title: Dominant component analysis of electrophysiological connectivity networks.
abstract: Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insight into brain processes. Their high dimensionality necessitates the development of methods for population-based statistics, in the face of small sample sizes. In this paper, we present such a method applicable to functional connectivity networks, based on identifying the basis of dominant connectivity components that characterize the patterns of brain pathology and population variation. Projection of individual connectivity matrices into this basis allows for dimensionality reduction, facilitating subsequent statistical analysis. We find dominant components for a collection of connectivity matrices by using the projective non-negative component analysis technique which ensures that the components have non-negative elements and are non-negatively combined to obtain individual subject networks, facilitating interpretation. We demonstrate the feasibility of our novel framework by applying it to simulated connectivity matrices as well as to a clinical study using connectivity matrices derived from resting state magnetoencephalography (MEG) data in a population of subjects diagnosed with autism spectrum disorder (ASD).
authors.affiliation: Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA. Yasser.Ghanbari@uphs.upenn.edu
authors.forename: Yasser
authors.initials: Y
authors.lastname: Ghanbari
authors.forename: Luke
authors.initials: L
authors.lastname: Bloy
authors.forename: Kayhan
authors.initials: K
authors.lastname: Batmanghelich
authors.forename: Timothy P L
authors.initials: TP
authors.lastname: Roberts
authors.forename: Ragini
authors.initials: R
authors.lastname: Verma
mesh_terms: D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000175:diagnosis* / Q000503:physiopathology*; D002675:Child, Preschool; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007223:Infant; D007231:Infant, Newborn; D015225:Magnetoencephalography / Q000379:methods*; D009415:Nerve Net / Q000503:physiopathology*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity