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Rare genetic variants that confer large effects on neurodevelopment and behavioral phenotypes can reveal novel gene-brain-behavior relationships relevant to autism. Copy number variation at the 22q11.2 locus offer one compelling example, as both the 22q11.2 deletion (22qDel) and duplication (22qDup) confer increased likelihood of autism spectrum disorders (ASD) and cognitive deficits, but only 22qDel confers increased psychosis risk. Here, we used the Penn Computerized Neurocognitive Battery (Penn-CNB) to characterized neurocognitive profiles of 126 individuals: 55 22qDel carriers (MAge=19.2 years, 49.1% male), 30 22qDup carriers (MAge=17.3 years, 53.3 % male), and 41 typically developing (TD) subjects (MAge=17.3 years, 39.0 % male). We performed linear mixed models to assess group differences in overall neurocognitive profiles, domain scores, and individual test scores. We found all three groups exhibited distinct overall neurocognitive profiles. 22qDel and 22qDup carriers showed significant accuracy deficits across all domains relative to controls (Episodic Memory, Executive Function, Complex Cognition, Social Cognition, and Sensorimotor Speed), with 22qDel carriers exhibiting more severe accuracy deficits, particularly in Episodic Memory. However, 22qDup carriers generally showed greater slowing than 22qDel carriers. Notably, slower social cognition speed was uniquely associated with increased global psychopathology and poorer psychosocial functioning in 22qDup. Compared to TD, 22q11.2 CNV carriers failed to show age-associated improvements in multiple cognitive domains. Exploratory analyses revealed 22q11.2 CNV carriers with ASD exhibited differential neurocognitive profiles, based on 22q11.2 copy number. These results suggest that there are distinct neurocognitive profiles associated with either a loss or gain of genomic material at the 22q11.2 locus.

Social-isolation has been linked to a range of psychiatric issues, but the behavioral component that drives it is not well understood. Here, a GWAS is carried out to identify genetic variants which contribute to Social-isolation behaviors in up to 449,609 participants from the UK Biobank. 17 loci were identified at genome-wide significance, contributing to a 4% SNP heritability estimate. Using the Social-isolation GWAS, polygenic risk scores (PRS) were derived in ALSPAC, an independent, developmental cohort, and used to test for association with friendship quality. At age 18, friendship scores were associated with the Social-isolation PRS, demonstrating that the genetic factors are able to predict related social traits. LD score regression using the GWAS demonstrated genetic correlation with autism spectrum disorder, schizophrenia, and major depressive disorder. However, no evidence of causality was found using a conservative Mendelian randomization approach other than that of autism spectrum disorder on Social-isolation. Our results show that Social-isolation has a small heritable component which may drive those behaviors which is associated genetically with other social traits such as friendship satisfaction as well as psychiatric disorders.

ObjectiveSchizophrenia is associated with widespread brain-morphological alterations, believed to be shaped by the underlying connectome architecture. This study tests whether large-scale structural reorganization in schizophrenia relates to normative network architecture, in particular regional centrality/hubness and connectivity patterns. We examine network effects in schizophrenia across different disease stages, and transdiagnostically explore consistency of such relationships in patients with bipolar and major depressive disorder.MethodsWe studied anatomical MRI scans from 2,439 adults with schizophrenia and 2,867 healthy controls from 26 ENIGMA sites. Case-control patterns of structural alterations were evaluated against two network susceptibility models: 1) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; 2) epicenter models, which identify regions whose typical connectivity profile most closely resembles the disease-related morphological alteration patterns. Both susceptibility models were tested across schizophrenia disease stages and compared to meta-analytic bipolar and major depressive disorder case-control maps.ResultsIn schizophrenia, regional gray matter reductions co-localized with interconnected hubs, in both the functional (r=0.58, pspinspin=0.01). Epicenters were identified in temporo-paralimbic regions, extending to frontal areas. We found unique epicenters for first-episode and early stages, and a shift from occipital to temporal-frontal epicenters in chronic stages. Transdiagnostic comparisons revealed shared epicenters in schizophrenia and bipolar, but not major depressive disorders.ConclusionsCortical reorganization over the course of schizophrenia closely reflects brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters. The observed overlapping epicenters for schizophrenia and bipolar disorder furthermore suggest shared pathophysiological processes within the schizophrenia-bipolar-spectrum.

SummaryMany scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose CoCoA (Conditional Correlation Model with Association Size), a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale, while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semi-parametric estimators adapted from genome association studies, and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modelling and partitioned correlation analyses.

