Modelling longitudinal structural change from serial MRI Ged
Ridgway [email protected]@ucl.ac.uk John
Ashburner Colleagues at the FIL (WTCN) and the Dementia Research
Centre Wellcome Trust Centre for Neuroimaging UCL Institute of
Neurology Slide 2 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 Slide 3
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 Slide 4
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
trainingDraganski et al. (2004) 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 learningDraganski et al. (2006) Slide 5 Example:
Training & structural plasticity Scholz et al. (2009) Training
induces changes in white matter architectureScholz et al. (2009)
Slide 6 Example: Training & structural plasticity Comments
& Controversies, NeuroImage, 2013, 73:225267 Thomas &
Baker: Teaching an adult brain new tricks: A critical review of
evidence for training-dependent structural plasticity in
humansTeaching 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)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 ]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
plasticityBeyond the one trick pony imaging strategyIn vivo
assessment of use-dependent brain plasticityBeyond 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
commentariesChanges in brain structure during learning: Fact or
artifact? Reply to Thomas and BakerOn evidence, biases and
confounding factors: Response to commentaries Slide 7 Example:
Alzheimers 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)Thompson et al. (2011) [source of figure on next slide] Slide
8 Example: Alzheimers disease evolution Slide 9 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
Cant 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 Slide 10 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. Xues CLASSIC, Wolzs 4D graph-cutCLASSIC4D graph-cut Slide 11
Longitudinal imaging animations Interpolating rigidly aligned
images Warping average by interpolated transform Interpolating
volume change (divergence) relative to the average Slide 12
Benefits of longitudinal image processing Anderson et al. (2012)
Gray matter atrophy rate as a marker of disease progression in
ADAnderson et al. (2012) Slide 13 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 Slide 14 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. 2001Ashburner 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 2011Thomas et al. 2009 Yushkevich et
al. 2010 Thompson & Holland 2011 Slide 15 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
estimatesBias 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 morphometryAccurate 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 trialsAlgorithms, atrophy and Alzheimer's disease:
Cautionary tales for clinical trials Reuter & Fischl: Avoiding
asymmetry-induced bias in longitudinal image processingAvoiding
asymmetry-induced bias in longitudinal image processing Slide 16
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 Slide 17 Slide 18 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 Slide
19 Slide 20 Slide 21 Slide 22 Slide 23 Example result Alzheimers
disease subject Above: Images aligned only rigidly (OASIS
data)OASIS Below: Non-rigid volume change (divergence) Slide 24
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 Slide 25 Terminology: TBM, DBM &
(longitudinal) VBM (Deformation) Tensor-based morphometry (TBM)
Davatzikos et al. (1996); Chung et al. (2001)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)Lepore et al. 2008 Deformation-based morphometry (DBM)
Ashburner et al. (1998)Ashburner et al. (1998) Multivariate
analysis of displacement vector patterns Longitudinal VBM (Kipps et
al. 2005)Kipps et al. 2005 Tissue-specific volume-change (using
segmentation) Slide 26 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)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) Slide
27 Other statistical modelling approaches Bernal-Rusiel et al.
(2012) Statistical analysis of longitudinal neuroimage data with
Linear Mixed Effects models. [FreeSurfer]Bernal-Rusiel et al.
(2012) Chen et al. (2013) Linear mixed-effects modeling approach to
FMRI group analysis. [AFNI]Chen et al. (2013) Li et al. (2013)
Multiscale adaptive generalized estimating equations for
longitudinal neuroimaging data. [unbalanced twin and familial
studies]Li et al. (2013) Bayesian spatio-temporal modelling in SPM
Slide 28 Demo of longitudinal imaging in SPM12 Beta version
released in December 2012 (phew!)
http://www.fil.ion.ucl.ac.uk/spm/software/spm12/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
(dont email John!) http://www.fil.ion.ucl.ac.uk/spm/support/ Slide
29 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. Slide 30 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) Slide 31
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) Slide 32
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) Slide
33 Modelling longitudinal structural change from serial MRI Ged
Ridgway [email protected]@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]
