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What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depres
What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depres

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Eiko Fried University of Leuven Network Analysis Approach to Psychopathology and Comorbidity ABCT November 14 2015 Diagnosis of Major Depression MD Reliable diagnosis is essential ID: 635022 Download Presentation

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Presentation on theme: "What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depres"— Presentation transcript

Slide1

What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis

Eiko Fried

University of Leuven

Network Analysis Approach to Psychopathology and

Comorbidity

ABCT, November 14, 2015Slide2

Diagnosis of Major Depression (MD)

Reliable diagnosis is essential to study and treat

mental disordersReliable diagnosis of MD

is difficult: biomarkers have very limited

explanatory

power,

and MD was among the least reliable diagnosis in DSM-5 field trials (kappa = 0.28)Current state: we measure depression symptoms to indicate the presence of MD. We add them to a sum-score, and suppose this adequately represents depression severity

2Slide3

Common cause model

3s1

s2

s3

s4

s5

MSlide4

Common cause model

4s1

s2

s3

s4

s5

M

Red eyesSlide5

Common cause model

5s1

s2

M

Red eyes

Fever

s3

s4

s5Slide6

Common cause model

6s1

s2

s3

s4

s5

M

Red eyes

Fever

Runny nose

Koplik's

spots

CoughSlide7

Common cause model

7s1

s2

s3

s4

s5

Red eyesFever

Runny nose

Koplik's

spots

CoughSlide8

Common cause model

8

There

is

a

specific relationship between symptoms of a disorder and the disorder itself (

common

cause

model

)

s1

s2

s3

s4

s5

M

Red eyes

Fever

Runny nose

Koplik's

spots

CoughSlide9

Common cause model

9There is a

specific relationship between

symptoms of a disorder

and

the disorder itself (common cause model)Symptoms are somewhat interchangeables1s2

s3

s5

M

Red eyes

Fever

Runny nose

Cough

s4

Koplik's

spotsSlide10

Common cause model

10There is a

specific relationship between

symptoms of a disorder

and

the disorder itself (common cause model)Symptoms are somewhat interchangeables1s3

s4

s5

M

Red eyes

Runny nose

Koplik's

spots

Cough

s2

FeverSlide11

Common cause model

11There is a

specific relationship between

symptoms of a disorder

and

the disorder itself (common cause model)Symptoms are somewhat interchangeableSymptoms are unrelated beyond their common causes1

s2

s3

s4

s5

M

Red eyes

Fever

Runny nose

Koplik's

spots

Cough

Red eyes

Fever

Runny nose

Koplik's

spotsSlide12

Common cause model

12There is a

specific relationship between

symptoms of a disorder

and

the disorder itself (common cause model)Symptoms are somewhat interchangeableSymptoms are unrelated beyond their common causeA 'good' symptom is one that indicates the latent disease well

M

s1

s2

s3

s4

s5

Red eyes

Fever

Runny nose

Koplik's

spots

CoughSlide13

Psychiatry13

s1

s2

s3

s4

s5

D

Common

cause

model

ubiquitous

in

psychiatrySlide14

Measuring Major Depression14

s1

s2

s3

s4

s5

MD

Insomnia

Fatigue

Concentration problems

Psychomotor problems

Weight loss

Common

cause

modelSlide15

Common cause modelWe

measure symptoms to indicate the disorderAdd symptoms

to total-score to indicate severity

Measuring Major Depression

15

s1

s2s3s4

s5

MD

Insomnia

Fatigue

Concentration problems

Psychomotor problems

Weight lossSlide16

Common cause modelWe

measure symptoms to indicate the disorderAdd symptoms

to total-score to indicate severity

Symptoms roughly interchangeableWe want to

treat

the disease so symptoms disappearMeasuring Major Depression16s1s2

s3

s4

s5

MD

Insomnia

Fatigue

Concentration problems

Psychomotor problems

Weight lossSlide17

Common cause model (

overly simplistic)We

measure symptoms

to indicate the

disorder

Add

symptoms to total-score to indicate severitySymptoms roughly interchangeableWe want to treat the disease so

symptoms

disappear

Measuring

Major Depression

17

s1

s2

s3

s4

s5

MD

Insomnia

Fatigue

Concentration problems

Psychomotor problems

Weight lossSlide18

Measuring Major Depression18

s1

s2

s3

s4

s5

MD

Insomnia

Fatigue

Concentration problems

Psychomotor problems

Weight loss

Problem

:

there

is

a

dramatic

lack

of

consensus

what

depression

symptoms

(

or

good

depression

symptoms

)

are

. Different

depression

instruments

measure

very

different

things

. Slide19

What are 'good' depression

symptoms?DSM-5: 9 symptomsNone of

the common rating scales

of depression measure all DSM symptoms; all of them

measure

a number of symptoms not featured in the DSMBDI: irritability, pessimism, feelings of being punished, …HRSD: anxiety, genital symptoms, hypochondriasis, insights into the depressive illness, paralysis, …CESD: frequent crying, talking less, perceiving others as unfriendly, …As a result, there is little consistency across depression studies because patients are enrolled based on very different criteria

19Slide20

20Slide21

Measurement of depression"The measurement of depression of depression is as confused as the basic construct of the state itself."

