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Mental problems are written in your face

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Systematic variations of the block design task were given to 20 autistic, 33 normal and 12 mildly retarded subjects. Designs were contrasted which were either "whole" or segmented, rotated or unrotated, and which did or did not contain obliques. Only segmentation, but neither of the spatial orientation factors, revealed a significant group difference. Autistic subjects, regardless of age and ability, performed better than controls when presented with unsegmented designs. This result suggests that they need less of the normally required effort to segment a gestalt, and thus supports the hypothesis of weak central coherence as a characteristic of information processing in autism.

 
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Abstract​


Objective​


There is ongoing uncertainty about the structure and definition of alexithymia. Conceptually, alexithymia has traditionally been defined as a multidimensional trait with four components: difficulty identifying feelings, difficulty describing feelings, externally orientated thinking, and difficulty fantasizing. However, some authors suggest that difficulty fantasizing might not be a component, and others suggest low emotional reactivity is a fifth component. In this study, we sought to clarify this issue using factor analysis.

Method​


In a sample of adults (N = 508), we administered a comprehensive battery of psychometric measures and analyzed their latent structure using exploratory factor analysis.

Results​


Subscales assessing difficulty identifying feelings, difficulty describing feelings, and externally orientated thinking all loaded well together on the alexithymia factor. However, none of the subscales assessing aspects of difficulty fantasizing (i.e., daydreaming frequency, vividness, content, or use of daydreams to regulate emotions) loaded on the alexithymia factor. Similarly, no emotional reactivity subscales loaded on the alexithymia factor, and alexithymia was associated with higher (not lower) levels of emotional reactivity for negative emotions.

Conclusions​


Difficulty fantasizing and low emotional reactivity are not components of the latent alexithymia construct. The traditional four-component definition of alexithymia likely warrants refinement to a more parsimonious three-component solution.

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Abstract​

This series of meta-analyses examined structural abnormalities of the hippocampus and other brain regions in persons with PTSD compared to trauma-exposed and non-exposed control groups. The findings were significantly smaller hippocampal volumes in persons with PTSD compared to controls with and without trauma exposure, but group differences were moderated by MRI methodology, PTSD severity, medication, age and gender. Trauma-exposed persons without PTSD also showed significantly smaller bilateral hippocampal compared to non-exposed controls. Meta-analyses also found significantly smaller left amygdala volumes in adults with PTSD compared to both healthy and trauma-exposed controls, and significantly smaller anterior cingulate cortex compared to trauma-exposed controls. Pediatric samples with PTSD exhibited significantly smaller corpus callosum and frontal lobe volumes compared to controls, but there were no group differences in hippocampal volume. The overall findings suggested a dimensional, developmental psychopathology systems model in which: (1) hippocampal volumetric differences covary with PTSD severity; (2) hippocampal volumetric differences do not become apparent until adulthood; and (3) PTSD is associated with abnormalities in multiple frontal–limbic system structures.

Introduction​

Exposure to trauma can precipitate the development of posttraumatic stress disorder (PTSD), a complex syndrome comprising re-experiencing symptoms (e.g., nightmares, flashbacks) hyperarousal symptoms (e.g., insomnia), numbing symptoms (e.g., restricted affect, anhedonia), and avoidance symptoms (e.g., avoiding trauma-related stimuli) (DSM-IV, American Psychiatric Association, 1994) in addition to poor concentration and difficulty explicitly recalling aspects of the traumatic event (DSM-IV, American Psychiatric Association, 1994). PTSD may be accompanied by other types of mild cognitive impairment, such as relatively impoverished autobiographic memory for positive events (Harvey et al., 1998; McNally et al., 1995) as well as problems with attention, working memory (Vasterling et al., 1998, Vasterling et al., 2002), and learning novel word associations (Golier et al., 2002). Studies of electro-encephalographic activity (Karl et al., 2006) have found that PTSD is associated with enhanced processing of trauma-related stimuli and reduced processing of neutral stimuli. Converging evidence from neuroimaging research suggests that this altered information processing is associated with differential functional neuroanatomical activity in PTSD (Bremner et al., 1999b, Bremner et al., 2003b; Clark et al., 2003; Matsuo et al., 2003; Rauch et al., 1996; Shaw et al., 2002; Shin et al., 2004a, Shin et al., 2004b).
Studies of structural brain abnormalities in PTSD have focused in particular on the hippocampus, a grey matter structure in the limbic system that is critically involved in explicit (declarative) memory, working memory (O’Keefe and Nadel, 1978; Squire, 1992), and memory for episodic events (Eldridge et al., 2000; Tulving, 1985; Wheeler and Buckner, 2004). The hippocampus also has an important role in the regulation of stress (Jacobson and Sapolsky, 1991), and findings from animal research suggest that chronic stress may affect the hippocampus through excess release of glucocorticoids (Sapolsky et al., 1990), corticotropin-releasing hormone (Brunson et al., 2001), and glutamate (Moghaddam, 2002; Moghaddam and Bolinao, 1994), inhibition of neurogenesis (Gould et al., 1997); impaired long-term potentiation induction (Li et al., 2005); inhibition of brain-derived neurotrophic factor (BDNF, Duric and McCarson, 2005) and altered serotonergic receptor function (Harvey et al., 2003).
Because of its critical role in learning and memory as well as stress regulation, alterations in the hippocampus have been proposed as contributing to the etiology of PTSD (Bremner, 2001; Sapolsky, 2000). However, findings from PTSD neuroimaging research are equivocal (Jelicic and Merckelbach, 2004). Some cross-sectional studies find reduced hippocampal volumes (e.g., Bremner et al., 1995; Gurvits et al., 1996; Stein et al., 1997) in PTSD but others do not (e.g., Pederson et al., 2004; Schuff et al., 2001). Right-sided (Bremner et al., 1995), left-sided (Gurvits et al., 1996) as well as bilateral (Bremner et al., 2003a) volumetric reductions have been reported. One longitudinal study failed to find reduced hippocampal volume at 6 months post-trauma (Bonne et al., 2001), but the sample in this study experienced only a single incident trauma rather than chronic trauma exposure. Smaller hippocampal volumes have been associated with longer time since trauma (Villarreal et al., 2002) as well as trauma severity (Bremner et al., 1997; Gurvits et al., 1996; Winter and Irle, 2004) but there are negative findings as well (Stein et al., 1997). Winter and colleagues (Winter and Irle, 2004) found reduced hippocampal volumes in burn survivors with and without PTSD, compared to non-exposed healthy controls, which suggests that trauma exposure may produce reductions in hippocampal volumes in the absence of a PTSD diagnosis. In contrast, in Gilbertson et al.'s (2002) twin study, smaller hippocampal volumes were only found in combat veterans with more severe PTSD compared to non-exposed controls, with no significant differences when veterans with less severe PTSD were included in the analyses. Perhaps most critically, they found no significant difference in hippocampal volumes between monozygotic twin pairs with and without PTSD, and concluded that smaller hippocampal volume is a premorbid risk factor for severe and chronic PTSD, rather than a consequence of PTSD or trauma exposure.
In their critical review, Jelicic and Merckelbach (2004) noted that PTSD hippocampus volumetric studies are beset by a number of limitations, including small study sample sizes and low statistical power, methodological heterogeneity (e.g., neuroimaging measurements, type of control sample), and sample heterogeneity (e.g., type and severity of trauma exposure, comorbid psychopathology, medication use). Meta-analysis is a technique that can address some of these limitations, and the results of two recent meta-analyses have provided further evidence of hippocampal volumentric reduction in PTSD. Smith (2005) meta-analyzed 13 studies of adult patients with PTSD and found that persons with PTSD had left and right hippocampal volumes that were 7.2% and 7.0% smaller, respectively, than those of non-exposed controls, and 4.3% and 4.5% smaller, respectively, than those of trauma-exposed controls. Kitayama and associates (2005) also found smaller bilateral hippocampal volume in PTSD compared to both trauma-exposed and non-exposed controls in a meta-analysis of nine studies of adult patients, the majority of whom had chronic trauma exposure (combat veterans and adult survivors of childhood abuse).
The objective of the research that we present in this paper was to quantitatively integrate the literature through a comprehensive series of meta-analyses of structural abnormalities in PTSD. We expanded upon the results of the two previous meta-analyses (Kitayama et al., 2005; Smith, 2005) in the following ways. As recommended by Glass et al. (1981) we did not restrict the study sample to only those studies with the best methodology, which yielded a larger and more inclusive sample of studies. We then used empirical methods to identify sample heterogeneity and to construct homogenous groups for analyses. To examine whether volumetric reductions were specific to PTSD, we also meta-analyzed comparisons of trauma-exposed samples without PTSD versus healthy controls. To address method and sample variance, we conducted an extensive series of analyses examining the effects of moderator variables, including MRI methodology, gender, age and age of trauma exposure, PTSD severity and duration, comorbid disorders, and medication. To examine whether volumetric reductions were restricted to the hippocampus, we meta-analyzed PTSD volumetric studies of other brain regions. For ease of apprehension, we have organized the series of meta-analyses into separate sections punctuated by summaries. In the discussion we summarize the overall results and explicate their implications for the formulation of comprehensive neurobiological models of PTSD.

