On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

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On the early diagnosis of Alzheimer’s Disease from

multimodal signals: A survey

Ane Alberdi Aramendi, Asier Aztiria, Adrian Basarab

To cite this version:

Ane Alberdi Aramendi, Asier Aztiria, Adrian Basarab. On the early diagnosis of Alzheimer’s Disease

from multimodal signals: A survey. Artificial Intelligence in Medicine, Elsevier, 2016, 71, pp.1-29.

�10.1016/j.artmed.2016.06.003�. �hal-01514635�

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To link to this article : DOI :

10.1016/j.artmed.2016.06.003

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http://dx.doi.org/10.1016/j.artmed.2016.06.003

To cite this version :

Alberdi Aramendi, Ane and Aztiria, Asier and

Basarab, Adrian On the early diagnosis of Alzheimer's Disease from

multimodal signals: A survey. (2016) Artificial Intelligence in

Medicine, vol. 71. pp. 1-29. ISSN 0933-3657

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On

the

early

diagnosis

of

Alzheimer’s

Disease

from

multimodal

signals:

A

survey

Ane

Alberdi

a,∗

_,

_Asier

_Aztiria

a

_,

_Adrian

_Basarab

b

a_Mondragon_University,_Electronics_and_Computing_Department,_Goiru_Kalea,_2,_Arrasate_20500,_Spain

b_Université_de_Toulouse,_Institut_de_Recherche_en_Informatique_de_Toulouse,_Centre_National_de_la_Recherche_{Scientifique,}_Unité_Mixte_de_Recherche_5505,

UniversitéPaulSabatier,118RoutedeNarbonne,31062Toulouse,France

Keywords: Multimodality Behaviour Physiology Alzheimer’sDisease Earlydetection

a

b

s

t

r

a

c

t

Introduction:ThenumberofAlzheimer’sDisease(AD)patientsisincreasingwithincreasedlifeexpectancy and115.4millionpeopleareexpectedtobeaffectedin2050.Unfortunately,ADiscommonlydiagnosed toolate,whenirreversibledamageshavebeencausedinthepatient.

Objective:Anautomatic,continuousandunobtrusiveearlyADdetectionmethodwouldberequiredto improvepatients’lifequalityandavoidbighealthcarecosts.Thus,theobjectiveofthissurveyisto reviewthemultimodalsignalsthatcouldbeusedinthedevelopmentofsuchasystem,emphasizingon theaccuracythattheyhaveshownuptodateforADdetection.Someusefultoolsandspecificissues towardsthisgoalwillalsohavetobereviewed.

Methods:Anextensiveliteraturereviewwasperformedfollowingaspecificsearchstrategy,inclusion criteria,dataextractionandqualityassessmentintheInspec,CompendexandPubMeddatabases. Results:Thisworkreviewstheextensivelistofpsychological,physiological,behaviouralandcognitive measurementsthatcouldbeusedforADdetection.Themostpromisingmeasurementsseemtobe mag-neticresonanceimaging(MRI)forADvscontrol(CTL)discriminationwithan98.95%accuracy,while electroencephalogram(EEG)showsthebestresultsformildcognitiveimpairment(MCI)vsCTL(97.88%) andMCIvsADdistinction(94.05%).AvailablephysiologicalandbehaviouralADdatasetsarelisted,aswell asmedicalimaginganalysisstepsandneuroimagingprocessingtoolboxes.Someissuessuchas“label noise”andmulti-sitedataarediscussed.

Conclusions:ThedevelopmentofanunobtrusiveandtransparentADdetectionsystemshouldbebased onamultimodalsysteminordertotakefulladvantageofallkindsofsymptoms,detecteventhesmallest changesandcombinethem,soastodetectADasearlyaspossible.Suchamultimodalsystemmight prob-ablybebasedonphysiologicalmonitoringofMRIorEEG,aswellasbehaviouralmeasurementslikethe onesproposedalongthearticle.ThementionedADdatasetsandimageprocessingtoolboxesare avail-ablefortheirusetowardsthisgoal.Issueslike“labelnoise”andmulti-siteneuroimagingincompatibilities mayalsohavetobeovercome,butmethodsforthispurposearealreadyavailable.

1. Introduction

Peoples’lifeexpectancyisgrowingcontinuouslyinthe devel-opedcountries,makingthepopulationincreasinglyold.Evenifthis isapositivereality,italsobringsunwantedconsequencessuchas anincreasingnumberofdiseases,includingtheAlzheimer’s Dis-ease(AD).ItisestimatedthatADwilldoubleitsfrequencyinthe next20years[1]andthat115.4millionpeoplewillsufferfromit

∗_{Corresponding}_author._Tel.:₊₃₄_647504215.

E-mailaddresses:aalberdiar@mondragon.edu(A.Alberdi), aaztiria@mondragon.edu(A.Aztiria),adrian.basarab@irit.fr(A.Basarab).

in2050[2].Furthermore,whiledeathsattributedtootherhealth problemssuchasheartdiseasehavedecreasedinthelastyears, deathsattributedtoADbetween2000and2010haveincreasedin 68%[3].Nowadays,itdoesnotexista cureforAD[4],neithera 100%reliablediagnosisuntilapost-mortemanalysisisdone.Only somesymptomatictreatmentsthatareeffectiveforlimitedperiods insubgroupsofpatientsareavailable[5].Lifeexpectancyofthe patientsdiagnosedwithADiscurrentlylessthan7years[4].

Even ifit is thought that AD is theresult of a combination ofgenetic,environmentalandlifestylefactors[6,7],theinitiating eventsthatmakesomeonedevelopthistypeofdementiaremain stillunknown[8].ThemosteffectivemethodofcontrollingAD’s progressionis believedtobebased onanearlydiagnosisand a

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goodmanagementstrategystartedfromtheverybeginningofthe cognitivedecline[9,10],but, nowadays,thediagnosisismainly accomplishedusingpsychologicalteststhatbecomepositivewhen thediseaseispracticallyirreversible[11].

Thispaperis structuredas follows.In theremainderof this section,ADand itsprogressareexplained(seeSections1.1 and

1.2), andtheneed foranearlydiagnosismethodishighlighted (Section1.3).InSection2themethodologyusedtoconductthe literaturereviewis explained.In Section3,thecurrent stateof cognitive,psychological,physiologicalandbehaviouraldiagnostic methodsandbiomarkersisexposedandinSection4,thereviewed state of the art is critically analysed highlighting the research gaps.InSection5someoftheusefultoolsforthemultimodalAD detectionresearchareconsidered,namely,thepubliclyavailable datasets(Section5.1),thestandardmethodsformedicalimaging analysis(Section5.2)andtheneuroimagingprocessingtoolboxes (Section5.3).InSection6,someofthereal-worlddatasets’issues arediscussed.Finally,inSection7,aconclusionisgivenalongwith cluesforfuturework.

1.1. Definition

ADisaprogressive,degenerativedisorderthatattacksbrain’s nervecells,orneurons,resultinginlossofmemory,thinkingand languageskills,andbehaviouralchanges[12].Itisaneurological disorderthatmostlyaffectspeopleover65yearsoldandwhose incidencerategrowsexponentiallywithage[13].It isthemost commonformofdementia[14]andunlikewhatsomepeoplemay think,ADisnotanormalpartofageing[15].

1.2. Phases

ItisbelievedthatpeopledevelopingADundergothreedifferent stages[16].ThefirstoneisthepreclinicalADstage,wherechanges inthebrain,inthebloodandinthecerebrospinalfluid(CSF)may starthappening,butthepatientdoesnotshowanysymptoms[3]. Therefore,nowadays,thisphasecannotstillbedetected.Infact,it isbelievedthatthisstagecanstart20yearsbeforeanysymptomis evidenced.TheNunStudy,oneofthemostsignificantlongitudinal studiesintheareaofADresearch,hasevenshownevidencesof cor-relationsbetweenyouthlinguisticabilityandlatelifeprogression toAD[17].

Thesecond stageof the diseaseis calledthemild cognitive impairment(MCI).Inthisstage,symptomsrelatedtothethinking abilitymaystarttobenoticeableforthepatientsthemselvesand forthenearestfamilymembers,buttheydonotaffecttheirdaily life[3].NotallthepeoplediagnosedwithMCIdevelopAD,butonly anestimated10–15%ofthemeveryyear[18,19]andthereason whysomepeopledevelopdementiaand othersdonot,remains stillunknown.Whena patientisdiagnosedwithMCI,aspecific diagnosisproceduremuststarttounderstandwhichdiseaseor con-ditionisresponsibleforthedeficit[20].TwodifferenttypesofMCI aredistinguished:amnesicMCI(aMCI)andnon-amnesicMCI[9]. Theformerreferstopatientswhohaveimpairmentinthememory domain,andthelattertopatientswhohaveimpairmentinoneor morenon-memorydomainsofcognition,as,forexample,attention orlanguageprocessing.Itisbelievedthatsubjectssufferingfrom aMCIaremorelikelytodevelopAD[21].

ThefinalstageofthediseaseiscalleddementiaduetoAD,where memory,thinkingandbehaviouralsymptomsarealreadyevident andaffectthepatient’sabilitytofunctionindailylife.

1.3. Theneedforanearlydiagnosismethod

ADsymptomsareoccasionallyrecognizedbythepatients them-selves,butinthevastmajorityofcases,thecaregiversortheclose

familiars and friends are theones who realize thebehavioural andcognitivechangessufferedbytheADpatients.Theseverity ofthesesymptomsisnotalwayseasytonotice.Theproblemisthat ADsymptomsareinmanycasesconfusedwithanormalageing process,andthus,doctorsarenotconsulteduntilbeingtoolate, resultinginalatediagnosis[22].Inthesurveycarriedoutin[23], 64%ofthecaregiversaffirmedthatbeforethediagnosis,they con-sideredthebehaviourchangessufferedfromthepatientsaspart ofthenormalageingprocess.67%ofthemagreethatthismade thediagnosistobedelayed.Furthermore,onceinthehandof spe-cialists,itisnotyeteasytocorrectlydiagnoseAD.Eventhemost experiencedspecialistsfailinabout10–15%ofcasestocorrectly diagnoseAD[24].Infact,thedefinitediagnosecanonlybemade byapost-mortemexaminationofthebrain.Nowadays,apatient suspiciousofsufferingfromADcanbeclinicallydiagnosedwith anaccuracyofabout90%,drawingonmedicalrecords,physical andneurologicalexamination,laboratorytests,neuroimagingand neuropsychologicalevaluation[25].Mostofthesemethodsusedfor ADdiagnosisaretime-consumingandtheyrequireaclinician inter-vention[26],involvingannoyingdisplacementstohospitals,which canbespeciallydifficultwithelderly.Moreover,themonitoring oftheprogression ofthediseaseisvery costly[27]and, there-fore,notwellenoughstudied.Neuroimagingisbeingincreasingly usedbecauseitofferstophysiciansthepossibilitytoanalysethe patients’brainswhiletheyarealive.Nevertheless,whenchanges canbeappreciatedwiththenakedeye,itisnormallytoolate.That meansthatthebrainhasevidentsignsofatrophy,orthattoomany neurofibrillarytangles(NFT)andAmyloidplaquedepositscanbe foundonit.Non-invasive,fast,inexpensiveandreliableAD diag-nosismethodsarestilltobedeveloped[4].

