NIH MRI Study of Normal Brain Development - PowerPoint PPT Presentation

Nih mri study of normal brain development l.jpg
Download
1 / 59

NIH MRI Study of Normal Brain Development AC Evans Ph.D. Brain Development Cooperative Group Pediatric Functional Neuroimaging: a Trans-NIH Workshop May 25, 2004 Contrast changes over time Problems with previous studies Sample sizes too small to detect subtle signals

Related searches for NIH MRI Study of Normal Brain Development

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

Download Presentation

NIH MRI Study of Normal Brain Development

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Nih mri study of normal brain development l.jpg

NIH MRI Study of Normal Brain Development

AC Evans Ph.D.

Brain Development Cooperative Group

Pediatric Functional Neuroimaging:

a Trans-NIH Workshop

May 25, 2004


Contrast changes over time l.jpg

Contrast changes over time


Problems with previous studies l.jpg

Problems with previous studies

  • Sample sizes too small to detect subtle signals

  • Heterogeneity of subject population

  • Little longitudinal data

  • Lack of demographic representativeness

  • Limited behavioral data for brain-behaviour correlation

  • Limited MRI data (typically T1 only)

  • Usually limited analysis techniques


Mri study of normal brain development n 500 l.jpg

The National Institute on Drug Abuse

MRI Study of Normal Brain Development(N=500)

Create a database of behavioral and brain MRI development data for 0-18 years

Analyze structural-behavioural relationships

Develop technique for dissemination of results


Rationale for project design l.jpg

Rationale for Project Design

  • Problem:

    Existing normative databases limited in size

    Pooling of databases difficult. Existing databases incompatible in

    • Slice thickness

    • Pulse sequence

    • Demographics

    • Behavioural tests

      One centre cannot collect large dataset fast enough to keep pace with technology

  • Solution:

    Clinical trial model: multi-centre acquisition, uniform protocol


  • Mri objectives l.jpg

    MRI Objectives

    • Objective 1: Anatomical MRI/Behavior (5-18)

    • Objective 2: Anatomical MRI/Behaviour (0-4)

    • Ancillary A: MR Spectroscopy

    • Ancillary B: Diffusion Tensor Imaging, Relaxometry


    Pediatric study centers pscs l.jpg

    Pediatric Study Centers (PSCs)

    • Neuropsychiatric Institute and Hospital, UCLA

      McCracken

    • Children’s Hospital, Boston

      Rivkin

    • Children’s Hospital of Philadelphia

      Wang

    • University of Texas-Houston Medical School

      Brandt

    • Children’s Hospital Medical Center, Cincinnati

      Ball

    • Washington University, St. Louis

      McKinstry


    Data coordinating center dcc l.jpg

    Data Coordinating Center (DCC)

    • Overall Direction Evans

    • Database Zijdenbos, Vins, Charlet, Harlap, Das

    • Behavioral Liaison Leonard, Milovan

    • MRI Acquisition Pike, Arnaoutelis

    • MRI Analysis Collins, Kitching, Lerch

    • Sampling Plan Lange (Harvard)

    • Data Transfer Zeffiro, Van Meter (Georgetown)

    • Scientific Liaison Paus

    • Clinical Liaison Ad-Dab’bagh, Webster


    Slide9 l.jpg

    Clinical Coordinating Center (CCC) – St. Louis

    Recruitment, behavioral measures selection/certification,

    exclusions etc. for Obj 1,2 – Botteron, Almli

    Behavioral QC – Rainey, Henderson,

    Singer, Smith, Dubois, Warren, Edwards

    DTI Processing Center (DPC) - NIH

    Pierpaoli, Basser, Rohde, Chang

    MRS Processing Center (MPC) – UCLA (?)

    Alger, O’Neill


    Slide10 l.jpg

    NIH MRI Study of Normal Brain Development

    DCC

    CCC

    DPC


    Recruitment procedure l.jpg

    Recruitment Procedure

    • Representative sample based on US 2002 census

    • Zip code demographic data

    • Telephone brief screener at recruitment

    • Telephone long screener for inclusion criteria

    • DISC, FIGS, CBCL

    • Hospital Visit (Neuro exam, Behaviour, MRI)

    • Objective 1 scans 3 times, every 2 years

    • Objective 2 scans 3-6 times

    • SES (3 levels) X age (0-18 yrs) X gender X ethnicity


    Accrual by age objective 1 l.jpg

    60

    50

    40

    30

    20

    10

    0

    4.5 6 7 8 9 10 11 12 13 14 15 16 17 18

    Accrual by Age (Objective 1)


    Slide13 l.jpg

    Family Income (Raw)

