Pattern Analysis of the Human Connectome

261 Pages·2019·9.151 MB·English

by  Dewen Hu, Ling-Li Zeng

Dewen Hu
Ling-Li Zeng
Pattern
Analysis of
the Human
Connectome
Pattern Analysis of the Human Connectome
Dewen Hu • Ling-Li Zeng
Pattern Analysis of the
Human Connectome
123
DewenHu Ling-LiZeng
CollegeofIntelligenceScience CollegeofIntelligenceScience
andTechnology andTechnology
NationalUniversityofDefenseTechnology NationalUniversityofDefenseTechnology
Changsha,Hunan,China Changsha,Hunan,China
ISBN978-981-32-9522-3 ISBN978-981-32-9523-0 (eBook)
https://doi.org/10.1007/978-981-32-9523-0
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Contents
1 Introduction................................................................. 1
1 MultimodalBrainImaging............................................. 1
2 sMRI-BasedStructuralConnectivity.................................. 2
3 DTI-BasedAnatomicalConnectivity.................................. 3
4 fMRI-BasedFunctionalConnectivity................................. 3
5 DynamicFunctionalConnectivity..................................... 5
6 MultivariatePatternAnalysis.......................................... 6
7 FeatureExtraction...................................................... 7
8 DimensionalityReduction ............................................. 8
9 ClassifierDesignandPerformanceEvaluation ....................... 8
10 TheContentoftheBook............................................... 9
References.................................................................... 9
2 Multivariate Pattern Analysis of Whole-Brain Functional
ConnectivityinMajorDepression........................................ 17
1 Introduction............................................................. 17
2 Subjects................................................................. 19
3 ImageAcquisitionandPreprocessing................................. 20
4 IdentificationofFeatureswithHighDiscriminativePower.......... 21
5 SupportVectorClassificationandPerformanceEvaluation.......... 23
6 Altered Resting-State Functional Connectivity
inMajorDepression.................................................... 24
7 Discussion .............................................................. 26
References.................................................................... 29
3 DiscriminativeAnalysisofNonlinearFunctionalConnectivity
inSchizophrenia............................................................ 35
1 Introduction............................................................. 35
2 Participants ............................................................. 37
3 ImagingAcquisitionandPreprocessing............................... 38
4 MICandeMIC ......................................................... 39
5 HighDiscriminativeConnectivityFeatures........................... 41
v
vi Contents
6 SupportVectorClassificationandPerformanceEvaluation.......... 42
7 FunctionalConnectivityChanges ..................................... 43
8 Discussion .............................................................. 45
References.................................................................... 50
4 PredictingIndividualBrainMaturityUsingWindow-Based
DynamicFunctionalConnectivity........................................ 55
1 Introduction............................................................. 55
2 Participants ............................................................. 57
3 ImageAcquisitionandPreprocessing................................. 58
4 ALFF-FCMapofDynamicFunctionalConnectivity ................ 59
5 PartialLeast-SquaresAnalysisandAgePrediction .................. 61
6 Age-DependentChangesintheVariabilityoftheDynamic
FCDuringMaturation ................................................. 63
7 ControlAnalysis........................................................ 65
8 Reproducibility......................................................... 70
9 Discussion .............................................................. 71
References.................................................................... 78
5 LocallyLinearEmbeddingofFunctionalConnectivityfor
Classification................................................................ 83
1 Introduction............................................................. 83
2 FeatureSelection....................................................... 85
3 LLE-BasedDimensionalityReduction................................ 86
4 K-MeansClustering-BasedClassifier................................. 90
5 EvaluationofClassificationPerformance............................. 91
6 ComparisonwithOtherClassificationMethods...................... 94
7 MostDiscriminativeFunctionalConnectivityFeatures.............. 97
8 Discussion .............................................................. 97
References.................................................................... 101
6 LocallyLinearEmbeddingofAnatomicalConnectivityfor
Classification................................................................ 103
1 Introduction............................................................. 103
2 ParticipantsandImagingProtocol..................................... 105
3 RegionofInterestSegmentationandFiberTracking................. 106
4 FeatureSelectionandClassification................................... 109
5 Discussion .............................................................. 111
References.................................................................... 117
7 LocalityPreservingProjectionofFunctionalConnectivityfor
Regression................................................................... 123
1 Introduction............................................................. 124
2 DataAcquisitionandPreprocessing................................... 125
3 Parametric Curve Fitting and Age-Related Changes
inInterregionalFunctionalConnectivity.............................. 127
4 Low-DimensionalEmbeddings........................................ 129
Contents vii
5 LocallyAdjustedSupportVectorRegression(LASVR)for
AgePrediction.......................................................... 133
6 Discussion .............................................................. 136
References.................................................................... 143
8 IntrinsicDiscriminantAnalysisofFunctionalConnectivity
forMulticlassClassification............................................... 149
1 Introduction............................................................. 149
2 Participants ............................................................. 151
3 DataAcquisitionandPreprocessing................................... 152
4 IDAAlgorithmandIntrinsicconnectomes ............................ 153
5 MulticlassClassificationBasedonIntrinsicDiscriminative
Analysis................................................................. 156
6 IdentificationofFeatureswithHighDiscriminativePower.......... 157
7 Discussion .............................................................. 158
References.................................................................... 164
