# Presentation: 720658

## Predicting Relapse in Methamphetamine Dependent Individuals

- Martin P Paulus
- Department of Psychiatry
- University of California San Diego
- mpaulus@ucsd.edu

## Stimulant Dependence

- Stimulants
- Cocaine
- Methamphetamine
- Amphetamine
- 12 – 15% ever tried stimulants
- 1-3% have stimulant dependence
- 50% of sober stimulant dependent individuals relapse within a year.

## Relapse

- An important public health problem.
- Predicting relapse may help to deliver targeted interventions to those individuals at risk.
- Current methods to predict relapse have
- Low specificity (many false positives)
- Moderate sensitivity (frequent false negatives)

## Decision Making and Relapse

- Decision-making:
- Person has to select among several options.
- Each option can be associated with positive or negative outcomes, which may be uncertain.
- Key elements of decision situations:
- Probability of an outcome associated with an option.
- The positive or negative consequence.
- The magnitude of the consequence

## Study Goals

- Neurobiology of decision-making dysfunctions in stimulant dependent subjects.
- Can functional magnetic resonance imaging be used as a tool to predict relapse?

## Subjects

## BOLD-fMRI

- Hemoglobin is **diamagnetic** when _oxygenated_ but **paramagnetic** when _deoxygenated_.

## Assessment Protocol

- Two-Choice Prediction Task

- Two-Choice Response Task

## Sobriety Survival Function

- Sobriety assessment:
- Semi Structured Assessment for the Genetics of Alcoholism.
- Relapse:
- any use of methamphetamine during any time after discharge.

## Subjects’ Socio-demographics

## Subjects’ Use Characteristics

## Behavioral Performance

## Nine brain areas differentiated relapsing and non-relapsing subjects:
prefrontal, parietal and insular cortex.  
Non-relapsing individuals showed more activation than relapsing individuals 

- prefrontal, parietal and insular cortex.
- Non-relapsing individuals showed more activation than relapsing individuals

## Prediction Accuracy

## Receiver Operator Curves

- With a specificity of at least 83.3%
- Sensitivity ranged from 54.5% to 90.9%.

## Neural Systems Predicting Time to Relapse

- Activation in three different brain areas predicted increased time to relapse:
- low activation in these areas at baseline was highly predictive of time to relapse (2 = 23.9, df=3, p < .01)

-  	**Area****	****	****Coefficient (SE)****	****Wald****	****p****	****Exp(B)****	****95% CI****	**
- R Middle Frontal Gyrus	-4.36	1.82	5.68	.017	.013	0.00 – 0.46
- R Middle Temporal Gyrus	-3.38	1.66	4.10	.043	.034	0.001 – 0.89
- R Posterior Cingulate	-5.960	2.22	7.18	.007	.003	0.000- 0.20

## Summary & Conclusions

- Functional Magnetic Resonance Imaging results predict relapse.
- Relapse = less activation in structures that are critical for decision-making
- Poor decision-making: “setting the stage” for relapse.

## Candidate Processes

- Insular cortex:
- Altered interoceptive processing during decision-making
- Internal feeling states have less influence on predicting optimal behavior
- Inferior parietal lobule:
- Poor assessment of the decision-making situation and subsequent reliance on habitual behavior.

## Take Home Message

- Methamphetamine dependent subjects
- Show brain patterns that can be used to predict **whether and when relapse** may occur.
- Future studies:
- What are the specific cognitive processes?
- Do interventions have an impact on relapse?
- Does this apply to other addictions?