    N = 409


    Slide14 l.jpg

    Parental Education

    N = 409

    NIH MRI Study of Normal

    Pediatric Development US Population


    Slide15 l.jpg

    Ethnicity

    N = 409


    Slide16 l.jpg

    Behavioral Maturation is multi-dimensional


    Test battery l.jpg

    MRI

    BEHAVIORAL

    T1W (Obj1+2+fallbacks)

    Full Interview

    T2W (Obj1+2+fallbacks)

    Bayley Mental

    Handedness

    PDW (Obj1+2+fallbacks)

    Bayley Motor

    JTCI

    MRS (Obj1+2)

    Bayley Behavioral

    Nepsy fluency

    MRSI (Obj1+2)

    Brief Interview

    Neurologicals

    DTI (Obj1+2)

    BRIEF Parent

    PSI

    Dual-contrast T2 (Obj2)

    CANTAB

    Pregnancy

    T1 Relaxometry (Obj2)

    Carey

    Purdue peg board

    T2 Relaxometry (Obj2)

    CBCL

    Tanner

    CVLT (C and II)

    TCI

    DAS

    Urine and Saliva

    DISC

    Digit span and coding

    DPS4

    WASI

    Exclusion (Obj2)

    FIGS

    Woodcock-Johnson III

    Test Battery


    Study organization l.jpg

    Study Organization

    Study

    Subject n-1

    Subject n

    Subject n+1

    Visit 1

    Visit 2

    Visit n

    Exclusionary Screening

    Behavioral Instruments

    MRI Procedures

    CBCL

    DISC

    CBCL

    DPS4

    JTCI

    CANTAB

    WJ3

    WASI

    MRI

    MRI


    System architecture l.jpg

    PSC

    DCC

    Mass Storage System

    Backup

    System

    Internet &

    DBMS

    Server(s)

    Study

    Work

    Station

    Data

    Warehouse

    MRI

    Scanner

    BVL

    BVL

    MRI

    INTERNET

    MRI

    MRI

    Data Marts

    MRI Console

    BVL

    Behavioral

    PC (laptop)

    Scientific

    Community

    Data Analysis

    Pipeline

    System Architecture


    Slide20 l.jpg

    New technology never works first time


    Slide21 l.jpg

    DCC-ID

    identifiedby

    candidate

    for each

    PSCID

    personal

    member of

    contains

    data on multiple

    visits

    recruited by

    SessionID

    Gender

    ethnic

    VisitNo

    psc

    DoB

    visit

    Objective

    EthnicID

    Weight

    CenterID

    Age

    stores data for a battery

    of administered MRI procedures

    & behavioral instruments

    Height

    ObjectiveID

    Objective

    MRI procedures

    behavioral battery of instruments

    bio

    figs

    apib

    das

    tanner

    psi

    cantab

    Screening

    Type

    DICOM

    T2W3D

    wisc

    exclus

    disc

    carey

    neuro

    wasi

    purdue

    cvltc

    MRS

    waisr

    MINC

    TestID

    brief int

    dps4

    hand

    pls3

    wj3

    saliva

    cvlt2

    MRSI

    header

    CommentID

    full int

    cbcl

    nepsy

    pregn

    bayley

    jtci

    urine

    T1W3D

    PD

    ScoreID

    Test ID

    Candidate Profile

    are

    identified

    by


    Dbms software platform l.jpg

    DBMS Software Platform

    • MySQL DBMS:

      • Cross-platform, open source

      • Robustness, speed, reliability

      • Low development cost

      • - Remote management

    • Graphical User Interface:

      • Cross platform, Internet enabled application

      • PHP-based application, complemented by

      • JavaScript, Java, and Perl for data

      • manipulation tasks.

      • Remote management, customizable


    Slide23 l.jpg

    Database GUI


    Database summary l.jpg

    Database Summary

    • Low-cost, extensible, secure

    • 61 tests, approx.

    • 20,000 possible data fields (~1000 filled/subject)

    • Laptop-based behavioral test battery

    • Automatic MRI data transfer

    • Web-based behavioral GUI

    • Interactive 3D MRI web viewer

    • Automatic QC procedures

    • Project web site


    Slide26 l.jpg

    N=449

    N=56

    N=188

    Obj 1

    Obj 2

    DTI


    Behavioral instrument status l.jpg

    Behavioral Instrument Status

    Total # of Instruments = 9827

    Objective 1 = 9029

    Objective 2 = 798

    Total # in Database = 9827

    As of May 20, 2004.


