Dentate Gyrus Microstructure Is Associated With Resilience After Exposure to Maternal Stress Across Two Human Cohorts.

BACKGROUND: Maternal stress (MS) is a well-documented risk factor for impaired emotional development in offspring. Rodent models implicate the dentate gyrus (DG) of the hippocampus in the effects of MS on offspring depressive-like behaviors, but mechanisms in humans remain unclear. Here, we tested whether MS was associated with depressive symptoms and DG micro- and macrostructural alterations in offspring across 2 independent cohorts. METHODS: We analyzed DG diffusion tensor imaging-derived mean diffusivity (DG-MD) and volume in a three-generation family risk for depression study (TGS; n = 69, mean age = 35.0 years) and in the Adolescent Brain Cognitive Development (ABCD) Study (n = 5196, mean age = 9.9 years) using generalized estimating equation models and mediation analysis. MS was assessed by the Parenting Stress Index (TGS) and a measure compiled from the Adult Response Survey from the ABCD Study. The Patient Health Questionnaire-9 and rumination scales (TGS) and the Child Behavior Checklist (ABCD Study) measured offspring depressive symptoms at follow-up. The Schedule for Affective Disorders and Schizophrenia-Lifetime interview was used to assign depression diagnoses. RESULTS: Across cohorts, MS was associated with future symptoms and higher DG-MD (indicating disrupted microstructure) in offspring. Higher DG-MD was associated with higher symptom scores measured 5 years (in the TGS) and 1 year (in the ABCD Study) after magnetic resonance imaging. In the ABCD Study, DG-MD was increased in high-MS offspring who had depressive symptoms at follow-up, but not in offspring who remained resilient or whose mother had low MS. CONCLUSIONS: Converging results across 2 independent samples extend previous rodent studies and suggest a role for the DG in exposure to MS and offspring depression.

Automated Multiclass Artifact Detection in Diffusion MRI Volumes via 3D Residual Squeeze-and-Excitation Convolutional Neural Networks.

Diffusion MRI (dMRI) is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts. Hence, artifact type identification in dMRI volumes is an essential pre-processing step prior to carrying out any further analysis. Manual artifact identification amongst a large pool of dMRI data is a highly labor-intensive task. Previous attempts at automating this process are often limited to a binary classification ("poor" vs. "good" quality) of the dMRI volumes or focus on detecting a single type of artifact (e.g., motion, Eddy currents, etc.). In this work, we propose a deep learning-based automated multiclass artifact classifier for dMRI volumes. Our proposed framework operates in 2 steps. In the first step, the model predicts labels associated with 3D mutually exclusive collectively exhaustive (MECE) sub-volumes or "slabs" extracted from whole dMRI volumes. In the second step, through a voting process, the model outputs the artifact class present in the whole volume under investigation. We used two different datasets for training and evaluating our model. Specifically, we utilized 2,494 poor-quality dMRI volumes from the Adolescent Brain Cognitive Development (ABCD) and 4,226 from the Healthy Brain Network (HBN) dataset. Our results demonstrate accurate multiclass volume-level main artifact type prediction with 96.61 and 97.52% average accuracies on the ABCD and HBN test sets, respectively. Finally, in order to demonstrate the effectiveness of the proposed framework in dMRI pre-processing pipelines, we conducted a proof-of-concept dMRI analysis exploring the relationship between whole-brain fractional anisotropy (FA) and participant age, to test whether the use of our model improves the brain-age association.

Normalized Brain Tissue-Level Evaluation of Volumetric Changes of Youth Athletes Participating in Collision Sports.

Observations of short-term changes in the neural health of youth athletes participating in collision sports (e.g., football and soccer) have highlighted a need to explore potential structural alterations in brain tissue volumes for these persons. Studies have shown biochemical, vascular, functional connectivity, and white matter diffusivity changes in the brain physiology of these athletes that are strongly correlated with repetitive head acceleration exposure. Here, research is presented that highlights regional anatomical volumetric measures that change longitudinally with accrued subconcussive trauma. A novel pipeline is introduced that provides simplified data analysis on standard-space template to quantify group-level longitudinal volumetric changes within these populations. For both sports, results highlight incremental relative regional volumetric changes in the subcortical cerebrospinal fluid that are strongly correlated with head exposure events greater than a 50-G threshold at the short-term post-season assessment. Moreover, longitudinal regional gray matter volumes are observed to decrease with time, only returning to baseline/pre-participation levels after sufficient (5-6 months) rest from collision-based exposure. These temporal structural volumetric alterations are significantly different from normal aging observed in sex- and age-matched controls participating in non-collision sports. Future work involves modeling repetitive head exposure thresholds with multi-modal image analysis and understanding the underlying physiological reason. A possible pathophysiological pathway is presented, highlighting the probable metabolic regulatory mechanisms. Continual participation in collision-based activities may represent a risk wherein recovery cannot occur. Even when present, the degree of the eventual recovery remains to be explored, but has strong implications for the well-being of collision-sport participants.