Can we use brain function to predict the course of cannabis use towards dependence? Results from a prospective neuroimaging studie Janna Cousijn ‐ lab, University of Amsterdam, The Netherlands
Amsterdam Institute for Addiction Research, AMC, The Netherlands
[email protected]
Learn about learning, 2013
INTRODUCTION
PREDICTORS
DISCUSSION
Outline • General introduction • (Neuro)cognitive predictors of future cannabis use and problem severity • Discussion
INTRODUCTION
PREDICTORS
DISCUSSION
Outline • General introduction • (Neuro)cognitive predictors of future cannabis use and problem severity • Discussion
INTRODUCTION
PREDICTORS
DISCUSSION
Why do only some individuals become addicted?
Transition
Sporadic drug use
INTRODUCTION
PREDICTORS
DISCUSSION
Dual‐process theories of addiction (Evans & Coventry, 2006; Strack & Deutch, 2006: Wiers & Stacy, 2006)
• Chronic relapsing disorder characterized by compulsive drug taking • Imbalance between approach‐oriented motivational system and regulatory executive system I
GO
s h STO ould P
Sensitized and conditioned responses to cues
INTRODUCTION
PREDICTORS
DISCUSSION
Cannabis addiction? YES! Treatment demands increase 9000
Primary
Secondary
8000 7000
5000 4000 3000 2000 1000 0
19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08
Number
6000
Year
National Drug Monitor 2009
INTRODUCTION
PREDICTORS
DISCUSSION
Outline • General introduction • (Neuro)cognitive predictors of future cannabis use and problem severity • Discussion
INTRODUCTION
PREDICTORS
DISCUSSION
Addicts and healthy individuals differ in many aspect
Substance use Genetics Brain function Social environment Potential predictors of addiction
Control Motivation
Control Motivation
Joint roken = belonend
Control Motivation CUE
Joint zien extreem sterkt verlangen (craving) automatische aandacht automatische toenadering
Control Motivation CUE
Disbalans verslaving
INTRODUCTION
PREDICTORS
DISCUSSION
Cognitive control • Working‐memory • Decision making
Can we use neurocognitive processes to predict cannabis use trajectories?
Motivation • Cue‐reactivity • Attentional bias fast allocation, sustained attention • Approach bias fast approach responses • Cue‐induced craving
INTRODUCTION
PREDICTORS
DISCUSSION
Design • Heavy cannabis users (n = 33, 18‐25 y) – Use: > 10 days/month, > 2 year, no treatment • Controls (n = 42)
T 1
‐brain structure ‐decision making ‐working memory ‐cue‐reactivity ‐cannabis use ‐problems
T 2 6 months
‐cannabis use ‐problems
T 3 3 year ‐brain structure ‐decision making ‐working memory ‐cue‐reactivity ‐cannabis use ‐problems
INTRODUCTION
PREDICTORS
DISCUSSION
Design • Heavy cannabis users (n = 33, 18‐25 y) – Use: > 10 days/month, > 2 year, no treatment • Controls (n = 42)
T 1
‐brain structure ‐decision making ‐working memory ‐cue‐reactivity ‐cannabis use ‐problems
T 2 6 months
‐cannabis use ‐problems
T 3 3 year ‐brain structure ‐decision making ‐working memory ‐cue‐reactivity ‐cannabis use ‐problems
INTRODUCTION
PREDICTORS
DISCUSSION
Approach bias • Relatively automatic tendency to approach rather than avoid • Possible predictor of drug abuse / dependence?
INTRODUCTION
PREDICTORS
DISCUSSION
Approach‐Avoidance Task (original Rinck & Becker, J Behav Ther Exp Psychiatry 2007)
• Arm flexion and extension: pulling and pushing a joystick • Irrelevant feature task: respond to format of the picture • Heavy drinkers faster to approach alcohol (Wiers et al. 2009) bias = RT avoid – RT approach
INTRODUCTION
PREDICTORS
DISCUSSION
• Heavy cannabis users are faster in approaching cannabis compared to controls
• Approach‐bias predicts escalation of cannabis use after 6 months
INTRODUCTION
PREDICTORS
DISCUSSION
Stimulus‐Response Compatibility Task (SRC)‐fMRI • Move a manikin towards or away from cannabis (blocked design) • Regular cannabis users are faster in approaching cannabis compared to controls (Mogg et al. 2006) • Cannabis approach (neutral avoid) vs. cannabis avoid (neutral approach)
INTRODUCTION
PREDICTORS
DISCUSSION
• No group differences for cannabis approach vs. avoid activity • Within cannabis users positive correlation lifetime use: 4.0
2.3 x = - 2 y = 12 z = 6
• DLPFC and ACC predict problem severity after six months 3.5
2.3 x = 34 y = 38 z = 32
INTRODUCTION
PREDICTORS
Iowa Gambling task fMRI Monetary decision‐making task
DISCUSSION
INTRODUCTION
PREDICTORS
DISCUSSION
• Win > loss evaluation in right insula, right caudate and right VLPFC was positively associated with weekly cannabis use
• Disadvantageous > advantageous decisions in frontal pole and middle and superior temporal gyrus was positively associated with change in weekly cannabis use
INTRODUCTION
PREDICTORS
DISCUSSION
N‐Back task fMRI • Working‐memory task (blocked design) • 3 levels: 0‐back, 1‐back, 2‐back A A
Y A Z X
1-back target
B 2-back target
0-back target 0-back 1-back 2-back
INTRODUCTION
• Tensor‐ICA analysis (FSL; Beckmann & Smith 2005)
• Individual differences in working‐memory network function predicts escalation in cannabis use after 6 months
PREDICTORS
DISCUSSION
INTRODUCTION
PREDICTORS
DISCUSSION
Integrated model of cannabis use
• Both working‐memory network function, the approach‐bias, IGT activations, and craving uniquely explain variance in future cannabis use.
INTRODUCTION
PREDICTORS
DISCUSSION
Outline • General introduction • (Neuro)cognitive predictors of future cannabis use and problem severity • Discussion
INTRODUCTION
PREDICTORS
DISCUSSION
• Can we use brain function to predict cannabis use? YES Brain activity: working‐memory, decision‐making Behavior: Approach‐bias, craving •Identify individuals at‐risk for the development of a cannabis use disorder •fMRI assessments add value •Targeted interventions: – Approach‐bias retraining (Wiers et al. 2010, 2011) – Working‐memory training
INTRODUCTION
PREDICTORS
DISCUSSION
Questions? Dr. Janna Cousijn Dr. Anneke Goudriaan Prof. Richard Ridderinkhof Prof. Reinout Wiers
Prof. Wim van den Brink Prof. Dick Veltman
