Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from crosssectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns

Recent Advances in Adult Post-Transplant Lymphoproliferative Disorder

PTLD is a rare but severe complication of hematopoietic or solid organ transplant recipients, with variable incidence and timing of occurrence depending on different patient-, therapy-, and transplant-related factors. The pathogenesis of PTLD is complex, with most cases of early PLTD having a strong association with Epstein-Barr virus (EBV) infection and the iatrogenic, immunosuppression-related decrease in T-cell immune surveillance. Without appropriate T-cell response, EBV-infected B cells persist and proliferate, resulting in malignant transformation. Classification is based on the histologic subtype and ranges from nondestructive hyperplasias to monoclonal aggressive lymphomas, with the most common subtype being diffuse large B-cell lymphoma-like PTLD. Management focuses on prevention of PTLD development, as well as therapy for active disease. Treatment is largely based on the histologic subtype. However, given lack of clinical trials providing evidence-based data on PLTD therapy-related outcomes, there are no specific management guidelines. In this review, we discuss the pathogenesis, histologic classification, and risk factors of PTLD. We further focus on common preventive and frontline treatment modalities, as well as describe the application of novel therapies for PLTD and elaborate on potential challenges in therapy

Choroid plexus enlargement is associated with neuroinflammation and reduction of blood brain barrier permeability in depression.

BACKGROUND: Recent studies have shown that choroid plexuses (CP) may be involved in the neuro-immune axes, playing a role in the interaction between the central and peripheral inflammation. Here we aimed to investigate CP volume alterations in depression and their associations with inflammation. METHODS: 51 depressed participants (HDRS score > 13) and 25 age- and sex-matched healthy controls (HCs) from the Wellcome Trust NIMA consortium were re-analysed for the study. All the participants underwent full peripheral cytokine profiling and simultaneous [11C]PK11195 PET/structural MRI imaging for measuring neuroinflammation and CP volume respectively. RESULTS: We found a significantly greater CP volume in depressed subjects compared to HCs (t(76) = +2.17) that was positively correlated with [11C]PK11195 PET binding in the anterior cingulate cortex (r = 0.28, p = 0.02), prefrontal cortex (r = 0.24, p = 0.04), and insular cortex (r = 0.24, p = 0.04), but not with the peripheral inflammatory markers: CRP levels (r = 0.07, p = 0.53), IL-6 (r = -0.08, p = 0.61), and TNF-α (r = -0.06, p = 0.70). The CP volume correlated with the [11C]PK11195 PET binding in CP (r = 0.34, p = 0.005). Integration of transcriptomic data from the Allen Human Brain Atlas with the brain map depicting the correlations between CP volume and PET imaging found significant gene enrichment for several pathways involved in neuroinflammatory response. CONCLUSION: This result supports the hypothesis that changes in brain barriers may cause reduction in solute exchanges between blood and CSF, disturbing the brain homeostasis and ultimately contributing to inflammation in depression. Given that CP anomalies have been recently detected in other brain disorders, these results may not be specific to depression and might extend to other conditions with a peripheral inflammatory component

Meta-analysis of regional white matter volume in bipolar disorder with replication in an independent sample using coordinates, T-maps, and individual MRI data

Converging evidence suggests that bipolar disorder (BD) is associated with white matter (WM) abnormalities. Meta-analyses of voxel based morphometry (VBM) data is commonly performed using published coordinates, however this method is limited since it ignores non-significant data. Obtaining statistical maps from studies (T-maps) as well as raw MRI datasets increases accuracy and allows for a comprehensive analysis of clinical variables. We obtained coordinate data (5-studies), T-Maps (12-studies, including unpublished data) and raw MRI datasets (5-studies) and analysed the 24 studies using Seed-based d Mapping (SDM). A VBM analysis was conducted to verify the results in an independent sample. The meta-analysis revealed decreased WM volume in the posterior corpus callosum extending to WM in the posterior cingulate cortex. This region was significantly reduced in volume in BD patients in the independent dataset (p=0.003) but there was no association with clinical variables. We identified a robust WM volume abnormality in BD patients that may represent a trait marker of the disease and used a novel methodology to validate the findings

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis

Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model

Large-scale collaborative analysis of brain imaging data, in psychiatry and neurology, offers a new source of statistical power to discover features that boost accuracy in disease classification, differential diagnosis, and outcome prediction. However, due to data privacy regulations or limited accessibility to large datasets across the world, it is challenging to efficiently integrate distributed information. Here we propose a novel classification framework through multi-site weighted LASSO: each site performs an iterative weighted LASSO for feature selection separately. Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature. This new weight is used to guide the LASSO process at the next iteration. Only the features that help to improve the classification accuracy are preserved. In tests on data from five sites (299 patients with major depressive disorder (MDD) and 258 normal controls), our method boosted classification accuracy for MDD by 4.9% on average. This result shows the potential of the proposed new strategy as an effective and practical collaborative platform for machine learning on large scale distributed imaging and biobank data