Coupling meditation-like practices with drug and behavior therapies more helpful in tackling addiction

Published on December 20, 2013 at 6:50 AM · No Comments

A new paper suggests that rehabilitation strategies coupling meditation-like practices with drug and behavior therapies are more helpful than drug-plus-talk therapy alone when helping people overcome addiction

Using a computational model of addiction, a literature review and an in silico experiment, theoretical computer scientist Yariv Levy and colleagues suggest in a new paper this week that rehabilitation strategies coupling meditation-like practices with drug and behavior therapies are more helpful than drug-plus-talk therapy alone when helping people overcome addiction.

Levy reports results of his survey of animal and human studies and a computational experiment in a special section on addictive disorders in the current issue of the open access journal Frontiers in Psychiatry. He conducted this investigation while a doctoral student at the University of Massachusetts Amherst with neuroscience researcher Jerrold Meyer, an expert in the neurochemistry of human psychiatric disorders, and computer scientist Andrew Barto, an expert in mathematical theory of learning and planning.

Levy says the goal is to translate what has been learned from animal and human studies to better understand addiction and explore new approaches to treatment. Another member of the research team was neuroeconomist Dino Levy of Tel Aviv University, an expert in decision-making processes who developed the core of the theoretical model. He is no relation to lead author Yariv Levy.

Levy says, "Our higher-level conclusion is that a treatment based on meditation-like techniques can be helpful as a supplement to help someone get out of addiction. We give scientific and mathematical arguments for this."

His theoretical research approach using virtual subjects is rather unusual, Levy acknowledges, but it's now gaining significant trust because it offers some strengths. In particular, because it relies on the increasing amount of available data and knowledge, in silico research offers quick preliminary tests of "rationally supported speculations," he says, before full-scale experiments are launched with human patients or animals.

"I am a theoretician, so I use other peoples' studies and try to see how they work together and how experiments fit in," Levy points out. "This work follows a knowledge repository (KR) model, where the knowledge comes from other peoples' theories and experiments. By consolidating them, we propose some hypotheses that we hope will subsequently be tested by experts in the field." The KR model used in his current work incorporates pharmacokinetic, pharmacodynamic, neuropsychological, cognitive and behavioral components, the researcher notes.

The researchers explored the allostatic theory of addiction by combining two existing computational models, one pharmacological and the other a more behavioral-cognitive model. The allostatic theory describes changes in the brain's reward and anti-reward systems and reward set points as substance misuse progresses. "Neural adaptations arising from the reward system itself and from the anti-reward system provide the subject with functional stability, while affecting the person's mood. We propose a computational hypothesis describing how a virtual subject's drug consumption, cognitive substrate and mood interface with reward and anti-reward systems," they write.

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