The purpose of this study was to test for differences in brain shape among children with cleft palate only (CP) (n = 22) children with cleft lip and palate (CLP) (n = 35) and controls (n = 39) using Euclidean TG-101348 distance matrix analysis. thalamus. Differences in brain shape unique to CP and to CLP were also identified. These results expand upon previous volumetric studies on brain morphology in individuals with CL/P and provide additional evidence that the primary defect in CL/P results in both facial and brain dysmorphology. = 0.427). The sample was ethnically homogenous in order to control for racial variation in skull morphology. Seventy-nine (82%) children self-identified as Caucasian eight (8%) as Asian American 1 (1%) as African American 2 (2%) as Hispanic/Latino Vax2 1 (1%) as Native Hawaiian/Pacific Islander 4 (4%) as biracial and 1 (1%) did not disclose his race. Table 1 Demographic variables of the sample Cognitive Assessment Cognition was assessed in every child using a battery of neuropsychological tests that measured IQ and several other cognitive domains. Neuropsychological tests were administered by cognitive specialists at the University of Iowa. A description of this assessment is available in Conrad et al. (2009). Approximately 90% of the children TG-101348 included in this study were included in the sample assessed in Conrad et al. (2009). Like in Conrad et al. (2009) after controlling for differences in socioeconomic status ANCOVA revealed no difference in full-scale IQ (FSIQ) (F = 1.84 df = 2 = 0.164) or performance IQ (PIQ) (F = 0.13 df = 2 = 0.880) among children with CP those with CLP and controls (Table 1). VIQ was significantly different among the three groups (F = 3.37 df = 2 = 0.039). Post-hoc analysis with Bonferroni correction revealed that VIQ was lower in children with CP (= 97) relative to controls (= 108) (95% CI: 0.26 TG-101348 – 22.62) but no difference in VIQ was detected between CLP (= 102) and controls (95% CI: ?2.66 – 15.68) or children with CP (95% CI: ?15.84 – 5.99). Imaging Methods Images were obtained with a 1.5-T Signa magnetic resonance scanner (General Electric Milwaukee Wisconsin) using a T1 sequencing protocol. Post-acquisition processing was completed by technicians at the University of Iowa using the software BRAINS (Brain Research: Analysis of Images Networks and Systems19-23). Statistical Shape Analysis Twenty-three (23) landmarks representing cortical and subcortical structures on the midline and left TG-101348 side of the brain were used to assess brain shape (Table 2 Figures 1-2). Landmarks were collected blind to sex and cleft status using eTDIPS a multidimensional volume visualization analysis software that allows landmarks to be placed on any of the three planar views or directly on a 3D reconstruction of the brain25-26. Landmarks were only collected on the left side of the brain because when Weinberg et al. (2009)16 analyzed brain shape in adults with CL/P they used unilateral left brain landmarks. For comparative purposes it was beneficial to employ the same technique. All of the landmarks TG-101348 that were used in this project were validated in an inter- and intra-observer error study. The average intraobserver error was 1.9 mm with a range of 0.72 to 5.6 mm and the average interobserver error was 1.1 mm with a range of 0.40 mm to 3.4 mm. Figure 1 Landmarks used to assess brain shape Figure 2 Wire frame representation of landmarks Table 2 Landmarks used to assess brain shape Landmarks were analyzed using Euclidean Distance Matrix Analysis (EDMA)27. Briefly EDMA assesses shape differences by comparing a matrix of the linear distances that connect pairs of landmarks (interlandmark distances (ILDs)) in one sample to a matrix of the linear distances that connect the corresponding pairs of landmarks in a second sample27-28. Corresponding ILDs are compared between samples as a ratio such that if a given ratio is equal to 1 the two samples do not differ with respect to that specific ILD. Statistical significance is assessed using a non-parametric bootstrapping algorithm that ultimately produces a confidence interval (α = 0.05) for each ILD29-30. Three pairwise comparisons were conducted in this study using EDMA: (1) CP vs. control; (2) CLP vs. control; (3) CLP vs. CP. RESULTS CP vs. Control Of the 231 interlandmark distances representing brain shape in children with CP and controls 22 (10%) were significantly smaller and 14 (6%) were significantly larger in children with CP relative to controls (Figure.