Table Of ContentUse R!
Jörg Polzehl
Karsten Tabelow
Magnetic
Resonance
Brain Imaging
Modeling and Data Analysis Using R
Use R!
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J rg Polzehl Karsten Tabelow
(cid:129)
Magnetic Resonance Brain
Imaging
R
Modeling and Data Analysis Using
123
Jörg Polzehl Karsten Tabelow
WIAS Berlin WIAS Berlin
Berlin, Germany Berlin, Germany
ISSN 2197-5736 ISSN 2197-5744 (electronic)
UseR!
ISBN978-3-030-29182-2 ISBN978-3-030-29184-6 (eBook)
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Für Louis Pascal, Maurice und Jean-Loup
Frederic
—Karsten Tabelow
To my friends and family
—Jörg Polzehl
Preface
Our interest in neuroimaging started some 20 years ago, initiated by talks given
by Fred Godtliebsen (UiT, Tromsø, Norway) and by Fridhjof Kruggel
(Max-Planck-Institute of Cognitive Neuroscience, Leipzig) on functional magnetic
resonance imaging in the colloquium of the Weierstrass Institute for Applied
Analysis and Stochastics (WIAS). At that time, Vladimir Spokoiny and one of the
authors of this book, Jörg Polzehl, were interested in imaging problems starting
from pure theoretic developments in nonparametric statistics, particularly in
adaptivesmoothingforregressionproblems.Thishasleadtointerestingalgorithms
for image denoising that rely on qualitative assumptions on the image structure,
adapt to this structure, and preserve essential spatial information (Polzehl and
Spokoiny 2000). fMRI seemed to be a perfect application for the methodology. It
resulted in a paper, Polzehl and Spokoiny (2001), and a successful grant proposal
withintheDFGResearchCenterMATHEON.Thisapproachwaspresentedasaposter
attheOrganizationofHumanBrainMapping(OHBM)MeetingatSanAntonioin
2000, which led to a first contact with a group of statisticians working on
neuroimaging problems and resulted in an invitation by Keith Worsley to partici-
pate in a statistics in neuroimaging workshop in Montreal in December 2000.
In 2005, the other author of this book, Karsten Tabelow, joints the project. We
met Henning U.Vossfrom Weill Cornell Medical College,NewYork,USA.This
collaborationwasveryimportant,asHenningisaphysicistandacquiresMRIdata
himself with clinical questions in the background. We always felt that the combi-
nationofmathematicaldevelopmentalongaspecificscientificquestioniskeytothe
success of the project. The first paper of this collaboration (Tabelow et al. 2006)
was a complete elaboration of a structural adaptive smoothing procedure for fMRI
but it also included the statistical inference on the fMRI activation areas.1 It was
importantto show theusefulness ofsuch anapproach inclinical context(Tabelow
et al. 2008b), specifically for better alignment of activation regions in presurgical
planning.
1AsKeithWorsleynicelystatedinoneofhisreviewsontheaccompanyingRpackagefmri.
vii
viii Preface
HenningU.Vossalsointroducedustodiffusionmagneticresonanceimagingand
diffusion models, e.g.,thediffusion tensormodel,butalso to theorientation distri-
butionfunction.Thus,westartedtodevelopastructuraladaptivesmoothingmethod
fordMRIdatainthecontextofthetensormodel(Tabelowetal.2008a).Presenting
the results at the Annual Meeting of the OHBM 2007, we met Michael Deppe
(University Hospital, Münster, Germany), Siawoosh Mohammadi and Alfred
Anwander(Max-Planck-InstituteofCognitiveNeuroscience,Leipzig),whichstim-
ulatedourinterestindMRIanalysis.Thefirstauthorgotthechancetoparticipatein
the 2nd International Summer School in Biomedical Engineering on Diffusion-
weightedMagneticResonanceImaging:PrinciplesandApplicationsin2007.
