The effect of d-cycloserine on brain processing of breathlessness over pulmonary rehabilitation: an experimental medicine study.

Research question: Pulmonary rehabilitation is the best treatment for chronic breathlessness in COPD but there remains an unmet need to improve efficacy. Pulmonary rehabilitation has strong parallels with exposure-based cognitive behavioural therapies (CBT), both clinically and in terms of brain activity patterns. The partial N-methyl-d-aspartate (NMDA)-receptor agonist d-cycloserine has shown promising results in enhancing efficacy of CBT, thus we hypothesised that it would similarly augment the effects of pulmonary rehabilitation in the brain. Positive findings would support further development in phase 3 clinical trials. Methods: 72 participants with mild-to-moderate COPD were recruited to a double-blind pre-registered (ClinicalTrials.gov identifier: NCT01985750) experimental medicine study running parallel to a pulmonary rehabilitation course. Participants were randomised to 250 mg d-cycloserine or placebo, administered immediately prior to the first four sessions of pulmonary rehabilitation. Primary outcome measures were differences between d-cycloserine and placebo in brain activity in the anterior insula, posterior insula, anterior cingulate cortices, amygdala and hippocampus following completion of pulmonary rehabilitation. Secondary outcomes included the same measures at an intermediate time point and voxel-wise difference across wider brain regions. An exploratory analysis determined the interaction with breathlessness anxiety. Results: No difference between d-cycloserine and placebo groups was observed across the primary or secondary outcome measures. d-cycloserine was shown instead to interact with changes in breathlessness anxiety to dampen reactivity to breathlessness cues. Questionnaire and measures of respiratory function showed no group difference. This is the first study testing brain-active drugs in pulmonary rehabilitation. Rigorous trial methodology and validated surrogate end-points maximised statistical power. Conclusion: Although increasing evidence supports therapeutic modulation of NMDA pathways to treat symptoms, we conclude that a phase 3 clinical trial of d-cycloserine would not be worthwhile.

Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure.

There is considerable interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of an average group network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two new extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects of subject-level covariates on individual differences in cluster structure. The proposed Multi-Subject Stochastic Blockmodels (MS-SBMs) can flexibly account for between-subject variability in terms of homogeneous or heterogeneous covariate effects on connectivity using subject demographics such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on the Wald, likelihood ratio and permutation tests. We show that the proposed multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition (i.e. the Fast Louvain and Newman Spectral algorithms). Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting-state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; n=268 brain regions), we show that Heterogeneous Stochastic Blockmodel (Het-SBM) identifies a range of network topologies simultaneously, including modular and core structures.

Long-term Influences of Prenatal Maternal Depressive Symptoms on the Amygdala-Prefrontal Circuitry of the Offspring From Birth to Early Childhood.

BACKGROUND: Prenatal maternal depression may have long-term impacts on amygdala-cortical development. This study explored associations of prenatal maternal depressive symptoms on the amygdala-cortical structural covariance of the offspring from birth to early childhood, derived from a longitudinal birth cohort. METHODS: Structural magnetic resonance imaging was performed to obtain the amygdala volume and cortical thickness at each time point. Prenatal maternal depressive symptoms were measured using the Edinburgh Postnatal Depression Scale at 26 weeks of pregnancy. Regression analysis was used to examine the effects of the Edinburgh Postnatal Depression Scale on a structural coupling between the amygdala volume and cortical thickness at birth (n = 167) and 4.5 years of age (n = 199). RESULTS: Girls whose mothers had high prenatal maternal depressive symptoms showed a positive coupling between the amygdala volume and insula thickness at birth (ß = .617, p = .001) but showed a negative coupling between the amygdala volume and inferior frontal thickness at 4.5 years of age (ß = -.369, p = .008). No findings were revealed in boys at any time point. CONCLUSIONS: The development of the amygdala-prefrontal circuitry is vulnerable to environmental factors related to depression. Such a vulnerability might be sex dependent.

Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: Application to Epigenome-Wide Association Studies.

Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to modify this selection adequately. The use of standard diagnostics, such as residual plotting, is clearly not practical for neuroimaging data. However, the selection of covariates is crucial for linear regression to ensure valid statistical inference. In particular, the mean model of regression needs to be reasonably well specified. Unfortunately, this issue is often overlooked in the field of neuroimaging. This study aims to adopt the existing Confounder Adjusted Testing and Estimation (CATE) approach and to extend it for use with neuroimaging data. We propose a modification of CATE that can yield valid statistical inferences using Principal Component Analysis (PCA) estimators instead of Maximum Likelihood (ML) estimators. We then propose a non-parametric hypothesis testing procedure that can improve upon parametric testing. Monte Carlo simulations show that the modification of CATE allows for more accurate modelling of neuroimaging data and can in turn yield a better control of False Positive Rate (FPR) and Family-Wise Error Rate (FWER). We demonstrate its application to an Epigenome-Wide Association Study (EWAS) on neonatal brain imaging and umbilical cord DNA methylation data obtained as part of a longitudinal cohort study. Software for this CATE study is freely available at http://www.bioeng.nus.edu.sg/cfa/Imaging_Genetics2.html.

FKBP5 Moderates the Association between Antenatal Maternal Depressive Symptoms and Neonatal Brain Morphology.

Antenatal maternal depressive symptoms influence fetal brain development and increase the risk for depression in offspring. Such vulnerability is often moderated by the offspring's genetic variants. This study aimed to examine whether FKBP5, a key regulator of the hypothalamic-pituitary-adrenal (HPA) axis, moderates the association between antenatal maternal depressive symptoms and in utero brain development, using an Asian cohort with 161 mother-offspring dyads. Antenatal maternal depressive symptoms were measured using the Edinburgh Postnatal Depression Scale (EPDS) during the second trimester of pregnancy. Neonatal structural brain images were acquired using magnetic resonance imaging (MRI) shortly after birth. Maternal and neonatal FKBP5 gene was genotyped using Illumina OmniExpress arrays. A gene set-based mixed effect model for gene-environment interaction (MixGE) was used to examine interactive effects between neonatal genetic variants of FKBP5 and antenatal maternal depressive symptoms on neonatal amygdala and hippocampal volumes, and cortical thickness. Our study revealed that genetic variants in neonatal FKBP5 moderate the association between antenatal maternal depressive symptoms and right hippocampal volume but only show a trend for such moderation on amygdala volumes and cortical thickness. Our findings are the first to reveal that the association between maternal depressive symptoms and in utero neurodevelopment of specific brain regions is modified through complex genetic variation in neonatal FKBP5. Our results suggest that an increased risk for depression may be transmitted from mother to child during fetal life and that the effect is dependent upon neonatal FKBP5 genotype.

A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes.

Imaging genetics is an emerging field for the investigation of neuro-mechanisms linked to genetic variation. Although imaging genetics has recently shown great promise in understanding biological mechanisms for brain development and psychiatric disorders, studying the link between genetic variants and neuroimaging phenotypes remains statistically challenging due to the high-dimensionality of both genetic and neuroimaging data. This becomes even more challenging when studying gene-environment interaction (G×E) on neuroimaging phenotypes. In this study, we proposed a set-based mixed effect model for gene-environment interaction (MixGE) on neuroimaging phenotypes, such as structural volumes and tensor-based morphometry (TBM). MixGE incorporates both fixed and random effects of G×E to investigate homogeneous and heterogeneous contributions of multiple genetic variants and their interaction with environmental risks to phenotypes. We discuss the construction of score statistics for the terms associated with fixed and random effects of G×E to avoid direct parameter estimation in the MixGE model, which would greatly increase computational cost. We also describe how the score statistics can be combined into a single significance value to increase statistical power. We evaluated MixGE using simulated and real Alzheimer's Disease Neuroimaging Initiative (ADNI) data, and showed statistical power superior to other burden and variance component methods. We then demonstrated the use of MixGE for exploring the voxelwise effect of G×E on TBM, made feasible by the computational efficiency of MixGE. Through this, we discovered a potential interaction effect of gene ABCA7 and cardiovascular risk on local volume change of the right superior parietal cortex, which warrants further investigation.

Fast and accurate modelling of longitudinal and repeated measures neuroimaging data.

Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e.g., assumption of Compound Symmetry--the state of all equal variances and equal correlations--or spatially homogeneous longitudinal correlations). While some new methods have been proposed to more accurately account for such data, these methods are based on iterative algorithms that are slow and failure-prone. In this article, we propose the use of the Sandwich Estimator method which first estimates the parameters of interest with a simple Ordinary Least Square model and second estimates variances/covariances with the "so-called" Sandwich Estimator (SwE) which accounts for the within-subject correlation existing in longitudinal data. Here, we introduce the SwE method in its classic form, and we review and propose several adjustments to improve its behaviour, specifically in small samples. We use intensive Monte Carlo simulations to compare all considered adjustments and isolate the best combination for neuroimaging data. We also compare the SwE method to other popular methods and demonstrate its strengths and weaknesses. Finally, we analyse a highly unbalanced longitudinal dataset from the Alzheimer's Disease Neuroimaging Initiative and demonstrate the flexibility of the SwE method to fit within- and between-subject effects in a single model. Software implementing this SwE method has been made freely available at http://warwick.ac.uk/tenichols/SwE.