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Morphometry BIRN - Overview - Scientific Achievements. Morphometry BIRN: Overview. Scientific Goal Methods Support multi-site structural MRI clinical studies or trials Multi-site MRI calibration, acquisition and analysis Integrate advanced image analysis and visualization tools Sites (9)

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morphometry birn overview
Morphometry BIRN:Overview
  • Scientific Goal
  • Methods
    • Support multi-site structural MRI clinical studies or trials
    • Multi-site MRI calibration, acquisition and analysis
    • Integrate advanced image analysis and visualization tools
  • Sites (9)

MGH, BWH, Duke, UCLA, UCSD, UCI, JHU, Wash U, MIT

correlates

human neuroanatomical data  clinical data

Diseases:Unipolar Depression, Alzheimer’s, Mild Cognitive Impairment

slide3

DB

Morphometry BIRN: Domain Areas

Integrate Analysis &

Visualization Tools

Data Management

Processing Workflows

Multi-site MRI

Calibration

HID

XNAT

Workflows (LONI/Kepler)

  • fBIRN
  • Mouse BIRN
  • BIRN CC

Application Cases

http://nbirn.net/Publications/Brochures/index.htm

morphometry birn manuscripts
Morphometry BIRN:manuscripts

Mouse – Morphometry BIRN paper

  • Ali et al., Automated segmentation of neuroanatomical structures in multispectral mouse MRI(NeuroImage, 2005)

Technical development papers:

  • Fennema-Notestine et al., Quantitative evaluation of automatic skull stripping methods(Human Brain Mapping, 2005)
  • Neu et al., The LONI debabeler: a mediator for neuroimaging software(NeuroImage, 2005)
  • Chen et al., Fast correction for direction-dependent distortions in DTI(NeuroImage, in press)
  • Jovicich et al., Reliability in multi-site structural MRI studies: unwarping effects(NeuroImage, in press)
  • Bischoff-Grethe, A Technique for the Deidentification of Structural Brain MRI(NeuroImage, submitted)
  • Farrell et al., Signal to noise ratio effects on fractional anisotropy reproducibility from DTI(to be submitted 2005)
  • Han et al., Reliability of MRI-derived measurements of cerebral cortical thickness(NeuroImage, submitted)

Clinical application papers:

  • Fennema-Notestine et al., Multi-site clilnical structural neuroimaging data of legacy data(to be submitted 2005)
  • MacFall et al., Lobar distribution of lesion volumes in late-life depression(Neuropsychopharmacology, submitted)
  • Beg et al, Pattern classification of hippocampal shape analysis in a study of AD (to be submitted 2006)
  • Marcus et al., The OASIS Project: a publicly available human brain imaging data resource(submitted)
morphometry birn manuscripts1
Morphometry BIRN:manuscripts

Mouse – Morphometry BIRN paper

  • Ali et al., Automated segmentation of neuroanatomical structures in multispectral mouse MRI(NeuroImage, 2005)

Technical development papers:

  • Fennema-Notestine et al., Quantitative evaluation of automatic skull stripping methods(Human Brain Mapping, 2005)
  • Neu et al., The LONI debabeler: a mediator for neuroimaging software(NeuroImage, 2005)
  • Chen et al., Fast correction for direction-dependent distortions in DTI(NeuroImage, in press)
  • Jovicich et al., Reliability in multi-site structural MRI studies: unwarping effects(NeuroImage, in press)
  • Bischoff-Grethe, A Technique for the Deidentification of Structural Brain MRI(NeuroImage, submitted)
  • Farrell et al., Signal to noise ratio effects on fractional anisotropy reproducibility from DTI(to be submitted 2005)
  • Han et al., Reliability of MRI-derived measurements of cerebral cortical thickness(NeuroImage, submitted)

Clinical application papers:

  • Fennema-Notestine et al., Multi-site clilnical structural neuroimaging data of legacy data(to be submitted 2005)
  • MacFall et al., Lobar distribution of lesion volumes in late-life depression(Neuropsychopharmacology, submitted)
  • Beg et al, Pattern classification of hippocampal shape analysis in a study of AD (to be submitted 2006)
  • Marcus et al., The OASIS Project: a publicly available human brain imaging data resource(submitted)
morphometry birn calibration cortical thickness reproducibility across mri system upgrade
Morphometry BIRN Calibration: Cortical thickness reproducibility across MRI system upgrade

Thickness variability maps: Group results (lh)

Global Thickness variability:

Group results (5 subjects)

Sonata-Sonata

~ 6%

Sonata-Avanto

~ 3.5%

Avanto-Avanto

morphometry birn manuscripts2
Morphometry BIRN:manuscripts

Mouse – Morphometry BIRN paper

  • Ali et al., Automated segmentation of neuroanatomical structures in multispectral mouse MRI(NeuroImage, 2005)

Technical development papers:

  • Fennema-Notestine et al., Quantitative evaluation of automatic skull stripping methods(Human Brain Mapping, 2005)
  • Neu et al., The LONI debabeler: a mediator for neuroimaging software(NeuroImage, 2005)
  • Chen et al., Fast correction for direction-dependent distortions in DTI(NeuroImage, in press)
  • Jovicich et al., Reliability in multi-site structural MRI studies: unwarping effects(NeuroImage, in press)
  • Bischoff-Grethe, A Technique for the Deidentification of Structural Brain MRI(NeuroImage, submitted)
  • Farrell et al., Signal to noise ratio effects on fractional anisotropy reproducibility from DTI(to be submitted 2005)
  • Han et al., Reliability of MRI-derived measurements of cerebral cortical thickness(NeuroImage, submitted)

