenia Bulletin23483501
  46. 46. McIntosh AM, Job DE, Moorhead TWJ, Harrison LK, Forrester K, et al. (2004) Voxel-based morphometry of patients with schizophrenia or bipolar disorder and their unaffected relatives. Biological Psychiatry 56: 544–552.AM McIntoshDE JobTWJ MoorheadLK HarrisonK. Forrester2004Voxel-based morphometry of patients with schizophrenia or bipolar disorder and their unaffected relatives.Biological Psychiatry56544552
  47. 47. Rose SE, Chalk JB, Janke AL, Strudwick MW, Windus LC, et al. (2006) Evidence of altered prefrontal-thalamic circuitry in schizophrenia: an optimized diffusion MRI study. NeuroImage 32: 16–22.SE RoseJB ChalkAL JankeMW StrudwickLC Windus2006Evidence of altered prefrontal-thalamic circuitry in schizophrenia: an optimized diffusion MRI study.NeuroImage321622
  48. 48. Sigman M, Ungerer J (1981) Sensorimotor skills and language comprehension in autistic children. J Abnorm Child Psychol 9: 149–165.M. SigmanJ. Ungerer1981Sensorimotor skills and language comprehension in autistic children.J Abnorm Child Psychol9149165
  49. 49. Rajji TK, Mulsant BH (2008) Nature and course of cognitive function in late-life schizophrenia: a systematic review. Schizophr Res 102: 122–140.TK RajjiBH Mulsant2008Nature and course of cognitive function in late-life schizophrenia: a systematic review.Schizophr Res102122140
  50. 50. Welham J, Isohanni M, Jones P, McGrath J (2009) The antecedents of schizophrenia: a review of birth cohort studies. Schizophr Bull 35: 603–623.J. WelhamM. IsohanniP. JonesJ. McGrath2009The antecedents of schizophrenia: a review of birth cohort studies.Schizophr Bull35603623
  51. 51. Gottesman II, Gould TD (2003) The endophenotype concept in psychiatry: etymology and strategic intentions. The American Journal of Psychiatry 160: 636–645.II GottesmanTD Gould2003The endophenotype concept in psychiatry: etymology and strategic intentions.The American Journal of Psychiatry160636645
  52. 52. Meyer-Lindenberg A, Weinberger DR (2006) Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nature Reviews Neuroscience 7: 818–827.A. Meyer-LindenbergDR Weinberger2006Intermediate phenotypes and genetic mechanisms of psychiatric disorders.Nature Reviews Neuroscience7818827
  53. 53. Tan HY, Callicott JH, Weinberger DR (2008) Intermediate phenotypes in schizophrenia genetics redux: is it a no brainer? Molecular Psychiatry 13: 233–238.HY TanJH CallicottDR Weinberger2008Intermediate phenotypes in schizophrenia genetics redux: is it a no brainer?Molecular Psychiatry13233238
  54. 54. Thorisson GA, Smith AV, Krishnan L, Stein LD (2005) The International HapMap Project Web site. Genome Research 15: 1592–1593.GA ThorissonAV SmithL. KrishnanLD Stein2005The International HapMap Project Web site.Genome Research1515921593
  55. 55. Radua J, Via E, Catani M, Mataix-Cols D (2010) Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls. Psychological Medicine 1–12.J. RaduaE. ViaM. CataniD. Mataix-Cols2010Voxel-based meta-analysis of regional white-matter volume differences in autism spectrum disorder versus healthy controls.Psychological Medicine112
  56. 56. Braff DL, Geyer MA (1990) Sensorimotor gating and schizophrenia. Human and animal model studies. Arch Gen Psychiatry 47: 181–188.DL BraffMA Geyer1990Sensorimotor gating and schizophrenia. Human and animal model studies.Arch Gen Psychiatry47181188
  57. 57. Curcio F (1978) Sensorimotor functioning and communication in mute autistic children. J Autism Child Schizophr 8: 281–292.F. Curcio1978Sensorimotor functioning and communication in mute autistic children.J Autism Child Schizophr8281292
