Mixed models and multiple comparisons in analysis of human neurochemical maps

Examination of brain regional neurochemistry in disease states reveals diff erences among brain regions. Knowing where alterations in brain function ar e located is crucial to understanding the disease effect. The anatomical di stribution of neurotransmitter receptors is now often studied using quantit ative autoradiography, but the large number of brain regions involved raise s serious problems for statistical analysis of such data. Due to the depend ence among the subjects in case control designs, statistical analysis based on a 'mixed model' is useful. Such an analysis is illustrated using a smal l autoradiographic data set. The Bonferroni method, the method of Helm, and two 'False Discovery Rate'-controlling methods for adjusting P-values for multiple comparisons are compared. (C) 2000 Elsevier Science Ireland Ltd. A ll rights reserved.