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Although there have been significant advances in the treatment of PTSD, treatment failures persist. A meta-analysis of 26 studies with 44 treatment conditions reported that overall, 56% of those enrolled in treatment and 67% of those who completed treatment no longer met criteria for PTSD after treatment and 44% of enrollees and 54% of completers had clinically meaningful improvement by standards defined by the authors (Bradley, Greene, Russ, Dutra, & Westen, 2005). While these rates are impressive for short-term treatment of an often chronic disorder, the high rate of treatment failures calls for the innovation and dissemination of alternative or augmented treatments.
This article will review emerging psychotherapeutic and pharmacologic treatments for PTSD. While the term “emerging treatments” has no uniform definition in the literature, we use it to refer to interventions with some theoretical basis that have garnered the beginnings of scientific and popular support. By definition, this excludes interventions that have a strong scientific foundation or to which significant study has been dedicated such as prolonged exposure therapy (PE), CPT and EMDR which will not be addressed here. Our synthesis of the literature is presented below, with a description followed by a brief analysis of each treatment and with special emphasis given to virtual reality (VR) exposure therapy and D-cycloserine as enhancements to traditional treatments as there is much excitement surrounding these approaches.

Section snippets​

Couple and family therapy​

PTSD has been associated with marital and relationship difficulty, aggression toward partners and children, sexual dysfunction and emotional distancing (Monson, Fredman, & Adair, 2008), with more than 75% of married or partnered Operation Iraqi Freedom/Operation Enduring Freedom (OIF/OEF) Veterans reporting problems with family relationships (Sayers, Farrow, Ross, & Oslin, 2009). Numerous couple and family treatment strategies have been developed but few have been studied. Riggs (2000)

Pharmacologic treatments​

Though recent guidelines suggest that psychotherapy should be initiated as a first-line treatment for PTSD before pharmacological options (National Collaborating Centre for Mental Health, 2005), medications are often necessary to palliate symptoms, and the pursuit of more effective medication is essential to developing a range of effective treatment options. The most commonly used medications have been antidepressants, and specifically SSRIs (Davidson, 2000, Davidson and Connor, 1999). Stein,

Conclusions​

The recent proliferation in treatments for PTSD suggests that researchers are beginning to address the need to develop and evaluate alternatives to the current armamentarium. While there are myriad treatments emerging, few, if any, have sufficient evidence to draw conclusions about their efficacy. However, technological based treatments have the strongest preliminary evidence. The possibility inherent in Internet and teleconferencing based interventions is especially important given the
 
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The absence of PPI and P50 suppression deficits in our patients in the psychostimulant-naïve state indicates no gating deficits. In turn, this suggests that the difficulties to inhibit distraction of attention by irrelevant stimuli that many patients with (adult) ADHD report, have a different origin than the theoretical causes of sensory overload frequently reported in studies on patients with schizophrenia.


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Consistent with increased neural maturity and partially remitted symptomatology, our results indicate intact sensorimotor gating for both tasks in adult ADHD with no comorbidity, independent of the subjects' gender and whether ADHD subjects were receiving ongoing stimulant treatment or not. Reduced PPI at 120-ms lead intervals, on the other hand, was recorded in a subset of 10 ADHD subjects who were taken off their prescribed regular stimulants for 24 h and tested in a randomized counterbalanced order for on vs. off medication. However, our data remained inconclusive as to whether this observation constitutes beneficial treatment or acute stimulant withdrawal effects on sensorimotor gating.

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Autism spectrum disorders (ASDs) are neurodegenerative disorders that are diagnosed primarily based on children’s behaviors and characterized by insufficient development of normal social interaction with other people, impaired development of communicative ability, a lack of imaginative ability, and repetitive and stereotyped movements.13 Anatomical and physiological changes such as frontal cortex overgrowth occur in the ADS brain during the prenatal period.4,5 Underdeveloped cognitive areas affect decision-making, communication, and language.5 A 2017 statistic suggests that the prevalence of ASD among children was 168, 161, 152, 100, 100, 69, 67, 49, 27, and 9.2 per 10,000 in the United States, Japan, Canada, the United Kingdom, Ireland, Denmark, Australia, China, Brazil, and Portugal, respectively.6 The latest published data indicate that the prevalence of ASD is greater than 2%.7 The diagnosis of ASD nevertheless currently lacks a unifying theory.8 Early theories about the cause of ASD mainly focused on substandard parenting.8 Newschaffer et al.9 suggested that the causes of ASD mainly fall into three categories: genetic, environmental, and neurobiological. Factors including toxicity, teratogenic effects, trauma, and infections can also cause ASD.
Several independent studies and substantial evidence confirm that transferrin (TF; chromosomal location: 3q22.1) is one of the genes that confer susceptibility to ASD.10,11 Transferrin is an iron-transporting plasma glycoprotein that controls iron levels in biological fluid.10 The glycoprotein has two iron binding sites; these irons accumulate rapidly at the onset of myelination. A very recent study suggested that an elevated amount of oxalate in plasma plays a role in ASD by binding to the bilobal iron transport protein transferrin (hTF), thereby interfering with iron metabolism by inhibiting iron delivery to cells.11 Therefore, the genetic modification of the transferrin gene may be linked to the development of ASD.12 An investigation of the rs1867503 polymorphism of the TF gene reported that this genetic polymorphism plays a significant role in producing cognitive disorders such as ASD.13
Another gene related to ASD is transcription factor 4, 18q21.2 (TCF4; also known as E2-2, SEF2, or ITF2), a basic helix–loop–helix transcription factor that is frequently associated with cognitive dysfunction.1416 The autosomal dominant mutation or deletion of TCF4 results in Pitt–Hopkins syndrome , 18q deletion syndrome, and three rare ASDs (autistic disorder, Asperger syndrome, and pervasive developmental disorder).1719 A previous study indicated that in neurodevelopmental pathways TCF4 target genes cluster mostly to schizophrenia, ASD, and ID risk genes.20 Studies such as this have proven the association of these genes with ASD in some ethnic groups.
A study to validate the link between the TCF4 rs9951150 and TF rs1867503 variants and ASD in Bangladeshi children has not yet been conducted. We performed this study because the diagnosis and treatment of ASD in Bangladeshi children are not adequately prioritized despite a prevalence nearly equal to that in other parts of the world. The study was conducted using polymerase chain reaction (PCR)-based amplification followed by a restriction fragment length polymorphism (RFLP) method to detect the associations of TF rs1867503 and TCF4 rs9951150 with ASD. We anticipate that this study will help further elucidate ASD and improve procedures for its diagnosis and treatment.
Go to:

Methods and Materials​

Study design and sample and data collection​

Two groups of children were selected for this case–control study. The first group consisted of 96 children with ASD (aged 3–15 years) who were recruited as cases from schools for children with ASD in Chittagong and Dhaka using the criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition. We de-identified all patient details. A total of 118 healthy children (aged 3–15 years) were recruited as controls from areas in Dhaka and Chittagong, Bangladesh. All participants were randomly selected to investigate the risk of ASD due to the TF rs1867503 and TCF4 rs9951150 polymorphisms.
Genotyping analysis was performed in the Laboratory of Pharmacogenomics and Molecular Biology, Department of Pharmacy, Faculty of Science, Noakhali Science and Technology University, Noakhali, Bangladesh. Ethical clearance was obtained from the ethical committee of the Noakhali Science and Technology University (ID-01/2018) and written consent was obtained from each participant before inclusion in the study. We collected consent from guardians for minors or those lacking the capacity to consent. Consent was obtained verbally and in writing (signature or fingerprints). The consent form was translated into the native language to ensure accurate understanding by participants.
The study was conducted in accordance with the International Conference of Harmonization for Good Clinical Practice and was in compliance with the Declaration of Helsinki and its further amendments.21 Moreover, the study was performed following the Strengthening the Reporting of Observational Studies in Epidemiology guidelines, as described by von Elm et al.22