9 SparseRepresentationofDynamicFunctionalConnectivityin
Depression................................................................... 169
1 Introduction............................................................. 169
2 DataAcquisitionandPreprocessing................................... 170
3 DynamicFunctionalConnectivity..................................... 171
4 SparseRepresentationandClassification ............................. 172
5 ExtractionofSpecificDynamicFunctionalConnectivity
Patterns.................................................................. 176
6 Discussion .............................................................. 177
References.................................................................... 179
10 Low-Rank Learning of Functional Connectivity Reveals
NeuralTraitsofIndividualDifferences.................................. 183
1 Introduction............................................................. 183
2 ParticipantsandDataAcquisition..................................... 185
3 fMRIandDTIDataProcessing........................................ 186
4 Background-SubtractiononIndividualFunctional
Connectivity ............................................................ 187
5 RelatingBackgroundSkeletonofFunctionalConnectivityto
AnatomicalConnectivityArchitecture................................ 190
6 SparseRepresentationofFunctionalConnectivityTraitsand
theRelationtoCognitiveBehaviors................................... 190
7 ControlAnalysis........................................................ 195
8 Discussion .............................................................. 197
References.................................................................... 201
11 Multi-taskLearningofStructuralMRIforMulti-site
Classification................................................................ 205
1 Introduction............................................................. 205
2 Participants ............................................................. 208
viii Contents
3 ImageAcquisitionandPreprocessing................................. 209
4 Multi-siteClassificationUsingMulti-taskLearning ................. 209
5 Experiment.............................................................. 211
6 Result ................................................................... 213
7 Discussion .............................................................. 216
References.................................................................... 218
12 DeepDiscriminantAutoencoderNetworkforMulti-sitefMRI
Classification................................................................ 227
1 Introduction............................................................. 227
2 Participants ............................................................. 228
3 ImageAcquisition...................................................... 229
4 DataPreprocessing..................................................... 230
5 ControlofMotionArtifact............................................. 231
6 FunctionalConnectivityMeasure ..................................... 233
7 DiscriminantAutoencoderNetworkwithSparsityConstraint
(DANS)................................................................. 233
8 MultivariatePatternClassification..................................... 237
9 Estimation of the Discriminative Power of Functional
Connectivity ............................................................ 243
10 Discussion .............................................................. 247
References.................................................................... 253
Chapter 1
Introduction
Abstract Pattern analysis of brain connectome attracts increasing attention in
recentyears.Inthischapter,wefirstgaveabriefreviewofmultimodalbrainimaging
andbrainconnectome,andthenwediscussedsomebasicconceptsofmultivariate
patternanalysisofbrainconnectome anditsapplicationinneuropsychiatricdisor-
ders, including feature extraction, dimensionality reduction, classifier design, and
performanceevaluation.Finally,weintroducedthecontentofthisbook.
Keywords Multivariatepatternanalysis · Brainconnectome · fMRI · DTI
1 MultimodalBrain Imaging
Inthepastdecades,thegreatprogressofthecognitive neuroscience benefits from
the rapid development of brain imaging techniques, including magnetic resonance
imaging (MRI), positron emission tomography (PET), electroencephalography
(EEG), electrocorticography (ECoG), magnetoencephalography(MEG), etc. There
are three popular MR imaging modalities in neuroscience research: structural
MRI (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI). As
a noninvasive in vivo functional imaging technique, fMRI has become a most
popular neuroimaging approach in the field of neuroscience due to its promising
spatiotemporalresolution(Fig.1.1)[1].
At an early stage, studies always used task-related brain imaging to explore
brainfunctionanddysfunctionbydetectingtask-inducedbrainactivation,termedas
functionalsegregation[2].However,therearearound100billionneuronsand100
trillionsynapsesinthehumanbrain;seriesofcomplexcognitivefunctioncannotbe
fulfilledbyasinglebrainregion.Nowadays,thereisawidespreadargumentthata
complextaskwillinvolveinmultipleregionsoranentirebrainparticipate,termed
as functional integration [2]. Thus, brain function research could be done from
a perspective of brain connectome, to systematically characterize neurobiological
basis of cognitive and affective functions at a large-scale level. In fact, MRI-
based brain connectome becomes a hot research topic in the field of cognitive
©SpringerNatureSingaporePteLtd.2019 1
D.Hu,L.-L.Zeng,PatternAnalysisoftheHumanConnectome,
https://doi.org/10.1007/978-981-32-9523-0_1
2 1 Introduction
Fig.1.1 Multimodalbrainimaging
neuroscience, providing a new prospective for studying the neural activity of the
brainandthepathogenesisofneuropsychiatricdisorders[3].Inthisbook,wewill
focusonMRI-basedconnectomeofthehumanbrain.
2 sMRI-BasedStructuralConnectivity
MRI was first introduced in the imaging of the human brain in 1979. Gray/white
matter tissues can be segmented from the brain MRI scans, and gray matter den-
sity/volumechangescouldrevealthemorphologicalcharacteristicssuchaslocation
andsizeofagivenlesionbrainregioninclinicalresearchofneurologicaldiseases.
Actually, sMRI has been widely used in the research of various neuropsychiatric
disorders,includingAlzheimer’sdisease(AD)[4],Parkinson’sdisease(PD)[5,6],
epilepsy[7],schizophrenia[8],depression[9,10],etc.sMRIhasbeenusedinbrain
evolution,development,andplasticityresearch[11–13].Heetal.firstlyproposeda
sMRI-based structural connectome analysis approach for the human brains [14].
They used sMRI to measure regional cortical thickness of a group of normal
subjects from the ICBM database and then constructed structural connectivity
network by calculating the correlation coefficients of cortical thickness between
brain regions. Bassett and colleagues reported aberrant topological patterns of
large-scale structural connectivity networks in schizophrenia [15]. In recent years,