Autism spectrum disorder (ASD) is associated with high structural heterogeneity in magnetic resonance imaging (MRI). This work uncovers three neuroanatomical dimensions of ASD (N = 307) using machine learning methods and constructs their characteristic MRI signatures. The presence of these signatures, along with their clinical profiles and genetic architectures, are investigated in the general population. High expression of the first dimension (A1, “aging-related”) is associated with globally reduced brain volume, cognitive dysfunction, and aging-related genetic variants. The second dimension (A2, “schizophrenia-like”) is characterized by enlarged subcortical volume, antipsychotic medication use, and partially overlapping genetic underpinnings to schizophrenia. The third dimension (A3, “classical ASD”) is distinguished by enlarged cortical volume, high non-verbal cognitive performance, and genes and biological pathways implicating brain development and abnormal apoptosis. Thus, we propose a three-dimensional endophenotypic representation to construe the heterogeneity in ASD, which can support precision medicine and the discovery of the biological mechanisms of ASD.

Mechanisms underpinning neurotypical age-related variations in cortical thickness in the human brain remain insufficiently specified. Here we used cell-specific marker genes, followed by gene ontology and enrichment analyses, to quantify the association between gene-expression levels and inter-regional age-related variations in neurotypical cortical thinning using multicohort neuroimaging data from 14,248 individuals ages 4-89 years. We found that early-life (60 years) were associated with distinct patterns of association between inter-regional profiles of cortical thickness and expression profiles of markers genes for CA1 and S1 pyramidal cells, astrocytes, and microglia. Gene ontology and enrichment analyses indicated each of the three life-stages was associated with different biological processes and cellular components; these related to synaptic modeling in early life, neurotransmission in mid-life, and neurodegeneration in late-life. These findings provide mechanistic insights on age-related cortical thinning during typical development and ageing.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to structural covariance patterns across brain regions and individuals. We present a mega-analysis of structural covariance with magnetic resonance imaging of 50,699 healthy and diseased individuals (12 studies, 130 sites, and 12 countries) over their lifespan (ages 5 through 97). Patterns of structural covariance (PSC) were highly heritable (0.05< h2 -log10[p-value] > 8.8): 1245 previously unreported, and 69% of them independently replicated (-log10[p-value] = 4.5). Associations revealed an imaging phenotypic landscape between 2003 PSCs and 49 clinical and cognitive traits at multiple scales. We constructed machine learning-derived individualized imaging signatures for various disease diagnoses using PSC features at multiple scales, suggesting that disease effects on the brain were better manifested in a multi-scale continuum than on any single scale. Experimental results were integrated into the Multi-scale Structural Imaging Covariance (MuSIC) atlas and made publicly accessible through the BRIDGEPORT web portal (https://www.cbica.upenn.edu/bridgeport/). Our results reveal strong associations between brain structural covariance, genetics, and clinical phenotypes, supporting that PSCs can serve as an endophenotypic anatomic dictionary in future research.

Inversion polymorphisms between low-copy repeats (LCRs) might predispose chromosomes to meiotic non-allelic homologous recombination (NAHR) events and thus lead to genomic disorders. However, for the 22q11.2 deletion syndrome (22q11.2DS), the most common genomic disorder, no such inversions have been uncovered as of yet. Using fiber-FISH, we demonstrate that parents transmitting the de novo 3 Mb LCR22A–D 22q11.2 deletion, the reciprocal duplication, and the smaller 1.5 Mb LCR22A–B 22q11.2 deletion carry inversions of LCR22B–D or LCR22C–D. Hence, the inversions predispose chromosome 22q11.2 to meiotic rearrangements and increase the individual risk for transmitting rearrangements. Interestingly, the inversions are nested or flanking rather than coinciding with the deletion or duplication sizes. This finding raises the possibility that inversions are a prerequisite not only for 22q11.2 rearrangements but also for all NAHR-mediated genomic disorders.

(The American Journal of Human Genetics 101, 616–622; October 5, 2017) This article has been retracted at the request of the authors. In this article, we claimed the presence of nested inversion polymorphisms, spanning the LCR22-B/D or LCR22-C/D locus. We detected an increased frequency of inversions in the chromosomes of the parental genome in which the 22qll.2 rearrangement occurred. Since this publication, we have continued working on an improved map of the 22qll Iocus. During these efforts, we were unable to confirm the presence of the inversion polymorphism. We proved the polymorphism to be absent and the original data to be biased. Absence of a double-blinded control left complex observations open to interpretation and hampered objective analysis. With this crucial flaw uncovered, we no longer support these data and are retracting the article. We apologize to the scientific community, the readers of AJHG, and the families who were involved in this research.

