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Research ArticleResearch Perspectives

Traumatic Brain Injury Imaging Research Roadmap

M. Wintermark, L. Coombs, T.J. Druzgal, A.S. Field, C.G. Filippi, R. Hicks, R. Horton, Y.W. Lui, M. Law, P. Mukherjee, A. Norbash, G. Riedy, P.C. Sanelli, J.R. Stone, G. Sze, M. Tilkin, C.T. Whitlow, E.A. Wilde, G. York and J.M. Provenzale on behalf of the American College of Radiology Head Injury Institute
American Journal of Neuroradiology March 2015, 36 (3) E12-E23; DOI: https://doi.org/10.3174/ajnr.A4254
M. Wintermark
aFrom the Neuroradiology Division (M.W.), Department of Radiology, Stanford University, Stanford, California
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L. Coombs
bAmerican College of Radiology (L.C., M.T., R. Horton), Reston, Virginia
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T.J. Druzgal
cDepartments of Radiology (T.J.D., J.R.S.)
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A.S. Field
eNeuroradiology Section (A.S.F.), Department of Radiology, University of Wisconsin, Madison, Wisconsin
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C.G. Filippi
fDepartment of Radiology (C.G.F.), Columbia University, New York, New York
gDepartment of Radiology (C.G.F., P.C.S.), North Shore–Long Island Jewish Health System, Manhasset, New York
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R. Hicks
hOne Mind (R. Hicks), Seattle, Washington
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R. Horton
bAmerican College of Radiology (L.C., M.T., R. Horton), Reston, Virginia
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Y.W. Lui
iNeuroradiology Division (Y.W.L.), Department of Radiology, NYU School of Medicine, New York, New York
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M. Law
jNeuroradiology Section (M.L.), Department of Radiology, University of Southern California, Los Angeles, California
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P. Mukherjee
kNeuroradiology Section (P.M.), Department of Radiology, University of California, San Francisco, San Francisco, California
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A. Norbash
lDepartment of Radiology (A.N.), Boston University School of Medicine, Boston, Massachusetts
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G. Riedy
mNational Intrepid Center of Excellence (G.R.), Washington, DC
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P.C. Sanelli
gDepartment of Radiology (C.G.F., P.C.S.), North Shore–Long Island Jewish Health System, Manhasset, New York
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J.R. Stone
cDepartments of Radiology (T.J.D., J.R.S.)
dMedical Imaging and Neurological Surgery (J.R.S.), University of Virginia, Charlottesville, Virginia
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G. Sze
nNeuroradiology Section (G.S.), Department of Radiology, Yale University, New Haven, Connecticut
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M. Tilkin
bAmerican College of Radiology (L.C., M.T., R. Horton), Reston, Virginia
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C.T. Whitlow
oDepartment of Radiology–Neuroradiology and Translational Science Institute (C.T.W.), Wake Forest School of Medicine, Winston-Salem, North Carolina
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E.A. Wilde
pDepartments of Physical Medicine and Rehabilitation, Neurology, and Radiology (E.A.W.), Baylor College of Medicine, Houston, Texas
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G. York
qSan Antonio Military Medical Center (G.Y.), San Antonio, Texas
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J.M. Provenzale
rDepartment of Radiology (J.M.P.), Duke University Medical Center, Durham, North Carolina.
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    Figure.

    Hypothetic distribution of an advanced neuroimaging-dependent measure for TBI (red) and non-TBI (blue) groups. A, Idealized separation of distributions between the TBI and non-TBI groups due to a very selective “normal” non-TBI control group (ie, supernormal, with no history of TBI, and no neurologic, psychiatric, or other detectable abnormalities), which would be amenable to conventional statistical analyses based on the general linear model. B, Marked overlap of distributions between the TBI and non-TBI groups due to a non-TBI control group comprising subjects with pre-existing abnormalities present in the general population, which would be unlikely to yield a statistically significant differentiation by using the general linear model. Despite relatively marked overlap between distributions, classification approaches may be able to identify features unique to each group and therefore discriminate whether an individual belongs in the TBI or non-TBI group. To pursue implementation of such a binary classification, it will be necessary to characterize the variability associated with neuroimaging methods expected from the general population in the absence of TBI, which can be facilitated by constructing a large comprehensive normative data base.

