Interdisciplinary Biomedical Research...BD2K: Four Programmatic Areas Policies • Data Sharing –...
Transcript of Interdisciplinary Biomedical Research...BD2K: Four Programmatic Areas Policies • Data Sharing –...
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Interdisciplinary Biomedical Research
Vinay Pai Division of Health Information Technology
NIBIB, NIH 6707 Democracy Blvd., Ste. 200, Bethesda, MD
April 27, 2015
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Systems we interact with....
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Sensors and Us
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Smart Environment Technologies for Health Assessment and Assistance
acquire and apply knowledge about the resident and the physical surroundings to improve the resident’s experience.
EnvironmentAgent
Percepts (sensors)
Actions (controllers)
Cook and Schmitter-Edgecombe, PIs, Washington State University
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Technology ChallengesLongitudinal smart home data collection
• Characterize daily lifestyle of smart environment residents through minimal-supervision activity recognition and activity discovery; collected data will allow for a better measure of “real-world” outcome
• Design software algorithm that detect trends in behavioral data; earlier, preventative health interventions
• Evaluate activity-aware automated prompting technology for extending functional independence and improving quality of life; ecological momentary intervention
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Devices, processes, non-integrated system ! errors
Many non-integrated infusion pumpsNo means to coordinate devices with delivery of care
No ability to synchronize imaging with ventilation No ability to integrate data for smart alarms
Medical Device Interoperability
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MEDICAL DEVICE INTEROPERABILITYQuantum Medical Device Interoperability (QMDI) project • 5 year project (2010) • www.mdpnp.org • Multiple institutions
• MGH, UPenn, KSU, UIUC • Anakena, DocBox, Moberg Research • FDA, VA. • Funded by NIH, NSF, DoD
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Medical Device Interoperability (MD PnP) PI: Julian Goldman,
Partners HealthCare System and Mass Gen U01 mechanism – Cooperative Agreement
Aims Use cases Requirements Software & architecture Test and validation Complete deliverable sets Industry adoption
Clinical Scenarios PCA Safety Interlock - component-level medical device interoperability ICU preparedness - from OR to ICU Tele-health (TH) devices – between home and hospital
http://www.mdpnp.org/
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Medical Device Interoperability (MD PnP) PI: Julian Goldman,
Smart America CPS Challenge
http://www.mdpnp.org/
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Longitudinal pattern recognition
Subject Center
Cell Phone or
Computer Connection
Adapting parameters
Subject
Healthcare professional
Problem: Patients with CVD have symptoms that frequently bring them to emergency care where there is limited baseline data Solution: Remote monitoring to create physiological cardiac activity “fingerprints” that alert professionals and patient when there are irregularities based on their own cardiac patterns
Vladimir Shusterman, PinMed, NHLBI, R43-44 HL0771160, R41HL093953
Disease Management
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Artificial Organs• Artificial Pancreas
• Continuous glucose monitoring • Collaborative effort with FDA, JDRF,
NIH
http://www.minimed.com/treatment-and-products/continuous-glucose-monitoring
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Developing an Artificial Pancreas
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Group Science
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Myriad Data Types
Other ‘Omic
Imaging Phenotypic
Clinical
Genomic
Exposure
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➢ NIH Data Science ‘Programmatic Czar’
(aka, Point Person, Strategic Leader, etc.)
