Prenatal exposure to cigarette smoke or alcohol and cerebellum volume in attention-deficit/hyperactivity disorder and typical development

Prenatal exposure to teratogenic substances, such as nicotine or alcohol, increases the risk of developing attention-deficit/hyperactivity disorder (ADHD). To date, studies examining this relationship have used symptom scales as outcome measures to assess the effect of prenatal exposure, and have not investigated the neurobiological pathways involved. This study explores the effect of prenatal exposure to cigarettes or alcohol on brain volume in children with ADHD and typically developing controls. Children with ADHD who had been exposed prenatally to either substance were individually matched to children with and without ADHD who had not been. Controls who had been exposed prenatally were also individually matched to controls who had not been. For prenatal exposure to both smoking and alcohol, we found a pattern where subjects with ADHD who had been exposed had the smallest brain volumes and unexposed controls had the largest, with intermediate volumes for unexposed subjects with ADHD. This effect was most pronounced for cerebellum. A similar reduction fell short of significance for controls who had been exposed to cigarettes, but not alcohol. Our results are consistent with an additive effect of prenatal exposure and ADHD on brain volume, with the effects most pronounced for cerebellum

Differential Brain Development with Low and High IQ in Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder (ADHD) and intelligence (IQ) are both heritable phenotypes. Overlapping genetic effects have been suggested to influence both, with neuroimaging work suggesting similar overlap in terms of morphometric properties of the brain. Together, this evidence suggests that the brain changes characteristic of ADHD may vary as a function of IQ. This study investigated this hypothesis in a sample of 108 children with ADHD and 106 typically developing controls, who participated in a cross-sectional anatomical MRI study. A subgroup of 64 children also participated in a diffusion tensor imaging scan. Brain volumes, local cortical thickness and average cerebral white matter microstructure were analyzed in relation to diagnostic group and IQ. Dimensional analyses investigated possible group differences in the relationship between anatomical measures and IQ. Second, the groups were split into above and below median IQ subgroups to investigate possible differences in the trajectories of cortical development. Dimensionally, cerebral gray matter volume and cerebral white matter microstructure were positively associated with IQ for controls, but not for ADHD. In the analyses of the below and above median IQ subgroups, we found no differences from controls in cerebral gray matter volume in ADHD with below-median IQ, but a delay of cortical development in a number of regions, including prefrontal areas. Conversely, in ADHD with above-median IQ, there were significant reductions from controls in cerebral gray matter volume, but no local differences in the trajectories of cortical development

Brain Structural Networks Associated with Intelligence and Visuomotor Ability

Increasing evidence indicates that multiple structures in the brain are associated with intelligence and cognitive function at the network level. The association between the grey matter (GM) structural network and intelligence and cognition is not well understood. We applied a multivariate approach to identify the pattern of GM and link the structural network to intelligence and cognitive functions. Structural magnetic resonance imaging was acquired from 92 healthy individuals. Source-based morphometry analysis was applied to the imaging data to extract GM structural covariance. We assessed the intelligence, verbal fluency, processing speed, and executive functioning of the participants and further investigated the correlations of the GM structural networks with intelligence and cognitive functions. Six GM structural networks were identified. The cerebello-parietal component and the frontal component were significantly associated with intelligence. The parietal and frontal regions were each distinctively associated with intelligence by maintaining structural networks with the cerebellum and the temporal region, respectively. The cerebellar component was associated with visuomotor ability. Our results support the parieto-frontal integration theory of intelligence by demonstrating how each core region for intelligence works in concert with other regions. In addition, we revealed how the cerebellum is associated with intelligence and cognitive functions

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging

Detecting neuroimaging biomarkers for schizophrenia:a meta-analysis of multivariate pattern recognition studies

Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7–83.5%) and a specificity of 80.3% (95% CI: 76.9–83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9–88.2%) and similar specificity (76.9%, 95% CI: 71.3–81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9–80.4%, specificity of 79.0%, 95% CI: 74.6–82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity

