Research Projects

Project Summary / Abstract This 4-year Neuroethics R01 based at the University of Minnesota (UMN) will convene a national Working Group of top neuroethics, neurolaw, and neuroscience experts to conduct empirical research and generate evidence-based consensus recommendations for the ethical conduct of population research using highly portable, cloud-enabled MRI in new and diverse populations in field settings.

Project Summary/Abstract The BRAIN Initiative has made a significant investment in invasive human neuroscientific studies that take advantage of unique neurosurgical opportunities to study basic human neuroscience without therapeutic intent. These non-therapeutic studies are of particular ethical interes

PROJECT SUMMARY The mouse brain is composed of thousands of highly specialized cell types, distributed across hundreds of anatomical regions. Recently, advances in DNA barcoding and sequencing have enabled large-scale surveys of transcriptional state (single cell RNAseq), and epigenetic state (single cell DNA methylation and ATACseq) across the brain.

Abstract Brain development is characterized by a diverse set of cell types that are born and connected into rapidly growing complex 3D structures across time. Quantitative understanding of cell type composition and distribution in different brain regions provides fundamental knowledge about the building blocks of the brain and serves as an essential baseline with which to assess changes that may occur in brain disorders.

Tools for surveying brain cell types and circuits must be scalable, both in the number of molecular targets visualizable at once, and in the size of the tissues that can be assessed. They also must be high resolution, since cellular compartments such as axons, dendrites, and synapses exhibit nanoscale feature sizes.

Abstract In order to fully understand the structural substrates underlying the brain function, it is central to curate multiple attributes of the same neurons. The important attributes include, but limited to morphology, connection properties and molecular patterns, such as the expression of functional genes and the distribution of synaptic densities. Light microscopy-based neuronal tracing has contributed our fundamental understanding of the heterogeneity of neuronal morphology.

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.