Circuit Diagrams

ABSTRACT (PROJECT SUMMARY) The classification of neurons in the mammalian brain has long been a focus of intensive investigation in neuroscience. Neurons are widely recognized as the fundamental computational elements of the nervous system, and the broad diversity of their morphological, physiological, and molecular properties may provide crucial insights into their function and involvement in disease. Long-range axonal projections, in particular, are the quintessential determinants of network connectivity, providing a key nexus between cellular organization and circuit architecture.

Project Summary The goal of this project is to create a unified framework for understanding the relationship between neuronal gene expression and connectivity in mouse visual cortex, by using morphology as a key linking modality. There now exist publicly available large-scale data sets that measure both these modalities in mouse visual cortex. One dataset is a large set of Patch-seq experiments from single cells, which provide measurements of gene expression, electrophysiological properties and morphology for individual cells.

ABSTRACT The non-human primate (NHP) model is critical to the advancement of translational neuroscience, as it allows researchers to link observations regarding macroscale brain dynamics and cognition in the human to underlying meso- and microscale phenomena that cannot be fully investigated in humans. Importantly, the ultimate value of findings from the NHP for informing human models relies on the adequacy of methods for cross-species anatomical and functional alignment.

SUMMARY Brain-mapping initiatives are acquiring increasingly large and comprehensive neuroimaging and multiomic— e.g. genomic and transcriptomic—datasets. Existing analyses of such data in human neuroscience tend to search for links between cognition, behavior or disease on the one hand, and properties of genomes, transcrip- tomes or brain morphology and connectivity on the other. Such valuable analyses have steadily advanced our knowledge of human brain function.

PROJECT SUMMARY/ABSTRACT The growing availability of large functional magnetic resonance imaging (fMRI) datasets has enabled new investigations into functional systems of the human brain. A challenge – but also opportunity – of fMRI arises from the fact that BOLD signal stems from multiple intertwined neural and physiological sources. One major contributor to fMRI signals arises from slow (

Technology for automating the segmentation of neurons from electron microscopic (EM) data has improved dramatically, making it now possible to obtain accurate reconstructions of neural circuits from large EM volumes. However, even the best automation still must be followed by human proofreading to attain high accuracy. Hundreds of neuroscientists are already using our ChunkedGraph system for proofreading neural circuits. The system is fully web- and cloud-ready, facilitating seamless collaboration.

Project Summary Recent advances in technology driven by the BRAIN Initiative have yielded new methods for characterizing the ac- tivity, transcriptome, and microscale anatomy of neurons throughout the brain, and have increased the throughput of these techniques by orders of magnitude.

This two-year project will advance, integrate, document, and promote the use of the Hierarchical Event Descriptor (HED) system to describe events in human neuroimaging and behavioral data from research experiments and other sources in sufficient detail to support comparative analysis of human brain dynamics across studies. Relating the recorded data dynamics to temporally- specifiable changes in subject experience, action, and cognition is a major goal (and challenge) for both neuroimaging and biomechanical imaging.

ABSTRACT Eating disorders are severe psychiatric conditions with a significant worldwide cost and disability burden. Binge eating (BE) is a behavior that cuts across nearly all eating disorder diagnoses. Unfortunately, psychological treatments for eating disorders/BE are limited, and targeted biological/pharmacological treatments have not yet been effective. In order to develop more effective targeted treatments, it is critical to understand the neural circuit abnormalities that contribute to the onset, expression, and maintenance of BE.

PROJECT SUMMARY Machine-learning-based classification of neuroimaging data (hereafter ML-MRI) to predict clinical diagnoses has increased substantially in the last decade. Despite the promise of ML for clinical classification and prediction, no work has been done to anticipate the ethical obligations and challenges that emerge when ML algorithms predict clinical diagnoses in pre-symptomatic individuals.