Together with our Ph.D. student Saskia Becker, we developed the model-free
structuraladaptivesmoothingmethodfordMRIdata,whichwecalled(multi-shell)
position-orientation adaptive smoothing (msPOAS, Becker et al. 2012, 2014). The
motivationfortheseapproachesstemsfromacollaborationwithRemcoDuits(TU
Eindhoven,Netherlands).Atthesametime,wealsocontributedtothemodelingof
diffusion data byproviding a tensor mixture model with automatic selection ofthe
number of tensor components (Tabelow et al. 2012).
Meanwhile, Siawoosh Mohammadi moved to the Wellcome Trust Centre for
Neuroimaging atUCL.There,we started discussionsonanotherimaging modality
based on multiparameter mapping with Nikolaus Weiskopf and Martina F.
Callaghan. One of the results of this collaboration is the structural adaptive
smoothing method for MPM data (Mohammadi et al. 2017).
Accompanying with the methodological development, we always implemented
the algorithms in packages for R, specifically aws, fmri, dti, and qMRI and
released them on CRAN under a GNU license. However, much more people
workedonmethodsforneuroimagingusingR.Around2011,wefelt,togetherwith
Brandon Whitcher, that time was ripe to summarize those activities in a special
volume of the Journal of Statistical Software on “Magnetic Resonance Imaging in
R”, see also Tabelow et al. (2011). At CRAN, the “Medical Imaging Task View”
was created by Brandon Whitcher. We used some of the material that finally went
into this book for a lecture course on Statistics in Neuroimaging at the Humboldt
University Berlin in winter 2010. The plan to write this book dates back to this
time. With a workshop on Neuroimaging Data Analysis in 2013 and the Program
on Challenges in Computational Neuroscience (CCNS)2 at SAMSI in 2015–2016,
both initiated by Hongtu Zhu (University of Chapel Hill, US), the statistics and
neuroscience communities tried to further link both disciplines and encourage
young statisticians to work on emerging problems at this interface of disciplines.
2SupportedbytheNationalScienceFoundationunderGrantDMS-1127914totheStatisticaland
AppliedMathematicalSciencesInstitute.
Preface ix
In 2017, the Neuroconductor project3 (Muschelli et al. 2019) went online col-
lecting and adding many R packages related to neuroimaging and providing an
easy-to-use access to them. This really enhanced the usage of R in neuroimaging,
e.g., because John Muschelli contributed a huge number of missing implementa-
tions as packages. This allowed us to present complete analysis pipelines solely
based on R packages in all chapters of this book.
Wehavetothankalotofpeople,notonlythosementionedpreviously.Without
the collaboration and support, our results and the writing of this book would not
havebeenpossible.TheWeierstrassInstituteforAppliedAnalysisandStochastics
(WIAS Berlin) provided the perfect environment. We want to specifically thank
Jürgen Sprekels for his support of the whole imaging project at WIAS. We are
thankful to many colleagues from neuroscience we closely collaborate with,
especially André Brechmann and Reinhard König (LIN, Magdeburg), John-Dylan
Haynes (Einstein Zentrum Berlin, Charité Berlin), with whom we organized a
workshop on “Statistics and Neuroimaging” in 2011, and Christophe Lenglet
(CMRR, University of Minnesota, US). They introduced us into real-world prob-
lems, explained the physics and neuroscientific background behind, and provided
us with extensive data or even performed specific experiments to validate our
modeling approaches.
This book is about processing and modeling of images (in a general sense)
from magnetic resonance imaging. For their implementation, we rely on R
(R Development Core Team, 2019), the software environment for statistical com-
puting andgraphics. Withthis book, we wantto bridge twooften distinct commu-
nities.Itisintendedforstatisticians,whoareinterestedinneuroimagingandlookfor
an introduction. On the other hand, we hope it will be suited for neuroimaging
students,whowanttolearnaboutthestatisticalmodelingandanalysisofMRIdata.
By providing full worked-out examples, the book shall serve as a tutorial for MRI
analysiswithR.Wewouldverymuchliketoseefurtherdevelopmentinthefield,as
RisalreadycapableofprovidingmostofMRIanalysis.
In this book, we cover the theory underlying the modeling and data analysis or
data description when we think it is necessary. For all imaging modalities, we
outline complete analysis pipelines and illustrate them by worked-out examples.
