NDLI: Neural Networks and Classi cation & Regression Trees Are Able to Distinguish Female with Major Depression from Healhy Controls Using Neuroimaging Data

Content Provider	IEEE Xplore Digital Library
Author	Floares, A. Jakary, A. Bornstein, A. Deicken, R.
Copyright Year	2006
Abstract	Recently, magnetic resonance imaging and proton magnetic resonance spectroscopy studies of major depression identified structural and neurochemical alterations in several brain regions, including the hippocampus and prefrontal cortex. However, many contradictory endings exist. Most previous studies used a few cases and features, and conventional statistics. Therefore, we decided to use computational intelligence tools -neural networks and decision trees. Our data mining approach was applied to a neuroimaging dataset from 23 depressed women and 25 female controls. The goal was to identify complex relationships between the spectroscopic and volumetric inputs and diagnostic output. We tried to overcome data-related problems by using neural networks combined with genetic algorithms, ensemble methods, resampling, and extensive data preprocessing. The approach seems very promising. Some neural networks and two classification & regression trees - one involving hippocampal subregional volume and the other related to limbic-cortical neuronal integrity - successfully classified the subjects with 100% accuracy.
Starting Page	4605
Ending Page	4611
File Size	396510
Page Count	7
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247090
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Regression tree analysis Neuroimaging Biological neural networks Spectroscopy Classification tree analysis Magnetic resonance imaging Protons Magnetic resonance Hippocampus
Content Type	Text
Resource Type	Article

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