Open AccessAn fMRI normative database for connectivity networks using one‐class support vector machines
Author(s) -
Sato João Ricardo,
da Graça Morais Martin Maria,
Fujita André,
MourãoMiranda Janaina,
Brammer Michael John,
Amaro Edson
Publication year - 2009
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20569
Subject(s) - functional magnetic resonance imaging , support vector machine , normative , hum , functional connectivity , psychology , artificial intelligence , computer science , neuroscience , class (philosophy) , construct (python library) , resting state fmri , pattern recognition (psychology) , brain activity and meditation , identification (biology) , task (project management) , brain mapping , machine learning , electroencephalography , biology , art , philosophy , botany , management , epistemology , performance art , programming language , economics , art history
The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal patterns and intersubject variability—two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one‐class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp, 2009. © 2008 Wiley‐Liss, Inc.