➢ Reports to NIH Director➢ Change the culture of NIH
Associate Director for Data Science: Overview
• Dr. Philip E. Bourne, PhD • Associate Vice Chancellor
for Innovation and Industry Alliances
• UC San Diego • Associate Director, RCSB,
PDB • From March 3, 2014
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I. Facilitating Broad Use of Biomedical
Big Data II. Developing and Disseminating
Analysis Methods and Software for Biomedical Big Data
III. Enhancing Training for Biomedical Big Data
IV. Establishing Centers of Excellence for Biomedical Big Data
BD2K: Four Programmatic Areas
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Policies• Data Sharing
– Genomic data sharing plan update – Data sharing plans on all research awards
• Data Citation – Goal: legitimize data as a form of scholarship
• Grant Mechanisms – Support for communities e.g. GA4GH – Matchmaking (biomedical researchers and
others…)
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BD2K Awards
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BD2K Centers PI PI Institution Focus Areas
Causal Modeling and Discovery of Biomedical Knowledge from Big Data
Greg Cooper
University of Pittsburgh
Predictive Computational Phenotyping
Mark Craven
University of Wisconsin
BC, AD, MI, Asthma, HOC
Mobilize Center for Mobility Data Integration into Insight
Scott Delp
Stanford University
CP, OA, limb loss, stroke
KnowEnG – Knowledge Network Jiawei Han
University of Illinois at Urbana-Champaign
BC, social behavior, antibiotics
Big Data in Translational Genomics
David Haussler
Univ. of California at Santa Cruz
Cancer; rare, complex and polygenic genetic disorders
Patient-centered Information Commons
Isaac Kohane
Harvard Medical School
Neurodevelopmental diseases
BD2K Centers of Excellence
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BD2K Centers PI PI Institution Biomedical Focus Areas
Center of Excellence for Mobile Sensor Data-to-Knowledge
Santosh Kumar
University of Memphis
CHF readmission reduction, prevent relapse in abstinent smokersCenter for Expanded Data
Annotation and Retrieval (CEDAR)
Mark Musen Stanford University Immunology, Digital Repositories
The Heart of Data Science Peipei Ping UCLA Cardiovascular research
ENIGMA Center for worldwide medicine, imaging and genomics
Paul Thompson
University of Southern California
Major brain diseases including AD, ADHD, PTSD, etc.
Big Data for Discovery Science
Arthur Toga
University of Southern California
Brain genetics, proteomics and neuroimaging.
LINCS Data Coordination and Integration Center
Avi Ma’ayan Icahn School of Medicine @Mt. Sinai
Neurodevelopmental diseases
BD2K Centers of Excellence
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E N I G M A
• Drawing together groups conducting brain imaging studies
• both patient and population samples
• with MRI, DTI, fMRI, and ASL
• with or collecting GWAS data
• Predominantly imaging groups moving into genetics
• Structure
• Support Group & working groups
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FROM LARGE MACHINES / ORGANIZATIONS...
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‹#› R01EB010065
GE Global Research – Mayo Clinic Collaboration
Affordable, Accessible, High Performance 3.0T Brain-only MR Scanner
Potential Clinical Impact:• Light-weight, easy-to-site, low cryogen MRI system• Lower cost specialty system – increased access to advanced MRI• Higher performance than whole-body 3.0T MRI• Enables advanced brain MRI :
– microstructure changes– functional & structural connectivity
Potential Global Impact:• Neuropsychiatric disorders - #1 global public health issue• Provides access to high quality, advanced brain imaging• No cryogens needed – expands MRI to underserved areas
A public-private-academic partnership
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‹#› R01EB010065
GE Global Research – Mayo Clinic Collaboration
Program Status
• High performance gradient coil completed and tested in whole-body 3.0T scanner– 26-cm FOV : first phantom image to first in-vivo image in 4 days– Distortion-free imaging down to C3/C4 junction– High gradient performance, 90 mT/m and 700 T/m/s– Systems level stability tests in progress
• Public-Private-Academic Partnership– Clinical evaluation of head-only MRI system at Mayo Clinic– Technology and systems development at GE Global Research– Support from NIBIB and NINDS
Volunteer test of head gradient in GE Discovery MR750w 3.0T MR
Sagittal 3D image showing distortion-free 26-cm FOV
C2C3C4
Combining MR systems technology and clinical / MR physics expertise to advance brain imaging
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‹#› R01EB010065
GE Global Research – Mayo Clinic Collaboration
High Performance Head Gradient – Initial Results
T2 Fast-recovery FSE (FRFSE) :- 7th and 8th cranial nerves
visualized within the internal auditory canal
3D TOF:- Intracranial circulation
and small distal vessels
T1 FLAIR:- Midline anatomical
structures
3D IR-FSPGR:- Coronal reformation
from sagittal acquisition
MRI with a 90 mT/m and 700 T/m/s gradient system
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To small / innovative approaches...