Selection of genes and single nucleotide polymorphisms​

We selected additional ASD-susceptible genes and polymorphisms by analyzing their possible association with ASD. Transferrin factor is important for iron transportation12 and lower iron levels are linked with ASD.23,24 A genome-wide association study found a link between the rs1867503 polymorphism and ASD.13 Another study reported the association of TCF4 with autism.20 Moreover, the frequency of the minor allele must be greater than 15% according to the 1000 Genomes database in the studied population.
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Single nucleotide polymorphisms (SNPs) within the MIR137, TCF4, and ZNF804A genes show genome-wide association to schizophrenia. However, the biological basis for the associations is unknown. Here, we tested the effects of these genes on brain structure in 1300 healthy adults. Using volumetry and voxel-based morphometry, neither gene-wide effects—including the combined effect of the genes—nor single SNP effects—including specific psychosis risk SNPs—were found on total brain volume, grey matter, white matter, or hippocampal volume. These results suggest that the associations between these risk genes and schizophrenia are unlikely to be mediated via effects on macroscopic brain structure.
 
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The transcription factor 4 (TCF4) gene encodes a helix-loop-helix transcription factor protein, which initiates neuronal differentiation and is primarily expressed during nervous system development. The aim of the present study is to investigate the association of the TCF4 rs9960767 polymorphism and bipolar disorder, which is highly heritable. DNA isolation was performed on 95 patients with bipolar disorder and 108 healthy control subjects to examine the TCF4 rs9960767 polymorphism. Genotypic and allelic frequencies were determined using the polymerase chain reaction-restriction fragment length polymorphism method designed in our laboratory. Statistical analysis was performed using χ2 test within the 95% confidence interval. Odds ratios were calculated and Hardy-Weinberg equilibrium (HWE) was verified for all control subjects and patients. The A allele frequency was 95.8% in the patients and 94.4% in the control subjects, and 4.2% in the patients and 5.6% in the control subjects for the C allele. The genotype frequencies of the TCF4 gene rs9960767 variant were as follows: AA, 91.6% and AC, 8.4% in patients with bipolar (CC genotype was not observed in cases); AA, 89.8%; AC, 9.3% and CC, 0.9% in the control subjects. No statistically significant difference was identified between the patients and control subjects (χ2=0.937; P=0.626). In addition, gender specific analysis was performed, although no significant association was found according to the gender distrubition. All patients and control subjects were in HWE (P>0.05). Statistical analysis of the data indicates that the TCF4 gene rs9960767 polymorphism is not an independent risk factor for bipolar disorder in the overall population or in terms of gender; however, an increased population size would improve the statistical power. Furthermore, additional gene variants that are specifically involved in neuronal development may be analyzed for revealing the complex genetic architecture of bipolar disorder. An improved approach would be better to evaluate the TCF4 gene in a pathway specific manner due to its role as a transcription factor.

Bipolar disorder is a polygenic, common and chronicpsychiatric disorder that has a lifetime risk of 1% worldwide(1). It is also termed manicdepression due to the dramatic mood changes, such as extreme maniaor severe depression, that patients experience. It is usuallyaccompanied by thinking and behavioral disturbances, and often bypsychotic features, such as delusions or hallucinations. Theseepisodic mood changes occur at an extreme level that significantlyaffects the individual's social and business life (2).


Studies on families and with twins have demonstratedthat bipolar disorder is highly heritable. First-degree relativesof affected individiuals are associated with a 5–10-fold increasedrisk of bipolar disorder compared to that of the generalpopulation. The risk may increase up to the 40–70-fold inmonozygous twins (2). Despite thisstrong familiality, identification of bipolar disordersusceptibility genes has been challenging due to the multifactorialgenetic architecture of the disease. Multiple candidate genes havebeen proposed by linkage analysis and genome-wide associationstudies (GWAs) although these results have not been consistentlyreplicated (3,4).


GWAs are systematic and objective studies based onthe ‘common disease, common variant’ hypothesis, which allow us toidentify population specific and disease-associated variants(5). Attempts to identify risk genesfor common disorders, such as bipolar disorder and schizophrenia inhumans have began to provide important findings. It is known thatschizophrenia and bipolar disorder overlap epidemiologically,symptomatically and genetically (6). Alarge scale GWAs demonstrated that schizophrenia and bipolardisorder patients carry the same disease-associated variants, suchas in the zinc finger protein 804A and calcium voltage-gatedchannel subunit α1 C (CACNA1C) genes (2).


Stefansson et al (7) combined single nucleotide polymorphism(SNP) data from various GWAs for schizophrenia and conducted ameta-analysis of the most significant associatied signals. Thegenome-wide scan results of 12,945 schizophrenia cases and 34,591controls were analyzed. Significant associations were identified onchromosomes 6p21.3–22.1 (major histocompatibility complex, fiveSNPs; rs6913660, rs13219354, rs6932590, rs13211507 and rs3131296),11q24.2 (neurogranin, rs12807809) and 18q21.2 (transcription factor4; TCF4, rs9960767) (7). One of theserisk genes, TCF4, is also involved in normal brain development andTCF4 mutations have been associated with Pitt-Hopkins syndrome, arare developmental disorder characterized by severe motor andmental retardation (8), additionallyTCF4 deletions have been identified as risk factors forautistic-like behaviours (9).


The TCF4 gene is a basic helix-loop-helix (bHLH)transcription factor, which is also known to regulate theexpression of many other genes that are involved in celldifferentiation, cell survival and neurodevelopment (10). These associations between TCF4 andneurodevelopmental diseases have resulted in the present evaluationof whether the rs9960767 variant of the TCF4 gene is alsoassociated with bipolar disorder as a neuropsychiatric disorder. Inthe present study, the allele and genotype frequencies of the TCF4gene rs9960767 variant were analyzed in 95 bipolar disorderpatients and 108 healthy control subjects from a Turkish populationusing polymerase chain reaction-restriction fragment lengthpolymorphism (PCR-RFLP) analysis. For this method, the PCR primerswere designed by the present authors and a suitable restrictionenzyme was selected for analysis of the polymorphic gene site. Theprimers and enzyme may be used for further investigations of thisparticular SNP (rs9960767
 
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Schizophrenia is a chronic, severe and disabling psychiatric disorder that affects 1% of the population worldwide. Schizophrenia is a complex major disease that manifests as psychotic behaviour (delusions and hallucinations), disorganisation, dysfunction in normal affective responses and altered cognitive functioning. Reference van Os and Kapur1 Twin, family and adoption studies have demonstrated that schizophrenia is highly heritable; the heritability of schizophrenia is estimated to be as high as 80%. Reference Lichtenstein, Yip, Bjork, Pawitan, Cannon and Sullivan2,Reference Sullivan, Kendler and Neale3 However, the genetic loci that contribute to the disease remain generally elusive. Recent genome-wide association studies (GWAS) have provided unbiased assessments of common sequence variations across the whole genome and may robustly map the loci involved in the pathology of complex diseases. In the past 5 years, a number of GWAS of schizophrenia have been published. Reference Lencz, Morgan, Athanasiou, Dain, Reed and Kane4Reference Shi, Levinson, Duan, Sanders, Zheng and Pe'er20 Several loci have surpassed the genome-wide significance threshold (P = 5×10−8) in more than one GWAS; for example the major histocompatibility complex (MHC) region has exhibited genome-wide significance in four independent large-scale GWAS of schizophrenia. Reference Stefansson, Ophoff, Steinberg, Andreassen, Cichon and Rujescu9,Reference Yue, Wang, Sun, Tang, Liu and Zhang14,Reference Shi, Levinson, Duan, Sanders, Zheng and Pe'er20,Reference Purcell, Wray, Stone, Visscher, O'Donovan and Sullivan21


