AI Institute: Planning: BI4ALL: Understanding Biological
NSF 20-503 (K. Kording.): 01-Oct-2020 to 31-Jul-2022

The goal of this project is to plan an AI institution via several meetings and workshops

Collaborative Research: Transferable, Hierarchical, Expensive, Optimal, Robust, Interpretable Networks
NSF 20-540 (R Vidal.): 01-Sep-2020 to 31-Aug-2025

The goal of this project is to develop a mathematical, statistical and computational frame- work that helps explain the success of current network arcitectures, understand its pitfalls, and guide the design of novel architectures with guaranteed confidence, robustness, inter- pretability, optimality, and transferability

Federated Causal Inference for Multi-site Real-World Evidence & Clinical Trial Analysis
Studies in Pandemic Preparedness (M. Powell.): 01-Aug-2020 to current

This project will conduct federated retrospective analyses designed to assess the benefit of off-label drug use by pooling multiple disparate databases, to help prioritize and guide subsequent initiation and recruitment of randomized clinical trials. This will include evaluating the impact of the target drugs on patient outcomes from diseases similar to COVID-19, such as pneumonia or acute respiratory distress, generating artificial datasets using generative adversarial networks to asses performance of methods when 'ground truth' is known, applying the best methods to analyze the effect of the target drugs on the outcomes of COVID-19 patients across hospital systems, and using the results to evaluate the potential of these drugs and suggest guidelines for clinical trials.

Graspy: A python package for rigorous statistical analysis of populations of attributed connectomes
NIH MH-19-147 (J. Vogelstein.): 01-Jul-2020 to 30-Jun-2023

The goal of this project is to establish a state-of-the-art toolbox for analysis of connectomes, spanning taxa, scale, and complexity. we will develop and extend implementations to enable neurobiologists to (1) estimate latent structure from attributed connectomes, (2) identify meaningful clusters among populations of connectomes, and (3) detect relationships be- tween connectomes and multivariate phenotypes

NeuroNex2: Enabling Identification and Impact of Synaptic Weight in Functional Networks
NSF 2014862 (K Harris.): 01-Apr-2020 to 31-Mar-2025

The goal is to develop the requisite technology to understand the impact of synaptic weight on functional networks

CAREER: Foundational Statistical Theory and Methods for Analysis of Populations of Attributed Connectomes
NSF 17-537 (J. Vogelstein.): 01-Jan-2020 to 31-Dec-2025

The goal is to establish foundaitonal theory and methods for analyzing populations of attributed connectomes

Brain Networks in Mouse Models of Aging
NIH RO1AG066184-01 (A. Badea.): 01-Dec-2019 to 30-Nov-2023

The goal of this grant is to generate connectomes and RNA-seq transcriptomes to characterize and differentiate APOE mice as a model of aging

Accessible technologies for high-throughput, whole-brain reconstructions of molecularly characterized mammalian neurons
NIH RFA-MH-19-148 (M. Muller.): 01-Sep-2019 to 31-Aug-2022

The overall goal of the proposal is to develop technologies for the brain wide reconstruction of axonal arbors of molecularly defined neurons. The proposal aims at overcoming barriers in neuronal labeling, imaging and computation to achieve this goal, and to develop a technology platform that can be scaled to all neurons of the brain

Microsoft Research Award
(J. Vogelstein.): Unrestricted Gift

Research and development of neuroscience and connectomes around neuronal circuit and system modeling, application of time-series-of-graphs and dynamics to neuronal signaling analysis and connectomes, and in the abstractions of matter, math, machines that point toward complex systems composed of low-level components

Continual Learning Across Synapses, Circuits, and Brain Areas
FA8650-18-2-7834 (A. Tolias.): 01-Nov-2017 to 30-Oct-2021

Develop the pre-processing analysis pipeline for the imaging data collected in this project

Lifelong Learning Forests
FA8650-18-2-7834 (J. Vogelstein.): 01-Nov-2017 to 31-Oct-2021

Lifelong Learning Forests (L2Fs) will learn continuously, selectively adapting to new environ- ments and circumstances utilizing top-down feedback to impact low-level processing, with provable statistical guarantees, while maintaining computational tractability at scale

Sensorimotor processing, decision making, and internal states: towards a realistic multiscale circuit model of the larval zebrafish brain
NIH 1U19NS104653-01 (F. Engert.): 01-Sep-2017 to 31-Aug-2022

Generate a realistic multiscale circuit model of the larval zebrafish’s brain – the multiscale virtual fish (MSVF). The model will span spatial ranges from the nanoscale at the synaptic level, to local microcircuits to inter-area connectivity - and its ultimate purpose is to explain and simulate the quantitative and qualitative nature of behavioral output across various timescales


Reproducible imaging-based brain growth charts for psychiatry
NIH R01MH120482-01 (T. Satterthwaite.): 01-Aug-2019 to 31-May-2020

Aggregate, harmonize, and analyze existing large-scale pediatric neuroimaging datasets to identify normative and clinical brain growth curves

SemiSynBio: Collaborative Research: YeastOns: Neural Networks Implemented in Communication Yeast Cells
NSF 1807369 (E. Schulman.): 16-Jul-2018 to 30-Jun-2021

Provide neuroscience and machine learning expertise to guide the design of the computa- tional learning capabilities of the system

Connectome Coding at the Synaptic Scale
Nascent Innovation Grant 128503 (J. Vogelstein.): 01-Jan-2018 to 31-Dec-2020

Study learning and plasticity at an unprecedented scale, revealing the dynamics of large populations of synapses comprising an entire local cortical circuit. No previously conducted experiment could answer the questions about the dynamics of large populations of synapses, which is crucial to understanding the learning process

NeuroNex Innovation Award: Towards Automatic Analysis of Multi-Terabyte Cleared Brains
NSF 1707298 (J. Vogelstein.): 01-Sep-2017 to 31-Aug-2020 (No Cost Extension)

We propose to lower the barrier to connecting data to analyses and models by providing a coherent cloud computational ecosystem that minimizes current bottlenecks in the scientific process

CRCNS US-German Res Prop: functional computational anatomy of the auditory cortex
NIH 1R01DC016784-01 (J. MRatnanather.): 01-Jul-2017 to 30-Jun-2020

Create a robust computational framework for analyzing the cortical ribbon in a specific region: the auditory cortex

Multiscale Generalized Correlation: A Unified Distance-Based Correlation Measure for Dependence Discovery
NSF 1921310 (S. Cencheng.): 01-May-2017 to 30-Apr-2020

Establish a unified methodology framework for statistical testing in high-dimensional, noisy, big data, through theoretical advancements, comprehensive simulations, and real data experiments

NeuroNex Technology Hub: Towards the International Brain Station for Accelerating and Democratizing Neuroscience Data Analysis and Modeling
NSF 16-569 (J. Vogelstein.): 2017 to 2019

We propose to lower the barrier to connecting data to analyses and models by providing a coherent cloud computational ecosystem that minimizes current bottlenecks in the scientific process

The International Brain Station
90071826 (J. Vogelstein.): 2017 to 2018

Take the first few steps towards building the international brain station

Brain Comp Infra: EAGER: BrainLab CI: Collaborative, Community Experiments
ACI-1649880 (B. Miller.): 2017 to 2018

The BrainLab CI prototype system will deploy an experimental-management infrastruc- ture that allows users to construct community-wide experiments that implement data and metadata controls on the inclusion and exclusion of data

The Brain Ark
90076467 (J. Vogelstein.): 2017 to 2018

Characterize the statistical properties of the individual graphs, to identify circuit motifs, both that specialize in a species specific fashion, and that are preserved across species. As a test, will compare the connectomes of sea lions and coyotes

D3M: What Would Tukey Do?
FA8750-17-2-0112 (C. Priebe.): 01-Oct-2016 to 30-Sep-2020

Develop theory and methods for generating a discoverable archive of data modeling primi- tives and for automatically selecting model primitives and for composing selected primitives into complex modeling pipelines based on user-specified data and outcome(s) of interest

A Scientific Planning Workshop for Coordinating Brain Research Around the Globe
NIH RFA-MH-19-148 (J. Vogelstein.): 2016 to 2019

This travel grant is for the expressed purposes of gathering researchers from around the globe to discuss the new way to further brain research during part one of a two day conference

A Scientific Planning Workshop for Coordinating Brain Research Around the Globe
NSF 1637376 (J. Vogelstein.): 2016 to 2019

This travel grant is for the expressed purposes of gathering researchers from around the globe to further discuss advancements in brain research during the second part of a two day conference

From RAGs to Riches: Utilizing Richly Attributed Graphs to Reason from
N66001-15-C-40401 (J. Vogelstein.): 01-Sep-2019 to 31-Aug-2022

Multiple, large, multifarious brain imaging datasets are rapidly becoming standards in neuroscience. Yet, we lack the tools to analyze individual datasets, much less populations thereof. Therefore, we will develop theory and methods to analyze and otherwise make such data available

Scalable Grain Graph Analyses Using Big-Memory, High-IPS Compute Architectures
N66001-14-1-4028 (R. Burns.): 2014 to 2016

Build software infrastructure to enable analytics on billion node, terabyte sized networks using commodity hardware

Synaptomes of Mouse and Man
NIH R01NS092474 (S. Smith.): 2014 to 2019

The major goals of this project are to discover the synaptic diversity and complexity in mammalian brains, specifically comparing and contrasting humans with mice, the leading experimental animal

CRCNS: Data Sharing: The EM open Connectome Project
RO1EB16411 (R. Burns.): 2012 to 2015

Develop cyberinfrastructure to support management, visualization, storage, and analysis of large-scale electron microscopy data