http://www.nitrc.org/forum/forum.php?forum_id=7652 <table border="0" width="100%"><tr><td align="left"/><td align="right"><a href="https://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&amp;cmd=Link&amp;LinkName=pubmed_pubmed&amp;from_uid=28747877">Related Articles</a></td></tr></table> <p><b>Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction.</b></p> <p>Front Hum Neurosci. 2017;11:362</p> <p>Authors: Ding X, Yang Y, Stein EA, Ross TJ</p> <p>Abstract<br/> Machine learning techniques have been applied to resting-state fMRI data to predict neurological or neuropsychiatric disease states. Existing studies have used either a single type of resting-state feature or a few feature types (&lt;4) in the prediction model. However, resting-state data can be processed in many different ways, yielding different feature types containing complementary and/or novel information, leaving uncertain the most informative features to provide to the classifier. In this study, multiple resting-state features were calculated from two main analytical categories: local measures and network measures. Feature selection was adopted using an optimized grid-search approach selecting top ranked features from statistical tests. We then tested three optimized frameworks: feature combination, kernel combination, and classifier combination, all using the support vector machine as an elementary classifier, to combine these resting-state feature types. When applied to nicotine addiction, with a cohort size of 100 smokers and 100 non-smokers, via a 10-fold cross-validation procedure, the feature combination and the classifier combination achieved an accuracy of 75.5%, while the kernel combination achieved a 73.0% accuracy; all three combination frameworks improved classification performance compared to the single feature type based results (best accuracy 70.5%). This study not only reveals the discriminative power of resting-state data, but also demonstrates the efficiency of combining multiple features from one data phenotype to improve classification performance.<br/> </p><p>PMID: 28747877 [PubMed]</p> en-us Copyright 2000-2026 NITRC OSI Mon, 04 May 2026 3:28:08 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator