A framework for a brain-derived nosology of psychiatric disorders.

Current psychiatric diagnoses are not defined by neurobiological measures which hinders the development of therapies targeting mechanisms underlying mental illness 1,2 . Research confined to diagnostic boundaries yields heterogeneous biological results, whereas transdiagnostic studies often investigate individual symptoms in isolation. There is currently no paradigm available to comprehensively investigate the relationship between different clinical symptoms, individual disorders, and the underlying neurobiological mechanisms. Here, we propose a framework that groups clinical symptoms derived from ICD-10/DSM-V according to shared brain mechanisms defined by brain structure, function, and connectivity. The reassembly of existing ICD-10/DSM-5 symptoms reveal six cross-diagnostic psychopathology scores related to mania symptoms, depressive symptoms, anxiety symptoms, stress symptoms, eating pathology, and fear symptoms. They were consistently associated with multimodal neuroimaging components in the training sample of young adults aged 23, the independent test sample aged 23, participants aged 14 and 19 years, and in psychiatric patients. The identification of symptom groups of mental illness robustly defined by precisely characterized brain mechanisms enables the development of a psychiatric nosology based upon quantifiable neurobiological measures. As the identified symptom groups align well with existing diagnostic categories, our framework is directly applicable to clinical research and patient care.

Alcohol dependence decreases functional activation of the caudate nucleus during model-based decision processes.

BACKGROUND: Impaired decision making, a key characteristic of alcohol dependence (AD), manifests in continuous alcohol consumption despite severe negative consequences. The neural basis of this impairment in individuals with AD and differences with known neural decision mechanisms among healthy subjects are not fully understood. In particular, it is unclear whether the choice behavior among individuals with AD is based on a general impairment of decision mechanisms or is mainly explained by altered value attribution, with an overly high subjective value attributed to alcohol-related stimuli. METHODS: Here, we use a functional magnetic resonance imaging (fMRI) monetary reward task to compare the neural processes of model-based decision making and value computation between AD individuals (n = 32) and healthy controls (n = 32). During fMRI, participants evaluated monetary offers with respect to dynamically changing constraints and different levels of uncertainty. RESULTS: Individuals with AD showed lower activation associated with model-based decision processes in the caudate nucleus than controls, but there were no group differences in value-related neural activity or task performance. CONCLUSIONS: Our findings highlight the role of the caudate nucleus in impaired model-based decisions of alcohol-dependent individuals.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

The interaction of child abuse and rs1360780 of the FKBP5 gene is associated with amygdala resting-state functional connectivity in young adults.

Extensive research has demonstrated that rs1360780, a common single nucleotide polymorphism within the FKBP5 gene, interacts with early-life stress in predicting psychopathology. Previous results suggest that carriers of the TT genotype of rs1360780 who were exposed to child abuse show differences in structure and functional activation of emotion-processing brain areas belonging to the salience network. Extending these findings on intermediate phenotypes of psychopathology, we examined if the interaction between rs1360780 and child abuse predicts resting-state functional connectivity (rsFC) between the amygdala and other areas of the salience network. We analyzed data of young European adults from the general population (N = 774; mean age = 18.76 years) who took part in the IMAGEN study. In the absence of main effects of genotype and abuse, a significant interaction effect was observed for rsFC between the right centromedial amygdala and right posterior insula (p < .025, FWE-corrected), which was driven by stronger rsFC in TT allele carriers with a history of abuse. Our results suggest that the TT genotype of rs1360780 may render individuals with a history of abuse more vulnerable to functional changes in communication between brain areas processing emotions and bodily sensations, which could underlie or increase the risk for psychopathology.

White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group.

Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.

Amygdala functional connectivity in major depression - disentangling markers of pathology, risk and resilience.