Nicotine withdrawal is associated with subtle working memory deficits that predict subsequent relapse. We examined the neural substrates underlying these processes in treatment-seeking smokers, and explored the moderating influence of age on abstinence-induced alterations in brain activity and performance. Sixty-three smokers participated in two blood-oxygen-level dependent (BOLD) functional magnetic resonance imaging (fMRI) scans while performing a visual N-back task on two separate occasions: smoking as usual and after 24 hours of biochemically confirmed abstinence (order counterbalanced). Abstinence (versus smoking) led to reduced accuracy, slower median correct response time, and reduced BOLD signal change in the 3 a priori regions of interest (ROIs): medial frontal/cingulate gyrus and right and left dorsolateral prefrontal cortex. Significant age x session effects were found for BOLD signal change in all three regions, as well as for withdrawal and craving; for all measures, abstinence effects were attenuated in smokers aged >50 years compared to those < 50 years old. These results suggest that abstinence effects on neurocognitive function may be more pronounced for younger smokers, and may indicate a new avenue for research exploring mechanisms underlying age differences in smoking cessation success.

The paper presents a method of creating abnormality classifiers learned from Diffusion Tensor Imaging (DTI) data of a population of patients and controls. The score produced by the classifier can be used to aid in diagnosis as it quantifies the degree of pathology. Using anatomically meaningful features computed from the DTI data we train a non-linear support vector machine (SVM) pattern classifier. The method begins with high dimensional elastic registration of DT images followed by a feature extraction step that involves creating a feature by concatenating average anisotropy and diffusivity values in anatomically meaningful regions. Feature selection is performed via a mutual information based technique followed by sequential elimination of the features. A non-linear SVM classifier is then constructed by training on the selected features. The classifier assigns each test subject with a probabilistic abnormality score that indicates the extent of pathology. In this study, abnormality classifiers were created for two populations; one consisting of schizophrenia patients (SCZ) and the other with individuals with autism spectrum disorder (ASD). A clear distinction between the SCZ patients and controls was achieved with 90.62% accuracy while for individuals with ASD, 89.58% classification accuracy was obtained. The abnormality scores clearly separate the groups and the high classification accuracy indicates the prospect of using the scores as a diagnostic and prognostic marker.

The growing clinical importance of diffusion tensor imaging (DTI) in disease investigation has prompted large population studies that require computational neuroanatomic techniques for tensor processing, as conventional analysis of scalar maps of DTI does not identify the full impact of pathology. In this paper we propose a comprehensive framework called manifold based morphometry (MBM) for the computational and statistical analysis of DTI datasets, consisting of spatial normalization to a template, followed by voxel-based analysis based on embedding the tensors to a linear submanifold using kernel-based manifold learning and applying statistics in this embedded space. Regions of significant difference are identified and compared with those found with conventional voxel-based analysis of scalar maps of anisotropy and diffusivity. MBM has then been applied to the group-based statistical analysis of dataset of schizophrenia patients and controls. The comparison yields that MBM consisting of the full tensor DTI analysis reveals regions of difference that encompass regions identified by the analysis of scalar maps thereby reinforcing the comprehensive nature of the designed framework.

Most of current computer-based facial expression analysis methods focus on the recognition of perfectly posed expressions, and hence are incapable of handling the individuals with expression impairments. In particular, patients with schizophrenia usually have impaired expressions in the form of "flat" or "inappropriate" affects, which make the quantification of their facial expressions a challenging problem. This paper presents methods to quantify the group differences between patients with schizophrenia and healthy controls, by extracting specialized features and analyzing group differences on a feature manifold. The features include 2D and 3D geometric features, and the moment invariants combining both 3D geometry and 2D textures. Facial expression recognition experiments on actors demonstrate that our combined features can better characterize facial expressions than either 2D geometric or texture features. The features are then embedded into an ISOMAP manifold to quantify the group differences between controls and patients. Experiments show that our results are strongly supported by the human rating results and clinical findings, thus providing a framework that is able to quantify the abnormality in patients with schizophrenia.