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Indo-US Collaborative Program on Affordable Medical Devices
• Small Research Grants, non-renewable • NIH funds US Investigators, DBT funds Indian
investigators • US PI: Direct cost $75,000 / year, for 2 years
max • Institutes: NIBIB, NICHD, NIAID, and NCI • Aim: Develop, translate and use medical
technologies to significantly impact underserved populations
• Appropriate medical technologies: usable, cost-effective, sustainable and effective
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Funded Projects• Single-motor propelled wheelchair (UPittsburgh
and Indian Spinal Injuries Center) • Cell-phone based protocols for managing
childhood pneumonia (PATH and INCLEN) • Low-cost cardiac annunciator for newborns
(Mich. Tech. and JN Medical College) • A low-cost simple oral cancer screening device
for low-resource settings. (UC Irvine and Mazumdar-Shaw Cancer Center)
• Combined testing to identify virologic failure and HIV-1 Drug resistance. (UCSD and YRG Medical, Engineering, and Research Foundation)
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PASSED ISO 7176 WC STANDARDS TESTING
Optimized Version 2 of the OCT system
Cardiac Annunciator Prototype. Cost: $18
Mobile-based Childhood Pneumonia management system
layout
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Summary
• Interdisciplinary is IN at NIH. •Get the job done •Your imagination is the limiting factor
Contact
• Vinay Pai ([email protected]) • Your IC-specific program officer • NIH Reporter (reporter.nih.gov)
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Thank you
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ALZHEIMER’S DISEASE NEUROIMAGING INITIATIVE
(ADNI)
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GOALS OF THE ADNI: LONGITUDINAL MULTI-SITE OBSERVATIONAL STUDY
• Major goal is collection of data and samples to establish a brain imaging, biomarker, and clinical database in order to identify the best markers or combinations for following disease progression and monitoring treatment response
• Determine the optimum methods for acquiring, processing, and distributing images and biomarkers in conjunction with clinical and neuropsychological data in a multi-site context
• “Validate” imaging and biomarker data by correlating with neuropsychological and clinical data.
• Rapid public access of all data and access to samples
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ADNI Public-Private Partnership Structure
FDA
NIBIB, NINDS, NIMH and other ICs
PI: Mike Weiner Administrative Core: UCSF
Biostatistics Core: UCD: Beckett
Biomarkers Core: UPenn: Trojanowski/ Shaw
MRI Core: Mayo: Jack
Clinical Core: UCSD: Aisen Mayo: Petersen
PET Core: Berkeley: Jagust
Informatics Core: UCLA: Toga
Publications Core: Harvard: Green
Pathology Core: WashU: Morris
57 Clinical Sites: ADNI PIs and Cores
ADNI Executive Steering Committee
Private/Philanthropic +
Public
Genetics Core: Indiana: Saykin
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Data and Sample Sharing
• Goal is rapid public access of all raw and processed data
• Central repository for all QA’d MRI and PET [Laboratory of Neuroimaging, USC (LONI)]
• Clinical data base at UCSD is linked to LONI • Databases- in the public domain, available to all
qualified investigators • Sample sharing-Resource Allocation Review
Committee • No special access • Data Sharing & Publication Committee (DPC)
-ADNI Data Use Agreement
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Shen et al 2010: Overview FreeSurfer: 56 volume
or cortical thickness
measures
VBM: 86 GM density measures
QC’ed genotyping data
GWAS of Imaging Phenotypes
Strong associations represented by heat maps
R L L R
R L R
GWAS of candidate QT VBM of candidate SNPRefined modeling of candidate association
530,992 SNPs142 QTs
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Gene Identification with Imaging “Deep Phenotypes”: GWAS
Conclusion: Imaging Gene Discovery
Structural MRI + 600k SNPs = GRIN2b as Novel Risk Factor for MTL deficits in Alzheimers
Stein et al 2010; ADNI