Search

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. [ABSTRACT FROM AUTHOR]

Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain‐age). The choice of the ML approach in estimating brain‐age in youth is important because age‐related brain changes in this age‐group are dynamic. However, the comparative performance of the available ML algorithms has not been systematically appraised. To address this gap, the present study evaluated the accuracy (mean absolute error [MAE]) and computational efficiency of 21 machine learning algorithms using sMRI data from 2105 typically developing individuals aged 5–22 years from five cohorts. The trained models were then tested in two independent holdout datasets, one comprising 4078 individuals aged 9–10 years and another comprising 594 individuals aged 5–21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree‐based, and kernel‐based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross‐validation folds, number of extreme outliers, and sample size. Tree‐based models and algorithms with a nonlinear kernel performed comparably well, with the latter being especially computationally efficient. Extreme Gradient Boosting (MAE of 1.49 years), Random Forest Regression (MAE of 1.58 years), and Support Vector Regression (SVR) with Radial Basis Function (RBF) Kernel (MAE of 1.64 years) emerged as the three most accurate models. Linear algorithms, with the exception of Elastic Net Regression, performed poorly. Findings of the present study could be used as a guide for optimizing methodology when quantifying brain‐age in youth. [ABSTRACT FROM AUTHOR]

Background: There is uncertainty whether unipolar mania is a discrete sub‐type of bipolar disorder. Disrupted rest/activity rhythms are a key feature of bipolar disorder (BD) but have not been well characterised in unipolar mania/hypomania (UM). We compared subjective and objective rest/activity patterns, demographic and mental health outcomes across BD, UM and control groups. Methods: UK residents aged 37–73 years were recruited into UK Biobank from 2006 to 2010. BD, UM and control groups were identified via a mental health questionnaire. Demographic, mental health and subjective sleep outcomes were self‐reported. Accelerometery data were available for a subset of participants, and objective measures of sleep and activity were derived. Results: A greater proportion of males met UM criteria, and more females were in the BD group. Both BD and UM groups had poor mental health outcomes vs. controls. Objectively measured activity differed between all three groups: UM had highest levels of activity and BD lowest. The UM group had shorter sleep duration compared to controls. Subjective rest/activity measures showed that both mood disorder groups (compared to controls) had later chronotype preference, more disturbed sleep and increased difficulty getting up in the morning. However, the UM group were more likely to report an early chronotype compared to BD and control groups. Conclusions: BD and UM share features in common, but key differences support the proposition that UM may be a distinct and more clinically homogenous disorder. UM was characterised by a higher proportion of males, early chronotype, increased activity and shorter sleep duration. [ABSTRACT FROM AUTHOR]

The brain‐age‐gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine‐learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G‐brainAGE and L‐brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22–37 years) participating in the Human Connectome Project. Sex differences were determined in G‐brainAGE and L‐brainAGE. Random forest regression was used to determine sex‐specific associations between G‐brainAGE and non‐imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L‐brainAGE showed sex‐specific differences; in females, compared to males, L‐brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G‐brainAGE were minimal, associations between G‐brainAGE and non‐imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G‐brainAGE was self‐identification as non‐white in males and systolic blood pressure in females. The results demonstrate the value of applying sex‐specific analyses and machine learning methods to advance our understanding of sex‐related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions. [ABSTRACT FROM AUTHOR]

Harmful alcohol use is a leading cause of premature death and is associated with age‐related disease. Biological ageing is highly variable between individuals and may deviate from chronological ageing, suggesting that biomarkers of biological ageing (derived from DNA methylation or brain structural measures) may be clinically relevant. Here, we investigated the relationships between alcohol phenotypes and both brain and DNA methylation age estimates. First, using data from UK Biobank and Generation Scotland, we tested the association between alcohol consumption (units/week) or hazardous use (Alcohol Use Disorders Identification Test [AUDIT] scores) and accelerated brain and epigenetic ageing in 20,258 and 8051 individuals, respectively. Second, we used Mendelian randomisation (MR) to test for a causal effect of alcohol consumption levels and alcohol use disorder (AUD) on biological ageing. Alcohol use showed a consistent positive association with higher predicted brain age (AUDIT‐C: β = 0.053, p = 3.16 × 10−13; AUDIT‐P: β = 0.052, p = 1.6 × 10−13; total AUDIT score: β = 0.062, p = 5.52 × 10−16; units/week: β = 0.078, p = 2.20 × 10−16), and two DNA methylation‐based estimates of ageing, GrimAge (units/week: β = 0.053, p = 1.48 × 10−7) and PhenoAge (units/week: β = 0.077, p = 2.18x10−10). MR analyses revealed limited evidence for a causal effect of AUD on accelerated brain ageing (β = 0.118, p = 0.044). However, this result should be interpreted cautiously as the significant effect was driven by a single genetic variant. We found no evidence for a causal effect of alcohol consumption levels on accelerated biological ageing. Future studies investigating the mechanisms associating alcohol use with accelerated biological ageing are warranted. [ABSTRACT FROM AUTHOR]

