# Presentation: 488467

## 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
- 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)

- 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

- 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

- 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)

- 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)

- 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

## 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

## NIH MRI Study of Normal Brain Development

- DCC

- CCC

- DPC

## 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)

## Family Income (Raw)

- N = 409

## Parental Education

- N = 409

-   NIH MRI Study of Normal
-      Pediatric Development                                    US  Population

## Ethnicity

- N = 409

## Behavioral Maturation is multi-dimensional

## Test Battery

## Study Organization

- DPS4

- Visit 2

- CBCL

- WJ3

- Visit n

- CANTAB

- Visit 1

- DISC

- JTCI

- CBCL

- Subject  n

- Subject n+1

- Subject n-1

- Study

- ...

**MRI**

**MRI**

- Exclusionary Screening
- Behavioral Instruments
- MRI Procedures

**Notes:**
Before I show how the Candidate registration procedure looks like, let me briefyou with several important concept of the database design:

The database is built with the focus on a candidate. Candidate is assigned a CandID by DCC. CandID is a randomly generated number from the range of available numbers given to each PSC...It is also identified by locally given PSCID. 
Each Candidate is further identified by the city/state of residence, the PSC,which registered it, Date of Birth, Ethnicity, Sex, Weight and Height (the last two aredynamically updated for each session).

Tests are created as modules, and are classified by Objective with which they areadministered, by domain and subdomain.

The next is the registration process:

## System Architecture

**Notes:**
Chat about the computer systems in general...

This is the schema of the “Computer” systems we have set up for our project’s implementation.

The systems are customized for each project. The customization usually involves the study sites’ setup, computer equipment and other devices purchase, on site personnel training, as needed. Hardware and software specification for study sites are developed respecting the study objectives and strategy of implementation.

The data is regularly backup and archived on the BIC raid systems. The usual scop of backed data includes, but is not limited to:
DCC data warehouse content and metadata; Analytical Databases metadata

All data is now shared over the internet or virtual private networks, if established. This reduces the transfer time of MR data from sites to BIC. It also enables virtually real-time view of processed images. (Consider presenting JIV viewer if applicable).

A multiple levels of quality controls are setup both at the sites and at BIC to insure providing the high quality outputs from the data processing Pipeline.

-> -> NEXT SLIDE

## New technology never works first time

## Candidate Profile

- are
- identified
- by

**Notes:**
Before I show how the Candidate registration procedure looks like, let me briefyou with several important concept of the database design:

The database is built with the focus on a candidate. Candidate is assigned a CandID by DCC. CandID is a randomly generated number from the range of available numbers given to each PSC...It is also identified by locally given PSCID. 
Each Candidate is further identified by the city/state of residence, the PSC,which registered it, Date of Birth, Ethnicity, Sex, Weight and Height (the last two aredynamically updated for each session).

Tests are created as modules, and are classified by Objective with which they areadministered, by domain and subdomain.

The next is the registration process:

## DBMS Software Platform

-  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

## Database GUI

## Slide 24

## 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

## N=449

- N=56

- N=188

- DTI

- Obj 1

- Obj 2

##  Behavioral Instrument Status

- Total # of Instruments =  9827
-                  Objective 1 =  9029
-                   Objective 2 =   798
-      Total # in Database = 9827

- As of May 20, 2004.

