Table Of ContentNEURAL ENGINEERING TECHNIQUES FOR
AUTISM SPECTRUM DISORDER, VOLUME 2
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NEURAL ENGINEERING
TECHNIQUES FOR
AUTISM SPECTRUM
DISORDER, VOLUME 2
DIAGNOSIS AND CLINICAL ANALYSIS
Editedby
Ayman S. El-Baz
UniversityofLouisville,Louisville,KY,UnitedStates;UniversityofLouisville
atAlameinInternationalUniversity(UofL-AIU)
Jasjit S. Suri
ATHEROPOINT,Roseville,CA,UnitedStates
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Dedication
Withloveandaffectiontomymotherandfather,whoselovingspiritsustainsmestill
(cid:1)AymanEl-Baz
Tomylatelovingparents,immediatefamily,andchildren
(cid:1)JasjitS.Suri
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Contents
List of contributors xiii 2.2.2 Effectofcytokine/chemokineproductionin
braindevelopment 23
About the editors xvii
2.2.3 Maternalimmunedysregulationand
Acknowledgments xix developmentaloutcomesofoffspring 25
2.2.4 Maternalimmuneactivationandautism
spectrumdisorder 26
PART 1 2.2.5 Maternalstressandautismspectrum
Autism and clinical analysis: Diagnosis disorder 27
2.2.6 Maternalgutmicrobiomeandautism
spectrumdisorder 28
1. Remote telehealth assessments for
2.2.7 Alterationsincytokineandchemokine
autism spectrum disorder 3
profilesduringgestationandtheneonatal
ANGELAV.DAHIYA,JENNIFERR.BERTOLLO, period 29
CHRISTINAG.MCDONNELLANDANGELASCARPA 2.3 Autoantibodiesreactivetobrainantigens 36
2.3.1 Autoantibodyoverview 36
1.1 Introduction 3
2.3.2 Autoantibodiesandbrainpathologies 37
1.1.1 In-personstandardizedassessmentsforautism
2.3.3 Autoantibodiesandautismspectrum
spectrumdisorder 4
disorder 38
1.1.2 Significanceofremoteassessmentsforautism
2.3.4 Maternalautoantibodiesand
spectrumdisorder 5
neurodevelopmentalalterations 39
1.2 Telehealthassessments 6
2.3.5 Maternalautoantibody-relatedautism
1.2.1 Videoconferencing(live/invivo) 7
spectrumdisorderoverview 40
1.2.2 Asynchronousvideoanalysis:current 9
2.3.6 MARASDanimalmodels 44
1.2.3 Asynchronousvideoanalysis:
2.3.7 Maternalautoantibody-related
retrospective 10
fetal-braintargetsandautismspectrum
1.2.4 Mobileapplications 11
disorder 46
1.2.5 Onlinewebsites 14
2.3.8 Maternalautoantibodiesaspotential
1.2.6 Otherformsoftechnology 15
autismspectrumdisorder-risk
1.3 Implications 16
biomarkers 48
1.3.1 Futuredirections 16
2.4 Concludingremarks 48
References 17
References 49
2. Maternal immune dysregulation and
autism spectrum disorder 21
3. Reading differences in eye-tracking data
ALEXANDRARAMIREZ-CELIS, as a marker of high-functioning autism in
DANIELLE(HYUNJUNG)KIMANDJUDYVANDEWATER
adults and comparison to results from
2.1 Introduction 21 web-related tasks 63
2.2 Cytokinesandchemokines(overview) 22
VICTORIAYANEVA,LEANHA,SUKRUERASLAN,
2.2.1 Cytokinesandchemokinesinthecentral YELIZYESILADAANDRUSLANMITKOV
nervoussystem 23
3.1 Introduction 63
3.2 Relatedwork 65
vii
viii
Contents
3.3 Automateddetectionofhigh-functioningautismin 5. Applicationsof machine learning
adultswitheye-trackingdatafromwebtasks 66 methodsto assist the diagnosis of autism
3.4 Theproposedapproach 67
spectrum disorder 99
3.4.1 Datacollection 68
3.4.2 Participants 68 MAHMOUDELBATTAH,ROMUALDCARETTE,
FEDERICACILIA,JEAN-LUCGUE´RINAND
3.4.3 Stimuliandtasks 69
GILLESDEQUEN
3.4.4 Apparatus 70
3.4.5 Procedure 70 5.1 Introduction 99
3.4.6 Datapreprocessing 71 5.2 Backgroundandrelatedwork 100
3.5 Experiments 71 5.2.1 Analysisofvisualattentionin
3.6 Results 73 autism 100
3.7 Discussion 75 5.2.2 Machinelearningforautism
3.8 Conclusion 77 diagnosis 101
3.9 Opendata 77 5.3 Datadescription 103
References 77 5.3.1 Participants 103
5.3.2 Experimentalprotocol 104
4. Parents of children with autism
5.3.3 Visualizationofeye-tracking
spectrum disorders: interventions with and scanpaths 104
for them 81 5.4 Unsupervisedlearning:clusteringofeye-tracking
scanpaths 106
LILIANAP.ROJAS-TORRES,YURENAALONSO-ESTEBAN
ANDFRANCISCOALCANTUD-MARI´N 5.4.1 Imagepreprocessing 107
5.4.2 Featureextractionusingprincipalcomponent
4.1 Introduction 81 analysisandt-SNE 107
4.2 Parentparticipationinearlycomprehensive 5.4.3 Featureextractionusingdeep
interventionprograms 82 autoencoder 107
4.2.1 Parentaltraining 83 5.4.4 K-Meansclustering 109
4.2.2 PivotalResponseTrainingProgram 84 5.4.5 Qualityofclusters 110
4.2.3 TreatmentandEducationofAutisticrelated 5.4.6 Clusteranalysis 111
CommunicationHandicappedChildren 5.5 Supervisedlearning:classificationmodel 113
Program 84 5.5.1 Datapreprocessingandaugmentation 113
4.2.4 EarlyStartDenverModel 85 5.5.2 Modeldesign 113
4.3 Programsforthedevelopmentofparent(cid:1)child 5.5.3 Classificationaccuracy 113
interaction 85 5.6 Demoapplication 114
4.3.1 Hanen’smorethanwords 85 5.7 Limitations 116
4.3.2 Preschoolautismcommunicationtrial 85 5.8 Conclusions 116
4.3.3 JointAttentionSymbolicPlay,Engagement, References 116
andRegulation 86
4.3.4 ImprovingParentsasCommunication
6. Potential approaches and recent
Teachers 86
4.3.5 Parent(cid:1)childinteractiontherapy 87 advances in biomarker discovery in autism
4.3.6 SteppingStonesTripleP 87 spectrum disorders 121
4.4 Parent(cid:1)childinterventionbasedonanxiety
SALAMSALLOUM-ASFAR,AHMEDK.ELSAYEDAND
reduction 88
SARAA.ABDULLA
4.4.1 Cognitivebehavioraltherapyforanxiety
reductioninchildrenwithautismspectrum 6.1 Introduction 121
disorderswithparentalintervention 88 6.2 Diagnosisandcategoriesofbiomarkers 122
4.4.2 Mindfulness-basedintervention 89 6.2.1 Humanbrainconnectome:structural,
4.5 Conclusion 90 functional,andmolecularneuroimaging
References 91 biomarkersforautismspectrumdisorder 122
ix
Contents
6.2.2 Molecularbiomarkers 122 8.2.3 Recommendations 180
6.2.3 Maternalandpaternalbiomarkers: 8.3 Featureanalysis 181
pregnancyanditspotentialassociationwith 8.3.1 Dimensionalityreduction 181
ASD 133 8.3.2 Featurerepresentation 184
6.2.4 Nextgenerationofdiagnostic 8.3.3 Recommendations 186
biomarkers 137 8.4 Technologicalapplications 187
6.3 Conclusion 139 8.5 Conclusion 190
References 140 References 190
7. Detection and identification of warning 9. Inhibition of lysine-specific demethylase
signs of autism spectrum disorder:
1 enzyme activity by TAK-418 as a novel
instruments and strategies for its
therapy for autism 195
application 147
SATORUMATSUDAANDHARUHIDEKIMURA
J.M.SALGADO-CACHO,M.R.GO´MEZ-SOLER,
M.L.RI´OS-RODRI´GUEZANDY.DEDIEGO-OTERO 9.1 Introduction 195
9.2 Lysine-specificdemethylase1asthepotential
7.1 Introduction 147
therapeutictargetforautismspectrumdisorder 196
7.2 Importanceofearlydetection 148
9.2.1 Druggabilityintargetingepigenetic
7.3 Differentialdiagnosis 149 factors 196
7.3.1 Abriefhistoryoftherelationshipbetween
9.2.2 Potentialtherapeuticfunctionsoflysine-
autismandpsychosis 150
specificdemethylase1inhibition 197
7.3.2 Similarities 150
9.2.3 Concernregardingtheon-targettoxicityof
7.3.3 Distinguishingfeatures 152 generallysine-specificdemethylase1
7.4 Detectionandscreeningprocess 155
inhibitors 197
7.5 SymptomdetectionvsDiagnosis 156
9.3 Discoveryofthe“enzymeactivity-specific”
7.6 Impactonthefamilyofdetectinganddiagnosing inhibitorsoflysine-specificdemethylase1 198
AutismSpectrumDisorder 158
9.3.1 Originalscreeningflow 198
7.7 Choiceofscreeninginstrumentsaccordingtoageof
9.3.2 DiscoveryofT-448andTAK-418 199
applicationandculturalenvironmentof 9.3.3 UniqueinhibitorymechanismofT-448and
implementation 159
TAK-418 199
7.8 Discussion 163
9.3.4 LowriskofhematologicaltoxicitybyT-448
7.9 Conclusions 166 andTAK-418inrodents 202
References 166
9.3.5 PreclinicalefficacyofT-448andTAK-
418 202
8. Machine learning in autism 9.3.6 HypothesisofthemechanismofactionofT-
spectrum disorder diagnosis and 448andTAK-418 205
9.4 Discussion 206
treatment: techniques and
9.5 Conclusion 207
applications 173
References 207
ARJUNSINGH,ZOYAFAROOQUI,BRANDENSATTLER,
EMILYLI,SRUSHTINERKAR,MICHAELHELDEAND
10. Behavioral phenotype features of
UNYIMEUSUA
autism 213
8.1 Introduction 173
HUIYUDUAN,JESU´SGUTIE´RREZ,ZHAOHUICHE,
8.2 Utilizingmachinelearningalgorithmstodiagnose
PATRICKLECALLETANDGUANGTAOZHAI
autismspectrumdisorder 175
8.2.1 Datasetwithbehavioralcharacteristics 176 10.1 Introduction 213
8.2.2 Datasetwithpersonal/cognitive 10.2 Eyemovementbehaviorphenotypeofautism 215
characteristics 178 10.2.1 Naturalstimuli 215