Clinical application papers:

  • Fennema-Notestine et al., Multi-site clilnical structural neuroimaging data of legacy data(to be submitted 2005)
  • MacFall et al., Lobar distribution of lesion volumes in late-life depression(Neuropsychopharmacology, submitted)
  • Beg et al, Pattern classification of hippocampal shape analysis in a study of AD (to be submitted 2006)
  • Marcus et al., The OASIS Project: a publicly available human brain imaging data resource(submitted)
morphometry birn shape analysis pipeline overview
Morphometry BIRN: Shape Analysis Pipeline Overview

4

JHU

Shape Analysis

of Segmented Structures

3

MGH

Segmentation

5

BWH

Visualization

Teragrid

Technical Goal: seamless

integration of tools and data flow during processing

1

BIRN

Virtual

Data Grid

Data Donor

Site (WashU)

Scientific Goal: correctly classify patient status from morphometric results

N=45

De-identification

And upload

2

slide9

Morphometry BIRN: Shape Analysis Pipeline Results

21 control subjects

18 Alzheimer subjects

6 semantic dementia subjects

Shape-derived metrics can be used to detect class-specific information

morphometry birn manuscripts3
Morphometry BIRN:manuscripts

Mouse – Morphometry BIRN paper

  • Ali et al., Automated segmentation of neuroanatomical structures in multispectral mouse MRI(NeuroImage, 2005)

Technical development papers:

  • Fennema-Notestine et al., Quantitative evaluation of automatic skull stripping methods(Human Brain Mapping, 2005)
  • Neu et al., The LONI debabeler: a mediator for neuroimaging software(NeuroImage, 2005)
  • Chen et al., Fast correction for direction-dependent distortions in DTI(NeuroImage, in press)
  • Jovicich et al., Reliability in multi-site structural MRI studies: unwarping effects(NeuroImage, in press)
  • Bischoff-Grethe, A Technique for the Deidentification of Structural Brain MRI(NeuroImage, submitted)
  • Farrell et al., Signal to noise ratio effects on fractional anisotropy reproducibility from DTI(to be submitted 2005)
  • Han et al., Reliability of MRI-derived measurements of cerebral cortical thickness(NeuroImage, submitted)

Clinical application papers:

  • Fennema-Notestine et al., Multi-site clilnical structural neuroimaging data of legacy data(to be submitted 2005)
  • MacFall et al., Lobar distribution of lesion volumes in late-life depression(Neuropsychopharmacology, submitted)
  • Beg et al, Pattern classification of hippocampal shape analysis in a study of AD (to be submitted 2006)
  • Marcus et al., The OASIS Project: a publicly available human brain imaging data resource(submitted)
slide11

Morphometry BIRN: Multi-site Alzheimer’s Disease Overview

Web

BIRN CC Portal

Multi-site data queries and statistics

Access to visualization and interpretation tools

MGH

Freesurfer

segmentations

2

3

BIRN

Virtual Data

Grid

MGH

Human Imaging

DB

UCSD

Human Imaging

DB

AD Project Data Flow

1) Retrospective data upload from UCSD and MGH sites

2) Semi-automated subcortical segmentation (MGH)

3) From any participating site: query, statistical analysiand visualization of the data through the BIRN Portal

3

3

1

MGH

Archives

UCSD

Archives

Data

Upload

BWH/MGH

N=118

UCSD

N=125

slide12

Hippocampal Volume Loss in Normal Aging

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Morphometry BIRN: Multi-site Alzheimer’s Disease Results

Hippocampal volume loss in

normal aging from

multi-site healthy data

Diagnostic classification

of multi-site healthy vs AD

  • Multi-site legacy data, if properly matched
  • and calibrated, can be combined
  • Linear and quadratic discriminant analysis applied
  • Classification success rate on test data 90%.
morphometry birn manuscripts4
Morphometry BIRN:manuscripts

Mouse – Morphometry BIRN paper

  • Ali et al., Automated segmentation of neuroanatomical structures in multispectral mouse MRI(NeuroImage, 2005)

Technical development papers:

  • Fennema-Notestine et al., Quantitative evaluation of automatic skull stripping methods(Human Brain Mapping, 2005)
  • Neu et al., The LONI debabeler: a mediator for neuroimaging software(NeuroImage, 2005)
  • Chen et al., Fast correction for direction-dependent distortions in DTI(NeuroImage, in press)
  • Jovicich et al., Reliability in multi-site structural MRI studies: unwarping effects(NeuroImage, in press)
  • Bischoff-Grethe, A Technique for the Deidentification of Structural Brain MRI(NeuroImage, submitted)
  • Farrell et al., Signal to noise ratio effects on fractional anisotropy reproducibility from DTI(to be submitted 2005)
  • Han et al., Reliability of MRI-derived measurements of cerebral cortical thickness(NeuroImage, submitted)

Clinical application papers:

  • Fennema-Notestine et al., Multi-site clilnical structural neuroimaging data of legacy data(to be submitted 2005)
  • MacFall et al., Lobar distribution of lesion volumes in late-life depression(Neuropsychopharmacology, submitted)
  • Beg et al, Pattern classification of hippocampal shape analysis in a study of AD (to be submitted 2006)
  • Marcus et al., The OASIS Project: a publicly available human brain imaging data resource(submitted)