“behavioral imaging”: II. Application of the quantitative algorithm to hypothesistesting in a population ofhemiparkinsonian patients

This study examines the potential of a "behavioral imaging" algorithm presented by Gur et al. (1988, first article of this series) as a method for quantitat-ing neurobehavioral data. Regional ("lobar") values were calculated for hypothesis testing, using neuropsychological test scores obtained in a sample of patients with hemiparkinsonism described in Blonder et al., in press. Patients with left extremity symptoms of Parkinson's disease (N = 7) were compared with patients with right-side symptoms (N = 14) and normal controls (N = 17). Regional lobar values were analyzed statistically using analysis of variance with diagnosis as a grouping factor and lobe and hemisphere as within-group factors. The hypothesis of neuropsychological deficits associated with the hemisphere ipsilateral to the side of presumed striatal deficiency was supported by a significant diagnosis × hemisphere interaction (p < 0.01), and regional specificity was suggested by a diagnosis × region × hemisphere interaction (p = 0.01). The effects were sustained when measures of motor functions were excluded. The results support the utility of the algorithm for statistical analysis of neuropsychological data to test hypotheses regarding brain-behavior relationships.