Heterogeneity An issue in design & analysis for population-based neuroscience

Heterogeneity An issue in design & analysis for population-

based neuroscience

James B Kirkbride Ph.D.Sir Henry Wellcome Research Fellow

University of Cambridge

Part II Neuroscience Workshop

“Heterogeneity”

“Population-based”

“Heterogeneity”

What if everyone had smoked?

What would we conclude about the relationship between smoking and lung cancer?

0 649

n.s 0 649

What if everyone had smoked?

What would we conclude about the relationship between smoking and lung cancer?

Other factors: alcohol, social class, genetics…

Importance of heterogeneity!!!

Heterogeneity An issue in design & analysis for population-

based neuroscience




What are psychotic disorders?

Population based neuroscience…

• Debilitating set of mental disorders, characterised by:– Positive symptoms

• Seeing or hearing things which aren’t there - hallucinations

• Erroneous beliefs held contrary to normal view of subject - delusions

– Negative symptoms (flat affect, anhedonia)– Affective symptoms – changes in mood

(bipolar disorder, psychotic depression)– Chronic, debilitating socially &

economically


Positive

Negative

Affective


How common are psychotic disorders?


• Prevalence of psychotic symptoms: – Median: 5.3% (IQR: 1.9-14.4%)

Van Os et al. 2009 • Prevalence of psychotic disorders:

– Lifetime prevalence: ~1%Saha et al. 2005; Goldner et al. 2002; Perala et al. 2007; Robins & Regier 1990; Kessler et al. 1994

– Annual prevalence: ~0.4% Kirkbride et al, In Press; Saha et al. 2005


• Incidence of psychotic disorders: – All psychotic disorders: 31.7 per 100k/pa

Kirkbride et al. PLoS One, 2012– Schizophrenia : 15.2 per 100k/pa

McGrath et al. 2004; Kirkbride et al. PLoS One, 2012

– Affective psychoses: 12.4 per 100k/paKirkbride et al. PLoS One, 2012

– Figures are for guidance only – preclude important heterogeneity along key socio-demographic dimensions


Kirkbride et al, 2006; Hafner et al 1993

No mind is an island… [Background]Age specific indicence rates of psychoses by sex

0.00

20.00

40.00

60.00

80.00

100.00

120.00

16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64

Ageband

Rat

e (p

er 1

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00 p

erso

n-ye

ars)

Males

Fem ales

Neighbourhoods & psychotic disorder

Is there an association?


Schizophrenia

• Highest rates of schizophrenia in inner city tracts

• These tracts tended to be more socially disorganized, socially isolated

• No comparable association with bipolar disorder

• Also observed elsewhere: Mannheim (Germany), Ireland, London, Bristol, Nottingham


No mind is an island…

(4) < 0 .1

(2) 0 .1 - 0 .2

(18) 0 .2 - 0 .8

(3) 0 .8 - 0 .9

(6) >= 0.9

probability of theta greater than 1.0

2.0km

N

“Don’t people with psychosis end up moving into inner-city

neighbourhoods?”


“Aren’t these results explained by reverse

causation (social drift)?”


• Prospectively collected national data on 1.75m Danish people

• Urban birthplace & upbringing associated with increased risk of schizophrenia in later life

• Cannot be explained by social drift• Dose-response relationship


• Dose-response relationship:

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

Rural area (baseline)

Provincial town

Provincial city Suburb of Capital

Capital

Adj

uste

d ra

te ra

tio

Urbanicity

Data from Mortensen et al. (1999). Adjusted for age-sex interaction, parental age, family history and season of birth


Heterogeneity An issue in design & analysis for

population-based neuroscience




“Population-based”

• Population-based studies:– Subjects are collected from the entire

population “at-risk”– Often random selection– Representative sample– Design of study adheres to the principles of

epidemiology:• Minimise chance, bias & confounding• Design may often be case-control or “cohort”

based• Analysis appropriate to design


• Case-control (ideal)– All cases in a given population of interest

identified – Cases invited to participate in study (sub-

sample)– Cases need to be representative of the total

case group– Controls selected to be representative of

population from which cases come from– i.e. had the case not become unwell they

could have been a control– Control selection random ideally


• Cohort (ideal)– Representative sample of disease free people

identified– Cohort followed up over time with assessment– Wait to see who develops disease (or not)– Look for differences in assessment between

the two groups– Cohort: prospective – Sample size for psychosis?


• Population-based neuroscience:– Both approaches can be used for

understanding differences in brain using neuroimaging studies

– Principles of epidemiology central to design & analysis of subjects

– Observational epidemiology may also inform the research question for neuroscience


What mechanisms might increase psychosis risk

due to exposure to certain environmental

factors?


How can population heterogeneity inform clinical neuroscience?

(and vice versa)

• Urban living and migrant status appear to increase psychosis risk

• May operate through social stress• i.e. greater social pressures lead to

dopamine dysregulation triggering psychotic symptoms

• If this is true, then you might expect to see neural differences in brains of those living in rural & urban areas (for example)


No mind is an island… [Mechanisms]

• 32 health German volunteers - students• Given a stress-test (arithmetic based) to

evaluate stress, while under fMRI• Asked about current & past upbringing• Second sample for validation (n=23)

Amygdala signals –ve affect & environmental threat

Perigenual anterior cingulate cortex – region controlling stress, negative affect & amygdala


• Suggests greater sensitivity to stress in city dwellers

• Amygdala signals –ve affect & environmental threat – important in psychosis symptoms

• Confirms & informs epidemiology• Heterogeneity of exposure critical to detecting

this


• Next steps• Cases vs. controls? • Migrants vs. non-migrants?• Larger samples?• Interplay with genetic factors?• Measuring (variation in) specific exposures

(beyond urbanicity)

• Heterogeneity provides us with a window on detecting associations between exposure & disorder

• Mental disorders are no exception• To detect risk factors for mental disorders,

heterogeneity in the risk factor (whether genetic or environmental) is important

• This is also true for neuroscientific research• To attribute differences in brain activity to a

proposed factor relies on variation in the factor • fMRI studies need to be designed to take this

heterogeneity into account


• Peter Jones• Jeremy Coid• ÆSOP study group• ELFEP study group

• Wellcome Trust, UK

Acknowledgements

• Gratitude & thanks to Amber Christian Osterhout, creator of the Gaining Insight campaign, www.gaining-insight.com for allowing me to use her images in this presentation


http://www.gaining-insight.com/

[email protected]

“Disease Onset" by Amber Christian Osterhout, creator of the Gaining Insight campaign, www.gaining-insight.com


mailto:[email protected]

http://www.gaining-insight.com/

Heterogeneity An issue in design & analysis for population-based neuroscience

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