Late-life Generalized Anxiety Disorder (GAD) is relatively understudied and the underlying structural and functional neuroanatomy has received little attention. In this study, we compare the brain structural characteristics in white and gray matter in 31 non-anxious older adults and 28 late-life GAD participants. Gray matter indices (cortical thickness and volume) were measured using FreeSurfer parcellation and segmentation, and mean diffusivity was obtained through Diffusion Tensor Imaging (DTI). We assessed both macroscopic white matter changes [using white matter hyperintensity (WMH) burden] and microscopic white matter integrity [using fractional anisotropy (FA)]. No differences in macro- or microscopic white matter integrity were found between GAD and non-anxious controls (HC). GAD participants had lower cortical thickness in the orbitofrontal cortex (OFC), inferior frontal gyrus, and pregenual anterior cingulate cortex (ACC). Higher worry severity was associated with gray matter changes in OFC, ACC and the putamen. The results did not survive the multiple comparison correction, but the effect sizes indicate a moderate effect. The study suggests that late-life GAD is associated with gray matter changes in areas involved in emotion regulation, more so than with white matter changes. We conclude that anxiety-related chronic hypercortisolemia may have a dissociative effect on gray and white matter integrity.

Childhood adversity is associated with altered or dysregulated stress reactivity; these altered patterns of physiological functioning persist into adulthood. Evidence from both preclinical animal models and human neuroimaging studies indicates that early life experience differentially influences stressor-evoked activity within central visceral neural circuits proximally involved in the control of stress responses, including the subgenual anterior cingulate cortex (sgACC), paraventricular nucleus of the hypothalamus (PVN), bed nucleus of the stria terminalis (BNST) and amygdala. However, the relationship between childhood adversity and the resting-state connectivity of this central visceral network remains unclear. To this end, we examined relationships between childhood threat and childhood socioeconomic deprivation, the resting-state connectivity between our regions of interest (ROIs), and affective symptom severity and diagnoses. We recruited a transdiagnostic sample of young adult males and females (n = 100; mean age = 27.28, SD = 3.99; 59 females) with a full distribution of maltreatment history and symptom severity across multiple affective disorders. Resting-state data were acquired using a 7.2-min functional magnetic resonance imaging (fMRI) sequence; noted ROIs were applied as masks to determine ROI-to-ROI connectivity. Threat was determined by measures of childhood traumatic events and abuse. Socioeconomic deprivation (SED) was determined by a measure of childhood socioeconomic status (parental education level). Covarying for age, race and sex, greater childhood threat was significantly associated with lower BNST-PVN, amygdala-sgACC and PVN-sgACC connectivity. No significant relationships were found between SED and resting-state connectivity. BNST-PVN connectivity was associated with the number of lifetime affective diagnoses. Exposure to threat during early development may entrain altered patterns of resting-state connectivity between these stress-related ROIs in ways that contribute to dysregulated neural and physiological responses to stress and subsequent affective psychopathology.

Objectives Hippocampal hyperactivation marks pre-clinical dementia pathophysiology, potentially due to differences in the connectivity of specific medial temporal lobe structures. Our aims were to characterize the resting-state functional connectivity of medial temporal lobe sub-structures in older adults, and evaluate whether specific sub-structural (rather than global) functional connectivity relates to memory function. Methods In 15 adults (mean age=69 years), we evaluated the resting-state functional connectivity of medial temporal lobe sub-structures: dentate/Cornu Ammonis (CA)-4, CA-1, CA-2/3, subiculum, the molecular layer, entorhinal cortex, and parahippocampus. We used 7-Tesla Susceptibility Weighted Imaging and Magnetization-Prepared Rapid Gradient Echo sequences to segment substructures of the hippocampus, which were used as structural seeds for examining functional connectivity in a resting BOLD sequence. We then assessed correlations between functional connectivity with memory performance (short and long delay free recall on the California Verbal Learning Test or CVLT). Results All the seed regions had significant connectivity within the temporal lobe (including the fusiform, temporal, and lingual gyri). The left CA1 was the only seeds with significant functional connectivity to the amygdala. The left entorhinal cortex was the only seed to have significant functional connectivity with frontal cortex (anterior cingulate and superior frontal gyrus). Only higher left dentate-left lingual connectivity was associated with poorer CVLT performance (Spearman r=−0.81, p=0.0003, Benjamini-Hochberg False Discovery Rate=0.01) after multiple comparison correction. Conclusions Rather than global hyper-connectivity of the medial temporal lobe, left dentate-lingual connectivity may provide a specific assay of medial temporal lobe hyper-connectivity relevant to memory in aging.

Researchers increasingly use passive sensing data and frequent self-report to implement personalized mobile health (mHealth) interventions. Yet, we know that certain populations may find these technical protocols burdensome and intervention uptake as well as treatment efficacy may be affected as a result. In the present study, we predicted feasibility (participant adherence to protocol) and acceptability (participant engagement with intervention content) as a function of baseline sociodemographic, mental health, and well-being characteristics of 99 women randomized in the personalized preventive intervention Wellness-for-Two (W-4-2), a randomized trial evaluating stress-related alterations during pregnancy and their effect on infant neurodevelopmental trajectories. The W-4-2 study used ecological momentary assessment (EMA) and wearable electrocardiograph (ECG) sensors to detect physiological stress and personalize the intervention. Participant adherence to protocols was 67% for EMAs and 52% for ECG bio-sensors. Higher baseline negative affect significantly predicted lower adherence to both protocols. Women assigned to the intervention group engaged on average with 42% of content they received. Women with higher annual household income were more likely to engage with more of the intervention content. Researchers should carefully consider tailoring of the intensity of technical intervention protocols to reduce fatigue, especially among participants with higher baseline negative affect, which may improve intervention uptake and efficacy findings at scale.

Background Snoring has been shown to be associated with adverse physical and mental health, independent of the effects of sleep disordered breathing. Despite increasing evidence for the risks of snoring, few studies on sleep and health include objective measures of snoring. One reason for this methodological limitation is the difficulty of quantifying snoring. Conventional methods may rely on manual scoring of snore events by trained human scorers, but this process is both time- and labor-intensive, making the measurement of objective snoring impractical for large or multi-night studies. Methods The current study is a proof-of-concept to validate the use of support vector machines (SVM), a form of machine learning, for the automated scoring of an objective snoring signal. An SVM algorithm was trained and tested on a set of approximately 150,000 snoring and non-snoring data segments, and F-scores for SVM performance compared to visual scoring performance were calculated using the Wilcoxon signed rank test for paired data. Results The ability of the SVM algorithm to discriminate snore from non-snore segments of data did not differ statistically from visual scorer performance (SVM F-score=82.46 ± 7.93 versus average visual F-score=88.35 ± 4.61, p=0.2786), supporting SVM snore classification ability comparable to visual scorers. Conclusion In this proof-of-concept, we established that the SVM algorithm performs comparably to trained visual scorers, supporting the use of SVM for automated snoring detection in future studies.