neuroinformatics challenges in mri data integration hugo schnack rudolf magnus institute of...

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Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre Utrecht www.smri.nl ([email protected])

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Page 1: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Neuroinformatics challenges in MRI data integration

Hugo Schnack

Rudolf Magnus Institute of NeuroscienceDepartment of Psychiatry

University Medical Centre Utrechtwww.smri.nl ([email protected])

Page 2: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Our recent acquisition:7 Tesla scanner

Officially opened, December 4th, 2007

Page 3: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Our research

Investigation of morphological brain abnormalities in psychiatric disorders (schizophrenia)Q: Are brains of schizophrenia patients smaller?

Magnetic Resonance Imaging (MRI) scans of patients and healthy comparison subjects

Image processing and statistical analysesA: Yes, statistically they are.

Page 4: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Schizophrenia patients have less gray matter than healthy subjects

N=310 for this resultLarge variation in brain morphology

Page 5: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Heritability of brain changes in schizophrenia

Page 6: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Heritable brain changes in schizophrenia

P-C N-N P-C N-N

N=2x44 for this result(Discordant) twins are sparse

Page 7: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

We Need More Twins!

International collaborations

EUTwinsS (European Twin Study Network on Schizophrenia)Germany, the UK, The Netherlands, Spain, Hungary and Switzerland

STAR (Schizophrenia Twin and Relatives) consortiumHeidelberg, Jena, London, Utrecht, Helsinki

Page 8: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Can we combine brain scans from different scanners (machines, manufacturers, acquisition protocols, field strength, in time, …)?

What do we mean by ‘Can’? (increase in power; closer to ‘the truth’ – can we know the truth?)

Is there a measure of goodness for (processed) MRI scan?

Multicenter MRI

Goal: combine MRI data from different scanners

Page 9: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Scanner

Truth

Derivatives:Segments, volumes, shapes, fiber tracts, …

Processing

Page 10: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Another

ScannerScannerNow

ScannerTwo years later

TruthNow

TruthTwo years later

Derivatives:Segments, volumes, shapes, fiber tracts, …

Derivatives:Segments, volumes, shapes, fiber tracts, …

Processing (+2 yr)

Page 11: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Multicenter MRI(STAR)

STAR multicenter MRI calibration study:Schnack et al. 2004. Human Brain Mapping 22: 312-320.

Site scanner manufacturer and type

acquisition summary

protocol / orientation / scan time

voxel dimensions (mm) / (no. slices)

TE

(ms)

TR

(ms)

flip

angle

Utrecht, reference

repeated 1

repeated 2

Philips NT 1.5 T 3D-FFE coronal 11 min

111.2 (180)4.6 30 30˚

London GE Signa 1.5 T 3D-SPGR coronal 19 min

0.7810.7811.5 (124)5 35 35˚

Heidelberg Picker Edge 1.5 T 3D-FLASH sagittal 13 min

111.5 (128)3 30 30˚

Jena Philips ACS II 1.5 T 3D-FFE sagittal 11 min

111 (256)5 13 25˚

Helsinki Siemens Magnetom Impact 1.0 T

MPRAGE sagittal 7 min

111.2 (128)4.4 11.4 12˚

Page 12: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Multicenter MRI(STAR)

STAR multicenter MRI calibration study:Schnack et al. 2004. Human Brain Mapping 22: 312-320.

Site scanner manufacturer and type

acquisition summary

protocol / orientation / scan time

voxel dimensions (mm) / (no. slices)

TE

(ms)

TR

(ms)

flip

angle

Utrecht, reference

repeated 1

repeated 2

Philips NT 1.5 T 3D-FFE coronal 11 min

111.2 (180)4.6 30 30˚

London GE Signa 1.5 T 3D-SPGR coronal 19 min

0.7810.7811.5 (124)5 35 35˚

Heidelberg Picker Edge 1.5 T 3D-FLASH sagittal 13 min

111.5 (128)3 30 30˚

Jena Philips ACS II 1.5 T 3D-FFE sagittal 11 min

111 (256)5 13 25˚

Helsinki Siemens Magnetom Impact 1.0 T

MPRAGE sagittal 7 min

111.2 (128)4.4 11.4 12˚

Page 13: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Multicenter MRI

6 healthy subjects scanned in Utrecht (twice), Heidelberg, Jena, London

Processed with image processing pipeline in Utrecht

1. Measure reliability (fixed algorithms)

2. Calibrate algorithms (tunable parameters)

Goal: combine MRI data from different scanners

Calibration study

Page 14: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Multicenter MRI

Reliability of tissue volumes (ICC)

Gray White Utrecht repeated 0.97 1.00 Utrecht – London 0.94 0.99 London – Jena 0.85 0.94 Jena – Utrecht 0.88 0.94

ICC = true variation / (true variation + error) > 0.7 = “good”

Page 15: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Voxelwise reliability (ICC)

Utrecht repeated scans:97% of the voxels has ICC > 0.7 (“good”)

Page 16: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Multicenter MRI:Voxelwise reliability (ICC)

Neff (gain factor)

Page 17: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

ChallengesComparability of MR images (scanners)Comparability of analysis tools (software)Comparability of their interactions

Creation of gold standards (“truths”)Create better simulated MR images

Other “calibration” mechanisms (insteadof sending out 6 people around Europe?)

How to present / summarize /visualize reliability? (generalizable?)

Other modalities…

Page 18: Neuroinformatics challenges in MRI data integration Hugo Schnack Rudolf Magnus Institute of Neuroscience Department of Psychiatry University Medical Centre

Rudolf Magnus Institute of NeuroscienceDepartment of Psychiatry

University Medical Centre Utrechtwww.smri.nl ([email protected])

ContributorsHugo SchnackNeeltje van HarenRachel BrouwerHilleke Hulshoff Pol (head Neuroimaging Psychiatry)René Kahn (head Dept. Psychiatry)