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    Table 1:

    Existing imaging repositories

    NDARFITBIRLORISLONI IDADARTXNATINDI/FCPCOINSBIRN HIDNIDB
    Core developersNational Institutes of HealthNational Institutes of Health NINDSMontreal Neurological InstituteUniversity of Southern CaliforniaAmerican College of RadiologyWashington UniversityNITRCMIND Research NetworkNational Institutes of Health NIGMSOlin Neuropsychiatry Research Center
    Types of data storedNeuroimagingNeuroimagingNeuroimagingNeuroimagingImagingNeuroimagingNeuroimagingNeuroimagingNeuroimagingNeuroimaging
    PhenomicPhenomicPhenomicPhenomicPhenomicPhenomicPhenomicPhenomicPhenomicPhenomic
    GeneticDemographicGeneticGeneticDemographicDemographicDemographicDemographicDemographic
    DemographicOther: extensibleDemographicDemographicOther: extensibleOther: extensibleOther: extensibleOther: extensible
    Other: extensibleOther: extensibleOther: extensibleOther: extensible
    Common data elementsRequired useRequired useFlexible usageFlexible usageFlexible usageFlexible usageNoneNoneNoneNone
    WorkflowYes
    Continuous project coordinationNoNoYesNoYesNoNoNoNoNo
    Monitoring of data acquisitionNoNoYesNoYesNoNoNoNoYes
    Data cleaningNoNoYesYesYesYesNoNoYesYes
    Data storageDeveloper hostingDeveloper hostingDeveloper hosting or local storageDeveloper hosting or local storageDeveloper hosting or local storageLocal storageDeveloper hostingDeveloper hostingLocal storageLocal storage
    Data queryYesYesYesYesYesYesNoYesYesYes
    Data downloadYesYesYesYesYesYesYesYesYesYes
    Interface with external data-processingNoNoYesYesYesYesNoNoYesNo
    Integrated data-processing toolsNoNoNoYesYes?YesNoNoNoNo
    Disease-specificYes: autismYes: TBINoNoNoNoNoNoNoNo
    Major datasets sharedData from 77,575 individuals related to autismIn developmentABIDEABIDEData from 150,000 individuals across a wide array of clinical domainsHCPABIDEABIDEABIDEABIDE
    AddNeuroMedAIBLADHD-200MCIC SchizophrenicBrainscape
    CBRAINADNICoRRNKI RocklandELUDE
    CITAADNIDODNKI RocklandfBIRN
    GUSTOHCPMIRIAD
    IBISICBMOASIS
    MAVANPPMI
    NeuGrid
    NeuroDevNet
    Access policiesGovernment-defined with institutional agreementGovernment-defined with institutional agreementUser-specifiedUser-specifiedUser-specifiedUser-specifiedUnrestrictedUser-specifiedUser-specifiedUser-specified
    Data contributionRequired as a condition of many NIH grantsRequired as a condition of many NIH grantsRequired for some multisite projectsRequired for some multisite projectsRequired for some multisite projectsVoluntaryVoluntaryVoluntaryVoluntary
    VoluntaryVoluntaryVoluntaryVoluntary
    • Note:—NDAR indicates National Database for Autism Research; NIGMS, National Institute of General Medical Sciences; INDI/FCP, International Neuroimaging Data Sharing Initiative/1000 Functional Connectome Project; IDA, Image Data Archive; COINS, Collaborative Informatics Neuroimaging Suite; NIDB, Neuroinformatics Database; HCP, Human Connectome Project; AIBL, Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing; ELUDE, Efficient Longitudinal Upload of Depression in the Elderly; CITA, Centro de Investigación y Terapias Avanzadas; ADNIDOD, Study of Brain Aging in Vietnam War Veterans; GUSTO, Singapore's birth cohort study; MIRIAD, Multisite Imaging Research In the Analysis of Depression; BIRN, Biomedical Informatics Research Network; fBIRN, Functional Bioinformatics Research Network; IBIS, Infant Imaging Brain Study; ICBM, International Consortium for Brain Mapping; OASIS, Open Access Structural Imaging Series; MAVAN, Maternal Adversity, Vulnerability, and Neurodevelopment; PPMI, Parkinson's Progressive Measures Initiative; XNAT, Extensible Neuroimaging Archive Toolkit; NINDS, National Institute of Neurologic Disorders and Stroke; ADHD, attention deficit/hyperactivity disorder; CoRR, Consortium for Reliability and Reproducibility; NKI Rockland, Nathan Kline Institute Rockland; MCIC, MIND Clinical Imaging Consortium.

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    Table 2:

    Core (required) demographic variables that should be collected for any normative data base

