Baby Open Brains: An open-source dataset of infant brain segmentations.

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold-standard brain segmentations. These segmentations are limited by the difficulty of infant brain segmentations, which require extensive neuroanatomical knowledge and are time-consuming in nature.

Prefrontal tDCS fails to modulate memory retrieval in younger and older adults.

Previous research shows that a single session of anodal transcranial direct current stimulation (tDCS) to the left dorsolateral prefrontal cortex (dlPFC) can improve the accuracy of episodic memory retrieval, but stimulation effects are not always found and may be moderated by time of day. Here, we report the results from a rigorous clinical trial (NCT03723850) designed to replicate these tDCS findings in younger adults and extend them to cognitively normal older adults. We conducted the largest double-blind, between-subjects tDCS study on memory retrieval in younger and older adults to date.

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.

High performers demonstrate greater neural synchrony than low performers across behavioral domains.

Heterogeneity in brain activity can give rise to heterogeneity in behavior, which in turn comprises our distinctive characteristics as individuals. Studying the path from brain to behavior, however, often requires making assumptions about how similarity in behavior scales with similarity in brain activity. Here, we expand upon recent work (Finn et al.

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

Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important for studying typical and atypical brain development. 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 for robust and generalizable brain segmentation leveraging data augmentation and a large sample size of manually annotated images.

Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds.

Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170).

Propofol selectively modulates functional connectivity signatures of sustained attention during rest and narrative listening.

Sustained attention is a critical cognitive function reflected in an individual's whole-brain pattern of functional magnetic resonance imaging functional connectivity. However, sustained attention is not a purely static trait. Rather, attention waxes and wanes over time.