STATISTICAL METHODS TO ANALYZE MASSIVE HIGH-DIMENSIONAL NEUROIMAGING DATA ...

114 Pages·2015·1.51 MB·English

STATISTICAL METHODS TO ANALYZE MASSIVE
HIGH-DIMENSIONAL NEUROIMAGING DATA
by
Shaojie Chen
A dissertationsubmittedtoTheJohnsHopkinsUniversityinconformitywiththe
requirementsforthedegreeofDoctorofPhilosophy.
Baltimore,Maryland
August,2015
©ShaojieChen 2015
Allrightsreserved
Abstract
Thestatisticalanalysisofneuroimagingdataposesseveralchallengestoday,partlydue
to their size, high dimensionalityand noise. In thiswork, we address three different meth-
ods foranalyzing massive,high-dimensionaland noisyfunctionalmagneticresonance im-
ages(fMRI) data. In thefrstmethod,parallelcomputingtechniquesarecombinedwithan
independent component analysis (ICA) algorithm to decompose resting state fMRI data.
The algorithm’s performance is greatly improved compared to existing methods. In the
second method, a graphical model, referred to as state space model (SSM) is extended by
enforcingL-1andL-2penaltiesonparameters. Themodelscaleswelltoveryhighdimen-
sionsand can beapplied to a vastclass ofdifferent neuroimaginganalysis applications. In
thethirdmethod,atwo-stagemethodisdevelopedtoextractinformationfromnoisyfMRI
data. WefrstusefunctionalregressiontoextractfeaturesfromfMRIdataandthenusethe
features to predicts physical pains that human feels. A support vector machine (SVM) is
trained forpredictionandit achieveshighpredictionaccuracy.
Advisor:
ii
ABSTRACT
Brian Caffo, PhD
Committee:
James Pekar, PhD (chair, SOMradiology& radiologicalscience)
Brian Caffo, PhD (advisor,SPH biostatistics)
MichelleCarlson, PhD (SPH mentalhealth& epidemiology)
MartinLindquist,PhD (SPH biostatistics)
Alternates:
Gregory Pontone,MD(SOM psychiatry)
Ani Eloyan,PhD (SPH biostatistics)
JonathanLinks,PhD (SPH environmentalhealth)
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Acknowledgments
FirstIwouldliketothankmyadvisor,BrianCaffo,whohasbeenagreatfriend,agreat
advisorandgreatmentor. Hehasbeenaconstantsourceofadviceandencouragement. His
nice personality and endless fow of ideas have made it a great pleasure to work with him.
I have learned from him how to be a good biostatistical researcher, and more importantly,
howtobeagreat person.
Thanks to my thesis committee for their advice over the years: James Pekar, Michelle
Carlson, MartinLindquist,GregoryPontone,Ani Eloyanand JonathanLinks.
Thanks to Joshua Vogelstein and Ciprian Crainiceanu for yourgreat advice on my dif-
ferent projects.
Thanks to all the SMARTies. It is so great to be part of the group. The group is like a
bigfamilyand isalways fullofgreat ideas and projects.
Warmestthankstothestudentsofthebiostatisticsdepartmentwhooverlappedwithmy
time here. Special thanks to Chen Yue, Lei Huang, Huitong Qiu, Detian Deng and Yuting
Xu: you guys are so amazing that I could not imagine what my PhD life will be without
you. Iam gratefultothestudentsthat begantheprogrambeforemefortheirhelpon every
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ACKNOWLEDGMENTS
aspect ofmyresearch and career (especiallyJueminYang andShanshan Li).
Finally,Iwouldsaythankstomyfamily. Thankstomyparentswhohavebeensupport-
ing me all the time on every decision I made. They do not speak English and might never
know what I am writing here, but I am sure they can feel it on the other side of the planet.
