A data-driven unsupervised learning such as an independent component analysis was gainfully applied to bloodoxygenation-
level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data compared to a model-based
general linear model (GLM). This is due to an ability of this unsupervised learning method to extract a meaningful
neuronal activity from BOLD signal that is a mixture of confounding non-neuronal artifacts such as head motions and
physiological artifacts as well as neuronal signals. In this study, we support this claim by identifying neuronal
underpinnings of cigarette craving and cigarette resistance. The fMRI data were acquired from heavy cigarette smokers
(n = 14) while they alternatively watched images with and without cigarette smoking. During acquisition of two fMRI
runs, they were asked to crave when they watched cigarette smoking images or to resist the urge to smoke. Data driven
approaches of group independent component analysis (GICA) method based on temporal concatenation (TC) and TCGICA
with an extension of iterative dual-regression (TC-GICA-iDR) were applied to the data. From the results, cigarette
craving and cigarette resistance related neuronal activations were identified in the visual area and superior frontal areas,
respectively with a greater statistical significance from the TC-GICA-iDR method than the TC-GICA method. On the
other hand, the neuronal activity levels in many of these regions were not statistically different from the GLM method
between the cigarette craving and cigarette resistance due to potentially aberrant BOLD signals.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.