We study morphologic characteristics and age-related changes in patients with schizophrenia to investigate whether abnormal neurodevelopment and brain structure have a role in the pathophysiological course of this disease.Our data consist of a set of cranial magnetic resonance images of 46 patients with schizophrenia and age- and sex-matched healthy controls. We deformed a template brain image to our set of subject images. Jacobian fields of these deformations were reduced to sets of 52 normalized region volumes for each subject by using a neuroanatomic atlas. Normalized regional volumes of the control and patient groups were compared by using Student t-test, and age correlation of each region volume was calculated for the two groups. All results were corrected for multiple comparisons by using permutation testing. We used a classifier based on support vector machines and a feature selection method to determine our ability to discriminate brains of controls from those of patients.Analysis of normalized region volumes shows enlargement of the third ventricle in patients. The age-correlation study showed a significant positive correlation in the third ventricle and right thalamus of controls, but not patients. Using an average of 6.5 features, our classifier was able to correctly identify 72% of patients and 70% of controls.In addition to enlargement of the third ventricle, brains of patients with schizophrenia show a different pattern of age-related changes.

Abnormal neurodevelopment may play a role in the pathophysiology of schizophrenia. We used deformation-based morphometry to examine voxel-wise age-related changes in patients with schizophrenia compared with healthy brains.We used a set of skull-stripped brains from an image database of cranial magnetic resonance images. We then deformed a template brain to the rest of the brains creating a set of deformation fields. Using the Jacobian values of these deformation fields, we calculated the voxel-wise t-score for comparison of controls with patients. We also calculated the voxel-wise Pearson correlation of Jacobian with age for both controls and patients.By examining the volume renderings of these statistical fields, we found that healthy people undergo age-related expansion of the ventricles, the surrounding periventricular white matter, and a corresponding decline in the frontal lobes and cingulate gyrus. In contrast, patients show much less of this age-related expansion of the ventricles and less atrophy in the cerebral cortex. In addition, patients have larger ventricles and reduced volume in the frontal/parietal lobes.These constellations of findings suggest that otherwise normal age-related ventricular enlargement and cortical loss occurs in schizophrenia patients, albeit at an earlier age.

Subject motion is well recognized as a significant impediment to resolution and sensitivity in functional magnetic resonance imaging (fMRI). A parallel confounder to fMRI data quality is geometric image distortion, particularly at high field strengths, due to susceptibility-induced magnetic field inhomogeneity. Consequently, many high-field echo-planar imaging methods incorporate a post-processing distortion correction by acquiring a field map of the sample prior to the fMRI measurement. However, field mapping methods impose a spatial mask on the data, since field information is only obtainable from regions with adequate signal-to-noise ratio (SNR). This masking, when applied to subsequent images in the fMRI time series, can clip the effects of motion, resulting in inaccurate estimation and correction of motion-based changes in the images.The effects of geometric distortion correction on automated realignment (motion correction) of fMRI data are investigated from data acquired at 4 T. The results of image realignment with and without prior application of distortion correction are compared, using the estimated motion parameters and overall image realignment as metrics.The application of field-map-based distortion correction prior to image realignment reduces the amount of motion detected by a standard motion correction algorithm. Moreover, motion correction applied before distortion correction is shown to result in superior realignment of motion-correction images.It is preferable to perform motion realignment prior to correcting for geometric distortion.

Research in detecting regional changes in brain metabolism related to functional activity is reviewed and supplemented by original results from positron emission tomography (PET) and the 18F-fluorodeoxyglucose method (FDG). A formula for calculating the value of local cerebral glucose metabolism (LCMRGlc) is discussed. Results concerning auditory stimulation suggest that metabolic responses are determined by the stimulus content and analysis strategy used by the subject rather than the side of stimulation. Tactile stimulation of the hand and fingers caused asymmetrical increases in LCMRGlc confirming topographic maps of the postcentral gyrus and other functional studies. Data from visually stimulated normal subjects show how the visual hemifields project to the opposite calcarine cortex. Studies of patients with hemianopsia or various field defects demonstrated that metabolic scanning can reveal alterations of cortical function not detectable by CT scan. Data obtained reveal the ability of metabolic mapping to subdivide the occipital cortex into distinct regions. Such measurements may also anticipate the course of visual recovery. Findings of increased overall right-hemispheric metabolism during the performance of verbal and spatial cognitive tasks are consistent with earlier results from right-handed males. Results for the frontal eye fields provide the first experimental evidence that lateralized metabolic activity, produced by cognitive tasks, causes similarly lateralized activity within a motor region. It is further demonstrated that FDG is able to provide information on such states as vigilance and anxiety.