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Modelling longitudinal structuralchange from serial MRIGed Ridgway Gerard.Ridgway@ucl.ac.ukJohn AshburnerColleagues at the FIL (WTCN) and theDementia Research Centre

Wellcome Trust Centre for NeuroimagingUCL Institute of NeurologyOverviewMotivation for longitudinal dataNeed for appropriate statistical analysisBenefits of longitudinal image processingRisk of bias from asymmetric processingLongitudinal imaging in SPM12Unbalanced data and further extensionsMotivation for longitudinal dataDevelopment, growth, plasticity, aging, degeneration, and treatment-response are inherently longitudinalSerial data have major advantages over multiple cross-sectional samples at different stagesIncreasing power Subtlety of change over time vs. inter-individual variationReducing confoundsDemonstrating causality with interventionsSeparating within-subject changes from cohort effectsExample: Training & structural plasticityIntervention (training) + longitudinal data allows causal interpretation of change, cf. just differenceDraganski et al. (2004) Neuroplasticity:Changes in grey matter induced by trainingvolunteers who learned to juggle transient and selective structural change in brain areas associated with processing and storage of complex visual motionDraganski et al. (2006) Temporal and spatial dynamics of brain structure changes during extensive learning2004 - Nature2006 - J Neuroscience (three different time points while medical students learned for their medical examination)

4Example: Training & structural plasticityScholz et al. (2009) Training induces changes in white matter architecture

Example: Training & structural plasticityComments & Controversies, NeuroImage, 2013, 73:225267

Thomas & Baker: Teaching an adult brain new tricks: A critical review of evidence for training-dependent structural plasticity in humansErickson: 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 plasticityBeyond the one trick pony imaging strategyFields: Changes in brain structure during learning: Fact or artifact? Reply to Thomas and Baker Thomas & Baker: On evidence, biases and confounding factors: Response to commentariesExample: Alzheimers disease evolutionMultiple sources of cohort effectsBirth-year (nutrition, etc.)Disease onset-time cohortsHealthy survivor effectTimescales too long for pure longitudinal studiesUnstructured multicohort longitudinal designsSee Thompson et al. (2011)[source of figure on next slide]7Example: Alzheimers disease evolution

Timescales too long for pure longitudinal studiesUnstructured multicohort longitudinal designs8Further statistical issuesEven simple designed experiments have pitfallsUsually seek group-by-time interactionNot significant change in one group but not anotherNot group difference at one time-point but not anotherCant ignore dependence within-subject over timeIn 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 processingSmaller within-subject variation motivates longitudinally-tailored image processing methodsBoundary shift integral (BSI)Intensity difference after rigid registration over region from brain masks more precise than mask volume diff.Non-rigid registration Jacobian-integrationJI over segmented region more precise than multiple independent segmentations (example following)Temporally-constrained/regularised 4D methodsE.g. Xues CLASSIC, Wolzs 4D graph-cutLongitudinal imaging animations

Interpolating rigidly aligned imagesWarping average by interpolated transformInterpolating volume change (divergence) relative to the averageNeed to click on video to start, and again on video to remove white rectangles covering second and third panels (clicking elsewhere advances slide instead)11

Benefits of longitudinal image processingAnderson et al. (2012) Gray matter atrophy rate as a marker of disease progression in AD

Risk of bias from asymmetric processingWithin-subject image processing often treats one time-point differently from the othersLater scans registered (rigidly or non-rigidly) to baselineBaseline scan segmented (manually or automatically)Asymmetry can introduce methodological biasesE.g. only baseline has no registration interpolation errorBaseline seg. more accurate than propagated segs.Change in later intervals more regularised/constrainedRisk of bias from asymmetric processingTheory known for a long time (but often ignored)Ashburner et al. 1999; Christensen, 1999;Cachier & Rey, 2000;Smith et al. 2001Demonstrated in practice recently as a serious issueThomas et al. 2009; Yushkevich et al. 2010; Thompson & Holland 2011

Risk of bias from asymmetric processingComments & Controversies, NeuroImage, 2011, 57:1-21

Thompson & Holland: Bias in tensor based morphometry Stat-ROI measures may result in unrealistic power estimatesHua et al: Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometryFox et al:Algorithms, atrophy and Alzheimer's disease: Cautionary tales for clinical trialsReuter & Fischl:Avoiding asymmetry-induced bias in longitudinal image processingLongitudinal image processing in SPM12Ashburner & 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 imagesConsistent with direct registration between pairDiffeomorphic complex warping without folding

Generative modelAverage imageInhomogeneity regularizationRegistr. (velocity) regularizationTime-pointNNon-rigid TransformVelocityInhomog.correction fieldRigid TransformRigid parametersNoise-level

Example result Alzheimers disease subjectAbove: Images aligned only rigidly (OASIS data)Below: Non-rigid volume change (divergence)Example result Group averages82 subjects from OASIS longitudinal data (part 1)DARTEL for between-subject spatial normalisationDivergences transformed without modulationNext 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 divSee also mass-multivar. generalized TBM (Lepore et al. 2008)Deformation-based morphometry (DBM)Ashburner et al. (1998)Multivariate analysis of displacement vector patternsLongitudinal VBM (Kipps et al. 2005)Tissue-specific volume-change (using segmentation)Longitudinal statistical modelling in SPMBalanced data (e.g. designed experiment)Same number (and timing) of time-points over subjectsRepeated-measures / within-subject ANOVADependence 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 statisticsE.g. straight line or polynomial regression coefficientsSub-optimal if times vary dramatically (singletons dropped)Other statistical modelling approachesBernal-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 SPMDemo of longitudinal imaging in SPM12Beta version released in December 2012 (phew!)http://www.fil.ion.ucl.ac.uk/spm/software/spm12/ Frequent updates until final releaseRecord (and ideally report) the SPM12 revision number (r5360)Longitudinal registration relatively stableNo longitudinal examples in SPM manual yetPossibly after SPM course in MaySupport on SPM list, or email me (dont 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 levelsVector (list) of time intervals (yr)

One moduleper subject (scripting required if many subjects!)Vector (list) of times relative to arbitrary datum (e.g. baseline=0)Select all scans for this subjectJacobian output useful to quantify interpretable ROI volumes (in litres)

Output/resultsAverage imageJacobians or divergencesDeformationsNext stepsSegment avgRun Dartel/ShootWarp e.g. dv to standard spaceSPM stats on dv (TBM)Or combine with seg of avg (VBM)Modelling longitudinal structuralchange from serial MRIGed 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]