Recently, the Schizophrenia Psychiatric GWAS Consortium (PGC) reported a large schizophrenia GWAS. 13 In this study, the authors conducted a mega-analysis of the combined genotyping data from 21 856 individuals of European ancestry from 17 separate studies and then performed a replication study in a sample of 29 839 participants from 19 populations. This study revealed seven loci with genome-wide significance, including two previously reported loci (i.e. the MHC region and TCF4) and the following five novel loci: 1p23.3 (MIR137), 2q32.3 (PCGEM1), 8p23.2 (CSMD1), 8q21.3 (MMP16), and 10q24.32–q24.33 (CNNM2/NT5C2). Moreover, a joint analysis of schizophrenia and bipolar disorder identified three additional genes that reached genome-wide significance: CACNA1C, ANK3 and ITIH3/4. The most significant new finding was the identification of MIR137, which encodes the microRNA 137, which is a known regulator of neuronal development. Notably, microRNA 137 may directly regulate some other schizophrenia susceptibility genes. Among the 301 high-confidence predicted MIR137 targets, 17 had at least one significant single nucleotide polymorphism (SNP) at P<10−4; these 17 targets included four genome-wide significant genes (i.e. TCF4, CACNA1C, CSMD1 and C10orf26). Subsequently, these four genes and ZNF804A, another compelling candidate gene for schizophrenia, were validated as MIR137 targets. Reference Kim, Parker, Williamson, McMichael, Fanous and Vladimirov22,Reference Kwon, Wang and Tsai23 These findings suggest that the MIR137-mediated pathway is involved in the aetiology of schizophrenia. In a replication study, Hamshere et al tested 78 of the 81 SNPs highlighted by the PGC in a UK population and found significant association for 37 (47%) of the SNPs. Remarkably, genetic variants in three new loci (i.e. ITIH3/4, CACNA1C and SDCCAG8) reached genome-wide significance after combining their new schizophrenia data (CLOZUK) with those of the PGC. Reference Hamshere, Walters, Smith, Richards, Green and Grozeva24 The CLOZUK sample is a series of 2640 UK individuals that were registered for clozapine treatment and had clinical diagnoses of schizophrenia and 2878 controls. Reference Hamshere, Walters, Smith, Richards, Green and Grozeva24


Only a few schizophrenia GWAS have been conducted in non-Western populations. One of these was our GWAS of the genotypes of 3750 individuals with schizophrenia and 6468 healthy controls from Han Chinese populations (BIOX GWAS). Reference Shi, Li, Xu, Wang, Li and Shen15 None of the SNPs within the 10 loci reported by PGC met the criteria for genome-wide significance in our data-set. However, the criteria for genome-wide significance minimises the occurrence of false positives (i.e. type I errors), whereas the occurrence of false negatives (i.e. type II errors) was almost certain. In another schizophrenia GWAS that was conducted in a Han Chinese population, Yue et al Reference Yue, Wang, Sun, Tang, Liu and Zhang14 identified the susceptibility locus at the MHC region. We ascertained significant associations between the MHC region, the TCF4 gene and schizophrenia in our previous study of 2496 patients with schizophrenia and 5184 normal controls drawn from a Han Chinese population, but we failed to confirm an association between the NRGN gene and schizophrenia. Reference Li, Li, Chen, Zhao, Wang and Huang25 Regarding the psychiatric traits, most of the associations were population specific; however, in some cases, the associations may exhibit convergence of risk genes (but not necessarily risk alleles) across populations. Reference Gelernter, Kranzler, Sherva, Almasy, Koesterer and Smith26 It is of great interest to replicate the findings related to the other loci that were identified by the PGC in the Han Chinese population. In the present study, we conducted a two-stage analysis. We first analysed the SNPs (n = 2595) of the eight newly identified loci, predicted the targets of microRNA 137 in the BIOX GWAS data and selected 18 candidate SNPs. These SNPs were genotyped and tested for their associations with schizophrenia in a replication cohort that consisted of 3585 patients with schizophrenia (the schizophrenia group) and 5496 controls (the control group) of Han Chinese ancestry. A meta-analysis was performed to combine the Chinese data-sets (BIOX GWAS and replication).


Method​


Participants​


Participants in the schizophrenia group were in-patients or out-patients who were recruited from various mental health centres. The patients were interviewed by two independent psychiatrists and were diagnosed according to DSM-IV criteria, 27 and had 2-year histories of the disorder. All met the following two criteria: preoccupation with one or more delusions and frequent auditory hallucinations. However, none of the following symptoms were prominent: disorganised speech, disorganised or catatonic behaviour, or flat or inappropriate affect. All healthy controls were randomly selected from Chinese Han volunteers (from hospitals and a community survey) who were asked to reply to a written invitation to evaluate their medical histories. Potential lists of controls were screened for suitable volunteers by excluding individuals with major mental illnesses. The sample consisted of 3585 people in the schizophrenia group (1901 men and 1684 women, the mean age at onset of schizophrenia was 35.0 years, s.d. = 11.0) and 5496 controls (2819 men and 2677 women with a mean age of 46.4 years, s.d. = 14.1). Of the participants, 1329 in the schizophrenia group and 2037 in the control group were northern Han Chinese; 1700 in the schizophrenia group and 2622 in the control group were central Han Chinese; and 556 in the schizophrenia group and 837 in the control group were southern Han Chinese. Approval was received for our study from the local Human Genetic Resources Ethics Committee of Human Genetic Resources. After providing a complete description of the study to the participants, written informed consent was obtained.

BIOX GWAS quality control​


The gender established via data genotyping was checked for each of the participants, and individuals in the schizophrenia group of unknown or inconsistent gender (compared with the sample record) were removed (n = 49). Arrays with call rates <95% were excluded (n = 276). SNPs with call rates <95% in either the schizophrenia or the control group were removed (n = 92 324). SNPs with minor allele frequencies <3% (n = 228 267) and those that significantly deviated from Hardy–Weinberg Equilibrium (HWE; P≤1×10−6) among the control group (n = 28 657) were also excluded. Heterozygosity rates were calculated with the intent of removing deviations that exceeded six standard deviations from the mean; however, no samples were excluded based on this criterion. PLINK's identity by descent analysis was used to detect cryptic relatedness. When a pair of individuals exhibited a PI_HAT >0.25, the member of the pair with the lower call rate was excluded from the analysis (n = 146). Population ancestry assessments were evaluated using principal components analysis, and all of the samples were of Chinese ancestry.

SNP selection and genotyping​


The allelic frequencies of the nine genome-wide significant SNPs reported in the PGC and PGC+CLOZUK studies differ widely between the European and Chinese populations (see online Table DS1, which is based on HAPMAP CEU (Utah residents with Northern and Western European ancestry from the Centre d'Etude du Polymorphisme Humain project collection) and CHB (Han Chinese in Beijing, China) data-sets), which indicates genetic heterogeneity. Therefore, we tended to select the SNPs that exhibited more significant associations in the BIOX GWAS dataset. Moreover, the linkage disequilibrium information about the chosen SNPs and those reported in the PGC and PGC+CLOZUK studies was also considered. Ultimately, 18 SNPs from 12 genes were selected for this study (online Table DS2) and further information about the selection of SNPs is provided in the Results section.

The MassARRAY iPLEX Gold platform (Sequenom, San Diego, California, USA) was used for genotyping. Polymerase chain reaction (PCR) amplification primers and single-base extension unextended primers were designed using the online software Human GenoTyping Tools (www.mysequenom.com/Tools). The genotyping analysis was performed according to the manufacturer's protocol (Sequenom). Genotype callings were extracted by the MassARRAY Typer software (version 4.0). In total 9173 individuals, including 3648 in the schizophrenia group and 5525 control groups, were genotyped. Individuals with less than 90% call rates (which may indicate poor DNA quality) were excluded from the analysis. Therefore, 9081 samples (3585 in the schizophrenia group and 5496 in the control group) were kept for further analysis. For each SNP, the call rates and P-values that resulted from HWE testing in the control group were calculated for data quality control.

Analyses​


HapMap genotypes and haplotype data were downloaded using Haploview. Reference Barrett, Fry, Maller and Daly28 HWE tests, allelic association tests and meta-analysis of each SNP were conducted using PLINK. Reference Purcell, Neale, Todd-Brown, Thomas, Ferreira and Bender29 The heterogeneity across studies was evaluated using the Q-test. The Mantel–Haenszel method was used to calculate the fixed-effect estimate. We stratified our samples into northern, central and southern groups according to their geographic region, performed analysis of each group and then combined the results via meta-analysis in the replication study.

Results​


Comparison of the BIOX and PGC/PGC+CLOZUK GWAS data-sets in terms of the newly identified loci​


Nine genome-wide significant SNPs within the eight loci were reported in the PGC and PGC+CLOZUK studies (PGC: 1p21.3, 2q32.3, 8p23.2, 8q21.3 and 10q24.32-q24.33, PGC+CLOZUK: ITIH3/4, CACNA1C and SDCCAG8), and their frequencies differed widely between the European and Chinese populations (Table DS1). In the PGC study, the strongest association signal was observed in locus 1p21.3 (MIR137, rs1625579, P= 1.59×10−11). rs1625529 was not genotyped in the BIOX GWAS data-set; however, a proxy SNP (rs1198588, D′ = 1 and r 2 = 1 in CHB) was present. This proxy SNP exhibited marginal significance (P = 6.91×10−2), and the direction of the effect size was consistent with the PGC report. Therefore, the rs1198588 SNP was selected in the follow-up stage. In 10q24.33 (NT5C2), the reported genome-wide significant SNP was rs11191580 (P = 1.11×10−8) in the PCG study. In the follow-up phase, we selected rs732998 (P = 9.15×10−3 in the BIOX GWAS data-set), which is in complete linkage disequilibrium with rs11191580 (D′ = 1, r 2 = 1) in the HAPMAP CHB data-set. In the locus ITIH3/4, no significant SNPs were observed in the BIOX GWAS data-set. However, rs2239547 was not genotyped in the BIOX GWAS, but the SNP was included in the follow-up phase. Within the other five loci (i.e. 8p23.2, 8q21.3, 10q24.32, SDCCAG8 and CACNA1C), the genome-wide significant SNPs reported in the PGC or PGC+CLOZUK studies were not genotyped or did not exhibit associations in the BIOX GWAS data-set; thus, we selected the other SNPs within each locus that exhibited the most significant associations (P<0.05) for replacement in the follow-up study. In the loci with multiple markers that were found to be significant in the BIOX GWAS data-set, all of the SNPs with P-values below 0.01 were selected. For each locus, at least one SNP was selected with the exception of locus 2q32.3 because no significant SNPs were observed in this locus in the BIOX GWAS data-set and because, as reported in the PGC study, rs17662626 was non-polymorphic in the HAPMAP CHB population.

Results for the 17 top-scoring MIR137 predicted target genes in the BIOX GWAS data-set​


Among the 17 MIR137 predicted target genes (i.e., C10orf26, TCF4, CSMD1, CACNA1C, SLC12A2, CALN1, GRIA1, ST13, CSDC2, LUZP2, GLIS2, EPHA7, CADPS2, RGS6, TBC1D12, FAM78A and C20orf108), a total of 15 (i.e. all but CSDC2 and GLIS2) with at least one SNP were genotyped in the BIOX GWAS data-set. Within these regions, 2230 genotyped SNPs were analysed and 29 SNPs (1.30%) at CSMD, CACNA1C, CALN1, CADPS2 and RGS6 exhibited significance at the level of P<0.01. Ten tagSNPs were selected for the follow-up phase.

Results of the follow-up phase and combined analysis​


The detailed SNP quality control information is given in Table DS2, and the full results for all SNPs are listed in online Table DS3. In the follow-up study, we replicated the associations of five markers (P<0.05, with effects in the same direction, and P-values of less than 0.001 in the meta-analysis of the combined Han Chinese samples). The summary results for the five SNPs are shown in Table 1. The SNP with the most significant association was located in an intron of ITIH3/4 (rs2239547, odds ratio (OR) = 0.81, P = 1.17×10−10), which is consistent with Hamshere et al's report Reference Hamshere, Walters, Smith, Richards, Green and Grozeva24 (OR = 0.90, P = 3.62×10−10 in the PGC+CLOZUK data-set). Meta-analysis of the combined European and Chinese samples (P = 7.54×10−17, OR = 0.88; the Cochran's Q-test of heterogeneity P>0.01) strongly supported the association of this SNP.

 
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Schizophrenia (SZ) is a severe mental disorder affecting about 1 % of the human population. Patients show severe deficits in cognitive processing often characterized by an improper filtering of environmental stimuli. Independent genome-wide association studies confirmed a number of risk variants for SZ including several associated with the gene encoding the transcription factor 4 (TCF4). TCF4 is widely expressed in the central nervous system of mice and humans and seems to be important for brain development. Transgenic mice overexpressing murine Tcf4 (Tcf4tg) in the adult brain display cognitive impairments and sensorimotor gating disturbances. To address the question of whether increased Tcf4 gene dosage may affect cognitive flexibility in an auditory associative task, we tested latent inhibition (LI) in female Tcf4tg mice. LI is a widely accepted translational endophenotype of SZ and results from a maladaptive delay in switching a response to a previously unconditioned stimulus when this becomes conditioned. Using an Audiobox, we pre-exposed Tcf4tg mice and their wild-type littermates to either a 3- or a 12-kHz tone before conditioning them to a 12-kHz tone. Tcf4tg animals pre-exposed to a 12-kHz tone showed significantly delayed conditioning when the previously unconditioned tone became associated with an air puff. These results support findings that associate TCF4 dysfunction with cognitive inflexibility and improper filtering of sensory stimuli observed in SZ patients.
 
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Background: Females generally perform better than males on some measures of social processing (e.g. Empathizing), while males typically perform better than females on some measures of non-social processing (e.g. Systemizing). Extremes of these sex-typical cognitive profiles are associated with the development and maintenance of certain psychiatric disorders. For example, the autism-psychosis model predicts psychosis spectrum conditions can be characterized as a cognitive pattern of very poor Systemizing alongside superior Empathizing ability (autism demonstrating the diametrically opposing cognitive profile). However, little experimental research has been carried out to date testing the cognitive profile associated with psychosis. Methods: The present study used a large non-clinical sample to investigate the relationship between a “jumping to conclusions” (JTC) reasoning bias commonly seen in patients with delusions and measures of Empathizing and Systemizing. Results: Those showing a JTC bias demonstrated greater Empathizing and reduced Systemizing compared to a non-JTC group, irrespective of biological sex. Sex differences were identified in Empathizing and Systemizing but not the JTC. Conclusions: These results show a cognitive pattern consistent with predictions from the autism-psychosis model. In a non-clinical population, a reasoning bias associated with delusions is associated with an Emphasizing/Systemizing profile opposite to that characteristic of autism.
 
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Crespi and Badcock’s (Behaviour Brain Sci 31: 241–261, 2008) novel theory, which presents autism and positive schizophrenia as diametrical opposites on a cognitive continuum, has received mixed support in the literature to date. The current study aimed to further assess the validity of this theory by investigating predictions in relation to empathizing and systemizing. Specifically, it is predicted by Crespi and Badcock that while mild autistic traits should be associated with a cognitive profile of superior mechanistic cognition (which overlaps with systemizing) but reduced mentalistic cognition (which overlaps with empathizing), positive schizotypy traits should be associated with the opposite profile of superior mentalistic but reduced mechanistic cognition. These predictions were tested in a student sample using a battery of self-report and behavioural measures. The pattern of results obtained provides no support for Crespi and Badcock’s theory.
 
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We argue that hyper-systemizing predisposes individuals to show talent, and review evidence that hyper-systemizing is part of the cognitive style of people with autism spectrum conditions (ASC). We then clarify the hyper-systemizing theory, contrasting it to the weak central coherence (WCC) and executive dysfunction (ED) theories. The ED theory has difficulty explaining the existence of talent in ASC. While both hyper-systemizing and WCC theories postulate excellent attention to detail, by itself excellent attention to detail will not produce talent. By contrast, the hyper-systemizing theory argues that the excellent attention to detail is directed towards detecting ‘if p, then q’ rules (or [input–operation–output] reasoning). Such law-based pattern recognition systems can produce talent in systemizable domains. Finally, we argue that the excellent attention to detail in ASC is itself a consequence of sensory hypersensitivity. We review an experiment from our laboratory demonstrating sensory hypersensitivity detection thresholds in vision. We conclude that the origins of the association between autism and talent begin at the sensory level, include excellent attention to detail and end with hyper-systemizing.