## IQ Scores (n=248)

- FIQ: 110.7+0.8

- VIQ: 109.9+08

- PIQ: 108.9+0.8

## Summary of Behavioral QC

| SITE (N) | ERRORS [Error rate = (Errors/1,000xN) x 100] |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | INPUT (%) | SCORING (%) | CLERICAL (%) | QUERIES (%) | OTHER (%) |
| BOS 1 (29) | 133 (0.4) |      3 (0.01) | 213 (0.7) | 13 (0.05) | 19 (0.06) |
| BOS 2 (4)  |   3 (0.07) |      0 (0.0) |   14 (0.4) |   5 (0.1) |   4 (0.1) |
| CIN (24) | 116 (0.5) |      3 (0.01) | 108 (0.4) |     1 (0.004) | 15 (0.06) |
| HOU (23) | 126 (0.5) |      7 (0.03) | 233 (1.0) |   5 (0.02) | 15 (0.06) |
| UCLA (18) |   46 (0.2) |      1 (0.005) | 155 (0.9) |   4 (0.02) |   8 (0.04) |
| PHI 1 (14) |   71 (0.5) |      1 (0.007) |   51 (0.4) | 0 (0.0) |   4 (0.03) |
| PHI 2 (1) |     0 (0.0) |      0 (0.0) |     0 (0.0) | 0 (0.0) |   2 (0.2) |
| STL1 (29) | 140 (0.5) |      4 (0.01) | 106 (0.4) | 12 (0.04) | 41 (0.1) |
| STL2 (12) |   99 (0.8) |      0 (0.0) |   86 (0.7) | 21 (0.2) | 17 (0.1) |
| Total (154) | 734 (0.5) | 19 (0.01) | 966 (0.6) | 61 (0.04) | 125 (0.08) |

## W-J: Passage Comprehension (n=278)

- r=0.85, p=0.000

## WASI: Vocabulary (n=248)

- r=0.86, p=0.000

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

- r=-0.75, p=0.000

## Objective 1

**Notes:**
Variability across sites regarding %recruited with St. Louis at 50%, UCLA at 19% and Houston (29%) closest to the average (28%) 

N-screened and N-recruited are fixed and will not change.  N-recruited is the number of subjects that will be tracked prospectively throughout the study.

“N” MRI is a subset of the other two and represents what number of recruited subjects completed an MRI for a specific visit.

## 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.**

**Notes:**
Need to check the numbers for Philadelphia which did not appear in the query.

## Objective 2

**Notes:**
Visits noted on the chart reflect how the scans were labeled.

## QC Overview

**Goal: quick turn-around time**
**mean time for expedited review****	****1.5 days**
**median time over all subjects ****	****7.0 days**
**Concentrated mostly on subject QC**

| Number of: | Oct 2003 | May 9, 2004 |
| --- | --- | --- |
|    MRI datasets received: | 492     (obj1 + obj2) | 582 (436 obj1 + 146 obj2) |
|    MRI datasets QC’d | 484 (98.4%) | 582 (100%) |
|    Total volumes rec’d | 8815 | 13102 |
| Time spent in MRI QC/subject | 2-100 minutes | unchanged |
| Total time spent in MRI QC | ~360 hours | ~530 hours (total) |

## Inter-packet movement

- After

- Before

- Separate volume into packets
- Register each packet to target
- Resample and interpolate to 1mm slice thickness

## 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

- Partial volume correction

- Voxel-based model fitting

- Registration
- aMRI - fMRI

- Voxel-based coherence analysis

- T1/T2/PD/...
- alignment

- Intensity non-uniformity correction

- 3D segmentation

- Stereotaxic Spatial Normalization

- Inter-volume normalization

- GLM analysis
- in 3, 4, or 5D

## Anatomical MRI analysis pipeline

**ASP**

## INSECT

- ANIMAL

- ASP

- SEAL

## Objective 1 – classification

## Obj 1 – Tissue SPAMs (n=337)

## Age-related changes in WM density

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

## 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.)

- Cortical Surface Extraction(Kim, Hanyang U.)

## Cortical Surface Extraction(Kim, Hanyang U.)

- Cortical Surface Extraction(Kim, Hanyang U.)

## Automated extraction of

- both cortical surfaces using
-       CLASP algorithm
-      (5 different brains)

## 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

- Average

## Required N to recover 0.5 mm

- Unblurred

- 5mm

- 10mm

- 20mm

- 200

- 0

## Recoverable change when N=25

- 2mm

- 0mm

## Prefrontal atrophy in normal aging (N=851)

- Slope
- (mm loss)

- 0.025

- 0.01

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

## Cortical thickness versus Age

## Slide 55

## Slide 56

## Obj 2 stereotaxic T1 average

- nihpd120_obj2

- icbm152

## 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