AnRDoC frameworkintegratingstructuralMRIwithcognitiveControlandWorkingMemory
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ArianaE.Anderson ,MaxwellMansolf ,AnthonyRodriguez ,StevenP.Reise ,SpencerFrei ,Levon
Demirdjian
3,RobertM.Bilder 1
(1)DepartmentofPsychiatryandBiobehavioral Sciences,(2)DepartmentofPsychology,(3)DepartmentofStatistics
UniversityofCalifornia,LosAngeles
INTRODUCTION
Thegrowthofbiologicalpsychiatryoverthelast50yearshas
increasedaccessibilityofmeasuresatmultiplelevelsfromthe
genomicthroughneuroimagingofbraincircuitstructureandfunction
tosophisticatedassessmentofcognitiveandbehavioralprocesses.
TheRDoC initiativeaimstoleverageourcapacitytoassessthese
underlyingdimensionswiththeaimtoprovidestrongerbasesfor
biologicalpsychiatry.Here,weidentifystructuralMRIcorrelatesof
workingmemory(visualandspatial)andcognitivecontrol(response
inhibition)usingBayesiannetworksin278subjectswithandwithout
aDSMdiagnoses.
Units of Analyses
DISC1
...
DRD1
non.WM.hypointensities
HOPKINS_INTSENSITIVITY
SWM_G
ADHD
BP
Control
SZ
SubCortGrayVol
SCHOOL_YRS
RI_G
Right.Thalamus.Proper
VWM_G
HOPKINS_OBSCOMP
n
Age
Gender(%Male)
Education(years)
SomatizationScore(Hopkins
ObsessiveCompulsive(Hopkins)
42.00
32.53
0.45
14.63
0.35
1.18
48.00
35.49
0.42
14.61
0.66
1.18
133.00
31.34
0.48
15.03
0.22
0.50
55.00
36.03
0.24
12.69
0.63
1.05
InterpersonalSensitivity(Hopkins)
Depression(Hopkins)
Anxiety(Hopkins)
ResponseInhibition(g)
VisualWorkingMemory(g)
SpatialWorkingMemory(g)
ChapmanPerceptionAberration
ChapmanSocialAnhedonia
ChapmanPhysicalAnhedonia
0.75
0.66
0.44
0.01
0.18
0.18
4.71
13.40
13.59
1.11
0.97
0.75
0.03
0.11
0.12
6.04
16.46
15.83
0.40
0.37
0.21
-0.28
0.28
0.37
3.14
9.62
11.47
0.94
0.84
0.84
0.37
-0.27
-0.20
8.93
15.14
17.41
Left.Amygdala
CSF
Left.Hippocampus
HOPKINS_ANXIETY
CHAPPER_TOTAL
Left.Caudate
Left.Pallidum
CHAPSOC_TOTAL
Right.Hippocampus
HOPKINS_SOMATIZATION
Right.Pallidum
CHAPPHY_TOTAL
Table1:ThefollowingmeasureswereusedinaBayesian networktoidentifythehierarchicalrelationshipaboutstructuralMRImarkers,
demographicfeatures,diagnosis,andworkingmemoryandcognitivecontrol.
Genes
Right.Caudate
Left.Accumbens.area
PPC
DLPFC
Total
Gray
Volume
...
CortexVol
Circuits
WM
CC
ADHDSelf-ReportScaleASRS-V1.1
X
X
AdultADHDInterview(modulefromKSADS-PL)
X
X
StructuredClinicalInterviewforDSM-IV,Axis1,PatientVersion
X
X
Left.Putamen
Screening/Diagnosis/ClinicalRatingScales
YoungManiaRatingScale
SCAP
SMNM
...
WMS-IV
Symbol Span
Right.Putamen
X
ScaleforAssessmentofNegativeSymptoms
X
ScaleforAssessmentofPositiveSymptoms
X
BriefPsychiatricRatingScale
X
Right.Accumbens.area
Brain.Stem
Personality/Temperament/SymptomQuestionnaires
...
Latent Specific
Factor k
X
Akiskal'sBipolarIIScale
X
BarrattImpulsivityScale
X
Eysenck'sImpulsivity,VenturesomeandEmpathyInventory
X
MPQ(Control-Impulsivityitems)
X
TheDickmanScaleofFunctionalvsDysfunctionalImpulsivity
X
ChapmanScales(RevisedPhysicalAnhedonia;RevisedSocialAnhedonia;PerceptualAberrations
METHODS
Right.Cerebellum.White.Matter
Left.Cerebellum.Cortex
X
VerbalMemoryandManipulationTask
X
SpatialMemoryandManipulationTask
X
VerbalWorkingMemoryCapacityTasks
X
SpatialWorkingMemoryCapacityTasks
X
WMS-IVSymbolSpan
X
WMS-IVDigitSpan
X
WMS-IVLetterNumberSequencing(LNS)
X
Right.Cerebellum.Cortex
TotalGrayVol
AGE
Stop-SignalTask
X
CNPCPT
X
ReversalLearning
X
ColorTrailsTest
X
TaskSetSwitching
X
StroopTest
X
AttentionNetworksTask(ExecCenter)
X
DelayDiscounting
X
BalloonAnalogRiskTask
X
lhCorticalWhiteMatterVol
rhCorticalWhiteMatterVol
Right.Amygdala
NeuroImagingMeasures
FunctionalMRI(FunctionalNetworkConnectivityfromICA-derivedbrainnetworks[seetext])
X
Physical Anhedonia (Chapman) and Left Cortical White Matter Volume
Verbal Working Memory and Right Pallidum Volume
Depression and Cortical Volume
Somatization and Left Cerebellar Cortical Volume
X
90000
300000
StructuralMRIvolumes:(totalbrain,totalcortical,DLPFC,VLPFC,PPC,totalgrey/whitecorticaland
subcortical,Ventrofronto-striatal,hippocampal,thalamic,globus pallidus)
X
X
6e+05
80000
Genotyping
OmniExpress
X
X
2000
Table2:Units ofanalysesavailableintheCNPdata
RESULTS
DX
5e+05
ADHD
BP
Control
SZ
1500
250000
DX
DX
ADHD
ADHD
BP
BP
Control
Control
SZ
SZ
70000
DX
ADHD
BP
60000
Control
SZ
50000
200000
HomegeneousItemParcels
Carelessness
Haphazardness
BlindActions
G
F1
F2
F3
F4
F5
Overspending
SpendVsSave
BuyingSpendingSprees
AvoidSnapDecisions
AvoidSimpleSnapDecisions
QuickDecisions
4e+05
p2
0.52
0.48
0.48
0.44
0.76
0.77
0.23
0.26
0.40
0.69
0.64
0.36
0.25
0.68
0.50
0.50
0.07
0.48
0.27
0.73
0.07
0.61
0.40
0.60
0.05
0.38
0.34
0.28
0.72
0.53
0.56
0.44
0.51
0.49
0.61
0.58
0.41
0.54
0.46
0.60
0.32
0.57
0.44
0.56
0.22
0.35
0.60
0.50
0.50
0.25
0.36
0.85
0.85
0.15
0.15
0.30
0.74
0.64
0.36
0.14
0.33
0.79
0.74
0.26
0.15
0.37
0.35
0.29
0.71
0.48
Table3:Bifactor analysesofself-reportparcels,yieldingasinglecommondimensionand5specificfactors.h2iscommunality,u2isuniquevariance,andp2ispercentofcommonvariance
attributabletothegeneralfactor.Loadings<.30notshown.
40000
1000
0
0.0
ThrillSeeking
Deliberation2
u2
0.47
Activities2
Organization
h2
0.51
SeekFrighteningThings
Deliberation1
StructuralMRI,demographicinformation,andmeasuresofworking
memory,responseinhibition,anhedonia(Chapmanscales),and
otherdimensionsoftheHopkinssymptomchecklistFinally,these
measurementswereintegratedusingaBayesiannetwork,where
themostlikelyrelationshipspanningalldimensionswereassessed
withoutassuminggivenhierarchyorcausality.Thisnetworkwas
learnedusingthescore-basedmethod,connecting44nodeswith
133 arcs,fitusingtheBICforconditionalGaussian.
Left.Cerebellum.White.Matter
NeurocognitiveMeasures
TheCNPdatasetconsistsofvolunteers(healthyandpatient)from
thegreaterLosAngelesareawhoenrolledandcompleted
diagnosticinterviews,self-reports,cognitiveexams,andcontributed
geneticdata.SamplecharacteristicsarelistedinTable1.
Neuroimagingdata(MRI,fMRI)werecollectedalongwithall
personality,neurocognitivemeasuresandgenotypinglistedinTable
2
StructuralMRIdatawasprocessedusingFreeSurfer,extracting
corticalandsubcorticalmeasurements.
Hierarchicalclusteranalyseswasusedtoidentifyredundant
variableswithnumerousoutcomemeasuresgeneratedbyeach
neurocognitivetask.Next,exploratorybifactor analysiswasusedto
developmeasurementmodelsforeachconstructofinterest,
whereinthebifactor approachallowsustoexaminewhatasetof
tasksshareincommon(generalfactor)anddata-drivenmodelsfor
specificvariancewouldallowustoexaminerelationshipsamong
neurocognitivevariablesthatremainafteraccountingforthe
generalfactor. Compositemeasuresofcognitivecontroland
workingmemorywerecreatedusingbifactor modeling,extracting
“g”generalfactormeasurementsofvisualandspatialworking
memoryseparately.
EckbladandChapman'sHypomanicPersonalityScale
Left Cortical White Matter Volume
Latent Specific
Factor 1
X
Right Pallidum Volume
Latent General
Factor
TheTemperamentandCharacterInventory
Cortical Volume
Self Reports
Optic.Chiasm
Left Cerebellar Cortex Volume
Paradigms
Left.Thalamus.Proper
DX
HOPKINS_DEPRESSION
Variable
WM.hypointensities
GENDER
Bayesian network modeling is a method of inference where the joint frequency of data is
explained via a product of marginal distributions (9). In this framework, multimodal
observations such as behavior, self-report, and paradigms represent observations recorded
on a single subject. In the Bayesian networks, nodes represent random variables, and an
arrow defines a probabilistic connection (dependence) among the variables. Formally, if the
variables are defined as discrete, then the joint probability distribution can be defined as the
product of the local probability distributions.
Bayesian Network
COMT
RESULTS
METHODS(Continued)
0.5
1.0
Depression (Hopkins)
1.5
2.0
1
2
Somatization (Hopkins)
-2
-1
0
1
Verbal Working Memory
2
0
10
20
30
40
Physical Anhedonia (Chapman)
CONCLUSIONS
REFERENCES
OurresultssuggestthatBayesiannetworkscanbeusedtoidentify
thedependenciesbetweenstructuralMRImeasurementsand
observedchangesincognitionandbehavior.Morebroadly,
diagnosisstillmodulatestherelationshipbetweensymptom
measuresandstructuralMRImeasures,suggestingthatdiagnosis
maybecapturinginformationnotcontainedalreadyinthestructural
andsymptombasedmeasures.Futureworkwillincludegenotype
andfunctionalMRImeasurestofurtherassessthehierarchical
structurespanningtheRDoC framework.
1.
CronbachLJ,Meehl PE.Constructvalidityinpsychologicaltests.Psychologicalbulletin1955;52(4):281.
2.
Borsboom D,Mellenbergh GJ,vanHeerden J.Theconceptofvalidity.Psychologicalreview2004;111(4):1061.
3.
CuthbertBN,Kozak MJ.Constructingconstructsforpsychopathology:TheNIMHresearchdomaincriteria.2013.
4.
Insel T,CuthbertB,GarveyM,Heinssen R,PineDS,QuinnK,Sanislow C,WangP.Researchdomaincriteria(RDoC):towardanew
classificationframeworkforresearchonmentaldisorders.AmericanJournalofPsychiatry2010;167(7):748-751.
5.
CuthbertBN,Insel TR.Towardthefutureofpsychiatricdiagnosis:thesevenpillarsofRDoC.BMCMedicine2013;11(1):126.
6.
Asparouhov T,Muthén B.Exploratorystructuralequationmodeling.StructuralEquationModeling:AMultidisciplinaryJournal
2009;16(3):397-438.
7.
Kievit RA,vanRooijen H,Wicherts JM,Waldorp LJ,Kan K-J,ScholteHS,Borsboom D.Intelligenceandthebrain:Amodel-based
approach.Cognitiveneuroscience2012;3(2):89-97.
8.
GreenlandS,Brumback B.Anoverviewofrelationsamongcausalmodellingmethods.Internationaljournalofepidemiology
2002;31(5):1030-1037.
9.
JensenFV.AnintroductiontoBayesiannetworks. Vol210:UCLpressLondon;1996.
SupportedinpartbytheNationalInstituteofHealth(NIH)–NIMHR03MH106922,NIA
K25AG051782.ArianaEAnderson,Ph.D.,holdsaCareerAwardattheScientificInterface
fromBWF.
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