  58. 58. Geyer MA, Swerdlow NR, Mansbach RS, Braff DL (1990) Startle response models of sensorimotor gating and habituation deficits in schizophrenia. Brain Res Bull 25: 485–498.MA GeyerNR SwerdlowRS MansbachDL Braff1990Startle response models of sensorimotor gating and habituation deficits in schizophrenia.Brain Res Bull25485498
  59. 59. Peng RY, Mansbach RS, Braff DL, Geyer MA (1990) A D2 dopamine receptor agonist disrupts sensorimotor gating in rats. Implications for dopaminergic abnormalities in schizophrenia. Neuropsychopharmacology 3: 211–218.RY PengRS MansbachDL BraffMA Geyer1990A D2 dopamine receptor agonist disrupts sensorimotor gating in rats. Implications for dopaminergic abnormalities in schizophrenia.Neuropsychopharmacology3211218
  60. 60. Dumontheil I, Burgess PW, Blakemore S-J (2008) Development of rostral prefrontal cortex and cognitive and behavioural disorders. Developmental Medicine and Child Neurology 50: 168–181.I. DumontheilPW BurgessS-J Blakemore2008Development of rostral prefrontal cortex and cognitive and behavioural disorders.Developmental Medicine and Child Neurology50168181
  61. 61. Barnea-Goraly N, Menon V, Eckert M, Tamm L, Bammer R, et al. (2005) White Matter Development During Childhood and Adolescence: A Cross-sectional Diffusion Tensor Imaging Study. Cerebral Cortex 15: 1848–1854.N. Barnea-GoralyV. MenonM. EckertL. TammR. Bammer2005White Matter Development During Childhood and Adolescence: A Cross-sectional Diffusion Tensor Imaging Study.Cerebral Cortex1518481854
  62. 62. Takarae Y, Minshew NJ, Luna B, Sweeney JA (2007) Atypical involvement of frontostriatal systems during sensorimotor control in autism. Psychiatry Research: Neuroimaging 156: 117–127.Y. TakaraeNJ MinshewB. LunaJA Sweeney2007Atypical involvement of frontostriatal systems during sensorimotor control in autism.Psychiatry Research: Neuroimaging156117127
  63. 63. Shukla DK, Keehn B, Lincoln AJ, Müller R-A (2010) White matter compromise of callosal and subcortical fiber tracts in children with autism spectrum disorder: a diffusion tensor imaging study. Journal of the American Academy of Child and Adolescent Psychiatry 49: 1269–1278.1278e1261–1262DK ShuklaB. KeehnAJ LincolnR-A Müller2010White matter compromise of callosal and subcortical fiber tracts in children with autism spectrum disorder: a diffusion tensor imaging study.Journal of the American Academy of Child and Adolescent Psychiatry49126912781278e1261–1262
  64. 64. Douaud G, Smith S, Jenkinson M, Behrens T, Johansen-Berg H, et al. (2007) Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain 130: 2375–2386.G. DouaudS. SmithM. JenkinsonT. BehrensH. Johansen-Berg2007Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia.Brain13023752386
  65. 65. Brambilla P, Hardan A, di Nemi SU, Perez J, Soares JC, et al. (2003) Brain anatomy and development in autism: review of structural MRI studies. Brain Res Bull 61: 557–569.P. BrambillaA. HardanSU di NemiJ. PerezJC Soares2003Brain anatomy and development in autism: review of structural MRI studies.Brain Res Bull61557569
  66. 66. Moreau MP, Bruse SE, David-Rus R, Buyske S, Brzustowicz LM (2011) Altered microRNA expression profiles in postmortem brain samples from individuals with schizophrenia and bipolar disorder. Biol Psychiatry 69: 188–193.MP MoreauSE BruseR. David-RusS. BuyskeLM Brzustowicz2011Altered microRNA expression profiles in postmortem brain samples from individuals with schizophrenia and bipolar disorder.Biol Psychiatry69188193
  67. 67. Ko J, Fuccillo MV, Malenka RC, Sudhof TC (2009) LRRTM2 functions as a neurexin ligand in promoting excitatory synapse formation. Neuron 64: 791–798.J. KoMV FuccilloRC MalenkaTC Sudhof2009LRRTM2 functions as a neurexin ligand in promoting excitatory synapse formation.Neuron64791798
  68. 68. Siddiqui TJ, Pancaroglu R, Kang Y, Rooyakkers A, Craig AM (2010) LRRTMs and neuroligins bind neurexins with a differential code to cooperate in glutamate synapse development. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 30: 7495–7506.TJ SiddiquiR. PancarogluY. KangA. RooyakkersAM Craig2010LRRTMs and neuroligins bind neurexins with a differential code to cooperate in glutamate synapse development.The Journal of Neuroscience: The Official Journal of the Society for Neuroscience3074957506
  69. 69. Coyle JT (1996) The glutamatergic dysfunction hypothesis for schizophrenia. Harvard Review of Psychiatry 3: 241–253.JT Coyle1996The glutamatergic dysfunction hypothesis for schizophrenia.Harvard Review of Psychiatry3241253
  70. 70. Thyagarajan A, Ting AY (2010) Imaging activity-dependent regulation of neurexin-neuroligin interactions using trans-synaptic enzymatic biotinylation. Cell 143: 456–469.A. ThyagarajanAY Ting2010Imaging activity-dependent regulation of neurexin-neuroligin interactions using trans-synaptic enzymatic biotinylation.Cell143456469
  71. 71. McTigue DM, Tripathi RB (2008) The life, death, and replacement of oligodendrocytes in the adult CNS. J Neurochem 107: 1–19.DM McTigueRB Tripathi2008The life, death, and replacement of oligodendrocytes in the adult CNS.J Neurochem107119
  72. 72. Etherton MR, Blaiss CA, Powell CM, Sudhof TC (2009) Mouse neurexin-1alpha deletion causes correlated electrophysiological and behavioral changes consistent with cognitive impairments. Proc Natl Acad Sci U S A 106: 17998–18003.MR EthertonCA BlaissCM PowellTC Sudhof2009Mouse neurexin-1alpha deletion causes correlated electrophysiological and behavioral changes consistent with cognitive impairments.Proc Natl Acad Sci U S A1061799818003
  73. 73. Tan GCY, Doke TF, Ashburner J, Wood NW, Frackowiak RSJ (2010) Normal variation in fronto-occipital circuitry and cerebellar structure with an autism-associated polymorphism of CNTNAP2. NeuroImage. GCY TanTF DokeJ. AshburnerNW WoodRSJ Frackowiak2010Normal variation in fronto-occipital circuitry and cerebellar structure with an autism-associated polymorphism of CNTNAP2.NeuroImage
  74. 74. Scott-Van Zeeland AA, Abrahams BS, Alvarez-Retuerto AI, Sonnenblick LI, Rudie JD, et al. (2010) Altered functional connectivity in frontal lobe circuits is associated with variation in the autism risk gene CNTNAP2. Science Translational Medicine 2: 56ra80.AA Scott-Van ZeelandBS AbrahamsAI Alvarez-RetuertoLI SonnenblickJD Rudie2010Altered functional connectivity in frontal lobe circuits is associated with variation in the autism risk gene CNTNAP2.Science Translational Medicine256ra80
  75. 75. Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9: 97–113.RC Oldfield1971The assessment and analysis of handedness: the Edinburgh inventory.Neuropsychologia997113
  76. 76. Hollingshead AB (1975) Four Factor Index of Social Status. Yale University. New Haven, Ct. AB Hollingshead1975Four Factor Index of Social StatusYale University. New Haven, Ct
  77. 77. First MB SR, Gibbon M, Williams JBW (1995) Strucutured Clinical Interview for DSM-IV Axis I Disorders, Patient Edition (SCID-P), version 2. New York: Biometrics Research. SR First MBM. GibbonJBW Williams1995Strucutured Clinical Interview for DSM-IV Axis I Disorders, Patient Edition (SCID-P), version 2New YorkBiometrics Research
  78. 78. Ad-Dab'bagh Y, Einarson D, Lyttelton O, Muehlboeck J, Mok K, et al. Y. Ad-Dab'baghD. EinarsonO. LytteltonJ. MuehlboeckK. MokThe CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research; 2006 June, 2006; Florence, Italy. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research; 2006 June, 2006; Florence, Italy.
  79. 79. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, et al. (2001) A four-dimensional probabilistic atlas of the human brain. J Am Med Inform Assoc 8: 401–430.J. MazziottaA. TogaA. EvansP. FoxJ. Lancaster2001A four-dimensional probabilistic atlas of the human brain.J Am Med Inform Assoc8401430
  80. 80. Collins D (1994) The crime of failing to record instructions. N Z Med J 107: 40–41.D. Collins1994The crime of failing to record instructions.N Z Med J1074041
  81. 81. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17: 87–97.JG SledAP ZijdenbosAC Evans1998A nonparametric method for automatic correction of intensity nonuniformity in MRI data.IEEE Trans Med Imaging178797
  82. 82. Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, et al. (2002) Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17: 479–489.SM SmithY. ZhangM. JenkinsonJ. ChenPM Matthews2002Accurate, robust, and automated longitudinal and cross-sectional brain change analysis.Neuroimage17479489
  83. 83. Zijdenbos AP, Forghani R, Evans AC (2002) Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 21: 1280–1291.AP ZijdenbosR. ForghaniAC Evans2002Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis.IEEE Trans Med Imaging2112801291
  84. 84. Tohka J, Zijdenbos A, Evans A (2004) Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 23: 84–97.J. TohkaA. ZijdenbosA. Evans2004Fast and robust parameter estimation for statistical partial volume models in brain MRI.Neuroimage238497
  85. 85. Collins D, Holmes C, Peters T, Evans A (1995) Automatic 3D model-based neuroanatomical segmentation. Hum Brain Mapp 3: 190–208.D. CollinsC. HolmesT. PetersA. Evans1995Automatic 3D model-based neuroanatomical segmentation.Hum Brain Mapp3190208
  86. 86. Chakravarty MM, Sadikot AF, Germann J, Bertrand G, Collins DL (2008) Towards a validation of atlas warping techniques. Medical Image Analysis 12: 713–726.MM ChakravartyAF SadikotJ. GermannG. BertrandDL Collins2008Towards a validation of atlas warping techniques.Medical Image Analysis12713726
  87. 87. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics (Oxford, England) 21: 263–265.JC BarrettB. FryJ. MallerMJ Daly2005Haploview: analysis and visualization of LD and haplotype maps.Bioinformatics (Oxford, England)21263265
  88. 88. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, et al. (2002) The structure of haplotype blocks in the human genome. Science (New York, NY) 296: 2225–2229.SB GabrielSF SchaffnerH. NguyenJM MooreJ. Roy2002The structure of haplotype blocks in the human genome.Science (New York, NY)29622252229
  89. 89. Reitan RM, Wolfson D (1985) The Halstead-Reitan Neuropsychological Test Battery: Theory and clinical interpretation. Tucson, AZ: Neuropsychology Press. RM ReitanD. Wolfson1985The Halstead-Reitan Neuropsychological Test Battery: Theory and clinical interpretationTucson, AZNeuropsychology Press
  90. 90. Reitan RM, Wolfson D (1993) The Halstead-Reitan Neuropsychological Test Battery: Theory and clinical interpretation. Tucson, AZ: Neuropsychology Press. RM ReitanD. Wolfson1993The Halstead-Reitan Neuropsychological Test Battery: Theory and clinical interpretationTucson, AZNeuropsychology Press
  91. 91. Lezak M (1995) Neuropsychological Assessment. Oxford: Oxford University Press, New York. M. Lezak1995Neuropsychological AssessmentOxfordOxford University Press, New York
  92. 92. Flashman LA, Flaum M, Gupta S, Andreasen NC (1996) Soft signs and neuropsychological performance in schizophrenia. The American Journal of Psychiatry 153: 526–532.LA FlashmanM. FlaumS. GuptaNC Andreasen1996Soft signs and neuropsychological performance in schizophrenia.The American Journal of Psychiatry153526532
  93. 93. Goldman S, Wang C, Salgado MW, Greene PE, Kim M, et al. (2009) Motor stereotypies in children with autism and other developmental disorders. Developmental Medicine and Child Neurology 51: 30–38.S. GoldmanC. WangMW SalgadoPE GreeneM. Kim2009Motor stereotypies in children with autism and other developmental disorders.Developmental Medicine and Child Neurology513038
  94. 94. Honey GD, Pomarol-Clotet E, Corlett PR, Honey RAE, McKenna PJ, et al. (2005) Functional dysconnectivity in schizophrenia associated with attentional modulation of motor function. Brain 128: 2597–2611.GD HoneyE. Pomarol-ClotetPR CorlettRAE HoneyPJ McKenna2005Functional dysconnectivity in schizophrenia associated with attentional modulation of motor function.Brain12825972611
  95. 95. Mostofsky SH, Powell SK, Simmonds DJ, Goldberg MC, Caffo B, et al. (2009) Decreased connectivity and cerebellar activity in autism during motor task performance. Brain: A Journal of Neurology 132: 2413–2425.SH MostofskySK PowellDJ SimmondsMC GoldbergB. Caffo2009Decreased connectivity and cerebellar activity in autism during motor task performance.Brain: A Journal of Neurology13224132425
  96. 96. Magri C, Sacchetti E, Traversa M, Valsecchi P, Gardella R, et al. (2010) New copy number variations in schizophrenia. PloS One 5: e13422.C. MagriE. SacchettiM. TraversaP. ValsecchiR. Gardella2010New copy number variations in schizophrenia.PloS One5e13422
  97. 97. Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, et al. (2010) Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466: 368–372.D. PintoAT PagnamentaL. KleiR. AnneyD. Merico2010Functional impact of global rare copy number variation in autism spectrum disorders.Nature466368372
  98. 98. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, et al. (2002) The human genome browser at UCSC. Genome Res 12: 996–1006.WJ KentCW SugnetTS FureyKM RoskinTH Pringle2002The human genome browser at UCSC.Genome Res129961006