1. Introduction​

Savantism is found more commonly in autism spectrum conditions (ASC) than in any other neurological group (see Howlin 2009), and the majority of those with savantism have an ASC (Hermelin 2002). This ‘comorbidity’ (or to use the more neutral term ‘co-occurrence’, since comorbidity is a strange term to use when one of the characteristics is not a disability) shows us that these two profiles are associated well above chance. This forces us to ask: why the link between talent and autism?
In this paper, we argue that while savantism (defined as prodigious talent) is only seen in a subgroup of people with ASC, a universal feature of the autistic brain is excellent attention to detail (Shah & Frith 1993; Jolliffe & Baron-Cohen 1997; O'Riordan et al. 2001). Furthermore, we argue that excellent attention to detail exists in ASC because of evolutionary forces positively selecting brains for strong systemizing, a highly adaptive human ability (Baron-Cohen 2008).
Strong systemizing requires excellent attention to detail, and in our view the latter is in the service of the former. Attention occurs at an early level of cognition, while systemizing is a fairly high-level aspect of cognition. Next, we argue that one can trace excellent attention to detail to its basis in sensory hypersensitivity in ASC. Finally, in this paper, we review an experiment from our laboratory in vision, which points to sensory hypersensitivity in ASC, and briefly describe our research programme exploring this in other modalities (olfaction, hearing and touch). But first, what is systemizing?

2. Systemizing​

Talent in autism comes in many forms, but a common characteristic is that the individual becomes an expert in recognizing repeating patterns in stimuli. We call this systemizing, defined as the drive to analyse or construct systems. These might be any kind of system. What defines a system is that it follows rules, and when we systemize we are trying to identify the rules that govern the system, in order to predict how that system will behave (Baron-Cohen 2006). These are some of the major kinds of system:
  1. collectible systems (e.g. distinguishing between types of stones or wood);
  2. mechanical systems (e.g. a video recorder or a window lock);
  3. numerical systems (e.g. a train timetable or a calendar);
  4. abstract systems (e.g. the syntax of a language or musical notation);
  5. natural systems (e.g. the weather patterns or tidal wave patterns);
  6. social systems (e.g. a management hierarchy or a dance routine with a dance partner); and
  7. motoric systems (e.g. throwing a Frisbee or bouncing on a trampoline).

In all these cases, one systemizes by noting regularities (or structure) and rules. The rules tend to be derived by noting if p and q are associated in a systematic way. The general formulation of what happens during systemizing is one looks for laws of the form ‘if p, then q’. If it is Friday, then we eat fish. If we multiply 3 by itself, then we get 9. If we turn the switch to the down position, then the light comes on. When we think about the kinds of domains in which savants typically excel, it is those domains that are highly systemizable.
Examples might be from numbers (e.g. spotting if a number is a prime number), calendrical calculation (e.g. telling which day of the week a given date will fall), drawing (e.g. analysing space into geometric shapes and the laws of perspective, and perfecting an artistic technique), music (e.g. analysing the sequence of notes in a melody, or the lawful regularities or structure in a piece), memory (e.g. recalling long sequences of digits or lists of information) or even learning foreign languages (e.g. learning vocabulary or the laws of grammar). In each of these domains, there is the opportunity to repeat behaviour in order to check if one gets the very same outcome every time. Multiplying 3 by itself always delivers 9, the key change in this specific musical piece always occurs in the 13th bar, throwing the ball at this particular angle and with this particular force always results in it landing in the hoop.

3. Systemizing the Rubik's Cube​

Let us take a cardinal example of savantism: a non-conversational child with autism who can solve the Rubik's Cube ‘problem’ in 1 min and 7 s. This is a nice example because it illustrates several things. First, that the child's non-verbal ability with the Rubik's Cube is at a much higher level than either his communication or social skills, or indeed what one would expect of his age. Second, it prompts us to ask: what are the processes involved in solving the Rubik's Cube? At a minimum, it involves analysing or memorizing the sequence of moves to produce the correct outcome. It is a series of ‘if p, then q’ steps. This child with autism appeared to have ‘discovered’ the layer-by-layer method to solve the 3×3×3 Rubik's Cube problem, which at best takes a minimum of 22 moves. (Note that he was not as fast as the current 2008 World Champion Erik Akkersdijk who in the Czech Open championship solved the Rubik's Cube in 7.08 s!).

4. Systemizing in autism spectrum conditions​

What is the evidence for intact or even unusually strong systemizing in ASC? First, such children perform above the level that one would expect on a physics test (Baron-Cohen et al. 2001). Children with Asperger's syndrome as young as 8–11 years old scored higher than a comparison group who were older (typical teenagers). Second, using the Systemizing Quotient (SQ), people with high-functioning autism or AS score higher on the SQ compared with general population controls (Baron-Cohen et al. 2003). Third, children with classic autism perform better than controls on the picture-sequencing test where the stories can be sequenced using physical-causal concepts (Baron-Cohen et al. 1986). They also score above average on a test of how to figure out how a Polaroid camera works, even though they have difficulties figuring out people's thoughts and feelings (Baron-Cohen et al. 1985; Perner et al. 1989). The Polaroid camera test was used as a mechanical equivalent to the false belief test, since, in the former, all one has to do is infer what will be represented in a photograph given the ‘line of sight’ between the camera and an object, whereas, in the latter, one has to infer what belief (i.e. mental representation) a person will hold given what they saw and therefore know about.
Strong systemizing is a way of explaining the non-social features of autism: narrow interests; repetitive behaviour; and resistance to change/need for sameness. This is because when one systemizes, it is best to keep everything constant, and to only vary one thing at a time. That way, one can see what might be causing what, and with repetition one can verify that one gets the very same pattern or sequence (if p, then q) every time, rendering the world predictable. One issue is whether hyper-systemizing only applies to the high-functioning individuals with ASC. While their obsessions (with computers or maths, for example) could be seen in terms of strong systemizing (Baron-Cohen et al. 1999), when we think of a child with low-functioning autism, many of the classic behaviours can be seen as a reflection of their strong systemizing. Some examples are listed in box 1.

Box 1. Systemizing in classic autism and/or Asperger's syndrome.​

Table​


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5. Systemizing and weak central coherence​

