Disease state prediction from resting state functional connectivity
Open accessAbstract
The application of multivoxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real‐time functional MRI applications. Support vector classification is the most popular of these techniques, owing to reports that it has better prediction accuracy and is less sensitive to noise. Support vector classification was applied to learn functional connectivity patterns that distinguish patients with depression from healthy volunteers. In addition, two feature selection algorithms were implemented (one filter method, one wrapper method) that incorporate reliability information into the feature selection process. These reliability feature selections methods were compared to two previously proposed feature selection methods. A support vector classifier was trained that reliably distinguishes healthy volunteers from clinically depressed patients. The reliability feature selection methods outperformed previously utilized methods. The proposed framework for applying support vector classification to functional connectivity data is applicable to other disease states beyond major depression. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.
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Several recent brain imaging studies have tried to distinguish depressed patients from healthy controls.
“Recently, several brain imaging studies have attempted to distinguish depressed patients from healthy controls (Fu et al., 2008;Costafreda et al., 2009;Craddock et al., 2009).”
From Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis (2012) · cites this paper as Craddock et al., 2009
This kind of analysis has previously been used to predict subjects' psychiatric condition and cognitive state.
“This kind of analysis has previously been used (albeit from full correlation network matrices) to predict subjects’ psychiatric condition [77] and cognitive state [78];”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [77]
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- Authors
- Craddock, R. C., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S.
- Affiliations
- Georgia Institute of Technology (US)
- Emory University (US)
- Corresponding author
- Craddock, R. Cameron
- Hu, Xiaoping P.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2009-10-26
- Volume
- 62
- Issue
- 6
- Pages
- 1619-1628
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- PMC
- PMC3749911
- OpenAlex
- W2013016035
- Cited by
- 411
- References
- 40
- Citations by year
- 2026: 3 · 2025: 14 · 2024: 16 · 2023: 15 · 2022: 21 · 2021: 21 · 2020: 21 · 2019: 33 · 2018: 22 · 2017: 33 · 2016: 32 · 2015: 38 · 2014: 36 · 2013: 44 · 2012: 32
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Emory University
- National Institutes of Health (UL1TR000454)
- National Institute of Mental Health (R01MH073719, P50MH077083, NIMH P50)
- National Institute of Biomedical Imaging and Bioengineering (R01EB002009)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Keywords
- resting-state functional connectivity, depression, support vector machine classification, feature selection, filter and wrapper methods, multi-voxel pattern analysis, brain state classification
- MeSH terms
- Adult, Algorithms, Artificial Intelligence, Major Depressive Disorder, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results
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