Lecture 2

Question 1

wat zijn de componenten van de network theory

Answer 1

symptom network A, symptom network B, bridge symptoms, external field. network is made up of links and nodes.

Question 2

wat is het idee van network theory

Answer 2

symptoms of mental disorders are components in a system that influence each other, you get feedback cycles in these networks of problems that keep on activating each other.

Question 3

low connectivity system =

Answer 3

desired network, because if someone develops a symptom (insomnia bv) they wont immediately develop all other problems.

Question 4

how does a low connectivity network behave

Answer 4

linearly: more stressors (external activation) in the network drive leads to higher symptom activation levels. if the stressor decreases, the symptoms decrease -> network moves back to its original state

Question 5

high connectivity of symptom network

Answer 5

symptoms are connected, undesired because if they get one symptom, high probability that other symptoms will occur as well.

Question 6

more stressors in high connectivity network =

Answer 6

higher levels of symptom activation. if the stressor decreases, the network does not move back to its original state. all symptoms can continue to feed on each other.

Question 7

even kijken naar schrift getekend

Answer 7

oke

Question 8

wat zie je bij states in een low connectivity model

Answer 8

state of this network is ball, if a perturbation hits the ball, it can go up, but without the external source the ball rolls back to the bottom of the well (the stable state).

dus… er kan wel een slechte invloed/verstoring zijn, maar als de external state goed is dan komen er geen symptoms. voorbeeld hiervan is mourning, grief. zonder external state zou dat gevoel er ook niet zijn. as time goes by, you will return to baseline

Question 9

wat zie je bij states in een high connectivity model

Answer 9

if the ball in the bottom of the well (stable state A) is perturbed, and it crosses the tipping point, then the ball will role to stable state B. (dus hier is een tweede stable state. als je stress weghaald bij een weakly connected network, gaat het gewoon terug. maar bij een strongly connected network rolt het niet terug naar zijn originele staat, omdat er 2 staten zijn).

Question 10

wat laat dit verschil in states zien over mental disorders

Answer 10

maybe mental disorders arent latent variables we need to figure out, but they are alternative stable states that are generated by the way many different symptoms interact together.

Question 11

hoe is hysteresis bij weakly connected network

Answer 11

black and green lines overlap. green is where you go up, black is where you go down. als je in het zwarte gedeelte zit is het dus heel makkelijk om terug te gaan naar het groene gedeelte.

Question 12

hoe is hysteresis bij strongly connected network

Answer 12

bij strongly connected moet je weer helemaal terug naar beneden gaan om het netwerk terug naar boven te krijgen. there is an inaccessible zone where the network can be. the symptoms are fueling each other, and keeping them in the active state => you need much more energy to break that.

Question 13

hysteresis =

Answer 13

hard to return to the previous stable state.

Question 14

2 woorden van weakly connected networks

Answer 14

resilient
spontaneous recovery

Question 15

2 woorden van strongly connected networks

Answer 15

vulnerable
hysteresis

Question 16

wat laat hysteresis dus zien in mental disorders

Answer 16

dat iemand vast zit in een mental disorder, want elke keer dat ze iets proberen op te lossen komt er iets anders voor in de plaats.

Question 17

the hysteresis effect occurs when you have… (2 factors)

Answer 17

• positive connections (one problem leads to another problem)
• network is connected (can reach every node from every other node)

Question 18

mental disorders are…

Answer 18

alternative stable states in a symptom network.

Question 19

dus basically comorbiditeit komt door

Answer 19

symptomen die elkaar versterken en tot nieuwe disorders lijken

Question 20

mental disorders are due to

Answer 20

local hyperconnectivity of the symptom network in combination with perturbations. this leads to a netwrok getting stuck in the alternative stable state of the mental disorder.

Question 21

whether the shift to the alternative stable state of the mental disorder is permanent, depends on..

Answer 21

the size of the hysteresis effect

Question 22

if the symptom network is huge…

Answer 22

many problems activate each other -> mental disorders are harder to treat.

Question 23

if the symptom network is smaller…

Answer 23

some links can be removed or interventions can be placed on nodes -> mental disorders may be easier to treat.

Question 24

wat was de eerste software voor deze binary data

Answer 24

IsingFit

Question 25

wat kunnen we nu met de software

Answer 25

binary, continuous and categorical data

Question 26

wat voor verschillende nodes heb je

Answer 26

peripheral is minder belangrijk (depressie: insomnia and fatigue) en central is meer belangrijk (depressie: worthlessness and death)

Question 27

hoeveel connections bij p nodes

Answer 27

(p*(p-1))/2

Question 28

hoeveel thresholds bij p nodes

Answer 28

= p

Question 29

p betekent

Answer 29

aantal nodes

Question 30

hoeveel cellen bij p als het binary is

Answer 30

2^p

Question 31

wat doet het elasso algoritme

Answer 31

uses a series of piecewise regressions, each variable features a dependent variable and then the rest is independent. dit gaat zo door.
try to predict one variable from all others, and try to see which of the other variables improve the prediction. try including only variables that improve the prediction enough to justify including the parameter.

Question 32

wat betekent neighbourhood

Answer 32

when variable x is included in the prediction for y, we say that x is in the neighbourhood of y

Question 33

network approach can be applied to…

Answer 33

• modeling external shocks
• examining long-term changes and pathways
• analyzing the effects of interventions
• networks and genetics

Question 34

how to assess robustness in a network

Answer 34

1. assume the estimated network is true
2. simulate repeatedly from the network
3. evaluate the expected replicability between the networks estimated on different samples
4. assess what proportion present links would be expected to be picked up (sensitivity)
5. assess what proportion of absent links would be expected to be correctly deemed absent (specificity)

Question 35

wha to complex systems do in the neighbourhood of a transition

Answer 35

complex systems slow down -> their state at time t becomes a better predictor of their state at time t+1

Question 36

hoe heet dit fenomeen van complex states die betere predictors worden

Answer 36

autoregression

Question 37

wat betekent autoregression voor individuals

Answer 37

individuals closer to a transition should slow down (have higher autoregression)

Question 38

early warning signs occur when then the transition happens. if ppl keep cutting down a rainforest, than resilience of the rainforest to random pertrbations decreases over time. if vulnerability increases and resilience continues to go down, there is a point at which the system starts recovering from perturbations in a slower way

Answer 38

oke

Question 39

critical slowing down refers to a signla that may characterise psychopathology networks before…

Answer 39

transition to an alternative stable state

Question 40

what is the big question now

Answer 40

how can we connect the theoretical models to data models