BACKGROUND: Limbic-cortical imbalance is an established model for the neurobiology of major depressive disorder (MDD), but imaging genetics studies have been contradicting regarding potential risk and resilience mechanisms. Here, we re-assessed previously reported limbic-cortical alterations between MDD relatives and controls in combination with a newly acquired sample of MDD patients and controls, to disentangle pathology, risk, and resilience. METHODS: We analyzed functional magnetic resonance imaging data and negative affectivity (NA) of MDD patients (n = 48), unaffected first-degree relatives of MDD patients (n = 49) and controls (n = 109) who performed a faces matching task. Brain response and task-dependent amygdala functional connectivity (FC) were compared between groups and assessed for associations with NA. RESULTS: Groups did not differ in task-related brain activation but activation in the superior frontal gyrus (SFG) was inversely correlated with NA in patients and controls. Pathology was associated with task-independent decreases of amygdala FC with regions of the default mode network (DMN) and decreased amygdala FC with the medial frontal gyrus during faces matching, potentially reflecting a task-independent DMN predominance and a limbic-cortical disintegration during faces processing in MDD. Risk was associated with task-independent decreases of amygdala-FC with fronto-parietal regions and reduced faces-associated amygdala-fusiform gyrus FC. Resilience corresponded to task-independent increases in amygdala FC with the perigenual anterior cingulate cortex (pgACC) and increased FC between amygdala, pgACC, and SFG during faces matching. CONCLUSION: Our results encourage a refinement of the limbic-cortical imbalance model of depression. The validity of proposed risk and resilience markers needs to be tested in prospective studies. Further limitations are discussed.

Cortical Surfaces Mediate the Relationship Between Polygenic Scores for Intelligence and General Intelligence.

Recent large-scale, genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with general intelligence. The cumulative influence of these loci on brain structure is unknown. We examined if cortical morphology mediates the relationship between GWAS-derived polygenic scores for intelligence (PSi) and g-factor. Using the effect sizes from one of the largest GWAS meta-analysis on general intelligence to date, PSi were calculated among 10 P value thresholds. PSi were assessed for the association with g-factor performance, cortical thickness (CT), and surface area (SA) in two large imaging-genetics samples (IMAGEN N = 1651; IntegraMooDS N = 742). PSi explained up to 5.1% of the variance of g-factor in IMAGEN (F1,1640 = 12.2-94.3; P < 0.005), and up to 3.0% in IntegraMooDS (F1,725 = 10.0-21.0; P < 0.005). The association between polygenic scores and g-factor was partially mediated by SA and CT in prefrontal, anterior cingulate, insula, and medial temporal cortices in both samples (PFWER-corrected < 0.005). The variance explained by mediation was up to 0.75% in IMAGEN and 0.77% in IntegraMooDS. Our results provide evidence that cumulative genetic load influences g-factor via cortical structure. The consistency of our results across samples suggests that cortex morphology could be a novel potential biomarker for neurocognitive dysfunction that is among the most intractable psychiatric symptoms.

Multimodal neuroimaging measures and intelligence influence pedophile child sexual offense behavior.

Pedophilia is a heterogeneous disorder for which the neurobiological correlates are not well established. In particular, there are no biological markers identifying individuals with high risk to commit child sexual offense (CSO). Pedophiles with CSO (P+CSO; N = 73), pedophiles without CSO (P-CSO; N = 77), and non-pedophilic controls (NPC; N = 133) were assessed using multimodal structural neuroimaging measures including: cortical thickness (CT), surface area (SA), and white matter fractional anisotropy (FA), as well as full scale IQ (FSIQ) performance. Cortex-wise mediation analyses were used to assess the relationships among brain structure, FSIQ and CSO behavior. Lower FSIQ performance was strongly predict with P+CSO (Wald Chi2 = 13.0, p = 3.1 × 10-5). P+CSO had lower CT in the right motor cortex and pronounced reductions in SA spanning the bilateral frontal, temporal, cingulate, and insular regions (PFWE-corrected < 0.05). P+CSO also had lower FA particularly in the corpus callosum (PFWE-corrected < 0.05). The relationship between SA and P+CSO was significantly mediated by FSIQ, particularly in the prefrontal and anterior insular cortices (PFWE-corrected < 0.05). Within P+CSO, left prefrontal and right anterior cingulate SA negatively correlated with number of CSOs (PFWE-corrected < 0.05). This study demonstrates converging neurobiological findings in which P+CSO had lower FSIQ performance, reduced CT, reduced SA, and reduced FA, compared to P-CSO as well as NPC. Further, FSIQ potentially mediates abuse by pedophiles via aberrant SA, whereas the CT and FA associations were independent of FSIQ differences. These findings suggest aberrant neuroanatomy and lower intelligence as a potential core feature underlying child sexual abuse behavior by pedophiles.

The influence of MIR137 on white matter fractional anisotropy and cortical surface area in individuals with familial risk for psychosis.

The rs1625579 variant near the microRNA-137 (MIR137) gene is one of the best-supported schizophrenia variants in genome-wide association studies (GWAS), and microRNA-137 functionally regulates other GWAS identified schizophrenia risk variants. Schizophrenia patients with the MIR137 rs1625579 risk genotype (homozygous for the schizophrenia risk variant) also have aberrant brain structure. It is unclear if the effect of MIR137 among schizophrenia patients is due to potential epistasis with genetic risk for schizophrenia or other factors of the disorder. Here, we investigated the effect of MIR137 genotype on white matter fractional anisotropy (FA), cortical thickness (CT), and surface area (SA) in a sample comprising healthy control subjects, and individuals with familial risk for psychosis (first-degree relatives of patients with schizophrenia or bipolar disorder; N=426). In voxel-wise analyses of FA, we observed a significant genotype-by-group interaction (PFWE<0.05). The familial risk group with risk genotype had lower FA (PFWE<0.05), but there was no genetic association in controls. In vertex-wise analyses of SA, we also observed a significant genotype-by-group interaction (PFWE<0.05). Relatives with MIR137 risk genotype had lower SA, however the risk genotype was associated with higher SA in the controls (all PFWE<0.05). These results show that MIR137 risk genotype is associated with lower FA in psychosis relatives that is similar to previous imaging-genetics findings in patients with schizophrenia. Furthermore, MIR137 genotype may also be a risk factor in a subclinical population with wide reductions in white matter FA and cortical SA.

Weather conditions influence the number of psychiatric emergency room patients.

The specific impact of weather factors on psychiatric disorders has been investigated only in few studies with inconsistent results. We hypothesized that meteorological conditions influence the number of cases presenting in a psychiatric emergency room as a measure of mental health conditions. We analyzed the number of patients consulting the emergency room (ER) of a psychiatric hospital in Berlin, Germany, between January 1, 2008, and December 31, 2014. A total of N = 22,672 cases were treated in the ER over the study period. Meteorological data were obtained from a publicly available data base. Due to collinearity among the meteorological variables, we performed a principal component (PC) analysis. Association of PCs with the daily number of patients was analyzed with autoregressive integrated moving average model. Delayed effects were investigated using Granger causal modeling. Daily number of patients in the ER was significantly higher in spring and summer compared to fall and winter (p < 0.001). Three PCs explained 76.8% percent of the variance with PC1 loading mostly on temperature, PC2 on cloudiness and low pressure, and PC3 on windiness. PC1 and PC2 showed strong association with number of patients in the emergency room (p < 0.010) indicating higher patient numbers on warmer and on cloudy days. Further, PC1, PC2, and PC3 predicted the number of patients presenting in the emergency room for up to 7 days (p < 0.050). A secondary analysis revealed that the effect of temperature on number of patients was mostly due to lower patient numbers on cold days. Although replication of our findings is required, our results suggest that weather influences the number of psychiatric patients consulting the emergency room. In particular, our data indicate lower patient numbers during very cold temperatures.

Cortical surface-based threshold-free cluster enhancement and cortexwise mediation.

Threshold-free cluster enhancement (TFCE) is a sensitive means to incorporate spatial neighborhood information in neuroimaging studies without using arbitrary thresholds. The majority of methods have applied TFCE to voxelwise data. The need to understand the relationship among multiple variables and imaging modalities has become critical. We propose a new method of applying TFCE to vertexwise statistical images as well as cortexwise (either voxel- or vertexwise) mediation analysis. Here we present TFCE_mediation, a toolbox that can be used for cortexwise multiple regression analysis with TFCE, and additionally cortexwise mediation using TFCE. The toolbox is open source and publicly available (https://github.com/trislett/TFCE_mediation). We validated TFCE_mediation in healthy controls from two independent multimodal neuroimaging samples (N = 199 and N = 183). We found a consistent structure-function relationship between surface area and the first independent component (IC1) of the N-back task, that white matter fractional anisotropy is strongly associated with IC1 N-back, and that our voxel-based results are essentially identical to FSL randomise using TFCE (all PFWE <0.05). Using cortexwise mediation, we showed that the relationship between white matter FA and IC1 N-back is mediated by surface area in the right superior frontal cortex (PFWE  < 0.05). We also demonstrated that the same mediation model is present using vertexwise mediation (PFWE  < 0.05). In conclusion, cortexwise analysis with TFCE provides an effective analysis of multimodal neuroimaging data. Furthermore, cortexwise mediation analysis may identify or explain a mechanism that underlies an observed relationship among a predictor, intermediary, and dependent variables in which one of these variables is assessed at a whole-brain scale. Hum Brain Mapp 38:2795-2807, 2017. © 2017 Wiley Periodicals, Inc.

Association study between the neurexin-1 gene and tardive dyskinesia.

OBJECTIVE: Tardive dyskinesia (TD) is a motor side effect that may develop after long-term antipsychotic treatment. Schizophrenia has recently been associated with the Neurexin-1 (NRXN1) gene that codes for a cell adhesion molecule in synaptic communication. METHODS: This study examined five NRXN1 single-nucleotide polymorphisms (SNPs) for possible association with the occurrence and severity of TD in 178 schizophrenia patients of European ancestry. RESULTS: We did not find these SNPs to be significantly associated with TD. CONCLUSIONS: More research is needed with additional SNPs and in bigger samples before we can completely rule out the role of NRXN1 in TD.

Pharmacogenetics and Imaging-Pharmacogenetics of Antidepressant Response: Towards Translational Strategies.

Genetic variation underlies both the response to antidepressant treatment and the occurrence of side effects. Over the past two decades, a number of pharmacogenetic variants, among these the SCL6A4, BDNF, FKBP5, GNB3, GRIK4, and ABCB1 genes, have come to the forefront in this regard. However, small effects sizes, mixed results in independent samples, and conflicting meta-analyses results led to inherent difficulties in the field of pharmacogenetics translating these findings into clinical practice. Nearly all antidepressant pharmacogenetic variants have potentially pleiotropic effects in which they are associated with major depressive disorder, intermediate phenotypes involved in emotional processes, and brain areas affected by antidepressant treatment. The purpose of this article is to provide a comprehensive review of the advances made in the field of pharmacogenetics of antidepressant efficacy and side effects, imaging findings of antidepressant response, and the latest results in the expanding field of imaging-pharmacogenetics studies. We suggest there is mounting evidence that genetic factors exert their impact on treatment response by influencing brain structural and functional changes during antidepressant treatment, and combining neuroimaging and genetic methods may be a more powerful way to detect biological mechanisms of response than either method alone. The most promising imaging-pharmacogenetics findings exist for the SCL6A4 gene, with converging associations with antidepressant response, frontolimbic predictors of affective symptoms, and normalization of frontolimbic activity following antidepressant treatment. More research is required before imaging-pharmacogenetics informed personalized medicine can be applied to antidepressant treatment; nevertheless, inroads have been made towards assessing genetic and neuroanatomical liability and potential clinical application.

Rapporteur summaries of plenary, symposia, and oral sessions from the XXIIIrd World Congress of Psychiatric Genetics Meeting in Toronto, Canada, 16-20 October 2015.

The XXIIIrd World Congress of Psychiatric Genetics meeting, sponsored by the International Society of Psychiatric Genetics, was held in Toronto, ON, Canada, on 16-20 October 2015. Approximately 700 participants attended to discuss the latest state-of-the-art findings in this rapidly advancing and evolving field. The following report was written by trainee travel awardees. Each was assigned one session as a rapporteur. This manuscript represents the highlights and topics that were covered in the plenary sessions, symposia, and oral sessions during the conference, and contains major notable and new findings.

Prefrontal White Matter Structure Mediates the Influence of GAD1 on Working Memory.

The glutamic acid decarboxylase 1 (GAD1) gene is a major determinant of γ-aminobutyric acid (GABA), the most abundant inhibitory neurotransmitter modulating local neuronal circuitry. GABAergic dysfunction and expression of GAD1 have been implicated in the pathophysiology of schizophrenia, and in working memory impairment. We examined the influence of the functional GAD1 rs3749034 variant on white matter fractional anisotropy (FA), cortical thickness, and working memory performance in schizophrenia patients and healthy controls (N=197). Using transcranial magnetic stimulation with electroencephalography (TMS-EEG), we subsequently examined the effect of rs3749034 on long-interval cortical inhibition (LICI) in the dorsolateral prefrontal cortex (DLPFC) in schizophrenia patients and healthy controls (N=66). We found that the rs3749034 T-allele carrier risk group had lower voxel-wise FA in the prefrontal cortex region (PFWE-corrected<0.05) but not cortical thickness. Mixed-model regression revealed a significant effect on attentional processing and working memory across four performance measures (F1,182=11.5, P=8 × 10(-4)). FA in the prefrontal cortex was associated with digit-span performance. Voxel-wise mediation analysis revealed that the effect GAD1 on poorer digit-span performance statistically predicted the lower white matter FA (PFWE-corrected<0.05). In exploratory analysis, we found a prominent GAD1 genotype-by-diagnosis interaction on DLPFC LICI (F1,56=14.3, P=4.1 × 10(-4)). Our findings converge on variation in GAD1 gene predicting a susceptibility mechanism that affects white matter FA, GABAergic inhibitory neurotransmission in the DLPFC, and working memory performance. Furthermore, via voxel mediation of FA and TMS-EEG intervention, we provide evidence for a potentially causal mechanism through which aberrant DLPFC GABA signaling may contribute to working memory dysfunction.

