Baby Open Brains: An Open-Source Repository of Infant Brain Segmentations.

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable protocols and processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold standard brain segmentations. Addressing this core limitation, we constructed the Baby Open Brains (BOBs) Repository, an open source resource comprising manually curated and expert-reviewed infant brain segmentations.

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 (aby and nfant rain egmentation Neural work), 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.

Distraction biases working memory for faces.

Working memory persists in the face of distraction, yet not without consequence. Previous research has shown that memory for low-level visual features is systematically influenced by the maintenance or presentation of a similar distractor stimulus. Responses are frequently biased in stimulus space towards a perceptual distractor, though this has yet to be determined for high-level stimuli.