There is increasing interest in using data‐driven unsupervised methods to identify structural underpinnings of common mental illnesses, including major depressive disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980) and UK Biobank (UKB, N = 8,900), for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K‐nearest neighbour graphs and graph partitioning with Markov stability to determine optimal clustering of participants. Resultant clusters were (1) checked whether they were replicated in an independent cohort and (2) tested for associations with depression status and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case–control status in either cohort (GS subsample: pFDR =.2239–.6585; UKB: pFDR =.2003–.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets, for CSA, CV and subCV (GS subsample: d = 0.2529–.3490, pFDR <.005; UKB: d = 0.0868–0.1070, pFDR <.005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case–control status. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to first discover plasma proteomic biomarkers relating to neurodegeneration (N) and vascular (V) damage in cognitively normal individuals and second to discover proteins mediating sex‐related difference in N and V pathology. Methods: Five thousand and thirty‐two plasma proteins were measured in 1061 cognitively normal individuals (628 females and 433 males), nearly 90% of whom had magnetic resonance imaging measures of hippocampal volume (as N) and white matter hyperintensities (as V). Results: Differential protein expression analysis and co‐expression network analysis revealed different proteins and modules associated with N and V, respectively. Furthermore, causal mediation analysis revealed four proteins mediated sex‐related difference in N and one protein mediated such difference in V damage. Discussion: Once validated, the identified proteins could help to select cognitively normal individuals with N and V pathology for Alzheimer's disease clinical trials and provide targets for further mechanistic studies on brain sex differences, leading to sex‐specific therapeutic strategies. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches. [ABSTRACT FROM AUTHOR]

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree‐based Pipeline Optimisation Tool (TPOT) which uses a tree‐based representation of ML pipelines and conducts a genetic programming‐based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state‐of‐the‐art accuracy for Freesurfer‐based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ±.124 years) and a relevance vector regression (MAE 5.474 ±.140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data‐driven approach to find optimal models for neuroimaging applications. [ABSTRACT FROM AUTHOR]

Objectives: Current research suggests significant disruptions in functional brain networks in individuals with mood disorder, and in those at familial risk. Studies of structural brain networks provide important insights into synchronized maturational change but have received less attention. We aimed to investigate developmental relationships of large‐scale brain networks in mood disorder using structural covariance (SC) analyses. Methods: We conducted SC analysis of baseline structural imaging data from 121 at the time of scanning unaffected high risk (HR) individuals (29 later developed mood disorder after a median time of 4.95 years), and 89 healthy controls (C‐well) with no familial risk from the Scottish Bipolar Family Study (age 15‐27, 64% female). Voxel‐wise analyses of covariance were conducted to compare the associations between each seed region in visual, auditory, motor, speech, semantic, executive‐control, salience and default‐mode networks and the whole brain signal. SC maps were compared for (a) HR(all) versus C‐well individuals, and (b) between those who remained well (HR‐well), versus those who subsequently developed mood disorder (HR‐MD), and C‐well. Results: There were no significant differences between HR(all) and C‐well individuals. On splitting the HR group based on subsequent clinical outcome, the HR‐MD group however displayed greater baseline SC in the salience and executive‐control network, and HR‐well individuals showed less SC in the salience network, compared to C‐well, respectively (P <.001). Conclusions: These findings indicate differences in network‐level inter‐regional relationships, especially within the salience network, which precede onset of mood disorder in those at familial risk. [ABSTRACT FROM AUTHOR]