Genetic variation in CYP3A43 is associated with response to antipsychotic medication.

Genetic variation in cytochrome enzymes is known to affect drug metabolism and influence treatment response. Recently, the rs472660 variant in CYP3A43 has been associated with olanzapine response and clearance. In this study, we investigated the impact of rs472660 and the putatively functional marker rs680055 on antipsychotic response. We genotyped the rs472660 and rs680055 single nucleotide polymorphisms (SNPs) in N = 152 schizophrenia patients of European descent collected at two sample sites who were predominately treated with second generation antipsychotics for up to 6 months. Treatment response was assessed prospectively using Brief Psychiatric Rating Scale (BPRS) scores. Statistical analysis was performed using Chi square and analysis of covariance. The rs680055 SNP was significantly associated with treatment response. Carriers of the minor allele had significantly lower BPRS scores at study end (p = 5.9 × 10(-4)) with 8 % of the variance being explained by rs680055 genotype. Post hoc analyses revealed that this effect was present in both samples and in both genders. The rs472660 SNP was also associated with response (p = 0.027); however, this finding was not significant after multiple test correction. This is the first evidence that the rs680055 missense mutation influences antipsychotic response. Although our finding for rs472660 was only a non-significant trend after correction, our results still support the notion that this SNP may play a role in antipsychotic response. Despite the fact that the functional role of CYP3A43 in antipsychotic metabolism is not fully understood yet, our study provides an important contribution to understanding genetic factors of antipsychotic response.

Brain white matter development is associated with a human-specific haplotype increasing the synthesis of long chain fatty acids.

The genetic and molecular pathways driving human brain white matter (WM) development are only beginning to be discovered. Long chain polyunsaturated fatty acids (LC-PUFAs) have been implicated in myelination in animal models and humans. The biosynthesis of LC-PUFAs is regulated by the fatty acid desaturase (FADS) genes, of which a human-specific haplotype is strongly associated with ω-3 and ω-6 LC-PUFA concentrations in blood. To investigate the relationship between LC-PUFA synthesis and human brain WM development, we examined whether this FADS haplotype is associated with age-related WM differences across the life span in healthy individuals 9-86 years of age (n = 207). Diffusion tensor imaging was performed to measure fractional anisotropy (FA), a putative measure of myelination, of the cerebral WM tracts. FADS haplotype status was determined with a single nucleotide polymorphism (rs174583) that tags this haplotype. Overall, normal age-related WM differences were observed, including higher FA values in early adulthood compared with childhood, followed by lower FA values across older age ranges. However, individuals homozygous for the minor allele (associated with lower LC-PUFA concentrations) did not display these normal age-related WM differences (significant age × genotype interactions, p(corrected) < 0.05). These findings suggest that LC-PUFAs are involved in human brain WM development from childhood into adulthood. This haplotype and LC-PUFAs may play a role in myelin-related disorders of neurodevelopmental origin.

Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates.

