Brain Images of Normal Subjects (BRAINS) Bank David Alexander Dickie Dr Dominic E. Job.

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Brain Images of Normal Subjects (BRAINS) Bank David Alexander Dickie Dr Dominic E. Job

Transcript of Brain Images of Normal Subjects (BRAINS) Bank David Alexander Dickie Dr Dominic E. Job.

Page 1: Brain Images of Normal Subjects (BRAINS) Bank David Alexander Dickie Dr Dominic E. Job.

Brain Images of Normal Subjects (BRAINS) Bank

David Alexander Dickie

Dr Dominic E. Job

Page 2: Brain Images of Normal Subjects (BRAINS) Bank David Alexander Dickie Dr Dominic E. Job.

Background

• Age and disease affect brain structure

• The effects are disparate

• Much MRI data are needed

• ~300 normal ageing (>60yrs) subjects

• “Atlases” of the aged brain are limited

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Background

• Brain Images of Normal Subjects (BRAINS) bank >1000 normal sbjs >60yrs

• BRAINS models and atlases calculate distributions (not assume)

• Data requires much image processing– Pilot ~200 normal, ~200 AD sjbs, 60-94 years

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MR Image processing

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Brain extraction

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Brain extraction

• Brain Extraction Tool (BET) commonly used

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Brain extraction

• Brain Extraction Tool (BET) commonly used

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Template based brain extraction

• Advanced Normalization Tools (ANTS) http://www.picsl.upenn.edu/ANTS/

• Uses diffeomorphic (super nonlinear) spatial normalisation

Page 9: Brain Images of Normal Subjects (BRAINS) Bank David Alexander Dickie Dr Dominic E. Job.

Image registration

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ANTS diffeomorphic spatial normalisation

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ANTS diffeomorphic spatial normalisation

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ANTS diffeomorphic spatial normalisation

• But catastrophes still happen

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ANTS diffeomorphic spatial normalisation

• ANTS takes ~1 hour per subject (computer)

• Still requires by slice checking– ~10 minutes checking per subject

• 460 subjects took ~2.5 months

• Catastrophes still happen

• >1000 subjects in full-scale study

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Data driven brain volume models

• Statistical models oft used in brain imaging– The general linear model (GLM)

• Assume data generation and distribution

• Transformations lose natural data, have risks, complexity

• Image banks support data driven models

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Brains are heteroscedastic

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See, they’re different

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Data driven vs. general models

DDPM has ~65% less error

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Data driven brain voxel models

• Statistical voxel based morphometry (VBM)– The general linear model (GLM)

• Assumes data generation and distribution

• Transformations lose natural data, smoothing

• Image banks support data driven models

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BRAINS atlasesBRAINS

MNI 152

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Image registration

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0

BRAINS atlases

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BRAINS atlases

Alzheimer’sNormal

Red=95th

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White matter lesions

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White matter lesions

95

75

50

25

5 A

Percentile

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95

75

50

25

5 A

Percentile

White matter lesions

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Percentiles of grey matter density in a normal ageing and Alzheimer’s disease subject

Alzheimer’sNormal

Alzheimer’s disease has lowest percentiles of GM in MTL

5th

95th

50th

25th

75th

Percentile

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Bad

OK

Good

Percentiles of grey matter density in a normal ageing and Alzheimer’s disease subject

Alzheimer’sNormal

Alzheimer’s disease has lowest percentiles of GM in MTL

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Alzheimer’s Control

5th

95th

50th

25th

75th

Percentile

Median percentile image of grey matter density in Alzheimer’s disease (n=49) and control (n=49) subjects

Alzheimer’s has lowest percentiles of GM across the cortex, specifically hippocampus.

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BRAINS vs. VBM

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BRAINS vs. VBM

• Needs data

• Less assumptions

• Anatomical resolution

• Specific anatomy

• Individuals

• Size of differences

• Simpler

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SINAPSE SPIRIT, MRC, Tony Watson

Thank you