Changes in community structure of resting state functional connectivity in unipolar depression

• Published in: PloS one Vol. 7; no. 8; p. e41282
• Main Authors: Lord, Anton, Horn, Dorothea, Breakspear, Michael, Walter, Martin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.08.2012; Public Library of Science (PLoS)
• Subjects: Adult; Alzheimer's disease; Artificial intelligence; Biology; Brain; Brain architecture; Brain mapping; Brain research; Case-Control Studies; Classification; Communities; Community structure; Cortex; Data acquisition; Data processing; Depression - physiopathology; Diagnosis; Diagnostic systems; Female; Functional magnetic resonance imaging; Humans; Laboratories; Learning algorithms; Machine learning; Magnetic Resonance Imaging; Male; Mathematical analysis; Matrix methods; Medical imaging; Medical research; Medicine; Mental depression; Mental health; Modularity; Multivariate analysis; Neural networks; Neurobiology; Neuroimaging; Neurology; Neurosciences; Psychiatry; Schizophrenia; Social and Behavioral Sciences; Queensland Australia; Australia; Germany; New South Wales Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0041282

Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of "resting state" functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects.We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions.We characterised the hierarchical organization of these matrices using network-based matrics, with an emphasis on their mid-scale "modularity" arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index.In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets.