Share Email Print

Proceedings Paper

Functional connectivity analysis of resting-state fMRI networks in nicotine dependent patients
Author(s): Aria Smith; Anahid Ehtemami; Daniel Fratte; Anke Meyer-Baese; Olmo Zavala-Romero; Anna E. Goudriaan; Lianne Schmaal; Mieke H. J. Schulte
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 29 March 2016
PDF: 6 pages
Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978827 (29 March 2016); doi: 10.1117/12.2217514
Show Author Affiliations
Aria Smith, Florida State Univ. (United States)
Anahid Ehtemami, Florida State Univ. (United States)
Daniel Fratte, Florida State Univ. (United States)
Anke Meyer-Baese, Florida State Univ. (United States)
Olmo Zavala-Romero, Florida State Univ. (United States)
Anna E. Goudriaan, Academic Medical Ctr., Univ. of Amsterdam (Netherlands)
Lianne Schmaal, VU Univ. Medical Ctr. (Netherlands)
Mieke H. J. Schulte, Academic Medical Ctr., Univ. of Amsterdam (Netherlands)

Published in SPIE Proceedings Vol. 9788:
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

© SPIE. Terms of Use
Back to Top