Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm
dc.contributor.author | Peltier, Scott J. | en_US |
dc.contributor.author | Polk, Thad A. | en_US |
dc.contributor.author | Noll, Douglas C. | en_US |
dc.date.accessioned | 2006-04-19T14:15:48Z | |
dc.date.available | 2006-04-19T14:15:48Z | |
dc.date.issued | 2003-12 | en_US |
dc.identifier.citation | Peltier, Scott J.; Polk, Thad A.; Noll, Douglas C. (2003)."Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm." Human Brain Mapping 20(4): 220-226. <http://hdl.handle.net/2027.42/35195> | en_US |
dc.identifier.issn | 1065-9471 | en_US |
dc.identifier.issn | 1097-0193 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/35195 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=14673805&dopt=citation | en_US |
dc.description.abstract | Low-frequency oscillations (<0.08 Hz) have been detected in functional MRI studies, and appear to be synchronized between functionally related areas. A current challenge is to detect these patterns without using an external reference. Self-organizing maps (SOMs) offer a way to automatically group data without requiring a user-biased reference function or region of interest. Resting state functional MRI data was classified using a self-organizing map (SOM). Functional connectivity between the left and right motor cortices was detected in five subjects, and was comparable to results from a reference-based approach. SOMs are shown to be an attractive option in detecting functional connectivity using a model-free approach. Hum. Brain Mapping 20:220–226, 2003. © 2003 Wiley-Liss, Inc. | en_US |
dc.format.extent | 396501 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Wiley Subscription Services, Inc., A Wiley Company | en_US |
dc.subject.other | Life and Medical Sciences | en_US |
dc.subject.other | Neuroscience, Neurology and Psychiatry | en_US |
dc.title | Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Neurosciences | en_US |
dc.subject.hlbsecondlevel | Kinesiology and Sports | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Applied Physics, University of Michigan, Ann Arbor, Michigan ; Emory University, Hospital Annex, 531 Asbury Circle, Suite N305, Atlanta, GA 30322-4600 | en_US |
dc.contributor.affiliationum | Department of Psychology, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan | en_US |
dc.identifier.pmid | 14673805 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/35195/1/10144_ftp.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1002/hbm.10144 | en_US |
dc.identifier.source | Human Brain Mapping | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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