INTRODUCTION: Advances in image segmentation of magnetic resonance images (MRI) have demonstrated that multi-atlas approaches improve segmentation over regular atlas-based approaches. These approaches often rely on a large number of manually segmented atlases (e.g. 30-80) that take significant time and expertise to produce. We present an algorithm, MAGeT-Brain (Multiple Automatically Generated Templates), for the automatic segmentation of the hippocampus that minimises the number of atlases needed whilst still achieving similar agreement to multi-atlas approaches. Thus, our method acts as a reliable multi-atlas approach when using special or hard-to-define atlases that are laborious to construct. METHOD: MAGeT-Brain works by propagating atlas segmentations to a template library, formed from a subset of target images, via transformations estimated by nonlinear image registration. The resulting segmentations are then propagated to each target image and fused using a label fusion method. We conduct two separate Monte Carlo cross-validation experiments comparing MAGeT-Brain and basic multi-atlas whole hippocampal segmentation using differing atlas and template library sizes, and registration and label fusion methods. The first experiment is a 10-fold validation (per parameter setting) over 60 subjects taken from the Alzheimer's Disease Neuroimaging Database (ADNI), and the second is a five-fold validation over 81 subjects having had a first episode of psychosis. In both cases, automated segmentations are compared with manual segmentations following the Pruessner-protocol. Using the best settings found from these experiments, we segment 246 images of the ADNI1:Complete 1Yr 1.5 T dataset and compare these with segmentations from existing automated and semi-automated methods: FSL FIRST, FreeSurfer, MAPER, and SNT. Finally, we conduct a leave-one-out cross-validation of hippocampal subfield segmentation in standard 3T T1-weighted images, using five high-resolution manually segmented atlases (Winterburn et al., 2013). RESULTS: In the ADNI cross-validation, using 9 atlases MAGeT-Brain achieves a mean Dice's Similarity Coefficient (DSC) score of 0.869 with respect to manual whole hippocampus segmentations, and also exhibits significantly lower variability in DSC scores than multi-atlas segmentation. In the younger, psychosis dataset, MAGeT-Brain achieves a mean DSC score of 0.892 and produces volumes which agree with manual segmentation volumes better than those produced by the FreeSurfer and FSL FIRST methods (mean difference in volume: 80 mm(3), 1600 mm(3), and 800 mm(3), respectively). Similarly, in the ADNI1:Complete 1Yr 1.5 T dataset, MAGeT-Brain produces hippocampal segmentations well correlated (r>0.85) with SNT semi-automated reference volumes within disease categories, and shows a conservative bias and a mean difference in volume of 250 mm(3) across the entire dataset, compared with FreeSurfer and FSL FIRST which both overestimate volume differences by 2600 mm(3) and 2800 mm(3) on average, respectively. Finally, MAGeT-Brain segments the CA1, CA4/DG and subiculum subfields on standard 3T T1-weighted resolution images with DSC overlap scores of 0.56, 0.65, and 0.58, respectively, relative to manual segmentations. CONCLUSION: We demonstrate that MAGeT-Brain produces consistent whole hippocampal segmentations using only 9 atlases, or fewer, with various hippocampal definitions, disease populations, and image acquisition types. Additionally, we show that MAGeT-Brain identifies hippocampal subfields in standard 3T T1-weighted images with overlap scores comparable to competing methods.

Treating working memory deficits in schizophrenia: a review of the neurobiology.

Cognitive deficits are a core feature of schizophrenia. Among these deficits, working memory impairment is considered a central cognitive impairment in schizophrenia. The prefrontal cortex, a region critical for working memory performance, has been demonstrated as a critical liability region in schizophrenia. As yet, there are no standardized treatment options for working memory deficits in schizophrenia. In this review, we summarize the neuronal basis for working memory impairment in schizophrenia, including dysfunction in prefrontal signaling pathways (e.g., γ-aminobutyric acid transmission) and neural network synchrony (e.g., gamma/theta oscillations). We discuss therapeutic strategies for working memory dysfunction such as pharmacological agents, cognitive remediation therapy, and repetitive transcranial magnetic stimulation. Despite the drawbacks of current approaches, the advances in neurobiological and translational treatment strategies suggest that clinical application of these methods will occur in the near future.

Exploratory study on association of genetic variation in TBC1D1 with antipsychotic-induced weight gain.

BACKGROUND: Previous studies have shown that antipsychotics with high propensity for antipsychotic-induced weight gain (AIWG) influence glucose transporter type 4 (GLUT4) mediated glucose intake. Variation in the gene encoding TBC1 domain family member 1 (TBC1D1), a Rab-GTPase activating protein regulating GLUT4 trafficking, has been associated with obesity. Therefore, we investigated the impact of TBC1D1 polymorphisms on AIWG. METHODS: We analyzed rs9852 and rs35859249 in TBC1D1 in 195 schizophrenia subjects treated mostly with clozapine or olanzapine for up to 14 weeks. Association was tested using analysis of variance and analysis of covariance with change (%) from baseline weight as the dependent variable. RESULTS: Analysis of covariance showed a non-significant trend for lower weight gain in carriers of the T-allele of rs9852 than in C-allele homozygotes (p = 0.063). This effect was more pronounced in the subgroup of patients treated with clozapine or olanzapine (p = 0.024). For rs35859249, no significant association with AIWG could be detected. CONCLUSIONS: This is the first study examining the association between TBC1D1 and AIWG. The moderate association of rs9852, located in the 3'UTR near a miRNA binding site, indicates an influence of TBC1D1 on AIWG. Further investigations remain necessary to elucidate the role of this gene in AIWG.