BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans.

Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations. Experimental Design: Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance. Principal Observations: Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better. Conclusions: BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.

Development of top-down cortical propagations in youth.

Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.

The goal priority network as a neural substrate of Conscientiousness.

Conscientiousness is a personality trait associated with many important life outcomes, but little is known about the mechanisms that underlie it. We investigated its neural correlates using functional connectivity analysis in fMRI, which identifies brain regions that act in synchrony. We tested the hypothesis that a broad network resembling a combination of the salience and ventral attention networks, which we provisionally label the goal priority network (GPN), is a neural correlate of Conscientiousness. Self- and peer-ratings of Conscientiousness were collected in a community sample of adults who underwent a resting-state fMRI scan (N = 218). An independent components analysis yielded five components that overlapped substantially with the GPN. We examined synchrony within and between these GPN subcomponents. Synchrony within one of the components-mainly comprising regions of anterior insula, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex-was significantly associated with Conscientiousness. Connectivity between this component and the four other GPN components was also significantly associated with Conscientiousness. Our results support the hypothesis that variation in a network that enables prioritization of multiple goals may be central to Conscientiousness.

Personality and Neural Correlates of Mentalizing Ability.

Theory of mind, or mentalizing, defined as the ability to reason about another's mental states, is a crucial psychological function that is disrupted in some forms of psychopathology, but little is known about how individual differences in this ability relate to personality or brain function. One previous study linked mentalizing ability to individual differences in the personality trait Agreeableness. Agreeableness encompasses two major subdimensions: Compassion reflects tendencies toward empathy, prosocial behavior, and interpersonal concern, whereas Politeness captures tendencies to suppress aggressive and exploitative impulses. We hypothesized that Compassion but not Politeness would be associated with better mentalizing ability. This hypothesis was confirmed in Study 1 (N = 329) using a theory of mind task that required reasoning about the beliefs of fictional characters. Post hoc analyses indicated that the honesty facet of Agreeableness was negatively associated with mentalizing. In Study 2 (N = 217), we examined whether individual differences in mentalizing and related traits were associated with patterns of resting-state functional connectivity in the brain. Performance on the theory of mind task was significantly associated with patterns of connectivity between the dorsal medial and core subsystems of the default network, consistent with evidence implicating these regions in mentalization.