Child maltreatment and NR3C1 exon 1F methylation, link with deregulated hypothalamus-pituitary-adrenal axis and psychopathology: A systematic review

Background Epigenetics offers one promising method for assessing the psychobiological response to stressful experiences during childhood. In particular, deoxyribonucleic acid (DNA) methylation has been associated with an altered hypothalamus–pituitary–adrenal (HPA) axis and the onset of mental disorders. Equally, there are promising leads regarding the association between the methylation of the glucocorticoid receptor gene (NR3C1-1F) and child maltreatment and its link with HPA axis and psychopathology. Objective The current study aimed to assess the evidence of a link among child maltreatment, NR3C1-1F methylation, HPA axis deregulation, and symptoms of psychopathology. Methods We followed the Prisma guidelines and identified 11 articles that met our inclusion criteria. Results We found that eight studies (72.72%) reported increased NR3C1-1F methylation associated with child maltreatment, specifically physical abuse, emotional abuse, sexual abuse, neglect, and exposure to intimate partner violence, while three studies (27.27%) found no significant association. Furthermore, a minority of studies (36.36%) provided additional measures of symptoms of psychopathology or cortisol in order to examine the link among NR3C1-1F methylation, HPA axis deregulation, and psychopathology in a situation of child maltreatment. These results suggest that NR3C1-1F hypermethylation is positively associated with higher HPA axis activity, i.e. increased production of cortisol, as well as symptoms of psychopathology, including emotional lability-negativity, externalizing behavior symptoms, and depressive symptoms. Conclusion NR3C1-1F methylation could be one mechanism that links altered HPA axis activity with the development of psychopathology

Connectome smoothing via low-rank approximations

In brain imaging and connectomics, the study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem. Often, this problem is especially difficult because the sample or cohort size is relatively small, sometimes even a single subject, while the number of nodes can be very large with noisy estimates of connectivity. While the element-wise sample mean of the adjacency matrices is a common approach, this method does not exploit underlying structural properties of the graphs. We propose using a low-rank method which incorporates dimension selection and diagonal augmentation to smooth the estimates and improve performance over the naäve methodology for small sample sizes. Theoretical results for the stochastic blockmodel show that this method offers major improvements when there are many vertices. Similarly, we demonstrate that the low-rank methods outperform the standard sample mean for a variety of independent edge distributions as well as human connectome data derived from magnetic resonance imaging, especially when sample sizes are small. Moreover, the low-rank methods yield “eigen-connectomes”, which correlate with the lobe-structure of the human brain and superstructures of the mouse brain. These results indicate that low-rank methods are an important part of the toolbox for researchers studying populations of graphs in general, and statistical connectomics in particular

Connectome smoothing via low-rank approximations

In statistical connectomics, the quantitative study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem. Often, this problem is especially difficult because the sample or cohort size is relatively small, sometimes even a single subject. While using the element-wise sample mean of the adjacency matrices is a common approach, this method does not exploit any underlying structural properties of the graphs. We propose using a low-rank method which incorporates tools for dimension selection and diagonal augmentation to smooth the estimates and improve performance over the naive methodology for small sample sizes. Theoretical results for the stochastic blockmodel show that this method offers major improvements when there are many vertices. Similarly, we demonstrate that the low-rank methods outperform the standard sample mean for a variety of independent edge distributions as well as human connectome data derived from magnetic resonance imaging, especially when sample sizes are small. Moreover, the low-rank methods yield "eigen-connectomes", which correlate with the lobe-structure of the human brain and superstructures of the mouse brain. These results indicate that low-rank methods are an important part of the tool box for researchers studying populations of graphs in general, and statistical connectomics in particular