Project Summary/Abstract Our project will create a computational resource, the Single Neuron Analyzer, to support the neuroscience community’s efforts to build a reproducible, comprehensive, data-driven atlas of brain cell types.
Project Summary/Abstract The connections between different brain regions play an important role in normal brain function. This project proposes to create an end-to-end pipeline for analysis of human white matter connections using “tractometry” methods. In tractometry, tissue properties are estimated in the long-range connections between remote brain regions. The project will focus on the analysis of the Human Connectome Project diffusion MRI dataset, which provides one of the largest available publicly available datasets of diffusion MRI from a sample of normal healthy individuals.
Abstract One of the high priority research areas listed in the BRAIN 2025 Report is a better understanding of the brain network dynamics across time and space from human electrophysiological recordings.
Linking molecular and anatomical features of brain cell identity through computational data integration Abstract The brain contains diverse cell types that vary widely in characteristic properties and function in complex, interconnected circuits. A complete definition of cellular identity in the brain requires incorporating both molecular and anatomical properties, including gene expression, epigenetic regulation, spatial position, and axonal projection patterns. However, existing experimental approaches cannot measure all of these features simultaneously within individual cells.
PROJECT SUMMARY The highly-organized intrinsic brain activity, as measured by resting-state functional magnetic resonance imaging (rsfMRI), is being widely used to measure functional brain connectivity in both healthy subjects and patient groups, despite the underlying neural mechanisms remain largely unclear. Converging evidence has suggested that infra-slow propagating activity may play an important role in generating rsfMRI connectivity and dynamics, and it thus could be the key to understanding the neural basis of rsfMRI connectivity and the functional role of intrinsic brain activity.
PROJECT SUMMARY Perivascular spaces are a critical component of the glia-lymphatic circuit, facilitating the clearance of soluble waste. The role of perivascular spaces and changes in the brain’s clearance system in normal development, aging, and cognition is not fully understood, mainly due to lack of neuroimaging capabilities. However, noninvasive in vivo mapping of the perivascular space fluid with high accuracy and reliability is now made possible with our recent analytical developments, using human connectome project (HCP) data.
Abstract This application will combine the strengths of two large scale NIH-funded initiatives to understand disorder- related patterns in the human brain: Connectomes Related to Human Disease (CRHD) and Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA). We will develop and evaluate novel brain vulnerability metrics - based on the idea of polygenic risk scores – that we expect to better predict diagnosis and cognitive performance than standard neuroimaging measures.
The human brain is an unimaginably complicated system of interconnected neurons that is capable of complex thought, emotion and behavior. Macroscale white matter connections quantified via the structural connectome (SC) act as the backbone for the flow of functional activation, which can be represented via the functional con- nectome (FC). Our group and others have shown that quantifying properties of the brain’s structural and func- tional connectomes and their relationship can inform understanding of brain-behavior associations and disease mechanisms4-9.
Project Abstract The first postnatal years are an exceptionally dynamic and critical period of structural and functional development of the human brain. Many neurodevelopmental disorders are the consequence of abnormal brain development during this stage. Several NIH-funded studies have recently acquired and released large-scale infant brain MRI datasets in the National Institute of Mental Health Data Archive (NDA), leading to over 3,000 publically-available infant MRI scans from multiple imaging sites.
ABSTRACT Understanding the biological principles of cell type diversity and organization is necessary for deciphering neural circuits underlying brain function. The recent rapid accumulation of single cell transcriptomic and epigenomic data sets provides unprecedented opportunity to explore the molecular genetic basis of cell type identity, diversity, and organization. However, analysis of multi-omics datasets have been largely driven by statistic methods that typically do not engage the deep knowledge of neurobiology and developmental biology.