Thanks to my brother, Shujie Chen, who has always been a last resort for inspiration and
strength when I got confused and don’t know what to do with my life. I feel so lucky to
have been born in such a great family. Thanks to Yao, my fancee, for all the care, support
and everythingwehaveexperienced together.
v
Contents
Abstract ii
Acknowledgments iv
ListofTables vii
ListofFigures viii
1 Introduction 1
1.1 Statisticalchallengesinneuroimagingdataanalysis . . . . . . . . . . . . . 2
1.2 Organizationaloverview . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 A ParallelGroup Independent Component AnalysisAlgorithm forMassivefMRIData Analysis
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Materialsand Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 TheICA model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
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CONTENTS
2.2.2 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.3 1000 FunctionalConnectomesProject Data . . . . . . . . . . . . . 14
2.2.4 AutismBrain ImagingDataExchange . . . . . . . . . . . . . . . . 15
2.2.5 SimulationStudies . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.1 1,000Functional ConnectomesProject Data . . . . . . . . . . . . . 21
2.3.2 AutismBrain ImagingDataExchange . . . . . . . . . . . . . . . . 24
2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3 A Sparse HighDimensional State-Space Model withanApplicationto Neuroimaging Data 31
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2 TheModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.3 ParameterEstimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.1 E Step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.3.2 M Step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.3.3 TheCompleteEM . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.4 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.4.1 Parameter Estimations . . . . . . . . . . . . . . . . . . . . . . . . 48
3.4.2 MakingPredictions . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.5 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
vii
CONTENTS
4 fMRI Based Biomarker forPhysicalPain 61
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2 FeatureExtraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.3 SupportVectorRegression . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.3.1 LinearRegression . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.3.2 Non-linearRegressionand theKernel Trick . . . . . . . . . . . . . 72
4.4 ApplicationtoPain Prediction . . . . . . . . . . . . . . . . . . . . . . . . 74
4.4.1 Prediction Acurracy . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.4.2 Further ImprovingPredictionAccuracy . . . . . . . . . . . . . . . 77
4.4.3 ClusteringVoxels . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.5 Discussionsand FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . 80
5 DiscussionandFuture Work 81
Appendices 84
A1 Appendixto Chapter3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Bibliography 87
Curriculum Vitae 97
viii
List of Tables
2.1 Summarymeasures ofthecorrelationsin thetwo simulationexamples. . . . 20
2.2 Speed increaseofPGICA . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1 PLDSRunningTime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2 SimilaritiesAmongEstimatedAMatrices . . . . . . . . . . . . . . . . . . 58
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List of Figures
2.1 Histogramsofage(left), IQ (middle),and SRS (right)forparticipantsinABIDE plottedand colored
2.2 Truesignalsforthesimulationexamples. Each componentis atwodimensionalarray wherethe
2.3 Boxplots(for bothfastICA and PGICA) oftheaveragecorrelations (log-transformed)ofthetrue
2.4 Axial,sagittal,and frontal (left toright)planes oftheauditory,control,defaultmodeandvisualnetw
2.5 3Dviewofauditory,control,defaultmodeand visualnetworks(from top). . 24
2.6 Axial,sagittal,and frontal (left toright)planes ofthedefault mode,auditoryand visualnetworks
3.1 xaxis istuningparameterλ underlogscaleand yaxis isthedistancebetween truthand estimations;
C
3.2 Row1: Atruth;non-penalizedestimationofA; optimallypenalized estimationofA. Row 2: C truth;
3.3 Estimationand predictionaccuracies. . . . . . . . . . . . . . . . . . . . . 53
3.4 Eigen-valuesand CorrespondingProfle LikelihoodPlot . . . . . . . . . . 56
3.5 ConnectivityGraph: Thewideredgemeansstrongerconnectivity;thered edgemeans negativeconnecti
3.6 3DRendering ofColumnsofMatrixC . . . . . . . . . . . . . . . . . . . . 58
3.7 Predictionaccuracies comparisononHCP data . . . . . . . . . . . . . . . 59
4.1 Threeweightsformultivariatetimeseries averaging . . . . . . . . . . . . . 66
4.2 Weightsestimatedwithfunctionalregression: whitecolorismoreweightwhilegreen colorisless
4.3 Predictionaccuracies comparison. Themean correlationsforfunctionalregressionweightsand box
4.4 Predictionaccuracies comparison. Themean correlationsforfunctionalregressionweightsand box
4.5 Predictionaccuracies comparison. Themean correlationsforfunctionalregressionweightsand box
4.6 Pick numberofclusters: theSSE dropsvery slowlywhenthenumberofclusters isover5. 79
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