Automated MRI analysis: academic and

commercial options

RASAD 2012

Clifford R Jack Jr

Alexander Family Professor of Alzheimer's Disease Research,

Dept Radiology, Mayo Clinic, Rochester, MN

Outline

Measurements covered

Cross sectional sMRI

Longitudinal sMRI

fcMRI

Current options

Caveats

Opportunities for innovation

Automated sMRI measures

Atlas on template

Atlas registered to

individual AD subject

that has been

segmented

Components

Template with atlas labels

Segmentation

Registration

Extract data from ROI parcellation

Current options: available MRI analysis SW

Freesurfer - Fischl, MGH

Neuroquant/Quark - Dale, Cortechs Labs

SPM/VBM (with atlas) - Ashburner

FSL - Smith

Automated Non-linear Image Matching and Anatomical Labeling

(Animal) - MNI

Caraet and Surface-based Atlases Wash U.

LONI Pipelines, UCLA

Medical Image Processing, Analysis, and Visualization – NIH

Advanced Normalization Tools (ANTS) – Avant, Penn

http://idoimaging.com/programs (lists >250 tools)

Opportunities for innovation - multi

voxel, multi ROI – “AD Signature”

Selecting Regions - 3 approaches

Use established knowledge of pathology to select ROIs.

NFT Braak stage neurodegenerative atrophy

Combine best performing ROIs diagnostically

Unbiased training algorithm – train on a ―gold standard‖

data set, test in independent data sets. Gold standard could

be autopsy confirmed AD, clinically diagnosed, etc

Voxel-wise methods capture time dependent progression from MCI to AD (n = 33)

MCI

3 years before conversion to AD

MCI

1 year before conversion to AD

AD

At time of conversion to AD

L R RL RL

3D Maps from Multiple MRI Illustrate Changing Atrophy Patterns as Subjects Progress from MCI to AD

Whitwell et al Brain 2007

Dickerson et al – “AD signature” in impaired and

pre clinical subjects – neocortical association +

medial temporal limbic

Dickerson BC, Bakkour A, Salat DH et al. Cereb

Cortex. 2009;19:497-510

Dickerson BC, Stoub TR, Shah RC et al. Neurology.

2011;76:1395-1402

Becker JA, Hedden T, Carmasin J et al. Annals of

neurology. 2010;Apr 13 [Epub]

Dickerson et al. Cereb Cortex, 2009

(A) Medial temporal cortex, (B) Inferior temporal gyrus, (C) Temporal pole, (D)

Angular Gyrus, (E) Superior frontal gyrus, (F) Superior parietal lobule, (G)

Supramarginal gyrus, (H) Precunes, (I) Inferior frontal sulcus, (J) visual reference

STAND algorithm for Individual Subject

Diagnosis - Vemuri et al Neuroimage 2008

Main Component of the STAND-Algorithm Large library of (AD and CN) MRI scans from which regions differentiating AD from

CN are detected and used to score new incoming cases.

MRI Scan STAND Algorithm ≥ 0 ABNORMAL

<0 NORMAL

STAND algorithm for Individual Subject

Diagnosis - Vemuri et al Neuroimage 2008

Large library of (AD and CN) MRI scans from which regions differentiating AD from CN

are detected and used to score new incoming cases. Accuracy of the method ~ 90 %

MRI Scan STAND Algorithm ≥ 0 ABNORMAL

<0 NORMAL

Longitudinal sMRI Measures

Measure each time point independently

Register serial images and compute change in image

space

Boundary Shift Integral

Freeborough and Fox, 1997

Affine registration

Time 1 Time 2

TBMSyN – CN vs AD rates

Two sample T-test of

TBMSyN atrophy maps

between AD (N=51) and

CN (N=51) Mayo 3T

subjects. FDR corrected

p<0.05 threshold.

Baseline and a follow-up

T1 MPRAGE, separated

by 12 to 18 months

--------------------------------

M Senjem

Bias in longitudinal sMRI measures

Holland and Thompson Neuroimage 2010 – bias in

Hua et al (Neuroimage 2010) TBM resulting in

underestimated sample sizes

Algorithms, atrophy and Alzheimer's disease:

Cautionary tales for clinical trials, Nick C. Fox,

Gerard R. Ridgway, Jonathan M. Schott, Neuroimage

2011

Bias in TBM – from Thompson and Holland Neuroimage

2011

Caveats: “ Fox rules”; bronze standard

Commutative or ―symmetric‖: Are the absolute changes

from A→B the same as from B→A?

Transitivity: if three sequential scans are available does

summing measures for A→B and B→C reproduce the

result of directly measuring A→C?

Comparison with low-bias (but high-variability) manual

measurements even though they are too imprecise for

typical trials

Comparison with other more established techniques on the

same data-set

Caveats: “ Fox rules”; bronze standard

Assessment of ―reproducibility‖ with short interval scans.

Atrophy should tend to zero as the interval shortens. Whilst

there may be a degree of variability, should be no mean

group change with ―same-day‖ scans.

Comparison with the known disease biology and

pathological studies, where available. Example, cross-

sectional hipp vol reduced by ~15 to 20% at time of AD

dementia diagnosis. If atrophy starts several years prior to

symptom onset, this is compatible with a measured rate of

hippocampal atrophy of no more than 3–5% per year –

50%/yr is not plausible.

MR measures beyond sMRI

MRS, ASL, DTI: Difficult to see how to standardize

first step, acquisition, using vendor product sequences

complex spin preparation and read out – differ among

vendors

phantoms for standardized measures difficult – diffusion

and perfusion

fcMRI echo planar imaging: spin preparation and

read out standard (caveats: ramp sampling, spirals)

Functional MRI – task free vs task

based

Imaging of extrinsic perturbations of fMRI

time course by task activation task fMRI

Imaging of intrinsic network connectivity

(fcMRI) task-free fMRI

Task Free fMRI - history

First description of task free fMRI - Biswal, B., et al.,

Functional connectivity in the motor cortex of resting human

brain using echo-planar MRI. Magn Reson Med, 1995,

spontaneous low-frequency fluctuations (0.1-0.01 Hz)

observed in BOLD) signal were highly correlated

within sensory motor cortex

BOLD

Signal

Acquired

Images (Sampling over time) Time (Sec)

Resting State - fMRI Acquisition

Task Free fMRI (resting state) –

functional connectivity (fcMRI)

TF-fMRI = functional connectivity. Represents a measure of

correlated signal from two or more spatially distinct regions over

time

Low-frequency fluctuations are specific to gray matter and can be

used to identify the spatial extent of temporally correlated

networks of functional connectivity within the brain

These large-scale networks are present at all times in the living

human brain

resting state networks are more accurately referred to as intrinsic

connectivity networks (ICNs)

Task Negative Network aka DMN. 342 CN. Positive correl, 6

mm seed PCC. ICA (20 components) within the TNN (red-

anterior DMN, blue-posterior DMN, green ventral DMN)

TPN, 342 CN. PCC seed, negative correlations, aka anti-

correlations. Four ICAs within TPN (red-salience, blue-dorsal

attention, green-left executive control, violet-right executive)

Slice timing correction

Realignment

Normalization

(to SPM EPI template)

Spatial smoothing

(FWHM = 4mm)

Linear temporal detrending

Temporal bandpass filtering

(ideal filter with cutoffs of 0.01 to 0.08 Hz)

Remove covariables

(realignment parameters, global signal, WM

signal, CSF signal)

Standard

Pre

processing

TF-fMRI data analysis methods

extract the spatial and temporal extent of ICNs

seed-based correlation

seeds may consist of an individual voxel, group of contiguous

voxels, or larger functionally/anatomically derived regions of

interest (e.g. Brodmann areas)

Seed to brain

Seed to seed (node to node)

In phase or out of phase masking

Graph theory – node to node connectivity matrices

data driven multivariate analysis techniques, eg

independent component analysis (ICA)

Popular fMRI software

Standard Preprocessing fMRI Pipelines

SPM (http://www.fil.ion.ucl.ac.uk/spm/)

FSL (http://www.fmrib.ox.ac.uk/fsl/)

AFNI (http://afni.nimh.nih.gov/afni/)

Brain Voyager (http://www.brainvoyager.com/)

Resting state funct connectivity and network analysis packages

REST (http://www.restfmri.net/)

GIFT ICA (http://mialab.mrn.org/software/#gica)

Melodic ICA (http://www.fmrib.ox.ac.uk/fsl/melodic/index.html)

BrainScape (http://nrg.wikispaces.com/Brainscape+About)

CONN Toolbox (http://web.mit.edu/swg/software.htm)

Brain Connectivity Toolbox (https://sites.google.com/a/brain-

connectivity-toolbox.net/bct/)

fcMRI Characteristic connectivity pattern with AD spectrum

Decreased in phase connectivity in posterior DMN

Increased in phase connectivity in anterior network regions

Anterior – posterior disconnection

Connectivity changes may occur early in disease process

CN amyloid positive – ie preclinical phase of AD

Prior to amyloid - Posterior DMN connectivity may be

increased in APOE 4 early in life

History of MRI technology arc

Considerable potential, but only if standardized – ie opportunities

exist

Conclusions

Opportunities: innovation that

conforms to standards set by the field

Multi voxel diagnostics cross sectional – sMRI

Multi voxel diagnostics longitudinal – sMRI

fcMRI – pre processing and analysis

the recipe outlined for HV proposed earlier is

rationale and would serve as a good temple for future

efforts

Conformity vs innovation Not conflicting goals

Many examples of standards agreed upon by industry

competitors – have not impeded innovation

SMTP (send mail transfer protocol) – email

DICOM (Digital Imaging and Communications in Medicine) -

standard for handling, storing, printing, and transmitting

information in medical imaging

WIFI (802.11 is main protocol, b,g,n are flavors) - nearly all pad

computers can communicate over (ie speak) 802.11. Doesn’t

prevent the creation of vertical markets for improvements —

$500 iPad vs $300 Galaxy Tab

Conformity = standardization = widespread utility

How are standardized MRI

measures best distributed to the

medical community?

Proposal

By MR manufactures

Historically this is how technology is distributed

integrated chain running on scanner

acquisition

QC

Preprocessing artifact correction

extract relevant diagnostic info from individual scan

Relate individual scan to appropriate standard data base

Output value – that conforms to agreed upon standards

manufacturer assumes responsibility for insuring forward

compatibilty – consistency - with every upgrade

Difficult for stand alone SW developer

Proposal for MR manufacturers

having complicated 1 of a kind pipelines for both data pre

processing and analysis (and normative data bases) running

at every medical center is not a path to standardization

MR vendors are not going to distribute identical products,

but could distribute their own pipelines where the final

output meets standards set by the field

Standards established by an accredited or empowered expert

board with appropriate expertise, appropriate

representativeness, and without commercial or institutional

bias