Unsupervised learning toward brain imaging data analysis: cigarette craving and resistance related neuronal activations from functional magnetic resonance imaging data analysis

Dong-Youl Kim; Jong-Hwan Lee

doi:10.1117/12.2053765

28 May 2014 Unsupervised learning toward brain imaging data analysis: cigarette craving and resistance related neuronal activations from functional magnetic resonance imaging data analysis

Dong-Youl Kim, Jong-Hwan Lee

Author Affiliations +

Proceedings Volume 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII; 91180L (2014) https://doi.org/10.1117/12.2053765
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

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.

Citation Download Citation

Dong-Youl Kim and Jong-Hwan Lee "Unsupervised learning toward brain imaging data analysis: cigarette craving and resistance related neuronal activations from functional magnetic resonance imaging data analysis", Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 91180L (28 May 2014); https://doi.org/10.1117/12.2053765

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Resistance

Functional magnetic resonance imaging

Data modeling

Independent component analysis

Machine learning

Data analysis

Brain

Show All Keywords

Keywords/Phrases

Search In:

Publication Years