Functional Connectivity Analysis of Resting-State fMRI Networks in Nicotine Dependent Patients

Abstract

Brain imaging studies identified brain networks that play a key role in nicotine dependence-related behavior. Functional connectivity of the brain is dynamic; it changes over time due to different causes such as learning, or quitting a habit. Functional connectivity analysis is useful in discovering and comparing patterns between functional magnetic resonance imaging (fMRI) scans of patients’ brains. In the resting state, the patient is asked to remain calm and not do any task to minimize the contribution of external stimuli. The study of resting-state fMRI networks have shown functionally connected brain regions that have a high level of activity during this state. In this project, we are interested in the relationship between these functionally connected brain regions to identify nicotine dependent patients, who underwent a smoking cessation treatment. Our approach is on the comparison of the set of connections between the fMRI scans before and after treatment. We applied support vector machines, a machine learning technique, to classify patients based on receiving the treatment or the placebo. Using the functional connectivity (CONN) toolbox, we were able to form a correlation matrix based on the functional connectivity between different regions of the brain. The experimental results show that there is inadequate predictive information to classify nicotine dependent patients using the SVM classifier. We propose other classification methods be explored to better classify the nicotine dependent patients.

Cite

@inproceedings{smith2016functional,
  title = {Functional Connectivity Analysis of Resting-State fMRI Networks in Nicotine Dependent Patients},
  author = {Smith, Aria and Ehtemami, Anahid and Fratte, Daniel and Meyer-Baese, Anke and Zavala-Romero, Olmo and Goudriaan, Anna E. and Schmaal, Lianne and J. Schulte, Mieke H.},
  year = {2016},
  doi = {10.1117/12.2217514},
  url = {https://doi.org/10.1117/12.2217514},
}

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Topics

• Deep Learning
• Health
• SciML