As with the weak central coherence (WCC) theory (Frith 1989; and discussed in this issue, Happé & Vital 2009), the hyper-systemizing theory is about a different cognitive style (Happé 1996). Similar to that theory, it also posits excellent attention to detail (in perception and memory), since when one systemizes one has to pay attention to the tiny details. This is because each tiny detail in a system might have a functional role leading to new information of the form ‘if p, then q’. Excellent attention to detail in autism has been repeatedly demonstrated (Shah & Frith 1983, 1993; Jolliffe & Baron-Cohen 2001; O'Riordan et al. 2001; Mottron et al. 2003).
One difference between these two theories is that the WCC theory sees people with ASC as drawn to detailed information (sometimes called a local processing bias) either for negative reasons (an inability to integrate was postulated in the original version of this theory) or because of stronger local processing (in the later version of this theory). By contrast, the hyper-systemizing theory sees this same quality (excellent attention to detail) as being highly purposeful: it exists in order to understand a system. Attention to detail is occurring for positive reasons: in the service of achieving an ultimate understanding of a system (however small and specific that system might be).
We can return to the Rubik's Cube problem to see the difference between these two theories more clearly. At one level, the Rubik's Cube is a three-dimensional Block Design Test but where the cubes are all connected. Recall that the Block Design Test is the subtest on Weschler IQ tests on which people with autism perform at their best (Shah & Frith 1993; Happé 1996). The Rubik's Cube contains 21 movable connected cubes (since the five central cubes do not move) with different coloured faces in the 3×3×3 version. According to the WCC theory, the reason why people with autism show superior performance on the Block Design Test is that their good local processing enables them to ‘see’ each individual cube even if the design to be copied is not ‘pre-segmented’ (Shah & Frith 1983). It is clear how good local processing would lead to faster ‘analysis’ of the whole (design) into constituent parts (the individual cubes), but to solve the Rubik's Cube (or the Block Design problem), more than just good local processing is needed. A strength in ‘if p, then q’-type reasoning is also required. On the classic Block Design subtest, one needs to mentally or manually rotate the cube to produce the relevant output. That is, one needs to perform an operation on the input to produce the relevant output. The same is true (but with more cubes and therefore more complexity) in the Rubik's Cube problem: ‘If the red cube with the green side is positioned on the top layer on the right side and I rotate the top layer anticlockwise by 90 degrees, then this will complete the top layer as all one colour’.
In earlier formulations of systemizing, the key cognitive process was held to be in terms of [input–operation–output] processing (Baron-Cohen 2006). In mathematics, if the input=3, and the operation=cubing, then the output=27. In the Rubik's Cube notional example above, the input=[the red cube with the green side is positioned on the top layer on the right side], the operation=[rotate the top layer anticlockwise by 90 degrees] and the output=[complete the top layer as all one colour]. Note that WCC makes no mention of the key part of this that is noting the consequences of an operation. Simply seeing the parts in greater detail would not by itself lead to understand the operations (the moves) needed to solve the Rubik's Cube.
Another difference between the WCC theory and the hyper-systemizing theory is that the latter (but not the former) predicts that over time, the person may achieve an excellent understanding of a whole system, given the opportunity to observe and control all the variables (all the ‘if p, then q’ rules) in that system. WCC would predict that even given all the time in the world, the individual will be forever lost in the detail. The existence of talented mathematicians with AS such as Richard Borcherds is proof that such individuals can integrate the details into a true understanding of the system (Baron-Cohen 2003). In the rule ‘if p, then q’, the terms ‘if’ and ‘then’ are how the details become integrated, albeit one small step at a time. The idea at the neurological level that ASC involves an abundance of local short-range connectivity (Belmonte et al. 2004) may explain this cognitive style of identifying one specific link between two details.

6. Hyper-systemizing: implications for education​

Teachers, whether of children with autism or adults with Asperger's syndrome, need to take into account that hyper-systemizing will affect not only how people with ASC learn but also how they should be assessed. IQ test items, essays and exam questions designed for individuals who are ‘neurotypical’ may lead to the person with ASC scoring zero when their knowledge is actually greater, deeper and more extensive than that of most people. What can appear as a slow processing style may be because of the massively greater quantity of information that is being processed.
A man with Asperger's syndrome reported recently that ‘I see all information in terms of links. All information has a link to something and I pay attention to these links. If I am asked a question in an exam I have great difficulty in completing my answer within the allocated 45 min for that essay, because every fact I include has thousands of links to other facts, and I feel my answer would be incorrect if I didn't report all of the linked facts. The examiner thinks he or she has set a nice circumscribed question to answer, but for someone with autism or Asperger's syndrome, no topic is circumscribed. There is ever more detail with ever more interesting links between the details’.
When asked about the concept of apple, for example, he could not give a short summary answer such as ‘an apple is a piece of fruit’ (i.e. referring to the prototypical level ‘apple’ as linked to the superordinate level ‘fruit’) but had to continue by also trying to link it to the 7500 different species of apple (the subordinate-level concepts), listing many of each type and the differences in terms of the history of each species, how they are cultivated, what they taste and look like, etc. When asked about the concept of beetle, he could not just give a summary answer such as ‘a beetle is an insect’ but had to mention as many of the 350 000 species of beetle that he knew existed.
This cognitive style is understandable in terms of the hyper-systemizing theory because a concept is a system. A concept is a way of using an ‘if p, then q’ rule to define what to include as members of a category (e.g. if it has scales and gills, then it is a fish). Furthermore, concepts exist within a classification system, which are rules for how categories are related to one another. So, the question ‘what is a beetle?’ is trivial for a neurotypical individual who simply answers in terms of a crude, imprecise and fuzzy category: ‘it is an insect’. It may, however, require a very long, exhaustive answer from someone with ASC: beetles are members of the category of animal (kingdom), arthropods (phylum), insects (class), pterygota (subclass), neoptera (infraclass), endopterygota (superorder), coleoptera (order), and could be in one of four suborders (adephaga, archostemata, mycophaga and polyphaga), each of which has an infraorder, a superfamily and a family. Even the previous sentence would for this man with Asperger's syndrome be a gross violation of the true answer to the question because so much important factual information has been left out. But for the hyper-systemizer, getting these details correct matters, because the concept—and the classification system linking concepts—is a system for predicting how this specific entity (this specific beetle) will behave or will differ from all other entities.

7. Hyper-systemizing theory versus executive dysfunction theory​

The executive dysfunction (ED) theory (Rumsey & Hamberger 1988; Ozonoff et al. 1991; Russell 1997) is the other major theory that has attempted to explain the non-social features of ASC, and particularly the repetitive behaviour and narrow interests that characterize ASC. According to this theory, aspects of executive function (action control) involved in flexible switching of attention and planning are impaired, leading to perseveration. The ED theory, similar to the WCC theory, has difficulty in explaining instances of good understanding of a whole system, such as calendrical calculation, since within the well-defined system (calendar) attention can switch very flexibly. The ED theory also predicts perseveration (so-called ‘obsessions’) but does not explain why in autism and Asperger's syndrome these should centre on systems (Baron-Cohen & Wheelwright 1999). Finally, the ED theory simply re-describes repetitive behaviour as an instance of ED without seeing what might be positive about the behaviour.
So, when the low-functioning person with classic autism has shaken a piece of string thousands of times close to his eyes, while the ED theory sees this as perseveration arising from some neural dysfunction which would normally enable the individual to shift attention, the hyper-systemizing theory sees the same behaviour as a sign that the individual ‘understands’ the physics (i.e. recognizes the patterns) behind the movement of that piece of string. He may be able to make it move in exactly the same way every time. Or to take another example, when he makes a long, rapid sequence of sounds, he may ‘know’ exactly that acoustic pattern, and get some pleasure from the confirmation that the sequence is the same every time. Much as a mathematician might feel an ultimate sense of pleasure that the ‘golden ratio’ (that (a+b)/a=a/b) and that this always comes out as 1.61803399, so the child—even with low-functioning autism—who produces the same outcome every time with their repetitive behaviour, appears to derive some emotional pleasure at the predictability of the world. This may be what is clinically described as ‘stimming’ (Wing 1997). Autism was originally described as involving ‘resistance to change’ and ‘need for sameness’ (Kanner 1943), and here we see that important clinical observation may be the hallmark of strong systemizing. It will be important for future neuroimaging studies to test if the reward systems in the brain (e.g. the dopaminergic or cannabinoid systems) are active during such repetitive behaviour.
If we return to the Rubik's Cube example, the ED theory would predict that an inability to ‘plan’ should make solving a Rubik's Cube impossible for a savant with autism. By contrast, as we saw earlier, the hyper-systemizing theory has no difficulty in explaining such talent.