Anearlydiagnosis of thedisorder canbe extremelyhelpful forthepatientsbecausetheycanhaveaccesstotreatmentsthat candelaysomesymptoms,beingmuchmoreeffectiveintheearly stages[4],aswellastoprogramsandsupportservices,whenthe disorderhasnoyetprogressedtoomuch[22].Furthermore,thiscan allowthemtotakepartinthedecisionoftheirfuture,as,for exam-ple,abouttheircareandeverydaylifeoraboutmoneyandlegal concerns.EarlydiagnosiscanalsohelptoimproveADsurvivalrate

[28].

Thereby,foranearlydiagnosisofADitisnecessarytobeableto detectthemostsubtlesymptomsofanytype.Takingintoaccount themultimodalnatureofADsymptomatology,itisclearthatthe mostefficientandreliableearlyADdetectionmethodologycannot onlyrelyonmeasurementsofauniquedomain,i.e.only physiolog-icalorbehaviouralsymptoms,butonthecombinationofseveral modalities,thatcouldallowtodetectallthesubtlechangesofall domainsfromtheverybeginningandtocontrastthemwithother typeofsymptomsforareliablediagnosis.Themultimodalnature ofotherdisorderssuch asstresshasalsobeenanalysed,andan approachforitsearlydetectionproposed[29],demonstratingthe feasibilityandapplicationofthesemethodstomultipledisorders. Nevertheless,subtlechangesarenoteasilynoticeable.People, withouttheaidoftechnology,arenotabletorecognisethesosmall behaviouralshiftsthatADpatientsmayundergo,notsuspecting theproblemuntilbeingtoolate.Technologythatcouldmakethis processeasierishighlyrequiredtospeedupthewholeprocess. PhysiciansmaynotbeabletocorrectlydiagnoseADiftheydonot findthenecessaryphysiologicaltracesforit.Automatedcomputer aideddiagnosis(CAD)techniquesareneededtofacilitate physi-cian’sdiagnosisof complexdiseasesin individualpatients [30]. Thus,technologythatcanhelpintheearlydetectionofADbasedon subtlebehaviouralandphysiologicalchangesmustbedeveloped.

Recently,areviewofnon-invasiveinnovativediagnostictools fortheearlydetectionofADhasbeenpublished[31]. Neverthe-less,thisarticledidnotemphasizeonthemultimodalnatureofthe disorder,neitherintheautomaticassessmentofdementiabased

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Fig.1.Searchmethodologyusedfortheliteraturereviewprocess.

onunobtrusively obtainedbehavioural data.Hence, theaim of thisarticleistocompletethepreviousworkbysummingupthe researchcarriedout intheearlyADdetectioninthefourmain modalities,namely,inthephysiological,cognitive,psychological and behaviouraldomains.Itis intendedtoemphasize themost reliableandusefulbiomarkersfounduptodate,andtherefore,to givecluesforthecombinationofmeasurementsandfeaturesthat shouldbeusedforanautomatic,unobtrusiveandreliableearlyAD detectionsystem.

2. Methods

ThefollowingreviewofthestateoftheartconcerningAD detec-tionwasundertakentoaddressthreespecificgoals:

1Toreviewthevarietyoffeatures,thatcancurrentlybeusedin ordertodetectAD,startingfromthemostwidelyaccepted meth-odswhicharealreadyincludedintheclinicaldiagnosisprocess, tothenewemergingways.

2Tocomparetheaccuraciesthatcanbeachievedwitheachsignal ormeasurement,soastohelptodecideamongthemostsuitable signalsforeachsituation.

3Tohighlightthestepsthatshouldbefollowedinordertoachieve anubiquitousearlyADdetectionsystem.

Toattainthesegoals anextensiveliteraturereviewwas per-formed,withthefollowingsearchstrategyandinclusioncriteria. 2.1. Searchstrategy

Publicationswereretrievedbymeansofacomputerizedsearch oftheCompendexandInspecdatabasesviaEngineeringVillage[32]

andofthePubMeddatabase[33]inordertofindrelevantstudies publishedinEnglishfromJanuary2005todate.

Thereviewwascarriedoutinaniterativeway:first,aglobal pointofviewofthecurrentstateinADdetectionwassearched. The search terms applied in the title field in this step were: “Alzheimer’s”OR“dementia”OR“AD”AND“detect*”OR“diagnos*” OR“measure*”AND“survey”OR“review”.Controlledtermswere usedinordertodiscardallthepublicationsrelatedtonon-relevant domains. Afterremoving duplicates, 70 results were achieved. Titles and abstracts of the remaining papers were reviewed, rejectingtheonesthat focusedonaspectsofADotherthanthe assessment.Twentypaperswereconsideredforfurtherreading.

Afteridentifyingthemaindomainsandmodalitiesinvolvedin thecurrentstateofADdetection,amorespecificsearchwascarried outforeachoneofthedomains.Thecombinationofsearchterms usedwerethefollowing:“Alzheimer’s”OR“dementia”OR“AD” AND“detect*”OR“recogn*”OR“identif*”OR“model*”OR“anal*” OR“diagnos*”AND“physiolog*”OR“behavio*”OR“psycholog*” OR“cogniti*”AND“accura*”.Afirstsetof649studyabstractswas retrievedforassessment.Controlledvocabularytermswereused inordertoexcludepublicationsrelatedtonon-relevantresearch areasandduplicateswererejected.Thebibliographiesofall rele-vantarticlesandreviewpaperswerealsohand-searched.Thetitles andabstractsoftheremainingarticleswerereviewedinapplying theinclusioncriteria.Forty-fourpaperswerein-depthread.

Asummaryoftheliteraturereviewmethodologyusedis pre-sentedinFig.1.

2.2. Inclusioncriteria

Allthe selectedpapers were original studiesand journal or conferencearticles,writteninEnglishandpublishedfrom2005 onwards.Forthefirststepofthisliteraturereview,onlythepapers in where objective AD detection systems were reviewed were accepted whilefor the secondstep, the inclusioncriteria were thefollowing:studiesofdiagnosticaccuracyofADusingatleast physiologicalorbehaviouraldataandvalidatedbymeansof cog-nitiveassessmenttestssuchastheMini-MentalStateExamination (MMSE),totalsubjectsinthestudyatleasttenandsufficientdata reportedeitherdirectlyorindirectlytoenabletheaccuracytable construction.

2.3. Dataextractionandqualityassessment

Adataextractionspreadsheetwascreatedforcollectingdata fromthepapers.Eachoneoftheselectedpaperswasfullyreadand assessedbyoneoftheauthors,whereastheresultswereverified byallofthem.Disagreementswereresolvedthroughdiscussion. 3. MeasuringADsymptoms

PeoplesufferingfromAD,showsymptomsofseveraltypesand in differentdegrees, depending ontheprogression level ofthe dementia.Thesesymptomscanbedistinguishedintofourmain modalities,whicharephysiological,psychological,cognitiveand behavioural.Thesymptomatologyinthesefourmodalitiescould

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ingeneralbeunderstoodasachainprocessthatstartswithsome physiologicalchangesinthepatient,mostlyinthebrain,thatlead tocognitivedifficultieswhichinturnprovokepsychologicaland behaviouralchangesonthepatient.

RegardingthephysiologicalsymptomsofAD,thebest-known changesaretheonesevidencedinthebrain.Ithasbeenfoundthat ADpatients’brainscontainahugenumberofAmyloidplaquesand NFT,whicharealsopresentinmanyhealthyagedsubjects’brains, butinmuchmoremoderateamounts.Amyloidplaquesrefertothe depositsofbeta-amyloid(ˇA)proteinfragments,whichare accu-mulatedbetweentheneuronsandNFTtothedepositsoftauprotein fragmentswhicharepiledupinsidetheneurons[22].The accumu-lationofˇAonthebrainisconsideredanecessarybutnotsufficient conditiontoproducetheclinicalsymptomsofMCIanddementia

[34].Thepresenceoftheseplaquesandtanglesiseventually accom-paniedbythedamageanddeathofneurons[3],andinfact,oneof themostfavourablehypothesisabouttheoriginofADnowadaysis theabnormaldepositionoftheseproteins[35–37].Cerebral hypo-perfusionhasalsobeenfoundtobemoreevidentinADpatients thaninnormaladults,sootherhypothesisblamingthevascular andcardiovascularproblemstobethecauseofthishypoperfusion whichinturncouldtriggerdementiahavebeendeveloped[38]. Corticalandhippocampalatrophiesofthebrainarealsoverywell knownADsymptoms[9].Reductionofthevolumeofthe hippocam-pusisprobablythemostcommonpronouncedchange[26],being asymptomwhichisalreadyevidencedinthemildstageandwhich worsensovertime.Infact,theauthorsof[39]affirmthatatthemild dementiastageofAD,hippocampalvolumeisalreadyreducedby 15–30%andinaMCIthevolumeisreducedby10–15%.Reduced brainactivityand communication betweennervecells hasalso beenfoundtobeanADsymptom,andeyedynamicpattershave alsobeendetectedtochangeinthesepatients.

Cognition-relatedsymptomsareprobablythebestknowninAD. TheclinicalhallmarkandearliestmanifestationofADisepisodic memoryimpairment[40].Notrememberingrecentlylearnt infor-mationisthemostcommonsymptom,whichisdiscerniblefrom theearlystagesof thedisease.Memoryalsostartstofailwhen rememberingimportantdatesorevents.PeopleinearlyADstages may alsohave difficulties solving daily problems, for example, withnumber-relatedtasksasmanagingfinances.Gettingconfused aboutthedates, seasonsand time, aswellas familiarplacesis anothersignofthedisease.Visionmayalsobeaffected,andthe patientsmaynotbeabletoread,tojudgedistancesorto distin-guishcoloursandcontrastsandolfactorydysfunctionhasalsobeen reported[41].Communicationproblemsmayalsoarise:patients maysufferfromdifficultieswhenexpressingthemselves,theymay repeatthingsortheymaystopinthemiddleofaconversation with-outknowinghowtocontinue.Vocabularylossisalsoacommon signofthedisease,aswellasmisplacingand notremembering wheretheyleftthingsandthus,losingthem.Asthedisease pro-gresses, this cognitive symptoms become even worse, and the patientsstarthavingtroublesrecognizingpeoplenearby, includ-ingfamilymembers[22,42].Reducedprevalenceofpaincanalso beanADsymptom[41].

