1 Douglas Mental Health Research Institute, McGill University, Montreal, QC, Canada; 2 Northwestern University, Chicago, US; 3 Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, US * These authors contributed equally to the work
Prediction of brain age using resting-state functional connectivity reveals accelerated aging in the preclinical phase of autosomal dominant Alzheimer’s disease, irrespectively of amyloid pathology
Usingresting-statefMRIgraphmetrics,wedevelopedamodelthatcanpredictbrainageacrossthewholelifespan.ApplyingthismodeltopredictbrainaginginthecontextofpreclinicalADrevealedthatthepresymptomaticphaseofADADischaracterizedbyacceleratedfunctionalbrainaging.Thisphenomenonisindependentfrom,andmightthereforeprecede,Aβaccumulation.
InindividualsatriskofsporadicAD,neitherAPOE4genotypeorAβburdenwasassociatedwithacceleratedbrainaging.
FurtherstudieswillbeneededtobetterunderstandthefactorsmodulatingacceleratedfunctionalbrainaginginthecontextofpreclinicalAD.
Overlaps exist between the neural systems vulnerable to aging andAlzheimer's disease(AD). It is a matter of debate whether aging and AD progression are independentphenomena.We aimed at developing amodel able to predict brain aging from resting-statefunctionalconnectivity(rsfMRI).Wethenusedthedifferencebetweenthepredictedageand thechronologicalage to testwhetherpresymptomaticautosomaldominantAD(ADAD)mutationcarriershaveprematureaging(DIANcohort).Wealsotestedifthebeta-amyloid(Aβ)status(positiveornegative)contributestothediscrepancybetweentheageestimated from brain functional connectivity and the actual age. We repeated theseanalyses inasymptomatic individualsat riskof sporadicAD,assessingboth theeffectofAPOE4genotypeandAβburden(PREVENT-ADcohort).
Funding
Julie Gonneaud,1* Etienne Vachon-Presseau,1,2* Alexis Baria,1 Alexa Pichet Binette,1 Tammie Benzinger,3 John Morris,3 Randall Bateman,3 John Breitner,1 Judes Poirier,1 and Sylvia Villeneuve1 for the Alzheimer’s Disease Neuroimaging Initiative, the Dominantly Inherited Alzheimer Network and the PREVENT-AD Research Group
NeuralNetworkdevelopment
Modelperformance–BrainAgepredictionagainstactualageacrossdatasets
Background and objectives Brain Aging in the preclinical phase of AD
Summary and conclusions
Brain Age Predictive Model
Increase in the number of features andarchitecture complexity introducedoverfitting(i.e. lowestrootmeansquareerrorinthetrainingsetbuthighesterrorinthevalidationset).Themodelusing10featuresand2layersof 5 and 2 nodes was the one showingthebetter generalizability (i.e. providingthe lowest error in the validation set)andwasselectedforthefinalmodel.
Regression tree ensembles1.00.80.60.40.20.00
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Rootmeansquareerrorinthedifferentsetsasfunctionofthenumberoffeaturesandnetworkarchitecture
Features(i.e.graphmetrics)rankedbyimportance
Cohorts Trainingset
Validationset
Testingset
DIANmut.noncarriers 105[19-69yo] - 30
[18-61yo]
DIANmutationcarriers - - 128[20-58yo]
PREVENT-AD 36[55-78yo] - 257
[55-84yo]
CamCAN 602[18-87yo] - 100
[18-88yo]
ADNI 30[65-90yo] - 15
[66-94yo]
ICBM - 47[19-79yo] -
Totalsample 773[18-90yo]
47[19-79yo]
530[18-94yo]
Datasets.Samplesize[age-range]
Actual Age (years)100806040200
Pre
dict
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ge (
year
s)
100
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Actual Age (years)100806040200
Pre
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ge (
year
s)
100
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Trainingset Validationset(ICBM)
1)Sub-graphcentrality2)Positive-weightedclusteringcoefficient3)Binarizedassortativity4)Weightedmodularitycoefficient5)Smallworldness6)Flowcoefficient7)Betweennesscentrality8)Negative-weightedparticipationcoefficient9)Positive-weighteddiversitycoefficient10)Negative-weightedclusteringcoefficient11)Eigenvectorcentrality12)Binarizedefficiency13)Weightedassortativity14)Negative-weightedgatewaycoefficient15)Binarizedclusteringcoefficient16)Resilience17)Binarizedmodularitycoefficient18)Participationcoefficient19)Negative-weighteddiversitycoefficient20)Positive-weightedparticipationcoefficient21)Characteristicpathdiameter22)Characteristicpatheccentricity23)Characteristicpathradius24)Positive-weightedgatewaycoefficient25)Characteristicpathlambda26)Characteristicpathefficiency
DIANmutationnoncarriers
DIANmutationcarriers
Samplesize 30 128
ActualAge 39.07±11.39 34.27±9.56*
PredictedAge 36.92±15.61 42.36±14.43t
DIANmutationnoncarriersAβ-
DIANmutationcarriersAβ-
DIANmutationcarriersAβ+
Samplesize 28 77 40
ActualAge 38.86±11.74 33.01±8.96** 38.20±9.39††
PredictedAge 37.89–15.43 40.83±12.89 45.68±16.30*†
PREVENT-ADAβ-
PREVENT-ADAβ+
Samplesize 51 11
ActualAge 62.82±4.25 65.45±6.22
PredictedAge 68.89±6.07 70.09±3.09
PREVENT-ADAPOE4noncarriers
PREVENT-ADAPOE4carriers
Samplesize 148 108
ActualAge 63.93±5.75 63.05±4.78
PredictedAge 68.96±5.14 68.46±5.01
Authors have no disclosure. Acknowledgments: We would like to thank all the participants, the collaborators from the DIAN and PREVENT-AD studies and all members of the Villeneuve lab who contributed to this study.
contact : [email protected] | poster downloadable @ villeneuvelab.com
Model was built on the training set, optimized based on itsgeneralizability in the validation set and, finally, the model’spredictionswereanalyzedinthetestingset.
