Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

Preliminary findings of four-week, task-based anodal prefrontal cortex transcranial direct current stimulation transferring to other cognitive improvements in schizophrenia

Most transcranial Direct Current Stimulation (tDCS) trials of schizophrenia administer few sessions and do not assess transfer effects to other cognitive domains. In a randomized, double-blind, sham-controlled, parallel groups trial, we determined the extent to which 4-weeks of 2 mA tDCS at 20 min/day totalling 20 tDCS sessions administered during a spatial working memory test, with anodal right dorsolateral prefrontal cortex (DLPFC) and cathodal left tempo-parietal junction (TPJ) placement, as an adjunct to antipsychotics reduced auditory hallucinations and improved cognition in 12 outpatients with schizophrenia. Anodal tDCS significantly improved language-based working memory after 2 weeks and verbal fluency after 2 and 4 weeks. Thus, four weeks of tDCS appears to be safe and elicits transfer benefits to other prefrontal-dependent cognitive abilities in schizophrenia

Cerebellum development during childhood and adolescence: A longitudinal morphometric MRI study

In addition to its well-established role in balance, coordination, and other motor skills, the cerebellum is increasingly recognized as a prominent contributor to a wide array of cognitive and emotional functions. Many of these capacities undergo dramatic changes during childhood and adolescence. However, accurate characterization of co-occurring anatomical changes has been hindered by lack of longitudinal data and methodologic challenges in quantifying subdivisions of the cerebellum. In this study we apply an innovative image analysis technique to quantify total cerebellar volume and 11 subdivisions (i.e. anterior, superior posterior, and inferior posterior lobes, corpus medullare, and three vermal regions) from anatomic brain MRI scans from 25 healthy females and 25 healthy males aged 5-24 years, each of whom was scanned at least three times at approximately 2-year intervals. Total cerebellum volume followed an inverted U shaped developmental trajectory peaking at age 11.8 years in females and 15.6 years in males. Cerebellar volume was 10% to 13% larger in males depending on the age of comparison and the sexual dimorphism remained significant after covarying for total brain volume. Subdivisions of the cerebellum had distinctive developmental trajectories with more phylogenetically recent regions maturing particularly late. The cerebellum's unique protracted developmental trajectories, sexual dimorphism, preferential vulnerability to environmental influences, and frequent implication in childhood onset disorders such as autism and ADHD make it a prime target for pediatric neuroimaging investigations

Identification of Genetically Mediated Cortical Networks: A Multivariate Study of Pediatric Twins and Siblings

Structural magnetic resonance imaging data from 308 twins, 64 singleton siblings of twins, and 228 singletons were analyzed using structural equation modeling and selected multivariate methods to identify genetically mediated intracortical associations. Principal components analyses (PCA) of the genetic correlation matrix indicated a single factor accounting for over 60% of the genetic variability in cortical thickness. When covaried for mean global cortical thickness, PCA, cluster analyses, and graph models identified genetically mediated fronto-parietal and occipital networks. Graph theoretical models suggest that the observed genetically mediated relationships follow small world architectural rules. These findings are largely concordant with other multivariate studies of brain structure and function, the twin literature, and current understanding on the role of genes in cortical neurodevelopment