Progressive deterioration of cognition leads to incoherent behaviourandlimitsthepatient’scapacitytoperformhistasksof everydaylife.Therefore,behaviouralsymptomsofADaredirect consequencesofthecognitivechanges.ADpatientsmaytakemuch moretimethanbeforeperformingdailyactivitiesdueto concen-trationdifficulties.Visualproblemsmayleadtomanybehavioural changes,as,forexample,indriving.Duetocommunication diffi-culties,thepatientmaysufferabigchangeofpersonality,anda personwhohasalwaysbeenverysociable,canbeanymore moti-vatedtodealwithpeopleandhavesociallife.Furthermore,they relymoreandmoreonotherpeopleforeverydayactivities,like eating,bathingor dressingbecausetheymayhaveproblemsto

Fig.2.ThemultimodalnatureofAD.

performwellphysically.Theymayalsobeunabletowalkproperly, duetogaitandbalancedysfunction[41],ortositbythemselves andincontinenceandswallowingproblemsmayalsoarise[22,42]. Psychologicalsymptomsincludechangesinmoodand person-ality.ADpatientscanbecomesuspiciousbecausetheymaythink theyhavebeenstolenwhentheylosethings,confusedwhenthey donotrememberthedayitisorhowtheyarrivedwheretheyare, anddepressed,fearfuloranxious,becausetheyrealizethattheydo notrememberbasicthingsandtheydonotknowhowfaritcan arrive[22].Depressionisthemostcommonpsychological symp-tominAD.Nevertheless,itisnotstillclearifdepressionisreallya consequenceofADorariskfactorbyitself[43].Apathy,irritability, agitation,euphoria,disinhibition,delusionsandhallucinationsare alsopartofADsymptomatology[41].

3.1. Cognitionanalysis

ADandMCIlevelscanbeevaluatedbymeansofmanytests, among which some are based on thecognitive abilities of the patients. Someexamplesare theMMSE[44]which isthemost frequentlyusedtestforADdiagnosis,theSevereCognitive Impair-ment Scale [45], the Alzheimer’s Disease Assessment Scale – Cognitive[46]whichfocusesonattention,orientation,language, executivefunctioningandmemoryskills,theNeuropsychological TestBattery[47]whichincludestreatmenteffects’measurements, theBlessedTestwhichassessesmemory,attention,concentration, and theabilitytocomplete Activitiesof DailyLiving(ADL)and theSevereImpairmentBattery[48]whichalternativelyfocuseson measuringtheunaffectedcognitivefunctions[49].

Some other tests are the Neurobehavioral Cognitive Status Examination,theDementiaRatingScale –2andtheCambridge NeuropsychologicalTestAutomatedBattery[50].TheReyAuditory VerbalLearningTestandtheCategoryFluencyTest,whichtestthe abilityofpatientsforrecallingwords,TheTrailMakingTestwhich measuresthefunctionofbrainingeneral,andothercognitivetasks liketheDigitSymbolSubstitutionTestandtheClockDrawingTest canalsohelpinmeasuringcognitivefunctionsofADpatients.

Thistypeofneuropsychologicaltestshavebeenshowntobe effectiveintheassessmentofAD.Nevertheless,theypresentsome

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drawbacks. Themost important one is that the assessment by meansofthesetestsislengthyandcomplicated[49].Furthermore, theyarenotsuitableforallthepatientsinallthestagesofthe dementia.Moreover,eveniftheycanmeasurethedementiastatein acertainmoment,itcanbecomplicatedtoearlydetectADbecause theymaynotshowenoughsensitivityorbecauseasinmanycases, itmaybetoolatewhenthetestisperformed.

3.2. Psychologicalevaluation

Asdepressionisoneofthemostfrequentnon-cognitive symp-tomsinAD(totheextentthatapathogeneticrelationbetween depressionandADhasbeensuggested[51]),psychological evalu-ationismainlyfocusedondepressionsymptoms’measurement.

Geriatric Depression Scale (GDS) is an instrument todetect depressionamongoldadults.Depressioncansometimesprovoke similarsymptomstothose of dementia,even areversiblestate called pseudodementia, and therefore, this test allows not to measure thecognitivestateof thepatients butto seewhether depressioncoexistswithADoranotherformofdementiaorto dis-missanykindofdementiaverifyingthatthesymptomsarerelated todepressionwithoutAD[52].

TheMontgomeryand ˚AspergDepressionScale,theCornellScale forDepressioninDementiaandtheNursesObservationScalefor GeriatricPatientsareotherpossibilitiestoassessthedepression levelsinADpatients[51].

Furtherresearchisstillneededinordertoverifyifdepressionis aconsequenceofdementia,or,onthecontraryifitisanotherrisk factor.Onceknowntherelationbetweenbothconcepts,thiskind ofpsychologicalassessmentcouldbeintroducedin aperiodical dementiaprogressiontest.Nevertheless,psychologicalevaluation beingcarriedoutbymeansoftestsandscales,hasthesame draw-backsasthecognitivetests,notbeingsuitableforanautomatic continuousmonitoringsystemfordementia.

3.3. Physiologicalsignals

NowadaysADresearchismainlybasedonphysiological mea-surements, making use of both biological signals and imaging methods.Especiallythelatterarebeingdevelopedandimproved straight off, and this is giving way to AD related physiological changesgettingbetteridentified.Thevolumetricanalysisofthe brain,whichallowstodetectatrophy,hasbeenthemain objec-tiveofimaging,andevenifithasbeendonemanuallyformany years,nowadays,itisevolvingandhasstartedtobedone auto-matically,thankstotechniqueslikethevoxel-basedmorphometry

[53,54], tensor-based morphometry [55,56], object-based mor-phometry[57] and feature-basedmorphometry [58].Currently, “neuroimagingplaysacentralroleintheclinicalresearchof cog-nitive disorders” [59]. Some of theneuroimaging methods are consideredforclinicaluse,namely,PositronEmission Tomogra-phy(PET),ComputedTomography(CT)andstructuralMRIwhile theothersneedstillfurtherresearchinordertobeaccepted.

In this section, thecurrent useand state of the biomedical signalsandimagesthathavebeenconsideredforADresearchis introduced.

3.3.1. CSF

Intherecentyears,someresearcheshavefocusedonidentifying reliableandvalidbiomarkersofADinbiofluids[60].Oneofthese biofluidsistheCSF,whichisaclearfluidthatsurroundsthebrain andspinal cordmainlyforprotection.CSFmustbeobtainedby lumbarpuncture[61].

CSF“istheonlybodyfluidindirectcontactwiththeextracellular spaceofthebrainandthusbiochemicalchangesdueto patholog-icalbrainprocessesaremoreprobabletobereflectedinCSFthan

inotherbodyfluids”[7].Thus,scientistshavemadethe hypoth-esisthattheaccumulationofAˇplaquesonthebraininvolvesa decreaseinCSFAˇ42levelsandthatthiscanbealready appreci-atedintheasymptomaticperiod.CSFtaulevelsarealsoknownto increase,butitisnotclearifthishappensaftertheAˇ accumula-tionstarts[62]orbothprocessesstartindependently[34],thus,the pathophysiologicalprocessoftaumightbeprecedenttotheoneof Aˇ.

Aˇ42 is probably the mosttypical CSF measurement in AD detection,andinthemajorityofthecasesdecreasedvalueshave beenfoundinADpatientscomparedtohealthysubjects[34].Aˇ40 isalsopresentintheliteraturebutnosignificantdifferenceshave beenfoundforADpatients[63],andsometimesratios between bothAˇspecieshavealsobeencomputed,suggestingthatithas potentialforbothfordistinguishingADpatientsfromhealthy sub-jectsandtopredictADinpeoplesufferingfromMCI[60,63–65].CSF totaltau,aswellassomespecifictauepitopes(p-tau231,p-tau181 andp-tau199),havebeenfoundtoincreaseinAD[34,66]andsome researchesalsoaffirmitspredictabilityfromMCItoAD[60,67]. Theratiooftau-epitopestoAˇ42,inagreementwiththe prece-dentresults,havealsobeenfoundtobepredictorsofADinMCI patients [67].OtherchemicalcomponentslikeCSF Isoprostanes which have beenfoundtobeincreasedin ADpatients even in thepreclinicalstage[68]and˛1-antichymotrypsin,Interleukin-6 andvariousmarkersofinflammationwhichhavegivenambiguous results[60],evenifmuchlessfrequently,arealsopresentinthe lit-erature.Recently,ithasbeenconcludedthattheamountofCSFin thehippocampalregionisalsorelatedtoAD[69].Thisisprobably duetothedecreasedsizeofthehippocampusinAD,whichleaves spaceformoreCSF.

“Numerous studies on CSF biomarkers for AD have been publishedduringthelastyears,howeverfrequentlyproviding con-tradictoryand inconclusive results”[34].Furthermore,many of thesebiomarkersarenotuniquetoADdiseasebuttoothertypesof dementia.Inaddition,thistechniqueisveryinvasivebecauseCSF mustbeobtainedbylumbarpuncture,andthus,itisdifficulttouse itasapreventionmethodoftheoverallpopulation.

3.3.2. Bloodtests

Bloodsamplescanbeobtainedinalessintrusiveandlesscostly way [61] andmore frequently thanCSF samples,and thus, AD biomarkersonbloodhavealsobeensearched.Moreprecisely,the bloodcomponentsplasmaandserumhavebeenanalysed,aswell asplatelets[70].

Featuresextractedfrombloodsamplesaresimilartotheones extractedfromCSF.Aˇ42andAˇ40,whichaccordingtothe major-ityoftheresearchesdonotshowsignificantdifferencesbetween ADandhealthysubjects[63,71]orgiveambiguousresults[72,73], Aˇ42/Aˇ40ratiowhichinthestudycarriedoutbyKoyamaetal.

[71],atoddswiththeoneperformedbyHanssonetal.[72],has showndecreasedvaluesinADpatientsand˛1-antichymotrypsin andvariousmarkersofinflammationwhichhavenotprovided evi-denceaboutthepotentialfordistinguishingbetweenADpatients andhealthysubjects[73].

