change from serial MRI

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6 Νοε 2013 (πριν από 3 χρόνια και 7 μήνες)

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Modelling longitudinal structural

change from serial MRI

Ged Ridgway



Gerard.Ridgway@ucl.ac.uk

John Ashburner

Colleagues at the FIL (WTCN) and the

Dementia Research Centre


Wellcome Trust Centre for Neuroimaging

UCL Institute of Neurology

Overview


Motivation for longitudinal data


Need for appropriate statistical analysis


Benefits of longitudinal image processing


Risk of bias from asymmetric processing


Longitudinal imaging in SPM12


Unbalanced data and further extensions

Motivation for longitudinal data


Development, growth, plasticity, aging, degeneration,
and treatment
-
response are inherently longitudinal


Serial data have major advantages over multiple
cross
-
sectional samples at different stages


Increasing power


Subtlety of change over time vs. inter
-
individual variation


Reducing confounds


Demonstrating causality with interventions


Separating within
-
subject changes from cohort effects

Example: Training & structural plasticity


Intervention (training) + longitudinal data allows
causal interpretation of change, cf. just difference


Draganski et al. (2004)

Neuroplasticity
:

Changes
in grey matter induced by training


“volunteers who learned to juggle … transient and
selective structural change in brain areas associated
with processing and storage of complex visual motion”


Draganski et al. (2006)

Temporal and spatial
dynamics of brain structure changes during
extensive learning

Example: Training & structural plasticity


Scholz et al. (2009)

Training induces changes in
white matter architecture

Example: Training & structural plasticity


Comments & Controversies
,
NeuroImage,
2013, 73:225

267



Thomas & Baker:
Teaching an adult brain new tricks: A critical review
of evidence for training
-
dependent structural plasticity in humans


Erickson:
Evidence for structural plasticity in humans: Comment on
Thomas and Baker (2012)


[ Jones et al:
White matter integrity,
fiber

count, and other fallacies:
The do's and don'ts of diffusion MRI

]


Draganski &
Kherif
:
In vivo assessment of use
-
dependent brain
plasticity

Beyond the “one trick pony” imaging strategy


Fields:
Changes in brain structure during learning: Fact or
artifact
?
Reply to Thomas and Baker


Thomas & Baker:
On evidence, biases and confounding factors:
Response to commentaries

Example: Alzheimer’s disease evolution


Multiple sources of cohort effects


Birth
-
year (nutrition, etc.)


Disease onset
-
time cohorts


“Healthy survivor effect”


Timescales too long for pure longitudinal studies


“Unstructured
multicohort

longitudinal designs”


See
Thompson et al. (2011)


[source of figure on next slide…]

Example: Alzheimer’s disease evolution

Further statistical issues


Even simple designed experiments have pitfalls


Usually seek group
-
by
-
time interaction


Not significant change in one group but not another


Not group difference at one time
-
point but not another


Can’t ignore dependence within
-
subject over time


In an ANOVA with group and time factors:


Time effects can relate to (smaller) within
-
subject var.


Group differences must relate to between
-
subject var.


Group
-
by
-
time interaction …

Benefits of longitudinal image processing


Smaller within
-
subject variation motivates
longitudinally
-
tailored image processing methods


Boundary shift integral (BSI)


Intensity difference after rigid registration over region from
brain masks more precise than mask volume diff.


Non
-
rigid registration “Jacobian
-
integration”


JI over segmented region more precise than multiple
independent segmentations (example following…)


Temporally
-
constrained/regularised “4D” methods


E.g.
Xue’s

CLASSIC
,
Wolz’s

4D graph
-
cut

Longitudinal imaging animations

Interpolating rigidly
aligned images

Warping average by
interpolated transform

Interpolating volume
change (divergence)
relative to the average

Benefits of longitudinal image processing


Anderson et al. (2012)

Gray matter atrophy rate
as a marker of disease progression in AD


Risk of bias from asymmetric processing


Within
-
subject image processing often treats one
time
-
point differently from the others


Later scans registered (rigidly or non
-
rigidly) to baseline


Baseline scan segmented (manually or automatically)


Asymmetry can introduce methodological biases


E.g. only baseline has no registration interpolation error


Baseline
seg
. more accurate than propagated
segs
.