AregeneticmutationandAβburdenassociatedwithacceleratedbrainaginginpreclinicalADAD?
AregeneticriskfactorandAβburdenassociatedwithacceleratedbrainaginginindividualsatriskofsporadicAD?
R2=0.53rmse=14.01
R2=0.49rmse=13.84
R2=-.23rmse=16.88
Acutal Age (years)100806040200
Pre
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ge (
year
s)
100
80
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R2=0.36rmse=13.02
Predicted age was overestimated inDIAN mutation carriers while it wasunder-estimatedinnoncarriers.
The overestimation inmutation carrierswas not associatedwithAβstatusorload.
Predicted age was overestimated in thePREVENT-ADwithnodifferencebetweenAPOE4 carriers (the main genetic riskfactorofsporadicAD)andnoncarriers.
Differentfrommutationnoncarriersattp<.10,*p<.05,**p<.01;DifferentfrommutationcarriersAβ-at†p<.05,††p<.01,ns:notsignificant
amyloid_positivity10
dif'
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mutation/apoe10
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Group_amyloid_mutMUT_posMUT_neg1_NC_neg
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Predicted age overestimation in the PREVENT-AD did notdiffereitherbetweenAβpositiveandnegativeindividuals.
MutationarriersAβ-
groupDIAN_mut1_DIAN_n
dif'
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PIB_fSUVR_TOT_CORTMEAN2,22,01,81,61,41,21,0
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amyloid_index3,02,52,01,51,0
dif'
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F2,142=8.44p=.004
p=.06
p=.92
F1,60<1P=0.57
Aβ- Aβ+ APOE4carriers
Mutationnoncarriers
Aβ-
MutationcarriersAβ+
Mutationnoncarriers
Mutationcarriers
60
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-20
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Pred
ictedAg
eDifferen
ce p p=.03 r=-.12
p=0.22
r=-.11p=0.45
F1,254<1p=.97
APOE4noncarriers
Pred
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eDifferen
ce
30
20
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-10
-20
-30
Pred
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MeanneocorticalAβ[PIB-PET]
MeanneocorticalAβ[NAV4964-PET]
1.01.21.41.61.82.02.2
1.01.52.02.53.0
DIANmutationnoncarriersDIANmutationcarriersPREVENT-ADCamCANADNI
Testset
Training set
5 10 15 20 25
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5 10 15 20 25
Number of features
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Null set
5 10 15 20 25
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Neu
ralNetworkArchite
cture
Rootmeansquareerror(rm
se)
Participants and Methods Resting-state functional magnetic resonance imaging (rsfMRI) scans were collected in 1,350cognitivelynormalparticipantsfrom18to94yearsoldprovidedbytheDIAN,PREVENT-AD,Cam-CAN,ADNI,andICBMcohortstotrainandtesta“BrainAge”predictivemodel.
CohortsDominantlyInheritedAlzheimerNetwork isamultisite longitudinalstudywhichenrolls individualsaged18andolderwhohaveabiologicalparentthatcarriesageneticmutationresponsibleforautosomaldominantAD(ADAD).Cognitivelynormalmutationcarriersandnoncarrierswereincludedinthepresentstudy.Pre-symptomaticEvaluationofExperimentalorNovelTreatmentsforAlzheimer’sDiseaseisamonocentriclongitudinalcohortwhichincludescognitivelynormalolderindividualsaged55andolderwithafamilyhistoryofsporadicAD.Cambridge Centre for Ageing and Neuroscience is a large-scale monocentric research project includingcognitivelynormalindividualsaged18to88yearsold.Alzheimer’sDiseaseNeuroimagingInitiativeisamultisitelongitudinalstudywhichenrollscognitivelynormalandimpairedolderindividuals.Onlycognitivelynormalolderadultswereincludedinthepresentstudy.InternationalConsortiumforBrainMappingisamultisitestudy.Cognitivelynormalindividualsaged19to85formtheMontreal’ssitearchivedinthe1000FunctionalConnectomesProject’srepositorywereincludedinthepresentstudy.
Resting-statefunctionalMRI(rsfMRI)
Resting-statescanswereallpreprocessedwithNIAK(http://niak.simexp-lab.org/)
Averaged BOLD signals were extracted using thePowerandPetersonparcellation(Poweretal.,2011).Some ROIs were removed due to low coverageresultingina238x238Pearsoncorrelationmatrix.
26graphmetricswerethenextractedusingtheBrainConnectivityToolbox(Rubinov&Sporn,2010;https://sites.google.com/site/bctnet/)
Amyloid(Aβ)Positronemissiontomography(PET)
AβscanswereacquiredusingC11-PIBinDIAN(N=145)andF18-NAV4694inPREVENT-AD(N=62).Standardized uptake value ratios (SUVR; reference region:cerebellum grey matter) were averaged across frontal,temporal,parietalandposteriorcingulatecorticestoobtainaglobalindexofAβburden.
NeuralNetworkwastrainedtopredictage
NeuralnetworkswereconstructedusingMatlab(https://www.mathworks.com/products/deep-learning.html)
PIB-PETNAV4964-PET
MeanneocorticalAβextraction