    Slide28 l.jpg

    40

    30

    20

    10

    Std. Dev = 12.50

    Mean = 110.6

    N = 248.00

    0

    75.0

    85.0

    95.0

    105.0

    115.0

    125.0

    135.0

    145.0

    80.0

    90.0

    100.0

    110.0

    120.0

    130.0

    140.0

    FIQ

    IQ Scores (n=248)

    FIQ: 110.7+0.8

    VIQ: 109.9+08

    PIQ: 108.9+0.8


    Summary of behavioral qc l.jpg

    Summary of Behavioral QC


    Slide30 l.jpg

    W-J: Passage Comprehension (n=278)

    r=0.85, p=0.000


    Slide31 l.jpg

    WASI: Vocabulary (n=248)

    r=0.86, p=0.000


    Slide32 l.jpg

    Spatial Working Memory (CANTAB): Errors (n=250)

    r=-0.75, p=0.000


    Objective 1 l.jpg

    Objective 1


    Objective 1 mrs i l.jpg

    Objective 1 – MRS/I

    Objective 1 – DTI

    *Corrected to exclude the early Cincinnati and St. Louis 1 subjects since the DTI product was not available.


    Objective 2 l.jpg

    Objective 2


    Qc overview l.jpg

    QC Overview

    • Goal: quick turn-around time

      • mean time for expedited review1.5 days

      • median time over all subjects 7.0 days

    • Concentrated mostly on subject QC


    Inter packet movement l.jpg

    Inter-packet movement

    • Separate volume into packets

    • Register each packet to target

    • Resample and interpolate to 1mm slice thickness

    After

    Before


    Data flow for brain mapping l.jpg

    Structural

    Probability

    Maps

    Functional

    Probability

    Maps

    Data Flow for Brain Mapping

    Data Acquisition : Reconstruction : Conversion to MINC Image Format

    PET

    fMRI

    aMRI

    Registration

    aMRI - ePET

    aMRI - tPET

    Frame alignment

    Inter-slice normalization

    T1/T2/PD/…

    alignment

    Registration

    aMRI - fMRI

    Partial volume correction

    Intensity non-uniformity correction

    Voxel-based model fitting

    Voxel-based coherence analysis

    3D segmentation

    Stereotaxic Spatial Normalization

    Inter-volume normalization

    GLM analysis

    in 3, 4, or 5D


    Anatomical mri analysis pipeline l.jpg

    Anatomical MRI analysis pipeline

    ASP


    Slide40 l.jpg

    Manual

    Auto

    INSECT

    ANIMAL

    SEAL

    ASP


    Slide41 l.jpg

    Objective 1 – classification


    Slide42 l.jpg

    Obj 1 – Tissue SPAMs (n=337)


    Slide43 l.jpg

    Age-related changes in WM density

    Paus et al, Science 1999; n=111

    NIHPD; n=204; 16 1 0 t=10.5


    Slide44 l.jpg

    WM density and Spatial Working Memory:

    Between Errors (Age removed)

    NIHPD; n=188; 7 -1 66 t=-4.0


    Cortical surface extraction kim hanyang u l.jpg

    Cortical Surface Extraction(Kim, Hanyang U.)


    Cortical surface extraction kim hanyang u46 l.jpg

    Cortical Surface Extraction(Kim, Hanyang U.)


    Slide47 l.jpg

    Automated extraction of

    both cortical surfaces using

    CLASP algorithm

    (5 different brains)


    Analysis of detection limits l.jpg

    Analysis of detection limits

    • 19 T1 MRIs of the same subject (Colin Holmes) with 1mm isotropic sampling.

    • Computation of standard deviations across cortex.

      • Across blurring kernels.

      • Across metrics.

    • Power analysis:

      • N needed to recover change of 0.5 mm

      • Change required at N=25 in each group.


    Colin s 19 brains l.jpg

    Colin’s 19 Brains

    Average


    Required n to recover 0 5 mm l.jpg

    Required N to recover 0.5 mm

    Unblurred

    5mm

    10mm

    20mm

    200

    0


    Recoverable change when n 25 l.jpg

    Unblurred

    5mm

    10mm

    20mm

    Recoverable change when N=25

    2mm

    0mm


    Prefrontal atrophy in normal aging n 851 l.jpg

    Prefrontal atrophy in normal aging (N=851)

    0.025

    Slope

    (mm loss)

    0.01


    Cortical thickness vs age between obj 1 4 18 slope in mm yr n 289 l.jpg

    Cortical thickness vs. age between (Obj 1, 4-18) (slope in mm/yr, N=289)


    Cortical thickness versus age l.jpg

    Cortical thickness versus Age


    Obj 2 stereotaxic t1 average l.jpg

    Obj 2 stereotaxic T1 average

    nihpd120_obj2

    icbm152


    Related large scale projects l.jpg

    Related large-scale projects

    • ICBM (7000)

    • Giedd and Rapoport (3000)

    • Brad Peterson (TS, OCD, ADHD 600)

    • Maternal Adversity (MAVAN 500)

    • Tourette’s Neuroimaging Consortium (500)

    • Japanese Human Brain Project (1200)


    Welcome to the good ship nihpd l.jpg

    Welcome to the good ship NIHPD


  • Login