Change in later intervals more regularised/constrained

Risk of bias from asymmetric processing


Theory known for
a long
time (
but
often ignored)


Ashburner et al. 1999
;
Christensen, 1999
;

Cachier & Rey, 2000
;

Smith et al. 2001


Demonstrated in
practice
recently as
a serious issue


Thomas et al. 2009
;
Yushkevich et al. 2010
;
Thompson & Holland 2011

Risk of bias from asymmetric processing


Comments & Controversies, NeuroImage,
2011, 57:1
-
21



Thompson & Holland:
Bias in tensor based morphometry
Stat
-
ROI measures may result in unrealistic power estimates


Hua

et al:
Accurate measurement of brain changes in
longitudinal MRI scans using tensor
-
based morphometry


Fox et al:

Algorithms, atrophy and Alzheimer's disease:
Cautionary tales for clinical trials


Reuter &
Fischl
:

Avoiding asymmetry
-
induced bias in
longitudinal image processing

Longitudinal image processing in SPM12


Ashburner & Ridgway (2013)


“Unified” rigid and non
-
rigid registration with
model of differential intensity inhomogeneity (bias)


“Generative”


each time
-
point is a reoriented,
spatially warped, intensity biased version of avg.


“Symmetric” with respect to permutation of images


“Consistent” with direct registration between pair


“Diffeomorphic”


complex warping without folding

Generative model

Average
image

Inhomogeneity
regularization

Registr. (velocity)
regularization

Time
-
point

N

Non
-
rigid
Transform

Velocity

Inhomog.

correction
field

Rigid
Transform

Rigid
parameters

Noise
-
level

Example result


Alzheimer’s disease subject


Above: Images aligned only rigidly (
OASIS

data)


Below: Non
-
rigid volume change (divergence)

Example result


Group averages


82 subjects from OASIS longitudinal data (part 1)


DARTEL for between
-
subject spatial normalisation


Divergences transformed without modulation


Next step could be SPM statistical analysis…

Terminology: TBM, DBM & (longitudinal) VBM


(Deformation) Tensor
-
based morphometry (TBM)


Davatzikos et al. (1996)
;
Chung et al. (2001)


SPM
-
like (mass
-
univariate) analysis of Jacobian or div


See also mass
-
multivar
. “generalized” TBM (
Lepore et al. 2008
)


Deformation
-
based morphometry (DBM)


Ashburner et al. (1998)


Multivariate analysis of displacement vector patterns


Longitudinal VBM (
Kipps et al. 2005
)


Tissue
-
specific volume
-
change (using segmentation)

Longitudinal statistical modelling in SPM


“Balanced” data (e.g. designed experiment)


Same number (and timing) of time
-
points over subjects


Repeated
-
measures / within
-
subject ANOVA


Dependence within specified factor(s)


“Unbalanced” data (e.g. observational study)


E.g. more frequent observation closer to onset (
DIAN
)


Two
-
stage (fMRI
-
like) analysis of summary statistics


E.g. straight line or polynomial regression coefficients


Sub
-
optimal if times vary dramatically (singletons dropped)

Other statistical modelling approaches


Bernal
-
Rusiel

et al. (2012)

Statistical analysis of
longitudinal
neuroimage

data with Linear Mixed
Effects models. [
FreeSurfer
]


Chen et al. (2013)

Linear mixed
-
effects
modeling

approach to FMRI group analysis. [
AFNI
]


Li et al. (2013)

Multiscale

adaptive generalized
estimating equations for longitudinal neuroimaging
data. [unbalanced … twin and familial studies]


Bayesian spatio
-
temporal modelling in SPM…

Demo of longitudinal imaging in SPM12


Beta version released in December 2012 (phew!)


http://www.fil.ion.ucl.ac.uk/spm/software/spm12/



Frequent updates until final release


Record (and ideally report) the SPM12 revision number (r5360)


Longitudinal registration relatively stable


No longitudinal examples in SPM manual yet


Possibly after SPM course in May…


Support on SPM list, or email me (don’t email John!)


http://www.fil.ion.ucl.ac.uk/spm/support/


No Longitudinal
button, but found
in Batch menu,
like
Dartel
, etc.

Choice of paired
or general serial.

No difference in
model, but easier
specification and
results for pairs.

Specify Time 1
scans for all
subjects, then all
Time 2 scans (in
same order!)

Default values
can be left;
NaN

to automatically
estimate (
Rician
)
noise levels

Vector (list) of
time intervals (yr)

One module

per subject
(scripting required
if many subjects!)

Vector (list) of
times relative to
arbitrary datum
(e.g. baseline=0)

Select all scans
for this subject

Jacobian output
useful to quantify
interpretable ROI
volumes (in litres)


Output/results


Average image


Jacobians

or
divergences


Deformations


Next steps


Segment
avg


Run
Dartel
/Shoot


Warp e.g.
dv

to
standard space


SPM stats on
dv

(TBM)


Or combine with
seg

of
avg

(VBM)

Modelling longitudinal structural

change from serial MRI

Ged Ridgway



Gerard.Ridgway@ucl.ac.uk


This work was supported by the Medical Research Council
[grant number MR/J014257/1]


The Wellcome Trust Centre for Neuroimaging is supported by
core funding from the Wellcome Trust [091593/Z/10/Z]