interdisciplinary biomedical research...bd2k: four programmatic areas policies • data sharing –...

Interdisciplinary Biomedical Research

Vinay Pai Division of Health Information Technology

NIBIB, NIH 6707 Democracy Blvd., Ste. 200, Bethesda, MD

April 27, 2015

Systems we interact with....

Sensors and Us

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

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

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

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

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/

Medical Device Interoperability (MD PnP) PI: Julian Goldman,

Smart America CPS Challenge

http://www.mdpnp.org/

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

Artificial Organs• Artificial Pancreas

• Continuous glucose monitoring • Collaborative effort with FDA, JDRF,

NIH

http://www.minimed.com/treatment-and-products/continuous-glucose-monitoring

Developing an Artificial Pancreas

Group Science

Myriad Data Types

Other ‘Omic

Imaging Phenotypic

Clinical

Genomic

Exposure

➢ 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

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

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…)

BD2K Awards

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

FROM LARGE MACHINES / ORGANIZATIONS...

‹#› 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

‹#› 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

‹#› 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

To small / innovative approaches...

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

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)

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

Summary

• Interdisciplinary is IN at NIH. •Get the job done •Your imagination is the limiting factor

Contact

• Vinay Pai (Vinay.pai@nih.gov) • Your IC-specific program officer • NIH Reporter (reporter.nih.gov)

Thank you

ALZHEIMER’S DISEASE NEUROIMAGING INITIATIVE

(ADNI)

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

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

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

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

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