Performance-based connectivity analysis: A path to convergence with clinical studies

Abstract

Connectivity analyses have become increasingly important in functional imaging. When used to describe the functional anatomy of a specific behavior, these analyses are generally applied to a subset of the data that demonstrate significant differences when experimental conditions are contrasted. Such data reduction is sub-optimal for a systems approach as it assumes that all data that survive the statistical contrast filter are related to the behavior and that none of the filtered data has a significant function. When such data filtering is applied to speech and language tasks, the resulting functional anatomy rarely reflects the brain lateralization established in over a century and a half of clinical studies. A two-step performance-based connectivity analysis is described in which the first step uses multiple linear regression to establish a direct relationship between regional brain activity and a measure of performance. The second step uses partial correlations to examine the functional relationships between the predictor regions and other brain regions. When applied to regional cerebral blood flow data obtained with positron emission tomography during a speech production task, the results demonstrate left lateralization of motor control areas, thalamic involvement in repetition rate, and auditory cortical suppression, all consistent with clinical observations. The integration of performance measures into the earliest stages of image analysis without reliance on data filtering based on decomposition may provide a path toward convergence with traditional descriptions of functional anatomy based on clinical studies.