Daniel Fratte, Aria G. Smith, & Anke Meyer-Baese


fMRI Data Analysis Techniques in Substance Abuse Conditions: A nicotine-addiction case study


Smoking addiction presents enormous health problems most notably leading to 12 million premature deaths of Americans and another 25 million deaths as a result of nicotine related illnesses. Deter- mining individual smoking cessation treatments and measures that improve treatment efficacy are of importance if we are to reduce the drastic health consequences associated with tobacco use. Brain imaging studies identified brain networks that play a key role in nicotine addiction-related behavior. Resting state networks reflect a basic property of functional brain organization and include areas be- lieved to play a key role in reward and addiction1.

This study will provide the clinical neuroscientist with a fast and accurate computational diagnosis support. Predicting from initial data the relapse likelihood will be beneficial when determining indi- vidual therapies for nicotine-addicted subjects and increase the awareness of the addiction problem in nicotine-support groups.