Background: Mood disorders are familial psychiatric diseases, in which patients show reduced white matter (WM) integrity. We sought to determine whether WM integrity was affected in young offspring at high‐familial risk of mood disorder before they go on to develop major depressive disorder (MDD). Methods: The Bipolar Family study is a prospective longitudinal study examining young individuals (age 16–25 years) at familial risk of mood disorder on three occasions 2 years apart. This study used baseline imaging data, categorizing groups according to clinical outcome at follow‐up. Diffusion tensor MRI data were acquired for 61 controls and 106 high‐risk individuals, the latter divided into 78 high‐risk subjects who remained well throughout the study (‘high‐risk well’) and 28 individuals who subsequently developed MDD (‘high‐risk MDD’). Voxel‐wise between‐group comparison of fractional anisotropy (FA) based on diagnostic status was performed using tract‐based spatial statistics (TBSS). Results: Compared to controls, both high‐risk groups showed widespread decreases in FA (pcorr < .05) at baseline. Although FA in the high‐risk MDD group negatively correlated with subthreshold depressive symptoms at the time of scanning (pcorr < .05), there were no statistically significant differences at p‐corrected levels between the two high‐risk groups. Conclusions: These results suggest that decreased FA is related to the presence of familial risk for mood disorder along with subdiagnostic symptoms at the time of scanning rather than predictive of subsequent diagnosis. Due to the difficulties performing such longitudinal prospective studies, we note, however, that this latter analysis may be underpowered due to sample size within the high‐risk MDD group. Further clinical follow‐up may clarify these findings. [ABSTRACT FROM AUTHOR]

Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network ( pFDR < 0.05). All metrics across networks were significantly associated with intelligence ( pFDR < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia ( r = −0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp 38:5919-5930, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Objectives Previous neuroimaging studies have reported abnormalities in white matter ( WM) pathways in subjects at high familial risk of mood disorders. In the current study, we examined the trajectory of these abnormalities during the early stages of illness development using longitudinal diffusion tensor imaging ( DTI) data. Methods Subjects (16−28 years old) were recruited in the Scottish Bipolar Family Study, a prospective longitudinal study that has examined individuals at familial risk of mood disorder on three occasions, 2 years apart. The current study concerns imaging data from the first and second assessments. We analysed DTI data for 43 controls and 69 high-risk individuals who were further subdivided into a group of 53 high-risk subjects who remained well (high-risk well) and 16 who met diagnostic criteria for major depressive disorder (high-risk MDD) at follow-up. Longitudinal differences in fractional anisotropy ( FA) between groups based on diagnostic status were investigated using the tract-based spatial statistics technique ( TBSS). Results We found a significant reduction in FA ( Pcorr<.05) across widespread brain regions over 2 years in all three groups. The trajectory of FA reduction did not differ significantly between groups. Conclusions These results suggest that there are similar trajectories of FA reductions for controls and high-risk young adults, despite high-risk individuals being at a disadvantaged starting point considering their reduced WM integrity detected at baseline in previous studies. Difference in WM integrity between high-risk individuals and controls could therefore occur in earlier childhood and be a necessary but not sufficient condition to develop future mood disorders. [ABSTRACT FROM AUTHOR]

Background: Studies have identified perturbations in facial processing in bipolar disorder and major depressive disorder (MDD), but their relationship to genetic risk and early development of illness is unclear. Methods: The Scottish Bipolar Family Study is a prospective longitudinal investigation examining young individuals (age 16-25) at familial risk of mood disorder. Participants underwent functional MRI using an implicit facial processing task employing angry and neutral faces. An explicit facial expression recognition task was completed outside the scanner. Clinical outcomes obtained 2 years after the scan were used to categorise participants into controls (n = 54), highrisk individuals who had developed MDD (HR MDD; n = 30) and high-risk individuals who remained well (HR Well, n = 43). Results: All groups demonstrated activation patterns typically observed during facial processing, including activation of the amygdala, hippocampus, fusiform gyrus and middle frontal regions. Notably, the HR MDD group showed reduced activation of the anterior cingulate gyrus versus both the control and HR Well group for angry faces, and versus the HR Well group for neutral faces. Outside the scanner, the HR MDD group was less accurate in recognising fearful expressions than the HR Well group. Conclusions: Here, we demonstrate functional abnormalities of the anterior cingulate cortex alongside facial emotional recognition deficits in high-risk individuals in the early stages of depression compared with both controls and at-risk individuals who remained well. These neural changes were associated with a current or future diagnosis of MDD and were not simply associated with increased familial risk. [ABSTRACT FROM AUTHOR]

Whalley HC, Papmeyer M, Sprooten E, Lawrie SM, Sussmann JE, McIntosh AM. Review of functional magnetic resonance imaging studies comparing bipolar disorder and schizophrenia. Bipolar Disord 2012: 14: 411-431. © 2012 The Authors. Journal compilation © 2012 John Wiley & Sons A/S. Objective: Although bipolar disorder (BD) and schizophrenia (SCZ) have a number of clinical features and certain susceptibility genes in common, they are considered separate disorders, and it is unclear which aspects of pathophysiology are specific to each condition. Here, we examine the functional magnetic resonance imaging (fMRI) literature to determine the evidence for diagnosis-specific patterns of brain activation in the two patient groups. Method: A systematic search was performed to identify fMRI studies directly comparing BD and SCZ to examine evidence for diagnosis-specific activation patterns. Studies were categorized into (i) those investigating emotion, reward, or memory, (ii) those describing executive function or language tasks, and (iii) those looking at the resting state or default mode networks. Studies reporting estimates of sensitivity and specificity of classification are also summarized, followed by studies reporting associations with symptom severity measures. Results: In total, 21 studies were identified including patients (n = 729) and healthy subjects (n = 465). Relative over-activation in the medial temporal lobe and associated structures was found in BD versus SCZ in tasks involving emotion or memory. Evidence of differences between the disorders in prefrontal regions was less consistent. Accuracy values for assignment of diagnosis were generally lower in BD than in SCZ. Few studies reported significant symptom associations; however, these generally implicated limbic regions in association with manic symptoms. Conclusions: Although there are a limited number of studies and a cautious approach is warranted, activation differences were found in the medial temporal lobe and associated limbic regions, suggesting the presence of differences in the neurobiological substrates of SCZ and BD. Future studies examining symptom dimensions, risk-associated genes, and the effects of medication will aid clarification of the mechanisms behind these differences. [ABSTRACT FROM AUTHOR]

Objectives: Although in current diagnostic criteria there exists a distinction between bipolar disorder and schizophrenia, many patients manifest features of both disorders, and it is unclear which aspects, if any, confer diagnostic specificity. In the present study, we investigate whether there are differences in medial temporal lobe (MTL) activation in bipolar disorder and schizophrenia. We also investigate associations between activation levels and symptom severity across the disorders. Methods: Functional magnetic resonance imaging scans were conducted on 14 healthy controls, 14 patients with bipolar disorder, and 15 patients with schizophrenia undergoing an emotional memory paradigm. Results: All groups demonstrated the expected pattern of behavioural responses during encoding and retrieval, and there were no significant group differences in performance. Robust MTL activation was seen in all three groups during viewing of emotional scenes, which correlated significantly with recognition memory for emotional stimuli. The bipolar group demonstrated relatively greater increases in activation for emotional versus neutral scenes in the left hippocampus than both controls and patients with schizophrenia. There was a significant positive correlation between mania scores and activation in the anterior cingulate, and a significant negative correlation between depression scores and activation in the dorsolateral prefrontal cortex. Conclusion: These results provide evidence that there are distinct patterns of activation in the MTL during an emotional memory task in bipolar disorder and schizophrenia. They also demonstrate that different mood states are associated with different neurobiological responses to emotion across the patient groups. [ABSTRACT FROM AUTHOR]

Background: Peripheral biomarkers reflecting early neurodegeneration change are critical to the development of treatments for Alzheimer's disease (AD). The most widely used indicator of AD neurodegeneration in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with neuroimaging in prodromal disease stage. Method: To identify blood‐based proteins relating to neurodegeneration ("N") in preclinical or prodromal disease stage, we used SOMAscan assay to measure 5033 proteins in two groups of healthy individuals (discovery group = 564, validation group = 497) selected from Generation Scotland cohort. We firstly performed both linear regression and protein co‐expression network to identify differentially expressed proteins and co‐expressed protein modules associated with "N" in the discovery group. We then validated such associations in the validation group. Result: Using linear regression, we identified different proteins that were significantly associated with whole brain volume, cortical volume and hippocampus volume after multiple correction, indicating that the associations between proteins and "N" were dependent on brain regions. From protein co‐expression network analysis, we identified seven modules (from 20 to 2088 proteins) in the discovery group, all of which were significantly preserved in the validation group. Furthermore, several modules were significantly associated with "N" and such associations were affected by sex and APOE genotype. Moreover, we replicated the associations between differentially expressed proteins and modules with "N" in the validation group. Conclusion: Our study is the largest we are aware of to report a plasma biomarker indicative of neurodegeneration in prodromal disease stage both in terms of the number of proteins assayed and in sample size. We identified and validated a groups of proteins in two large independent groups of healthy individuals. These proteins provide tractable targets for further mechanistic studies of neurodegeneration pathology, particularly in preclinical stages of Alzheimer's. [ABSTRACT FROM AUTHOR]

Language impairments are a characteristic feature of autism and related autism spectrum disorders (ASDs). Autism is also highly heritable and one of the most promising candidate genes implicated in its pathogenesis is contactin-associated protein-like 2 (CNTNAP2), a gene also associated with language impairment. In the current study we investigated the functional effects of variants of CNTNAP2 associated with autism and language impairment (rs7794745 and rs2710102; presumed risk alleles T and C, respectively) in healthy individuals using functional magnetic resonance imaging (fMRI) during performance of a language task (n = 66). Against a background of normal performance and lack of behavioral abnormalities, healthy individuals with the putative risk allele versus those without demonstrated significant increases in activation in the right inferior frontal gyrus (Broca's area homologue) and right lateral temporal cortex. These findings demonstrate that risk associated variation in the CNTNAP2 gene impacts on brain activation in healthy non-autistic individuals during a language processing task providing evidence of the effect of genetic variation in CNTNAP2 on a core feature of ASDs., (Copyright © 2011 Wiley Periodicals, Inc.)

A single nucleotide polymorphism (val66met) in the brain derived neurotrophic factor (BDNF) gene has been shown to be a risk factor for a number of psychiatric disorders, including schizophrenia. This polymorphism has also been shown to have effects on prefrontal brain morphology and function. This study aims to clarify the effects of the val66met polymorphism on prefrontal brain function in a population at high genetic risk for schizophrenia. The Edinburgh High Risk Study has followed young individuals who had one first- or second-degree relative with schizophrenia and a minimum of one further genetic relative with the illness. A sample of 62 individuals provided both genetic and functional imaging data using the Hayling sentence completion task. Individuals with the BDNF ValVal (presumed risk) genotype (n = 41) showed relatively increased activation of the anterior cingulate cortex in relation to Met carrier individuals (n = 21) during sentence completion conditions versus baseline, against a background of similar levels of task performance. It appeared from further investigation that this relatively increased activation was attributable to a failure to disengage or suppress activation in the high risk ValVal group during the task condition, suggesting that BDNF may contribute to the abnormal default network reported in schizophrenia. These results suggest that this gene affects prefrontal brain function in those at high genetic risk for the disorder, unconfounded by medication effects. BDNF may therefore be one of the heritable factors involved in the development of abnormal prefrontal function in schizophrenia. © 2010 Wiley-Liss, Inc., (Copyright © 2010 Wiley-Liss, Inc.)

Bipolar disorder and schizophrenia are highly heritable conditions that are associated with structural brain abnormalities. Although brain abnormalities are found in the well relatives of people with schizophrenia, the extent to which genetic liability relates to brain structure in either disorder is still unclear. This study sought to ascertain the effects of genetic liability to schizophrenia and bipolar disorder on white and grey matter volume in patients with these diagnoses and their well relatives. Seventy-one patients and 72 unaffected relatives were recruited for the study. Patients included those with schizophrenia from families affected by schizophrenia alone, those with bipolar disorder from families affected by bipolar disorder alone and those with bipolar disorder from families affected by both bipolar disorder and schizophrenia. Samples of unaffected relatives of each patient group were also recruited. Subjects underwent an MRI scan of the brain, which was analysed using optimised voxel-based morphometry (VBM). Grey and white matter volume was then related to a continuous measure of genetic liability based on a threshold-liability model. Genetic liability to schizophrenia was associated with decreased grey matter volume in dorso- (DLPFC) and ventrolateral prefrontal (VLPFC) cortices. The relationship remained after diagnostic status had been taken into account. Complementary white matter changes were also demonstrated. No relationship was demonstrated between a genetic liability to bipolar disorder and either white or grey matter volume. Genes that raise the likelihood of developing schizophrenia may exert their effects by diminishing grey matter volume in the DLPFC and VLPFC and their associated white matter connections. Genes for bipolar illness might have subtle effects on brain structure, which may need particularly large samples to detect., ((c) 2005 Wiley-Liss, Inc.)