Meso- and macrozooplankton communities in the Weddell Sea, Antarctica

The present paper describes composition and abundance of meso- and macrozooplankton in the epipelagic zone of the Weddell Sea and gives a systematic review of encountered species regarding results of earlier expeditions. Material was sampled from 6 February to 10 March 1983 from RV Polarstern with a RMT 1+8 m (320 and 4500 μm mesh size). In agreement with topography and water mass distribution three distinct communities were defined, clearly separated by cluster analysis: The Southern Shelf Community has lowest abundances (approx. 9000 ind./1000 m3). Euphausia crystallorophias and Metridia gerlachei are predominating. Compared with the low overall abundance the number of regularly occurring species is high (55) due to many neritic forms. Herbivores and omnivores are dominating (58% and 35%). The North-eastern Shelf Community has highest abundances (about 31 000 ind./1000 m3). It is predominated by copepodites I–III of Calanus propinquus and Calanoides acutus (61%). The faunal composition is characterized by both oceanic and neritic species (64). Fine-filter feeders are prevailing (65%). The Oceanic Community has a mean abundance of approximately 23 000 ind./1000 m3, consisting of 61 species. Dominances are not as pronounced as in the shelf communities. Apart from abundant species like Calanus propinquus, Calanoides acutus, Metridia gerlachei, Oithona spp. and Oncaea spp. many typical inhabitants of the Eastwind Drift are encountered. All feeding types have about the same importance in the Oceanic Community

Machine learning for genetic prediction of psychiatric disorders: a systematic review

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48–0.95 AUC) and differed between schizophrenia (0.54–0.95 AUC), bipolar (0.48–0.65 AUC), autism (0.52–0.81 AUC) and anorexia (0.62–0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning

Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of the disease. Combined with the usually scarcer longitudinal data and the variability in the definition of outcomes/transition, this makes prognostic predictions a challenging endeavor. Employing neuroimaging data in this endeavor introduces the additional hurdle of high dimensionality. Machine learning techniques are especially suited to tackle this challenging problem. This review starts with a brief introduction to machine learning in the context of its application to clinical neuroimaging data. We highlight a few issues that are especially relevant for prediction of outcome and transition using neuroimaging. We then review the literature that discusses the application of machine learning for this purpose. Critical examination of the studies and their results with respect to the relevant issues revealed the following: 1) there is growing evidence for the prognostic capability of machine learning–based models using neuroimaging; and 2) reported accuracies may be too optimistic owing to small sample sizes and the lack of independent test samples. Finally, we discuss options to improve the reliability of (prognostic) prediction models. These include new methodologies and multimodal modeling. Paramount, however, is our conclusion that future work will need to provide properly (cross-)validated accuracy estimates of models trained on sufficiently large datasets. Nevertheless, with the technological advances enabling acquisition of large databases of patients and healthy subjects, machine learning represents a powerful tool in the search for psychiatric biomarkers

Magnetic resonance imaging of boys with attention-deficit/hyperactivity disorder and their unaffected siblings.

OBJECTIVE: To study the influence of increased familial risk for attention-deficit/hyperactivity disorder (ADHD) on brain morphology. METHOD: Volumetric cerebral measures based on whole brain magnetic resonance imaging scans from 30 boys with ADHD, 30 of their unaffected siblings, and 30 matched controls were compared. RESULTS: Both subjects with ADHD and their unaffected siblings displayed reductions in right prefrontal gray matter and left occipital gray and white matter of up to 9.1% (p < 0.05). Right cerebellar volume was reduced by 4.9% in subjects with ADHD (p = 0.026) but not in their unaffected siblings (p = 0.308). A 4.0% reduction in intracranial volume was found in subjects with ADHD (p = 0.031), while a trend was observed in their unaffected siblings (p = 0.068). CONCLUSIONS: The volumetric reductions in cortical gray and white matter in subjects with ADHD are also present in their unaffected siblings, suggesting that they are related to an increased familial risk for the disorder. In contrast, the cerebellum is unaffected in siblings, suggesting that the reduction in volume observed in subjects with ADHD may be more directly related to the pathophysiology of this disorder