PMID- 19144586
OWN - NLM
STAT- MEDLINE
DCOM- 20090406
LR  - 20141120
IS  - 1558-0210 (Electronic)
IS  - 1534-4320 (Linking)
VI  - 16
IP  - 6
DP  - 2008 Dec
TI  - Interpretable classifiers for FMRI improve prediction of purchases.
PG  - 539-48
LID - 10.1109/TNSRE.2008.926701 [doi]
AB  - Despite growing interest in applying machine learning to neuroimaging analyses, 
      few studies have gone beyond classifying sensory input to directly predicting 
      behavioral output. With spatial resolution on the order of millimeters and 
      temporal resolution on the order of seconds, functional magnetic resonance 
      imaging (fMRI) is a promising technology for such applications. However, fMRI 
      data's low signal-to-noise ratio, high dimensionality, and extensive 
      spatiotemporal correlations present formidable analytic challenges. Here, we 
      apply different machine-learning algorithms to previously acquired data to 
      examine the ability of fMRI activation in three regions-the nucleus accumbens 
      (NAcc), medial prefrontal cortex (MPFC), and insula-to predict purchasing. Our 
      goal was to improve spatiotemporal interpretability as well as classification 
      accuracy. To this end, sparse penalized discriminant analysis (SPDA) enabled 
      automatic selection of correlated variables, yielding interpretable models that 
      generalized well to new data. Relative to logistic regression, linear 
      discriminant analysis, and linear support vector machines, SPDA not only 
      increased interpretability but also improved classification accuracy. SPDA 
      promises to allow more precise inferences about when specific brain regions 
      contribute to purchasing decisions. More broadly, this approach provides a 
      general framework for using neuroimaging data to build interpretable models, 
      including those that predict choice.
FAU - Grosenick, Logan
AU  - Grosenick L
AD  - Neuroscience Institute at Stanford, Stanford University, Stanford, CA 94305 USA.
FAU - Greer, Stephanie
AU  - Greer S
FAU - Knutson, Brian
AU  - Knutson B
LA  - eng
GR  - R21 030778/PHS HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - IEEE Trans Neural Syst Rehabil Eng
JT  - IEEE transactions on neural systems and rehabilitation engineering : a 
      publication of the IEEE Engineering in Medicine and Biology Society
JID - 101097023
SB  - IM
MH  - Adult
MH  - *Artificial Intelligence
MH  - Brain/*physiology
MH  - Brain Mapping/*methods
MH  - Choice Behavior/*physiology
MH  - *Consumer Behavior
MH  - Female
MH  - Humans
MH  - Magnetic Resonance Imaging/*methods
MH  - Male
MH  - Pattern Recognition, Automated/*methods
MH  - Reproducibility of Results
MH  - Sensitivity and Specificity
EDAT- 2009/01/16 09:00
MHDA- 2009/04/07 09:00
CRDT- 2009/01/16 09:00
PHST- 2009/01/16 09:00 [entrez]
PHST- 2009/01/16 09:00 [pubmed]
PHST- 2009/04/07 09:00 [medline]
AID - 10.1109/TNSRE.2008.926701 [doi]
PST - ppublish
SO  - IEEE Trans Neural Syst Rehabil Eng. 2008 Dec;16(6):539-48. doi: 
      10.1109/TNSRE.2008.926701.