The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity
Open accessAbstract
Recently, functional network connectivity (FNC, defined as the temporal correlation among spatially distant brain networks) has been used to examine the functional organization of brain networks in various psychiatric illnesses. Dynamic FNC is a recent extension of the conventional FNC analysis that takes into account FNC changes over short periods of time. While such dynamic FNC measures may be more informative about various aspects of connectivity, there has been no detailed head-to-head comparison of the ability of static and dynamic FNC to perform classification in complex mental illnesses. This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features. Also, we compare cross-validated classification performance between static and dynamic FNC. Results show that the dynamic FNC significantly outperforms the static FNC in terms of predictive accuracy, indicating that features from dynamic FNC have distinct advantages over static FNC for classification purposes. Moreover, combining static and dynamic FNC features does not significantly improve the classification performance over the dynamic FNC features alone, suggesting that static FNC does not add any significant information when combined with dynamic FNC for classification purposes. A three-way classification methodology based on static and dynamic FNC features discriminates individual subjects into appropriate diagnostic groups with high accuracy. Our proposed classification framework is potentially applicable to additional mental disorders.
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Citations
Cites
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Cited by
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
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Data-Driven Subgroups in Depression Derived from Directed Functional Connectivity Paths at Rest
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The Necessity of Leave One Subject Out (LOSO) Cross Validation for EEG Disease Diagnosis
Kunjan, S., Grummett, T. S., Pope, K. J., Powers, D. M. W., Fitzgibbon, S. P., Bastiampillai, T., Battersby, M., & Lewis, T. W. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-86993-9_50 · Cited by 80
Metadata
- Authors
- Rashid, B., Arbabshirani, M. R., Damaraju, E., Cetin, M. S., Miller, R., Pearlson, G. D., & Calhoun, V. D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 134
- Pages
- 645-657
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2336687820
- Cited by
- 334
- References
- 13
- FWCI
- 21.35
- Type
- journal article
- Language
- en
- Open access
- green
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