Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2: Diagnosis and Clinical Analysis PDF
Preview Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2: Diagnosis and Clinical Analysis
NEURAL ENGINEERING TECHNIQUES FOR AUTISM SPECTRUM DISORDER, VOLUME 2 Thispageintentionallyleftblank 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 AcademicPressisanimprintofElsevier 125LondonWall,LondonEC2Y5AS,UnitedKingdom 525BStreet,Suite1650,SanDiego,CA92101,UnitedStates 50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom Copyright©2023ElsevierInc.Allrightsreserved. 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Notices Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperiencebroadenour understanding,changesinresearchmethods,professionalpractices,ormedicaltreatmentmaybecomenecessary. Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluatingandusingany information,methods,compounds,orexperimentsdescribedherein.Inusingsuchinformationormethodstheyshouldbe mindfuloftheirownsafetyandthesafetyofothers,includingpartiesforwhomtheyhaveaprofessionalresponsibility. Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,assumeanyliabilityforany injuryand/ordamagetopersonsorpropertyasamatterofproductsliability,negligenceorotherwise,orfromanyuseor operationofanymethods,products,instructions,orideascontainedinthematerialherein. ISBN:978-0-12-824421-0 ForInformationonallAcademicPresspublications visitourwebsiteathttps://www.elsevier.com/books-and-journals Publisher:MaraE.Conner AcquisitionsEditor:ChrisKatsaropoulos EditorialProjectManager:EmilyThomson ProductionProjectManager:FahmidaSultana CoverDesigner:MilesHitchen TypesetbyMPSLimited,Chennai,India Dedication Withloveandaffectiontomymotherandfather,whoselovingspiritsustainsmestill (cid:1)AymanEl-Baz Tomylatelovingparents,immediatefamily,andchildren (cid:1)JasjitS.Suri Thispageintentionallyleftblank 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
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