Can big data yield big

insights for depression?

www.gillanlab.com

AWARE Conference, Dublin 2019

Claire Gillan, PhD

Assistant Professor of Psychology and MQ Fellow

Trinity College Dublin

@clairegillanTCD

OCD

Bipolar

Schizophrenia

Depression

***

0

20

40

60

80

100

Resp

on

ses (

%)

Contr

ol

OC

D

Gillan et al., American Journal of Psychiatry, 2011

Habit

Same goes for schizophrenia!

And social anxiety…

And cocaine addiction…

…and binge-eating disorder, alcohol use disorder,

mixed results in anorexia…

OCD

OCD

Bipolar

Schizophrenia

Depression

OCD

BipolarDepression

Schizophrenia

*p<.05 ** p<.01 ***p<.001

Co

ntr

ol o

ver

hab

it

**** ***

p >.14

1,413 participants

(general population)

Eating D

isord

ers

Imp

uls

ivity

OC

D

Alc

ohol A

ddic

tion

Schiz

oty

py

Depre

ssio

n

Tra

it A

nxie

ty

Apath

y

Socia

l Anxie

ty

Gillan et al., eLife, 2016

Habits forming, trans-diagnostically

Anxious-Depression

Co

mp

uls

ivit

y

Social Withdrawal

0.5

0.0

0.5

0.0

-

0.5

0.0

0.5 -

0.5

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Inter-correlation of 209

individual self-report

questionnaire items

Gillan et al., eLife, 2016

Contr

ol over

habit

***

**

Dimension or Disorder?

Gillan et al., JAMA Psychiatry, 2019

Contr

ol over

habit

Contr

ol over

habit (

beta

)

N=285 diagnosed patients

GAD OCD GAD

+

OCD

This is interesting, but is this

valuable?

33% remission rate

47% response rateTrivedi et al., 2006

Antidepressants work!

They just don’t work for everyone.

Several candidate predictors of treatments response

But :

• Non-specific

• Weak, one-dimensional predictors

• Impractical (too expensive)

None are in clinical use

www.AntidepressantResearch.com

Can we use big data to make psychiatry’s first

objective tests?

Precision Treatment

Several candidate predictors of treatments response

But :

• Non-specific

• Weak, one-dimensional predictors

• Impractical (too expensive)

None are in clinical use

Our Objective:

• Specific – which treatment will work for you?

• Strong, multidimensional predictors

• Scalable, cheap and easy to access

Make this tool available

www.AntidepressantResearch.com

A novel method to achieve these goals

www.AntidepressantResearch.com

• Scalable: We want to recruit as we mean to implement

• Strong: We need a large sample (N=1000) to use machine

learning to capture multidimensional space

• Specific: We need to test comparative treatments

Early Identification

• Prevention is better than treatment.

• Can we detect an episode before it happens?

• Network theories suggest we might be able to pin it

down to a matter of weeks

anxietysleep motivation

self-esteem

moodappetite

Are tightly connected

networks are more

vulnerable?

Borsboom & Kramer, 2008

Weakly connected(‘resilient’)

Strongly connected(‘vulnerable’)

Baseline Network

Future diagnoses

Future treatments

freq

uenc

y

A BLow Disability OCD Patients (N=102)

High Disability OCD Patients (N=101)

Weakly connected(‘resilient’)

Strongly connected(‘vulnerable’)

Baseline Network

Future diagnoses

Future treatments

freq

uen

cy

A BLow Disability OCD Patients (N=102)

High Disability OCD Patients (N=101)

Kelley, …., & Gillan, in preparation

Early Identification

N=200 patients

Higher ImpairmentLower Impairment

episode

begins

weeks

co

nnectivity

@use_mybrain

Can we use signals like this to predict the future?

Want to help?

VISIT: www.AntidepressantResearch.com

Seeking:

• GPs

• Pharmacists

• Individuals +/- 2 days of starting antidepressants

@use_mybrain

Gillan LabTricia Seow

Andrew Pringle

Kevin Lynch

Eoghan Gallagher

Sean Kelley

Thank You

www.AntidepressantResearch.com

@use_mybrain