21Slide22

Network modelSymptoms co-occur

due to their common cause

22Slide23

Network modelSymptoms

co-occur because they cause

each other

23

Psychomotor problems

Insomnia

Concentration problemss1s2

s3

s4

s5

Fatigue

Weight lossSlide24

Network modelSymptoms

co-occur because they cause

each otherSymptoms are

roughly equally important indicators

24

Psychomotor problems

InsomniaConcentration problemss1

s2

s3

s4

s5

Fatigue

Weight lossSlide25

Network modelSymptoms

co-occur because they cause

each otherSymptoms

are distinct entities

with

different

characteristics25Psychomotor problemsInsomniaConcentration problems

s1

s2

s3

s4

s5

Fatigue

Weight lossSlide26

Network modelSymptoms

co-occur because they cause

each otherSymptoms

are distinct entities

with

different

characteristicsReinforcing feedback loops (attractor state)26Psychomotor problemsInsomnia

Concentration problems

s1

s2

s3

s4

s5

Fatigue

Weight lossSlide27

Network modelImportant

new questions arise: what symptoms are most

central to driving depressive processes?

27

Psychomotor problems

Insomnia

Concentration problemss1s2

s3

s4

s5

Fatigue

Weight lossSlide28

Network modelImportant

new questions arise: what symptoms are most

central to driving depressive processes?

28

Psychomotor problems

Insomnia

Concentration problemss1s2

s3

s5

Fatigue

s4

Weight lossSlide29

Network modelImportant

new questions arise: what symptoms are most

central to driving depressive processes?

29

Psychomotor problems

Concentration problems

s2s3s4

s5

Fatigue

Weight loss

Insomnia

s1Slide30

What are 'good' depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis

30

Journal of Affective Disorders

Eiko I. Fried

Sacha

Epskamp

Randolph M. NesseFrancis TuerlinckxDenny BorsboomSlide31

Research questionsWhat

is the network structure of depression?

DSM symptomsA large number of symptoms

above and beyond the DSM criteria

What

symptoms are most central, i.e. most connected in the network?31Slide32

Sample3463 depressed

outpatients from the enrollment stage of the

STAR*D studyMean age 41 years (SD=13), 63%

femaleIDS-C: 28-item questionnaire that covers 15 disaggregated

DSM

symptoms

and 13 common non-DSM symptoms (e.g., anxiety, irritability)Network estimationGaussian graphical model (special case of the Pairwise Markov Random Field): edges are partial correlation coefficientsRegularization via least absolute shrinkage and selection operator (lasso); very small edges set exactly to 0, results in a conservative (sparse) network32Slide33

33

Estimation

Edges

equal

partial

correlationsSparse network InterpretationHeterogeneous networkSome clusters emergeNetwork structure of MD

DOI

|

10.1016

/j.jad.2015.09.005Slide34

34

Symptom

importance

DOI

|

10.1016

/j.jad.2015.09.005Slide35

35

Symptom

importance

DOI

|

10.1016/j.jad.2015.09.005Slide36

Permutation test to examine

differences in centrality between DSM and non-DSM symptoms:Betweenness centrality: p

= 0.12 Closeness centrality: p = 0.64 Node strength:

p = 0.03 (0.08)Controlling for outliers: Betweenness centrality: p = 0.28

Closeness centrality:

p

= 1Node strength: p = 0.13 DSM symptoms are not more central than non-DSM symptoms36Full IDS symptom networkDOI | 10.1016/j.jad.2015.09.005Slide37

Robustness analysis

37Slide38

ConclusionsCore assumptions

of the common cause model do not seem remotely

tenable for depression"Depression sum-scores don't

add up: why analyzing specific

depression

symptoms is essential" (Fried & Nesse, 2015)Centrality measures may provide new insights regarding the clinical significance of specific depression symptoms. These insights likely have major clinical implications and suggest new approaches that may better predict outcomes such as the course of illness, probability of relapse, and treatment response.38Slide39

LimitationsSTAR*D population

Cross-sectional (indegree vs outdegree centrality)

Heterogeneity of depressionTopological overlap

39Slide40

Thank youSlide41

Eiko FriedUniversity of Leuven

University of Amsterdameiko-fried.comeiko.fried@gmail.comSlide42

DiscussionRobustness: DSM

and non-DSM symptoms do not differ regarding means (W = 121, p = 0.30) or SD (W = 89, p = 0.72)

10 disaggregated symptoms not more central than the other 18 symptoms (node strength: p = 0.86; betweenness and closeness: p = 1

)42Slide43

43