IsoprostanesandInterleukin-6havealsobeenextractedfrom plasma,but theyhave resultedin thesamekindof ambiguous results[60].Inblood platelets,AmyloidPrecursorProtein(APP) forms,beta-secretaseenzyme(BACE)andalpha-secretase(ADAM 10)havealsobeenmeasured.Studieshavereportedthataltered valuesofthisbiologicalparameterscanbefoundinADpatients, evenintheveryearlystagesofthedisease[74,75].

Thus, uptonow, it is notclear ifblood samplescouldhelp indiscriminating ADandhealthypatients, neitheriftheycould serveasapredictor.Thedifferentresultsobtainedcouldbedueto thedifficultytomeasureAˇ42inplasmaortothemethodused for extractingthe peptides,as wellastothedifferences inthe

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populationsstudied.Hansonetal.[72]statethatwhetherplasma Aˇ concentrations reflect Aˇ metabolism in the brain is very unclearandothers affirmthereisnorelationatall[76]. Conse-quently,blood-basedbiomarkersofADhavenotbeenstillaccepted duetothe“failuretoreplicatefindings”[61]andtothe ambigu-ousresultsobtainedindifferentstudies.Nevertheless,itwouldbe interestingtoresearchfurtherbecausebloodcanbeeasilyobtained inroutinetests.Recently,ablood-baseddiagnosisprocedurehas beenpatentedbutitsvalidityforclinicaldiagnosisremainstobe seen.

3.3.3. CTscans

CTscanisastructuralimagingmethodthatusesX-raysto cre-atepicturesofcross-sectionsofthebody,achievingwiththesame dosageofradiation,100timesmoreclearimages[77]thanthe reg-ularX-rays.Togetthiskindofimages,specificCT scannersare used.

CThasbeenusedtoobservetheatrophyofmedial temporal regionsyearsago,butitisnoteasytofindrecentstudiesaboutCT asanADdiagnosissource.Vargheseetal.affirmthat[9]CTisnot usedasastandardtechniqueforearlydiagnosisofAD.Thiscould bebecauseothermethodshavedemonstratedtoprovidegreater accuracy,manipulabilityandprecision[60]andbecauseCTisonly capabletoshowlatechangesinAD[9].

EvenifsomestudieshavetriedtoverifyitsutilityinAD diag-nosis[78]due toitssimplicity,availabilityandinexpensiveness comparedtoothermethodssuchasMRI,nowadays,itisonlyused toruleoutotherbrainproblems,liketumoursorhaemorrhages, andit“doesnothaveanyotherroleintheearlydiagnosisofAD”

[9].

3.3.4. PETscans

PETimaging isa molecular imagingtechnique thatprovides three-dimensionalimagesofabrainatthemolecularand cellu-larlevel[79].Itconsistsofinjectingormakinginhaleasubstance, calledradiotracer,thatcontainsapositronemittertothepatients, detectingtheemittedradiationbyascannerandcomputinga dig-italimagethatrepresentsthedistributionoftheradiotracerinthe body[80].Dependingonthechosenradiotracer,differentkindsof PETscanscanbedone.PETScansaredonewithPETscanners,but theuseofcyclotronsforthepreparationoftheradiotracersisalso necessary,elevatingthecostoftheequipment.

InADdiagnosis,manydifferentradiotracershavebeenusedfor fourmainpurposes:Mainlythe11_C-PIB_to_image_the_accumulation

oftheˇAplaquesonthebrain,18_F-FDG_to_image_the_glucose

con-sumptionofthebrain,11_C-PMP,11_C-MP4A,11_C-MP4B,11_C-Nicotine

andotherstoimagetheneurotransmittersystemsofthebrainand finally11_{C-(R)-PK11195}_to_image_the_inflammation_in_the_central

nervoussystem(CNS)whichcancauseneuronaldeath[5].The glu-coseconsumptionimagingisbasedontheideathatasbrainmainly usesglucoseforenergyproduction,glucosemetabolismisclosely relatedtoneuronal function,bothatrestand duringfunctional activation[20,81].

CADsystemshavebeendevelopedtotrytoautomatically diag-nose AD and MCI. Su et al. [82] proposeda methodbased on automaticallyselectedROIfeatures,andclassifiedwithasupport vectormachine(SVM)classifierwithalinearkernel,andachieved accuracyratesofupto91.1%indistinguishingADfromcontrols, 79.41%withADandMCIand78.13%withMCIandcontrols.They alsotestedprincipalcomponentanalysis(PCA)andlinear discrimi-nantanalysis(LDA)basedfeatureswithbothlinearandradialbasis function(RBF)kernelSVMsandachievedaccuraciesupto94.6%, 81%and79.7%forthesamecasesasbefore.Dehghan[83]improved theseresultscombiningbothFDGand PiBPETscans,andusing PCAandSVMalgorithmsforfeatureextractionandclassification, theyachieved94.12%ofaccuracydistinguishingADfromhealthy

controlsand82.05% inthecaseofMCIandcontrols.Agroupof investigatorsoftheUniversityofGranadahaspublishedseveral importantworksproposingautomaticPETbasedADdiagnosistools

[84–86],reportinghighaccuraciesofupto98.3%distinguishingAD patientsandhealthycontrols,77.47%separatingCTLsfromboth ADandMCIpatientsand68.79%inclassifyingMCIpatients and controls.

TheadvantageofPETisthatithastheabilitytodisplayverymild symptoms[83].Unfortunately,whiletheoreticallyisnotahighrisk forthepatients,itinvolvesexposuretoradiationandradioactivity, and,therefore,itisamethodthatshouldbetterbeavoided. Fur-thermore,itisanexpensivemethodandisnothighlyavailable, althoughthisfactischanginginrecentyears[87].Thesereasons leadustobelievethatPETimagingisnotthebest-suitedmethod formassivemonitoringofthepopulation.

3.3.5. Singlephotonemissioncomputedtomography(SPECT) SPECTorperfusionSPECTisatypeofradionuclidebrainscan thattrackscerebralbloodflow(CBF)andmeasuresbrainactivity

[88].Itconsistsofinjectingormakingthepatientswallow radioac-tivesubstancesanddistinguishingthebraintissuesbytheradiation emittedbyeachoneofthemduetotheparticularabilityofeach tis-suetoabsorbthiskindofsubstances.ItisasimilarmethodtoPET,as bothconsistonintroducingshort-livedradionuclidesintoan amy-loidbindingmolecule,beingdifferenttheradionuclidesusedfor thetwotechniques:whilePETusesemittingpositrons,SPECTneeds photons[89].ThetwocommonestradiotracersusedforSPECTare

99m_{Tc-hexamethylpropyleneamine}_oxime_and99m_{Tc-ethylcysteine}

dimer[90].

SPECThasshowntobeavaluableaidfortheearlydiagnosisof AD[91],becauseitallowstoimagethehypo-perfusionsufferedby ADpatients.AcorrelationbetweentheprogressionofADandthe lossofcorticalCBFinvariousbrainregions[92]hasbeenfoundwith SPECT.Asignificantcorrelationwasalsofoundbetweenthetotal tauandphosphorylatedtauconcentrationsinCSFandperfusionin theleftparietalcortex[93].Nevertheless,itisnotyetclearinwhich brainareasthishypoperfusionismostevidencedandthuswhich onewouldbethemostaccurateoneforADdiagnosis. Temporo-parietalregionhasbeenconsideredpracticalfortheearlydetection ofAD[94],butitssensitivityandspecificityisstillquestioned[91]. Somesuggestthatposteriorcingulategyriandprecuneiregions couldbemoreuseful[95]whilemedialtemporallobe(MTL)and hippocampusregionscannotbeanalysedduetothedepthtowhich theyarelocated[96].

CADsystems have been developed using SPECTimages and machine-learning techniques[84,86,94,97,98]. Lopez et al. [84]

havebeenabletodistinguishADpatientsofAlzheimer’sDisease neuroimaginginitiative(ADNI)database[99]fromCTLswith96.7% accuracy,usingPCAbasedfeaturesofpreselectedslicesof inter-estandanSVMclassifierwithaquadratickernel.Ramirezetal.

[97]usedSPECTimagesof52subjects,andamethodologybased onfirstandsecondorderimageparameterselectionandSVM clas-sification.Featureselectiontechniquesyieldedafeaturevectorof onlytwocoefficients,thatcouldstillprovideahighclassification accuracyof90.38%.Theseresultsstronglysuggestthepotentialof suchasystemtoearlydetectAD.

SPECTshowslowerresolutionandhighervariability[100]than PET,butitsradiotracersarecheaperandeasiertoacquire[101], beingprobablybettersuitedforlongitudinalrepetitivestudies. Fur-thermore,SPECTcanbecarriedoutbymeansofaGammacamera, adevicethatisalreadyavailableinmostofthegreaterhospitals

[102]. SPECThasalsoshown thepotentialtoaiddistinguishing betweenADandotherdementias,namely,frontotemporal demen-tia(FTD),vasculardementia(VD)anddementiawithLewybodies, aswellasbetweenADpatientsandhealthycontrols[90,102]. Nev-ertheless,theheterogeneityoftheresultssuggestthatitshould

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becombinedwithothermethods.Weihetal.suggestedintheir review[102]thatSPECTcouldbebetterusedtoruleoutADinstead offordiagnosingit,asitpresentsamuchhigherspecificitythan accuracybothindistinguishingADpatientsfromhealthycontrols andinpredictingprogressionfromMCItoAD.Theresultsreported aboveencourageSPECT-basedADdiagnosisresearch,nonetheless, thiscanbequestionedduetoitsinvasivenatureprovokedbythe useofradiotracers.

3.3.6. StructuralMRI

MRIisanon-invasiveimagingtechniqueforstructural analy-sis.Shortly,itconsistsofapplyingstrongmagneticfieldstothe areathatiswantedtoimagewhilethedifferenttissuesare dis-tinguishedthankstotheirparticularrelaxationresponses,i.e.the radiofrequencysignalemittedbytheprotonsofeachtissue,[87]

whenthemagnetizationstops.ThisisdonewithanMRIscanner. StructuralbrainMRIimaginghasbeenwidelyconsideredfor earlydetectionanddiagnosisofAD[28].Thistechniquecanhelp diagnosingADintwoways:ononehand,itallowstomeasureMTL’s atrophy,whichiscloselyrelatedtocognitionandmemory,with veryhighdefinition[103,104]andontheotherhand,itenables changes ontissue characteristicsdue tovascular damagetobe detected[87].MTL’satrophyisearliestevidencedinthe hippocam-pusandtheentorhinalcortex[28,105–107],followedcloselybythe parahippocampusandtheamygdala[87].

Inthelastyears,thenumberoflongitudinalstudiesbasedon MRIimageshasincreased,thankstodatabasessuchasADNI[99]. Thishasallowedtobetteranalyseandmodeltheprogressionof thedisease [108]and its effects onindividuals’ spatiotemporal brainatrophy patterns[109]. Moreover,ithasbeenpossibleto verifythatthebrainatrophyinADpatientsandinsubjects con-vertingfromMCItoADhappensmuchfasterthaninhealthyadults

[110,111].

Makinguseofimageprocessing techniques,automatic diag-nosissystemshavebeendeveloped,achievingsatisfactoryresults distinguishingAD patients fromCTLs.Recently, Yepes-Calderón etal.[105]havedevelopedarelativelysimpleclassification sys-temtodistinguishbetweenAD,MCIandcontrolpatientswithMRI andtheyachievedclassificationsaccuraciesof98.95%when distin-guishingADfromcontrolpatients.Othershavereportedaccuracies of92%[30],89.22%[112],89%[56],88.9%[106],88.49%[28],87%

[26]and83%[113].Farzanetal.[114]haveachievedcomparable resultsinADdiagnosisfromlongitudinalMRIdata.Theyused per-centageofbrainvolumechangesinformationofaperiodoftwo years,andafterapplyingdiscriminativeanalysis(DA)toselectthe bestsubsetoffeatures,theyachievedaclassificationaccuracyof 91.7%indiscriminatingADpatientsfromCTL.Others[115]have recentlyanalysedwhetheritispossibletopredictADconversion fromMCIpatientsusinglongitudinalMRIdata.Apartfrom affirm-ingthispossibility,theyfoundoutadifferencebetweenmaleand femalepatients:whiletheyachievedanaccuracyof61%inmales, infemalesthisvalueraisedupto84%.

Asdistinguishingcontrolpeoplefromthosewhoaredeveloping MCImightbeparticularlyusefulinearlyADdetection,researches have alsofocusedonthis. Yepes-Calderónet al.[105]achieved 87.3%ofaccuracydistinguishingADandMCIcohortsand90.64% inthecaseofMCIandcontrol.MCIandcontrolsubjectshavealso beendistinguishedinotherstudieswithaccuracyratesof85.4%

[28],84%[56],81.3%[106]and78.22%[26].

Currently,MR’sroleisquiteblurryintheearlydiseasestages

[11].AtrophyofthehippocampuscanbedifferencedclearlyinAD patientscomparedtohealthypeople,but,unfortunately,itmaynot besoobviousattheearlystages,hinderingtheuseofMRIforearly detection.Furthermore,brainatrophyisnotspecifictoADbut char-acteristicofdifferentdiseases[87],orthebraincanevensuffer vol-umechangesduetoreasonsotherthanneuronalloss.Nevertheless,

someresearchersaffirmthepossibilityofpredictingand distin-guishingbetweenthedifferentstagesofADusingautomaticMRI analysisandtheresultsreportedhereinsuggestthatMRIcan con-tributepositivelytoanautomaticADdiagnosissystem,eveninits earlystages.Furthermore,MRIscannersarehighlyavailable nowa-daysandtheyareeasytouse,sofurtherresearchisworth. 3.3.7. FunctionalMRI(fMRI)

FunctionalMRIisanon-invasiveimagingtechniquefor func-tionalanalysisthatallowstodetectsomeoftheabnormalitiesof ADpatientsbrain’sperformance[9].Morespecifically,fMRI con-sistsofmeasuringtheoxygenconcentrationofthedifferentbrain areaswhenthesubjectisdevelopingdifferenttasksorwhenheisat thereststateforevaluatingthedefaultmodenetwork.Theseway, brainareasinvolvedoneachtaskoratthereststatecanbedetected

[116].Thus,abloodoxygenlevel-dependent(BOLD)imagecontrast thatprovidesanindirectmeasureofneuronalactivityisachieved

[59,87].Asintheprecedentcase,anMRIscannerisneededforthis typeofimaging.

“TheuseoffMRIinaging,MCI,andADpopulationsthusfarhas beenlimitedtoarelativelysmallnumberofresearchgroups”[87]. Notwithstanding,fMRIcanhelpdiagnosingADbyobtaining infor-mationabouteachbrainpart’sactivity.IthasbeenfoundthatAD patientshavereducedactivityintheMTL[117],particularlyinthe hippocampus[117–121],butalsointheentorhinalcortex[117], whileanincreasedactivationhasbeenreportedintheprefrontal cortex, probably,due to a compensationmechanism [122,123]. Deactivationinposteromedialcorticalareassuchastheposterior cingulateandthemedialparietalcortexhasalsobeenfoundtobe anomalousinADpatients[124,125].Nevertheless,these anoma-liesaremuchlessevidentinMCIpatients,whichcouldcomplicate theuseoffMRIasanearlydetectioncomponent.Insomecases, conflictingfindingshavebeendoneinhippocampal[118,126]and intheMTL[127,128]activationinMCIpatients.Thesedifferencesin resultsmightbeduetoacompensatoryeffect,wheresomebrain regionsmustactivateinordertocarryouttheworkthatothers cannotdoanymore.Someresearcheshavesuggestedthat some brainpartsfollow aU-curvepatternfor activation[129].Inthe defaultmodenetworkevaluations,a “significantalteration”has beenfoundintheconnectivitybetweenthehippocampusandits surroundingbrainareas[130].DifferencesinBOLDsignalshave alsobeenfoundbetweenADpatientsandotherdementiasufferers

[131–133].

Eveniftheyarefewerthanin thecaseof MRI,some exam-plesofautomaticanalysisoffMRIimagesinADdetectioncanbe foundintheliterature.Khazaeeetal. [134]developed an auto-maticclassificationsystembasedonSVMandfMRIimages,where anaccuracyof97.5%wasachieveddistinguishingADfromhealthy people.Tripolitietal.havecarriedoutseveralworks[135–137]

whereanaccuracyof88%wasachievedinthesametwo-class clas-sification problem. Furthermore,theyalso distinguishedelderly CTLs,patientswithverymildADandthosewithmildADwith80.5% ofaccuracyandintroducingafourthclassofhealthyyoungpeople theyachieved87%ofaccuracy.

ThebiggestadvantagesoffMRIareprobablyitsnoninvasiveand noradioactivenature,allowingitssafeutilization[59]ina repeti-tivemannerandthusfacilitatinglongitudinalstudies.fMRIoffers arelativelyhighspatialandtemporalresolution[9]ofthe activa-tionmapofthebrain,but,unfortunately,itisverysensitivetohead motion.Thiscouldbeaprobleminpeoplethatareinanadvanced stageofcognitiveimpairment,aswellasthefactthattheycanhave difficultiesinperformingthecognitiveactivitiesthatareneededfor thetest[87].Nevertheless,thelattershouldnotbeaproblemfor theearlydiagnosisofthedementiaandfurthermore,theresting statemethodologycanhelpovercomethisobstacle.

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3.3.8. Magneticresonancespectroscopicimaging(MRSI)

MRSI, also known as Chemical Shift Imaging, Spectroscopic ImagingorMultivoxelSpectroscopy(orMultivoxelMRS)[138],isa non-invasiveimagingmethodthatcanbeperformedinastandard MRIscanner.UnlikeMRIthatvisualisesanatomyinlivingtissueby onlyusingthesignalofwater,MRSImakesalsouseofMRS tech-nologythatcandetectbiochemistrybyusingsignalsfromorganic molecules,allowinginvivodetectionandmeasureof concentra-tionofsomelowmolecularweightmetabolites[59,138,139].“This techniqueisbasedonthephenomenonofchemicalshiftto dis-tinguishbetweenvariouscerebralmetabolites, whereby theH1 signalsfromthemetabolitesexhibitslightlydifferentresonant fre-quenciesdependentontheirspecificchemicalenvironment”[140]. Asthechemicalshiftofasinglemetaboliteisconstant,itwillalways peakat thesame frequency[141]andthereby, MRSprovidesa spectrain whicheachpeakrepresents ametaboliteorgroupof metabolites.Theareaunderthepeakisrelatedtothe concentra-tionofthemetabolite.Thesemetabolitesincludemyo-Inositol(mI), choline(Cho),N-acetylaspartate(NAA),creatine (Cr),glutamate andglutamine(Glu)[142].

AD patients have shown metabolite abnormalities like decreasedNAAorNAA/Crlevels[143–149],elevatedmI/Crratio

[144,147],increasedordecreasedCho/Crratiolevelsdependingon thestageofthedisease[150]anddecreasedGlulevels[147–149]

inthegraymatter(GM).NAA/mIratiohasbeenfoundtobeuseful fordistinguishingbetweenADpatientsandhealthysubjects.Infact, someaffirm[141,151,152]thatthisisthemostrobustmarkerofthe disease.MRSIcouldalsohelpinthepredictionfromMCIto demen-tia.SomestudieshavereportedlowerNAA/Cr[153–157]andhigher Cho/Cr[158]levelsinseveralbrainregionsinMCIpatientswho developeddementiathanstableMCIsubjects.Nevertheless,some disagreewiththesefindings[159,160]sofurtherresearchisneeded toverifyMRSI’spredictabilityfromMCItoAD.Someresearches havealsoaffirmedthepotentialofMRSItohelpindistinguishing differenttypesofdementiafromAD,suchasFTD[161]or subcor-ticalischemicVD[162,163].

Nevertheless,MRShassomedrawbacks.Theconcentrationof metabolitesinthebodycomparedtowaterconcentrationissmall, resultinginlowSNRimagesandlongacquisitiontimes[139],which inturnmakesthissystemsensitivetomotionartifacts[138]. Fur-thermore,itprovidesalowspatialresolution.Consequently,MRS “islittleusedintheclinicalevaluationofsubjectswithdementia”

[164].Furthermore,forthebestofourknowledge,automaticAD diagnosissystemsbasedonMRSIandmachinelearningtechniques havenotbeenreporteduptodate.

3.3.9. Diffusiontensorimaging(DTI)

DTI is a MRI technique that can provide information about braintissuemicrostructure.Itcanbeobtainednon-invasivelyusing an ordinary MRI scanner. It takes advantage of the Brownian motionphenomenonsufferedbywatermoleculesinhuman tis-sues,whichmakesthemcolliderandomlybetweenthemandwith othermolecules.Inpurewater,moleculesmoveinalldirections isotropically,i.e.withequalprobability,butthecellmembranes and thelargeprotein moleculesof thehumantissues limitthe rateand theorientationof thewatermolecules’ diffusion, ren-deringmovementsan-isotropic[59].Thus,themicrostructureof thehumantissuescanbeinferredfromthewatermolecules’ dif-fusionpatterns[165].Inotherwords,DTIidentifiesindirectly“the microscopicaspectsthatprovidemeasuresreflectingthepatterns in size, orientation and organization of tissue which are sup-posedprecursorstothefinalstageofmacroscopictissueatrophy”

[166,167].