Still, the field of neuroimaging is huge, so the book remains incomplete. For
example,wedonotcovergroupfMRIanalysis,butthesecond-levelanalysiscanbe
performed using existing R functionality. We do not address clinical diagnostics
based on multimodal structural MRI, see, e.g., Crainiceanu et al. (2017).
Furthermore, we also dropped the use of machine learning techniques in multi-
variate pattern analysis, which still would require some implementation in R to
become a feasible analysis tool. We also leave out important diffusion models like
NODDI or g-ratio maps for the lack of implementations in R. We are, however,
convinced that R provides a rich environment for further developments.
3Neuroconductor and itsmaintainers have beenpartially supportedby theR01 grant NS060910
from the National Institute of Neurological Disorders and Stroke at the National Institutes of
Health(NINDS/NIH).
x Preface
Somewhat parallel to the development and success of the imaging technologies
in neurosciences, the reproducibility crisis in science reached the field with the
general question on how many of the results are false. In fact, most are (Ioannidis
2005). The dispute raised important issues on the handling of the ever-growing
amount of data, i.e., its organizations, description, and publication, as well as the
reportingofthemethodsthathavebeenusedfortheanalysisofthedataandleadto
the scientific findings. It also met the discussion on the way of the publication of
scientific data, analysis, and results promoting Open Access publication, Open
Data, and more general Open Science. Specifically, data sharing repositories like
OpenNeuro (Stanford Center for Reproducible Neuroscience, 2019; Poldrack and
Gorgolewski 2017) or repositories providing access for scientific purposes, see
Turneretal.(2016,2017)andalsoAppendixBemerged.Withtheadvanceofnew
standards for data organization like the Brain Imaging Data Structure (BIDS)
(Gorgolewski et al. 2016) for the organization of MRI data in NIfTI format, such
repositories become an easy-to-use source for neuroimaging data to acquire new
andinitiallyunintendedscientificresultsor,liketheauthorsofthisbook,todevelop
new methodology. They also, together with a complete description of the corre-
spondingmetadataandpreferablytheanalysissoftware,enablethereproductionof
published results by other scientists than the original authors. Furthermore, for the
neuroimaging community appropriate recommendations for reporting the path to
thescientificresultsaredeveloped,e.g.,bytheCommitteeonBestPracticesinData
Analysis and Sharing (COBIDAS) of the Organization for Human Brain Mapping
(OHBM) (Nichols et al. 2017).
WithinRaworkflowisavailablethatnaturallysupportsthecreationofscientific
reports, e.g., based on LATEX, that are directly linked to the data and the code to
produce the results. Originally, the approach was to use R function Sweave
(Leisch 2002a,b),whichbycallingRSweave onaLATEXdocumentthat includes
R code executes the latter and includes the resulting numbers, statistics, or even
figures together with the commands that create them within the document. Later,
theknitrpackage(Xie2019)wasdevelopedtosolvesomelong-standingproblems
inSweaveandtoserveasatransparentenginefordynamicreportgenerationwith
R(Xie2015).Inprinciple,suchadynamicreportiscreatedfromaLATEXdocument
that includes so-called chunks of code in a ⋆.Rnw file. R with the knitr package
then creates the actual ⋆.tex that contains highlighted code and the results of its
execution. The system is flexible enough to work with other programming lan-
guages, like Python, Julia, or awk, and produce any output markup, like LATEX,
HTML, or Markdown.
In an attempt for full reproducibility, this book completely relies on such a
dynamic report generation: It uses neuroimaging data publicly available on
repositories; the PDF was created running the R code in the included chunks and
thenrunLATEXonthe⋆.texmarkupcode.Thus,almostallfigures,numbers,and
resultsaregeneratedwhileproducingthePDFfromthesources.Theonlyexception
wemadetothisprinciplewasduetothefactthatsomeneuroimageanalysisrunsfor
manyhoursorevendays.Themostextremeexampleisthecalltoxfibresforthe
ball-and-stickmodel inSect. 5.2.5whichrequiredalmost2weeksofcomputation.
