Brain volume increases throughout early development in predictable patterns and is an important indicator of brain health. Hippocampal development and volume are related to many complex phenotypes, such as depression and anxiety. While basic brain development is genetically driven, environmental influences also influence individualized brain growth and regression. Epigenetics is one mechanism by which development is impacted by environment. Both animal and post-mortem human studies suggest that early life environments shape epigenetic regulation of genes involved in depression and anxiety in the hippocampus. Further, much research suggests that these environmentally driven changes in epigenetics are also reflected in buccal cells. However, little is known about the relationship between peripheral and brain epigenetics, especially in young and healthy cohorts. In an effort to close the gap between the peripheral epigenome and brain structure in a pediatric population, it was investigated whether DNA methylation (DNAm) levels of stress-related genes (NR3C1, FKBP5, and SLC6A4) measured in buccal cells predict hippocampal volume in a healthy, pediatric population (N = 255; females = 113; age range < 2 months – 14 years, Mage = 5.17, SDage = 3.61). Using multiple linear regression, results indicate that DNAm values across the whole gene and individual CpG sites of NR3C1, FKBP5, and SLC6A4 predict bilateral hippocampal volume. Results also indicate an age interaction such that the relationship between hippocampal volume and HPA gene DNAm is stronger in younger participants (0-6 years old), whereas serotonin transporter gene DNAm is stronger in older participants (6-14 years old). These results indicate that buccal DNAm of NR3C1, FKBP5, and SLC6A4 may be useful predictors of hippocampal volume early in development. These results validate the utility of peripheral epigenetics in the study of early life stress and brain structure. Further, these results emphasize the importance of considering developmental stages between which the relationship between brain and peripheral epigenetics may differ and highlight the possibility that diverse biological systems may be more or less relevant at different ages.

Autism spectrum disorder (ASD) is a developmental neuropsychiatric condition with early childhood onset, thus most research has focused on characterizing brain function in young individuals. Little is understood about brain function differences in middle age and older adults with ASD, despite evidence of persistent and worsening cognitive symptoms. Functional Magnetic Resonance Imaging (MRI) in younger persons with ASD demonstrate that large-scale brain networks containing the prefrontal cortex are affected. A novel, threshold-selection-free graph theory metric is proposed as a more robust and sensitive method for tracking brain aging in ASD and is compared against five well-accepted graph theoretical analysis methods in older men with ASD and matched neurotypical (NT) participants. Participants were 27 men with ASD (52 +/- 8.4 years) and 21 NT men (49.7 +/- 6.5 years). Resting-state functional MRI (rs-fMRI) scans were collected for six minutes (repetition time=3s) with eyes closed. Data was preprocessed in SPM12, and Data Processing Assistant for Resting-State fMRI (DPARSF) was used to extract 116 regions-of-interest defined by the automated anatomical labeling (AAL) atlas. AAL regions were separated into six large-scale brain networks. This proposed metric is the slope of a monotonically decreasing convergence function (Integrated Persistent Feature, IPF; Slope of the IPF, SIP). Results were analyzed in SPSS using ANCOVA, with IQ as a covariate. A reduced SIP was in older men with ASD, compared to NT men, in the Default Mode Network [F(1,47)=6.48; p=0.02; 2=0.13] and Executive Network [F(1,47)=4.40; p=0.04; 2=0.09], a trend in the Fronto-Parietal Network [F(1,47)=3.36; p=0.07; 2=0.07]. There were no differences in the non-prefrontal networks (Sensory motor network, auditory network, and medial visual network). The only other graph theory metric to reach significance was network diameter in the Default Mode Network [F(1,47)=4.31; p=0.04; 2=0.09]; however, the effect size for the SIP was stronger. Modularity, Betti number, characteristic path length, and eigenvalue centrality were all non-significant. These results provide empirical evidence of decreased functional network integration in pre-frontal networks of older adults with ASD and propose a useful biomarker for tracking prognosis of aging adults with ASD to enable more informed treatment, support, and care methods for this growing population.