IthasbeenproventhatDTIcanproviderelevantinformation aboutaperson’scognitivestate,beingmean-diffusivity(MD)and fractionalanisotropy(FA)themainmeasuresusedforit[59].FAhas

shownsignificantdifferencesinthecingulum,spleniumofthe cor-puscallosum,uncinatefasciculus,superiorlongitudinalfasciculus andfrontallobesbetweenADpatientsandhealthycontrols,andMD inthehippocampus,spleniumofthecorpuscallosum,parietallobes andtemporallobes.MDhasbeenfoundtoincreasewithcognitive performancedecline,especiallyinthetemporalstructureswhile FAdecreases[168].Thehippocampalarea,theposteriorcingulate andthecorpuscallosumhavealsoshownmoderateearly cogni-tivedysfunctionevidencein DTIimages[166,169],whichcould allowearlydetectionofAD.Forthispurpose,DTIhasshown supe-rioreffectsizescomparedtovolumetricMTLmeasurements[170]. SomefewstudieshavealsoshownabnormalitiesinMDvaluesin healthysubjectsatriskofAD[171,172],whichcouldleadtoavery earlydiagnosiswhencognitivechangeshavenotyetstarted.

Machinelearning methods havebeenappliedtoDTIimages inseveralresearchesbothfor automaticMCIandADdiagnosis. O’Dwyeretal.[19]madeuseofDTIimagesandanSVMalgorithm withanRBFkernelandachieved92.9%accuracyin distinguish-ingMCIfromcontrolhealthysubjects,andaverysimilarresultof 92.785%consideringathreeclassclassificationproblemwithaMCI, non-amnesicMCIandcontrolsubjects.Weeetal.[173]combined bothDTIandfMRIimagesinordertoobtaincomplementary fea-turesrelatedtothewhitematter(WM)andtotheGMrespectively. Thecombinationof thetwotechniquesandtheSVM algorithm withalinearkernelgavesignificantlyhigherclassification accu-raciesdistinguishingMCIfromhealthycontrolsubjectsthanusing eachoneofthetechniquesalone.Specifically,96.3%accuracywas achievedwiththecombinationmethod,comparedto88.89% accu-racyusingDTIaloneand70.37%usingfMRIalone.Dyrbaetal.[174]

createda diagnosismethodologyforADemphasizinginitsreal futureapplicationandtakingintoaccountthevariabilitythatcan befoundinDTIimagestakenwithdifferentMRIscanners.Forthat, theymadeuseofDTItakenfrom9differentscannersandcreated amethodologytodistinguishADfromcontrolsubjects.Thebest classificationaccuracyof83.3%wasachievedfortheMD informa-tionextractedfromtheimages,usinganSVMalgorithmwithRBF kernel.TheirworkshowedthatDTIcanberobustenoughtobe incorporatedtoADdiagnosissystemsifthenecessarytreatments areapplied.

DTIhasshowntobeaverypotentialtoolintheearlydiagnosis ofAD,becauseitcandetectalterationsthatcannotbedetected,for example,byconventionalMRI[175].Itstillpresentssome draw-backs,becausethereisstilluncertaintyaboutthebestchoiceof diffusionparameters and aboutthemethods touse tomanage crossingfibres[59].Nevertheless,theseobstaclesarebeing over-come,sobeforelongDTIcouldbeacceptedasaclinicaldiagnose tool.

3.3.10. TranscranialDoppler(TCD)ultrasonography

TCDultrasonographyisanimagingtechnologythathasbeen usedtoassesscerebralhemodynamics[176],namely,CBF. Tran-quartetal.[177]wereamongthefirstinmeasuringCBFusingthis technology,testingitinrabbits,whileMacéetal.[178]havemore recentlypresentedahighresolutionCBFimagingtechniquebased onthesameprinciple.ThismethodisbasedontheDopplereffect, andisexecutedwithan“ultrasoundprobethatsendshigh-pitched inaudibleandinvisiblesoundwavesintothebody,which“bounce” offofthetissuesinvaryingpatterns”[179].Theparametersofthese patternsallowtocomputethedirectionandspeedoftheblood flow.Theexperienceoftheoperatorandtheindirectparameters likethedepthofthesamplevolume,thepositionofthetransducer andthedirectionoftheflowallowtoassignthereceivedDoppler signaltoaspecificartery[180].

Studiesbasedonthistechnologyhavebeencarriedoutinorder toverifyitspotentialtodetectADandtopredictit.Several find-ingshavebeendone[181]. Silvestriniet al.[182]foundoutan

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increasedcarotidintima-mediathickness(IMT),whichisa parame-terofthearterialwall,inADpatientscomparedtohealthysubjects. ThisincreaseinIMTcouldalsoindicateahighershort-termriskof developingADortoconvertfromMCItoAD[183].Ahigherdegree ofcarotidatherosclerosishasbeenfoundtobecorrelatedthesame waywiththediseaseandahigherriskofdevelopingit[184].The total CBFis alsodecreased inAD patients [185,186],aswellas thecerebrovascularreservecapacity(CVRC)and themeanflow velocity(MFV)[176,180,187,188].CVRC“isa parameterof cere-brovascularautoregulationdescribingtheabilityforvasodilation ofcerebralarteriolesinsettingoflowcerebralperfusionpressure”

[180].DecreasedCVRCormiddlecerebralarteryflowvelocitycould alsorevealahigherriskfordevelopingadementia[189].The pul-satilityindex(PI)hasbeenfoundtoincreaseinAD[176].Afew studieshavealsofocusedoncerebralmicro-embolization,andhave concludedthatpeoplesufferingfromdementia,bothADorVD,are moreaffectedbythiseffect[190,191].

Despitethesefindings,fortheverybestofourunderstanding, researchesaimingtocreateanautomaticearlydiagnosissystem forADhavenotyetbeenreported.Nevertheless,advanceshave beendoneinthisareadevelopingimageprocessingalgorithmsto betterfocusonregionsofinterest,i.e.thecarotidarterywall,in ultrasoundimages[192].

Vascularimpairmentcanbedetectedbyseveralimaging meth-odslikePETorSPECT,butultrasonographycanbeanon-invasive andcheaperalternative,thusnoradiationorinjectionsareneeded. Unfortunately,ultrasonographyhasalsosomedrawbacks.Mistakes canbedoneintheidentificationofindividualvesselsandinthe esti-mationofbloodvelocityduetotheanglebetweenthevesseland theultrasonicbeam[180].Nowadays,colour-codedduplex ultra-sonographymightovercomesomeofthesedrawbacks,butitoffers lowerperformanceforlongmonitoring.Furthermore,evenifsome researchessuggestthepossibilityofusingultrasonographyto dis-tinguishbetweenAD’ssymptomsfromotherdementiassuchasthe VaD[193],thisisnotstillpossible[181].Evenworse,giventhatthe relationshipbetweenvasculardegenerationanddementiaisnot clear,itcannotevenbeknownifthistechniquecouldreallyserve asadiagnosismethod.Nowadays,itcanjustserveformonitoring thevascularsystem’sstateforADprevention.

3.3.11. Electroencephalogram(EEG)

EEGsarecalledtotherecordingsoftheelectricfieldofthescalp causedbytheelectricalsignalsexchangedbetweenneurons[4]. Thus,theyreflectthecommunicationactivitybetweennervecells, whichisofgreatimportanceinneurologicaldiseaseslikeAD.

StudieshaveshownthatEEGmayhavethepotentialforanearly ADdetection.Ithasbeenwidelyacceptedthatatleast3typesof changesoccurinADpatients’EEGsignals:theyslowdown(i.e.the poweroflowfrequenciesisfoundtobeincreasedwhilethepower ofthehighfrequenciesisdecreased),theircomplexity,whichis themeasureofthenumberofdifferentpatternsinthesignal[4],is reducedandsynchronyorcorrelationbetweenEEGsignalsofthe differentpartsofthebrainisreduced[4,194].

In therecentyears, EEGhasshown promising resultsin AD and MCIdetection. AnItalian groupof researchershave devel-opedautomaticEEGbaseddiagnosismethods,usinganalgorithm calledIFAST,whichisbasedonArtificialNeuralNetworks(ANN). IFAST consists in synthesizing EEG data by computing spatial featuresofEEGsthatarerepresentedbysomeANNbased connec-tionparameters[195].Usingthesetechniquestheyhaveachieved 93.46% accuracyseparatingMCI fromhealthyelderly[196]and 92.33%distinguishingADpatientsfromMCI[197].Theyhavealso shown thevalidity of the IFAST method to predictconversion fromaMCItoADwithhighaccuracy(85.98%)ina1-year follow-upstudy[195].Recently,theyhaveimprovedtheirmethod[198]

by using a feature extraction technique called MS-ROM and a

combinationofsomeclassificationalgorithms,namely,k-nearest neighbours,naïveBayesandquadraticdiscriminantclassifier.They havereportedverysatisfactoryresults:anaverageof93.48%for ADdetection,97.88%forMCIdetectionand94.05%forADvsMCI discrimination.Trambaiollietal.[199]haveusedSVMtoclassify healthypeopleandprobableADpatients,usingEEGsignals,and 79.9%accuracywasachieved.Individualmodelswerealsotested byanalysingfor eachoneofthesubjectstheratiobetweenthe numberofcorrectlyclassifiedEEGepochsandthetotalnumber ofEEGepochs,resultinginhigheraccuraciesofupto87%.Inthe studycarriedoutbyMcBrideetal.[35],EEGsignalshavebeenused todiscriminatecontrol,MCIandADpatients.Spectraland com-plexityfeatureswereusedforthreeSVM classifiers,that aimed tosolvethreetwo classclassificationproblems:controlvsMCI, controlvsADandMCIvsAD.Amajorityvotingsystemwasused tosolvetheoverallclassificationproblembasedontheresultsof thepreviousclassifiers.91.4%,84.4% and89.9%ofaccuracy was achievedinthefirst,secondandthirdtwoclassclassification prob-lemsrespectively,andanoverallclassificationaccuracyof82.6% ontheclassificationof thethree classes.Recently,anothervery promising methodbased onEEGsynchronization analysishave beenproposedfortheearlydiagnosisofAD[200].

Manyresearchers[1,4]supporttheuseofEEGforalongitudinal monitoringofchanges inthebrain, duetothecheapand non-invasivenatureofthismethodandbecauseoftheeasewithwhich anybodycantakesampleswithouttheneedofgoingtoamedical facilityeachtime.Itisa“simple,relativelyinexpensiveand poten-tiallymobilebrainimagingtechnology”[201]butfurtherresearch isneededforEEGtobeincludedinaclinicalADdiagnosis.

3.3.12. Magnetoencephalogram(MEG)

MEGisanon-invasivemedicalimagingtechnology.MEG iden-tifiesthebrainactivitybymeasuringthemagneticfield created by theelectric current flowing withintheneurons. Thus, mea-surementsfollowasimilarprincipletotheonesobtainedbyEEG becausebothmeasurethesamesourcesofbrainactivity.AMEG scannerisneededforthisimagingpurpose.

MEGfindingsrelatedtoADaresimilartothoseofEEG.Increased delta and theta activity [202–205] in frontal and central areas

[206] and decreased alpha activity in posterior and temporal regions[206]hasbeenreportedinseveralpiecesofresearch,i.e. slowersignals.A generalizedlossof functionalinteractions (i.e. decreasedsynchrony)hasalsobeenfound[202,207,208].People withMCIhave alsobeeninvestigated withMEG, verifyingthat theirsymptomsaresomewherebetweenthoseofADandcontrols

[203,209,210].

MEG hasbeenless studied thanEEG, probably,becausethe resultsobtaineduptodatewiththismethodsuggestthatithasa lowerdiscriminativepotentialthanEEG.Gómezetal.havehighly contributedtotheuseofthesesignalsonADdiagnosis, publish-ing severalresearches based onit. Theyhave analysed several MEG features’ ability to discriminatebetween AD patients and healthycontrols,includingSampleEntropy(SampEn)and Lempel-Ziv complexity(LZC)[211,212],Shannonspectralentropy(SSE), approximate entropy(ApEn), Higuchi’s fractaldimension(HFD)

[213],Maragos and Sun’s fractal dimension (MSFD)and Cross-approximateentropy(Cross-ApEn)[214].Theyhaveconcludedthat MEGreallyhasthepotentialtodiscriminatebetweenthesetwo groups,astheyhaveachievedaccuraciesof70.83%usingthe Cross-ApEn,77.42%withSSE,87.8%inthecaseofHFDand85.37%with SampEnandLZCparametersintroducedtoanANFISclassifier. Nev-ertheless,forthebestofourknowledge,noresearchhasanalysedif theresultsremainsohighwhenanMCIgroupisconsidered,which couldbeinterestingtoanalysethepredictabilitytoADandearly detectionpotentialofMEGsignals.

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MEGcanbedonewithoutplacinguncomfortableelectrodesand itislessaffectedbyconductivityissuesrelatedtotheskullandscalp

[215],theydonotrequireareference,theyarelessaffectedby vol-umeconduction,andfurthermore,theycanobtainmoresensitive measurementsofthecorticalactivitythanscalpEEG.The disadvan-tageofMEGistheinterferencethatEarth’smagneticfieldorthe electricaldevicescanintroduce,someasurementsmustbedone inaheavilyshieldedroomwithalltheelectricaldevicesaround switchedoff,whichcomplicatesitsuseaspartofaglobalroutine monitoringsystem.Furthermore,resultsshowthattheaccuracy obtaineduptodatewithMEGdoesn’treachtheoneobtainedby EEGs.

3.3.13. Eyedynamics

Ithasbeenhypothesizedthat“thepatternofAD-specific neu-rodegenerationmayaffectneuralcircuitryoftheeyemovement systemin a uniquemannerthat allows theclinical differentia-tionofADfromothercognitivedisorders”[216].Inordertoverify thishypothesis,eyemovementsofADpatients havebeen com-paredtothoseofhealthysubjectsinmanystudiesandeffectively, ithasbeenproventhatADpatientssufferfromchangesin ocu-lomotorandpupillaryfunctions[217].Moreprecisely,changesin saccades,smoothpursuitfunctionandinthepupillaryresponse havebeenfoundbysomeresearchers.Saccadesare“rapid, conju-gatemovementsoftheeyes,whichservetoorientthehighacuity fovealregionoftheretinaontoaspecificregionofvisualspace”

[218]. Saccadicmovementscan bedistinguishedintothree dif-ferenttypes: prosaccades,which areeyemovementstowardsa target,antisaccades,whicharemovementsintheopposite direc-tionofatarget,andfinally,microsaccadesandsaccadicintrusions, which are minuscule eye movementsthat happen during fixa-tion.Itisthoughtthatsaccadesareofparticularinterestbecause theyareveryrelatedtoattentionandthus,theyarelikelytobe disturbedbycognitiveimpairmentsassociatedwith neurodegen-erativedisorders suchasAD,aswellasbydysfunctions related purelytooculomotorexecution[219].Allthesebehaviourscanbe measuredeasilyandinanon-invasivewayinalaboratory,making thepatientscarryoutspecifictaskslikethereflexiveparadigm,the memory-guidedparadigm[219],thegap/overlaptaskorthe anti-saccadetask[220]whiletheireyesarebeingtrackedbycamerasor infraredsystems[221]andimageprocessingtechniques.Reading taskshavealsobeenused[222].

Some researches have revealed that AD patients show higherlatencythanhealthy subjectswhenstarting prosaccades

[223–226],thatthevelocityoftheseprosaccadesis lower[226]

andthattheaccuracywhenreachingthetargetalsoworsens[220]. Antisaccadeshavealsobeenanalysedinsomeresearches.Ithas beenfoundthatinthiscasetoo,latencyincreasesinADpatients comparedtohealthypeople,andthatthenumberofincorrect sac-cadestowardthetargetincreaseswhilethenumberofcorrections aftertheerrordecreases[220,223].Peltschetal.[227]compared aMCI,mildADandhealthypeople’santisaccadesconcludingthat aMCIandmildADpatientsshowedverysimilarperformanceinthe antisaccadetask.RelatedtoADpatients’microsaccadesand sac-cadicintrusions,ithasbeenfoundthattheyaremuchmoreoblique, frequentandgreaterinamplitude[223]thanhealthypeoples’ones. Hence,theirgaze-fixationismuch moreunstable.Nevertheless, disagreementsexistwiththeseresults.Somehavenotfound dif-ferencesinspeedandaccuracyofADpatients’saccadicmovements

[224],neitheringaptests’results.Smoothpursuitsareslowereye movementsthatservetokeepanobjectfoveatedifitmovesacross ourfieldofvision[218].IthasbeenfoundthatADpatients’smooth pursuitfunctionisaffectedinasimilarwayassaccadicfunctions, i.e.theyshowincreasedlatencyanddecreasedvelocity,velocity gain(pursuitvelocity/targetvelocity)andinitialacceleration.They alsotendtoanticipatetothetargets’movement,resultinginmore

compensatorysaccades.Pupillaryresponseshavealsobeenstudied bysomeresearches,andtheyhavefoundoutthattheseresponses showagreaterlatency,andsmalleramplitude,velocityand accel-erationinADpatientsthatinhealthypeople[228].

Despitethepoweroftheeyedynamics’forADdetection,no manyresearchershavetriedtodevelopanautomaticADdetection systemusingeyemovementfeaturesandmachinelearning tech-niques.Lagunetal.[221]areabouttheonlyonestakingadvantage ofthesebiomarkersforADrecognition.Theyusedthevisualpaired comparisontask,whichconsistsonidentifyingtheeyemovement patternofthepatientswhileintroducingnewvisualstimulus.It hasbeenseenthatcontrolpatientsspend70%ofthetimelooking atthenewstimuluswhereasMCIpatientsspendthesametime lookingattheoldandnewpictures,suggestingthattheyarenot familiarizedwithanyoneofthem[229].Theyusedtheseandother eyedynamics’featuresandanSVMclassifier,achievinganaccuracy of86.9%distinguishingbetweenMCIandcontrolpatients.

The problemwiththese measurementsis that these abnor-malitiesarenotalwayspresentinADpatientsandfurthermore, theyarenotuniquetothem[219].Moreover,itisnotclearifMCI patientsalsoshowsignsoftheseabnormalitiesbecausewhilesome refusethisfact[224],othershavefoundsomeevidences[220,225]. Crutcheret al. [229] have alsofound that someMCI preferred thenewimagesthesameasthecontrolpatients,demonstrating thevariabilitybetweenpatients’patterns.Consequently,further researchis needed toverify if theymight beused both as AD biomarkersandaspredictorsintheearlystages.

3.3.14. Summary

The great effort beingmade in Alzheimer’s research in the lastyearshasallowedtoidentifymanyAD-relatedphysiological biomarkerssothattheADdiagnosisisbecomingmoreandmore accurate.Table1summarizesthephysiologicalbiomarkersofAD thathavebeenfoundintheliterature.

As it has been seen in this section, many types of signal processingtechniquesandimagingmodalities havebeentested soastofindoutthebestsignalsand featuresthatcanbeused todiagnoseADautomatically,oratleasttoassistthespecialistin

Table1

PhysiologicalADbiomarkersoftheliterature. Method Biomarkers

CSF Aˇ42↓,Aˇ40,Aˇ42/Aˇ40↓,totaltau↑,p-tau231↑, p-tau181↑,p-tau199↑,[p-tau231,p-tau181,

p-tau199]/Aˇ42↑,Isoprostanes↑,˛1-antichymotrypsin, Interleukin-6,markersofinflammation,amountofCSFin thehippocampus↑

Blood Aˇ42,Aˇ40,Aˇ42/Aˇ40,˛1-antichymotrypsin,various markersofinflammation,IsprostanesandInterleukin-6, APP,ADAM10,BACE

CT Brainproblemsotherthandementia MRI MTL’satrophy,vasculardamage

fMRI ActivityintheMTL↓,prefrontalcortex↑,capacityof deactivationinPMC↓

MRSI NAA↓,NAA/Cr↓,mI/Cr↑,Cho/Cr↑,Glu↓,NAA/mI↓ TCD CarotidIMT↑,totalCBF↓,CVRC↓,MFV↓,PI↑,cerebral

microenbolization↑ DTI MD↑,FA↓

PET ˇ-Amyloidplaques↑,glucoseconsumption↑,anomaliesin neurotransmittersystems,inflammationintheCNS↑ SPECT CBF↓,CSF↑,perfusion↓

MEG/EEG Deltaandthetaactivity↑,alphaactivity↓,complexity↓, synchrony↓

Eyedyn. Prosaccades’andantisaccades’latency↑,velocity↓and accuracy↓,no.ofincorrectsaccades↑,no.ofcorrections↓, obliquity↑,frequency↑andamplitude↑ofmicrosaccades andsaccadicintrusions,gaze-fixations’stability↓, anomaliesinsmoothpursuitfunction,pupillaryresponses’ latency↑,amplitude↓,velocity↓andacceleration↓

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theharddecisionofthediagnosis.Table2showsthesignalsand imagingmodalitiesusedintheliteraturewiththeirrespective fea-tures.Nevertheless,muchlessworkhasbeendonerelatedtothe automaticearlydiagnosisofMCIandAD,whichisthekeyforthe preventionofdementia’sprogression.

Table3showsthebestaccuraciesachievedinthestateofthe artdistinguishingADpatientsfromCTLsusingphysiological sig-nalsandimages,whileTable4isitsequivalentforMCIdiagnosis and Table5 forMCI andAD discrimination.Theresultsforthe threeclassificationproblemsareverypromising.Itisevidentthat

Table2

Physiologicalfeaturesusedintheliterature.

Method References Features Parameters MRI [135–137,113,106,28,56,

26,112,30,105]

GMvoxelvalues Mean,SD

GMvolume Voxellocation,PCAeigenbrains,PLSa_-brains,_sICA_basis

functions

WMvolume PCAeigenbrains,PLS-brains GM+WMvolume PCAeigenbrains,PLS-brains

Hippocampalvolume Totalvolume,CHF(visualfeatures),CSFvolume Totalbrainvolume PCAeigenbrains,GMvolume/totalvolume Corticalthickness AveragewithinaROI

MBLb_features _Coordinates

Atrophicvoxels averageJacobianwithinaROI

MTLvolume Grey-leveldata,volumechangeestimation 35ROIs Structuralthickness,contourarea,volume,structural

curvature

fMRI [134–137,173] Headmotion Pathlength(see[230])

Activationpatterns No.ofactivatedvoxels,maxz-score, sizeoftheclusterwherethemax z-scorebelongsto,no.ofsignificant clusters,%ofactivatedregionsthat belongtoaROI,totalactivationofROIs, atlasbasedROIs’clusteringcoefficients offunctionalconnectivitynetworksof severalfrequencysub-bands BOLDresponse Amplitude,undershootandtransittime

CBF Amplitude

Venousvolume Amplitude Vascularsignal Amplitude DeoxyHbsignal Amplitude

Brainnetworkgraph Degree,participationcoefficient,betweennesscentrality,local efficiency,local/globalefficiency

DTI [174,19,173] FA Mean

MD Mean

Axialdiffusion Mean Radialdiffusion Mean Fibreconnectivitynetwork

PET [82–86] Intensityofthewholebrain(VAFc₎ _Eigenbrains_(PCA),_ICA_based_features,_LDA_projections,₂₂

ROIs,averageof48ROIs IntensityofVOId _Eigenbrains_(PCA)

SPECT [97,86,84] Firstorderhistogram Mean,variance,entropy

Co-occurrencematrix Angularsecondmoment,contrast,inversedifferencemoment, entropy,correlation

SOIs(slicesofinterest) Eigenbrains(PCA) Voxelsofinterest Eigenbrains(PCA) VOIs NMSEe_features

EEG [196,35,199,197] Delta,theta,alpha,betaandgammabands’spectrum powerdensities,totalspectralpower,specificspectralpower ratios(see[35]),coherencebetweenseveralcombinationsof pairsofelectrodes,peakalphaband,spectralpeaksof biauricularreferences,spectralpeaksofbipolarreferences, medianfrequency,spectralentropy

TemporalsignalsofFp1,Fp2,F7,F3,Fz,F4,F8,T3,C3, Cz,C4,T4,T5,P3,Pz,P4,T6,O1,O2

ConnectionweightsderivedfromIFASTmethodology,activity, mobility,complexity,SampEn,LZC

Firstderivative Totalspectralpower,peakalphabandfrequency,median frequency,spectralentropy,SampEn,LZC

MEG [211–214] Temporalsignals SampEn,LZC,ApEn,MSFD,Higuchi’sfractaldimension(HFD), cross-ApEn

Spectrum SSE

Eyedyn. [221,229] Pupildiameter SD/meanduringtestsandoutsidethem,pupildilatationin tests((meantest−meanfam)/meantest)

Fixations Medianduration,meanre-fixationdepth,totalduration,total no.offixations,totalfixationtime,noveltypreference Saccades Orientation

a_Partial_Least_Squares. b_{Manifold-based}_learning. c _{Voxels-as-features.} d_Voxels_of_interest.

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Table3

AccuracyratesreportedintheliteratureforADvsCTLdiscrimination. Reference Signal Accuracy(%) [105] MRI 98.95 [86] PET 98.3 [94] SPECT 98.3 [40] Speech 97.7 [134] fMRI 97.5 [98] SPECT 96.91 [35] EEG 96.9 [84] SPECT 96.7 [82] PET 94.6 [83] PET 94.12 Table4

AccuracyratesreportedintheliteratureforMCIvsCTLdiscrimination. Reference Signal Accuracy(%) [198] EEG 97.88 [35] EEG 96.8 [173] DTI+fMRI 96.3 [196] EEG 93.48 [19] DTI 92.9 [105] MRI 90.64 [221] Eyedyn. 87 [28] MRI 85.4 [56] MRI 84 [83] PET 82.05 Table5

AccuracyratesreportedintheliteratureforMCIvsADdiscrimination. Reference Signal Accuracy(%) [198] EEG 94.05 [19] DTI 92.785 [136] fMRI 93 [197] EEG 92.33 [35] EEG 90.9 [105] MRI 87.3 [135] fMRI+MRI 87 [195] EEG 85.98 [82] PET 81 [115] MRI 83.78a [28] MRI 78.92 a_In_females.

diagnosticratesinreallifeusingthesemethodswouldnotbeas highastheonesthathavebeenachievedinlaboratoryexperiments becauseinreallifethereismuch morevarietyofdiseases, and probably,alsomuchmorevariationintheprogressionof demen-tia.However,theseresultsverifythatautomaticsignalandimage processingmethodshavethepotentialforbothADdiagnosisand earlyADdiagnosis(orMCI)whentheyarecombinedwithother methods.

Nonetheless,mostoftheanalysedneuroimagingmethodsstill lacktohaveestablishedvalidity,sensitivity,specificity,predictive value,repeatabilityandconcordance[231]tohaverealdiagnostic value.Variabilityinparticipantselection,methodological incon-sistency and useofdifferent acquisitionprotocolsbetweenthe differentresearches arethe maincauses forthis problem[59]. Thereby,furtherresearchisrequiredinordertoovercomethese barriersandestablishavalidearlydiagnosismethodbasedon phys-iologicaldata.

3.4. Behaviouralresponses

Althoughbehaviouralchangesarenotlessimportantthan phys-iologicalones,theyhavebeenmuchlessconsideredinADdetection research.Sometestsandscaleshavebeendevelopedinorderto assess patients’ autonomy tocarry out everyday activities, but

muchlesshasbeendonetowardsanautomaticdetectionsystem. Thefollowinglinessumupthebehaviouralassessmentmethods forADpatients.

3.4.1. Generalbehaviourassessmenttests

Behaviouralchanges sufferedby ADpatients mightbe mea-suredbymeansofquestionnairesortests.Thesetoolsmayhelpthe patienthimselfandhisrelativestotakeconscienceoftherealstate ofthedisease.TestsliketheBehaviouralPathologyinADRating Scale(BEHAVE-AD)[232],theBriefPsychiatricRatingScale[233], theBehaviorRatingScaleforDementiaoftheConsortiumto Estab-lishaRegistryforAD[234],NeuropsychiatricInventory(NPI)[50]

andtheDementiaBehaviourDisturbanceScale[235]are question-nairesusedtomeasurethebehaviouralabnormalitiesthattheAD patientscanundergo[49].

Nevertheless,asdo allassessmentsbasedonquestionnaires, theyhavesomedrawbacks,includingthesocommonbelated diag-nosis.

3.4.2. ADLscales

Testswiththemainobjectiveofmeasuringtheprogressofthe dementiasbyanalysingtheabilitiesofthepatientsforcarryingout typicaldailyactivitieswithnormalityhavebeendesigned.These testsofferadditional informationtotheone givenbycognitive tests,becauseeven ifa patienthasachievedquiteencouraging results,hemayhaveproblemsintegratingvisual,motorand cog-nitiveskills,makinghimperformpoorlyinADLs.Often,thereal stateofthedementiacanbebetterassessedandthelevelof sup-portneededcanbemuchbetterunderstoodseeingtheminaction andrecordingthelevelofcognitivesupportrequiredtocomplete acertaintasksuccessfully[236].Hence,ADLscanbeassessedboth bymeansofquestionnairesandbyspecificallydesignedtasks.

Sometestsarebasedonthemostbasicactivities(ADL),like feed-ing,walkingordressing,whileothersmeasuretheabilitiesformore complextasks,calledinstrumentalactivities(IADL)[49].Examples ofIADLarecooking,taskswhichinvolvetheuseofmoney,etc. KatzIndexofADL[237]andtheADCS-ADL19[238]testmeasure theformerkindofactivitieswhiletheADCS-ADL23[238]issuited fortheIADLactivities.Otherquestionnairesandinterviewslikethe DisabilityAssessmentforDementia[239],Interviewfor Deteriora-tioninDailyLivingActivitiesinDementia[240]ortheFunctional AutonomyMeasurementSystem[241]alsoservetomeasurethe behaviourregardingtheADLactivities.

Amongthespecifictasksthatallowtoevaluatetheabilitiesof thepatientinvivo,themostwellknownisprobablythekitchentask assessment[236].Itisafunctionalmeasurethataimstoevaluate theprocessingskillsofinitiation,organization,inclusionofallsteps, sequencing,safetyandjudgement,andcompletionof acooking taskformeasuringthecognitiveaspectsofperformancebymeans ofbehaviour.

3.4.3. Smarthomes

Asmarthomeisaregularhomethathasbeenaugmentedwith varioustypesofsensorsandactuators[242],beingitsmain objec-tivetoovercomethecognitivedisordersofpeopletoenhancetheir autonomy[243].

Sensorized devices and environments allow to capture the actionsoftheresidentswhileactuatorscanserveforautomation orforprovidingcomfort,makingtaskseasierorfinishingthetasks thathavenotbeenaccomplishedbythepatients,forexample,for securityreasons(turnofftheovenafteracertaintime).Prompts orsuggestionscanalsobemadetorecalltothepatientshowto continueaninterruptedtaskandtoprovidethempunctual assis-tancewhenneeded[244].Alltheseactionsshouldbecarriedoutin anon-intrusive[245]andtransparentway,respectingtheprivacy ofthepatients,tomakeiteasierforadultstoacceptthistechnology