    Recommended Core (Required) VariablesCDE Variable NamePermissible ValuesNotes, Including Those from the CDE Web Site
    AgeAge value0–120 yr?For children younger than 1 year born at <36 weeks' gestation, it is recommended to also collect gestational age. Because of potential violation of privacy legislation and specifically HIPAA regulations, the calculated age should be recorded rather than using the actual date of birth.
    GenderGender typeFemale, male, unknown, unspecified, not reportedNote that “gender” refers to self-reported gender of the participant. Gender is the socially constructed identity of sex and is equated with phenotypic sex. Gender may differ from the sex of an individual determined genetically. The use of “gender” as opposed to “sex” is to facilitate the ease of self-report data collection.
    HandednessHand preference typeLeft hand, right hand, both hands, unknownHand which the participant/subject uses predominantly, not necessarily the hand he/she writes with exclusively
    RaceRace USA categoryAmerican Indian or Alaska native, Asian, black or African-American, Native Hawaiian or other Pacific Islander, white, unknown, not reportedThe patient's self-declared racial origination, independent of ethnic origination, using OMB-approved categories. We note that these may not be applicable to all non-USA regions.
    EthnicityEthnicity USA categoryHispanic or Latino, not Hispanic or Latino, unknown, not reportedCategory of ethnicity the participant/subject most closely identifies with. We note that these may not be applicable to all non-USA regions.
    Socioeconomic statusEducation level USA-typeNever attended/kindergarten only; 1st grade; 2nd grade; 3rd grade; 4th grade; 5th grade; 6th grade; 7th grade; 8th grade; 9th grade; 10th grade; 11th grade; 12th grade, no diploma; high school graduate; GED or equivalent; some college, no degree; associate degree: occupational/technical/vocational program; associate degree: academic program; bachelor's degree (eg, BA, AB, BS, BBA); master's degree (eg, MA, MS, MEng, MEd, MBA); professional school degree (eg, MD, DDS, DVM, JD); doctoral degree (eg, PhD, EdD); unknownHighest grade or level of school participant/subject has completed or the highest degree received. Note that for children, these should reflect the highest education level of the primary caregiver.
    Job classification categoryOfficial/manager, professional, technician, sales worker, administrative support worker, craft worker, operative, laborer/helper, service worker, social worker, unknown, noneCategory that classifies work performed by participant or, in the case of children, the work performed by the primary caregiver.
    Family income range$15,000 to $24,999, $25,000 to $34,999, $35,000 to $49,999, $50,000 to $74,999, $75,000 to $99,999, ≥$100,000, refused, unknown, <$15,000;Range, in US dollars, of the annual pretax, prededuction total income, of the household of which the participant/subject is a member
    Academic achievementEducation service typeSpecial education, regular education, none, unknown, early interventionType of educational services received (as a child)
    • Note:—GED indicates General Educational Development; HIPPA, Health Insurance Portability and Accountability Act; OBM, Office of Budget and Management.

    • View popup
    Table 3:

    “Core” and “preferred” imaging protocols for diffusion-weighted imaging

    Parameter“Core”“Preferred” No. 1 (High Angular Resolution)“Preferred” No. 2 (More b-Values)
    OrientationAxialAxialAxial
    CoilAnyPhased array ≥8 channelsPhased array ≥8 channels
    ReadoutEPIEPIEPI
    TR (ms)∼9000∼9000∼9000
    TE (ms)MinMin (<100)Min (<100)
    FOV (mm2)256 × 256 or 350 (66% phase)256 × 256 or 350 (66% phase)256 × 256 or 350 (66% phase)
    Matrix size128 × 128128 × 128128 × 128
    Sections/thickness (mm)Any/≤359/2.759/2.7
    Section gap (mm)000
    Voxel size (mm)≤3 in all dimensionsIsotropic 2.73Isotropic 2.73
    Directions≥1264≥12
    Dual-echoAnyNoNo
    Fat-suppressionAnyYesYes
    Phase-encode directionA to PA to PA to P
    BW (Hz/pixel)134613461346
    Parallel imaging factorAny22
    b-value (s/mm2)2 (0, ∼1000)2 (0, 1300)≥3 (eg, 0, ∼1000, ∼2000)
    No. b=0 images11 per 8 directions if allowed1 per 8 directions if allowed
    • Note:—Min indicates minimum; A to P, anterior to posterior; BW, bandwidth.

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M. Wintermark, L. Coombs, T.J. Druzgal, A.S. Field, C.G. Filippi, R. Hicks, R. Horton, Y.W. Lui, M. Law, P. Mukherjee, A. Norbash, G. Riedy, P.C. Sanelli, J.R. Stone, G. Sze, M. Tilkin, C.T. Whitlow, E.A. Wilde, G. York, J.M. Provenzale
Traumatic Brain Injury Imaging Research Roadmap
American Journal of Neuroradiology Mar 2015, 36 (3) E12-E23; DOI: 10.3174/ajnr.A4254

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Traumatic Brain Injury Imaging Research Roadmap
M. Wintermark, L. Coombs, T.J. Druzgal, A.S. Field, C.G. Filippi, R. Hicks, R. Horton, Y.W. Lui, M. Law, P. Mukherjee, A. Norbash, G. Riedy, P.C. Sanelli, J.R. Stone, G. Sze, M. Tilkin, C.T. Whitlow, E.A. Wilde, G. York, J.M. Provenzale
American Journal of Neuroradiology Mar 2015, 36 (3) E12-E23; DOI: 10.3174/ajnr.A4254
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