Independent component analysis and statistical parametric mapping of the relationship between personality and brain blood flow in normal males

In Kang
University of Canterbury

Marco Reale
University of Canterbury

Carl Scarrott
University of Canterbury

Irene L Hudson
University of South Australia

Robin Tuner
University of New South Wales

Medical images are an important source of information about physiological processes, but they are often deteriorated by noise due to various sources of interference and other phenomena that affect the measurement processes in imaging and data acquisition systems. The images are mixtures of unknown combinations of sources summing differently at each of the sensors. Independent component analysis (ICA) [Hyv´┐Żrinen, A. and Oja, E., 2001] is an effective method for removing artifacts and separating sources of the brain signals from medical images. In this study, we assess the relationship between regional cerebral blood flow (rCBF) and all seven of the Temperament and Character Index (TCI) personality traits using ICA. ICA can assess the difference in rCBF between quartile groups for each personality trait to identify brain regions. Significant clusters of activation (increasing level of trait associated with increasing blood flow) or deactivation (decreasing level of trait associated with increasing level of blood flow) were found in relation to all seven TCI traits. The ICA linear model results showed that a significant relationship in specific regions of the brain. Graphs of the average regional cerebral blood flow highlighted the existence of non-linear relationships delineated by the independent components. These results support previous work showing a biological basis for the TCI model [Cloninger, C., 2002] and non-linear model [Turner et al 2003, Turner, R. 2005].

Session 3d, Medical Statistics: 16:40 — 17:00, Room 445

Presentation Program