Award Abstract # 1452485
CAREER: Modeling Personalized Brain Development with Big Data

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: VANDERBILT UNIVERSITY
Initial Amendment Date: January 27, 2015
Latest Amendment Date: June 25, 2019
Award Number: 1452485
Award Instrument: Continuing Grant
Program Manager: Sylvia Spengler
sspengle@nsf.gov
 (703)292-7347
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: February 1, 2015
End Date: August 31, 2021 (Estimated)
Total Intended Award Amount: $435,951.00
Total Awarded Amount to Date: $465,951.00
Funds Obligated to Date: FY 2015 = $267,827.00
FY 2017 = $93,363.00

FY 2018 = $94,761.00

FY 2019 = $10,000.00
History of Investigator:
  • Bennett Landman (Principal Investigator)
    bennett.landman@vanderbilt.edu
Recipient Sponsored Research Office: Vanderbilt University
110 21ST AVE S
NASHVILLE
TN  US  37203-2416
(615)322-2631
Sponsor Congressional District: 05
Primary Place of Performance: Vanderbilt University
2301 Vanderbilt Place
Nashville
TN  US  37235-0002
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): GTNBNWXJ12D5
Parent UEI: K9AHBDTKCB55
NSF Program(s): Info Integration & Informatics
Primary Program Source: 01001516DB NSF RESEARCH & RELATED ACTIVIT
01001718DB NSF RESEARCH & RELATED ACTIVIT

01001819DB NSF RESEARCH & RELATED ACTIVIT

01001920DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 1045, 7218, 7364, 9150, 9151
Program Element Code(s): 736400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Big data offer an opportunity to study specific control populations (age / sex / environmental factors / demographics / genetics) and identify substantive homogeneous sub-cohorts so that one may understand the roles that potential factors play in brain development, differentiating abnormal trajectories from normal development. The image processing, statistical, and informatics tools to effectively and efficiently use big data imaging archives for quantitative population-level research and personalized medicine do not yet exist. This research will enable discovery science on a scale considerably larger than routinely possible with traditional study designs by creating novel informatics resources that tie archives of 3-D images into accessible research databases. This research will discover genetic and environmental factors that influence an individual's brain development and characterize the developing human brain through personal developmental trajectories. To accomplish this goal, new informatics technologies will be created to enable (1) image processing and segmentation based on image content in the context of heterogeneous, low quality, and error prone data with minimal human oversight and (2) routine archival, query, and image processing of large medical imaging datasets. This research will impact the areas of (1) informatics via novel computation models, (2) neuroscience via a new structural model of brain development, and (3) public health via newly accessible data sets for research. The science and technology innovations enabled by using big data to understand personalized brain development will be communicated in a tiered method. Outreach to the K-12 audience will target conceptualizing design criteria, inspiring students with interactive demonstrations, and providing capabilities for students to apply key concepts in hands-on engineering projects. For advanced students and researchers, new accessible course materials and online modules will be developed so that others may build upon the foundations established by this research.

Novel software, data wrangling tools, and resources will be created through two research thrusts organized around a novel test bed infrastructure and synthesized in a third education/outreach thrust. Thrust 1 (Personal Brain Trajectories) will focus on extracting meaningful information from medical images when performed at scale through (1) creating automated methods robust to variations in image quality, acquisition, and transfer errors, and (2) enabling efficient human-in-loop control at scale. The research will extend novel statistical models for image content labeling while adapting quality control techniques from industrial engineering. Thrust 2 (Novel Storage & Processing) will create novel medical imaging data models to describe data acquisition / retrieval, storage, cleaning, access / security, query and processing by integrating of medical imaging standards with big data architecture derived from social network and e-commerce communities. This infrastructure will provide practical access to petabyte imaging archives, integrate with existing data workflows, and effectively function with commodity hardware. The PI will develop and release a reference test bed to evaluate new technologies in the context of computer-aided detection (CADe) of brain abnormalities while considering age, sex, and demographics. Using the test bed, researchers and students will be able to efficiently evaluate existing and emerging image processing software to screen for potential prognostic markers. In Thrust 3 (Education and Outreach), the research results will be integrated into two classes targeting undergraduate students and interactive online modules created and released through an established graduate student/faculty training program. Each summer, an undergraduate and high school student will participate in research by implementing and extending research contributions within an interactive demonstration platform. In the second through fifth summers, a high school teacher will assist in the development of curricula targeting high school students using the demonstration platform. High school students and teachers will be recruited from Nashville Metro schools with a high underrepresented minority / reduced cost lunch populations. These efforts will create an open-source, open-hardware system for public demonstration and K-12 classroom exercises.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

(Showing: 1 - 10 of 134)
Aboud, Katherine S. and Huo, Yuankai and Kang, Hakmook and Ealey, Ashley and Resnick, Susan M. and Landman, Bennett A. and Cutting, Laurie E. "{Structural covariance across the lifespan: Brain development and aging through the lens of inter-network relationships}" Human Brain Mapping , v.40 , 2019 , p.125--136 10.1002/hbm.24359
Ana Gainaru, Hongyang Sun, Guillaume Aupy, Padma Raghavan and Bennett A. Landman "On-the-fly scheduling vs. reservation-based scheduling for unpredictable workflows" The International Journal of High Performance Computing Applications , 2019
Andrew J. Plassard, Maureen McHugo, Stephan Heckers, Bennett A. Landman "Multi-Scale Hippocampal Parcellation Improves Atlas-Based Segmentation Accuracy" In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. , 2017
Andrew J. Plassard, Pierre F. D'Haese, Srivatsan Pallavaram, Allen T. Newton, Daniel O. Claassen, Benoit M. Dawant, Bennett A. Landman "Multi-Modal and Targeted Imaging Improves Automated Mid-Brain Segmentation" In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation. , 2017
Andrew J. Plassard, Pierre F. DHaese, Srivatsan Pallavaram, Daniel O. Claassen, Benoit M. Dawant, Bennett A. Landman "Multi-Modal Imaging with Specialized Sequences Improves Accuracy of the Automated Sub-Cortical Grey Matter Segmentation" Magnetic Resonance Imaging , 2019
Andrew J. Plassard, Robert L. Harrigan, Allen T. Newton, Swati D. Rane, Srivatsan Pallavaram, Pierre F. D'Haese, Benoit M. Dawant, Daniel O. Claassen, Bennett A. Landman "On the Fallacy of Quantitative Segmentation for T1-Weighted MRI" In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016.  , 2016
Andrew J. Plassard, Zhen Yang, Swati D. Rane, Jerry L. Prince, Daniel O. Claassen, Bennett A. Landman "Improving Cerebellar Segmentation with Statistical Fusion. " In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016.  , 2016
Andrew Plassard, Bennett A. Landman. "?Multi-Protocol, Multi-Atlas Statistical Fusion: Theory and Application.?" Journal of Medical Imaging (JMI). In Press July 2017 , 2017
Bao, Shunxing and Bermudez, Camilo and Huo, Yuankai and Parvathaneni, Prasanna and Rodriguez, William and Resnick, Susan M. and D'Haese, Pierre Fran{\c{c}}ois and McHugo, Maureen and Heckers, Stephan and Dawant, Benoit M. and Lyu, Ilwoo and Landman, Benne "{Registration-based image enhancement improves multi-atlas segmentation of the thalamic nuclei and hippocampal subfields}" Magnetic Resonance Imaging , v.59 , 2019 , p.143--152 10.1016/j.mri.2019.03.014
Bermudez, Camilo and Blaber, Justin and Remedios, Samuel W. and Reynolds, Jess E. and Lebel, Catherine and McHugo, Maureen and Heckers, Stephan and Huo, Yuankai and Landman, Bennett A. "{Generalizing deep whole brain segmentation for pediatric and post-contrast MRI with augmented transfer learning}" Journal of Medical Imaging , v.7 , 2020 , p.20 10.1117/12.2548622
Bermudez, Camilo and Plassard, Andrew J. and Chaganti, Shikha and Huo, Yuankai and Aboud, Katherine S. and Cutting, Laurie E. and Resnick, Susan M. and Landman, Bennett A. "{Anatomical context improves deep learning on the brain age estimation task}" Magnetic Resonance Imaging , v.62 , 2019 , p.70--77 10.1016/j.mri.2019.06.018
(Showing: 1 - 10 of 134)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The overall objective of this CAREER proposal was to answer question on what impacts brain development by evaluating subject-specific factors using big data medical imaging. 

The essential premise of this project was two-fold: (1) big data are necessary to understand the impacts of genetic and environmental factors on brain development, and (2) medical imaging computing with big data is achievable with practical and pragmatic resources (cost, effort, hardware) using informatics to distribute data and computation together. To address these fundamental problems facing neuroscience and image science, we sought to understand how big data approaches could be developed for medical imaging within the context of existing standards and information technologies. 

 

This project pursued a fundamental rethinking of how medical image computing and big data could guide neuroscience, image processing algorithm engineering, and, eventually, medical science. The research created novel informatics and medical imaging technologies that led to a large data processing center with over 100 hundred thousand subjects. The effort to construct personalized trajectories of brain development led to novel innovations in robust computing and protocol harmonization. These effort in big data were essential to the deep learning revolution and our current abilities to rapidly prototype image segmentation algorithms. We have extensively published using the resources and ideas created under this project. Moreover, these efforts have helped to provided mentorship and training to more than two dozen graduate trainees, many of whom are now pursuing their own careers in imaging science. 

 

 


Last Modified: 11/03/2021
Modified by: Bennett A Landman

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page