Can big data yield big insights for depression?...Can big data yield big insights for depression?...
Transcript of Can big data yield big insights for depression?...Can big data yield big insights for depression?...
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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
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OCD
Bipolar
Schizophrenia
Depression
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***
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
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OCD
Bipolar
Schizophrenia
Depression
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OCD
BipolarDepression
Schizophrenia
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*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
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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
***
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**
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?
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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?
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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
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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
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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
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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?
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Want to help?
VISIT: www.AntidepressantResearch.com
Seeking:
• GPs
• Pharmacists
• Individuals +/- 2 days of starting antidepressants
@use_mybrain
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Gillan LabTricia Seow
Andrew Pringle
Kevin Lynch
Eoghan Gallagher
Sean Kelley
Thank You
www.AntidepressantResearch.com
@use_mybrain