8. Sensory hypersensitivity​

Rather than assuming that the strong systemizing in ASC is ultimately reducible to excellent attention to detail, in this section we pursue the idea that the excellent attention to detail is itself reducible to sensory hypersensitivity. Mottron & Burack (2001) postulated the ‘enhanced perceptual functioning’ model of ASC, characterized by superior low-level perceptual processing. To what extent is this a feature of basic sensory physiology?
Studies using questionnaires such as the sensory profile have revealed sensory abnormalities in over 90 per cent of children with ASC (Leekam et al. 2001; Kern et al. 2006; Tomchek & Dunn 2007). In vision, Bertone et al. (2003) found that individuals with ASC are more accurate at detecting the orientation of first-order gratings (simple, luminance-defined) but less accurate at identifying second-order gratings (complex, texture-defined). In the auditory modality, superior pitch processing has been found in ASC (Mottron et al. 1999; Bonnel et al. 2003; Heaton et al. 2008). In a case study, Mottron et al. (1999) reported exceptional absolute judgement and production of pitch. Bonnel et al. (2003) found superior pitch discrimination and processing abilities in individuals with high-functioning autism. O'Riordan & Passetti (2006) also reported superior auditory discrimination ability in children with ASC, and Järvinen-Pasley et al. (2002) showed superior perceptual processing of speech in children with autism.
In the tactile modality, Blakemore et al. (2006) showed hypersensitivity to vibrotactile stimulation to a frequency of 200 Hz but not for 30 Hz. In addition, the ASC group rated suprathreshold tactile stimulation as significantly more tickly and intense than did the control group. Tommerdahl et al. (2007) reported participants with ASC outperformed controls in tactile acuity after short adaptation to a vibrotactile stimulus period of 0.5 s. (Note that this hypersensitivity is not always observed. On a tactile discrimination task, O'Riordan & Passetti (2006) found no differences in children with autism compared with controls.) Cascio et al. (2008) investigated tactile sensation and reported increased sensitivity to vibrations and thermal pain in ASC, while detection to light touch and warmth/cold was similar in both groups.
Only two previous studies have been reported investigating olfaction in ASC, and unlike the research into the other senses which consistently find hypersensitivity, both of these studies reported deficits in identifying odours despite intact odour detection (Suzuki et al. 2003; Bennetto et al. 2007). Looking more closely at the two previous studies into olfaction in ASC, both required participants to explicitly identify the odour from a choice of responses, and methodology likely to involve both executive function and memory. For example, the study by Bennetto and colleagues required participants to decide which of four possible responses an odour matched. A simpler task might provide a purer test of low-level olfactory discrimination in ASC.
In the final section of this paper, we summarize an experiment from our laboratory looking at vision in ASC, in terms of basic sensory detection thresholds (acuity). Ongoing studies from our laboratory are also testing sensory detection thresholds in other modalities (touch, audition and olfaction). Full details of these experiments are reported elsewhere (Ashwin et al. 2009, submitted; Tavassoli et al. submitted).
Participants were administered the Freiburg Visual Acuity and Contrast Test, a standardized optometric test that uses the Landholt-C optotype (Bertone et al. 2003). The gaps in the C-shape range from 0.4 to 25 mm and appear in one of four positions: up; down; left; or right. Participants sat at a fixed distance of 60 cm from the computer screen and identified the location of the ‘missing’ part of the C-shaped stimulus by selecting one of four arrow keys on the keyboard. Participants had 3 s to respond on each of the 150 trials. The results generated a Snellen decimal, where a value of 1.0 represents ‘normal’ 20 : 20 vision (Heaton et al. 2008). A score of 20 : 10 is regarded as excellent vision, and means an object normally detected at 10 feet can be detected at 20 feet. Thus, Snellen values above 1.0 represent increasingly accurate vision, and values below 1.0 represent worse vision. The ASC group scored a mean acuity measure of 2.79 (s.d.=±0.37), which was significantly better than the control group mean of 1.44 (s.d.=±0.26), t(40)=4.63; p<0.001. The Snellen score of 2.79 for the ASC group represents acuity 2.79 times better than normal, and translates to vision of 20 : 7. This approaches the range reported for birds of prey.
Results from this and other experiments demonstrated greater sensory perception in ASC across multiple modalities. In the context of the earlier discussion of hyper-systemizing and excellent attention to detail, we surmise that these sensory differences in functioning may be affecting information processing at an early stage (in terms of both sensation/cognition and development) in ways that could both cause distress but also predispose to unusual talent. These results of hypersensitivity confirm previous findings and mirror anecdotal reports of individuals with ASC (Grandin 1996). For example, Temple Grandin writes that ‘overly sensitive skin can be a big problem…Shampooing actually hurt my skin…To be lightly touched appeared to make my nervous system whimper, as if the nerve ends were curling up’. In terms of increased sensitivity to certain types of auditory stimuli (high frequencies), there are anecdotal reports that individuals with autism tend to avoid certain sounds. Grandin states ‘I can shut out my hearing and withdraw from most noise, but certain frequencies cannot be shut out…High pitched, shrill noises are the worst’. Mottron et al. (1999) reported the case of a woman with autism who was hypersensitive to frequencies from 1 to 5 kHz at 13 years of age, and to 4 kHz at 18 years.
Enhanced sensitivity may be specific to certain stimuli in all modalities. In vision, Bertone et al. (2003) pointed out the importance of specific stimuli in investigating visual differences in ASC. In touch, Blakemore et al. (2006) reported hypersensitivity for higher frequency (200 Hz) vibrotactile stimulation, but not for lower (30 Hz). Pinpointing the precise stimuli in which enhanced sensitivity occur in ASC will be important for future research. To our knowledge, the highest frequency that has been used to investigate hearing in ASC is 8 kHz (Bonnel et al. 2003). Our ongoing study investigates very high frequencies, up to 18 kHz (Tavassoli et al. submitted). The reported hypersensitivity through frequencies above 16 kHz is especially important since some environmental sounds operate at or above this range of frequencies. Grandin reported ‘Some of the sounds that are most disturbing to autistic children are the high-pitched, shrill noises made by electrical drills, blenders, saws, and vacuum cleaners’.
Hypersensitivity could result from a processing difference at various sensory levels including the density or sensitivity of sensory receptors, inhibitory and exhibitory neurotransmitter imbalance or speed of neural processing. Belmonte et al. (2004) suggested local range neural overconnectivity in posterior, sensory parts of the cerebral cortex is responsible for the sensory ‘magnification’ in people with ASC. While our laboratory and others have tested sensory profiles in ASC using functional magnetic resonance imaging (fMRI) (Gomot et al. 2006, 2008), the combination of imaging and genetic approaches to study sensory perception in fMRI may lead towards a more complete picture. We conclude that the search for the association between autism and talent should start with the sensory hypersensitivity, which gives rise to the excellent attention to detail, and which is a prerequisite for hyper-systemizing.
 
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Background​

Schizophrenia is a chronic and severe mental disorder, and it has been predicted to be highly polygenic. Common SNPs located in or near BNIP3L were found to be genome-wide significantly associated with schizophrenia in recent genome-wide association studies. The purpose of our study is to investigate potential causal variants in BNIP3L gene.

Results​

We performed targeted sequencing for all exons and un-translated regions of BNIP3L gene among 1806 patients with schizophrenia and 998 healthy controls of Han Chinese origin. Three rare nonsynonymous mutations, BNIP3L (NM_004331): c.52A>G, c.167G>A and c.313A>T, were identified in schizophrenia cases, and two of them were newly reported. The frequencies of these rare nonsynonymous mutations were significantly different between schizophrenia cases and healthy controls. For the common variants, rs147389989 achieved significance in both allelic and genotypic distributions with schizophrenia. Rs1042992 and rs17310286 were significantly associated with schizophrenia in meta-analyses using PGC, CLOZUK, and our new datasets in this study.

Conclusions​

Our findings provided further evidence that BNIP3L gene is a susceptibility gene of schizophrenia and revealed functional and potential causal mutations in BNIP3L. However, more functional validations are suggested to better understand the role of BNIP3L in the etiology of schizophrenia.
Keywords: BNIP3L gene, Schizophrenia, Targeted next-generation sequencing, Case–control study

Schizophrenia is a severe mental disorder that affects about 1% of the world population. The core features of schizophrenia are positive symptoms including delusions and hallucinations, negative symptoms (especially impaired motivation, reduction in spontaneous speech, and social withdrawal) and cognitive impairment [1, 2]. The first episode of schizophrenia usually occurs in late adolescence or early adulthood [3, 4]. Around 20% of schizophrenia patients have chronic symptoms and disability, and over 50% have intermittent but long-term psychiatric problems [5]. The etiology and pathogenesis of schizophrenia are not very clear, but genetic and environmental synergistic pathogeneses are generally accepted. The estimated heritability of schizophrenia is about 70–85% [6].

Schizophrenia is highly polygenic, as predicted by several genetic epidemiological researches many years ago [7]. Previous genome-wide association studies (GWASs) have revealed more than 100 distinct genetic loci are genome-wide associated with schizophrenia, which have en masse effects [1, 8, 9]. In 2017, Li et al. reported 30 new susceptibility loci of schizophrenia including rs73219805 locus near BNIP3L gene and predicting that the BNIP3L gene may be a susceptibility gene of schizophrenia [10]. Recent research about comparative genetic architectures of schizophrenia also revealed that intron variant of BNIP3L gene, rs117325001, was significantly associated with schizophrenia in a fixed-effect meta-analysis including individuals from East Asian and European ancestries [11].

 

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